AmalBabuPuthumana - Griffith University

Effect of feed ratio and pre-treatment on methane yieldsduring anaerobic co-digestion of sugarcane bagasse andtrash with chicken manure

Author

Puthumana Amal Babu

Published

2020-05-15

Thesis Type

Thesis (Masters)

School

School of Eng amp Built Env

DOI

httpsdoiorg102590419123687

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The author owns the copyright in this thesis unless stated otherwise

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1

Effect of feed ratio and pre-treatment onmethane yields during anaerobic co-digestionof sugarcane bagasse and trash with chicken

manure

Amal Babu PuthumanaBachelor of Technology (BTech) Mahatma Gandhi

University India

Master of Technology (MTech) Cochin University of Scienceand Technology India

Griffith School of Engineering and Built

Environment Griffith Sciences

Griffith University Nathan QLD 4111 Australia

Submitted in the fulfillment of the requirements of thedegree of Master of Philosophy

November 2019

2ii

iii

ABSTRACT

Australia is one of the major producers and exporter of agricultural products Annually

Australian agriculture produces approximately 151 Tg CO2 equivalent emissions The

use of fossil fuels in crop cultivation harvesting and transportation are considered as the

primary source of these greenhouse gas (GHG) emissions Moreover agronomic

management and crop residues left in the field also contribute to these GHG emissions

Alternative waste management practices include the use of crop residues and

agro-wastes as feedstocks for bioenergy production Anaerobic digestion is considered

as sustainable environmental technology to convert industrial sugarcane residues to

carbon dioxide (CO2) - neutral biogas The biogas thus produced can be used to produce

heat electricity and upgrade to biomethane for vehicle use The produced biomethane

can replace the diesel consumption associated with GHG emission in cane transport

Sugarcane is one among the most cultivated crop in the world Australia alone produced

nearly 335 million tonnes of cane in 2018 (FAO 2018) These large production of

sugarcane lead to an increase in crop residues and agro-wastes from the sugarcane

industry In this study an investigation regarding the anaerobic co-digestion of crop

residues and agro-wastes from sugarcane industry viz sugarcane trash (SCT) or

sugarcane bagasse (SCB) with chicken manure (CM) was investigated in a batch

experiment at 37 degC In spite of various researches conducted till date about

co-digestion of lignocellulosic waste with manure no research data was available

regarding the effect of feed ratio on co-digestion of SCTSCB with CM This research

gap was investigated in this study In addition to this steam explosion pre-treatment of

SCTSCB was included to investigate how the pre-treatment influence methane yield

among different feed ratios of SCTSCB with CM

iv

At first SCT and SCB were subjected to steam explosion pre-treatment (steam

impregnation at 130 degC for 5 minutes followed by steam explosion) Later two sets of

biochemical methane potential (BMP) tests were conducted at an Inoculum to Substrate

Ratio (ISR) of 2 Co-digestion of untreated and steam exploded SCT or SCB with CM

was investigated at feed ratios of 7525 5050 and 2575 on volatile solids (VS) basis

Assays with 100 untreated and steam exploded SCT or SCB were also included

Chemical analysis revealed that the steam explosion improved the VS content in

pre-treated biomass compared with untreated biomass The increase in VS was 16

and 57 in SCT and SCB respectively

On the other hand a slight reduction in total solids (TS) of nearly 4 and 1 were

observed in the case of SCT and SCB respectively BMP results showed that the steam

explosion had a profound effect on the methane production rates and yields especially

for SCB than SCT Methane (CH4) yields of 2018 and 199 ml CH4gVSadded were

obtained during the mono-digestion of untreated SCT and SCB respectively The

corresponding values for 100 steam-exploded SCT and SCB were 2075 and 2256

mlgVSadded respectively In comparison to mono-digestion the co-digestion of SCB or

SCT with CM did not improve the methane yields

Nevertheless pre-treatment improved the methane production rates and yields of

pre-treated biomass than untreated biomass Among the studied feed ratios best

methane yields of 2065 mlgVSadded were obtained when steam-exploded SCT was

co-digested with CM at 7525 ratio However methane yields decreased with an

increase in the amount of CM added SCB also showed a similar trend The best

methane yield of 1995 mlgVSadded was obtained when steam-exploded SCB was

co-digested with CM at 7525 ratio Among the tested feed ratios all co-digestion

mixtures except for 7525 and 5050 ratios of untreated SCT to CM showed synergistic

v

effects The best synergistic effect of 1857 was observed when untreated SCB was

co-digested with CM at 2575 ratio Kinetic modelling results confirmed that the steam

explosion pre-treatment improved the methane production rates and yields by increasing

the hydrolysis rate constant values However a higher hydrolysis rate constant was

noticed for SCT than SCB The highest hydrolysis rate constant of 016 d-1 was

achieved at feed ratios of 5050 and 2575 of pre-treated SCTCM

Interestingly more than 75 of methane in pre-treated assays was produced by Day 11

The study thus suggests that the steam explosion can improve the methane production

rates yields and productivity of SCT and SCB However the use of CM as co-substrate

did not improve the methane yields when compared to the mono-digestion of SCT or

SCB but a positive synergism was evident in most of the co-digestion feed ratios

Keywords lignocellulosic biomass co-digestion anaerobic digestion (AD)

chicken manure steam explosion pre-treatment feed ratio synergistic effect

vi

STATEMENT OF ORIGINALITY

This work has not previously been submitted for a degree or diploma in any

university To the best of my knowledge and belief the thesis contains no

material previously published or written by another person except where due

reference is made in the thesis itself

Amal Babu Puthumana (-sd)

vii

TABLE OF CONTENTS

Abstract iii

Statement of originality vi

Table of contents vii

List of figures xi

List of tables xiii

List of symbols and abbreviations xiv

Acknowledgments xvi

Chapter 1 Introduction 1

11 Background 3

12 Aim and Objectives 5

13 Thesis outline 5

Chapter 2 Literature Review 7

21 Agro-waste generation and their management 9

22 Sugarcane cultivation in Australia 10

23 Impact of sugarcane agricultural residue on the environment 15

24 Energy potential of sugarcane agricultural residue 16

25 Lignocellulosic biomass structure 16

26 Anaerobic digestion process 18

27 AD process parameters 21

271 Process temperature 21

272 pH 24

273 CN ratio and trace nutrients 25

28 Pre-treatment of lignocellulosic substrates 26

viii

281 Physical pre-treatment 27

282 Chemical pre-treatment 33

283 Biological pre-treatment 34

29 Anaerobic co-digestion 35

291 Chicken manure as a co-digestion substrate 39

210 Overview of literature review 42

Chapter 3 Materials and Methods 43

31 Substrates 45

311 Lignocellulosic substrates 45

312 Manure 45

32 Inoculum 45


34 Experimental design 46

35 Analytical methods 49

351 Determination of TS and VS 49

352 Determination of Chemical Oxygen Demand (COD) 49

353 Determination of Total Kjeldahl Nitrogen (TKN) Total

Kjeldahl Phosphorous (TKP) soluble and total metal ions 49

354 Determination of CN ratio 50

355 Determination of cellulose hemicellulose and lignin 50

356 Determination of VFA 50

357 Determination of ammoniacal nitrogen (NH4-N) and

phosphate (PO43-) 51

36 Biochemical Methane Potential (BMP) test 51

37 Data Processing 54

371 Theoretical BMP 54

372 Experimental BMP 54

ix

373 Synergistic effects analysis 56

374 Anaerobic biodegradability 56

375 BMP mathematical models 57

3751 First-order kinetic model 58

3752 Modified Gompertz model 58

376 Statistical analysis 59

Chapter 4 Results and Discussions 61

41 Physical properties and chemical composition of substrates

and Inoculum 63

42 Anaerobic co-digestion of untreated and steam exploded SCT

with CM 67

421 Effect of different feed ratio on methane yields

during anaerobic co-digestion of SCT with CM 67

422 Effect of steam explosion pre-treatment on anaerobic

co-digestion of SCT with CM at different feed ratios 69

423 Synergistic effects on anaerobic digestibility and methane yields


43 Anaerobic co-digestion of untreated and steam exploded SCB

with CM 72


during anaerobic co-digestion of SCB with CM 73


co-digestion of SCB with CM at different feed ratios 75



44 Post BMP analysis of SCTSCB when co-digested with CM 77

441 Cumulative methane yield and statistical analysis 77


443 VFA analysis of digestates 81

x

444 FIA analysis of digestates 84

45 Mathematical modelling 86

Chapter 5 Conclusions 95

51 Conclusion to this research work 97

52 Related future research works 99

Appendix 1 Statistical Analysis of Untreated SCT with CM 101

Appendix 2 Statistical Analysis of Pre-treated SCT with CM 106

Appendix 3 Statistical Analysis of Untreated SCB with CM 111

Appendix 4 Statistical Analysis of Pre-treated SCB with CM 116

References 121

xi

LIST OF FIGURES

Figure 21 Sugarcane mills and crop areas in Australia 11

Figure 22 Flowchart of production of SCB and SCT during sugar cultivation 13

Figure 23 (a) Plant tops and dry leaves of the sugarcane plant constituting

the SCT 14

Figure 23 (b) Dry fibre pulp left over after extracting juice from the stem

of sugarcane plant forming the SCB 14

Figure 24 Australian sugarcane residue burnings from 2013-2017 14

Figure 25 Structure of lignocellulosic biomass 17

Figure 26 Anaerobic digestion pathway 20

Figure 27 Rate of AD process based on different process temperatures 23

Figure 28 Pre-treatment of lignocellulosic biomass 27

Figure 31 (a) Untreated SCT 47

Figure 31 (b) Pre-treated SCT 47

Figure 31 (c) Untreated SCB 47

Figure 31 (d) Pre-treated SCB 47

Figure 31 (e) CM 47

Figure 32 Flowchart of methodology adopted in research study 48

Figure 33 Sealed 160mL serum bottle for BMP test with inoculum

substrate and headspace flushed with nitrogen gas 52

Figure 34 Gas chromatography machine (GC-2014) 53

Figure 41 Cumulative methane production during the anaerobic

co-digestion of untreated SCT (USCT) and pre-treated SCT (PSCT) with

CM at different feed ratios incubated in batch experiment at 37 oC

for 83 days 68

Figure 42 Cumulative methane yield among different feed ratios of

xii

Untreated SCTCM and Pre-treated SCTCM on day 11 (T11) 70

Figure 43 Synergistic and antagonistic effects of various co-digestion feed ratios

during anaerobic co-digestion of SCT with CM in batch assays at 37 oC 72


co-digestion of untreated SCB (USCB) and pre-treated SCB (PSCB) with


for 83 days 74


Untreated SCBCM and Pre-treated SCBCM on day 11 (T11) 76


during anaerobic co-digestion of SCB with CM in batch assays at 37 oC 77

Figure 47 VFAs of post BMP digestates in various feed ratio assays 83

Figure 48 Comparison of first-order and modified Gompertz kinetic model

predictions with experimental cumulative methane yield for different

co-digestion ratios of SCT with CM 89



co-digestion ratios of SCB with CM 91

xiii

LIST OF TABLES

Table 21 Major agricultural crops and their area of cultivation in Australia

(2012-2017) 10

Table 22 Composition of various lignocellulosic biomasses 18

Table 23 Comparison of mesophilic and thermophilic operation 23

Table 24 Optimal pH requirement for each stage of the AD process 24

Table 25 CN ratio of different lignocellulosic material and manure from

previous studies 26

Table 26 Previous studies and their results on mechanical pre-treatments 31

Table 27 Results of previous studies on the effect of feed ratio during the

anaerobic co-digestion of manure and lignocellulosic agro-wastes 37

Table 28 Previous studies reporting improvement in methane yields as a

result of co-digesting with manure 41

Table 31 Different feed ratios in anaerobic co-digestion of SCT and SCB

with CM tested in this study 47

Table 41 Physio-chemical properties of substrates and inoculum 65

Table 42 Macro and micro nutrients of substrates and inoculum 66

Table 43 Theoretical and experimental results of anaerobic biodegradability

during anaerobic co-digestion of SCTSCB with CM 80

Table 44 Results of flow injection analysis for various feed ratios of SCTSCB

with CM 85

Table 45 Modelling parameters for first-order and modified Gompertz model

for different feed ratios of SCTSCB co-digested with CM 87

xiv

LIST OF ABBREVIATIONS

AD Anaerobic Digestion

ANOVA Analysis of Variances

APHA American Public Health Association

ASMC Australian Sugar Milling Council

BD Biodegradability

BMP Bio-chemical Methane Potential

CN Carbon to Nitrogen

CEC Clean Energy Council

CM Chicken Manure

COD Chemical Oxygen Demand

CSTR Continuous Stirred Tank Reactor

DAWR Department of Agriculture and Water Resources

FAO Food and Agricultural Organisation

FIA Flow Injection Analyser

GC Gas Chromatography

GHG Green House Gases

HPLC High Performance Liquid Chromatography

HRT Hydraulic Retention Time

ICP Inductivity Coupled Plasma

ISR Inoculum to Substrate Ratio

NREL National Renewable Energy Laboratory

OES Optical Emission Spectroscopy

PSCB Pre-treated Sugarcane Bagasse

xv

PSCT Pre-treated Sugarcane Trash

SCB Sugarcane Bagasse

SCT Sugarcane Trash

STP Standard Temperature and Pressure

TCD Thermal Conductivity Detector

TKN Total Kjeldahl Nitrogen

TKP Total Kjeldahl Phosphorous

TS Total Solids

USCB Untreated Sugarcane Bagasse

USCT Untreated Sugarcane Trash

UV Ultra-Violet

VFA Volatile Fatty Acids

VOC Volatile Organic Compounds

VS Volatile Solids

xvi

ACKNOWLEDGMENTS

All glory and honours to God the Almighty for providing me an opportunity to conduct

this research work His blessings and mercy have given me strength throughout my

MPhil program at Griffith University Australia

I want to express my sincere gratitude to my principal supervisor Dr Prasad Kaparaju

Senior Lecturer School of Engineering and Built Environment for his patience

guidance technical assistance and productive discussions His motivation and

dedication towards work inspired me to work harder and overcome the hurdles in my

research pathway He also gave insightful comments through discussions and reviews

for the successful completion of my research and thesis writing process I am

acknowledging his efforts in sharpening my skills and moulding me to be a successful

researcher

I would also like to use this opportunity to thank my Associate supervisor Dr Sunil

Herat and HDR convenor of the Griffith School of Engineering and Built Environment

Nathan Campus Dr Jimmy Yu for their innovative ideas and suggestions that helped me

to improve my skills during my tenure as an MPhil researcher at Griffith University I

would also extend my gratitude to Dr Asif Latif and Dr Thiago Edwiges for their

support in my research work

I am also thankful to my colleagues Mr Paulose Paulose Mrs Thi Kim Chi Do and Ms

Erika Maria Conde Espitia for their sincere supports and laboratory assistance that

helped me to complete my research work efficiently much before the expected

completion date I would also like to acknowledge the technical supports provided by

Mr Scott Byrnes and Mr Rad Bak in my analysis I would also thank Mrs Lynda

xvii

Ashworth Administrative Support Officer Griffith School of Engineering and Built

Environment for providing me all non-technical supports during my tenure

I am also thankful for all the support and guidelines provided by the Griffith Higher

Degree by Research Office Even though I had to undergo some complex process

associated with the candidature change HDR staff supported me and showed patience

to hear understand and clear my quires I also take this opportunity to thank all

academic and non-academic staff of Griffith University Nathan Campus for providing

me all support to complete my research successfully

Last but not least I am thanking all my friends especially Mr Pranav Sajeevu Mr Alan

Jacob Antony Ms Megha Roy Mr Nikhil Aravindakshan Mrs Priya Philip and Ms

Jagriti Tiwari for their incredible support along with my family members including my

father - Mr Babu P John mother - Mrs Surya Kala Babu brother - Mr Adharsh B

Puthumana and my wife - Mrs Jisna Johnson who supported and motivated me during

my hard times It is because of their kind words and endless support I was able to

achieve this milestone

AMAL BABU PUTHUMANA

18

Chapter 1 General Introduction

1

Chapter 1 General introduction


2


3

11 Background

Australia is one of the major producers and exporter of agricultural crops producing

about 838 million tonnes of crops annually (FAO 2018) The agro-based industries in

Australia which process these crops emit nearly 151 Tg of CO2 equivalent (FAO 2018)

The GHG emissions from them are mainly associated with the use of fossil fuels during

cultivation harvesting and transportation of associated raw materials or products

Further surplus crop residues are either left on the field or burnt to reduce the

transportation cost (Nachiappan Fu and Holtzapple 2011) Accumulation of

agricultural residues on the field may also lead to environmental and health issues (Sud

Mahajan and Kaur 2008) Thus the need to develop an efficient waste management

technology to utilise these agro-wastes is increasing The utilisation of these biomass

resources for biofuel generation may reduce the GHG emissions associated with waste

management and replaces fossil fuel use in agriculture (Paudel et al 2017)

Among the different technologies used for conversion of agricultural feedstocks into

useful bioenergy anaerobic digestion (AD) is considered as the most economical and

environmentally sustainable technology (Chandra Takeuchi and Hasegawa 2012)

The produced biogas can be utilised for heat or electricity generation or even upgraded

to bio-methane for vehicle fuel use (Kaparaju and Rintala 2013)

The dry fibre pulp obtained after extracting the cane juice known as the sugarcane

bagasse (SCB) is estimated to be 30 of the weight of the cane (CEC 2019) Large

quantities of SCB are generally used as a biomass resource to produce process steam to

use onsite (Rabelo Carrere Maciel Filho and Costa 2011) It can also be used to

produce paper and paperboard products (Rainey and Covey 2016) On the other hand

the dry and green leaves and tops of the cane known as the sugarcane trash (SCT) is

estimated to be about 33 of the total plant weight (CEC 2019) Although the heat


4

content of SCT is much more than SCB (Eggleston Grisham and Antoine 2009) SCT

is generally left on the field to decay due to its bulkiness and high cost associated with

the collection storing and transportation (Janke et al 2015)

Both SCT and SCB are lignocellulosic materials that consist of cellulose hemicellulose

and lignin (Sawatdeenarunat Surendra Takara Oechsner and Khanal 2015) Different

pre-treatment methods can be used to break the complex structure of lignocellulosic

biomass (Neshat Mohammadi Najafpour and Lahijani 2017) Pre-treatment of

lignocellulosic substrates can be mainly classified as physical chemical and biological

pre-treatment Steam-explosion is a physical pre-treatment method which is more

economical and efficient for disrupting the lignocellulosic structure to improve the

anaerobic biodegradability and rate of methane production among these substrates

(Neshat et al 2017) Thus steam-explosion was adopted as the pre-treatment

methodology in this study to pre-treat SCT and SCB

Lignocellulosic materials have a high potential in producing biogas using AD

technology However the high carbon content in these substrates can cause inhibition to

the process resulting in a lower rate of production According to the studies conducted

by Neshat et al (2017) carbon to nitrogen (CN) ratio of 20-301 is to be maintained for

optimal microbial growth (Neshat et al 2017) Co-digesting these lignocellulosic

materials with animal manure can be done to reduce the CN ratio Many researchers

had worked to find out the best feed ratio in co-digesting animal manure with

lignocellulosic material For instance Cheng and Zhong (2014) conducted a research

study to investigate the best feed ratio among 1000 7525 5050 and 2575 ratios while

co-digesting cotton stalk with swine manure 5050 ratio was reported to produce more

yield in this study (Cheng and Zhong 2014) However not many previous studies have

been reported which investigated the amount of chicken manure to be added to enhance


5

biogas production while co-digesting with SCT and SCB In other words there is a need

to examine which feed ratio of SCTSCB to CM has the best biogas production

12 Aim and Objectives

The overall aim of this project is to investigate the effect of feed ratio on methane

production from sugarcane industry wastes through anaerobic co-digestion with

nitrogen-rich livestock manures Different feed ratios were tested with both untreated

and pre-treated lignocellulosic waste materials Moreover the utilisation of these two

waste streams will help in sustainable waste management and energy renewable energy

production Specific objectives included in this project to attain the aim are as follows

Objective 1 To study the effect of different feed ratios on methane production during

anaerobic co-digestion of untreated and pre-treated sugarcane bagassetrash with

chicken manure

Objective 2 To evaluate the anaerobic digestibility and kinetics of methane production

during anaerobic co-digestion of sugarcane bagassetrash with chicken manure

13 Thesis outline

This thesis is organised into five different chapters

Chapter 1 presents the background of the study the need for such research

identification of research gap the formation of aim and specific objectives of this

project

Chapter 2 reviews literature on the importance of sugarcane agro-wastes and as a

potential source for bioenergy production in Australia It also describes the anaerobic


6

digestion process and the various stages and process parameters affecting the anaerobic

digestion process Pre-treatment of the lignocellulosic substrates and co-digestion of

lignocellulosic waste with animal manure is discussed with some previous studies

Chapter 3 explains the various materials used describes the experimental design and

methodologies used in this study

Chapter 4 presents the main results of this research work and discusses the reasons for

obtaining those results along with supportive literature studies BMP results along with

the synergistic effects of co-digestion for both untreated and pre-treated SCT and SCB

were discussed Finally mathematical modelling anaerobic biodegradability and post

BMP analysis were also presented to understand the kinetics of biogas production and

the influence of pre-treatment on the biomass

Chapter 5 summarises the results of this study and arrives at the main conclusions This

chapter also provides some direction for future work that can be incorporated to have a

broader discussion on commercialising such a plant

Chapter 2 Literature Review

7



8


9

This chapter aims to describe different aspects associated with the anaerobic digestion

of lignocellulosic biomass factors affecting AD and methods to enhance methane yield

from lignocellulosic biomass It also provides the importance of sugarcane wastes and

their generation in Australia current management practices and their impact on the

environment Practical usage of these organic wastes to improve bioenergy production

from the sugarcane industry wastes are also discussed here This section also presents

previous studies dealing with the anaerobic co-digestion of lignocellulosic waste with

nitrogen-rich animal manure

21 Agro-waste generation and their management

Agro-wastes and organic wastes produced from the agricultural crops after crop harvest

are considered as a valuable renewable resource with excellent potential for generating

bio-energy (Anwar Gulfraz and Irshad 2014) Plant stalks straw hulls leaves and

vegetable matter are the significant agricultural residues generated from crop cultivation

(Lim and Matu 2015) In 2017 Australia alone produced 3835 gigagrams of CO2

equivalent crop residues (FAO 2018) The quantity of these wastes produced and their

physical and chemical properties are primarily depended on the crop species soil type

cultivation practices harvest time harvesting methods and processing technology

Burning of agro-wastes can lead to greenhouse gas (GHG) emissions and eventually

lead to global warming (Paul and Dutta 2018) On the other hand the disposal of these

organic wastes in landfills or dumpsites can also lead to severe health and

environmental issues (Sud et al 2008) Thus utilisation of this agro-waste for energy

recovery nutrients and chemicals is considered as sustainable management of these

organic wastes (Caramiello et al 2013 Lancellotti Righi Tatagraveno Taurino and

Barbieri 2013)


10

22 Sugarcane cultivation in Australia

The major agricultural crops cultivated in Australia are wheat sugarcane cotton maize

and rice Among these crops sugarcane is the second-largest cultivated crop Table 21

shows the major crops cultivated across Australia and their area of cultivation since

2012 It can be noted from Table 21 that there is a significant increase in the area of

cultivation of sugarcane in recent years

Table 21 Major agricultural crops and their area of cultivation in Australia (2012-17)

Source (FAO 2018)

Crop

Cultivate area (x 103ha)

2012 2013 2014 2015 2016 2017

Maize 696 786 524 596 533 678

Rice 1031 1136 767 697 266 822

Cotton 5965 444 3901 197 2804 NA

Soybean 381 411 37 301 292 300

Sugarcane 3386 3293 3752 3765 4472 4534

Sunflower 402 303 27 295 23 27

Wheat 139021 129793 126132 123837 112822 1219115

Despite being the second-largest cultivated crop in Australia sugar cultivation is mainly

limited to Queensland (Linnenluecke Zhou Smith Thompson and Nucifora 2019)

Australian sugar production is concentrated primarily in Queensland which constitutes

about 95 of the total output and the remaining is being produced in New South Wales

(Magarey et al 2019) It is spread along the eastern coastline for about 2100 km

between Mossman in northern Queensland and Grafton in north NSW (DAWR 2017)

The main sugarcane growing areas in Australia are located near Townsville and

Bundaberg as shown in Figure 21 In 2017 Australia produced about 365 million

tonnes of sugarcane (FAO 2018)


11

Figure 21 Sugarcane mills and crop area in Australia (ASMC 2019)

The harvested sugarcane plant from the fields generally consists of sugarcane plant stem

plant tops leaves and roots (Figure 22) The plant stem is being transported the sugar


12

mill for further processing The main products produced after processing the sugarcane

stem are sugar bagasse molasses and filter cake (George Cabello Eras Sagastume

Hens and Vandecasteele 2010) In the sugar mill the cane is crushed to extract juice

leaving behind a dry fibre pulp known as bagasse or SCB (Figure 23b) It is estimated

that bagasse constitutes about 25-30 of the total weight of cane (CEC 2019) Gabov

et al (2017) reported that around 260-280 kg of wet bagasse per ton of sugarcane is

produced from the sugar mills (Gabov Hemming and Fardim 2017) Bagasse

produced from the sugar industry is generally used for paper and paperboard production

(Sahu 2018) It is also used as fuel for the industrial boilers used for process steam

generation (George et al 2010) In Australia out of the total bagasse generated 107

million tonnes of bagasse was burnt producing 102 PJ of heat and power during 2015-

16 (Ramirez and Rainey 2019)

Green leaves of the sugarcane plant along with the plant top and plant roots are dried

together on field These trash substances are generally known as SCT (Figure 22

Figure 23a) It is estimated that almost one-third of the sugarcane plant weight is

contributed by the trash (Sahu 2018) After harvesting trash is left on the field to decay

or burnt mainly due to the high cost associated with the collection and transportation of

these trash from the fields (Janke et al 2015)


13

Figure 22 Flowchart of production of SCB and SCT during sugar cultivation

Sugarcane residue burnings in Australia from 2013 to 2017 are shown in Figure 24

Data shows that trash burnings associated with sugarcane cultivation had increased by

38 in Australia This trash management practice can contribute to air pollution

respiratory diseases and GHG emissions (Cerri et al 2011) Thus the need for efficient

and sustainable technology to utilise these organic wastes for generating energy and

other high-value products is required


14

Figure 23 (a) Plant tops and dry leaves of the sugarcane plant constituting the SCT

(b) Dry fibre pulp left over after extracting juice from the stem of sugarcane plant

forming the SCB

Figure 24 Australian sugarcane residue burnings from 2013 to 2017 (Source (FAO

2018)


15

23 Impacts of sugarcane agricultural residue on the environment

During the early 1980s green cane harvesting was one of the innovative techniques

adopted to utilise the vast quantities of trash produced in sugarcane cultivation

(Thorburn Horan and Biggs 2004) Even though trash has been used as soil much and

proved to improve the nutrient availability and soil moisture content its spread on land

had increased the chances of spreading diseases due to humidity and lower soil

temperatures (Goacutemez Torres De Souza and Jackson 2014) It also increased the risk

of spreading diseases such as the Weilrsquos disease caused due to the contact of wet soil or

water contaminated by an infected animal such as rat (Thorburn et al 2004) The most

cost-effective solution to this issue was to burn the trash in the fields after harvesting the

crop (Jain Bhatia and Pathak 2014) Burning of sugarcane trash had become a

standard policy throughout the industry as it turned out to be the most natural solution to

prevent the spread of diseases (Cerri et al 2011) Soon the negative impacts of the

burning sugarcane residues became evident It paved the way for many respiratory

disorders (Cerri et al 2011) Environmental implications of trash burnings include air

pollution in terms of volatile organic compounds (VOC) nitrogen oxide (NOx) nitrous

oxide (N2O) carbon monoxide (CO) carbon dioxide (CO2) and sulphur oxides (SOx)

(Jain et al 2014)

With the increase in demand for sugar and related products the sugar cultivation area

increased throughout the world including Australia (George et al 2010) The current

global sugarcane cultivation area is 26 million hectares with a total sugarcane

production of 18 billion tonnes (FAO 2018) Thus the need for developing alternative

technologies to utilise the wastes generated from sugarcane industries is necessary The

use of SCT and SCB to produce biogas is considered as one of the best sustainable

solutions to solve waste management without causing many environmental issues or


16

health hazards The ability of SCT and SCB to be used as a source for biogas can be

justified only by determining the energy potential of these agro wastes

24 Energy potential of sugarcane agricultural residue

Both SCT and SCB are considered as lignocellulosic biomass and are made of

carbohydrate polymers such as cellulose and hemicellulose and an aromatic polymer

known as lignin and a small number of acetyl groups minerals and phenolic compounds

(Sahu 2018) The arrangement of lignin cellulose and hemicellulose vary according to

the type of lignocellulosic biomass (Isikgor and Becer 2015) The distribution of these

compounds also depends on the maturity of the plant and species (Murphy Braun

Weiland and Wellinger 2011) On average the composition of cellulose

hemicellulose and lignin are approximately 35-50 20-35 and 10-25 respectively

(Cherubini 2010) The cross-linked structure of lignin binds the cellulose and

hemicellulose in the lignocellulosic biomass to form a complex cross-linked structure

(Eskicioglu et al 2017) The lignin bond thus hinders the accessibility of cellulose and

hemicellulose for the microbes in an anaerobic digestion process (Sawatdeenarunat et

al 2015)

25 Lignocellulosic biomass structure

Despite having high biomass yield lignocellulosic biomass does not contribute much to

the food and feed production It was inferred that lignocellulosic materials could

produce a better yield even in low energy input conditions which makes it a better

option for biogas production (Sawatdeenarunat et al 2015) High resistants and

recalcitrance of lignocellulosic biomasses are due to the interaction of cellulose

hemicellulose and lignin present in the lignocellulosic structure (Figure 25) Thus

hydrolysis is considered as the rate limiter for the AD process (Khanal 2011) These


17

major components of the lignocellulosic materials form a non-uniform complex

structure with a relatively different composition according to the type of biomass These

complex structures resist the cellulose crystallinity lignin phobicity and cellulose

encapsulation by the lignin-hemicellulose matrix inside the lignocellulose stem (Barakat

de Vries and Rouau 2013 Moraiumls et al 2012)

Figure 25 Structure of lignocellulosic biomass (Jensen Rodriguez Guerrero Karatzos

Olofsson and Iversen 2017)

Lignin is responsible for providing compressive strength to the plant tissues and to resist

the plant stem from pathogen or insect attack (War et al 2012) These long-chain

compound needs to be broken in order to attain access to the cellulose and

hemicellulose during the AD process (Paul and Dutta 2018) Composition of cellulose

hemicellulose and lignin for various lignocellulosic materials available are as shown in

Table 22


18

Table 22 Composition of various lignocellulosic biomasses Source (Cherubini 2010

Isikgor and Becer 2015 Menon and Rao 2012)

Biomass Cellulose () Hemicellulose () Lignin ()

Wheat Straw 350ndash390 230ndash300 120ndash160

Barley Hull 34 36 138ndash190

Barley Straw 360ndash430 240ndash330 63ndash98

Rice Straw 292ndash347 230ndash259 170ndash190

Rice Husks 287ndash356 120ndash293 154ndash200

Oat Straw 310ndash350 200ndash260 100ndash150

Ray Straw 362ndash470 190ndash245 99ndash240

Corn Cobs 337ndash412 319ndash360 61ndash159

Corn Stalks 350ndash396 168ndash350 70ndash184

Sugarcane Bagasse 250ndash450 280ndash320 150ndash250

Sorghum Straw 320ndash350 240ndash270 150ndash210

26 Anaerobic digestion process

Bio-chemical conversion of the agro-wastes into energy is one of the most efficient and

eco-friendly process compared to the direct combustion or thermochemical process

(Renouf Pagan and Wegener 2013) Anaerobic digestion is a biochemical process

where organic matter is decomposed by microbial community in the absence of oxygen

to produce CH4 and CO2 (Gumisiriza Hawumba Okure and Hensel 2017 Gumisiriza

Mshandete Rubindamayugi Kansiime and Kivaisi 2009) In addition to the major

components such as CH4 and CO2 traces of nitrogen (N2) oxygen (O2) and hydrogen

sulphide (H2S) together is known as the biogas

AD process can be broadly classified into four sub-processes namely hydrolysis

acidogenesis acetogenesis and methanogenesis (Figure 26) Acidogenic acetogenic

and methanogenic bacteria act on the available organic matter in the anaerobic condition

to produce biogas (Kothari Pandey Kumar Tyagi and Tyagi 2014) Organic matter

available in the anaerobic digesters generally consists of complex compounds such as


19

carbohydrates (cellulose hemicellulose starch) proteins and lipids The concentration

of these organic compounds varies according to the type of feedstock used The

complexity of these compounds hinders the action of microbes (Gumisiriza et al 2017)

Hydrolysis of these compounds to smaller units and solubilising them to make them

available for the microbial uptake (Sawatdeenarunat et al 2015) and further

degradation is necessary for proceeding the AD process The biochemical equation for

the hydrolysis of complex organic matter is presented as below (Gunes Stokes Davis

Connolly and Lawler 2019)

C6H10O4 + 2H2O C6H12O6 + H2 Eq21

In the subsequent phase the acid formic acidogenic bacteria use the monomeric sugars

amino acids and fatty acids obtained in the hydrolysis step to produce intermediate

volatile fatty acids (VFArsquos) (Neshat et al 2017) VFAs consist of short-chain organic

acids such as acetic acid propionic acid butyric acid valeric acid and caproic acid

Acidogenic bacteria use CO2 H2O and H2 to produce VFArsquos and have a doubling time

of approximately 30 minutes The acidogenic reactions can be written as follows (Gunes

et al 2019)

C6H12O6 2CH3CH2OH + 2CO2 Eq22

C6H12O6 + 2H2 2CH3CH2COOH + 2H2O Eq23

CH3CH2COOH + 2H2O CH3COOH + CO2 + 3H2 Eq24


20

Figure 26 Anaerobic digestion pathway Source (Si 2016)

Acetogenesis is the process in which the acetogenic bacteria converts the VFAs such as

propionic acid butyric acid and valeric acid to acetic acid and H2 (Si 2016) It is a

comparatively slow process and takes place usually in 15 to 4 days (Gunes et al 2019)

In the final stage the acetic acid H2 and CO2 are utilised by the methanogens to

produce CH4 and CO2 (Neshat et al 2017) This process of formation of methane from

the products of acetogenesis is known as methanogenesis It is the slowest growing

process and its doubling rate is 2 to 4 days (Kainthola Kalamdhad and Goud 2019)

Methanogens which produce methane by consuming H2 and CO2 are known as the

hydrogenotrophic methanogens (Xu et al 2019) On the other hand methanogens that

produce CH4 and CO2 from acetic acid are called acetoclastic methanogens (Kainthola


21

et al 2019) Methanogenesis can be represented by using the following equations

(Gumisiriza et al 2017 Kothari et al 2014)

2CH3CH2OH + CO2 2CH3COOH + CH4 Eq25

CH3COOH + CO2 CH4 + 2CO2 Eq26

CH3OH + H2 CH4 + H2O Eq27

CO2 + 4H2 CH4 + 2H2O Eq28

HCO-3 + 4H2 + H+ CH4 + 3H2O Eq29

27 AD process parameters

The microbial activity on the degradation of organic matter to produce CH4 is

dependent on various process parameters such as type of organic matter the

temperature of operation pH of the medium Carbon to Nitrogen (CN) ratio and trace

elements (Neshat et al 2017) In order to attain maximum yield from the organic

substance using AD these process parameters should be optimum (Kainthola et al

2019) The techno-economic feasibility of the experimental set-up in maintaining these

process parameters should also be considered while designing a commercial AD plant

271 Process temperature

The temperature of the process is one of the most influential parameters in an anaerobic

digestion process Effect of temperature can affect the metabolic activity of the

microorganisms resulting in variation of biogas produced Acidogenic bacteria are

active in a particular temperature range and a variation from that optimum temperature


22

range can result in acidification due to VFA accumulation (Neshat et al 2017) Based

on the experimental results of various researchers and by thermodynamic and kinetic

factors it can be concluded that the temperature of operation of an AD process

dramatically influences the methane production from an AD process (Gumisiriza et al

2017)

Based on the microbial growth rate process temperature range of an AD process can be

broadly classified as follows (Neshat et al 2017)

Psychrophilic Cryophilic 10 ndash 15 oC

Mesophilic 30 ndash 40 oC

Thermophilic 45 ndash 65 oC

AD process generally takes place between 10 oC (psychrophilic) to 65 oC (thermophilic)

(Figure 27) Based on experimental researches the AD process generally takes place at

an optimal range of 37 oC for mesophilic operation and 55 oC for thermophilic operation

(Mata-Aacutelvarez 2002 van Lier et al 2001) Advantages and disadvantages of both

mesophilic and thermophilic range of operation in anaerobic digestion are listed in

Table 23

Higher rate of microbial growth and lesser retention time along with the lesser reactor

volume makes the thermophilic operation the most favourable among the various

operating temperature (Gumisiriza et al 2017 Neshat et al 2017)According to the

literature higher temperature of operation is more sensitive to toxicity due to the

imbalance created due to high amount of VFA production and its low conversion at

high temperatures (Kainthola et al 2019)


23

Table 23 Comparison of mesophilic and thermophilic operation Source (Gumisiriza

et al 2017 Kainthola et al 2019 Neshat et al 2017)

Advantages Disadvantages

Mesophilic Wide range of operation More digester volume

Common anaerobic bacteria More retention time

More stable

Less energy input

Thermophilic higher organic loading rate increases inhibitory effect

a faster rate of biogas production Less range of feedstock

smaller reactor volume More sensitive to toxicity

Figure 27 Rate of AD process based on different process temperatures (Mojiri Aziz

Zaman Aziz and Hamidi 2012)

Fluctuations in the operating temperature by a fewer degree also results in a

considerable variation of the AD operation (Chae Jang Yim and Kim 2008) So

maintaining a constant temperature of operation throughout the AD process is very

important


24

272 pH

pH can be described as the measure of acidity or alkalinity of a substrate mixture

usually expressed in parts per million (ppm) (Kainthola et al 2019) It also represents

the concentration of hydrogen ions in the aqueous solution The pH of the substrate

inoculum mixture plays a vital role in the AD process as the microorganismrsquos reaction

and inhibition are directly related to the acidity or alkalinity of the medium

(Srisowmeya Chakravarthy and Nandhini Devi 2019) Table 24 summarises the

optimal pH value for different stages of the AD process

Table 24 Optimal pH requirement for each stage of the AD process

StagesProcess Temperature pH range Reference

Hydrolysis Mesophilic 55 (Kim et al 2003)

Acidogenesis Mesophilic 65 (Kim et al 2003)

Thermophilic 60 -70(Park Tsuno Hidaka and

Cheon 2008)

Methanogenesis Mesophilic 70(Huber Thomm Koumlnig Thies

and Stetter 1982)

Mesophilic 65-82(Lee Behera Kim and Park

2009 Yang and Okos 1987)

AD Mesophilic 70 -72 (Ağdağ and Sponza 2007)

Mesophilic 68 -72(Ward Hobbs Holliman and

Jones 2008)

Maximum methane yield Mesophilic 65 -75(Liu Yuan Zeng Li and Li

2008)

Neshat et al (2017) reported that the growth of methane producing methanogenic

bacteria is limited to a narrow band of pH 68 to 72 (Neshat et al 2017) The optimum

pH for acidogenic and hydrolytic microorganism growth is much lower than that of the

methanogenic community (Kainthola et al 2019) Therefore maintaining the optimum

pH for each process is essential to obtain maximum methane yields (Neshat et al 2017)


25

273 CN ratio and trace nutrients

CN ratio is also an essential parameter in determining the overall methane production

Low gas production is experienced in case of high CN ratio as the methanogenic

bacterial community consumes the available nitrogen to meet their requirements of

proteins and hence leaving behind the carbon content of the substrate resulting in less

biogas production (Wu Hickey Bhatnagar and Zeikus 2018) Low CN ratio of the

inoculum substrate mixture can result in the liberation of ammonium ions (NH4+) which

will lead to the gradual pH increase of the assay and finally results in toxicity and

inhibition of methane production (Fernandez Srinivas Schmidt Swita and Ahring

2018) Thus an optimal CN ratio of 20-301 is required to efficiently convert the

organic matter into biogas without any inhibition (Neshat et al 2017) Table 25

tabulates the CN ratio of various substrates that are subjected to anaerobic digestion

Agricultural residues and agro-wastes have a high CN ratio which makes them less

favourable for anaerobic digestion as the optimum CN ratio is not obtained (Risberg

Sun Leveacuten Horn and Schnuumlrer 2013) Whereas the low CN of manure and its lesser

carbon content retards the process and thus does not make it favourable for anaerobic

digestion as a sole substrate (Li et al 2014) However when these two substrates are

being co-digested a more preferred CN ratio can be obtained and thus an improvement

in the biogas production can be expected For instance Cheng and Zhong (2014)

reported CN of the cotton stalk as 501 which was higher than the considered optimum

CN ratio for an AD process The addition of swine manure resulted in attaining an

optimum CN ratio of 251 which resulted in an enhanced biogas production (Cheng and

Zhong 2014) Similar results were also achieved by Zhang et al (2016) when sorghum

stem and cow manure were co-digested An improvement of 26 from the control was

reported in this study (Zhang Zhang Li Li and Xu 2016)


26

Table 25 CN ratio of different lignocellulosic materials and manure from previous

studies

Biomass CN ratio Reference

Corn Stover 63 (Li et al 2014)

Switch grass 90 (Brown Shi and Li 2012)

Wheat straw 60 (Brown et al 2012)

Oat straw 46 (Wu Yao Zhu and Miller 2010)

Rice straw 47 (Ye et al 2013)

Cotton stalk 50 (Cheng and Zhong 2014)

Sugarcane bagasse 118 (Karthikeyan and Visvanathan 2013)

Chicken manure 10 (Li et al 2013)

Cattle manure 24 (Chandra et al 2012)

Swine manure 17 (Ye et al 2013)

Various micronutrients such as iron nickel zinc selenium molybdenum manganese

and tungsten along with macronutrients such as carbon nitrogen phosphorous

potassium calcium magnesium and sulphur are essential for the survival and growth of

the microorganisms that convert the organic matter to biogas (Paul and Dutta 2018)

However these micro macronutrients are being supplemented by the activated sludge

inoculum which constitutes nearly 95 of the volume in all the batch assays

28 Pre-treatment of lignocellulosic substrates

Pre-treatment of a substrate is the process of conditioning the substrate before

subjecting it to an AD process in order to enhance the methane yield of the substrate

with a comparatively lesser retention time (Maniet et al 2017) Pre-treatment helps to

break the structural bonding in the substrates and thus makes the organic matter readily

available for the microorganisms (Saidu Yuzir Salim Azman and Abdullah 2014) In

addition to these primary functions pre-treatment also helps to make the feedstock

available in a moderate size to the AD process and thereby reduces the energy

consumption for the process (Bauer et al 2014a) Pre-treatment of lignocellulosic


27

biomass is mainly done to reduce the cellulose crystallinity and to reduce lignin (Maniet

et al 2017) The lignocellulosic structure of the agricultural residues and the breakage

of complex lignin structure as a result of pre-treatment is as shown in Figure 28

Figure 28 Pre-treatment of lignocellulosic biomass (Kumar Barrett Delwiche and

Stroeve 2009)

The main types of lignocellulosic pre-treatments include physical chemical and

biological pre-treatments (Lizasoain et al 2016) Physical pre-treatment includes

mechanical grinding irradiation steam explosion and hydro-thermolysis (Wang et al

2018) Chemical pre-treatments are those pre-treatment methods using acid base

catalyst organic solvents and oxidising agents (Kumar et al 2009) On the other hand

biological pre-treatment is the type of pre-treatment in which the substrates are

subjected to microbial and enzymatic hydrolysis (Yang and Wyman 2008)

281 Physical pre-treatment

Physical pre-treatment methods include the treatment of lignocellulosic materials by

using mechanical operations such as grinding milling and chipping along with


28

ultrasound microwaves liquid hot water and steam explosion (Neshat et al 2017)

Methane yield and hydrolysis kinetics largely depend on the pre-treatment methodology

final particle size and physical structure of the lignocellulosic substrates (Eskicioglu et

al 2017) Reduction in particle size in order to increase the surface area and pore size is

one of the main characteristics of such mechanical pre-treatments (Galbe and Zacchi

2007 Palmowski and Muumlller 2000 Taherzadeh and Karimi 2008) Table 26 tabulates

the improvement in the methane yield of lignocellulosic substrates due to different

physical pre-treatment methodologies However significant energy consumption for

mechanical pre-treatment is one of its major disadvantages (Paudel et al 2017)

Ultrasound and microwave technologies have also proved to be efficient in increasing

the methane yields by cleavage of β-14 glucan bonds which increases the accessibility

for microbes(Chandra et al 2012) However the high cost associated with mechanical

pre-treatments made it less popular on commercial-scale operations (Chandra et al

2012) Production of inhibiting by-products high cost and higher energy consumption

prevents the commercial use of these technologies (Neshat et al 2017)

Liquid hot water pre-treatment is a methodology in which pressure is generated so as to

maintain water at an elevated temperature in the liquid state for a few minutes (Kumar

et al 2009) This process doesnrsquot require rapid decomposition Catalyst is also not used

in liquid hot water pre-treatment technique Pressurised water enters into the biomass

increasing the surface area and thereby removing hemicellulose and lignin (Mosier et al

2005) Liquid hot water pre-treatment methodology is attractive as it is a low-cost

process and doesnrsquot involve the use of any catalyst for the pre-treatment However

water and energy requirements are higher

The steam explosion was introduced tested and patented by Mason et al (1926) In the

steam explosion the substrates were subjected to hot steam (180 oC to 240 oC) and


29

pressure (1 to 35 MPa) followed by explosive decompression of the biomass resulting

in the rupture of the rigid structure of biomass fibres (Stelte 2013) The sudden pressure

release will cause the cellulose bundles to defibrillate and thus increases the

accessibility of the cellulose for enzymatic hydrolysis and fermentation (Stelte 2013)

Steam explosion is a cost-effective pre-treatment methodology as it avoids the usage of

any chemicals Equipment corrosion is minimum in steam explosion pre-treatment due

to the absence of chemicals A good yield of hemicellulose with low degraded by-

products could be obtained from steam explosion pre-treatment (Stelte 2013)

During the steam explosion process lignin is softened causing it to be released from

the cell wall and leads to a uniform distribution of lignin into the raw material (Kumar

et al 2009) Steam explosion reduces the time required by the microorganisms to

access the cellulose and hemicellulose (Hendriks and Zeeman 2009) resulting in an

increase in the rate of hydrolysis and more methane can be produced from the substrate

in comparatively lesser time compared to the untreated substrates (Paudel et al 2017)

The hydraulic retention time of the digester is also reduced as a result of steam

explosion (Bauer et al 2014a) The absence of acid or base will reduce the risk of high

or low pH of the inoculum substrate mixture (Lizasoain et al 2016) Even though the

steam explosion process is one among the easiest and cheapest process of pre-treatment

of lignocellulosic substrates the main challenges faced in this pre-treatment method is

the formation of toxic compounds and destruction of xylan fraction (Hendriks and

Zeeman 2009)

Previous studies on the steam explosion showed an improvement in methane yield For

instance the steam explosion of potato pulp reported by Kryvoruchko et al (2009)

produced 373 mlCH4gVSadded which was 12 more than the untreated substrate

(Kryvoruchko Machmuumlller Bodiroza Amon and Amon 2009) Similarly Bauer et al

(2009) reported a higher methane yield of 331 mLCH4gVSadded (an increase of 20


30

CH4) when wheat straw was subjected to steam explosion pre-treatment (Bauer Boumlsch

Friedl and Amon 2009) Table 26 summarises similar studies that include various

mechanical pre-treatment


31

Table 26 Previous studies and their results on mechanical pre-treatments

Sl

No

Pre-treatment

method

Lignocellulosic

substrates

Pre-treatment

condition

CH4 production

after pre-

treatment

(mLCH4gVSadded)

Increase in

CH4 ()

References

1 Milling Maize silage 2 mm 410 11 (Bruni Jensen Pedersen

and Angelidaki 2010)

Maize stalks 2 mm 272 0 (Menardo Airoldi and

Balsari 2012)

Barley straw 5 mm 370 54 (Menardo et al 2012)

2 Steam explosion Potato pulp 107 oC 15 minutes 373 12 (Kryvoruchko et al 2009)

Cane leaf matter 200 oC 10 minutes 237 3 (Mokomele et al 2019)

Harvested hay 175 oC 10 minutes 281 16 (Bauer et al 2014b)

Rice straw 200 oC 2 minutes 329 51 (Zhou et al 2016)

Wheat straw 180 oC 15 minutes 361 20 (Bauer et al 2009)

Kitchen waste 120 oC 15 minutes 930 43 (Li Jin Li Li and Yu

2016)

Reed biomass 200 oC 15 minutes 350 89 (Lizasoain et al 2016)

Olive mill solid

waste

200 oC 5 minutes 589 61 (Rincoacuten Rodriacuteguez-

Gutieacuterrez Bujalance

Fernaacutendez-Bolantildeos and

Borja 2016)


32

salix 230 oC 5 minutes 241 50 (Estevez Linjordet and

Morken 2012)

3 Microwave Switchgrass 150 oC 2450

MHz 1600 W

320 8 (Jackowiak Frigon

Ribeiro Pauss and Guiot

2011)

Wheat straw 150 oC 2450

MHz 1600 W

345 28 (Jackowiak Bassard

Pauss and Ribeiro 2011)

4 Liquid Hot Water Wheat straw 120 oC 1 hour 299 64 (Menardo et al 2012)

Sunflower stalks 170 oC 1 hour 219 14 (Monlau Barakat Steyer

and Carrere 2012)

Cynara stalks 160 oC 1 hour 24 (Oliveira Gominho

Diberardino and Duarte

2012)

Rice straw 120 oC 1 hour 261 32 (Menardo et al 2012)

Barley straw 90 oC 1 hour 340 42 (Menardo et al 2012)

Maize stalks 120 oC 1 hour 267 9 (Menardo et al 2012)


33

282 Chemical Pre-treatment

Disruption in the structure of lignocellulosic biomass as a result of chemical treatment is

known as chemical pre-treatment The main types of chemical pre-treatment can be

broadly classified as oxidative acidic alkaline ionic and inorganic salts pre-treatments

(Chen et al 2017) Oxidative pre-treatment is done to enhance methane yield of a

lignocellulosic substrate by subjecting the substrate to an oxidising alkaline medium at a

high temperature (~ 190 oC) for a short period (~ 30 minutes) (Teghammar Yngvesson

Lundin Taherzadeh and Horvaacuteth 2010) This chemical action is solubilising soluble

lignin and hemicellulose in the lignocellulosic materials and thereby the surface area of

cellulose is being increased (Monlau et al 2012)

Bases such as calcium potassium and sodium hydroxides are used to perform alkaline

chemical pre-treatment on lignocellulosic substrates (Chen et al 2017) Alkaline pre-

treatment is reported to be an efficient technique in the removal of lignin and preserving

carbohydrates and cellulose to enhance microbial activity which leads to a faster and

increased methane yield from the substrates (Zhu Wan and Li 2010) Alkaline pre-

treatment can also be used to increase pore size and accessible surface area (Datta 1981

Gharpuray Lee and Fan 1983) However these alkaline chemical pre-treatment

methodologies were restricted to minimal usage due to its chemical requirements and

formation of inhibitory compounds

Dilute acids are generally used for acidic chemical pre-treatment of lignocellulosic

substrates Acidic pre-treatments are generally carried out for short retention times (~ 1-

5 minutes) and high temperature (~120 ndash 170 oC) or long retention time (~15 minutes-1

day) and low temperature (~25 oC) Sulfuric acid hydrochloric acid and nitric acid are

the primary reagents used in acidic chemical pre-treatments (Gunes et al 2019) Acidic

pre-treatment breaks the bonds between ligninphenolics and carbohydrates complexes


34

so that cellulose and hemicellulose are readily available for the microbes (Knappert

Grethlein and Converse 1980) Similar to alkaline pre-treatment acidic pre-treatment

also has a significant drawback as the requirement of chemicals and the formation of

inhibitory compounds from the hemicellulose degradation (Larsson et al 1999)

Ionic liquids are also used for pre-treating the lignocellulosic biomass These liquids are

capable of dissolving lignocellulosic materials producing cellulose with a little residual

crystallinity (Samayam and Schall 2010) Despite having limited environmental issues

as these ionic liquids are capable of reusing even after pre-treatment they are less

popular due to the high cost of these compounds (Nguyen et al 2010)

Some of the inorganic salts also have been tested as a catalyst in chemical pre-treatment

of lignocellulosic substrates Generally such pre-treatments are being conducted at a

higher temperature (~160 oC) for few minutes (~45 minutes) (Liu et al 2009a Liu et

al 2009b Monlau et al 2012) Chemical pre-treatment is efficient in breaking the

ester linkage and the bonds between carbohydrates and lignin However the effect on

delignification is limited in chemical pre-treatment (Liu et al 2009a Liu et al 2009b)

The main drawback of inorganic salts pre-treatment is the presence of an excessive

amount of trace elements which will lead to inhibition of the process (Liu et al 2009a)

283 Biological pre-treatment

Destruction of the lignocellulosic structure of the biomass using any living organisms is

known as biological pre-treatment (Neshat et al 2017) Unlike physical and chemical

pre-treatment biological pre-treatments are more sustainable for the environment (Lee

1997 Taherzadeh and Karimi 2008) Commercial enzymes or fungi are being used to

break the complex bonding structure of lignocellulosic materials Different types of

commercial enzymes such as the endoglucanase exoglucanase and xylanase are being

synthesised and used for biological pre-treatment techniques (Lopez et al 2007) The


35

high enzymatic activity leads to the breakdown of complex lignocellulosic structure

(Lopez et al 2007) The main drawback of biological pre-treatment is the dependency

of enzymatic activity on temperature and pH (Kainthola et al 2019) Ensiling of

biomass is also considered as a biological pre-treatment (Janke et al 2019) Ensiling

induces an anaerobic condition that will convert many insoluble sugars into organic

acids and helps in preserving structural proteins and carbohydrates (Vervaeren Hostyn

Ghekiere and Willems 2010)

29 Anaerobic co-digestion

Anaerobic co-digestion is the process of combining two or more substrates to

supplement the deficiency of a substrate if subjected to the AD process as mono-

digestion (Song and Zhang 2015) Moreover anaerobic co-digestion of lignocellulosic

substrates with manure helps to maintain a stable pH within the methanogenesis range

(Kaparaju and Rintala 2005) Improved biogas yield and economic advantages

associated with the sharing of equipment resulted in an increased interest among co-

digestion of lignocellulosic substrates with manure (Li Li Zheng Fu and Lar 2009)

Mata-Alvarez et al (2002) reported that anaerobic co-digestion of substrates established

a positive synergism in the digester (Mata-Aacutelvarez 2002)

SCT and SCB are substrates with a high CN ratio that can be co-digested with

substrates with a lower CN ratio such as the cow manure chicken manure pig manure

etc In addition to improving the CN ratio of the substrate inoculum mixture co-

digestion also improves the trace nutrients content requirement for the AD process

(Sawatdeenarunat et al 2015) It also helps in the effective utilisation of the digester

volumes and more methane yield can be attained from the digester of the same volume

by incorporating co-digestion techniques (Li et al 2009)Co-digestion of energy crops

and animal manure resulted in increased plant output without changing the reactor


36

volume (Lindorfer Loacutepez Resch Braun and Kirchmayr 2007) For instance

Mokomele et al (2019) reported an increase of 7 in cumulative methane in co-

digesting sugarcane leaves and cow manure in 7525 ratio than the mono-digestion of

dairy cow manure (Mokomele et al 2019) Similarly an 18 increase in methane yield

was reported by Zheng et al (2015) when switchgrass and diary manure was co-

digested (Zheng et al 2015)

Co-digestion reduces the waste disposal issues related to the animal excreta and thereby

reduces GHG emissions (Shen et al 2018) On the other hand the co-digested digestate

has a greater fertilizer value compared to the digestate of mono-digestion (Li et al

2014) Thus co-digesting the lignocellulosic waste from the agricultural residue of

sugarcane along with the cattle manure can improve the overall production yield of the

AD process

Many studies have been conducted regarding the co-digestion of nitrogen-rich

substrates with carbon-rich lignocellulosic materials to supplement the deficiencies of

these batch assays Table 27 summarises some of the significant studies in this field

along with their results


37

Table 27 Results of previous studies on the effect of feed ratios during the anaerobic co-digestion of manure and lignocellulosic agro

wastes

Sl

No

Substrates Operating

conditions

Feed Ratios

tested

Pre-treatment Results Reference

1 Sugarcane leaves

+ Diary cow

manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 7525

5050 2575

0100 (VS basis)

Steam explosion 0100 (274

LCH4kgVSadded)

(Mokomele et al

2019)

2 Sugarcane

Bagasse + Diary

cow manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 5050

0100 (VS basis)


LCH4kgVSadded)

(Mokomele et al

2019)

3 Sugarcane leaves

+ Diary cow

manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 7525

5050 2575

0100 (VS basis)

NA 7525 (292

LCH4kgVSadded)

(Mokomele et al

2019)

4 Switchgrass +

Diary manure

500 mL Mesophilic

temperature ISR =

067 30 days

1000 7525

5050 2575

0100 (TS basis)

NA 5050 18 increment

in methane yield than

mono-digestion of

switchgrass

(Zheng et al 2015)

5 Salix viminalis +

Cow manure

1125 mL

mesophilic

temperature ISR =

067 57 days

1000 8020

7030 6040

5050 4060

3070 2080

0100 (VS basis)


mLCH4gVS)

pre-treated salix (1976

mLgVS) has 23

(Estevez et al 2012)


38

improvement over

untreated salix

6 Corn Stover + Pig

Manure

250 mL Mesophilic

temperature ISR =

026 70 days

1000 8020

6733 5050

3367 2080

0100 (VS basis)

NA 0100 (889

mLCH4gVS)

(Li et al 2018b)

7 Cotton stalk +

swine manure

500 mL Mesophilic

temperature ISR =

025 45 days

1000 7525

5050 2575 (TS

basis)

NA 5050 19-fold increase

in yield when

compared to control

(102 mLCH4gVS)

(Cheng and Zhong

2014)

8 Apple waste +

Chicken manure

250 mL Mesophilic

temperature ISR =

026 70 days

1000 8020

6733 5050

3367 2080

0100 (VS basis)

NA 3367 (827

mLCH4gVS)

(Li et al 2018b)

9 Whey + cow

manure

400 mL Mesophilic

temperature 55 days

1000 8515

7525 5050

2575 1585

0100 (VS basis)

NA 8515 (266

mLCH4gVS)

(Vivekanand Mulat

Eijsink and Horn

2018)


39

291 Chicken manure as a co-digestion substrate

Chicken manure also known as poultry litter can be described as a mixture of excreted

manure water spilled feed feathers and bedding materials from a poultry farm

Traditionally the chicken manure was spread on land as a fertilizer or disposed of to

landfill (Abouelenien Namba Kosseva Nishio and Nakashimada 2014) With

increasing environmental concerns related to spreading litter on land and the rising cost

of energy more attention was given for developing more value from these kinds of

wastes in the form of energy (Li et al 2014)

The potential of manure being used to produce biogas using AD technology was

identified and various experiments were conducted by different researchers

(Abouelenien et al 2014 Kalamaras and Kotsopoulos 2014 Masih-Das and Tao 2018

Mokomele et al 2019 Shen et al 2018) However one of the main drawbacks of the

manure as a mono-digestion substrate was the low CN ratio which caused the system

to inhibit the process and result in low gas production (Abouelenien Kitamura Nishio

and Nakashimada 2009) Later with the advancement of co-digestion technology the

potential of manure to be used as co-substrate in the AD process started to get its

priority (Mokomele et al 2019) Using poultry manure for the AD process not only

improved the biogas potential of substrates with a high CN ratio but also helped in

solving various issues related to the waste management from poultry farms (Neshat et

al 2017)

Table 28 tabulates a list of studies that were conducted by various researchers which

used different kind of manures to enhance biodegradability and to improve methane

yield using AD technology Cow manure pig manure horse manure chicken manure

etc were used by various researchers such as Mao et al (2017) Zhang et al (2017) and

Moset et al (2017) in their studies All these studies reported an improvement in


40

methane yield as a result of co-digestion with manures Studies conducted by Awasthi

et al (2019) reported chicken manure to produce highest biogas production rate of 04

m3kg of dry matter while comparing it with pig sheep cattle horse and duck manure

Similar results were also reported by Neshat et al (2017) Thus use of chicken manure

as a co-substrate in co-digestion with lignocellulosic substrates can result in a better

degradation of organic matter and thus better methane yield can be expected (Neshat et

al 2017)


41

Table 28 Previous studies reporting improvement in methane yields as a result of co-digesting with manure

Sl No Base substrates Manure Results References

1 Grass silage Pig manure

Co-digestion ratio of 31 (grass silagepig manure) resulted in 87 more

methane yield compared to mono-digestion of PM (2798

LCH4kgVSadded) (Xie et al 2011)

2 Grass Cow manure

Up to 20 improvement in methane yield in co-digestion than mono-

digestion of cow manure (276 NLCH4kginfluent) (Moset et al 2017)

3 Corn straw Pig manure

Co-digestion ratio of 37 (corn strawpig manure) produced maximum

methane yield of 220 LCH4kgVSadded (Mao et al 2017)

4 Food waste Horse manure

Co-digestion ratio of 11 (food wastehorse manure) resulted in 227

more methane than mono-digestion of horse manure (370

LCH4kgVSadded) (Zhang et al 2017)

5 Meadow grass Pig manure

Co-digestion ratio of 19 (meadow grasspig manure) resulted in 159

more methane yield compared to mono-digestion of pig mnaure

(337 LCH4kgVSadded) (Tsapekos et al 2017)

6

Organic fraction of

municipal solid waste Chicken manure

Co-digestion ratio of 11 produced more methane yield than the mono-

digestion of chicken manure (1500Nml) (Matheri et al 2017)

7 Apple waste Chicken manure

Co-digestion ratio of 12 (apple wastechicken manure) produced highest

methane yield (827 LCH4kgVSadded) (Li et al 2018)


42

210 Overview of Literature review

The increasing agro-wastes in Australia facilitates the use of anaerobic digestion

techniques to produce energy from wastes Sugarcane agricultural wastes viz the SCT

and SCB contribute a large quantity in the total agro wastes produced in Australia (FAO

2018) Various factors contribute to maximising energy production using this technique

The use of nitrogen-rich manure with sugarcane agricultural wastes could be favouring

more methane yield from these wastes The usage of manure for increasing nitrogen

content is more environmentally sustainable than using chemicals to improve the CN

ratio of the digester Moreover it helps in the efficient usage of poultry wastes Based

on the literature study conducted it was found that not much research has been done on

co-digesting the carbon-rich lignocellulosic sugarcane wastes with nitrogen-rich chicken

manure There exists a research gap in this field regarding the effect of different feed

ratios on methane yields during anaerobic co-digestion of sugarcane waste with chicken

manure which is investigated in this study This study is also extended to investigate

the behaviour of pre-treated SCT and SCB with different feed ratios while co-digesting

with CM This study aims to reveal which feed ratio produces more yield and the

synergistic or antagonistic effects of co-digestion of pre-treateduntreated SCT and SCB

with CM

Chapter 3 Materials and Methods

43

Chapter 3 Materials and methods


44


45

31 Substrates

Substrates used in this study include SCT SCB and CM These three substrates were

used to study the effect of feed ratio and pre-treatment on methane yields during

anaerobic co-digestion of the agricultural residue of sugarcane with CM SCT and SCB

were the substrates subjected to pre-treatment to study the effect of pre-treatment

311 Lignocellulosic substrates

SCT was collected from the sugarcane fields of the Racecourse sugar mill Mackay

Queensland The collected SCT was then milled to nearly 2 mm particle size The

prepared SCT was then stored at 4 oC until further use On the other hand the SCB was

collected from the Racecourse sugar mill Mackay Queensland SCB was also

subjected to a similar milling process to reduce the particle size to nearly 2 mm in order

to use it for the BMP test and then stored at 4 oC till further use

312 Manure

CM used in this experiment was collected from poultry farms in Mareeba (northern

Queensland) CM mainly composed of the poultry feed beddings excreta etc The

collected CM was stored at 4 oC until further use

32 Inoculum

The inoculum used in this experiment was collected from a full-scale biogas plant

treating sewage sludge under mesophilic conditions (Queensland Urban Utilities

Luggage point Pinkenba Brisbane) The collected inoculum was stored at 4 oC and was

pre-incubated at 37 oC for one week at the bioprocess engineering laboratory of Griffith

University Nathan campus Brisbane Australia in order to reactivate the micro-

organisms and any to remove any residual methane from the inoculum


46


Based on the various pre-treatment studied in section 28 steam explosion pre-treatment

had a clear advantage over the other types of pre-treatment mainly due to its low cost in

pre-treating lignocellulosic substrates Thus steam explosion pre-treatment was adopted

as the pre-treatment method for this study Steam explosion pre-treatment was

conducted in a two-step pilot-scale reactor at the Renewable bio-commodities pilot

plant Racecourse sugar mill Mackay Queensland designed and built by Andrtiz Inc

(USA) 20 kg of the substrate (SCTSCB) was mixed with 20 kg of water (50

moisture by weight) in a first stage stainless steel horizontal reactor with a capacity of

150L and incubated at room temperature for one hour The substrates were then loaded

to a second stage vertical reactor with 22 m lengthy and diameter 02 m in which the

steam impregnation was done at 130 oC for 5 minutes followed by a steam explosion

(explosion pressure = 2 MPa) (Harrison et al 2013 Zhang Wong Albertson Doherty

and OrsquoHara 2013b)

34 Experimental Design

SCT and SCB were co-digested with CM in different feed ratios in order to investigate

the effect of feed ratios on methane yields during anaerobic digestion The sugarcane

residue used were further divided into pre-treated (steam explosion) and untreated

substrates The same feed ratios were designed for pre-treated sugarcane trash (Pre-

treated SCT) untreated sugarcane trash (Untreated SCT) pre-treated sugarcane bagasse

(Pre-treated SCB) and untreated sugarcane bagasse (Untreated SCB) with chicken

manure (CM) Table 31 shows the various feed ratios tested in this work such as 1000

7525 5050 2575 0100 for Pre-treated SCTUntreated SCTPre-treated

SCBUntreated SCBCM Various substrates used in this study are shown in Figure 31

and the flowchart of methodology of this research work is shown in figure 32


47

Figure 31 Substrates used in this study (A) Untreated SCT (B) Pre-treated SCT (C)

Untreated SCB (D) Pre-treated SCB (E) CM

Table 31 Different feed ratios in anaerobic co-digestion of SCT and SCB with CM

tested in this study

Sample Untreated

SCT ()

Pre-treated

SCT ()

Untreated

SCB ()

Pre-treated

SCB ()

CM

()

Untreated SCT 100 0 0 0 0

Co-digestion 1 75 0 0 0 25



Pre-treated SCT 0 100 0 0 0




Untreated SCB 0 0 100 0 0




Pre-treated SCB 0 0 0 100 0




CM 0 0 0 0 100


48

Figure 32 Flowchart of methodology adopted in this research study

Initially the substrates and inoculum is subjected to a set of pre-BMP analysis Based

on the results obtained from these analysis BMP preparation calculations are done to

estimate the quantity of substrate and inoculum required for the designed parameters

BMP assays are prepared based on these design parameters and incubated at the

designed temperature GC analysis is performed in calculated intervals to obtain the

quantity and composition of biogas produced in the assays This process is continued


49

until the BMP termination where there is no significant gas production from any of the

assays in successive GC test intervals After the BMP termination digestates are

collected for post-BMP analysis Data obtained from the pre-BMP analysis GC analysis

and post-BMP analysis are integrated and results are formed based on various data

processing techniques

35 Analytical methods

351 Determination of TS and VS

TS and VS of the substrates and inoculum were determined before the start of the

experiment using the Standard Methods (APHA 1995)

352 Determination of Chemical Oxygen Demand (COD)

The COD of the substrates and inoculum was determined according to the standard

methods described elsewhere (Latif Mehta and Batstone 2017) Briefly high range

COD vials (500 ndash 10000 mg L-1) were used to make dilutions using suitable dilution

factors Deionised water was used to make these dilutions The prepared dilutions were

then thermally digested using Spectroquant TR 620 COD digester at 148 oC for 2 hours

After the vials being cooled down to the room temperature COD values were examined

using a spectrophotometer Move 100 (Merck)

353 Determination of Total Kjeldahl Nitrogen (TKN) Total Kjeldahl

Phosphorous (TKP) soluble and total metal ions

The soluble and total metal ions of the sample were measured along with TKP and TKN

using Perkin Elmer (USA) Optima 7300DV inductivity coupled plasma ndash optical

emission spectroscopy (ICP-OES) equipped with WinLab32 software (Latif Mehta and

Batstone 2015)


50

354 Determination of CN ratio

The CN ratio of the sample was determined using Sercon Europa EA-GSL elemental

analyser by combusting the sample at 1050 oC The sample was then inter-phased to a

Sercon Hydra 20-22 (IRMS) as per the standard methods described elsewhere (Beckett

Cresswell Grice and Carter 2015)

355 Determination of cellulose hemicellulose and lignin

The chemical composition of the samples was analysed using the standard procedures

described in Pasangulapati et at (2012) (Pasangulapati et al 2012) Briefly dried

samples were sieved through 250 μm sieve plates according to the procedures

demonstrated in Hames et al (2008) of the National Renewable Energy Laboratory

(NREL) Biomass retained on the sieve plate was subjected to the accelerated solvent

extractor to remove any non-structural material (Sluiter et al 2008) A two-step acid

hydrolysis methodology was used to analyse structural carbohydrates lignin acetyl and

ash content from the residual material To determine the acid-insoluble lignin a muffle

furnace was used HPLC with a refractive index detector (Bio-Rad Aminex HPX-87P

column) was used to analyse structural carbohydrates During a total run time of 30

minutes column temperature of 85 oC an eluent flow rate of 06 mLmin (deionized

water) was maintained A UV-vis spectrophotometer was used to analyse the acid

soluble lignin at a wavelength of 205 nm and an extinction coefficient of 1101g-cm

356 Determination of VFA

VFAs of the samples were determined using a gas chromatograph (Agilent model

78090A USA) with flame ionization detection according to the standard methods and

protocols described elsewhere (Latif et al 2015)


51

357 Determination of ammoniacal nitrogen (NH4-N) and phosphate

(PO43-)

NH4-N and PO43- contents in the sample were determined using flow injection analyser

(FIA) Lachat Instruments USA Quick Chem 8000 according to the methods described

elsewhere (Latif et al 2015)

36 Biochemical Methane Potential (BMP) tests

BMP tests were conducted for all the feed ratios mentioned above as in Table 31 An

inoculum to substrate ratio of 2 was maintained in all the assays BMP test was

conducted in 160 mL serum bottles with 100 mL working volume as per the standard

BMP protocols described elsewhere (Angelidaki et al 2009) A feeding load of 08gVS

was maintained in all assays Distilled water was used to make the working volume of

100mL pH in all assays was monitored before sealing the cap and was made neutral

(69 ndash 72) by the addition of an adequate amount of 1M HCl or NaOH solution The

anaerobic condition was maintained in the headspace by flushing the headspace with

99 pure nitrogen gas for 3 minutes The prepared assays (Figure 33) were then closed

with a butyl rubber stopper and sealed with aluminium crimps Sealed assays were then

incubated at mesophilic conditions (37 plusmn 01 oC)


52

Figure 33 sealed 160 mL serum bottle for BMP test with inoculum substrate and

headspace flushed with nitrogen gas

Quantity of biogas produced from the assays was calculated by measuring the pressure

developed in the headspace by using the manometer displacement method A sample

gas of known volume was drawn from the headspace of the assay and subjected to a U-

tube manometer to find the volume of water displaced by the headspace pressure The

sample gas which was normalised to atmospheric pressure was then subjected to gas-

chromatography (Schimadzu GC-2014 Japan) (Figure 34) to examine the composition

of the gas from the headspace


53

Figure 34 Gas chromatography machine (GC-2014)

The gas chromatograph used for this research was equipped with a Thermal

Conductivity Detector (TCD) and chin carbon ST 100120 packed column It had a

length of 2 m an outer diameter of 116rsquorsquo and an inner diameter of 1 mm A sample

loop of 1 mL (Valco GC valve) was used for injection A temperature of 75 oC and

110 oC was set for the injector and oven of the gas chromatograph However the

detector temperature of the gas chromatograph was set to 120 oC Argon gas of ultra-

high purity at a flow rate of 275 mL min-1 and 7238 kPa was used as the carrier gas


54

37 Data Processing

371 Theoretical BMP

The maximum biochemical methane potential of a substrate or a combination of

substrates estimated based on the amount of COD that is fed to the assays through these

substrates or combination of these substrates is known as the theoretical BMP

(BMPthCOD) The theoretical BMP of each feed ratio was calculated based on the

chemical analysis done before the start of the BMP test Theoretical BMP can be

calculated based on equation 31 and is expressed as mlCH4gVSadded (Nielfa Cano and

Fdz-Polanco 2015)

ᆒ㢐ᆒ썈υ =ᆒt

tttEq31

R represents the universal gas constant (0082 atm Lmol K) Temperature of operation

is denoted by T (31015K) the pressure is given by p (1 atm) VSadded is the amount of

VS added in the assay through the substrate The number of moles of CH4 can be

calculated using equation 32 (Nielfa et al 2015) and is denoted by ηCH4

ᆒt =ᆒ썈υ

Eq32

COD denotes the total COD in the assay from the substrate or combination of substrates

372 Experimental BMP

The amount of methane produced experimentally per gram of VS (gVS) added to the

assay through the substrates is termed as the experimental BMP and is expressed as mL-

CH4gVSadded To estimate the methane potential of the substrate assay with the same

quantity of inoculum alone is operated as blank and the methane production from the

blank is subtracted from the methane produced from the assays with substrates and


55

inoculum (Zhang et al 2014) Thus the experimental BMP values can be calculated

using equation 33

ᆒ = 䇅ᆒ㢐戴戴ᆒ㢐戴 Eq33

The volume of methane produced per gVS of the substrate from the assay containing

substrates and inoculum at STP is denoted by VsubSTPmethane and volume of methane

produced per gVS of inoculum from the assay containing inoculum alone at STP is

expressed as VinoSTPmethane To reduce the error in experimental data analysis all batch

assays were prepared in triplicates and the average production from these three assays

are used for calculations

The volume of methane produced per gram VS of substrate added at STP during a

sample event can be calculated using equation 34 and equation 35 (Kafle Kim and

Sung 2012)

䇅ᆒ㢐戴 =s ᆒ㢐戴 Σ gVS䇅ݏ

Eq34

戴ᆒ㢐戴 =s ᆒ㢐戴 gVS戴䇅䇅

Eq35

Where Vbiogas is the volume of biogas produced in the sample event Cmethane is the

concentration of methane in the headspace of assays Rvol is the Ratio used to normalise

the temperature during volume measurement (Rvol = 111 for mesophilic condition and

volume measurement is done at 30 oC ndash room temperature calculated based on the ideal

gas equation) ƩgVSsubstrate is the sum of VS added to the assays through substrate or the

mixture of substrates and gVSinoculum is the grams of volatile solids in the blank assays

through the inoculum


56

373 Synergistic effects analysis

The synergistic effects analysis of the co-digestion mixture is done in order to evaluate

the influence of each substrate in different feed ratio mixtures It also helps to estimate

the synergetic effects that could be produced during the biodegradation process The

synergetic effect (α) was calculated using equation 36 (Nielfa et al 2015)

= 戴ݏ t䇅戴ݏ㢐ݏ t䇅戴ݏ

Eq36

Experimental production is the experimental cumulative methane yield from the co-

digestion assay at the end of the BMP test and theoretical production is the methane

yield calculated from the sole substrates considering the contribution of VS from them

to form the co-digestion mixture

374 Anaerobic biodegradability

Anaerobic biodegradability of a batch assay is the measure of the amount of degradation

in VS or COD of an assay as a result of the anaerobic digestion process happening

during the BMP test Anaerobic biodegradability is calculated in this experiment

according to the standard methods and protocols outlined in Nielfa et al 2015 COD

and VS in each bottle is measured before the start of BMP and also after terminating the

BMP CODi and CODf represent the initial and final COD of the assays and VSi and VSf

indicate the initial and final VS in the assays respectively Biodegradability (BD)

calculated based on the COD values are done according to equation 6 BD calculated

based on COD is expressed as BDexpCOD as in equation 37 (Nielfa et al 2015)

υᆒ썈υ = ᆒ썈υ ᆒ썈υᆒ썈υ

tt Eq37


57

BD calculated based on the VS of assays can be done according to equation 7 BD

calculated based on VS is expressed as BDexpVS as in equation 38 (Nielfa et al 2015)

υ =

tt Eq38

Theoretical maximum methane production estimated in equation one was corrected with

biodegradability to obtain the BD corrected BMP which was then compared with the

experimental BMP results to validate the experimental methane production from batch

assays BD corrected BMP values can be written as in equation 39 (Nielfa et al 2015)

ᆒ㢐υ = ᆒ㢐ᆒ썈υ υᆒ썈υ Eq39

The relative error between the experimental BMP and BD corrected BMP can be

calculated using equation 310 (Nielfa et al 2015) A smaller value of error indicates a

minor deviation of experimental results with the predicted results

ݏݏݏ = ᆒ ᆒ㢐υᆒ

Eq310

375 BMP mathematical Models

The experimental biochemical methane potential of different feed ratios was modelled

based on the first-order kinetic model and modified Gompertz model Different model

variables of these two models were estimated using Microsoft excels solver function

These model parameters were used to determine the rate and performance of the AD

process The experimental data were compared with both the first-order model and the

modified Gompertz model to test the curve fitting following these models


58

3751 First-order kinetic model

The anaerobic digestion process in batch assays was assumed to follow the first-order

kinetic model as the substrates used in this AD process are lignocellulosic materials

and hydrolysis is considered as the rate-limiting step for such substrates (Angelidaki et

al 2009) The first-order kinetic model equation is as given in equation 311 (Zhang et

al 2014)

= t 㢐t) Eq311

The cumulative methane yield of the substrate is denoted by B and is measured in ml-

CH4gVSadded The ultimate methane potential of the assay is denoted by Bo and has the

same unit as that of the biochemical methane potential of the assay The hydrolysis rate

constant of the AD process is denoted by khyd It represents the rate of hydrolysis of the

AD process and has a unit of d-1 The time of the process is denoted by t and is

calculated as the difference between cumulative time from the start of the experiment

and the time delay of the assay

3752 Modified Gompertz model

The cumulative methane production from the batch assays was also modelled using the

modified Gompertz model according to equation 312 (Shen et al 2018) The modified

Gompertz model assumes that biogas production in the assays was directly proportional

to the microbial activity in the assay

= t

t

Eq312

The ultimate methane production from the assays in an infinite period was given by Go

(ml-CH4gVSadded) G gives the modelled biochemical methane potential of the assay at

time t and has the same unit as that of the ultimate biochemical methane potential Rm


59

denotes the rate of reaction in the AD process (ml-CH4gVSaddedd and λ denote the lag

phase time constant for the assay (d)

376 Statistical Analysis

In order to evaluate the data-set for any significant difference within the groups and

between the groups statistical analysis of cumulative methane produced from triplicates

assays were performed using one-way ANOVA using SPSS software (Kalamaras and

Kotsopoulos 2014) The test was conducted at a 5 level of significance Multiple

comparison statistical test was also conducted among different feed ratios of the same

treatment using the post hoc (Tukey) test to check the mean difference of the assays at a

5 level of significance


60

1

Chapter 4 Results and Discussions

61

Chapter 4 Results and discussions


62


63

41 Physical properties and chemical composition of substrates and

inoculum

Various physio-chemical properties of the substrates and inoculum used in this study

are presented in Table 41 The chemical composition of the substrates indicates that the

pre-treatment of SCT and SCB had a significant influence on their properties Untreated

SCT had a TS content of 913 The steam explosion of SCT resulted in a slight

reduction in TS content (Table 41) Similarly Untreated SCB contained 828 of

solids which was also reduced by nearly 1 as a result of the steam explosion pre-

treatment A similar reduction of TS content as a result of pre-treatment was reported by

Lizasoain et al (2016) Direct injection of steam during steam explosion pre-treatment

leading to solubilisation caused this reduction in dry matter content of pre-treated SCT

and SCB (Lizasoain et al 2016)

The organic matter content of Untreated SCT was 909 of dry matter (Table 41)

Steam explosion pre-treatment increased the VS content of SCT by 18 Similarly

VS content in Untreated SCB was 894 of TS and the steam explosion increased its

VS by 64 (Table 41) Estevez et al (2012) also reported a similar improvement in

VS content during the steam explosion of salix (Estevez et al 2012) Solubilisation of

organic matter as a result of steam explosion pre-treatment resulted in a higher VS

content in pre-treated SCT and SCB (Neshat et al 2017) The present study results are

in accord with those reported by Mokomele et al (2019) where the steam explosion

pre-treatment improved the VS of sugarcane bagasse by 4 (Mokomele et al 2019)

The CN ratio of SCT increased from 831 in untreated SCT to 953 in steam-exploded

SCT The corresponding values for SCB were 1465 and 1919 respectively (Table 41)

Mokomele et al (2019) also reported an increase from 152 to 171 in the CN ratio of

sugarcane bagasse as a result of the steam explosion pre-treatment (Mokomele et al


64

2019) Enrichment of cellulose and lignin contents in steam-exploded SCT and SCB

biomass resulted in a higher CN ratio However the use of substrates with a high CN

ratio for biogas production can lead to VFA inhibition (Neshat et al 2017) Previously

several studies have shown the optimal CN ratio for biogas production is 20-301

(Abouelenien et al 2014 Li Liu Cui Yu and Ma 2018a Matheri et al 2018

Mokomele et al 2019 Neshat et al 2017 Sawatdeenarunat et al 2015) Thus to

improve the anaerobic biodegradability and biogas production SCT and SCB can be co-

digested with co-substrates rich in nitrogen CN ratio of nitrogen-rich manure is much

lower than that of the lignocellulosic agro-wastes (Song and Zhang 2015) For instance

Li et al (2014) reported the CN ratio of chicken manure as 101 (Li et al 2014) which

is similar to the results obtained in this study Similarly Mokomele et al (2019)

reported high CN values for SCB and cane leaf matter in his studies A CN ratio of

153 and 107 was reported for SCB and cane leaf matter respectively in the studies of

Mokomele et al (2019) which is in accord with the results attained in this study

(Mokomele et al 2019) Thus to optimise the CN ratio co-digestion of these two co-

substrates was done and the preferred feed ratio of co-digestion was investigated in this

study

Glucan concentration of both SCT and SCB was improved due to pre-treatment The

increase in glucan content after the steam explosion was 156 and 77 in SCT and

SCB respectively (Table 41) Similar results were also reported during the steam

explosion of bulrush (Wang et al 2010)In this study the glucan content of the

lignocellulosic bulrush increased from 361 to 398 due to pre-treatment Similarly

a slight reduction in total lignin content (about 1) was evident in the studies by Wang

et al (2010) The increase in glucan content with a corresponding decrease in

hemicellulose (xylan and arabinan) and lignin contents in steam-exploded


65

lignocellulosic substrates was obviously due to disruption of cellulose structure as a

result of steam explosion pre-treatment (Wang et al 2010) During steam explosion

pre-treatment hemicellulose and a part of cellulose are hydrolysed into simple sugars

resulting in an increase in glucan content with a corresponding decrease in xylan

arabinan and lignin contents (Rodriacuteguez Rodriacuteguez Jimeacutenez Guilleacuten and Fernaacutendez-

Bolantildeos 2007)

Table 41 Physio-chemical properties of substrates and inoculum

Parameter Unit CMUntreated

SCT

Pre-treated

SCT

Untreated

SCB

Pre-treated

SCBInoculum

TS 721 913 872 828 819 19

VS TS 807 909 925 894 951 722

COD gl 7487 11598 12099 7939 9156 211

TSVS - 12 11 11 11 11 14

CN ratio - 124 831 953 1465 1919 49

Glucan ND 307 463 351 428 ND

Xylan ND 168 117 20 146 ND

Arabinan ND 28 14 21 16 ND

Lignin ND 298 283 315 282 ND

TKP gPkg 82 11 07 05 06 312

TKN gNkg 836 97 108 512 71 1113

Note ND Not determined

The macro- (P S Ca Na K and Mg) and micro- (Co Cu Fe Mn Mo Ni Se and Zn)

nutrient contents in the studied substrates are presented in Table 42 Results showed

that macro-micro nutrient contents in the pre-treated biomass were lower than the

corresponding untreated substrates except for Na and S


66

Table 42 Macro and micronutrients of substrates and inoculum

Sample Units CMUntreated

SCT

Pre-treated

SCT

Untreated

SCB

Pre-treated

SCBInoculum

Al mgg 103 123 099 084 024 817

As mgg 000 000 000 000 000 000

B mgg 003 000 000 000 000 004

Ba mgg 004 002 002 001 001 019

Ca mgg 1687 312 167 025 015 1973

Cd mgg 000 000 000 000 000 000

Co mgg 000 000 000 000 000 001

Cr mgg 004 001 001 001 001 005

Cu mgg 011 000 000 000 000 056

Fe mgg 107 092 019 086 026 1972

K mgg 1977 601 283 293 076 1582

Mg mgg 461 188 138 036 028 666

Mn mgg 036 010 006 005 003 017

Mo mgg 001 000 000 000 000 003

Na mgg 171 007 034 001 004 1644

Ni mgg 002 000 000 000 000 005

P mgg 796 101 041 015 010 2735

Pb mgg 001 000 000 000 000 003

S mgg 321 082 809 018 524 1469

Se mgg 001 000 001 000 001 000

Zn mgg 027 000 000 000 000 067

Mokomele et al (2019) also reported a similar reduction in micro and macronutrients

possibly due to solubilisation of these nutrients in the water as a result of pre-treatment

(Mokomele et al 2019) Several previous studies show that the improvement in

sulphate or phosphate contents or the improvement in these two elements together

improved the rate of degradation of lignocellulosic biomass (Scherer Neumann

Demirel Schmidt and Unbehauen 2009) However the micro- and macro-nutrient

contents in this study are largely contributed by the inoculum as it is a major source for


67

micro- and macro-nutrient Chicken manure a co-substrate in this study also contained

a higher amount of micro-and macro-nutrients which are required for the optimum AD

process (Hagos Zong Li Liu and Lu 2017)

42 Anaerobic co-digestion of untreated and steam exploded SCT with

CM

The effect of different feed ratios such as 7525 5050 and 2575 on methane yields

during the anaerobic co-digestion of SCT with CM was tested in this section Methane

yields of all the tested co-digestion ratios were compared with the mono-digestion of

both SCT and CM Different feed ratios of both untreated and pre-treated SCT were

compared and the results are presented in Table 43

421 Effect of different feed ratios on methane yields during anaerobic

co-digestion of SCT with CM

Results showed that feed ratio had a profound effect on methane production rates and

yields for both untreated and pre-treated SCT (Figure 41) For untreated SCT methane

production started after a lag phase of 3-4 days which could be possibly due to the lack

of readily available cellulose and hemicellulose in the assays as the untreated SCT

contained highly resistant and recalcitrant lignocellulosic structure (Sawatdeenarunat et

al 2015)

Thereafter methane production in all assays increased steadily and exhibited a similar

methane production rate during the initial seven days of incubation From day 7 to 14

methane production increased and higher methane production rates were noticed in

assays with a higher amount of CM than SCT Higher nitrogen contents in the assays

with more CM would have reduced the CN ratio to an optimum range which caused


68

the methane yield to increase sharply (Li et al 2014) However the lesser amount of

carbon in assays with lesser Untreated SCT caused it to cease methane production at a

very early stage indicating a lower HRT cycle for the co-digestion feed ratio with lesser

Untreated SCT

Figure 41 Cumulative methane production during the anaerobic co-digestion of

untreated SCT (USCT) and Pre-treated SCT (PSCT) with CM at different feed ratios

incubated in a batch experiment at 37 oC for 83 days

On the other hand for steam-exploded SCT methane production started immediately

without any lag phase A similar trend in methane production without any lag phase was

reported during anaerobic digestion steam-exploded olive mill solid waste (Rincoacuten et al

2016) However methane production rates with pre-treated SCT were higher than those

of untreated SCT The ease in availability of the cellulose and hemicellulose in a pre-

treated substrate resulted in more accessibility of these elements to the microbes

Similar results were observed in the studies conducted elsewhere (Paudel et al 2017)

Thus a faster production of biogas in the pre-treated substrates assays than that of the

untreated substrates assays was observed Among the feed ratios methane production


69

increased with an increase in the amount of pre-treated SCT This trend was noticed

throughout the experimental run Lower carbon contents in the assays with lesser SCT

would be the possible reason for such a lower production rate among them (Vivekanand

et al 2018) To compare the assays with the same amount of carbon mono-digestion

assays were used Synergistic and antagonistic effects of co-digestion ratios were

discussed in Section 423

422 Effect of steam explosion pre-treatment on anaerobic co-digestion

of SCT with CM at different feed ratios

In Pre-treated SCTCM assays more than 75 of methane production was obtained

within the initial 11 days of incubation when the rate of production of methane dropped

below 10 in all assays (Figure 42) However on an average only less than 45 of

methane production was observed among the Untreated SCTCM assays in the same 11

days of incubation This could be possibly due to the destruction of the lignocellulosic

structure as a result of the steam explosion pre-treatment leading to the availability of

cellulose and hemicellulose (Hendriks and Zeeman 2009) Thus the rate of hydrolysis

in pre-treated SCT was improved leading to more methane being produced rapidly in a

short period than with untreated SCT Similar results were achieved in the studies

conducted elsewhere (Mokomele et al 2019)

Figure 42 illustrates the cumulative methane produced among various feed ratios until

day 11 It is evident that among all the co-digestion ratios and mono-digestion of SCT

all Pre-treated SCTCM ratios produced much more methane than the corresponding

feed ratios of Untreated SCTCM till day 11 A similar positive effect of pre-treatment

was reported during the sodium hydroxide pre-treatment of sorghum by Sambusiti et al

(2013) (Sambusiti Ficara Malpei Steyer and Carregravere 2013) Steam explosion and

AFEX pre-treatment on sugarcane agricultural residues producing a higher methane


70

yield during the initial days of incubation were reported by Mokomele et al (2109)

(Mokomele et al 2019)

Figure 42 Cumulative methane yield among different feed ratios of Untreated

SCTCM and Pre-treated SCTCM on day 11 (T11)

It can also be seen that from Figure 42 the cumulative methane yield of Pre-treated

SCTCM assays show a decreasing trend as the SCT contents decreased possibly due to

the significant reduction in carbon content within feed ratios with lesser amount of SCT

contents A similar trend was also observed during the anaerobic co-digestion of whey

and manure when they were co-digested in different feed ratios such as 1000 8515

7525 5050 2575 1585 and 0100 (Vivekanand et al 2018) In this study

Vivekanand et al (2018) reported a decrease in methane yield from 264

mLCH4gVSadded to 147 mLCH4gVSadded as the percentage of manure in the co-

digestion mixture increased from 0 to 100 This might be due to the lesser carbon

content in the assays at lower amounts of SCT (Li et al 2009) The slightly better

methane yields obtained for untreated SCT with an increase in the amount of CM was

probably due to the availability of easily accessible cellulose and hemicellulose and due


71

to a better CN ratio contributed by CM (Vivekanand Olsen Eijsink and Horn 2013)

Both these results were in agreement with previous studies reported in the literature

(Ferreira Donoso-Bravo Nilsen Fdz-Polanco and Peacuterez-Elvira 2013)


during anaerobic co-digestion of SCT with CM

The amount of methane produced in different co-digestion feed ratios can be compared

with an equivalent amount of SCT and CM contributed from their mono-digestion

assays Additional methane yield obtained in the co-digestion mixtures compared to the

expected output from mono-digestion assays is known as synergism (Nielfa et al 2015)

On the contrary the reduction in methane yield in the co-digestion assays compared

with the expected methane production from the sole substrates (SCT and CM) alone is

known as antagonism (Nielfa et al 2015) Synergism observed in the co-digestion

assays might be due to the improvement in any of the factors affecting anaerobic

digestion (Mata-Aacutelvarez 2002) Nitrogen-rich manure used in this co-digestion study

improved the methane yield by lowering the CN ratio of high CN sugarcane

agricultural residue In this experiment the highest synergistic effect of 1196 was

observed in the 7525 ratio of Pre-treated SCTCM (Figure 43)

However in the case of Untreated SCTCM both 7525 and 5050 ratio exhibited

antagonistic effects A Similar antagonistic effect was observed during the anaerobic co-

digestion of whey and fish ensilage (Vivekanand et al 2018) In this study Vivekanand

et al (2018) reported an antagonistic effect in the co-digestion ratios 5050 2575 and

1585 A decrease in the extent of bio-degradation was reported for co-digestion ratios

with more than 50 of fish ensilage among the tested co-digestion ratios of 8515

7525 5050 2575 and 1585 (Vivekanand et al 2018) Among the tested feed ratios

in the current study 2575 ratio of Untreated SCTCM was the only co-digestion ratio


72

that produced a synergistic effect Thus the results of this study indicate that anaerobic

co-digestion is best suited for steam-exploded SCT than for the untreated SCT when co-

digested with CM


during anaerobic co-digestion of SCT with CM in batch assays at 37 oC

43 Anaerobic co-digestion of untreated and steam exploded SCB with

CM

The second set of experiments was conducted under the same conditions to study the

effect of different feed ratios such as 7525 5050 and 2575 on methane yields during

the anaerobic co-digestion of SCB with CM Methane yields of all the tested co-

digestion ratios were compared with the mono-digestion of both SCB and CM

Different feed ratios of both untreated and pre-treated SCB were compared and the

results are presented in Table 43


73


co-digestion of SCB with CM


yields for both untreated and pre-treated SCB Methane production in all untreated SCB

assays was low and delayed by four days (Figure 44) A similar lag phase of nearly

four days was reported by Estevez et al (2012) in his studies on untreated salix (Estevez

et al 2012) However an increase in the methane production rate was observed around

day 4 From day 4 to day 15 higher methane production rates and yields were noticed

(Figure 44) A similar observation in the initial lag phase and an increase in methane

production rates after a lag-phase of 4 ndash 5 days was reported during the anaerobic co-

digestion of goat manure with wheat straw (Zhang et al 2013a) The lower methane

production rates noticed in assays with high CM content was probably due to the lack of

readily available carbon contents in these assays A reverse trend of increased methane

production with an increase in SCT amount was evident from day 15 onwards

However higher methane production rates and relatively small lag-phase were noticed

in assays with steam-exploded SCB compared with untreated SCB A similar trend was

observed during the anaerobic digestion of steam-exploded salix (Estevez et al 2012)

Estevez et al (2012) reported the highest methane yield of 166 mLCH4gVSadded for

steam-exploded salix at 210oC for 10 minutes against the untreated salix which

produced 97 mLCH4gVSadded after 22 days of incubation (Estevez et al 2012)


74


untreated SCB (USCB) and Pre-treated SCB (PSCB) with CM at different feed ratios


A steep increase in methane production was observed from day 4 through day 10 and

thereafter methane production rates were lower indicating that the hydrolysis was the

rate-limiting step (Zhang et al 2013a) Towards the end of the experiment a clear trend

of increased methane production was noticed in all assays with a higher amount of SCT

The availability of more carbon contents to be converted into methane could be the

possible reason for this higher production in these assays (Vivekanand et al 2018)

However the biodegradability check on both untreated and pre-treated substrates shows

that only about 25 of the substrates were degraded during this BMP analysis (Table

43) This might be due to process inhibition in all assays as a result of high ammoniacal

nitrogen accumulation in them as it is a batch test (Table 44) A synergistic study

among the different co-digestion ratios tested was conducted and presented in Section

433 to estimate the performance of co-digestion


75


of SCB with CM at different feed ratios

The cumulative methane yield among different feed ratios until day 11 when the rate of

methane production drops below 10 among all assays is shown in Figure 45 Among

untreated SCB cumulative methane yield increases as the amount of chicken manure

increases in the assays The chemical analysis of untreated SCB showed that only a

lesser amount of cellulose and hemicellulose is available compared to the pre-treated

SCB for the microorganisms to feed A similar trend was observed when SCB was co-

digested with dairy cow manure by Mokomele et al (2019) Methane yield of SCB

increased from nearly 75 to 120 mLCH4gVSadded on the 11th day of incubation when

the co-digestion ratio was increased from 0 to 50 (Mokomele et al 2019) This is due

to the fact that the assays with more CM reduce the CN ratio of high CN SCB to an

optimum range which favours more methane production (Neshat et al 2017) This

causes the assays with high CM content to produce more yield on day 11 in the case of

untreated SCB

However in the case of pre-treated substrates the variation in feed ratios did not affect

the cumulative methane production in them All the Pre-treated SCBCM assays

produced nearly 85 mlCH4gVSadded till day 11 Similar results were reported by

Mokomele et al (2019) when cane leaf matter was co-digested with dairy manure

When cane leaf matter was subjected to AFEX pre-treatment the increase in manure

content during co-digestion does not show much difference in the methane yields

between the mono-digestion and the co-digestion mixtures after 10days of incubation

(Mokomele et al 2019) The availability of biodegradable fibres among the pre-treated

SCB in all co-digestion ratios must have reduced the effect of co-digestion with chicken

manure in this study Pre-treated SCB among all feed ratios tested here must be having


76

nutrient balance and buffer capacity to negate the benefits of co-digestion (Mokomele et

al 2019) Thus all feed ratios of pre-treated SCB produced almost the same methane

yield after 11 days of incubation


SCBCM and Pre-treated SCBCM on day 11 (T11)

433 Synergistic effect on anaerobic digestibility and methane yields

during anaerobic co-digestion of SCB with CM

Unlike the SCT all assays of untreated and pre-treated substrates of SCB has a

synergistic effect while co-digesting with CM Untreated SCBCM assays show a

higher synergistic effect when compared to the Pre-treated SCBCM for the same feed

ratio Both untreated and pre-treated substrates co-digestion show an increasing trend in

the synergistic effect as the amount of CM increases among the co-digestion assays

Supplementing the deficient components by the co-digestion caused an improvement in

overall cumulative methane yield (Aichinger et al 2015) Figure 46 shows the

improvement in co-digestion when compared to the expected methane production from

mono-digestion


77


during anaerobic co-digestion of SCB with CM in batch assays at 37 oC

The highest synergistic effect of 1857 was observed in the 2575 ratio of Untreated

SCBCM Lesser synergistic effect among the pre-treated substrates of SCBCM shows

that the co-digestion SCB with CM is more suitable for untreated SCB than pre-treated

SCB However the better synergistic effect alone does not mean that the co-digestion

ratio is the best choice for anaerobic digestion The synergistic effect shows how good

the co-digestion performed than the expected methane production from the same VS

contents from sole substrates alone (Kim Chowdhury Nakhla and Keleman 2017)

44 Post-BMP analysis of SCTSCB when co-digested with CM

441 Cumulative methane yield and statistical analysis

Cumulative methane yield obtained from all BMP assays including co-digestion

mixtures and mono-digestion of sole substrates is detailed in Table 43 Towards the

end of experiment all sets of co-digestion ratios showed a decrease in cumulative


78

methane yield with the increase in chicken manure The lesser amount of carbon

molecules in assays with lesser SCTSCB resulted in the low methane yield during

longer degradation time (Vivekanand et al 2018) Among Untreated SCTCM the

mono-digestion of Untreated SCT achieved the highest cumulative methane yield of

202 mlCH4gVSadded As the VS from chicken manure was increased by 25 of total VS

a decrease of 11 22 30 and 43 was observed among the cumulative methane

yield at the end of 83 days of incubation On the other hand for Pre-treated SCTCM

ratios a slightly higher cumulative methane yield of 2075 mlCH4gVSadded was

achieved by the mono-digestion of Pre-treated SCT All feed ratios of Pre-treated

SCTCM produced more cumulative methane yield than the corresponding feed ratios

of Untreated SCTCM assays

A similar trend was noticed in the case of SCB Highest cumulative methane yield of

199 mlCH4gVSadded among the Untreated SCBCM assays was attained when Untreated

SCB was mono-digested (Table 43) The decline in cumulative methane yield as the

amount of chicken manure increases is evident in the case of both Untreated SCB and

Pre-treated SCB co-digestion assays 185171162115 mlCH4gVSadded were the

cumulative methane yield obtained from 7525 5050 2575 and 0100 feed ratios of

Untreated SCBCM Similarly the highest methane yield of 225 mlCH4gVSadded was

produced by anaerobic digestion of Pre-treated SCB alone While increasing the

percentage of chicken manure in the feed a decrease of 12 23 31 and 49 was

observed among the tested feed ratios of 7525 5050 2575 and 0100 respectively

Cumulative methane yields of all batch assays were tested for statistical significance

using one-way ANOVA It was observed that all cumulative methane yield was distinct

and hence statistically significant at 95 confidence level except 1000 and 7525 ratio

of Pre-treated SCTCM assay All assays within the groups and between the groups


79

were tested Test of homogeneity of variances ANOVA and multiple comparisons

including the Tukey HSD LSD and Dunnett 2-sided t-test was done and tabulated in

the appendix for Untreated SCTCM Pre-treated SCTCM Untreated SCBCM and

Pre-treated SCBCM


Anaerobic biodegradability describes the amount of organic matter degraded as a result

of anaerobic digestion (Panigrahi Sharma and Dubey 2020) The theoretical methane

potential of the BMP assays can be corrected using the biodegradability factor and can

be used to compare for error with the experimentally achieved values (Nielfa et al

2015)

Table 43 also shows the theoretical methane yield expected from the assays per gram

VS of substrate added along with the experimental methane yield per gram of VS added

It also shows the biodegradability factor based on VS reduction and COD reduction

The percentage of VS destruction occurring in the assays as a result of the anaerobic

digestion process is known as the VS destruction (Wei et al 2018) Whereas the

reduction in COD as a result of the anaerobic digestion is termed as the COD

destruction Theoretical methane corrected with the biodegradability factor is the

product of biodegradability factor from COD destruction and the theoretical expected

methane yield from the assays calculated based on the moles of methane that can be

produced from initial COD (Nielfa et al 2015)


80

Table 43 Theoretical and experimental results of anaerobic biodegradability during anaerobic co-digestion of SCTSCB with CMNote denotes statistically significant values at 95 confidence level a denotes these values are not statistically significant at 95 confidence level

Substrates Feed ratio Theoretical BMP Experimental resultsBMPth BMPexpCOD BMPexpVS BDexpVS BDexpCOD BMPthBD Error

(mlCH4gVSadded) (mlCH4gCODadded) (mlCH4gVSadded) () () (mlCH4gVSadded) ()

Untreated

SCTCM

1000 5552 1444 2018 215 353 1960 28

7525 5444 1307 1791 247 307 1671 67

5050 5336 1178 1582 248 308 1642 -38

2575 5228 1074 1413 244 270 1413 00

Pre-treated

SCTCM

1000 5073 1625 2075a 265 410 2082 -03

7525 5085 1614 2065a 252 391 1988 37

5050 5097 1318 1691 235 319 1628 37

2575 5109 1106 1422 190 255 1302 85

Untreated

SCBCM

1000 4364 1812 1990 262 421 1839 76

7525 4553 1615 1850 237 391 1780 38

5050 4742 1429 1706 232 350 1660 27

2575 4931 1302 1615 179 301 1486 80

Pre-treated

SCBCM

1000 2882 3110 2256 251 715 2061 86

7525 3442 2304 1995 220 538 1852 72

5050 4000 1729 1741 235 414 1656 49

2575 4561 1357 1558 194 348 1586 -18

CM 1000 5121 895 1153 216 206 1056 84


81

It can be observed in general that the pre-treated substrates (Pre-treated SCTPre-treated

SCBCM) have a higher value of biodegradability compared to the untreated substrates

(Untreated SCTUntreated SCBCM) Readily available cellulose and hemicellulose

content among the pre-treated substrates causes the assays with these substrates to have

more degradation due to anaerobic digestion (Panigrahi et al 2020) It can also be

noted that the nitrogen-rich CM has the lowest value of biodegradability The lack of

degrading compounds in these kinds of animal manures is the reason for such a low

biodegradability among them (Gonzaacutelez-Fernaacutendez Leoacuten-Cofreces and Garciacutea-Encina

2008) The antibiotic feed given to the animals results in a higher quantity of antibiotic

contents in their manure which resulted in an inhibitory effect as the antibiotics kill or

deactivate living micro-organisms (Neshat et al 2017) Since the animal manures have

a lower biodegradability the same trend can be seen in the co-digestion assays Assays

with a higher amount of CM has a lower biodegradability This means that the

anaerobic biodegradability decreases with the increase in CM content among the co-

digestion assays Table 43 shows the theoretical BMP experimental BMP VS and

COD destruction biodegradability biodegradability corrected BMP and the relative

error for different feed ratios tested in this study A smaller value of error validates that

the experimentally achieved value matches with the theoretically expected value

443 VFA analysis of digestates

Digestates of the BMP assays were subjected to Volatile Fatty Acids (VFA) tests in

order to check the presence of any inhibitory long-chain volatile fatty acids All the co-

digestion mixtures and the individual mono-digestion of all the substrates used in this

study were tested All the assays had a considerable amount of acetic acids which

could have been converted into methane if all other parameters for anaerobic digestion

were optimum (Li et al 2018b) Figure 47 shows the percentage of VFAs in each


82

assay after 83 days of incubation All the assays consisted of about 40-50 of acetic

acid in them They degrade to acetic acid and then to methane by acetogenic microbes

A similar result was also observed by Bolado et al (2016) (Bolado-Rodriacuteguez Toquero

Martiacuten-Juaacuterez Travaini and Garciacutea-Encina 2016) However the presence of about

20-30 of valeric acids in these digestates could have caused an inhibitory effect on the

anaerobic digestion process Butyric acid also contributed to about 15-20 of the total

VFA concentration in these digestates High concentrations of butyric acid can inhibit

the activity of methanogens (Mamimin Prasertsan Kongjan and O-Thong 2017

Wagner Malin Lins and Illmer 2011) Small quantities of hexanoic acid heptanoic

acid and propionic acid were also present in the digestates of these assays


83

Figure 47 VFAs of post BMP digestates in various feed ratio assays

Note USCT Untreated SCT PSCT Pre-treated SCT USCB Untreated SCB PSCB Pre-treated SCB


84

444 FIA analysis of digestates

Flow Injection analysis was performed on the digestates from the BMP assays in this

study It tests the presence and amount of NO2-N NOx-N NH4-N and PO4-P ions in the

digestates These tests are usually done to check for the existence of any of the above

mentioned ions in the assays that resulted in the ceasing of biogas production For

instance higher quantity of NH4-N ions in the digestates indicates the presence of

ammoniacal nitrogen which hinders the microbial action and thereby reduces the net

biogas production from the substrates (Neshat et al 2017)

Table 44 shows the results of flow injection analysis on various co-digestion assays and

mon-digestion substrates used in this experiment Results show that ~1600 mgL of

ammoniacal nitrogen were present in all assay digestates However we can see a slight

increasing trend in ammoniacal nitrogen as the amount of chicken manure is being

increased among the feed ratios This trend is evident in all four type of co-digestion

mixtures Among untreated SCTCM assays NH4-N increased from 1616 mgL to

1727 mgL as the ratio of CM increased from 0 to 75 Similarly an increase of 1566

to 1687 1636 to 1727 and 1576 to 1727 mgL of NH4-N was observed in pre-treated

SCTCM untreated SCBCM and pre-treated SCBCM assays respectively Highest

value of NH4-N was observed in the digestates of mono-digestion of chicken manure

and was about 1747 mgL

On contrary the PO4P values shows a decreasing trend as the percentage of CM

increased in all four types of co-digestion mixtures Highest value of PO4P (165 mgL)

was observed in the digestates of mono-digesting untreated bagasse As the amount of

chicken manure increased a reduction in PO4P value was observed Among untreated

SCBCM assays the least value of PO4P was obtained in 2575 ratio and was 144 mgL


85

Similarly a decrease from 154 to 140 164 to 141 and 157 to 144 mgL was observed

among untreated SCTCM pre-treated SCTCM and pre-treated SCBCM respectively

The lowest value of PO4P was obtained from the digestates of 100 CM and was

136 mgL

Table 44 Results of flow injection analysis for various feed ratios of SCTSCB with

CM

Sample Feed ratioNO2-N NOx-N NH4-N PO4-P

mgL mgL mgL mgL

Untreated

SCTCM

1000 0 548 16160 1535

7525 0 554 16160 1465

5050 0 550 16867 1444

2575 0 553 17271 1404

Pre-treated

SCTCM

1000 0 556 15655 1636

7525 0 547 16059 1596

5050 0 551 16362 1505

2575 0 550 16867 1414

Untreated

SCBCM

1000 0 548 16362 1646

7525 0 561 16564 1586

5050 0 535 17069 1515

2575 0 551 17271 1444

Pre-treated

SCBCM

1000 0 364 15756 1566

7525 0 549 16665 1576

5050 0 543 16968 1505

2575 0 552 17271 1444

CM 1000 0 553 17473 1364

It can be also noted from table 43 and table 44 that the higher the amount of

ammoniacal nitrogen in the digestates lesser is the amount of PO4P and lesser is the

amount of cumulative methane yield obtained from those assays Higher concentrations

of ammoniacal nitrogen reduced the CN ratio of the digestates as the anaerobic


86

digestion progresses (Vats Khan and Ahmad 2019) It also could lead to an increase

in pH which would result in a lesser cumulative methane yield (Li et al 2013)

However in a batch assay system large quantity of minerals ions and trace elements

are being contributed by the inoculum This reduces the effect of co-digestion feed ratio

in terms of supplementing such elements or ions Non-removal of digestates in a batch

assay unlike a continuous feeding system caused the accumulation of ammoniacal

nitrogen in the assays (Neshat et al 2017) This in turns increases the pH of the system

(Li et al 2013) Continuous type reactor studies can be done for best ratios of the BMP

tests to eliminate the accumulation of ammoniacal nitrogen in the digestates Results of

such continuous type reactors after few HRT can reduce the influence of inoculum in

supplementing such nutrients

45 Mathematical modelling

Mathematical modelling of the mono-digestion of various substrates along with the co-

digestion of these substrates with CM was done in order to compare the fit of these

plots to the pre-defined models The two models used in this study were the first-order

kinetic model and the modified Gompertz model These models consider the

experimental biodegradability of the substrates during the process which also includes

the microbial growth

Table 45 presents the various parameters of the modified Gompertz model and the first-

order kinetic model for the cumulative methane production from the mono-digestion of

Untreated SCT Pre-treated SCT Untreated SCB and Pre-treated SCB along with the

co-digestion of these substrates with CM These calculated parameters are used to

establish a fit between the experimental cumulative methane yields and the expected

modelled data


87

Table 45 Modelling parameters for first-order model and modified Gompertz model for different feed ratios of SCTSCB co-digested with CMNote USCT Untreated SCT PSCT Pre-treated SCT USCB Untreated SCB PSCB Pre-treated SCB

SubstratesFeed

ratioModified Gompertz Model First-order model

B B0 Rm λR2

B0 khyd Time delayR2

(ml CH4gVSadded) (ml CH4gVSadded) (mlgVSd) (d) (ml CH4gVSadded) (d-1) (d)

USCTCM 100 0 20175 19347 712 264 09942 20753 005 266 09988

75 25 17909 16507 623 128 09772 17679 005 183 09956

50 50 15820 14442 648 122 09701 15343 006 188 09911

25 75 14128 12653 858 198 09727 13238 010 255 09898

PSCTCM 100 0 20748 19853 2142 168 09927 20397 015 185 09867

75 25 20650 19748 1819 123 09905 20331 014 167 09905

50 50 16910 16360 1766 142 09920 16782 016 174 09896

25 75 14224 13498 1521 157 09887 13889 016 178 09881

USCBCM 100 0 19900 19394 797 521 09972 20561 006 538 09889

75 25 18503 17700 528 197 09864 19472 004 197 09989

50 50 17056 16131 492 045 09734 17440 004 119 09940

25 75 16154 14420 767 109 09691 15218 003 178 09897

PSCBCM 100 0 22557 21982 702 154 09815 23570 004 188 09934

75 25 19955 19203 660 007 09787 20400 005 089 09941

50 50 17407 16241 718 015 09745 17090 007 110 09928

25 75 15577 14114 806 040 09624 14869 008 119 09837

CM 100 0 11532 10787 1237 280 09858 11135 016 299 09919


88

Hydrolysis process is considered as the rate-limiting step in the first-order kinetic model

whereas the bacterial growth and inhibition process is considered as the rate-limiting

step of the modified Gompertz model (Kafle et al 2012 Patil Raj Muralidhara Desai

and Raju 2012 Vats et al 2019) The term khyd of the first-order kinetic model

describes the rate of hydrolysis of the substrate and is known as the hydrolysis

coefficient Larger values of khyd imply the hydrolysis of that batch is at a faster rate

Untreated SCT had the lowest khyd value of 005 (Table 45) indicating that the rate of

hydrolysis was the slowest among the assays This was similar to the result of khyd value

of 006 d-1 reported elsewhere (Kafle et al 2012) for Untreated SCB This would be

possible due to the complex lignocellulosic structure consisting of a lesser amount of

soluble organic compounds Even though the mon-digestion of Untreated SCT had the

lowest khyd value its ultimate methane potential (B0) was highest when compared with

different co-digestion ratios for Untreated SCT with CM The B0 value for mono-

digested Untreated SCT was 20753 ml CH4gVSadded (Table 45) This value was

similar to the methane yield obtained by Pre-treated SCT towards the end of the

experimental run (Figure 48) It can be noticed that the khyd value increased with an

increase in the amount of CM On the other hand the B0 value decreased with an

increase in the amount of CM Both these results indicate that the rate of hydrolysis

improved with an increase in the amount of CM


89

Figure 48 Comparison of first-order (∆ - Untreated SCT and ndash Pre-treated SCT) and

modified Gompertz kinetic model ( ndash Untreated SCT and ndash Pre-treated SCT)

predictions with experimental cumulative methane yield (ndash) for different co-digestion

ratios of SCT with CM


90

At the same feed ratio the hydrolysis rate constant for Pre-treated SCT mono-digestion

and co-digestion samples was higher than that of Untreated SCT samples (Table 45)

This implies that the steam explosion pretreatment improved the hydrolysis of the

substrate and resulted in smaller khyd values This is due to the fact that steam explosion

pretreatment improved the solubilization of cellulose and hemicellulose and increased

the accessibility of substrate for the microorganisms (Sawatdeenarunat et al 2015)

Similarly B0 values obtained for Pre-treated SCB substrates were higher than that of the

Untreated SCB substrates for the same feed ratios This resulted in higher cumulative

methane yields produced at the end of the experimental run The high khyd for Pre-

treated SCT substrates resulted in a faster methane production rate among the same feed

Among the different co-digestion ratios for Untreated SCB the highest khyd value was

noticed for mono-digestion of Untreated SCB followed by the co-digestion ratios of

7525 and 5050 (Table 45) This could be possibly due to the weak hydrolysis of

Untreated SCB and inhibition caused due low pH as a result of VFA formation (Chen

Cheng and Creamer 2008 Risberg et al 2013 Weil et al 2002) A similar trend was

also observed for the B0 values (Table 45) Nevertheless the time delay of 538 days

was noticed for the mono-digestion of Untreated SCB Both these results indicate that

although the hydrolysis rate was slightly better the effect of time delay caused methane

production to follow the same trend as in the case of Untreated SCT assays (Figure 49)

The Pre-treated SCB also followed the same trend as that of the Pre-treated SCT It was

noticed that the khyd values increased with the increase in CM amount (Table 45)

Similarly a higher rate of hydrolysis was observed with an increase in CM content

Ultimate methane production for Pre-treated SCB also followed the same trend as that

of Pre-treated SCT B0 value decreased with the increase in CM content resulting in

faster hydrolysis but lower methane production towards the end of the experimental run


91

Figure 49 Comparison of first-order (∆ - Untreated SCB and ndash Pre-treated SCB)

and modified Gompertz kinetic model ( ndash Untreated SCB and ndash Pre-treated SCB)


ratios of SCB with CM


92

Modified Gompertz model uses three parameters for the prediction of methane

production from the assays Maximum methane potential of the assay (B0) specific rate

constant (Rm) and the lag phase (λ) (Nielfa et al 2015) The maximum methane

potential of Untreated SCT assays decreased with an increase in CM content (Table 45)

The specific rate constant and lag phase for the different feed ratios of Untreated SCT

were 712 623 648 858 and 264 128 122 198 respectively for the feed ratio of

1000 7525 5050 2575

A similar trend was also noticed with Pre-treated SCT (Table 45) B0 value decreased

with an increase in CM amount This resulted in lower methane production towards the

end of the experimental run (Figure 47) Rm also showed a decreasing trend with an

increase in CM amount The higher value of Rm will result in a faster rate of methane

production (Xie et al 2016) This in turn can be observed in terms of faster hydrolysis

for the Pre-treated SCT assays than the Untreated SCT assays at the same feed ratio

From Table 45 it can be noticed that Untreated SCB assays also followed the same

trend for B0 when the modified Gompertz model was applied B0 decreased with an

increase in the amount of CM Thus a high amount of SCB will lead to increased

methane production Rm values showed a decreasing trend until the feed ratio of 5050

for Untreated SCBCM Thereafter an increase in Rm values was noticed

Similar to that of the first-order model a significant lag phase was evident for the

mono-digestion of Untreated SCB The lag phase also followed the same trend as that of

the methane production rate (Table 45) For Pre-treated SCB assays a decrease in B0

values was noticed with an increase in CM amount On the other hand Rm and λ

showed a decreasing trend till 7525 feed ratio and thereafter it increased steadily as the

amount of CM increased The low B0 noticed at higher CM amount resulted in low

methane production at the end of the experimental run Thus we can conclude that


93

different co-digestion ratios for both SCT and SCB with and without steam explosion

pre-treatment had a profound influence on both methane production and its rate of

production


94

1

Chapter 5 Conclusions and Future works

95

Chapter 5 Conclusions and Future Works


96


97

51 Conclusion to this research work

The effect of feed ratio and pre-treatment on methane yields during anaerobic co-digestion of

sugarcane trash and bagasse was investigated in this study Chemical analysis shows that pre-

treatment of lignocellulosic substrates viz SCT and SCB had a profound effect Steam explosion

pre-treatment helped to break the complex lignocellulosic substrates and thus resulted in faster

and higher methane yields among the pre-treated substrates

BMP analysis of both SCT and SCB proved that a faster rate of production of biogas was

evident in all feed ratios for both substrates Even though the final methane yields are within the

comparable range the initial faster rate of methane production provides an opportunity to

implement a lower HRT among pre-treated substrates when compared to the untreated

substrates Results showed that about 75 of the total yield was obtained within the first 11

days in the case of pre-treated substrates whereas it took almost 45 days for the untreated

substrates to produce similar yields This implies that using pre-treated substrates can improve

net productivity by at least four times Thus the methane yield can be increased by about four

times if pre-treated substrates instead of untreated substrates

Considering the HRT of the reactor as 11 days pre-treated substrates had a clear advantage over

the untreated substrates (both SCT and SCB) in all the tested feed ratios and mono-digestion of

SCT and SCB An improvement of 156 122 83 and 37 was observed as a result of pre-

treatment among 1000 7525 5050 and 2575 ratios of SCT on day 11 The corresponding

improvement observed in SCB assays were 120 54 39 and 9 respectively

Among the different feed ratios tested for both untreated and pre-treated SCT and SCB an

overall improvement in methane yields with the addition of chicken manure was not observed

The cumulative methane yields were reduced with the addition of chicken manure which

implies that the addition of chicken manure had a negative impact on cumulative methane yields

However with the addition of chicken manure the amount of carbon molecules is considerably

reduced as the same VS was maintained among all co-digestion assays This caused a reduction

in methane yield from the co-digestion mixture Synergistic studies were done in this research


98

work to investigate whether co-digestion improved the synergism of the mixture The effect of

co-digestion was analysed by comparing the expected methane yields from the same amount of

VS content of the sole substrates digested separately and the methane yields produced from the

co-digestion mixture with the same VS content Synergism was observed among most of the co-

digestion mixtures which show co-digestion has a positive effect However the reduction of

carbon contents due to lesser carbon molecules being fed to the reactors resulted in a lesser yield

with the addition of chicken manure

Among the tested feed ratios addition of chicken manure helped to achieve a positive

synergism in all assays except 7525 and 5050 ratio of untreated SCTCM The only feed ratio

that had a synergistic effect in untreated SCTCM was 2575 ratio and was observed to be

318 Among pre-treated SCT assays the percentage improvement in cumulative methane

based on synergistic studies was 1196 477 and 281 among the 7525 5050 and 2575

ratios respectively Similar improvement observed in untreated SCB and pre-treated SCB were

390 853 1857 and 078 213 902 respectively for 7525 5050 and 2575 ratios An

improvement based on synergistic studies implies that the co-digestion helped in increasing the

methane yield from the same amount of VS from substrates digested separately

Mathematical modelling was done for all the batch assays using the first-order kinetic model

and the modified Gompertz model and different associated parameters were analysed It was

observed that time delay could be considerably reduced in the case of pre-treated substrates

when compared to the untreated substrates Both models produced a higher rate of methane

yield while using pre-treated substrates Anaerobic biodegradability post BMP analysis and

statistical analysis were also done in this research work to substantiate the results obtained in

this research The BMP analysis done to study the effect of co-digestion ratios of sugarcane

wastes with chicken manure opens up an opportunity to facilitate an efficient utilisation

technique for both SCTSCB and CM Further research works with different ISR ratios and

continuous type reactors need to be done to study how the feed ratios behave in such a reactor

before commercialising this technique


99

52 Related future research works

Testing of different ISR ratios to study the effect of feed ratio and co-digestion of

SCTSCB with CM

Testing different feed ratios of co-digesting SCTSCB with CM on a continuous type

CSTR reactor to conduct a scale down lab-scale study of the commercial plant

Testing the effect of feed ratio and co-digestion of SCTSCB with CM within different

incubation temperatures

100

101

APPENDIX 1

Statistical analysis of Untreated SCT with CM

The statistical significance of the mono-digestion of Untreated SCT along with the

different co-digestion mixtures of Untreated SCT with CM is analysed in this section

Various statistical parameters within the group of mono-digesting and co-digesting

Untreated SCT with CM are as follows

Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error

95 Confidence Interval

for Mean

Minimum MaximumLower Bound

Upper

Bound

100USCT +

0CM

3 2017333 223010 128755 1961935 2072732 19920 20340

75USCT +

25CM

3 1791000 34641 20000 1782395 1799605 17890 17950

50USCT +

50CM

3 1581667 85049 49103 1560539 1602794 15730 15900

25USCT +

75CM

3 1413000 338674 195533 1328869 1497131 13750 14400

0USCT +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1591267 3092455 798468 1420012 1762521 11460 20340

Test of Homogeneity of Variances

Levene Statistic df1 df2 Sig

Methane Yields Based on Mean 4590 4 10 023

Based on Median 938 4 10 481

102

Based on Median and with

adjusted df

938 4 4174 521

Based on trimmed mean 4144 4 10 031

ANOVA

Methane Yields

Sum of Squares df Mean Square F Sig

Between Groups 13352489 4 3338122 924688 000

Within Groups 36100 10 3610

Total 13388589 14

Multiple Comparisons

Dependent Variable Methane Yields

(I) treatment (J) treatment

Mean

Difference (I-

J) Std Error Sig


Lower

Bound

Upper

Bound

Tukey

HSD

100USCT

+ 0CM

75USCT +

25CM

2263333 155134 000 175277 277389

50USCT +

50CM

4356667 155134 000 384611 486723

25USCT +

75CM

6043333 155134 000 553277 655389

0USCT +

100CM

8640000 155134 000 812944 915056

75USCT +

25CM

100USCT +

0CM

-2263333 155134 000 -277389 -175277

50USCT +

50CM

2093333 155134 000 158277 260389

25USCT +

75CM

3780000 155134 000 326944 429056

0USCT +

100CM

6376667 155134 000 586611 688723

103

50USCT +

50CM

100USCT +

0CM

-4356667 155134 000 -486723 -384611

75USCT +

25CM

-2093333 155134 000 -260389 -158277

25USCT +

75CM

1686667 155134 000 117611 219723

0USCT +

100CM

4283333 155134 000 377277 479389

25USCT +

75CM

100USCT +

0CM

-6043333 155134 000 -655389 -553277

75USCT +

25CM

-3780000 155134 000 -429056 -326944

50USCT +

50CM

-1686667 155134 000 -219723 -117611

0USCT +

100CM

2596667 155134 000 208611 310723

0USCT +

100CM

100USCT +

0CM

-8640000 155134 000 -915056 -812944

75USCT +

25CM

-6376667 155134 000 -688723 -586611

50USCT +

50CM

-4283333 155134 000 -479389 -377277

25USCT +

75CM

-2596667 155134 000 -310723 -208611

LSD 100USCT

+ 0CM

75USCT +

25CM

2263333 155134 000 191767 260899

50USCT +

50CM

4356667 155134 000 401101 470233

25USCT +

75CM

6043333 155134 000 569767 638899

0USCT +

100CM

8640000 155134 000 829434 898566

75USCT +

25CM

100USCT +

0CM

-2263333 155134 000 -260899 -191767

104

50USCT +

50CM

2093333 155134 000 174767 243899

25USCT +

75CM

3780000 155134 000 343434 412566

0USCT +

100CM

6376667 155134 000 603101 672233

50USCT +

50CM

100USCT +

0CM

-4356667 155134 000 -470233 -401101

75USCT +

25CM

-2093333 155134 000 -243899 -174767

25USCT +

75CM

1686667 155134 000 134101 203233

0USCT +

100CM

4283333 155134 000 393767 462899

25USCT +

75CM

100USCT +

0CM

-6043333 155134 000 -638899 -569767

75USCT +

25CM

-3780000 155134 000 -412566 -343434

50USCT +

50CM

-1686667 155134 000 -203233 -134101

0USCT +

100CM

2596667 155134 000 225101 294233

0USCT +

100CM

100USCT +

0CM

-8640000 155134 000 -898566 -829434

75USCT +

25CM

-6376667 155134 000 -672233 -603101

50USCT +

50CM

-4283333 155134 000 -462899 -393767

25USCT +

75CM

-2596667 155134 000 -294233 -225101

Dunnett

t (2-sided)b

100USCT

+ 0CM

0USCT +

100CM

8640000 155134 000 819159 908841

75USCT +

25CM

0USCT +

100CM

6376667 155134 000 592825 682508

105

50USCT +

50CM

0USCT +

100CM

4283333 155134 000 383492 473175

25USCT +

75CM

0USCT +

100CM

2596667 155134 000 214825 304508

The mean difference is significant at the 005 level

b Dunnett t-tests treat one group as a control and compare all other groups against it

106

APPENDIX2

Statistical analysis of Pre-treated SCT with CM

The statistical significance of the mono-digestion of Pre-treated SCT along with the

different co-digestion mixtures of Pre-treated SCT with CM is analysed in this section

Various statistical parameters within the group of mono-digesting and co-digesting Pre-

treated SCT with CM are as follows

Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100PSCT +

0CM

3 2075000 629603 363501 1918598 2231402 20050 21270

75PSCT +

25CM

3 2065000 580259 335012 1920856 2209144 20310 21320

50PSCT +

50CM

3 1691000 272213 157162 1623379 1758621 16600 17110

25PSCT +

75CM

3 1422667 250067 144376 1360547 1484787 13980 14480

0PSCT +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1681400 3743018 966443 1474119 1888681 11460 21320





107


adjusted df

501 4 4789 739


ANOVA

Methane Yields


Between Groups 19438783 4 4859696 276948 000


Total 19614256 14




Mean

Difference (I-

J) Std Error Sig


Lower

Bound

Upper

Bound

Tukey

HSD

100PSCT

+ 0CM

75PSCT +

25CM

100000 342027 998 -102564 122564

50PSCT +

50CM

3840000 342027 000 271436 496564

25PSCT +

75CM

6523333 342027 000 539770 764897

0PSCT +

100CM

9216667 342027 000 809103 1034230

75PSCT +

25CM

100PSCT +

0CM

-100000 342027 998 -122564 102564

50PSCT +

50CM

3740000 342027 000 261436 486564

25PSCT +

75CM

6423333 342027 000 529770 754897

108

0PSCT +

100CM

9116667 342027 000 799103 1024230

50PSCT +

50CM

100PSCT +

0CM

-3840000 342027 000 -496564 -271436

75PSCT +

25CM

-3740000 342027 000 -486564 -261436

25PSCT +

75CM

2683333 342027 000 155770 380897

0PSCT +

100CM

5376667 342027 000 425103 650230

25PSCT +

75CM

100PSCT +

0CM

-6523333 342027 000 -764897 -539770

75PSCT +

25CM

-6423333 342027 000 -754897 -529770

50PSCT +

50CM

-2683333 342027 000 -380897 -155770

0PSCT +

100CM

2693333 342027 000 156770 381897

0PSCT +

100CM

100PSCT +

0CM

-9216667 342027 000 -1034230 -809103

75PSCT +

25CM

-9116667 342027 000 -1024230 -799103

50PSCT +

50CM

-5376667 342027 000 -650230 -425103

25PSCT +

75CM

-2693333 342027 000 -381897 -156770

LSD 100PSCT

+ 0CM

75PSCT +

25CM

100000 342027 776 -66208 86208

50PSCT +

50CM

3840000 342027 000 307792 460208

25PSCT +

75CM

6523333 342027 000 576125 728542

0PSCT +

100CM

9216667 342027 000 845458 997875

109

75PSCT +

25CM

100PSCT +

0CM

-100000 342027 776 -86208 66208

50PSCT +

50CM

3740000 342027 000 297792 450208

25PSCT +

75CM

6423333 342027 000 566125 718542

0PSCT +

100CM

9116667 342027 000 835458 987875

50PSCT +

50CM

100PSCT +

0CM

-3840000 342027 000 -460208 -307792

75PSCT +

25CM

-3740000 342027 000 -450208 -297792

25PSCT +

75CM

2683333 342027 000 192125 344542

0PSCT +

100CM

5376667 342027 000 461458 613875

25PSCT +

75CM

100PSCT +

0CM

-6523333 342027 000 -728542 -576125

75PSCT +

25CM

-6423333 342027 000 -718542 -566125

50PSCT +

50CM

-2683333 342027 000 -344542 -192125

0PSCT +

100CM

2693333 342027 000 193125 345542

0PSCT +

100CM

100PSCT +

0CM

-9216667 342027 000 -997875 -845458

75PSCT +

25CM

-9116667 342027 000 -987875 -835458

50PSCT +

50CM

-5376667 342027 000 -613875 -461458

25PSCT +

75CM

-2693333 342027 000 -345542 -193125

100PSCT

+ 0CM

0PSCT +

100CM

9216667 342027 000 822805 1020529

110

Dunnett

t (2-

sided)b

75PSCT +

25CM

0PSCT +

100CM

9116667 342027 000 812805 1010529

50PSCT +

50CM

0PSCT +

100CM

5376667 342027 000 438805 636529

25PSCT +

75CM

0PSCT +

100CM

2693333 342027 000 170471 368195



111

APPENDIX 3

Statistical analysis of Untreated SCB with CM

The statistical significance of the mono-digestion of Untreated SCB along with the

different co-digestion mixtures of Untreated SCB with CM is analysed in this section


Untreated SCB with CM are as follows

Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100USCB +

0CM

3 1990000 481248 277849 1870451 2109549 19400 20360

75USCB +

25CM

3 1850333 221209 127715 1795382 1905285 18270 18710

50USCB +

50CM

3 1705667 51316 29627 1692919 1718414 17000 17100

25USCB +

75CM

3 1615333 187172 108064 1568837 1661829 15940 16290

0USCB +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1662933 2957005 763495 1499180 1826687 11460 20360





112

Based on Median and

with adjusted df

1518 4 4354 339


ANOVA

Methane Yields


Between Groups 12176263 4 3044066 467120 000


Total 12241429 14




Mean

Difference (I-J) Std Error Sig


Lower

Bound

Upper

Bound

Tukey

HSD

100USCB

+ 0CM

75USCB +

25CM

1396667 208433 000 71070 208264

50USCB +

50CM

2843333 208433 000 215736 352930

25USCB +

75CM

3746667 208433 000 306070 443264

0USCB +

100CM

8366667 208433 000 768070 905264

75USCB +

25CM

100USCB +

0CM

-1396667 208433 000 -208264 -71070

50USCB +

50CM

1446667 208433 000 76070 213264

25USCB +

75CM

2350000 208433 000 166403 303597

113

0USCB +

100CM

6970000 208433 000 628403 765597

50USCB +

50CM

100USCB +

0CM

-2843333 208433 000 -352930 -215736

75USCB +

25CM

-1446667 208433 000 -213264 -76070

25USCB +

75CM

903333 208433 000 21736 158930

0USCB +

100CM

5523333 208433 000 483736 620930

25USCB +

75CM

100USCB +

0CM

-3746667 208433 000 -443264 -306070

75USCB +

25CM

-2350000 208433 000 -303597 -166403

50USCB +

50CM

-903333 208433 000 -158930 -21736

0USCB +

100CM

4620000 208433 000 393403 530597

0USCB +

100CM

100USCB +

0CM

-8366667 208433 000 -905264 -768070

75USCB +

25CM

-6970000 208433 000 -765597 -628403

50USCB +

50CM

-5523333 208433 000 -620930 -483736

25USCB +

75CM

-4620000 208433 000 -530597 -393403

LSD 100USCB

+ 0CM

75USCB +

25CM

1396667 208433 000 93225 186109

50USCB +

50CM

2843333 208433 000 237891 330775

25USCB +

75CM

3746667 208433 000 328225 421109

0USCB +

100CM

8366667 208433 000 790225 883109

114

75USCB +

25CM

100USCB +

0CM

-1396667 208433 000 -186109 -93225

50USCB +

50CM

1446667 208433 000 98225 191109

25USCB +

75CM

2350000 208433 000 188558 281442

0USCB +

100CM

6970000 208433 000 650558 743442

50USCB +

50CM

100USCB +

0CM

-2843333 208433 000 -330775 -237891

75USCB +

25CM

-1446667 208433 000 -191109 -98225

25USCB +

75CM

903333 208433 000 43891 136775

0USCB +

100CM

5523333 208433 000 505891 598775

25USCB +

75CM

100USCB +

0CM

-3746667 208433 000 -421109 -328225

75USCB +

25CM

-2350000 208433 000 -281442 -188558

50USCB +

50CM

-903333 208433 000 -136775 -43891

0USCB +

100CM

4620000 208433 000 415558 508442

0USCB +

100CM

100USCB +

0CM

-8366667 208433 000 -883109 -790225

75USCB +

25CM

-6970000 208433 000 -743442 -650558

50USCB +

50CM

-5523333 208433 000 -598775 -505891

25USCB +

75CM

-4620000 208433 000 -508442 -415558

100USCB

+ 0CM

0USCB +

100CM

8366667 208433 000 776419 896914

115

Dunnett

t (2-

sided)b

75USCB +

25CM

0USCB +

100CM

6970000 208433 000 636753 757247

50USCB +

50CM

0USCB +

100CM

5523333 208433 000 492086 612581

25USCB +

75CM

0USCB +

100CM

4620000 208433 000 401753 522247



116

APPENDIX 4

Statistical analysis of Pre-treated SCB with CM

The statistical significance of the mono-digestion of Pre-treated SCB along with the

different co-digestion mixtures of Pre-treated SCB with CM is analysed in this section


treated SCB with CM are as follows

Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100PSCB +

0CM

3 2255667 539290 311359 2121700 2389634 21940 22940

75PSCB +

25CM

3 1995667 427239 246667 1889535 2101799 19470 20270

50PSCB +

50CM

3 1740333 234592 135442 1682057 1798609 17140 17590

25PSCB +

75CM

3 1557667 281129 162310 1487830 1627503 15390 15900

0PSCB +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1740533 3908233 1009101 1524103 1956964 11460 22940





117


adjusted df

407 4 5808 798


ANOVA

Methane Yields


Between Groups 21260984 4 5315246 432087 000


Total 21383997 14




Mean



Lower

Bound

Upper

Bound

Tukey

HSD

100PSCB

+ 0CM

75PSCB +

25CM

2600000 286372 000 165753 354247

50PSCB +

50CM

5153333 286372 000 421086 609581

25PSCB +

75CM

6980000 286372 000 603753 792247

0PSCB +

100CM

11023333 286372 000 1008086 1196581

75PSCB +

25CM

100PSCB

+ 0CM

-2600000 286372 000 -354247 -165753

50PSCB +

50CM

2553333 286372 000 161086 349581

25PSCB +

75CM

4380000 286372 000 343753 532247

118

0PSCB +

100CM

8423333 286372 000 748086 936581

50PSCB +

50CM

100PSCB

+ 0CM

-5153333 286372 000 -609581 -421086

75PSCB +

25CM

-2553333 286372 000 -349581 -161086

25PSCB +

75CM

1826667 286372 000 88419 276914

0PSCB +

100CM

5870000 286372 000 492753 681247

25PSCB +

75CM

100PSCB

+ 0CM

-6980000 286372 000 -792247 -603753

75PSCB +

25CM

-4380000 286372 000 -532247 -343753

50PSCB +

50CM

-1826667 286372 000 -276914 -88419

0PSCB +

100CM

4043333 286372 000 310086 498581

0PSCB +

100CM

100PSCB

+ 0CM

-11023333 286372 000 -1196581 -1008086

75PSCB +

25CM

-8423333 286372 000 -936581 -748086

50PSCB +

50CM

-5870000 286372 000 -681247 -492753

25PSCB +

75CM

-4043333 286372 000 -498581 -310086

LSD 100PSCB

+ 0CM

75PSCB +

25CM

2600000 286372 000 196192 323808

50PSCB +

50CM

5153333 286372 000 451526 579141

25PSCB +

75CM

6980000 286372 000 634192 761808

0PSCB +

100CM

11023333 286372 000 1038526 1166141

119

75PSCB +

25CM

100PSCB

+ 0CM

-2600000 286372 000 -323808 -196192

50PSCB +

50CM

2553333 286372 000 191526 319141

25PSCB +

75CM

4380000 286372 000 374192 501808

0PSCB +

100CM

8423333 286372 000 778526 906141

50PSCB +

50CM

100PSCB

+ 0CM

-5153333 286372 000 -579141 -451526

75PSCB +

25CM

-2553333 286372 000 -319141 -191526

25PSCB +

75CM

1826667 286372 000 118859 246474

0PSCB +

100CM

5870000 286372 000 523192 650808

25PSCB +

75CM

100PSCB

+ 0CM

-6980000 286372 000 -761808 -634192

75PSCB +

25CM

-4380000 286372 000 -501808 -374192

50PSCB +

50CM

-1826667 286372 000 -246474 -118859

0PSCB +

100CM

4043333 286372 000 340526 468141

0PSCB +

100CM

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121

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Scherer P Neumann L Demirel B Schmidt O amp Unbehauen M (2009) Long termfermentation studies about the nutritional requirements for biogasification of fodderbeet silage as mono-substrate Biomass and Bioenergy 33 873-881doi101016jbiombioe200901011

Shen J Zhao C Liu Y Zhang R Liu G amp Chen C (2018) Biogas production from anaerobicco-digestion of durian shell with chicken dairy and pig manures Energy Conversion andManagement doihttpsdoiorg101016jenconman201806099

Si K (2016) Review Anaerobic Co-Digestion of Slaughterhouse Waste and Food and VegetableWaste Inhibition Factors and its Reverting Methods

133

Sluiter A Hames B Ruiz R Scarlata C Sluiter J Templeton D amp Crocker D (2008)Determination of Structural Carbohydrates and Lignin in BiomassmdashNRELTP-510-42618Laboratory Analytical Procedure (LAP)

Song Z amp Zhang C (2015) Anaerobic codigestion of pretreated wheat straw with cattlemanure and analysis of the microbial community Bioresource technology 186 128-135doihttpsdoiorg101016jbiortech201503028

Srisowmeya G Chakravarthy M amp Nandhini Devi G (2019) Critical considerations in two-stage anaerobic digestion of food waste ndash A review Renewable and Sustainable EnergyReviews 109587 doihttpsdoiorg101016jrser2019109587

Stelte W (2013) Steam explosion for biomass pre-treatment Danish Technological Institute

Sud D Mahajan G amp Kaur M P (2008) Agricultural waste material as potential adsorbentfor sequestering heavy metal ions from aqueous solutions ndash A review Bioresourcetechnology 99(14) 6017-6027 doihttpsdoiorg101016jbiortech200711064

Taherzadeh M J amp Karimi K (2008) Pretreatment of lignocellulosic wastes to improve ethanoland biogas production a review International journal of molecular sciences 9(9) 1621-1651 doi103390ijms9091621

Teghammar A Yngvesson J Lundin M Taherzadeh M J amp Horvaacuteth I S (2010)Pretreatment of paper tube residuals for improved biogas production Bioresourcetechnology 101(4) 1206-1212 doihttpsdoiorg101016jbiortech200909029

Thorburn P Horan H amp Biggs J (2004) The impact of trash management on sugarcaneproduction and nitrogen management a simulation study

Tsapekos P Kougias P G Treu L Campanaro S amp Angelidaki I (2017) Processperformance and comparative metagenomic analysis during co-digestion of manureand lignocellulosic biomass for biogas production Applied Energy 185 126-135doihttpsdoiorg101016japenergy201610081

van Lier J B Tilche A Ahring B K Macarie H Moletta R Dohanyos M Verstraete W(2001) New perspectives in anaerobic digestion Water Science and Technology 43(1)1-18 doi102166wst20010001

Vats N Khan A A amp Ahmad K (2019) Anaerobic co-digestion of thermal pre-treatedsugarcane bagasse using poultry waste Journal of Environmental Chemical Engineering7(5) 103323 doihttpsdoiorg101016jjece2019103323

Vervaeren H Hostyn K Ghekiere G amp Willems B (2010) Biological ensilage additives aspretreatment for maize to increase the biogas production Renewable Energy 35(9)2089-2093 doihttpsdoiorg101016jrenene201002010

Vivekanand V Mulat D G Eijsink V G H amp Horn S J (2018) Synergistic effects of anaerobic

134

co-digestion of whey manure and fish ensilage Bioresource technology 249 35-41doihttpsdoiorg101016jbiortech201709169

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135

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136

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ABSTRACT


LIST OF FIGURES

LIST OF TABLES


ACKNOWLEDGMENTS

APPENDIX 1

APPENDIX 3

APPENDIX 4

REFERENCES

1

Effect of feed ratio and pre-treatment onmethane yields during anaerobic co-digestionof sugarcane bagasse and trash with chicken

manure

Amal Babu PuthumanaBachelor of Technology (BTech) Mahatma Gandhi

University India

Master of Technology (MTech) Cochin University of Scienceand Technology India

Griffith School of Engineering and Built

Environment Griffith Sciences

Griffith University Nathan QLD 4111 Australia

Submitted in the fulfillment of the requirements of thedegree of Master of Philosophy

November 2019

2ii

iii

ABSTRACT
























iv


























v














vi







vii

TABLE OF CONTENTS

Abstract iii



List of figures xi

List of tables xiii


Acknowledgments xvi


11 Background 3


13 Thesis outline 5










272 pH 24



viii








31 Substrates 45


312 Manure 45

32 Inoculum 45

















ix









and Inoculum 63


with CM 67








with CM 72











x










References 121

xi

LIST OF FIGURES




the SCT 14












Figure 31 (e) CM 47








for 83 days 68


xii







for 83 days 74












xiii

LIST OF TABLES


(2012-2017) 10





previous studies 26













with CM 85



xiv






BD Biodegradability




CM Chicken Manure















xv



SCT Sugarcane Trash





TS Total Solids



UV Ultra-Violet



VS Volatile Solids

xvi

ACKNOWLEDGMENTS











researcher












xvii
















AMAL BABU PUTHUMANA

18


1



2


3

11 Background


























4



























5












chicken manure



13 Thesis outline




project




6
















7



8


9
























Barbieri 2013)


10








Source (FAO 2018)

Crop


2012 2013 2014 2015 2016 2017

Maize 696 786 524 596 533 678

Rice 1031 1136 767 697 266 822

Cotton 5965 444 3901 197 2804 NA

Soybean 381 411 37 301 292 300

Sugarcane 3386 3293 3752 3765 4472 4534

Sunflower 402 303 27 295 23 27

Wheat 139021 129793 126132 123837 112822 1219115











11





12




















13









14



forming the SCB


2018)


15


















(Jain et al 2014)









16
















al 2015)










17













Table 22


18






























19









C6H10O4 + 2H2O C6H12O6 + H2 Eq21







et al 2019)





20













21






CO2 + 4H2 CH4 + 2H2O Eq28

HCO-3 + 4H2 + H+ CH4 + 3H2O Eq29















22





2017)











Table 23








23






More stable

Less energy input









important


24

272 pH













Cheon 2008)


and Stetter 1982)





Jones 2008)


2008)







25



























26


studies




























27





Stroeve 2009)












28



























29





















Zeeman 2009)







30





31


Sl

No

Pre-treatment

method

Lignocellulosic

substrates

Pre-treatment

condition

CH4 production

after pre-

treatment

(mLCH4gVSadded)

Increase in

CH4 ()

References




Balsari 2012)








2016)


Olive mill solid

waste




Borja 2016)


32


Morken 2012)


MHz 1600 W



2011)


MHz 1600 W





and Carrere 2012)



2012)





33



























34



























35



























36












AD process






37


wastes

Sl

No


conditions

Feed Ratios

tested


1 Sugarcane leaves

+ Diary cow

manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 7525

5050 2575

0100 (VS basis)


LCH4kgVSadded)

(Mokomele et al

2019)

2 Sugarcane

Bagasse + Diary

cow manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 5050

0100 (VS basis)


LCH4kgVSadded)

(Mokomele et al

2019)

3 Sugarcane leaves

+ Diary cow

manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 7525

5050 2575

0100 (VS basis)

NA 7525 (292

LCH4kgVSadded)

(Mokomele et al

2019)

4 Switchgrass +

Diary manure

500 mL Mesophilic

temperature ISR =

067 30 days

1000 7525

5050 2575

0100 (TS basis)



mono-digestion of

switchgrass

(Zheng et al 2015)

5 Salix viminalis +

Cow manure

1125 mL

mesophilic

temperature ISR =

067 57 days

1000 8020

7030 6040

5050 4060

3070 2080

0100 (VS basis)


mLCH4gVS)


mLgVS) has 23



38

improvement over

untreated salix

6 Corn Stover + Pig

Manure

250 mL Mesophilic

temperature ISR =

026 70 days

1000 8020

6733 5050

3367 2080

0100 (VS basis)

NA 0100 (889

mLCH4gVS)

(Li et al 2018b)

7 Cotton stalk +

swine manure

500 mL Mesophilic

temperature ISR =

025 45 days

1000 7525

5050 2575 (TS

basis)


in yield when

compared to control

(102 mLCH4gVS)

(Cheng and Zhong

2014)

8 Apple waste +

Chicken manure

250 mL Mesophilic

temperature ISR =

026 70 days

1000 8020

6733 5050

3367 2080

0100 (VS basis)

NA 3367 (827

mLCH4gVS)

(Li et al 2018b)

9 Whey + cow

manure

400 mL Mesophilic

temperature 55 days

1000 8515

7525 5050

2575 1585

0100 (VS basis)

NA 8515 (266

mLCH4gVS)

(Vivekanand Mulat

Eijsink and Horn

2018)


39




















al 2017)







40







al 2017)


41







2 Grass Cow manure














6

Organic fraction of








42


















with CM


43



44


45

31 Substrates












312 Manure




32 Inoculum








46



























47





Sample Untreated

SCT ()

Pre-treated

SCT ()

Untreated

SCB ()

Pre-treated

SCB ()

CM

()

















CM 0 0 0 0 100


48









49























Batstone 2015)


50

























51


(PO43-)

















52











53










54

37 Data Processing

371 Theoretical BMP







Fdz-Polanco 2015)


tttEq31





ᆒt =ᆒ썈υ

Eq32









55


using equation 33










Sung 2012)


Eq34


Eq35









56







Eq36
















tt Eq37


57



υ =

tt Eq38










Eq310









58






al 2014)

= t 㢐t) Eq311













= t

t

Eq312





59












60

1


61



62


63


inoculum

























64

















study










65






Bolantildeos 2007)



SCT

Pre-treated

SCT

Untreated

SCB

Pre-treated

SCBInoculum

TS 721 913 872 828 819 19

VS TS 807 909 925 894 951 722

COD gl 7487 11598 12099 7939 9156 211

TSVS - 12 11 11 11 11 14

CN ratio - 124 831 953 1465 1919 49

Glucan ND 307 463 351 428 ND

Xylan ND 168 117 20 146 ND


Lignin ND 298 283 315 282 ND

TKP gPkg 82 11 07 05 06 312

TKN gNkg 836 97 108 512 71 1113







66



SCT

Pre-treated

SCT

Untreated

SCB

Pre-treated

SCBInoculum

Al mgg 103 123 099 084 024 817

As mgg 000 000 000 000 000 000

B mgg 003 000 000 000 000 004

Ba mgg 004 002 002 001 001 019

Ca mgg 1687 312 167 025 015 1973

Cd mgg 000 000 000 000 000 000

Co mgg 000 000 000 000 000 001

Cr mgg 004 001 001 001 001 005

Cu mgg 011 000 000 000 000 056

Fe mgg 107 092 019 086 026 1972

K mgg 1977 601 283 293 076 1582

Mg mgg 461 188 138 036 028 666

Mn mgg 036 010 006 005 003 017

Mo mgg 001 000 000 000 000 003

Na mgg 171 007 034 001 004 1644

Ni mgg 002 000 000 000 000 005

P mgg 796 101 041 015 010 2735

Pb mgg 001 000 000 000 000 003

S mgg 321 082 809 018 524 1469

Se mgg 001 000 001 000 001 000

Zn mgg 027 000 000 000 000 067









67





CM













al 2015)







68




Untreated SCT














69



























70


















71



























72



digested with CM




CM








73






















74

















75















untreated SCB












76
















mono-digestion


77
















78



























79




Pre-treated SCBCM






2015)












80




Untreated

SCTCM

1000 5552 1444 2018 215 353 1960 28

7525 5444 1307 1791 247 307 1671 67

5050 5336 1178 1582 248 308 1642 -38

2575 5228 1074 1413 244 270 1413 00

Pre-treated

SCTCM

1000 5073 1625 2075a 265 410 2082 -03

7525 5085 1614 2065a 252 391 1988 37

5050 5097 1318 1691 235 319 1628 37

2575 5109 1106 1422 190 255 1302 85

Untreated

SCBCM

1000 4364 1812 1990 262 421 1839 76

7525 4553 1615 1850 237 391 1780 38

5050 4742 1429 1706 232 350 1660 27

2575 4931 1302 1615 179 301 1486 80

Pre-treated

SCBCM

1000 2882 3110 2256 251 715 2061 86

7525 3442 2304 1995 220 538 1852 72

5050 4000 1729 1741 235 414 1656 49

2575 4561 1357 1558 194 348 1586 -18

CM 1000 5121 895 1153 216 206 1056 84


81



























82












83




84


























85




136 mgL


CM


mgL mgL mgL mgL

Untreated

SCTCM

1000 0 548 16160 1535

7525 0 554 16160 1465

5050 0 550 16867 1444

2575 0 553 17271 1404

Pre-treated

SCTCM

1000 0 556 15655 1636

7525 0 547 16059 1596

5050 0 551 16362 1505

2575 0 550 16867 1414

Untreated

SCBCM

1000 0 548 16362 1646

7525 0 561 16564 1586

5050 0 535 17069 1515

2575 0 551 17271 1444

Pre-treated

SCBCM

1000 0 364 15756 1566

7525 0 549 16665 1576

5050 0 543 16968 1505

2575 0 552 17271 1444

CM 1000 0 553 17473 1364






86
























modelled data


87


SubstratesFeed


B B0 Rm λR2



USCTCM 100 0 20175 19347 712 264 09942 20753 005 266 09988

75 25 17909 16507 623 128 09772 17679 005 183 09956

50 50 15820 14442 648 122 09701 15343 006 188 09911

25 75 14128 12653 858 198 09727 13238 010 255 09898

PSCTCM 100 0 20748 19853 2142 168 09927 20397 015 185 09867

75 25 20650 19748 1819 123 09905 20331 014 167 09905

50 50 16910 16360 1766 142 09920 16782 016 174 09896

25 75 14224 13498 1521 157 09887 13889 016 178 09881

USCBCM 100 0 19900 19394 797 521 09972 20561 006 538 09889

75 25 18503 17700 528 197 09864 19472 004 197 09989

50 50 17056 16131 492 045 09734 17440 004 119 09940

25 75 16154 14420 767 109 09691 15218 003 178 09897

PSCBCM 100 0 22557 21982 702 154 09815 23570 004 188 09934

75 25 19955 19203 660 007 09787 20400 005 089 09941

50 50 17407 16241 718 015 09745 17090 007 110 09928

25 75 15577 14114 806 040 09624 14869 008 119 09837

CM 100 0 11532 10787 1237 280 09858 11135 016 299 09919


88





















89






90



























91






92







1000 7525 5050 2575




















93



production


94

1


95



96


97





























98





























99



SCTSCB with CM





100

101

APPENDIX 1






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean


Upper

Bound

100USCT +

0CM

3 2017333 223010 128755 1961935 2072732 19920 20340

75USCT +

25CM

3 1791000 34641 20000 1782395 1799605 17890 17950

50USCT +

50CM

3 1581667 85049 49103 1560539 1602794 15730 15900

25USCT +

75CM

3 1413000 338674 195533 1328869 1497131 13750 14400

0USCT +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1591267 3092455 798468 1420012 1762521 11460 20340





102


adjusted df

938 4 4174 521


ANOVA

Methane Yields


Between Groups 13352489 4 3338122 924688 000


Total 13388589 14




Mean

Difference (I-

J) Std Error Sig


Lower

Bound

Upper

Bound

Tukey

HSD

100USCT

+ 0CM

75USCT +

25CM

2263333 155134 000 175277 277389

50USCT +

50CM

4356667 155134 000 384611 486723

25USCT +

75CM

6043333 155134 000 553277 655389

0USCT +

100CM

8640000 155134 000 812944 915056

75USCT +

25CM

100USCT +

0CM

-2263333 155134 000 -277389 -175277

50USCT +

50CM

2093333 155134 000 158277 260389

25USCT +

75CM

3780000 155134 000 326944 429056

0USCT +

100CM

6376667 155134 000 586611 688723

103

50USCT +

50CM

100USCT +

0CM

-4356667 155134 000 -486723 -384611

75USCT +

25CM

-2093333 155134 000 -260389 -158277

25USCT +

75CM

1686667 155134 000 117611 219723

0USCT +

100CM

4283333 155134 000 377277 479389

25USCT +

75CM

100USCT +

0CM

-6043333 155134 000 -655389 -553277

75USCT +

25CM

-3780000 155134 000 -429056 -326944

50USCT +

50CM

-1686667 155134 000 -219723 -117611

0USCT +

100CM

2596667 155134 000 208611 310723

0USCT +

100CM

100USCT +

0CM

-8640000 155134 000 -915056 -812944

75USCT +

25CM

-6376667 155134 000 -688723 -586611

50USCT +

50CM

-4283333 155134 000 -479389 -377277

25USCT +

75CM

-2596667 155134 000 -310723 -208611

LSD 100USCT

+ 0CM

75USCT +

25CM

2263333 155134 000 191767 260899

50USCT +

50CM

4356667 155134 000 401101 470233

25USCT +

75CM

6043333 155134 000 569767 638899

0USCT +

100CM

8640000 155134 000 829434 898566

75USCT +

25CM

100USCT +

0CM

-2263333 155134 000 -260899 -191767

104

50USCT +

50CM

2093333 155134 000 174767 243899

25USCT +

75CM

3780000 155134 000 343434 412566

0USCT +

100CM

6376667 155134 000 603101 672233

50USCT +

50CM

100USCT +

0CM

-4356667 155134 000 -470233 -401101

75USCT +

25CM

-2093333 155134 000 -243899 -174767

25USCT +

75CM

1686667 155134 000 134101 203233

0USCT +

100CM

4283333 155134 000 393767 462899

25USCT +

75CM

100USCT +

0CM

-6043333 155134 000 -638899 -569767

75USCT +

25CM

-3780000 155134 000 -412566 -343434

50USCT +

50CM

-1686667 155134 000 -203233 -134101

0USCT +

100CM

2596667 155134 000 225101 294233

0USCT +

100CM

100USCT +

0CM

-8640000 155134 000 -898566 -829434

75USCT +

25CM

-6376667 155134 000 -672233 -603101

50USCT +

50CM

-4283333 155134 000 -462899 -393767

25USCT +

75CM

-2596667 155134 000 -294233 -225101

Dunnett

t (2-sided)b

100USCT

+ 0CM

0USCT +

100CM

8640000 155134 000 819159 908841

75USCT +

25CM

0USCT +

100CM

6376667 155134 000 592825 682508

105

50USCT +

50CM

0USCT +

100CM

4283333 155134 000 383492 473175

25USCT +

75CM

0USCT +

100CM

2596667 155134 000 214825 304508



106

APPENDIX2






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100PSCT +

0CM

3 2075000 629603 363501 1918598 2231402 20050 21270

75PSCT +

25CM

3 2065000 580259 335012 1920856 2209144 20310 21320

50PSCT +

50CM

3 1691000 272213 157162 1623379 1758621 16600 17110

25PSCT +

75CM

3 1422667 250067 144376 1360547 1484787 13980 14480

0PSCT +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1681400 3743018 966443 1474119 1888681 11460 21320





107


adjusted df

501 4 4789 739


ANOVA

Methane Yields


Between Groups 19438783 4 4859696 276948 000


Total 19614256 14




Mean

Difference (I-

J) Std Error Sig


Lower

Bound

Upper

Bound

Tukey

HSD

100PSCT

+ 0CM

75PSCT +

25CM

100000 342027 998 -102564 122564

50PSCT +

50CM

3840000 342027 000 271436 496564

25PSCT +

75CM

6523333 342027 000 539770 764897

0PSCT +

100CM

9216667 342027 000 809103 1034230

75PSCT +

25CM

100PSCT +

0CM

-100000 342027 998 -122564 102564

50PSCT +

50CM

3740000 342027 000 261436 486564

25PSCT +

75CM

6423333 342027 000 529770 754897

108

0PSCT +

100CM

9116667 342027 000 799103 1024230

50PSCT +

50CM

100PSCT +

0CM

-3840000 342027 000 -496564 -271436

75PSCT +

25CM

-3740000 342027 000 -486564 -261436

25PSCT +

75CM

2683333 342027 000 155770 380897

0PSCT +

100CM

5376667 342027 000 425103 650230

25PSCT +

75CM

100PSCT +

0CM

-6523333 342027 000 -764897 -539770

75PSCT +

25CM

-6423333 342027 000 -754897 -529770

50PSCT +

50CM

-2683333 342027 000 -380897 -155770

0PSCT +

100CM

2693333 342027 000 156770 381897

0PSCT +

100CM

100PSCT +

0CM

-9216667 342027 000 -1034230 -809103

75PSCT +

25CM

-9116667 342027 000 -1024230 -799103

50PSCT +

50CM

-5376667 342027 000 -650230 -425103

25PSCT +

75CM

-2693333 342027 000 -381897 -156770

LSD 100PSCT

+ 0CM

75PSCT +

25CM

100000 342027 776 -66208 86208

50PSCT +

50CM

3840000 342027 000 307792 460208

25PSCT +

75CM

6523333 342027 000 576125 728542

0PSCT +

100CM

9216667 342027 000 845458 997875

109

75PSCT +

25CM

100PSCT +

0CM

-100000 342027 776 -86208 66208

50PSCT +

50CM

3740000 342027 000 297792 450208

25PSCT +

75CM

6423333 342027 000 566125 718542

0PSCT +

100CM

9116667 342027 000 835458 987875

50PSCT +

50CM

100PSCT +

0CM

-3840000 342027 000 -460208 -307792

75PSCT +

25CM

-3740000 342027 000 -450208 -297792

25PSCT +

75CM

2683333 342027 000 192125 344542

0PSCT +

100CM

5376667 342027 000 461458 613875

25PSCT +

75CM

100PSCT +

0CM

-6523333 342027 000 -728542 -576125

75PSCT +

25CM

-6423333 342027 000 -718542 -566125

50PSCT +

50CM

-2683333 342027 000 -344542 -192125

0PSCT +

100CM

2693333 342027 000 193125 345542

0PSCT +

100CM

100PSCT +

0CM

-9216667 342027 000 -997875 -845458

75PSCT +

25CM

-9116667 342027 000 -987875 -835458

50PSCT +

50CM

-5376667 342027 000 -613875 -461458

25PSCT +

75CM

-2693333 342027 000 -345542 -193125

100PSCT

+ 0CM

0PSCT +

100CM

9216667 342027 000 822805 1020529

110

Dunnett

t (2-

sided)b

75PSCT +

25CM

0PSCT +

100CM

9116667 342027 000 812805 1010529

50PSCT +

50CM

0PSCT +

100CM

5376667 342027 000 438805 636529

25PSCT +

75CM

0PSCT +

100CM

2693333 342027 000 170471 368195



111

APPENDIX 3






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100USCB +

0CM

3 1990000 481248 277849 1870451 2109549 19400 20360

75USCB +

25CM

3 1850333 221209 127715 1795382 1905285 18270 18710

50USCB +

50CM

3 1705667 51316 29627 1692919 1718414 17000 17100

25USCB +

75CM

3 1615333 187172 108064 1568837 1661829 15940 16290

0USCB +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1662933 2957005 763495 1499180 1826687 11460 20360





112

Based on Median and

with adjusted df

1518 4 4354 339


ANOVA

Methane Yields


Between Groups 12176263 4 3044066 467120 000


Total 12241429 14




Mean



Lower

Bound

Upper

Bound

Tukey

HSD

100USCB

+ 0CM

75USCB +

25CM

1396667 208433 000 71070 208264

50USCB +

50CM

2843333 208433 000 215736 352930

25USCB +

75CM

3746667 208433 000 306070 443264

0USCB +

100CM

8366667 208433 000 768070 905264

75USCB +

25CM

100USCB +

0CM

-1396667 208433 000 -208264 -71070

50USCB +

50CM

1446667 208433 000 76070 213264

25USCB +

75CM

2350000 208433 000 166403 303597

113

0USCB +

100CM

6970000 208433 000 628403 765597

50USCB +

50CM

100USCB +

0CM

-2843333 208433 000 -352930 -215736

75USCB +

25CM

-1446667 208433 000 -213264 -76070

25USCB +

75CM

903333 208433 000 21736 158930

0USCB +

100CM

5523333 208433 000 483736 620930

25USCB +

75CM

100USCB +

0CM

-3746667 208433 000 -443264 -306070

75USCB +

25CM

-2350000 208433 000 -303597 -166403

50USCB +

50CM

-903333 208433 000 -158930 -21736

0USCB +

100CM

4620000 208433 000 393403 530597

0USCB +

100CM

100USCB +

0CM

-8366667 208433 000 -905264 -768070

75USCB +

25CM

-6970000 208433 000 -765597 -628403

50USCB +

50CM

-5523333 208433 000 -620930 -483736

25USCB +

75CM

-4620000 208433 000 -530597 -393403

LSD 100USCB

+ 0CM

75USCB +

25CM

1396667 208433 000 93225 186109

50USCB +

50CM

2843333 208433 000 237891 330775

25USCB +

75CM

3746667 208433 000 328225 421109

0USCB +

100CM

8366667 208433 000 790225 883109

114

75USCB +

25CM

100USCB +

0CM

-1396667 208433 000 -186109 -93225

50USCB +

50CM

1446667 208433 000 98225 191109

25USCB +

75CM

2350000 208433 000 188558 281442

0USCB +

100CM

6970000 208433 000 650558 743442

50USCB +

50CM

100USCB +

0CM

-2843333 208433 000 -330775 -237891

75USCB +

25CM

-1446667 208433 000 -191109 -98225

25USCB +

75CM

903333 208433 000 43891 136775

0USCB +

100CM

5523333 208433 000 505891 598775

25USCB +

75CM

100USCB +

0CM

-3746667 208433 000 -421109 -328225

75USCB +

25CM

-2350000 208433 000 -281442 -188558

50USCB +

50CM

-903333 208433 000 -136775 -43891

0USCB +

100CM

4620000 208433 000 415558 508442

0USCB +

100CM

100USCB +

0CM

-8366667 208433 000 -883109 -790225

75USCB +

25CM

-6970000 208433 000 -743442 -650558

50USCB +

50CM

-5523333 208433 000 -598775 -505891

25USCB +

75CM

-4620000 208433 000 -508442 -415558

100USCB

+ 0CM

0USCB +

100CM

8366667 208433 000 776419 896914

115

Dunnett

t (2-

sided)b

75USCB +

25CM

0USCB +

100CM

6970000 208433 000 636753 757247

50USCB +

50CM

0USCB +

100CM

5523333 208433 000 492086 612581

25USCB +

75CM

0USCB +

100CM

4620000 208433 000 401753 522247



116

APPENDIX 4






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100PSCB +

0CM

3 2255667 539290 311359 2121700 2389634 21940 22940

75PSCB +

25CM

3 1995667 427239 246667 1889535 2101799 19470 20270

50PSCB +

50CM

3 1740333 234592 135442 1682057 1798609 17140 17590

25PSCB +

75CM

3 1557667 281129 162310 1487830 1627503 15390 15900

0PSCB +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1740533 3908233 1009101 1524103 1956964 11460 22940





117


adjusted df

407 4 5808 798


ANOVA

Methane Yields


Between Groups 21260984 4 5315246 432087 000


Total 21383997 14




Mean



Lower

Bound

Upper

Bound

Tukey

HSD

100PSCB

+ 0CM

75PSCB +

25CM

2600000 286372 000 165753 354247

50PSCB +

50CM

5153333 286372 000 421086 609581

25PSCB +

75CM

6980000 286372 000 603753 792247

0PSCB +

100CM

11023333 286372 000 1008086 1196581

75PSCB +

25CM

100PSCB

+ 0CM

-2600000 286372 000 -354247 -165753

50PSCB +

50CM

2553333 286372 000 161086 349581

25PSCB +

75CM

4380000 286372 000 343753 532247

118

0PSCB +

100CM

8423333 286372 000 748086 936581

50PSCB +

50CM

100PSCB

+ 0CM

-5153333 286372 000 -609581 -421086

75PSCB +

25CM

-2553333 286372 000 -349581 -161086

25PSCB +

75CM

1826667 286372 000 88419 276914

0PSCB +

100CM

5870000 286372 000 492753 681247

25PSCB +

75CM

100PSCB

+ 0CM

-6980000 286372 000 -792247 -603753

75PSCB +

25CM

-4380000 286372 000 -532247 -343753

50PSCB +

50CM

-1826667 286372 000 -276914 -88419

0PSCB +

100CM

4043333 286372 000 310086 498581

0PSCB +

100CM

100PSCB

+ 0CM

-11023333 286372 000 -1196581 -1008086

75PSCB +

25CM

-8423333 286372 000 -936581 -748086

50PSCB +

50CM

-5870000 286372 000 -681247 -492753

25PSCB +

75CM

-4043333 286372 000 -498581 -310086

LSD 100PSCB

+ 0CM

75PSCB +

25CM

2600000 286372 000 196192 323808

50PSCB +

50CM

5153333 286372 000 451526 579141

25PSCB +

75CM

6980000 286372 000 634192 761808

0PSCB +

100CM

11023333 286372 000 1038526 1166141

119

75PSCB +

25CM

100PSCB

+ 0CM

-2600000 286372 000 -323808 -196192

50PSCB +

50CM

2553333 286372 000 191526 319141

25PSCB +

75CM

4380000 286372 000 374192 501808

0PSCB +

100CM

8423333 286372 000 778526 906141

50PSCB +

50CM

100PSCB

+ 0CM

-5153333 286372 000 -579141 -451526

75PSCB +

25CM

-2553333 286372 000 -319141 -191526

25PSCB +

75CM

1826667 286372 000 118859 246474

0PSCB +

100CM

5870000 286372 000 523192 650808

25PSCB +

75CM

100PSCB

+ 0CM

-6980000 286372 000 -761808 -634192

75PSCB +

25CM

-4380000 286372 000 -501808 -374192

50PSCB +

50CM

-1826667 286372 000 -246474 -118859

0PSCB +

100CM

4043333 286372 000 340526 468141

0PSCB +

100CM

100PSCB

+ 0CM

-11023333 286372 000 -1166141 -1038526

75PSCB +

25CM

-8423333 286372 000 -906141 -778526

50PSCB +

50CM

-5870000 286372 000 -650808 -523192

25PSCB +

75CM

-4043333 286372 000 -468141 -340526

100PSCB

+ 0CM

0PSCB +

100CM

11023333 286372 000 1019558 1185109

120

Dunnett

t (2-

sided)b

75PSCB +

25CM

0PSCB +

100CM

8423333 286372 000 759558 925109

50PSCB +

50CM

0PSCB +

100CM

5870000 286372 000 504225 669775

25PSCB +

75CM

0PSCB +

100CM

4043333 286372 000 321558 487109



121

REFERENCES











122












123













124












125













126












127












128












129













130












131













132












133














134













135













136




ABSTRACT


LIST OF FIGURES

LIST OF TABLES


ACKNOWLEDGMENTS

APPENDIX 1

APPENDIX 3

APPENDIX 4

REFERENCES

2ii

iii

ABSTRACT
























iv


























v














vi







vii

TABLE OF CONTENTS

Abstract iii



List of figures xi

List of tables xiii


Acknowledgments xvi


11 Background 3


13 Thesis outline 5










272 pH 24



viii








31 Substrates 45


312 Manure 45

32 Inoculum 45

















ix









and Inoculum 63


with CM 67








with CM 72











x










References 121

xi

LIST OF FIGURES




the SCT 14












Figure 31 (e) CM 47








for 83 days 68


xii







for 83 days 74












xiii

LIST OF TABLES


(2012-2017) 10





previous studies 26













with CM 85



xiv






BD Biodegradability




CM Chicken Manure















xv



SCT Sugarcane Trash





TS Total Solids



UV Ultra-Violet



VS Volatile Solids

xvi

ACKNOWLEDGMENTS











researcher












xvii
















AMAL BABU PUTHUMANA

18


1



2


3

11 Background


























4



























5












chicken manure



13 Thesis outline




project




6
















7



8


9
























Barbieri 2013)


10








Source (FAO 2018)

Crop


2012 2013 2014 2015 2016 2017

Maize 696 786 524 596 533 678

Rice 1031 1136 767 697 266 822

Cotton 5965 444 3901 197 2804 NA

Soybean 381 411 37 301 292 300

Sugarcane 3386 3293 3752 3765 4472 4534

Sunflower 402 303 27 295 23 27

Wheat 139021 129793 126132 123837 112822 1219115











11





12




















13









14



forming the SCB


2018)


15


















(Jain et al 2014)









16
















al 2015)










17













Table 22


18






























19









C6H10O4 + 2H2O C6H12O6 + H2 Eq21







et al 2019)





20













21






CO2 + 4H2 CH4 + 2H2O Eq28

HCO-3 + 4H2 + H+ CH4 + 3H2O Eq29















22





2017)











Table 23








23






More stable

Less energy input









important


24

272 pH













Cheon 2008)


and Stetter 1982)





Jones 2008)


2008)







25



























26


studies




























27





Stroeve 2009)












28



























29





















Zeeman 2009)







30





31


Sl

No

Pre-treatment

method

Lignocellulosic

substrates

Pre-treatment

condition

CH4 production

after pre-

treatment

(mLCH4gVSadded)

Increase in

CH4 ()

References




Balsari 2012)








2016)


Olive mill solid

waste




Borja 2016)


32


Morken 2012)


MHz 1600 W



2011)


MHz 1600 W





and Carrere 2012)



2012)





33



























34



























35



























36












AD process






37


wastes

Sl

No


conditions

Feed Ratios

tested


1 Sugarcane leaves

+ Diary cow

manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 7525

5050 2575

0100 (VS basis)


LCH4kgVSadded)

(Mokomele et al

2019)

2 Sugarcane

Bagasse + Diary

cow manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 5050

0100 (VS basis)


LCH4kgVSadded)

(Mokomele et al

2019)

3 Sugarcane leaves

+ Diary cow

manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 7525

5050 2575

0100 (VS basis)

NA 7525 (292

LCH4kgVSadded)

(Mokomele et al

2019)

4 Switchgrass +

Diary manure

500 mL Mesophilic

temperature ISR =

067 30 days

1000 7525

5050 2575

0100 (TS basis)



mono-digestion of

switchgrass

(Zheng et al 2015)

5 Salix viminalis +

Cow manure

1125 mL

mesophilic

temperature ISR =

067 57 days

1000 8020

7030 6040

5050 4060

3070 2080

0100 (VS basis)


mLCH4gVS)


mLgVS) has 23



38

improvement over

untreated salix

6 Corn Stover + Pig

Manure

250 mL Mesophilic

temperature ISR =

026 70 days

1000 8020

6733 5050

3367 2080

0100 (VS basis)

NA 0100 (889

mLCH4gVS)

(Li et al 2018b)

7 Cotton stalk +

swine manure

500 mL Mesophilic

temperature ISR =

025 45 days

1000 7525

5050 2575 (TS

basis)


in yield when

compared to control

(102 mLCH4gVS)

(Cheng and Zhong

2014)

8 Apple waste +

Chicken manure

250 mL Mesophilic

temperature ISR =

026 70 days

1000 8020

6733 5050

3367 2080

0100 (VS basis)

NA 3367 (827

mLCH4gVS)

(Li et al 2018b)

9 Whey + cow

manure

400 mL Mesophilic

temperature 55 days

1000 8515

7525 5050

2575 1585

0100 (VS basis)

NA 8515 (266

mLCH4gVS)

(Vivekanand Mulat

Eijsink and Horn

2018)


39




















al 2017)







40







al 2017)


41







2 Grass Cow manure














6

Organic fraction of








42


















with CM


43



44


45

31 Substrates












312 Manure




32 Inoculum








46



























47





Sample Untreated

SCT ()

Pre-treated

SCT ()

Untreated

SCB ()

Pre-treated

SCB ()

CM

()

















CM 0 0 0 0 100


48









49























Batstone 2015)


50

























51


(PO43-)

















52











53










54

37 Data Processing

371 Theoretical BMP







Fdz-Polanco 2015)


tttEq31





ᆒt =ᆒ썈υ

Eq32









55


using equation 33










Sung 2012)


Eq34


Eq35









56







Eq36
















tt Eq37


57



υ =

tt Eq38










Eq310









58






al 2014)

= t 㢐t) Eq311













= t

t

Eq312





59












60

1


61



62


63


inoculum

























64

















study










65






Bolantildeos 2007)



SCT

Pre-treated

SCT

Untreated

SCB

Pre-treated

SCBInoculum

TS 721 913 872 828 819 19

VS TS 807 909 925 894 951 722

COD gl 7487 11598 12099 7939 9156 211

TSVS - 12 11 11 11 11 14

CN ratio - 124 831 953 1465 1919 49

Glucan ND 307 463 351 428 ND

Xylan ND 168 117 20 146 ND


Lignin ND 298 283 315 282 ND

TKP gPkg 82 11 07 05 06 312

TKN gNkg 836 97 108 512 71 1113







66



SCT

Pre-treated

SCT

Untreated

SCB

Pre-treated

SCBInoculum

Al mgg 103 123 099 084 024 817

As mgg 000 000 000 000 000 000

B mgg 003 000 000 000 000 004

Ba mgg 004 002 002 001 001 019

Ca mgg 1687 312 167 025 015 1973

Cd mgg 000 000 000 000 000 000

Co mgg 000 000 000 000 000 001

Cr mgg 004 001 001 001 001 005

Cu mgg 011 000 000 000 000 056

Fe mgg 107 092 019 086 026 1972

K mgg 1977 601 283 293 076 1582

Mg mgg 461 188 138 036 028 666

Mn mgg 036 010 006 005 003 017

Mo mgg 001 000 000 000 000 003

Na mgg 171 007 034 001 004 1644

Ni mgg 002 000 000 000 000 005

P mgg 796 101 041 015 010 2735

Pb mgg 001 000 000 000 000 003

S mgg 321 082 809 018 524 1469

Se mgg 001 000 001 000 001 000

Zn mgg 027 000 000 000 000 067









67





CM













al 2015)







68




Untreated SCT














69



























70


















71



























72



digested with CM




CM








73






















74

















75















untreated SCB












76
















mono-digestion


77
















78



























79




Pre-treated SCBCM






2015)












80




Untreated

SCTCM

1000 5552 1444 2018 215 353 1960 28

7525 5444 1307 1791 247 307 1671 67

5050 5336 1178 1582 248 308 1642 -38

2575 5228 1074 1413 244 270 1413 00

Pre-treated

SCTCM

1000 5073 1625 2075a 265 410 2082 -03

7525 5085 1614 2065a 252 391 1988 37

5050 5097 1318 1691 235 319 1628 37

2575 5109 1106 1422 190 255 1302 85

Untreated

SCBCM

1000 4364 1812 1990 262 421 1839 76

7525 4553 1615 1850 237 391 1780 38

5050 4742 1429 1706 232 350 1660 27

2575 4931 1302 1615 179 301 1486 80

Pre-treated

SCBCM

1000 2882 3110 2256 251 715 2061 86

7525 3442 2304 1995 220 538 1852 72

5050 4000 1729 1741 235 414 1656 49

2575 4561 1357 1558 194 348 1586 -18

CM 1000 5121 895 1153 216 206 1056 84


81



























82












83




84


























85




136 mgL


CM


mgL mgL mgL mgL

Untreated

SCTCM

1000 0 548 16160 1535

7525 0 554 16160 1465

5050 0 550 16867 1444

2575 0 553 17271 1404

Pre-treated

SCTCM

1000 0 556 15655 1636

7525 0 547 16059 1596

5050 0 551 16362 1505

2575 0 550 16867 1414

Untreated

SCBCM

1000 0 548 16362 1646

7525 0 561 16564 1586

5050 0 535 17069 1515

2575 0 551 17271 1444

Pre-treated

SCBCM

1000 0 364 15756 1566

7525 0 549 16665 1576

5050 0 543 16968 1505

2575 0 552 17271 1444

CM 1000 0 553 17473 1364






86
























modelled data


87


SubstratesFeed


B B0 Rm λR2



USCTCM 100 0 20175 19347 712 264 09942 20753 005 266 09988

75 25 17909 16507 623 128 09772 17679 005 183 09956

50 50 15820 14442 648 122 09701 15343 006 188 09911

25 75 14128 12653 858 198 09727 13238 010 255 09898

PSCTCM 100 0 20748 19853 2142 168 09927 20397 015 185 09867

75 25 20650 19748 1819 123 09905 20331 014 167 09905

50 50 16910 16360 1766 142 09920 16782 016 174 09896

25 75 14224 13498 1521 157 09887 13889 016 178 09881

USCBCM 100 0 19900 19394 797 521 09972 20561 006 538 09889

75 25 18503 17700 528 197 09864 19472 004 197 09989

50 50 17056 16131 492 045 09734 17440 004 119 09940

25 75 16154 14420 767 109 09691 15218 003 178 09897

PSCBCM 100 0 22557 21982 702 154 09815 23570 004 188 09934

75 25 19955 19203 660 007 09787 20400 005 089 09941

50 50 17407 16241 718 015 09745 17090 007 110 09928

25 75 15577 14114 806 040 09624 14869 008 119 09837

CM 100 0 11532 10787 1237 280 09858 11135 016 299 09919


88





















89






90



























91






92







1000 7525 5050 2575




















93



production


94

1


95



96


97





























98





























99



SCTSCB with CM





100

101

APPENDIX 1






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean


Upper

Bound

100USCT +

0CM

3 2017333 223010 128755 1961935 2072732 19920 20340

75USCT +

25CM

3 1791000 34641 20000 1782395 1799605 17890 17950

50USCT +

50CM

3 1581667 85049 49103 1560539 1602794 15730 15900

25USCT +

75CM

3 1413000 338674 195533 1328869 1497131 13750 14400

0USCT +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1591267 3092455 798468 1420012 1762521 11460 20340





102


adjusted df

938 4 4174 521


ANOVA

Methane Yields


Between Groups 13352489 4 3338122 924688 000


Total 13388589 14




Mean

Difference (I-

J) Std Error Sig


Lower

Bound

Upper

Bound

Tukey

HSD

100USCT

+ 0CM

75USCT +

25CM

2263333 155134 000 175277 277389

50USCT +

50CM

4356667 155134 000 384611 486723

25USCT +

75CM

6043333 155134 000 553277 655389

0USCT +

100CM

8640000 155134 000 812944 915056

75USCT +

25CM

100USCT +

0CM

-2263333 155134 000 -277389 -175277

50USCT +

50CM

2093333 155134 000 158277 260389

25USCT +

75CM

3780000 155134 000 326944 429056

0USCT +

100CM

6376667 155134 000 586611 688723

103

50USCT +

50CM

100USCT +

0CM

-4356667 155134 000 -486723 -384611

75USCT +

25CM

-2093333 155134 000 -260389 -158277

25USCT +

75CM

1686667 155134 000 117611 219723

0USCT +

100CM

4283333 155134 000 377277 479389

25USCT +

75CM

100USCT +

0CM

-6043333 155134 000 -655389 -553277

75USCT +

25CM

-3780000 155134 000 -429056 -326944

50USCT +

50CM

-1686667 155134 000 -219723 -117611

0USCT +

100CM

2596667 155134 000 208611 310723

0USCT +

100CM

100USCT +

0CM

-8640000 155134 000 -915056 -812944

75USCT +

25CM

-6376667 155134 000 -688723 -586611

50USCT +

50CM

-4283333 155134 000 -479389 -377277

25USCT +

75CM

-2596667 155134 000 -310723 -208611

LSD 100USCT

+ 0CM

75USCT +

25CM

2263333 155134 000 191767 260899

50USCT +

50CM

4356667 155134 000 401101 470233

25USCT +

75CM

6043333 155134 000 569767 638899

0USCT +

100CM

8640000 155134 000 829434 898566

75USCT +

25CM

100USCT +

0CM

-2263333 155134 000 -260899 -191767

104

50USCT +

50CM

2093333 155134 000 174767 243899

25USCT +

75CM

3780000 155134 000 343434 412566

0USCT +

100CM

6376667 155134 000 603101 672233

50USCT +

50CM

100USCT +

0CM

-4356667 155134 000 -470233 -401101

75USCT +

25CM

-2093333 155134 000 -243899 -174767

25USCT +

75CM

1686667 155134 000 134101 203233

0USCT +

100CM

4283333 155134 000 393767 462899

25USCT +

75CM

100USCT +

0CM

-6043333 155134 000 -638899 -569767

75USCT +

25CM

-3780000 155134 000 -412566 -343434

50USCT +

50CM

-1686667 155134 000 -203233 -134101

0USCT +

100CM

2596667 155134 000 225101 294233

0USCT +

100CM

100USCT +

0CM

-8640000 155134 000 -898566 -829434

75USCT +

25CM

-6376667 155134 000 -672233 -603101

50USCT +

50CM

-4283333 155134 000 -462899 -393767

25USCT +

75CM

-2596667 155134 000 -294233 -225101

Dunnett

t (2-sided)b

100USCT

+ 0CM

0USCT +

100CM

8640000 155134 000 819159 908841

75USCT +

25CM

0USCT +

100CM

6376667 155134 000 592825 682508

105

50USCT +

50CM

0USCT +

100CM

4283333 155134 000 383492 473175

25USCT +

75CM

0USCT +

100CM

2596667 155134 000 214825 304508



106

APPENDIX2






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100PSCT +

0CM

3 2075000 629603 363501 1918598 2231402 20050 21270

75PSCT +

25CM

3 2065000 580259 335012 1920856 2209144 20310 21320

50PSCT +

50CM

3 1691000 272213 157162 1623379 1758621 16600 17110

25PSCT +

75CM

3 1422667 250067 144376 1360547 1484787 13980 14480

0PSCT +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1681400 3743018 966443 1474119 1888681 11460 21320





107


adjusted df

501 4 4789 739


ANOVA

Methane Yields


Between Groups 19438783 4 4859696 276948 000


Total 19614256 14




Mean

Difference (I-

J) Std Error Sig


Lower

Bound

Upper

Bound

Tukey

HSD

100PSCT

+ 0CM

75PSCT +

25CM

100000 342027 998 -102564 122564

50PSCT +

50CM

3840000 342027 000 271436 496564

25PSCT +

75CM

6523333 342027 000 539770 764897

0PSCT +

100CM

9216667 342027 000 809103 1034230

75PSCT +

25CM

100PSCT +

0CM

-100000 342027 998 -122564 102564

50PSCT +

50CM

3740000 342027 000 261436 486564

25PSCT +

75CM

6423333 342027 000 529770 754897

108

0PSCT +

100CM

9116667 342027 000 799103 1024230

50PSCT +

50CM

100PSCT +

0CM

-3840000 342027 000 -496564 -271436

75PSCT +

25CM

-3740000 342027 000 -486564 -261436

25PSCT +

75CM

2683333 342027 000 155770 380897

0PSCT +

100CM

5376667 342027 000 425103 650230

25PSCT +

75CM

100PSCT +

0CM

-6523333 342027 000 -764897 -539770

75PSCT +

25CM

-6423333 342027 000 -754897 -529770

50PSCT +

50CM

-2683333 342027 000 -380897 -155770

0PSCT +

100CM

2693333 342027 000 156770 381897

0PSCT +

100CM

100PSCT +

0CM

-9216667 342027 000 -1034230 -809103

75PSCT +

25CM

-9116667 342027 000 -1024230 -799103

50PSCT +

50CM

-5376667 342027 000 -650230 -425103

25PSCT +

75CM

-2693333 342027 000 -381897 -156770

LSD 100PSCT

+ 0CM

75PSCT +

25CM

100000 342027 776 -66208 86208

50PSCT +

50CM

3840000 342027 000 307792 460208

25PSCT +

75CM

6523333 342027 000 576125 728542

0PSCT +

100CM

9216667 342027 000 845458 997875

109

75PSCT +

25CM

100PSCT +

0CM

-100000 342027 776 -86208 66208

50PSCT +

50CM

3740000 342027 000 297792 450208

25PSCT +

75CM

6423333 342027 000 566125 718542

0PSCT +

100CM

9116667 342027 000 835458 987875

50PSCT +

50CM

100PSCT +

0CM

-3840000 342027 000 -460208 -307792

75PSCT +

25CM

-3740000 342027 000 -450208 -297792

25PSCT +

75CM

2683333 342027 000 192125 344542

0PSCT +

100CM

5376667 342027 000 461458 613875

25PSCT +

75CM

100PSCT +

0CM

-6523333 342027 000 -728542 -576125

75PSCT +

25CM

-6423333 342027 000 -718542 -566125

50PSCT +

50CM

-2683333 342027 000 -344542 -192125

0PSCT +

100CM

2693333 342027 000 193125 345542

0PSCT +

100CM

100PSCT +

0CM

-9216667 342027 000 -997875 -845458

75PSCT +

25CM

-9116667 342027 000 -987875 -835458

50PSCT +

50CM

-5376667 342027 000 -613875 -461458

25PSCT +

75CM

-2693333 342027 000 -345542 -193125

100PSCT

+ 0CM

0PSCT +

100CM

9216667 342027 000 822805 1020529

110

Dunnett

t (2-

sided)b

75PSCT +

25CM

0PSCT +

100CM

9116667 342027 000 812805 1010529

50PSCT +

50CM

0PSCT +

100CM

5376667 342027 000 438805 636529

25PSCT +

75CM

0PSCT +

100CM

2693333 342027 000 170471 368195



111

APPENDIX 3






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100USCB +

0CM

3 1990000 481248 277849 1870451 2109549 19400 20360

75USCB +

25CM

3 1850333 221209 127715 1795382 1905285 18270 18710

50USCB +

50CM

3 1705667 51316 29627 1692919 1718414 17000 17100

25USCB +

75CM

3 1615333 187172 108064 1568837 1661829 15940 16290

0USCB +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1662933 2957005 763495 1499180 1826687 11460 20360





112

Based on Median and

with adjusted df

1518 4 4354 339


ANOVA

Methane Yields


Between Groups 12176263 4 3044066 467120 000


Total 12241429 14




Mean



Lower

Bound

Upper

Bound

Tukey

HSD

100USCB

+ 0CM

75USCB +

25CM

1396667 208433 000 71070 208264

50USCB +

50CM

2843333 208433 000 215736 352930

25USCB +

75CM

3746667 208433 000 306070 443264

0USCB +

100CM

8366667 208433 000 768070 905264

75USCB +

25CM

100USCB +

0CM

-1396667 208433 000 -208264 -71070

50USCB +

50CM

1446667 208433 000 76070 213264

25USCB +

75CM

2350000 208433 000 166403 303597

113

0USCB +

100CM

6970000 208433 000 628403 765597

50USCB +

50CM

100USCB +

0CM

-2843333 208433 000 -352930 -215736

75USCB +

25CM

-1446667 208433 000 -213264 -76070

25USCB +

75CM

903333 208433 000 21736 158930

0USCB +

100CM

5523333 208433 000 483736 620930

25USCB +

75CM

100USCB +

0CM

-3746667 208433 000 -443264 -306070

75USCB +

25CM

-2350000 208433 000 -303597 -166403

50USCB +

50CM

-903333 208433 000 -158930 -21736

0USCB +

100CM

4620000 208433 000 393403 530597

0USCB +

100CM

100USCB +

0CM

-8366667 208433 000 -905264 -768070

75USCB +

25CM

-6970000 208433 000 -765597 -628403

50USCB +

50CM

-5523333 208433 000 -620930 -483736

25USCB +

75CM

-4620000 208433 000 -530597 -393403

LSD 100USCB

+ 0CM

75USCB +

25CM

1396667 208433 000 93225 186109

50USCB +

50CM

2843333 208433 000 237891 330775

25USCB +

75CM

3746667 208433 000 328225 421109

0USCB +

100CM

8366667 208433 000 790225 883109

114

75USCB +

25CM

100USCB +

0CM

-1396667 208433 000 -186109 -93225

50USCB +

50CM

1446667 208433 000 98225 191109

25USCB +

75CM

2350000 208433 000 188558 281442

0USCB +

100CM

6970000 208433 000 650558 743442

50USCB +

50CM

100USCB +

0CM

-2843333 208433 000 -330775 -237891

75USCB +

25CM

-1446667 208433 000 -191109 -98225

25USCB +

75CM

903333 208433 000 43891 136775

0USCB +

100CM

5523333 208433 000 505891 598775

25USCB +

75CM

100USCB +

0CM

-3746667 208433 000 -421109 -328225

75USCB +

25CM

-2350000 208433 000 -281442 -188558

50USCB +

50CM

-903333 208433 000 -136775 -43891

0USCB +

100CM

4620000 208433 000 415558 508442

0USCB +

100CM

100USCB +

0CM

-8366667 208433 000 -883109 -790225

75USCB +

25CM

-6970000 208433 000 -743442 -650558

50USCB +

50CM

-5523333 208433 000 -598775 -505891

25USCB +

75CM

-4620000 208433 000 -508442 -415558

100USCB

+ 0CM

0USCB +

100CM

8366667 208433 000 776419 896914

115

Dunnett

t (2-

sided)b

75USCB +

25CM

0USCB +

100CM

6970000 208433 000 636753 757247

50USCB +

50CM

0USCB +

100CM

5523333 208433 000 492086 612581

25USCB +

75CM

0USCB +

100CM

4620000 208433 000 401753 522247



116

APPENDIX 4






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100PSCB +

0CM

3 2255667 539290 311359 2121700 2389634 21940 22940

75PSCB +

25CM

3 1995667 427239 246667 1889535 2101799 19470 20270

50PSCB +

50CM

3 1740333 234592 135442 1682057 1798609 17140 17590

25PSCB +

75CM

3 1557667 281129 162310 1487830 1627503 15390 15900

0PSCB +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1740533 3908233 1009101 1524103 1956964 11460 22940





117


adjusted df

407 4 5808 798


ANOVA

Methane Yields


Between Groups 21260984 4 5315246 432087 000


Total 21383997 14




Mean



Lower

Bound

Upper

Bound

Tukey

HSD

100PSCB

+ 0CM

75PSCB +

25CM

2600000 286372 000 165753 354247

50PSCB +

50CM

5153333 286372 000 421086 609581

25PSCB +

75CM

6980000 286372 000 603753 792247

0PSCB +

100CM

11023333 286372 000 1008086 1196581

75PSCB +

25CM

100PSCB

+ 0CM

-2600000 286372 000 -354247 -165753

50PSCB +

50CM

2553333 286372 000 161086 349581

25PSCB +

75CM

4380000 286372 000 343753 532247

118

0PSCB +

100CM

8423333 286372 000 748086 936581

50PSCB +

50CM

100PSCB

+ 0CM

-5153333 286372 000 -609581 -421086

75PSCB +

25CM

-2553333 286372 000 -349581 -161086

25PSCB +

75CM

1826667 286372 000 88419 276914

0PSCB +

100CM

5870000 286372 000 492753 681247

25PSCB +

75CM

100PSCB

+ 0CM

-6980000 286372 000 -792247 -603753

75PSCB +

25CM

-4380000 286372 000 -532247 -343753

50PSCB +

50CM

-1826667 286372 000 -276914 -88419

0PSCB +

100CM

4043333 286372 000 310086 498581

0PSCB +

100CM

100PSCB

+ 0CM

-11023333 286372 000 -1196581 -1008086

75PSCB +

25CM

-8423333 286372 000 -936581 -748086

50PSCB +

50CM

-5870000 286372 000 -681247 -492753

25PSCB +

75CM

-4043333 286372 000 -498581 -310086

LSD 100PSCB

+ 0CM

75PSCB +

25CM

2600000 286372 000 196192 323808

50PSCB +

50CM

5153333 286372 000 451526 579141

25PSCB +

75CM

6980000 286372 000 634192 761808

0PSCB +

100CM

11023333 286372 000 1038526 1166141

119

75PSCB +

25CM

100PSCB

+ 0CM

-2600000 286372 000 -323808 -196192

50PSCB +

50CM

2553333 286372 000 191526 319141

25PSCB +

75CM

4380000 286372 000 374192 501808

0PSCB +

100CM

8423333 286372 000 778526 906141

50PSCB +

50CM

100PSCB

+ 0CM

-5153333 286372 000 -579141 -451526

75PSCB +

25CM

-2553333 286372 000 -319141 -191526

25PSCB +

75CM

1826667 286372 000 118859 246474

0PSCB +

100CM

5870000 286372 000 523192 650808

25PSCB +

75CM

100PSCB

+ 0CM

-6980000 286372 000 -761808 -634192

75PSCB +

25CM

-4380000 286372 000 -501808 -374192

50PSCB +

50CM

-1826667 286372 000 -246474 -118859

0PSCB +

100CM

4043333 286372 000 340526 468141

0PSCB +

100CM

100PSCB

+ 0CM

-11023333 286372 000 -1166141 -1038526

75PSCB +

25CM

-8423333 286372 000 -906141 -778526

50PSCB +

50CM

-5870000 286372 000 -650808 -523192

25PSCB +

75CM

-4043333 286372 000 -468141 -340526

100PSCB

+ 0CM

0PSCB +

100CM

11023333 286372 000 1019558 1185109

120

Dunnett

t (2-

sided)b

75PSCB +

25CM

0PSCB +

100CM

8423333 286372 000 759558 925109

50PSCB +

50CM

0PSCB +

100CM

5870000 286372 000 504225 669775

25PSCB +

75CM

0PSCB +

100CM

4043333 286372 000 321558 487109



121

REFERENCES











122












123













124












125













126












127












128












129













130












131













132












133














134













135













136




ABSTRACT


LIST OF FIGURES

LIST OF TABLES


ACKNOWLEDGMENTS

APPENDIX 1

APPENDIX 3

APPENDIX 4

REFERENCES

iii

ABSTRACT
























iv


























v














vi







vii

TABLE OF CONTENTS

Abstract iii



List of figures xi

List of tables xiii


Acknowledgments xvi


11 Background 3


13 Thesis outline 5










272 pH 24



viii








31 Substrates 45


312 Manure 45

32 Inoculum 45

















ix









and Inoculum 63


with CM 67








with CM 72











x










References 121

xi

LIST OF FIGURES




the SCT 14












Figure 31 (e) CM 47








for 83 days 68


xii







for 83 days 74












xiii

LIST OF TABLES


(2012-2017) 10





previous studies 26













with CM 85



xiv






BD Biodegradability




CM Chicken Manure















xv



SCT Sugarcane Trash





TS Total Solids



UV Ultra-Violet



VS Volatile Solids

xvi

ACKNOWLEDGMENTS











researcher












xvii
















AMAL BABU PUTHUMANA

18


1



2


3

11 Background


























4



























5












chicken manure



13 Thesis outline




project




6
















7



8


9
























Barbieri 2013)


10








Source (FAO 2018)

Crop


2012 2013 2014 2015 2016 2017

Maize 696 786 524 596 533 678

Rice 1031 1136 767 697 266 822

Cotton 5965 444 3901 197 2804 NA

Soybean 381 411 37 301 292 300

Sugarcane 3386 3293 3752 3765 4472 4534

Sunflower 402 303 27 295 23 27

Wheat 139021 129793 126132 123837 112822 1219115











11





12




















13









14



forming the SCB


2018)


15


















(Jain et al 2014)









16
















al 2015)










17













Table 22


18






























19









C6H10O4 + 2H2O C6H12O6 + H2 Eq21







et al 2019)





20













21






CO2 + 4H2 CH4 + 2H2O Eq28

HCO-3 + 4H2 + H+ CH4 + 3H2O Eq29















22





2017)











Table 23








23






More stable

Less energy input









important


24

272 pH













Cheon 2008)


and Stetter 1982)





Jones 2008)


2008)







25



























26


studies




























27





Stroeve 2009)












28



























29





















Zeeman 2009)







30





31


Sl

No

Pre-treatment

method

Lignocellulosic

substrates

Pre-treatment

condition

CH4 production

after pre-

treatment

(mLCH4gVSadded)

Increase in

CH4 ()

References




Balsari 2012)








2016)


Olive mill solid

waste




Borja 2016)


32


Morken 2012)


MHz 1600 W



2011)


MHz 1600 W





and Carrere 2012)



2012)





33



























34



























35



























36












AD process






37


wastes

Sl

No


conditions

Feed Ratios

tested


1 Sugarcane leaves

+ Diary cow

manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 7525

5050 2575

0100 (VS basis)


LCH4kgVSadded)

(Mokomele et al

2019)

2 Sugarcane

Bagasse + Diary

cow manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 5050

0100 (VS basis)


LCH4kgVSadded)

(Mokomele et al

2019)

3 Sugarcane leaves

+ Diary cow

manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 7525

5050 2575

0100 (VS basis)

NA 7525 (292

LCH4kgVSadded)

(Mokomele et al

2019)

4 Switchgrass +

Diary manure

500 mL Mesophilic

temperature ISR =

067 30 days

1000 7525

5050 2575

0100 (TS basis)



mono-digestion of

switchgrass

(Zheng et al 2015)

5 Salix viminalis +

Cow manure

1125 mL

mesophilic

temperature ISR =

067 57 days

1000 8020

7030 6040

5050 4060

3070 2080

0100 (VS basis)


mLCH4gVS)


mLgVS) has 23



38

improvement over

untreated salix

6 Corn Stover + Pig

Manure

250 mL Mesophilic

temperature ISR =

026 70 days

1000 8020

6733 5050

3367 2080

0100 (VS basis)

NA 0100 (889

mLCH4gVS)

(Li et al 2018b)

7 Cotton stalk +

swine manure

500 mL Mesophilic

temperature ISR =

025 45 days

1000 7525

5050 2575 (TS

basis)


in yield when

compared to control

(102 mLCH4gVS)

(Cheng and Zhong

2014)

8 Apple waste +

Chicken manure

250 mL Mesophilic

temperature ISR =

026 70 days

1000 8020

6733 5050

3367 2080

0100 (VS basis)

NA 3367 (827

mLCH4gVS)

(Li et al 2018b)

9 Whey + cow

manure

400 mL Mesophilic

temperature 55 days

1000 8515

7525 5050

2575 1585

0100 (VS basis)

NA 8515 (266

mLCH4gVS)

(Vivekanand Mulat

Eijsink and Horn

2018)


39




















al 2017)







40







al 2017)


41







2 Grass Cow manure














6

Organic fraction of








42


















with CM


43



44


45

31 Substrates












312 Manure




32 Inoculum








46



























47





Sample Untreated

SCT ()

Pre-treated

SCT ()

Untreated

SCB ()

Pre-treated

SCB ()

CM

()

















CM 0 0 0 0 100


48









49























Batstone 2015)


50

























51


(PO43-)

















52











53










54

37 Data Processing

371 Theoretical BMP







Fdz-Polanco 2015)


tttEq31





ᆒt =ᆒ썈υ

Eq32









55


using equation 33










Sung 2012)


Eq34


Eq35









56







Eq36
















tt Eq37


57



υ =

tt Eq38










Eq310









58






al 2014)

= t 㢐t) Eq311













= t

t

Eq312





59












60

1


61



62


63


inoculum

























64

















study










65






Bolantildeos 2007)



SCT

Pre-treated

SCT

Untreated

SCB

Pre-treated

SCBInoculum

TS 721 913 872 828 819 19

VS TS 807 909 925 894 951 722

COD gl 7487 11598 12099 7939 9156 211

TSVS - 12 11 11 11 11 14

CN ratio - 124 831 953 1465 1919 49

Glucan ND 307 463 351 428 ND

Xylan ND 168 117 20 146 ND


Lignin ND 298 283 315 282 ND

TKP gPkg 82 11 07 05 06 312

TKN gNkg 836 97 108 512 71 1113







66



SCT

Pre-treated

SCT

Untreated

SCB

Pre-treated

SCBInoculum

Al mgg 103 123 099 084 024 817

As mgg 000 000 000 000 000 000

B mgg 003 000 000 000 000 004

Ba mgg 004 002 002 001 001 019

Ca mgg 1687 312 167 025 015 1973

Cd mgg 000 000 000 000 000 000

Co mgg 000 000 000 000 000 001

Cr mgg 004 001 001 001 001 005

Cu mgg 011 000 000 000 000 056

Fe mgg 107 092 019 086 026 1972

K mgg 1977 601 283 293 076 1582

Mg mgg 461 188 138 036 028 666

Mn mgg 036 010 006 005 003 017

Mo mgg 001 000 000 000 000 003

Na mgg 171 007 034 001 004 1644

Ni mgg 002 000 000 000 000 005

P mgg 796 101 041 015 010 2735

Pb mgg 001 000 000 000 000 003

S mgg 321 082 809 018 524 1469

Se mgg 001 000 001 000 001 000

Zn mgg 027 000 000 000 000 067









67





CM













al 2015)







68




Untreated SCT














69



























70


















71



























72



digested with CM




CM








73






















74

















75















untreated SCB












76
















mono-digestion


77
















78



























79




Pre-treated SCBCM






2015)












80




Untreated

SCTCM

1000 5552 1444 2018 215 353 1960 28

7525 5444 1307 1791 247 307 1671 67

5050 5336 1178 1582 248 308 1642 -38

2575 5228 1074 1413 244 270 1413 00

Pre-treated

SCTCM

1000 5073 1625 2075a 265 410 2082 -03

7525 5085 1614 2065a 252 391 1988 37

5050 5097 1318 1691 235 319 1628 37

2575 5109 1106 1422 190 255 1302 85

Untreated

SCBCM

1000 4364 1812 1990 262 421 1839 76

7525 4553 1615 1850 237 391 1780 38

5050 4742 1429 1706 232 350 1660 27

2575 4931 1302 1615 179 301 1486 80

Pre-treated

SCBCM

1000 2882 3110 2256 251 715 2061 86

7525 3442 2304 1995 220 538 1852 72

5050 4000 1729 1741 235 414 1656 49

2575 4561 1357 1558 194 348 1586 -18

CM 1000 5121 895 1153 216 206 1056 84


81



























82












83




84


























85




136 mgL


CM


mgL mgL mgL mgL

Untreated

SCTCM

1000 0 548 16160 1535

7525 0 554 16160 1465

5050 0 550 16867 1444

2575 0 553 17271 1404

Pre-treated

SCTCM

1000 0 556 15655 1636

7525 0 547 16059 1596

5050 0 551 16362 1505

2575 0 550 16867 1414

Untreated

SCBCM

1000 0 548 16362 1646

7525 0 561 16564 1586

5050 0 535 17069 1515

2575 0 551 17271 1444

Pre-treated

SCBCM

1000 0 364 15756 1566

7525 0 549 16665 1576

5050 0 543 16968 1505

2575 0 552 17271 1444

CM 1000 0 553 17473 1364






86
























modelled data


87


SubstratesFeed


B B0 Rm λR2



USCTCM 100 0 20175 19347 712 264 09942 20753 005 266 09988

75 25 17909 16507 623 128 09772 17679 005 183 09956

50 50 15820 14442 648 122 09701 15343 006 188 09911

25 75 14128 12653 858 198 09727 13238 010 255 09898

PSCTCM 100 0 20748 19853 2142 168 09927 20397 015 185 09867

75 25 20650 19748 1819 123 09905 20331 014 167 09905

50 50 16910 16360 1766 142 09920 16782 016 174 09896

25 75 14224 13498 1521 157 09887 13889 016 178 09881

USCBCM 100 0 19900 19394 797 521 09972 20561 006 538 09889

75 25 18503 17700 528 197 09864 19472 004 197 09989

50 50 17056 16131 492 045 09734 17440 004 119 09940

25 75 16154 14420 767 109 09691 15218 003 178 09897

PSCBCM 100 0 22557 21982 702 154 09815 23570 004 188 09934

75 25 19955 19203 660 007 09787 20400 005 089 09941

50 50 17407 16241 718 015 09745 17090 007 110 09928

25 75 15577 14114 806 040 09624 14869 008 119 09837

CM 100 0 11532 10787 1237 280 09858 11135 016 299 09919


88





















89






90



























91






92







1000 7525 5050 2575




















93



production


94

1


95



96


97





























98





























99



SCTSCB with CM





100

101

APPENDIX 1






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean


Upper

Bound

100USCT +

0CM

3 2017333 223010 128755 1961935 2072732 19920 20340

75USCT +

25CM

3 1791000 34641 20000 1782395 1799605 17890 17950

50USCT +

50CM

3 1581667 85049 49103 1560539 1602794 15730 15900

25USCT +

75CM

3 1413000 338674 195533 1328869 1497131 13750 14400

0USCT +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1591267 3092455 798468 1420012 1762521 11460 20340





102


adjusted df

938 4 4174 521


ANOVA

Methane Yields


Between Groups 13352489 4 3338122 924688 000


Total 13388589 14




Mean

Difference (I-

J) Std Error Sig


Lower

Bound

Upper

Bound

Tukey

HSD

100USCT

+ 0CM

75USCT +

25CM

2263333 155134 000 175277 277389

50USCT +

50CM

4356667 155134 000 384611 486723

25USCT +

75CM

6043333 155134 000 553277 655389

0USCT +

100CM

8640000 155134 000 812944 915056

75USCT +

25CM

100USCT +

0CM

-2263333 155134 000 -277389 -175277

50USCT +

50CM

2093333 155134 000 158277 260389

25USCT +

75CM

3780000 155134 000 326944 429056

0USCT +

100CM

6376667 155134 000 586611 688723

103

50USCT +

50CM

100USCT +

0CM

-4356667 155134 000 -486723 -384611

75USCT +

25CM

-2093333 155134 000 -260389 -158277

25USCT +

75CM

1686667 155134 000 117611 219723

0USCT +

100CM

4283333 155134 000 377277 479389

25USCT +

75CM

100USCT +

0CM

-6043333 155134 000 -655389 -553277

75USCT +

25CM

-3780000 155134 000 -429056 -326944

50USCT +

50CM

-1686667 155134 000 -219723 -117611

0USCT +

100CM

2596667 155134 000 208611 310723

0USCT +

100CM

100USCT +

0CM

-8640000 155134 000 -915056 -812944

75USCT +

25CM

-6376667 155134 000 -688723 -586611

50USCT +

50CM

-4283333 155134 000 -479389 -377277

25USCT +

75CM

-2596667 155134 000 -310723 -208611

LSD 100USCT

+ 0CM

75USCT +

25CM

2263333 155134 000 191767 260899

50USCT +

50CM

4356667 155134 000 401101 470233

25USCT +

75CM

6043333 155134 000 569767 638899

0USCT +

100CM

8640000 155134 000 829434 898566

75USCT +

25CM

100USCT +

0CM

-2263333 155134 000 -260899 -191767

104

50USCT +

50CM

2093333 155134 000 174767 243899

25USCT +

75CM

3780000 155134 000 343434 412566

0USCT +

100CM

6376667 155134 000 603101 672233

50USCT +

50CM

100USCT +

0CM

-4356667 155134 000 -470233 -401101

75USCT +

25CM

-2093333 155134 000 -243899 -174767

25USCT +

75CM

1686667 155134 000 134101 203233

0USCT +

100CM

4283333 155134 000 393767 462899

25USCT +

75CM

100USCT +

0CM

-6043333 155134 000 -638899 -569767

75USCT +

25CM

-3780000 155134 000 -412566 -343434

50USCT +

50CM

-1686667 155134 000 -203233 -134101

0USCT +

100CM

2596667 155134 000 225101 294233

0USCT +

100CM

100USCT +

0CM

-8640000 155134 000 -898566 -829434

75USCT +

25CM

-6376667 155134 000 -672233 -603101

50USCT +

50CM

-4283333 155134 000 -462899 -393767

25USCT +

75CM

-2596667 155134 000 -294233 -225101

Dunnett

t (2-sided)b

100USCT

+ 0CM

0USCT +

100CM

8640000 155134 000 819159 908841

75USCT +

25CM

0USCT +

100CM

6376667 155134 000 592825 682508

105

50USCT +

50CM

0USCT +

100CM

4283333 155134 000 383492 473175

25USCT +

75CM

0USCT +

100CM

2596667 155134 000 214825 304508



106

APPENDIX2






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100PSCT +

0CM

3 2075000 629603 363501 1918598 2231402 20050 21270

75PSCT +

25CM

3 2065000 580259 335012 1920856 2209144 20310 21320

50PSCT +

50CM

3 1691000 272213 157162 1623379 1758621 16600 17110

25PSCT +

75CM

3 1422667 250067 144376 1360547 1484787 13980 14480

0PSCT +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1681400 3743018 966443 1474119 1888681 11460 21320





107


adjusted df

501 4 4789 739


ANOVA

Methane Yields


Between Groups 19438783 4 4859696 276948 000


Total 19614256 14




Mean

Difference (I-

J) Std Error Sig


Lower

Bound

Upper

Bound

Tukey

HSD

100PSCT

+ 0CM

75PSCT +

25CM

100000 342027 998 -102564 122564

50PSCT +

50CM

3840000 342027 000 271436 496564

25PSCT +

75CM

6523333 342027 000 539770 764897

0PSCT +

100CM

9216667 342027 000 809103 1034230

75PSCT +

25CM

100PSCT +

0CM

-100000 342027 998 -122564 102564

50PSCT +

50CM

3740000 342027 000 261436 486564

25PSCT +

75CM

6423333 342027 000 529770 754897

108

0PSCT +

100CM

9116667 342027 000 799103 1024230

50PSCT +

50CM

100PSCT +

0CM

-3840000 342027 000 -496564 -271436

75PSCT +

25CM

-3740000 342027 000 -486564 -261436

25PSCT +

75CM

2683333 342027 000 155770 380897

0PSCT +

100CM

5376667 342027 000 425103 650230

25PSCT +

75CM

100PSCT +

0CM

-6523333 342027 000 -764897 -539770

75PSCT +

25CM

-6423333 342027 000 -754897 -529770

50PSCT +

50CM

-2683333 342027 000 -380897 -155770

0PSCT +

100CM

2693333 342027 000 156770 381897

0PSCT +

100CM

100PSCT +

0CM

-9216667 342027 000 -1034230 -809103

75PSCT +

25CM

-9116667 342027 000 -1024230 -799103

50PSCT +

50CM

-5376667 342027 000 -650230 -425103

25PSCT +

75CM

-2693333 342027 000 -381897 -156770

LSD 100PSCT

+ 0CM

75PSCT +

25CM

100000 342027 776 -66208 86208

50PSCT +

50CM

3840000 342027 000 307792 460208

25PSCT +

75CM

6523333 342027 000 576125 728542

0PSCT +

100CM

9216667 342027 000 845458 997875

109

75PSCT +

25CM

100PSCT +

0CM

-100000 342027 776 -86208 66208

50PSCT +

50CM

3740000 342027 000 297792 450208

25PSCT +

75CM

6423333 342027 000 566125 718542

0PSCT +

100CM

9116667 342027 000 835458 987875

50PSCT +

50CM

100PSCT +

0CM

-3840000 342027 000 -460208 -307792

75PSCT +

25CM

-3740000 342027 000 -450208 -297792

25PSCT +

75CM

2683333 342027 000 192125 344542

0PSCT +

100CM

5376667 342027 000 461458 613875

25PSCT +

75CM

100PSCT +

0CM

-6523333 342027 000 -728542 -576125

75PSCT +

25CM

-6423333 342027 000 -718542 -566125

50PSCT +

50CM

-2683333 342027 000 -344542 -192125

0PSCT +

100CM

2693333 342027 000 193125 345542

0PSCT +

100CM

100PSCT +

0CM

-9216667 342027 000 -997875 -845458

75PSCT +

25CM

-9116667 342027 000 -987875 -835458

50PSCT +

50CM

-5376667 342027 000 -613875 -461458

25PSCT +

75CM

-2693333 342027 000 -345542 -193125

100PSCT

+ 0CM

0PSCT +

100CM

9216667 342027 000 822805 1020529

110

Dunnett

t (2-

sided)b

75PSCT +

25CM

0PSCT +

100CM

9116667 342027 000 812805 1010529

50PSCT +

50CM

0PSCT +

100CM

5376667 342027 000 438805 636529

25PSCT +

75CM

0PSCT +

100CM

2693333 342027 000 170471 368195



111

APPENDIX 3






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100USCB +

0CM

3 1990000 481248 277849 1870451 2109549 19400 20360

75USCB +

25CM

3 1850333 221209 127715 1795382 1905285 18270 18710

50USCB +

50CM

3 1705667 51316 29627 1692919 1718414 17000 17100

25USCB +

75CM

3 1615333 187172 108064 1568837 1661829 15940 16290

0USCB +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1662933 2957005 763495 1499180 1826687 11460 20360





112

Based on Median and

with adjusted df

1518 4 4354 339


ANOVA

Methane Yields


Between Groups 12176263 4 3044066 467120 000


Total 12241429 14




Mean



Lower

Bound

Upper

Bound

Tukey

HSD

100USCB

+ 0CM

75USCB +

25CM

1396667 208433 000 71070 208264

50USCB +

50CM

2843333 208433 000 215736 352930

25USCB +

75CM

3746667 208433 000 306070 443264

0USCB +

100CM

8366667 208433 000 768070 905264

75USCB +

25CM

100USCB +

0CM

-1396667 208433 000 -208264 -71070

50USCB +

50CM

1446667 208433 000 76070 213264

25USCB +

75CM

2350000 208433 000 166403 303597

113

0USCB +

100CM

6970000 208433 000 628403 765597

50USCB +

50CM

100USCB +

0CM

-2843333 208433 000 -352930 -215736

75USCB +

25CM

-1446667 208433 000 -213264 -76070

25USCB +

75CM

903333 208433 000 21736 158930

0USCB +

100CM

5523333 208433 000 483736 620930

25USCB +

75CM

100USCB +

0CM

-3746667 208433 000 -443264 -306070

75USCB +

25CM

-2350000 208433 000 -303597 -166403

50USCB +

50CM

-903333 208433 000 -158930 -21736

0USCB +

100CM

4620000 208433 000 393403 530597

0USCB +

100CM

100USCB +

0CM

-8366667 208433 000 -905264 -768070

75USCB +

25CM

-6970000 208433 000 -765597 -628403

50USCB +

50CM

-5523333 208433 000 -620930 -483736

25USCB +

75CM

-4620000 208433 000 -530597 -393403

LSD 100USCB

+ 0CM

75USCB +

25CM

1396667 208433 000 93225 186109

50USCB +

50CM

2843333 208433 000 237891 330775

25USCB +

75CM

3746667 208433 000 328225 421109

0USCB +

100CM

8366667 208433 000 790225 883109

114

75USCB +

25CM

100USCB +

0CM

-1396667 208433 000 -186109 -93225

50USCB +

50CM

1446667 208433 000 98225 191109

25USCB +

75CM

2350000 208433 000 188558 281442

0USCB +

100CM

6970000 208433 000 650558 743442

50USCB +

50CM

100USCB +

0CM

-2843333 208433 000 -330775 -237891

75USCB +

25CM

-1446667 208433 000 -191109 -98225

25USCB +

75CM

903333 208433 000 43891 136775

0USCB +

100CM

5523333 208433 000 505891 598775

25USCB +

75CM

100USCB +

0CM

-3746667 208433 000 -421109 -328225

75USCB +

25CM

-2350000 208433 000 -281442 -188558

50USCB +

50CM

-903333 208433 000 -136775 -43891

0USCB +

100CM

4620000 208433 000 415558 508442

0USCB +

100CM

100USCB +

0CM

-8366667 208433 000 -883109 -790225

75USCB +

25CM

-6970000 208433 000 -743442 -650558

50USCB +

50CM

-5523333 208433 000 -598775 -505891

25USCB +

75CM

-4620000 208433 000 -508442 -415558

100USCB

+ 0CM

0USCB +

100CM

8366667 208433 000 776419 896914

115

Dunnett

t (2-

sided)b

75USCB +

25CM

0USCB +

100CM

6970000 208433 000 636753 757247

50USCB +

50CM

0USCB +

100CM

5523333 208433 000 492086 612581

25USCB +

75CM

0USCB +

100CM

4620000 208433 000 401753 522247



116

APPENDIX 4






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100PSCB +

0CM

3 2255667 539290 311359 2121700 2389634 21940 22940

75PSCB +

25CM

3 1995667 427239 246667 1889535 2101799 19470 20270

50PSCB +

50CM

3 1740333 234592 135442 1682057 1798609 17140 17590

25PSCB +

75CM

3 1557667 281129 162310 1487830 1627503 15390 15900

0PSCB +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1740533 3908233 1009101 1524103 1956964 11460 22940





117


adjusted df

407 4 5808 798


ANOVA

Methane Yields


Between Groups 21260984 4 5315246 432087 000


Total 21383997 14




Mean



Lower

Bound

Upper

Bound

Tukey

HSD

100PSCB

+ 0CM

75PSCB +

25CM

2600000 286372 000 165753 354247

50PSCB +

50CM

5153333 286372 000 421086 609581

25PSCB +

75CM

6980000 286372 000 603753 792247

0PSCB +

100CM

11023333 286372 000 1008086 1196581

75PSCB +

25CM

100PSCB

+ 0CM

-2600000 286372 000 -354247 -165753

50PSCB +

50CM

2553333 286372 000 161086 349581

25PSCB +

75CM

4380000 286372 000 343753 532247

118

0PSCB +

100CM

8423333 286372 000 748086 936581

50PSCB +

50CM

100PSCB

+ 0CM

-5153333 286372 000 -609581 -421086

75PSCB +

25CM

-2553333 286372 000 -349581 -161086

25PSCB +

75CM

1826667 286372 000 88419 276914

0PSCB +

100CM

5870000 286372 000 492753 681247

25PSCB +

75CM

100PSCB

+ 0CM

-6980000 286372 000 -792247 -603753

75PSCB +

25CM

-4380000 286372 000 -532247 -343753

50PSCB +

50CM

-1826667 286372 000 -276914 -88419

0PSCB +

100CM

4043333 286372 000 310086 498581

0PSCB +

100CM

100PSCB

+ 0CM

-11023333 286372 000 -1196581 -1008086

75PSCB +

25CM

-8423333 286372 000 -936581 -748086

50PSCB +

50CM

-5870000 286372 000 -681247 -492753

25PSCB +

75CM

-4043333 286372 000 -498581 -310086

LSD 100PSCB

+ 0CM

75PSCB +

25CM

2600000 286372 000 196192 323808

50PSCB +

50CM

5153333 286372 000 451526 579141

25PSCB +

75CM

6980000 286372 000 634192 761808

0PSCB +

100CM

11023333 286372 000 1038526 1166141

119

75PSCB +

25CM

100PSCB

+ 0CM

-2600000 286372 000 -323808 -196192

50PSCB +

50CM

2553333 286372 000 191526 319141

25PSCB +

75CM

4380000 286372 000 374192 501808

0PSCB +

100CM

8423333 286372 000 778526 906141

50PSCB +

50CM

100PSCB

+ 0CM

-5153333 286372 000 -579141 -451526

75PSCB +

25CM

-2553333 286372 000 -319141 -191526

25PSCB +

75CM

1826667 286372 000 118859 246474

0PSCB +

100CM

5870000 286372 000 523192 650808

25PSCB +

75CM

100PSCB

+ 0CM

-6980000 286372 000 -761808 -634192

75PSCB +

25CM

-4380000 286372 000 -501808 -374192

50PSCB +

50CM

-1826667 286372 000 -246474 -118859

0PSCB +

100CM

4043333 286372 000 340526 468141

0PSCB +

100CM

100PSCB

+ 0CM

-11023333 286372 000 -1166141 -1038526

75PSCB +

25CM

-8423333 286372 000 -906141 -778526

50PSCB +

50CM

-5870000 286372 000 -650808 -523192

25PSCB +

75CM

-4043333 286372 000 -468141 -340526

100PSCB

+ 0CM

0PSCB +

100CM

11023333 286372 000 1019558 1185109

120

Dunnett

t (2-

sided)b

75PSCB +

25CM

0PSCB +

100CM

8423333 286372 000 759558 925109

50PSCB +

50CM

0PSCB +

100CM

5870000 286372 000 504225 669775

25PSCB +

75CM

0PSCB +

100CM

4043333 286372 000 321558 487109



121

REFERENCES











122












123













124












125













126












127












128












129













130












131













132












133














134













135













136




ABSTRACT


LIST OF FIGURES

LIST OF TABLES


ACKNOWLEDGMENTS

APPENDIX 1

APPENDIX 3

APPENDIX 4

REFERENCES

iv


























v














vi







vii

TABLE OF CONTENTS

Abstract iii



List of figures xi

List of tables xiii


Acknowledgments xvi


11 Background 3


13 Thesis outline 5










272 pH 24



viii








31 Substrates 45


312 Manure 45

32 Inoculum 45

















ix









and Inoculum 63


with CM 67








with CM 72











x










References 121

xi

LIST OF FIGURES




the SCT 14












Figure 31 (e) CM 47








for 83 days 68


xii







for 83 days 74












xiii

LIST OF TABLES


(2012-2017) 10





previous studies 26













with CM 85



xiv






BD Biodegradability




CM Chicken Manure















xv



SCT Sugarcane Trash





TS Total Solids



UV Ultra-Violet



VS Volatile Solids

xvi

ACKNOWLEDGMENTS











researcher












xvii
















AMAL BABU PUTHUMANA

18


1



2


3

11 Background


























4



























5












chicken manure



13 Thesis outline




project




6
















7



8


9
























Barbieri 2013)


10








Source (FAO 2018)

Crop


2012 2013 2014 2015 2016 2017

Maize 696 786 524 596 533 678

Rice 1031 1136 767 697 266 822

Cotton 5965 444 3901 197 2804 NA

Soybean 381 411 37 301 292 300

Sugarcane 3386 3293 3752 3765 4472 4534

Sunflower 402 303 27 295 23 27

Wheat 139021 129793 126132 123837 112822 1219115











11





12




















13









14



forming the SCB


2018)


15


















(Jain et al 2014)









16
















al 2015)










17













Table 22


18






























19









C6H10O4 + 2H2O C6H12O6 + H2 Eq21







et al 2019)





20













21






CO2 + 4H2 CH4 + 2H2O Eq28

HCO-3 + 4H2 + H+ CH4 + 3H2O Eq29















22





2017)











Table 23








23






More stable

Less energy input









important


24

272 pH













Cheon 2008)


and Stetter 1982)





Jones 2008)


2008)







25



























26


studies




























27





Stroeve 2009)












28



























29





















Zeeman 2009)







30





31


Sl

No

Pre-treatment

method

Lignocellulosic

substrates

Pre-treatment

condition

CH4 production

after pre-

treatment

(mLCH4gVSadded)

Increase in

CH4 ()

References




Balsari 2012)








2016)


Olive mill solid

waste




Borja 2016)


32


Morken 2012)


MHz 1600 W



2011)


MHz 1600 W





and Carrere 2012)



2012)





33



























34



























35



























36












AD process






37


wastes

Sl

No


conditions

Feed Ratios

tested


1 Sugarcane leaves

+ Diary cow

manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 7525

5050 2575

0100 (VS basis)


LCH4kgVSadded)

(Mokomele et al

2019)

2 Sugarcane

Bagasse + Diary

cow manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 5050

0100 (VS basis)


LCH4kgVSadded)

(Mokomele et al

2019)

3 Sugarcane leaves

+ Diary cow

manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 7525

5050 2575

0100 (VS basis)

NA 7525 (292

LCH4kgVSadded)

(Mokomele et al

2019)

4 Switchgrass +

Diary manure

500 mL Mesophilic

temperature ISR =

067 30 days

1000 7525

5050 2575

0100 (TS basis)



mono-digestion of

switchgrass

(Zheng et al 2015)

5 Salix viminalis +

Cow manure

1125 mL

mesophilic

temperature ISR =

067 57 days

1000 8020

7030 6040

5050 4060

3070 2080

0100 (VS basis)


mLCH4gVS)


mLgVS) has 23



38

improvement over

untreated salix

6 Corn Stover + Pig

Manure

250 mL Mesophilic

temperature ISR =

026 70 days

1000 8020

6733 5050

3367 2080

0100 (VS basis)

NA 0100 (889

mLCH4gVS)

(Li et al 2018b)

7 Cotton stalk +

swine manure

500 mL Mesophilic

temperature ISR =

025 45 days

1000 7525

5050 2575 (TS

basis)


in yield when

compared to control

(102 mLCH4gVS)

(Cheng and Zhong

2014)

8 Apple waste +

Chicken manure

250 mL Mesophilic

temperature ISR =

026 70 days

1000 8020

6733 5050

3367 2080

0100 (VS basis)

NA 3367 (827

mLCH4gVS)

(Li et al 2018b)

9 Whey + cow

manure

400 mL Mesophilic

temperature 55 days

1000 8515

7525 5050

2575 1585

0100 (VS basis)

NA 8515 (266

mLCH4gVS)

(Vivekanand Mulat

Eijsink and Horn

2018)


39




















al 2017)







40







al 2017)


41







2 Grass Cow manure














6

Organic fraction of








42


















with CM


43



44


45

31 Substrates












312 Manure




32 Inoculum








46



























47





Sample Untreated

SCT ()

Pre-treated

SCT ()

Untreated

SCB ()

Pre-treated

SCB ()

CM

()

















CM 0 0 0 0 100


48









49























Batstone 2015)


50

























51


(PO43-)

















52











53










54

37 Data Processing

371 Theoretical BMP







Fdz-Polanco 2015)


tttEq31





ᆒt =ᆒ썈υ

Eq32









55


using equation 33










Sung 2012)


Eq34


Eq35









56







Eq36
















tt Eq37


57



υ =

tt Eq38










Eq310









58






al 2014)

= t 㢐t) Eq311













= t

t

Eq312





59












60

1


61



62


63


inoculum

























64

















study










65






Bolantildeos 2007)



SCT

Pre-treated

SCT

Untreated

SCB

Pre-treated

SCBInoculum

TS 721 913 872 828 819 19

VS TS 807 909 925 894 951 722

COD gl 7487 11598 12099 7939 9156 211

TSVS - 12 11 11 11 11 14

CN ratio - 124 831 953 1465 1919 49

Glucan ND 307 463 351 428 ND

Xylan ND 168 117 20 146 ND


Lignin ND 298 283 315 282 ND

TKP gPkg 82 11 07 05 06 312

TKN gNkg 836 97 108 512 71 1113







66



SCT

Pre-treated

SCT

Untreated

SCB

Pre-treated

SCBInoculum

Al mgg 103 123 099 084 024 817

As mgg 000 000 000 000 000 000

B mgg 003 000 000 000 000 004

Ba mgg 004 002 002 001 001 019

Ca mgg 1687 312 167 025 015 1973

Cd mgg 000 000 000 000 000 000

Co mgg 000 000 000 000 000 001

Cr mgg 004 001 001 001 001 005

Cu mgg 011 000 000 000 000 056

Fe mgg 107 092 019 086 026 1972

K mgg 1977 601 283 293 076 1582

Mg mgg 461 188 138 036 028 666

Mn mgg 036 010 006 005 003 017

Mo mgg 001 000 000 000 000 003

Na mgg 171 007 034 001 004 1644

Ni mgg 002 000 000 000 000 005

P mgg 796 101 041 015 010 2735

Pb mgg 001 000 000 000 000 003

S mgg 321 082 809 018 524 1469

Se mgg 001 000 001 000 001 000

Zn mgg 027 000 000 000 000 067









67





CM













al 2015)







68




Untreated SCT














69



























70


















71



























72



digested with CM




CM








73






















74

















75















untreated SCB












76
















mono-digestion


77
















78



























79




Pre-treated SCBCM






2015)












80




Untreated

SCTCM

1000 5552 1444 2018 215 353 1960 28

7525 5444 1307 1791 247 307 1671 67

5050 5336 1178 1582 248 308 1642 -38

2575 5228 1074 1413 244 270 1413 00

Pre-treated

SCTCM

1000 5073 1625 2075a 265 410 2082 -03

7525 5085 1614 2065a 252 391 1988 37

5050 5097 1318 1691 235 319 1628 37

2575 5109 1106 1422 190 255 1302 85

Untreated

SCBCM

1000 4364 1812 1990 262 421 1839 76

7525 4553 1615 1850 237 391 1780 38

5050 4742 1429 1706 232 350 1660 27

2575 4931 1302 1615 179 301 1486 80

Pre-treated

SCBCM

1000 2882 3110 2256 251 715 2061 86

7525 3442 2304 1995 220 538 1852 72

5050 4000 1729 1741 235 414 1656 49

2575 4561 1357 1558 194 348 1586 -18

CM 1000 5121 895 1153 216 206 1056 84


81



























82












83




84


























85




136 mgL


CM


mgL mgL mgL mgL

Untreated

SCTCM

1000 0 548 16160 1535

7525 0 554 16160 1465

5050 0 550 16867 1444

2575 0 553 17271 1404

Pre-treated

SCTCM

1000 0 556 15655 1636

7525 0 547 16059 1596

5050 0 551 16362 1505

2575 0 550 16867 1414

Untreated

SCBCM

1000 0 548 16362 1646

7525 0 561 16564 1586

5050 0 535 17069 1515

2575 0 551 17271 1444

Pre-treated

SCBCM

1000 0 364 15756 1566

7525 0 549 16665 1576

5050 0 543 16968 1505

2575 0 552 17271 1444

CM 1000 0 553 17473 1364






86
























modelled data


87


SubstratesFeed


B B0 Rm λR2



USCTCM 100 0 20175 19347 712 264 09942 20753 005 266 09988

75 25 17909 16507 623 128 09772 17679 005 183 09956

50 50 15820 14442 648 122 09701 15343 006 188 09911

25 75 14128 12653 858 198 09727 13238 010 255 09898

PSCTCM 100 0 20748 19853 2142 168 09927 20397 015 185 09867

75 25 20650 19748 1819 123 09905 20331 014 167 09905

50 50 16910 16360 1766 142 09920 16782 016 174 09896

25 75 14224 13498 1521 157 09887 13889 016 178 09881

USCBCM 100 0 19900 19394 797 521 09972 20561 006 538 09889

75 25 18503 17700 528 197 09864 19472 004 197 09989

50 50 17056 16131 492 045 09734 17440 004 119 09940

25 75 16154 14420 767 109 09691 15218 003 178 09897

PSCBCM 100 0 22557 21982 702 154 09815 23570 004 188 09934

75 25 19955 19203 660 007 09787 20400 005 089 09941

50 50 17407 16241 718 015 09745 17090 007 110 09928

25 75 15577 14114 806 040 09624 14869 008 119 09837

CM 100 0 11532 10787 1237 280 09858 11135 016 299 09919


88





















89






90



























91






92







1000 7525 5050 2575




















93



production


94

1


95



96


97





























98





























99



SCTSCB with CM





100

101

APPENDIX 1






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean


Upper

Bound

100USCT +

0CM

3 2017333 223010 128755 1961935 2072732 19920 20340

75USCT +

25CM

3 1791000 34641 20000 1782395 1799605 17890 17950

50USCT +

50CM

3 1581667 85049 49103 1560539 1602794 15730 15900

25USCT +

75CM

3 1413000 338674 195533 1328869 1497131 13750 14400

0USCT +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1591267 3092455 798468 1420012 1762521 11460 20340





102


adjusted df

938 4 4174 521


ANOVA

Methane Yields


Between Groups 13352489 4 3338122 924688 000


Total 13388589 14




Mean

Difference (I-

J) Std Error Sig


Lower

Bound

Upper

Bound

Tukey

HSD

100USCT

+ 0CM

75USCT +

25CM

2263333 155134 000 175277 277389

50USCT +

50CM

4356667 155134 000 384611 486723

25USCT +

75CM

6043333 155134 000 553277 655389

0USCT +

100CM

8640000 155134 000 812944 915056

75USCT +

25CM

100USCT +

0CM

-2263333 155134 000 -277389 -175277

50USCT +

50CM

2093333 155134 000 158277 260389

25USCT +

75CM

3780000 155134 000 326944 429056

0USCT +

100CM

6376667 155134 000 586611 688723

103

50USCT +

50CM

100USCT +

0CM

-4356667 155134 000 -486723 -384611

75USCT +

25CM

-2093333 155134 000 -260389 -158277

25USCT +

75CM

1686667 155134 000 117611 219723

0USCT +

100CM

4283333 155134 000 377277 479389

25USCT +

75CM

100USCT +

0CM

-6043333 155134 000 -655389 -553277

75USCT +

25CM

-3780000 155134 000 -429056 -326944

50USCT +

50CM

-1686667 155134 000 -219723 -117611

0USCT +

100CM

2596667 155134 000 208611 310723

0USCT +

100CM

100USCT +

0CM

-8640000 155134 000 -915056 -812944

75USCT +

25CM

-6376667 155134 000 -688723 -586611

50USCT +

50CM

-4283333 155134 000 -479389 -377277

25USCT +

75CM

-2596667 155134 000 -310723 -208611

LSD 100USCT

+ 0CM

75USCT +

25CM

2263333 155134 000 191767 260899

50USCT +

50CM

4356667 155134 000 401101 470233

25USCT +

75CM

6043333 155134 000 569767 638899

0USCT +

100CM

8640000 155134 000 829434 898566

75USCT +

25CM

100USCT +

0CM

-2263333 155134 000 -260899 -191767

104

50USCT +

50CM

2093333 155134 000 174767 243899

25USCT +

75CM

3780000 155134 000 343434 412566

0USCT +

100CM

6376667 155134 000 603101 672233

50USCT +

50CM

100USCT +

0CM

-4356667 155134 000 -470233 -401101

75USCT +

25CM

-2093333 155134 000 -243899 -174767

25USCT +

75CM

1686667 155134 000 134101 203233

0USCT +

100CM

4283333 155134 000 393767 462899

25USCT +

75CM

100USCT +

0CM

-6043333 155134 000 -638899 -569767

75USCT +

25CM

-3780000 155134 000 -412566 -343434

50USCT +

50CM

-1686667 155134 000 -203233 -134101

0USCT +

100CM

2596667 155134 000 225101 294233

0USCT +

100CM

100USCT +

0CM

-8640000 155134 000 -898566 -829434

75USCT +

25CM

-6376667 155134 000 -672233 -603101

50USCT +

50CM

-4283333 155134 000 -462899 -393767

25USCT +

75CM

-2596667 155134 000 -294233 -225101

Dunnett

t (2-sided)b

100USCT

+ 0CM

0USCT +

100CM

8640000 155134 000 819159 908841

75USCT +

25CM

0USCT +

100CM

6376667 155134 000 592825 682508

105

50USCT +

50CM

0USCT +

100CM

4283333 155134 000 383492 473175

25USCT +

75CM

0USCT +

100CM

2596667 155134 000 214825 304508



106

APPENDIX2






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100PSCT +

0CM

3 2075000 629603 363501 1918598 2231402 20050 21270

75PSCT +

25CM

3 2065000 580259 335012 1920856 2209144 20310 21320

50PSCT +

50CM

3 1691000 272213 157162 1623379 1758621 16600 17110

25PSCT +

75CM

3 1422667 250067 144376 1360547 1484787 13980 14480

0PSCT +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1681400 3743018 966443 1474119 1888681 11460 21320





107


adjusted df

501 4 4789 739


ANOVA

Methane Yields


Between Groups 19438783 4 4859696 276948 000


Total 19614256 14




Mean

Difference (I-

J) Std Error Sig


Lower

Bound

Upper

Bound

Tukey

HSD

100PSCT

+ 0CM

75PSCT +

25CM

100000 342027 998 -102564 122564

50PSCT +

50CM

3840000 342027 000 271436 496564

25PSCT +

75CM

6523333 342027 000 539770 764897

0PSCT +

100CM

9216667 342027 000 809103 1034230

75PSCT +

25CM

100PSCT +

0CM

-100000 342027 998 -122564 102564

50PSCT +

50CM

3740000 342027 000 261436 486564

25PSCT +

75CM

6423333 342027 000 529770 754897

108

0PSCT +

100CM

9116667 342027 000 799103 1024230

50PSCT +

50CM

100PSCT +

0CM

-3840000 342027 000 -496564 -271436

75PSCT +

25CM

-3740000 342027 000 -486564 -261436

25PSCT +

75CM

2683333 342027 000 155770 380897

0PSCT +

100CM

5376667 342027 000 425103 650230

25PSCT +

75CM

100PSCT +

0CM

-6523333 342027 000 -764897 -539770

75PSCT +

25CM

-6423333 342027 000 -754897 -529770

50PSCT +

50CM

-2683333 342027 000 -380897 -155770

0PSCT +

100CM

2693333 342027 000 156770 381897

0PSCT +

100CM

100PSCT +

0CM

-9216667 342027 000 -1034230 -809103

75PSCT +

25CM

-9116667 342027 000 -1024230 -799103

50PSCT +

50CM

-5376667 342027 000 -650230 -425103

25PSCT +

75CM

-2693333 342027 000 -381897 -156770

LSD 100PSCT

+ 0CM

75PSCT +

25CM

100000 342027 776 -66208 86208

50PSCT +

50CM

3840000 342027 000 307792 460208

25PSCT +

75CM

6523333 342027 000 576125 728542

0PSCT +

100CM

9216667 342027 000 845458 997875

109

75PSCT +

25CM

100PSCT +

0CM

-100000 342027 776 -86208 66208

50PSCT +

50CM

3740000 342027 000 297792 450208

25PSCT +

75CM

6423333 342027 000 566125 718542

0PSCT +

100CM

9116667 342027 000 835458 987875

50PSCT +

50CM

100PSCT +

0CM

-3840000 342027 000 -460208 -307792

75PSCT +

25CM

-3740000 342027 000 -450208 -297792

25PSCT +

75CM

2683333 342027 000 192125 344542

0PSCT +

100CM

5376667 342027 000 461458 613875

25PSCT +

75CM

100PSCT +

0CM

-6523333 342027 000 -728542 -576125

75PSCT +

25CM

-6423333 342027 000 -718542 -566125

50PSCT +

50CM

-2683333 342027 000 -344542 -192125

0PSCT +

100CM

2693333 342027 000 193125 345542

0PSCT +

100CM

100PSCT +

0CM

-9216667 342027 000 -997875 -845458

75PSCT +

25CM

-9116667 342027 000 -987875 -835458

50PSCT +

50CM

-5376667 342027 000 -613875 -461458

25PSCT +

75CM

-2693333 342027 000 -345542 -193125

100PSCT

+ 0CM

0PSCT +

100CM

9216667 342027 000 822805 1020529

110

Dunnett

t (2-

sided)b

75PSCT +

25CM

0PSCT +

100CM

9116667 342027 000 812805 1010529

50PSCT +

50CM

0PSCT +

100CM

5376667 342027 000 438805 636529

25PSCT +

75CM

0PSCT +

100CM

2693333 342027 000 170471 368195



111

APPENDIX 3






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100USCB +

0CM

3 1990000 481248 277849 1870451 2109549 19400 20360

75USCB +

25CM

3 1850333 221209 127715 1795382 1905285 18270 18710

50USCB +

50CM

3 1705667 51316 29627 1692919 1718414 17000 17100

25USCB +

75CM

3 1615333 187172 108064 1568837 1661829 15940 16290

0USCB +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1662933 2957005 763495 1499180 1826687 11460 20360





112

Based on Median and

with adjusted df

1518 4 4354 339


ANOVA

Methane Yields


Between Groups 12176263 4 3044066 467120 000


Total 12241429 14




Mean



Lower

Bound

Upper

Bound

Tukey

HSD

100USCB

+ 0CM

75USCB +

25CM

1396667 208433 000 71070 208264

50USCB +

50CM

2843333 208433 000 215736 352930

25USCB +

75CM

3746667 208433 000 306070 443264

0USCB +

100CM

8366667 208433 000 768070 905264

75USCB +

25CM

100USCB +

0CM

-1396667 208433 000 -208264 -71070

50USCB +

50CM

1446667 208433 000 76070 213264

25USCB +

75CM

2350000 208433 000 166403 303597

113

0USCB +

100CM

6970000 208433 000 628403 765597

50USCB +

50CM

100USCB +

0CM

-2843333 208433 000 -352930 -215736

75USCB +

25CM

-1446667 208433 000 -213264 -76070

25USCB +

75CM

903333 208433 000 21736 158930

0USCB +

100CM

5523333 208433 000 483736 620930

25USCB +

75CM

100USCB +

0CM

-3746667 208433 000 -443264 -306070

75USCB +

25CM

-2350000 208433 000 -303597 -166403

50USCB +

50CM

-903333 208433 000 -158930 -21736

0USCB +

100CM

4620000 208433 000 393403 530597

0USCB +

100CM

100USCB +

0CM

-8366667 208433 000 -905264 -768070

75USCB +

25CM

-6970000 208433 000 -765597 -628403

50USCB +

50CM

-5523333 208433 000 -620930 -483736

25USCB +

75CM

-4620000 208433 000 -530597 -393403

LSD 100USCB

+ 0CM

75USCB +

25CM

1396667 208433 000 93225 186109

50USCB +

50CM

2843333 208433 000 237891 330775

25USCB +

75CM

3746667 208433 000 328225 421109

0USCB +

100CM

8366667 208433 000 790225 883109

114

75USCB +

25CM

100USCB +

0CM

-1396667 208433 000 -186109 -93225

50USCB +

50CM

1446667 208433 000 98225 191109

25USCB +

75CM

2350000 208433 000 188558 281442

0USCB +

100CM

6970000 208433 000 650558 743442

50USCB +

50CM

100USCB +

0CM

-2843333 208433 000 -330775 -237891

75USCB +

25CM

-1446667 208433 000 -191109 -98225

25USCB +

75CM

903333 208433 000 43891 136775

0USCB +

100CM

5523333 208433 000 505891 598775

25USCB +

75CM

100USCB +

0CM

-3746667 208433 000 -421109 -328225

75USCB +

25CM

-2350000 208433 000 -281442 -188558

50USCB +

50CM

-903333 208433 000 -136775 -43891

0USCB +

100CM

4620000 208433 000 415558 508442

0USCB +

100CM

100USCB +

0CM

-8366667 208433 000 -883109 -790225

75USCB +

25CM

-6970000 208433 000 -743442 -650558

50USCB +

50CM

-5523333 208433 000 -598775 -505891

25USCB +

75CM

-4620000 208433 000 -508442 -415558

100USCB

+ 0CM

0USCB +

100CM

8366667 208433 000 776419 896914

115

Dunnett

t (2-

sided)b

75USCB +

25CM

0USCB +

100CM

6970000 208433 000 636753 757247

50USCB +

50CM

0USCB +

100CM

5523333 208433 000 492086 612581

25USCB +

75CM

0USCB +

100CM

4620000 208433 000 401753 522247



116

APPENDIX 4






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100PSCB +

0CM

3 2255667 539290 311359 2121700 2389634 21940 22940

75PSCB +

25CM

3 1995667 427239 246667 1889535 2101799 19470 20270

50PSCB +

50CM

3 1740333 234592 135442 1682057 1798609 17140 17590

25PSCB +

75CM

3 1557667 281129 162310 1487830 1627503 15390 15900

0PSCB +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1740533 3908233 1009101 1524103 1956964 11460 22940





117


adjusted df

407 4 5808 798


ANOVA

Methane Yields


Between Groups 21260984 4 5315246 432087 000


Total 21383997 14




Mean



Lower

Bound

Upper

Bound

Tukey

HSD

100PSCB

+ 0CM

75PSCB +

25CM

2600000 286372 000 165753 354247

50PSCB +

50CM

5153333 286372 000 421086 609581

25PSCB +

75CM

6980000 286372 000 603753 792247

0PSCB +

100CM

11023333 286372 000 1008086 1196581

75PSCB +

25CM

100PSCB

+ 0CM

-2600000 286372 000 -354247 -165753

50PSCB +

50CM

2553333 286372 000 161086 349581

25PSCB +

75CM

4380000 286372 000 343753 532247

118

0PSCB +

100CM

8423333 286372 000 748086 936581

50PSCB +

50CM

100PSCB

+ 0CM

-5153333 286372 000 -609581 -421086

75PSCB +

25CM

-2553333 286372 000 -349581 -161086

25PSCB +

75CM

1826667 286372 000 88419 276914

0PSCB +

100CM

5870000 286372 000 492753 681247

25PSCB +

75CM

100PSCB

+ 0CM

-6980000 286372 000 -792247 -603753

75PSCB +

25CM

-4380000 286372 000 -532247 -343753

50PSCB +

50CM

-1826667 286372 000 -276914 -88419

0PSCB +

100CM

4043333 286372 000 310086 498581

0PSCB +

100CM

100PSCB

+ 0CM

-11023333 286372 000 -1196581 -1008086

75PSCB +

25CM

-8423333 286372 000 -936581 -748086

50PSCB +

50CM

-5870000 286372 000 -681247 -492753

25PSCB +

75CM

-4043333 286372 000 -498581 -310086

LSD 100PSCB

+ 0CM

75PSCB +

25CM

2600000 286372 000 196192 323808

50PSCB +

50CM

5153333 286372 000 451526 579141

25PSCB +

75CM

6980000 286372 000 634192 761808

0PSCB +

100CM

11023333 286372 000 1038526 1166141

119

75PSCB +

25CM

100PSCB

+ 0CM

-2600000 286372 000 -323808 -196192

50PSCB +

50CM

2553333 286372 000 191526 319141

25PSCB +

75CM

4380000 286372 000 374192 501808

0PSCB +

100CM

8423333 286372 000 778526 906141

50PSCB +

50CM

100PSCB

+ 0CM

-5153333 286372 000 -579141 -451526

75PSCB +

25CM

-2553333 286372 000 -319141 -191526

25PSCB +

75CM

1826667 286372 000 118859 246474

0PSCB +

100CM

5870000 286372 000 523192 650808

25PSCB +

75CM

100PSCB

+ 0CM

-6980000 286372 000 -761808 -634192

75PSCB +

25CM

-4380000 286372 000 -501808 -374192

50PSCB +

50CM

-1826667 286372 000 -246474 -118859

0PSCB +

100CM

4043333 286372 000 340526 468141

0PSCB +

100CM

100PSCB

+ 0CM

-11023333 286372 000 -1166141 -1038526

75PSCB +

25CM

-8423333 286372 000 -906141 -778526

50PSCB +

50CM

-5870000 286372 000 -650808 -523192

25PSCB +

75CM

-4043333 286372 000 -468141 -340526

100PSCB

+ 0CM

0PSCB +

100CM

11023333 286372 000 1019558 1185109

120

Dunnett

t (2-

sided)b

75PSCB +

25CM

0PSCB +

100CM

8423333 286372 000 759558 925109

50PSCB +

50CM

0PSCB +

100CM

5870000 286372 000 504225 669775

25PSCB +

75CM

0PSCB +

100CM

4043333 286372 000 321558 487109



121

REFERENCES











122












123













124












125













126












127












128












129













130












131













132












133














134













135













136




ABSTRACT


LIST OF FIGURES

LIST OF TABLES


ACKNOWLEDGMENTS

APPENDIX 1

APPENDIX 3

APPENDIX 4

REFERENCES

v














vi







vii

TABLE OF CONTENTS

Abstract iii



List of figures xi

List of tables xiii


Acknowledgments xvi


11 Background 3


13 Thesis outline 5










272 pH 24



viii








31 Substrates 45


312 Manure 45

32 Inoculum 45

















ix









and Inoculum 63


with CM 67








with CM 72











x










References 121

xi

LIST OF FIGURES




the SCT 14












Figure 31 (e) CM 47








for 83 days 68


xii







for 83 days 74












xiii

LIST OF TABLES


(2012-2017) 10





previous studies 26













with CM 85



xiv






BD Biodegradability




CM Chicken Manure















xv



SCT Sugarcane Trash





TS Total Solids



UV Ultra-Violet



VS Volatile Solids

xvi

ACKNOWLEDGMENTS











researcher












xvii
















AMAL BABU PUTHUMANA

18


1



2


3

11 Background


























4



























5












chicken manure



13 Thesis outline




project




6
















7



8


9
























Barbieri 2013)


10








Source (FAO 2018)

Crop


2012 2013 2014 2015 2016 2017

Maize 696 786 524 596 533 678

Rice 1031 1136 767 697 266 822

Cotton 5965 444 3901 197 2804 NA

Soybean 381 411 37 301 292 300

Sugarcane 3386 3293 3752 3765 4472 4534

Sunflower 402 303 27 295 23 27

Wheat 139021 129793 126132 123837 112822 1219115











11





12




















13









14



forming the SCB


2018)


15


















(Jain et al 2014)









16
















al 2015)










17













Table 22


18






























19









C6H10O4 + 2H2O C6H12O6 + H2 Eq21







et al 2019)





20













21






CO2 + 4H2 CH4 + 2H2O Eq28

HCO-3 + 4H2 + H+ CH4 + 3H2O Eq29















22





2017)











Table 23








23






More stable

Less energy input









important


24

272 pH













Cheon 2008)


and Stetter 1982)





Jones 2008)


2008)







25



























26


studies




























27





Stroeve 2009)












28



























29





















Zeeman 2009)







30





31


Sl

No

Pre-treatment

method

Lignocellulosic

substrates

Pre-treatment

condition

CH4 production

after pre-

treatment

(mLCH4gVSadded)

Increase in

CH4 ()

References




Balsari 2012)








2016)


Olive mill solid

waste




Borja 2016)


32


Morken 2012)


MHz 1600 W



2011)


MHz 1600 W





and Carrere 2012)



2012)





33



























34



























35



























36












AD process






37


wastes

Sl

No


conditions

Feed Ratios

tested


1 Sugarcane leaves

+ Diary cow

manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 7525

5050 2575

0100 (VS basis)


LCH4kgVSadded)

(Mokomele et al

2019)

2 Sugarcane

Bagasse + Diary

cow manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 5050

0100 (VS basis)


LCH4kgVSadded)

(Mokomele et al

2019)

3 Sugarcane leaves

+ Diary cow

manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 7525

5050 2575

0100 (VS basis)

NA 7525 (292

LCH4kgVSadded)

(Mokomele et al

2019)

4 Switchgrass +

Diary manure

500 mL Mesophilic

temperature ISR =

067 30 days

1000 7525

5050 2575

0100 (TS basis)



mono-digestion of

switchgrass

(Zheng et al 2015)

5 Salix viminalis +

Cow manure

1125 mL

mesophilic

temperature ISR =

067 57 days

1000 8020

7030 6040

5050 4060

3070 2080

0100 (VS basis)


mLCH4gVS)


mLgVS) has 23



38

improvement over

untreated salix

6 Corn Stover + Pig

Manure

250 mL Mesophilic

temperature ISR =

026 70 days

1000 8020

6733 5050

3367 2080

0100 (VS basis)

NA 0100 (889

mLCH4gVS)

(Li et al 2018b)

7 Cotton stalk +

swine manure

500 mL Mesophilic

temperature ISR =

025 45 days

1000 7525

5050 2575 (TS

basis)


in yield when

compared to control

(102 mLCH4gVS)

(Cheng and Zhong

2014)

8 Apple waste +

Chicken manure

250 mL Mesophilic

temperature ISR =

026 70 days

1000 8020

6733 5050

3367 2080

0100 (VS basis)

NA 3367 (827

mLCH4gVS)

(Li et al 2018b)

9 Whey + cow

manure

400 mL Mesophilic

temperature 55 days

1000 8515

7525 5050

2575 1585

0100 (VS basis)

NA 8515 (266

mLCH4gVS)

(Vivekanand Mulat

Eijsink and Horn

2018)


39




















al 2017)







40







al 2017)


41







2 Grass Cow manure














6

Organic fraction of








42


















with CM


43



44


45

31 Substrates












312 Manure




32 Inoculum








46



























47





Sample Untreated

SCT ()

Pre-treated

SCT ()

Untreated

SCB ()

Pre-treated

SCB ()

CM

()

















CM 0 0 0 0 100


48









49























Batstone 2015)


50

























51


(PO43-)

















52











53










54

37 Data Processing

371 Theoretical BMP







Fdz-Polanco 2015)


tttEq31





ᆒt =ᆒ썈υ

Eq32









55


using equation 33










Sung 2012)


Eq34


Eq35









56







Eq36
















tt Eq37


57



υ =

tt Eq38










Eq310









58






al 2014)

= t 㢐t) Eq311













= t

t

Eq312





59












60

1


61



62


63


inoculum

























64

















study










65






Bolantildeos 2007)



SCT

Pre-treated

SCT

Untreated

SCB

Pre-treated

SCBInoculum

TS 721 913 872 828 819 19

VS TS 807 909 925 894 951 722

COD gl 7487 11598 12099 7939 9156 211

TSVS - 12 11 11 11 11 14

CN ratio - 124 831 953 1465 1919 49

Glucan ND 307 463 351 428 ND

Xylan ND 168 117 20 146 ND


Lignin ND 298 283 315 282 ND

TKP gPkg 82 11 07 05 06 312

TKN gNkg 836 97 108 512 71 1113







66



SCT

Pre-treated

SCT

Untreated

SCB

Pre-treated

SCBInoculum

Al mgg 103 123 099 084 024 817

As mgg 000 000 000 000 000 000

B mgg 003 000 000 000 000 004

Ba mgg 004 002 002 001 001 019

Ca mgg 1687 312 167 025 015 1973

Cd mgg 000 000 000 000 000 000

Co mgg 000 000 000 000 000 001

Cr mgg 004 001 001 001 001 005

Cu mgg 011 000 000 000 000 056

Fe mgg 107 092 019 086 026 1972

K mgg 1977 601 283 293 076 1582

Mg mgg 461 188 138 036 028 666

Mn mgg 036 010 006 005 003 017

Mo mgg 001 000 000 000 000 003

Na mgg 171 007 034 001 004 1644

Ni mgg 002 000 000 000 000 005

P mgg 796 101 041 015 010 2735

Pb mgg 001 000 000 000 000 003

S mgg 321 082 809 018 524 1469

Se mgg 001 000 001 000 001 000

Zn mgg 027 000 000 000 000 067









67





CM













al 2015)







68




Untreated SCT














69



























70


















71



























72



digested with CM




CM








73






















74

















75















untreated SCB












76
















mono-digestion


77
















78



























79




Pre-treated SCBCM






2015)












80




Untreated

SCTCM

1000 5552 1444 2018 215 353 1960 28

7525 5444 1307 1791 247 307 1671 67

5050 5336 1178 1582 248 308 1642 -38

2575 5228 1074 1413 244 270 1413 00

Pre-treated

SCTCM

1000 5073 1625 2075a 265 410 2082 -03

7525 5085 1614 2065a 252 391 1988 37

5050 5097 1318 1691 235 319 1628 37

2575 5109 1106 1422 190 255 1302 85

Untreated

SCBCM

1000 4364 1812 1990 262 421 1839 76

7525 4553 1615 1850 237 391 1780 38

5050 4742 1429 1706 232 350 1660 27

2575 4931 1302 1615 179 301 1486 80

Pre-treated

SCBCM

1000 2882 3110 2256 251 715 2061 86

7525 3442 2304 1995 220 538 1852 72

5050 4000 1729 1741 235 414 1656 49

2575 4561 1357 1558 194 348 1586 -18

CM 1000 5121 895 1153 216 206 1056 84


81



























82












83




84


























85




136 mgL


CM


mgL mgL mgL mgL

Untreated

SCTCM

1000 0 548 16160 1535

7525 0 554 16160 1465

5050 0 550 16867 1444

2575 0 553 17271 1404

Pre-treated

SCTCM

1000 0 556 15655 1636

7525 0 547 16059 1596

5050 0 551 16362 1505

2575 0 550 16867 1414

Untreated

SCBCM

1000 0 548 16362 1646

7525 0 561 16564 1586

5050 0 535 17069 1515

2575 0 551 17271 1444

Pre-treated

SCBCM

1000 0 364 15756 1566

7525 0 549 16665 1576

5050 0 543 16968 1505

2575 0 552 17271 1444

CM 1000 0 553 17473 1364






86
























modelled data


87


SubstratesFeed


B B0 Rm λR2



USCTCM 100 0 20175 19347 712 264 09942 20753 005 266 09988

75 25 17909 16507 623 128 09772 17679 005 183 09956

50 50 15820 14442 648 122 09701 15343 006 188 09911

25 75 14128 12653 858 198 09727 13238 010 255 09898

PSCTCM 100 0 20748 19853 2142 168 09927 20397 015 185 09867

75 25 20650 19748 1819 123 09905 20331 014 167 09905

50 50 16910 16360 1766 142 09920 16782 016 174 09896

25 75 14224 13498 1521 157 09887 13889 016 178 09881

USCBCM 100 0 19900 19394 797 521 09972 20561 006 538 09889

75 25 18503 17700 528 197 09864 19472 004 197 09989

50 50 17056 16131 492 045 09734 17440 004 119 09940

25 75 16154 14420 767 109 09691 15218 003 178 09897

PSCBCM 100 0 22557 21982 702 154 09815 23570 004 188 09934

75 25 19955 19203 660 007 09787 20400 005 089 09941

50 50 17407 16241 718 015 09745 17090 007 110 09928

25 75 15577 14114 806 040 09624 14869 008 119 09837

CM 100 0 11532 10787 1237 280 09858 11135 016 299 09919


88





















89






90



























91






92







1000 7525 5050 2575




















93



production


94

1


95



96


97





























98





























99



SCTSCB with CM





100

101

APPENDIX 1






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean


Upper

Bound

100USCT +

0CM

3 2017333 223010 128755 1961935 2072732 19920 20340

75USCT +

25CM

3 1791000 34641 20000 1782395 1799605 17890 17950

50USCT +

50CM

3 1581667 85049 49103 1560539 1602794 15730 15900

25USCT +

75CM

3 1413000 338674 195533 1328869 1497131 13750 14400

0USCT +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1591267 3092455 798468 1420012 1762521 11460 20340





102


adjusted df

938 4 4174 521


ANOVA

Methane Yields


Between Groups 13352489 4 3338122 924688 000


Total 13388589 14




Mean

Difference (I-

J) Std Error Sig


Lower

Bound

Upper

Bound

Tukey

HSD

100USCT

+ 0CM

75USCT +

25CM

2263333 155134 000 175277 277389

50USCT +

50CM

4356667 155134 000 384611 486723

25USCT +

75CM

6043333 155134 000 553277 655389

0USCT +

100CM

8640000 155134 000 812944 915056

75USCT +

25CM

100USCT +

0CM

-2263333 155134 000 -277389 -175277

50USCT +

50CM

2093333 155134 000 158277 260389

25USCT +

75CM

3780000 155134 000 326944 429056

0USCT +

100CM

6376667 155134 000 586611 688723

103

50USCT +

50CM

100USCT +

0CM

-4356667 155134 000 -486723 -384611

75USCT +

25CM

-2093333 155134 000 -260389 -158277

25USCT +

75CM

1686667 155134 000 117611 219723

0USCT +

100CM

4283333 155134 000 377277 479389

25USCT +

75CM

100USCT +

0CM

-6043333 155134 000 -655389 -553277

75USCT +

25CM

-3780000 155134 000 -429056 -326944

50USCT +

50CM

-1686667 155134 000 -219723 -117611

0USCT +

100CM

2596667 155134 000 208611 310723

0USCT +

100CM

100USCT +

0CM

-8640000 155134 000 -915056 -812944

75USCT +

25CM

-6376667 155134 000 -688723 -586611

50USCT +

50CM

-4283333 155134 000 -479389 -377277

25USCT +

75CM

-2596667 155134 000 -310723 -208611

LSD 100USCT

+ 0CM

75USCT +

25CM

2263333 155134 000 191767 260899

50USCT +

50CM

4356667 155134 000 401101 470233

25USCT +

75CM

6043333 155134 000 569767 638899

0USCT +

100CM

8640000 155134 000 829434 898566

75USCT +

25CM

100USCT +

0CM

-2263333 155134 000 -260899 -191767

104

50USCT +

50CM

2093333 155134 000 174767 243899

25USCT +

75CM

3780000 155134 000 343434 412566

0USCT +

100CM

6376667 155134 000 603101 672233

50USCT +

50CM

100USCT +

0CM

-4356667 155134 000 -470233 -401101

75USCT +

25CM

-2093333 155134 000 -243899 -174767

25USCT +

75CM

1686667 155134 000 134101 203233

0USCT +

100CM

4283333 155134 000 393767 462899

25USCT +

75CM

100USCT +

0CM

-6043333 155134 000 -638899 -569767

75USCT +

25CM

-3780000 155134 000 -412566 -343434

50USCT +

50CM

-1686667 155134 000 -203233 -134101

0USCT +

100CM

2596667 155134 000 225101 294233

0USCT +

100CM

100USCT +

0CM

-8640000 155134 000 -898566 -829434

75USCT +

25CM

-6376667 155134 000 -672233 -603101

50USCT +

50CM

-4283333 155134 000 -462899 -393767

25USCT +

75CM

-2596667 155134 000 -294233 -225101

Dunnett

t (2-sided)b

100USCT

+ 0CM

0USCT +

100CM

8640000 155134 000 819159 908841

75USCT +

25CM

0USCT +

100CM

6376667 155134 000 592825 682508

105

50USCT +

50CM

0USCT +

100CM

4283333 155134 000 383492 473175

25USCT +

75CM

0USCT +

100CM

2596667 155134 000 214825 304508



106

APPENDIX2






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100PSCT +

0CM

3 2075000 629603 363501 1918598 2231402 20050 21270

75PSCT +

25CM

3 2065000 580259 335012 1920856 2209144 20310 21320

50PSCT +

50CM

3 1691000 272213 157162 1623379 1758621 16600 17110

25PSCT +

75CM

3 1422667 250067 144376 1360547 1484787 13980 14480

0PSCT +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1681400 3743018 966443 1474119 1888681 11460 21320





107


adjusted df

501 4 4789 739


ANOVA

Methane Yields


Between Groups 19438783 4 4859696 276948 000


Total 19614256 14




Mean

Difference (I-

J) Std Error Sig


Lower

Bound

Upper

Bound

Tukey

HSD

100PSCT

+ 0CM

75PSCT +

25CM

100000 342027 998 -102564 122564

50PSCT +

50CM

3840000 342027 000 271436 496564

25PSCT +

75CM

6523333 342027 000 539770 764897

0PSCT +

100CM

9216667 342027 000 809103 1034230

75PSCT +

25CM

100PSCT +

0CM

-100000 342027 998 -122564 102564

50PSCT +

50CM

3740000 342027 000 261436 486564

25PSCT +

75CM

6423333 342027 000 529770 754897

108

0PSCT +

100CM

9116667 342027 000 799103 1024230

50PSCT +

50CM

100PSCT +

0CM

-3840000 342027 000 -496564 -271436

75PSCT +

25CM

-3740000 342027 000 -486564 -261436

25PSCT +

75CM

2683333 342027 000 155770 380897

0PSCT +

100CM

5376667 342027 000 425103 650230

25PSCT +

75CM

100PSCT +

0CM

-6523333 342027 000 -764897 -539770

75PSCT +

25CM

-6423333 342027 000 -754897 -529770

50PSCT +

50CM

-2683333 342027 000 -380897 -155770

0PSCT +

100CM

2693333 342027 000 156770 381897

0PSCT +

100CM

100PSCT +

0CM

-9216667 342027 000 -1034230 -809103

75PSCT +

25CM

-9116667 342027 000 -1024230 -799103

50PSCT +

50CM

-5376667 342027 000 -650230 -425103

25PSCT +

75CM

-2693333 342027 000 -381897 -156770

LSD 100PSCT

+ 0CM

75PSCT +

25CM

100000 342027 776 -66208 86208

50PSCT +

50CM

3840000 342027 000 307792 460208

25PSCT +

75CM

6523333 342027 000 576125 728542

0PSCT +

100CM

9216667 342027 000 845458 997875

109

75PSCT +

25CM

100PSCT +

0CM

-100000 342027 776 -86208 66208

50PSCT +

50CM

3740000 342027 000 297792 450208

25PSCT +

75CM

6423333 342027 000 566125 718542

0PSCT +

100CM

9116667 342027 000 835458 987875

50PSCT +

50CM

100PSCT +

0CM

-3840000 342027 000 -460208 -307792

75PSCT +

25CM

-3740000 342027 000 -450208 -297792

25PSCT +

75CM

2683333 342027 000 192125 344542

0PSCT +

100CM

5376667 342027 000 461458 613875

25PSCT +

75CM

100PSCT +

0CM

-6523333 342027 000 -728542 -576125

75PSCT +

25CM

-6423333 342027 000 -718542 -566125

50PSCT +

50CM

-2683333 342027 000 -344542 -192125

0PSCT +

100CM

2693333 342027 000 193125 345542

0PSCT +

100CM

100PSCT +

0CM

-9216667 342027 000 -997875 -845458

75PSCT +

25CM

-9116667 342027 000 -987875 -835458

50PSCT +

50CM

-5376667 342027 000 -613875 -461458

25PSCT +

75CM

-2693333 342027 000 -345542 -193125

100PSCT

+ 0CM

0PSCT +

100CM

9216667 342027 000 822805 1020529

110

Dunnett

t (2-

sided)b

75PSCT +

25CM

0PSCT +

100CM

9116667 342027 000 812805 1010529

50PSCT +

50CM

0PSCT +

100CM

5376667 342027 000 438805 636529

25PSCT +

75CM

0PSCT +

100CM

2693333 342027 000 170471 368195



111

APPENDIX 3






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100USCB +

0CM

3 1990000 481248 277849 1870451 2109549 19400 20360

75USCB +

25CM

3 1850333 221209 127715 1795382 1905285 18270 18710

50USCB +

50CM

3 1705667 51316 29627 1692919 1718414 17000 17100

25USCB +

75CM

3 1615333 187172 108064 1568837 1661829 15940 16290

0USCB +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1662933 2957005 763495 1499180 1826687 11460 20360





112

Based on Median and

with adjusted df

1518 4 4354 339


ANOVA

Methane Yields


Between Groups 12176263 4 3044066 467120 000


Total 12241429 14




Mean



Lower

Bound

Upper

Bound

Tukey

HSD

100USCB

+ 0CM

75USCB +

25CM

1396667 208433 000 71070 208264

50USCB +

50CM

2843333 208433 000 215736 352930

25USCB +

75CM

3746667 208433 000 306070 443264

0USCB +

100CM

8366667 208433 000 768070 905264

75USCB +

25CM

100USCB +

0CM

-1396667 208433 000 -208264 -71070

50USCB +

50CM

1446667 208433 000 76070 213264

25USCB +

75CM

2350000 208433 000 166403 303597

113

0USCB +

100CM

6970000 208433 000 628403 765597

50USCB +

50CM

100USCB +

0CM

-2843333 208433 000 -352930 -215736

75USCB +

25CM

-1446667 208433 000 -213264 -76070

25USCB +

75CM

903333 208433 000 21736 158930

0USCB +

100CM

5523333 208433 000 483736 620930

25USCB +

75CM

100USCB +

0CM

-3746667 208433 000 -443264 -306070

75USCB +

25CM

-2350000 208433 000 -303597 -166403

50USCB +

50CM

-903333 208433 000 -158930 -21736

0USCB +

100CM

4620000 208433 000 393403 530597

0USCB +

100CM

100USCB +

0CM

-8366667 208433 000 -905264 -768070

75USCB +

25CM

-6970000 208433 000 -765597 -628403

50USCB +

50CM

-5523333 208433 000 -620930 -483736

25USCB +

75CM

-4620000 208433 000 -530597 -393403

LSD 100USCB

+ 0CM

75USCB +

25CM

1396667 208433 000 93225 186109

50USCB +

50CM

2843333 208433 000 237891 330775

25USCB +

75CM

3746667 208433 000 328225 421109

0USCB +

100CM

8366667 208433 000 790225 883109

114

75USCB +

25CM

100USCB +

0CM

-1396667 208433 000 -186109 -93225

50USCB +

50CM

1446667 208433 000 98225 191109

25USCB +

75CM

2350000 208433 000 188558 281442

0USCB +

100CM

6970000 208433 000 650558 743442

50USCB +

50CM

100USCB +

0CM

-2843333 208433 000 -330775 -237891

75USCB +

25CM

-1446667 208433 000 -191109 -98225

25USCB +

75CM

903333 208433 000 43891 136775

0USCB +

100CM

5523333 208433 000 505891 598775

25USCB +

75CM

100USCB +

0CM

-3746667 208433 000 -421109 -328225

75USCB +

25CM

-2350000 208433 000 -281442 -188558

50USCB +

50CM

-903333 208433 000 -136775 -43891

0USCB +

100CM

4620000 208433 000 415558 508442

0USCB +

100CM

100USCB +

0CM

-8366667 208433 000 -883109 -790225

75USCB +

25CM

-6970000 208433 000 -743442 -650558

50USCB +

50CM

-5523333 208433 000 -598775 -505891

25USCB +

75CM

-4620000 208433 000 -508442 -415558

100USCB

+ 0CM

0USCB +

100CM

8366667 208433 000 776419 896914

115

Dunnett

t (2-

sided)b

75USCB +

25CM

0USCB +

100CM

6970000 208433 000 636753 757247

50USCB +

50CM

0USCB +

100CM

5523333 208433 000 492086 612581

25USCB +

75CM

0USCB +

100CM

4620000 208433 000 401753 522247



116

APPENDIX 4






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100PSCB +

0CM

3 2255667 539290 311359 2121700 2389634 21940 22940

75PSCB +

25CM

3 1995667 427239 246667 1889535 2101799 19470 20270

50PSCB +

50CM

3 1740333 234592 135442 1682057 1798609 17140 17590

25PSCB +

75CM

3 1557667 281129 162310 1487830 1627503 15390 15900

0PSCB +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1740533 3908233 1009101 1524103 1956964 11460 22940





117


adjusted df

407 4 5808 798


ANOVA

Methane Yields


Between Groups 21260984 4 5315246 432087 000


Total 21383997 14




Mean



Lower

Bound

Upper

Bound

Tukey

HSD

100PSCB

+ 0CM

75PSCB +

25CM

2600000 286372 000 165753 354247

50PSCB +

50CM

5153333 286372 000 421086 609581

25PSCB +

75CM

6980000 286372 000 603753 792247

0PSCB +

100CM

11023333 286372 000 1008086 1196581

75PSCB +

25CM

100PSCB

+ 0CM

-2600000 286372 000 -354247 -165753

50PSCB +

50CM

2553333 286372 000 161086 349581

25PSCB +

75CM

4380000 286372 000 343753 532247

118

0PSCB +

100CM

8423333 286372 000 748086 936581

50PSCB +

50CM

100PSCB

+ 0CM

-5153333 286372 000 -609581 -421086

75PSCB +

25CM

-2553333 286372 000 -349581 -161086

25PSCB +

75CM

1826667 286372 000 88419 276914

0PSCB +

100CM

5870000 286372 000 492753 681247

25PSCB +

75CM

100PSCB

+ 0CM

-6980000 286372 000 -792247 -603753

75PSCB +

25CM

-4380000 286372 000 -532247 -343753

50PSCB +

50CM

-1826667 286372 000 -276914 -88419

0PSCB +

100CM

4043333 286372 000 310086 498581

0PSCB +

100CM

100PSCB

+ 0CM

-11023333 286372 000 -1196581 -1008086

75PSCB +

25CM

-8423333 286372 000 -936581 -748086

50PSCB +

50CM

-5870000 286372 000 -681247 -492753

25PSCB +

75CM

-4043333 286372 000 -498581 -310086

LSD 100PSCB

+ 0CM

75PSCB +

25CM

2600000 286372 000 196192 323808

50PSCB +

50CM

5153333 286372 000 451526 579141

25PSCB +

75CM

6980000 286372 000 634192 761808

0PSCB +

100CM

11023333 286372 000 1038526 1166141

119

75PSCB +

25CM

100PSCB

+ 0CM

-2600000 286372 000 -323808 -196192

50PSCB +

50CM

2553333 286372 000 191526 319141

25PSCB +

75CM

4380000 286372 000 374192 501808

0PSCB +

100CM

8423333 286372 000 778526 906141

50PSCB +

50CM

100PSCB

+ 0CM

-5153333 286372 000 -579141 -451526

75PSCB +

25CM

-2553333 286372 000 -319141 -191526

25PSCB +

75CM

1826667 286372 000 118859 246474

0PSCB +

100CM

5870000 286372 000 523192 650808

25PSCB +

75CM

100PSCB

+ 0CM

-6980000 286372 000 -761808 -634192

75PSCB +

25CM

-4380000 286372 000 -501808 -374192

50PSCB +

50CM

-1826667 286372 000 -246474 -118859

0PSCB +

100CM

4043333 286372 000 340526 468141

0PSCB +

100CM

100PSCB

+ 0CM

-11023333 286372 000 -1166141 -1038526

75PSCB +

25CM

-8423333 286372 000 -906141 -778526

50PSCB +

50CM

-5870000 286372 000 -650808 -523192

25PSCB +

75CM

-4043333 286372 000 -468141 -340526

100PSCB

+ 0CM

0PSCB +

100CM

11023333 286372 000 1019558 1185109

120

Dunnett

t (2-

sided)b

75PSCB +

25CM

0PSCB +

100CM

8423333 286372 000 759558 925109

50PSCB +

50CM

0PSCB +

100CM

5870000 286372 000 504225 669775

25PSCB +

75CM

0PSCB +

100CM

4043333 286372 000 321558 487109



121

REFERENCES











122












123













124












125













126












127












128












129













130












131













132












133














134













135













136




ABSTRACT


LIST OF FIGURES

LIST OF TABLES


ACKNOWLEDGMENTS

APPENDIX 1

APPENDIX 3

APPENDIX 4

REFERENCES

vi







vii

TABLE OF CONTENTS

Abstract iii



List of figures xi

List of tables xiii


Acknowledgments xvi


11 Background 3


13 Thesis outline 5










272 pH 24



viii








31 Substrates 45


312 Manure 45

32 Inoculum 45

















ix









and Inoculum 63


with CM 67








with CM 72











x










References 121

xi

LIST OF FIGURES




the SCT 14












Figure 31 (e) CM 47








for 83 days 68


xii







for 83 days 74












xiii

LIST OF TABLES


(2012-2017) 10





previous studies 26













with CM 85



xiv






BD Biodegradability




CM Chicken Manure















xv



SCT Sugarcane Trash





TS Total Solids



UV Ultra-Violet



VS Volatile Solids

xvi

ACKNOWLEDGMENTS











researcher












xvii
















AMAL BABU PUTHUMANA

18


1



2


3

11 Background


























4



























5












chicken manure



13 Thesis outline




project




6
















7



8


9
























Barbieri 2013)


10








Source (FAO 2018)

Crop


2012 2013 2014 2015 2016 2017

Maize 696 786 524 596 533 678

Rice 1031 1136 767 697 266 822

Cotton 5965 444 3901 197 2804 NA

Soybean 381 411 37 301 292 300

Sugarcane 3386 3293 3752 3765 4472 4534

Sunflower 402 303 27 295 23 27

Wheat 139021 129793 126132 123837 112822 1219115











11





12




















13









14



forming the SCB


2018)


15


















(Jain et al 2014)









16
















al 2015)










17













Table 22


18






























19









C6H10O4 + 2H2O C6H12O6 + H2 Eq21







et al 2019)





20













21






CO2 + 4H2 CH4 + 2H2O Eq28

HCO-3 + 4H2 + H+ CH4 + 3H2O Eq29















22





2017)











Table 23








23






More stable

Less energy input









important


24

272 pH













Cheon 2008)


and Stetter 1982)





Jones 2008)


2008)







25



























26


studies




























27





Stroeve 2009)












28



























29





















Zeeman 2009)







30





31


Sl

No

Pre-treatment

method

Lignocellulosic

substrates

Pre-treatment

condition

CH4 production

after pre-

treatment

(mLCH4gVSadded)

Increase in

CH4 ()

References




Balsari 2012)








2016)


Olive mill solid

waste




Borja 2016)


32


Morken 2012)


MHz 1600 W



2011)


MHz 1600 W





and Carrere 2012)



2012)





33



























34



























35



























36












AD process






37


wastes

Sl

No


conditions

Feed Ratios

tested


1 Sugarcane leaves

+ Diary cow

manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 7525

5050 2575

0100 (VS basis)


LCH4kgVSadded)

(Mokomele et al

2019)

2 Sugarcane

Bagasse + Diary

cow manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 5050

0100 (VS basis)


LCH4kgVSadded)

(Mokomele et al

2019)

3 Sugarcane leaves

+ Diary cow

manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 7525

5050 2575

0100 (VS basis)

NA 7525 (292

LCH4kgVSadded)

(Mokomele et al

2019)

4 Switchgrass +

Diary manure

500 mL Mesophilic

temperature ISR =

067 30 days

1000 7525

5050 2575

0100 (TS basis)



mono-digestion of

switchgrass

(Zheng et al 2015)

5 Salix viminalis +

Cow manure

1125 mL

mesophilic

temperature ISR =

067 57 days

1000 8020

7030 6040

5050 4060

3070 2080

0100 (VS basis)


mLCH4gVS)


mLgVS) has 23



38

improvement over

untreated salix

6 Corn Stover + Pig

Manure

250 mL Mesophilic

temperature ISR =

026 70 days

1000 8020

6733 5050

3367 2080

0100 (VS basis)

NA 0100 (889

mLCH4gVS)

(Li et al 2018b)

7 Cotton stalk +

swine manure

500 mL Mesophilic

temperature ISR =

025 45 days

1000 7525

5050 2575 (TS

basis)


in yield when

compared to control

(102 mLCH4gVS)

(Cheng and Zhong

2014)

8 Apple waste +

Chicken manure

250 mL Mesophilic

temperature ISR =

026 70 days

1000 8020

6733 5050

3367 2080

0100 (VS basis)

NA 3367 (827

mLCH4gVS)

(Li et al 2018b)

9 Whey + cow

manure

400 mL Mesophilic

temperature 55 days

1000 8515

7525 5050

2575 1585

0100 (VS basis)

NA 8515 (266

mLCH4gVS)

(Vivekanand Mulat

Eijsink and Horn

2018)


39




















al 2017)







40







al 2017)


41







2 Grass Cow manure














6

Organic fraction of








42


















with CM


43



44


45

31 Substrates












312 Manure




32 Inoculum








46



























47





Sample Untreated

SCT ()

Pre-treated

SCT ()

Untreated

SCB ()

Pre-treated

SCB ()

CM

()

















CM 0 0 0 0 100


48









49























Batstone 2015)


50

























51


(PO43-)

















52











53










54

37 Data Processing

371 Theoretical BMP







Fdz-Polanco 2015)


tttEq31





ᆒt =ᆒ썈υ

Eq32









55


using equation 33










Sung 2012)


Eq34


Eq35









56







Eq36
















tt Eq37


57



υ =

tt Eq38










Eq310









58






al 2014)

= t 㢐t) Eq311













= t

t

Eq312





59












60

1


61



62


63


inoculum

























64

















study










65






Bolantildeos 2007)



SCT

Pre-treated

SCT

Untreated

SCB

Pre-treated

SCBInoculum

TS 721 913 872 828 819 19

VS TS 807 909 925 894 951 722

COD gl 7487 11598 12099 7939 9156 211

TSVS - 12 11 11 11 11 14

CN ratio - 124 831 953 1465 1919 49

Glucan ND 307 463 351 428 ND

Xylan ND 168 117 20 146 ND


Lignin ND 298 283 315 282 ND

TKP gPkg 82 11 07 05 06 312

TKN gNkg 836 97 108 512 71 1113







66



SCT

Pre-treated

SCT

Untreated

SCB

Pre-treated

SCBInoculum

Al mgg 103 123 099 084 024 817

As mgg 000 000 000 000 000 000

B mgg 003 000 000 000 000 004

Ba mgg 004 002 002 001 001 019

Ca mgg 1687 312 167 025 015 1973

Cd mgg 000 000 000 000 000 000

Co mgg 000 000 000 000 000 001

Cr mgg 004 001 001 001 001 005

Cu mgg 011 000 000 000 000 056

Fe mgg 107 092 019 086 026 1972

K mgg 1977 601 283 293 076 1582

Mg mgg 461 188 138 036 028 666

Mn mgg 036 010 006 005 003 017

Mo mgg 001 000 000 000 000 003

Na mgg 171 007 034 001 004 1644

Ni mgg 002 000 000 000 000 005

P mgg 796 101 041 015 010 2735

Pb mgg 001 000 000 000 000 003

S mgg 321 082 809 018 524 1469

Se mgg 001 000 001 000 001 000

Zn mgg 027 000 000 000 000 067









67





CM













al 2015)







68




Untreated SCT














69



























70


















71



























72



digested with CM




CM








73






















74

















75















untreated SCB












76
















mono-digestion


77
















78



























79




Pre-treated SCBCM






2015)












80




Untreated

SCTCM

1000 5552 1444 2018 215 353 1960 28

7525 5444 1307 1791 247 307 1671 67

5050 5336 1178 1582 248 308 1642 -38

2575 5228 1074 1413 244 270 1413 00

Pre-treated

SCTCM

1000 5073 1625 2075a 265 410 2082 -03

7525 5085 1614 2065a 252 391 1988 37

5050 5097 1318 1691 235 319 1628 37

2575 5109 1106 1422 190 255 1302 85

Untreated

SCBCM

1000 4364 1812 1990 262 421 1839 76

7525 4553 1615 1850 237 391 1780 38

5050 4742 1429 1706 232 350 1660 27

2575 4931 1302 1615 179 301 1486 80

Pre-treated

SCBCM

1000 2882 3110 2256 251 715 2061 86

7525 3442 2304 1995 220 538 1852 72

5050 4000 1729 1741 235 414 1656 49

2575 4561 1357 1558 194 348 1586 -18

CM 1000 5121 895 1153 216 206 1056 84


81



























82












83




84


























85




136 mgL


CM


mgL mgL mgL mgL

Untreated

SCTCM

1000 0 548 16160 1535

7525 0 554 16160 1465

5050 0 550 16867 1444

2575 0 553 17271 1404

Pre-treated

SCTCM

1000 0 556 15655 1636

7525 0 547 16059 1596

5050 0 551 16362 1505

2575 0 550 16867 1414

Untreated

SCBCM

1000 0 548 16362 1646

7525 0 561 16564 1586

5050 0 535 17069 1515

2575 0 551 17271 1444

Pre-treated

SCBCM

1000 0 364 15756 1566

7525 0 549 16665 1576

5050 0 543 16968 1505

2575 0 552 17271 1444

CM 1000 0 553 17473 1364






86
























modelled data


87


SubstratesFeed


B B0 Rm λR2



USCTCM 100 0 20175 19347 712 264 09942 20753 005 266 09988

75 25 17909 16507 623 128 09772 17679 005 183 09956

50 50 15820 14442 648 122 09701 15343 006 188 09911

25 75 14128 12653 858 198 09727 13238 010 255 09898

PSCTCM 100 0 20748 19853 2142 168 09927 20397 015 185 09867

75 25 20650 19748 1819 123 09905 20331 014 167 09905

50 50 16910 16360 1766 142 09920 16782 016 174 09896

25 75 14224 13498 1521 157 09887 13889 016 178 09881

USCBCM 100 0 19900 19394 797 521 09972 20561 006 538 09889

75 25 18503 17700 528 197 09864 19472 004 197 09989

50 50 17056 16131 492 045 09734 17440 004 119 09940

25 75 16154 14420 767 109 09691 15218 003 178 09897

PSCBCM 100 0 22557 21982 702 154 09815 23570 004 188 09934

75 25 19955 19203 660 007 09787 20400 005 089 09941

50 50 17407 16241 718 015 09745 17090 007 110 09928

25 75 15577 14114 806 040 09624 14869 008 119 09837

CM 100 0 11532 10787 1237 280 09858 11135 016 299 09919


88





















89






90



























91






92







1000 7525 5050 2575




















93



production


94

1


95



96


97





























98





























99



SCTSCB with CM





100

101

APPENDIX 1






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean


Upper

Bound

100USCT +

0CM

3 2017333 223010 128755 1961935 2072732 19920 20340

75USCT +

25CM

3 1791000 34641 20000 1782395 1799605 17890 17950

50USCT +

50CM

3 1581667 85049 49103 1560539 1602794 15730 15900

25USCT +

75CM

3 1413000 338674 195533 1328869 1497131 13750 14400

0USCT +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1591267 3092455 798468 1420012 1762521 11460 20340





102


adjusted df

938 4 4174 521


ANOVA

Methane Yields


Between Groups 13352489 4 3338122 924688 000


Total 13388589 14




Mean

Difference (I-

J) Std Error Sig


Lower

Bound

Upper

Bound

Tukey

HSD

100USCT

+ 0CM

75USCT +

25CM

2263333 155134 000 175277 277389

50USCT +

50CM

4356667 155134 000 384611 486723

25USCT +

75CM

6043333 155134 000 553277 655389

0USCT +

100CM

8640000 155134 000 812944 915056

75USCT +

25CM

100USCT +

0CM

-2263333 155134 000 -277389 -175277

50USCT +

50CM

2093333 155134 000 158277 260389

25USCT +

75CM

3780000 155134 000 326944 429056

0USCT +

100CM

6376667 155134 000 586611 688723

103

50USCT +

50CM

100USCT +

0CM

-4356667 155134 000 -486723 -384611

75USCT +

25CM

-2093333 155134 000 -260389 -158277

25USCT +

75CM

1686667 155134 000 117611 219723

0USCT +

100CM

4283333 155134 000 377277 479389

25USCT +

75CM

100USCT +

0CM

-6043333 155134 000 -655389 -553277

75USCT +

25CM

-3780000 155134 000 -429056 -326944

50USCT +

50CM

-1686667 155134 000 -219723 -117611

0USCT +

100CM

2596667 155134 000 208611 310723

0USCT +

100CM

100USCT +

0CM

-8640000 155134 000 -915056 -812944

75USCT +

25CM

-6376667 155134 000 -688723 -586611

50USCT +

50CM

-4283333 155134 000 -479389 -377277

25USCT +

75CM

-2596667 155134 000 -310723 -208611

LSD 100USCT

+ 0CM

75USCT +

25CM

2263333 155134 000 191767 260899

50USCT +

50CM

4356667 155134 000 401101 470233

25USCT +

75CM

6043333 155134 000 569767 638899

0USCT +

100CM

8640000 155134 000 829434 898566

75USCT +

25CM

100USCT +

0CM

-2263333 155134 000 -260899 -191767

104

50USCT +

50CM

2093333 155134 000 174767 243899

25USCT +

75CM

3780000 155134 000 343434 412566

0USCT +

100CM

6376667 155134 000 603101 672233

50USCT +

50CM

100USCT +

0CM

-4356667 155134 000 -470233 -401101

75USCT +

25CM

-2093333 155134 000 -243899 -174767

25USCT +

75CM

1686667 155134 000 134101 203233

0USCT +

100CM

4283333 155134 000 393767 462899

25USCT +

75CM

100USCT +

0CM

-6043333 155134 000 -638899 -569767

75USCT +

25CM

-3780000 155134 000 -412566 -343434

50USCT +

50CM

-1686667 155134 000 -203233 -134101

0USCT +

100CM

2596667 155134 000 225101 294233

0USCT +

100CM

100USCT +

0CM

-8640000 155134 000 -898566 -829434

75USCT +

25CM

-6376667 155134 000 -672233 -603101

50USCT +

50CM

-4283333 155134 000 -462899 -393767

25USCT +

75CM

-2596667 155134 000 -294233 -225101

Dunnett

t (2-sided)b

100USCT

+ 0CM

0USCT +

100CM

8640000 155134 000 819159 908841

75USCT +

25CM

0USCT +

100CM

6376667 155134 000 592825 682508

105

50USCT +

50CM

0USCT +

100CM

4283333 155134 000 383492 473175

25USCT +

75CM

0USCT +

100CM

2596667 155134 000 214825 304508



106

APPENDIX2






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100PSCT +

0CM

3 2075000 629603 363501 1918598 2231402 20050 21270

75PSCT +

25CM

3 2065000 580259 335012 1920856 2209144 20310 21320

50PSCT +

50CM

3 1691000 272213 157162 1623379 1758621 16600 17110

25PSCT +

75CM

3 1422667 250067 144376 1360547 1484787 13980 14480

0PSCT +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1681400 3743018 966443 1474119 1888681 11460 21320





107


adjusted df

501 4 4789 739


ANOVA

Methane Yields


Between Groups 19438783 4 4859696 276948 000


Total 19614256 14




Mean

Difference (I-

J) Std Error Sig


Lower

Bound

Upper

Bound

Tukey

HSD

100PSCT

+ 0CM

75PSCT +

25CM

100000 342027 998 -102564 122564

50PSCT +

50CM

3840000 342027 000 271436 496564

25PSCT +

75CM

6523333 342027 000 539770 764897

0PSCT +

100CM

9216667 342027 000 809103 1034230

75PSCT +

25CM

100PSCT +

0CM

-100000 342027 998 -122564 102564

50PSCT +

50CM

3740000 342027 000 261436 486564

25PSCT +

75CM

6423333 342027 000 529770 754897

108

0PSCT +

100CM

9116667 342027 000 799103 1024230

50PSCT +

50CM

100PSCT +

0CM

-3840000 342027 000 -496564 -271436

75PSCT +

25CM

-3740000 342027 000 -486564 -261436

25PSCT +

75CM

2683333 342027 000 155770 380897

0PSCT +

100CM

5376667 342027 000 425103 650230

25PSCT +

75CM

100PSCT +

0CM

-6523333 342027 000 -764897 -539770

75PSCT +

25CM

-6423333 342027 000 -754897 -529770

50PSCT +

50CM

-2683333 342027 000 -380897 -155770

0PSCT +

100CM

2693333 342027 000 156770 381897

0PSCT +

100CM

100PSCT +

0CM

-9216667 342027 000 -1034230 -809103

75PSCT +

25CM

-9116667 342027 000 -1024230 -799103

50PSCT +

50CM

-5376667 342027 000 -650230 -425103

25PSCT +

75CM

-2693333 342027 000 -381897 -156770

LSD 100PSCT

+ 0CM

75PSCT +

25CM

100000 342027 776 -66208 86208

50PSCT +

50CM

3840000 342027 000 307792 460208

25PSCT +

75CM

6523333 342027 000 576125 728542

0PSCT +

100CM

9216667 342027 000 845458 997875

109

75PSCT +

25CM

100PSCT +

0CM

-100000 342027 776 -86208 66208

50PSCT +

50CM

3740000 342027 000 297792 450208

25PSCT +

75CM

6423333 342027 000 566125 718542

0PSCT +

100CM

9116667 342027 000 835458 987875

50PSCT +

50CM

100PSCT +

0CM

-3840000 342027 000 -460208 -307792

75PSCT +

25CM

-3740000 342027 000 -450208 -297792

25PSCT +

75CM

2683333 342027 000 192125 344542

0PSCT +

100CM

5376667 342027 000 461458 613875

25PSCT +

75CM

100PSCT +

0CM

-6523333 342027 000 -728542 -576125

75PSCT +

25CM

-6423333 342027 000 -718542 -566125

50PSCT +

50CM

-2683333 342027 000 -344542 -192125

0PSCT +

100CM

2693333 342027 000 193125 345542

0PSCT +

100CM

100PSCT +

0CM

-9216667 342027 000 -997875 -845458

75PSCT +

25CM

-9116667 342027 000 -987875 -835458

50PSCT +

50CM

-5376667 342027 000 -613875 -461458

25PSCT +

75CM

-2693333 342027 000 -345542 -193125

100PSCT

+ 0CM

0PSCT +

100CM

9216667 342027 000 822805 1020529

110

Dunnett

t (2-

sided)b

75PSCT +

25CM

0PSCT +

100CM

9116667 342027 000 812805 1010529

50PSCT +

50CM

0PSCT +

100CM

5376667 342027 000 438805 636529

25PSCT +

75CM

0PSCT +

100CM

2693333 342027 000 170471 368195



111

APPENDIX 3






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100USCB +

0CM

3 1990000 481248 277849 1870451 2109549 19400 20360

75USCB +

25CM

3 1850333 221209 127715 1795382 1905285 18270 18710

50USCB +

50CM

3 1705667 51316 29627 1692919 1718414 17000 17100

25USCB +

75CM

3 1615333 187172 108064 1568837 1661829 15940 16290

0USCB +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1662933 2957005 763495 1499180 1826687 11460 20360





112

Based on Median and

with adjusted df

1518 4 4354 339


ANOVA

Methane Yields


Between Groups 12176263 4 3044066 467120 000


Total 12241429 14




Mean



Lower

Bound

Upper

Bound

Tukey

HSD

100USCB

+ 0CM

75USCB +

25CM

1396667 208433 000 71070 208264

50USCB +

50CM

2843333 208433 000 215736 352930

25USCB +

75CM

3746667 208433 000 306070 443264

0USCB +

100CM

8366667 208433 000 768070 905264

75USCB +

25CM

100USCB +

0CM

-1396667 208433 000 -208264 -71070

50USCB +

50CM

1446667 208433 000 76070 213264

25USCB +

75CM

2350000 208433 000 166403 303597

113

0USCB +

100CM

6970000 208433 000 628403 765597

50USCB +

50CM

100USCB +

0CM

-2843333 208433 000 -352930 -215736

75USCB +

25CM

-1446667 208433 000 -213264 -76070

25USCB +

75CM

903333 208433 000 21736 158930

0USCB +

100CM

5523333 208433 000 483736 620930

25USCB +

75CM

100USCB +

0CM

-3746667 208433 000 -443264 -306070

75USCB +

25CM

-2350000 208433 000 -303597 -166403

50USCB +

50CM

-903333 208433 000 -158930 -21736

0USCB +

100CM

4620000 208433 000 393403 530597

0USCB +

100CM

100USCB +

0CM

-8366667 208433 000 -905264 -768070

75USCB +

25CM

-6970000 208433 000 -765597 -628403

50USCB +

50CM

-5523333 208433 000 -620930 -483736

25USCB +

75CM

-4620000 208433 000 -530597 -393403

LSD 100USCB

+ 0CM

75USCB +

25CM

1396667 208433 000 93225 186109

50USCB +

50CM

2843333 208433 000 237891 330775

25USCB +

75CM

3746667 208433 000 328225 421109

0USCB +

100CM

8366667 208433 000 790225 883109

114

75USCB +

25CM

100USCB +

0CM

-1396667 208433 000 -186109 -93225

50USCB +

50CM

1446667 208433 000 98225 191109

25USCB +

75CM

2350000 208433 000 188558 281442

0USCB +

100CM

6970000 208433 000 650558 743442

50USCB +

50CM

100USCB +

0CM

-2843333 208433 000 -330775 -237891

75USCB +

25CM

-1446667 208433 000 -191109 -98225

25USCB +

75CM

903333 208433 000 43891 136775

0USCB +

100CM

5523333 208433 000 505891 598775

25USCB +

75CM

100USCB +

0CM

-3746667 208433 000 -421109 -328225

75USCB +

25CM

-2350000 208433 000 -281442 -188558

50USCB +

50CM

-903333 208433 000 -136775 -43891

0USCB +

100CM

4620000 208433 000 415558 508442

0USCB +

100CM

100USCB +

0CM

-8366667 208433 000 -883109 -790225

75USCB +

25CM

-6970000 208433 000 -743442 -650558

50USCB +

50CM

-5523333 208433 000 -598775 -505891

25USCB +

75CM

-4620000 208433 000 -508442 -415558

100USCB

+ 0CM

0USCB +

100CM

8366667 208433 000 776419 896914

115

Dunnett

t (2-

sided)b

75USCB +

25CM

0USCB +

100CM

6970000 208433 000 636753 757247

50USCB +

50CM

0USCB +

100CM

5523333 208433 000 492086 612581

25USCB +

75CM

0USCB +

100CM

4620000 208433 000 401753 522247



116

APPENDIX 4






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100PSCB +

0CM

3 2255667 539290 311359 2121700 2389634 21940 22940

75PSCB +

25CM

3 1995667 427239 246667 1889535 2101799 19470 20270

50PSCB +

50CM

3 1740333 234592 135442 1682057 1798609 17140 17590

25PSCB +

75CM

3 1557667 281129 162310 1487830 1627503 15390 15900

0PSCB +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1740533 3908233 1009101 1524103 1956964 11460 22940





117


adjusted df

407 4 5808 798


ANOVA

Methane Yields


Between Groups 21260984 4 5315246 432087 000


Total 21383997 14




Mean



Lower

Bound

Upper

Bound

Tukey

HSD

100PSCB

+ 0CM

75PSCB +

25CM

2600000 286372 000 165753 354247

50PSCB +

50CM

5153333 286372 000 421086 609581

25PSCB +

75CM

6980000 286372 000 603753 792247

0PSCB +

100CM

11023333 286372 000 1008086 1196581

75PSCB +

25CM

100PSCB

+ 0CM

-2600000 286372 000 -354247 -165753

50PSCB +

50CM

2553333 286372 000 161086 349581

25PSCB +

75CM

4380000 286372 000 343753 532247

118

0PSCB +

100CM

8423333 286372 000 748086 936581

50PSCB +

50CM

100PSCB

+ 0CM

-5153333 286372 000 -609581 -421086

75PSCB +

25CM

-2553333 286372 000 -349581 -161086

25PSCB +

75CM

1826667 286372 000 88419 276914

0PSCB +

100CM

5870000 286372 000 492753 681247

25PSCB +

75CM

100PSCB

+ 0CM

-6980000 286372 000 -792247 -603753

75PSCB +

25CM

-4380000 286372 000 -532247 -343753

50PSCB +

50CM

-1826667 286372 000 -276914 -88419

0PSCB +

100CM

4043333 286372 000 310086 498581

0PSCB +

100CM

100PSCB

+ 0CM

-11023333 286372 000 -1196581 -1008086

75PSCB +

25CM

-8423333 286372 000 -936581 -748086

50PSCB +

50CM

-5870000 286372 000 -681247 -492753

25PSCB +

75CM

-4043333 286372 000 -498581 -310086

LSD 100PSCB

+ 0CM

75PSCB +

25CM

2600000 286372 000 196192 323808

50PSCB +

50CM

5153333 286372 000 451526 579141

25PSCB +

75CM

6980000 286372 000 634192 761808

0PSCB +

100CM

11023333 286372 000 1038526 1166141

119

75PSCB +

25CM

100PSCB

+ 0CM

-2600000 286372 000 -323808 -196192

50PSCB +

50CM

2553333 286372 000 191526 319141

25PSCB +

75CM

4380000 286372 000 374192 501808

0PSCB +

100CM

8423333 286372 000 778526 906141

50PSCB +

50CM

100PSCB

+ 0CM

-5153333 286372 000 -579141 -451526

75PSCB +

25CM

-2553333 286372 000 -319141 -191526

25PSCB +

75CM

1826667 286372 000 118859 246474

0PSCB +

100CM

5870000 286372 000 523192 650808

25PSCB +

75CM

100PSCB

+ 0CM

-6980000 286372 000 -761808 -634192

75PSCB +

25CM

-4380000 286372 000 -501808 -374192

50PSCB +

50CM

-1826667 286372 000 -246474 -118859

0PSCB +

100CM

4043333 286372 000 340526 468141

0PSCB +

100CM

100PSCB

+ 0CM

-11023333 286372 000 -1166141 -1038526

75PSCB +

25CM

-8423333 286372 000 -906141 -778526

50PSCB +

50CM

-5870000 286372 000 -650808 -523192

25PSCB +

75CM

-4043333 286372 000 -468141 -340526

100PSCB

+ 0CM

0PSCB +

100CM

11023333 286372 000 1019558 1185109

120

Dunnett

t (2-

sided)b

75PSCB +

25CM

0PSCB +

100CM

8423333 286372 000 759558 925109

50PSCB +

50CM

0PSCB +

100CM

5870000 286372 000 504225 669775

25PSCB +

75CM

0PSCB +

100CM

4043333 286372 000 321558 487109



121

REFERENCES











122












123













124












125













126












127












128












129













130












131













132












133














134













135













136




ABSTRACT


LIST OF FIGURES

LIST OF TABLES


ACKNOWLEDGMENTS

APPENDIX 1

APPENDIX 3

APPENDIX 4

REFERENCES

vii

TABLE OF CONTENTS

Abstract iii



List of figures xi

List of tables xiii


Acknowledgments xvi


11 Background 3


13 Thesis outline 5










272 pH 24



viii








31 Substrates 45


312 Manure 45

32 Inoculum 45

















ix









and Inoculum 63


with CM 67








with CM 72











x










References 121

xi

LIST OF FIGURES




the SCT 14












Figure 31 (e) CM 47








for 83 days 68


xii







for 83 days 74












xiii

LIST OF TABLES


(2012-2017) 10





previous studies 26













with CM 85



xiv






BD Biodegradability




CM Chicken Manure















xv



SCT Sugarcane Trash





TS Total Solids



UV Ultra-Violet



VS Volatile Solids

xvi

ACKNOWLEDGMENTS











researcher












xvii
















AMAL BABU PUTHUMANA

18


1



2


3

11 Background


























4



























5












chicken manure



13 Thesis outline




project




6
















7



8


9
























Barbieri 2013)


10








Source (FAO 2018)

Crop


2012 2013 2014 2015 2016 2017

Maize 696 786 524 596 533 678

Rice 1031 1136 767 697 266 822

Cotton 5965 444 3901 197 2804 NA

Soybean 381 411 37 301 292 300

Sugarcane 3386 3293 3752 3765 4472 4534

Sunflower 402 303 27 295 23 27

Wheat 139021 129793 126132 123837 112822 1219115











11





12




















13









14



forming the SCB


2018)


15


















(Jain et al 2014)









16
















al 2015)










17













Table 22


18






























19









C6H10O4 + 2H2O C6H12O6 + H2 Eq21







et al 2019)





20













21






CO2 + 4H2 CH4 + 2H2O Eq28

HCO-3 + 4H2 + H+ CH4 + 3H2O Eq29















22





2017)











Table 23








23






More stable

Less energy input









important


24

272 pH













Cheon 2008)


and Stetter 1982)





Jones 2008)


2008)







25



























26


studies




























27





Stroeve 2009)












28



























29





















Zeeman 2009)







30





31


Sl

No

Pre-treatment

method

Lignocellulosic

substrates

Pre-treatment

condition

CH4 production

after pre-

treatment

(mLCH4gVSadded)

Increase in

CH4 ()

References




Balsari 2012)








2016)


Olive mill solid

waste




Borja 2016)


32


Morken 2012)


MHz 1600 W



2011)


MHz 1600 W





and Carrere 2012)



2012)





33



























34



























35



























36












AD process






37


wastes

Sl

No


conditions

Feed Ratios

tested


1 Sugarcane leaves

+ Diary cow

manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 7525

5050 2575

0100 (VS basis)


LCH4kgVSadded)

(Mokomele et al

2019)

2 Sugarcane

Bagasse + Diary

cow manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 5050

0100 (VS basis)


LCH4kgVSadded)

(Mokomele et al

2019)

3 Sugarcane leaves

+ Diary cow

manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 7525

5050 2575

0100 (VS basis)

NA 7525 (292

LCH4kgVSadded)

(Mokomele et al

2019)

4 Switchgrass +

Diary manure

500 mL Mesophilic

temperature ISR =

067 30 days

1000 7525

5050 2575

0100 (TS basis)



mono-digestion of

switchgrass

(Zheng et al 2015)

5 Salix viminalis +

Cow manure

1125 mL

mesophilic

temperature ISR =

067 57 days

1000 8020

7030 6040

5050 4060

3070 2080

0100 (VS basis)


mLCH4gVS)


mLgVS) has 23



38

improvement over

untreated salix

6 Corn Stover + Pig

Manure

250 mL Mesophilic

temperature ISR =

026 70 days

1000 8020

6733 5050

3367 2080

0100 (VS basis)

NA 0100 (889

mLCH4gVS)

(Li et al 2018b)

7 Cotton stalk +

swine manure

500 mL Mesophilic

temperature ISR =

025 45 days

1000 7525

5050 2575 (TS

basis)


in yield when

compared to control

(102 mLCH4gVS)

(Cheng and Zhong

2014)

8 Apple waste +

Chicken manure

250 mL Mesophilic

temperature ISR =

026 70 days

1000 8020

6733 5050

3367 2080

0100 (VS basis)

NA 3367 (827

mLCH4gVS)

(Li et al 2018b)

9 Whey + cow

manure

400 mL Mesophilic

temperature 55 days

1000 8515

7525 5050

2575 1585

0100 (VS basis)

NA 8515 (266

mLCH4gVS)

(Vivekanand Mulat

Eijsink and Horn

2018)


39




















al 2017)







40







al 2017)


41







2 Grass Cow manure














6

Organic fraction of








42


















with CM


43



44


45

31 Substrates












312 Manure




32 Inoculum








46



























47





Sample Untreated

SCT ()

Pre-treated

SCT ()

Untreated

SCB ()

Pre-treated

SCB ()

CM

()

















CM 0 0 0 0 100


48









49























Batstone 2015)


50

























51


(PO43-)

















52











53










54

37 Data Processing

371 Theoretical BMP







Fdz-Polanco 2015)


tttEq31





ᆒt =ᆒ썈υ

Eq32









55


using equation 33










Sung 2012)


Eq34


Eq35









56







Eq36
















tt Eq37


57



υ =

tt Eq38










Eq310









58






al 2014)

= t 㢐t) Eq311













= t

t

Eq312





59












60

1


61



62


63


inoculum

























64

















study










65






Bolantildeos 2007)



SCT

Pre-treated

SCT

Untreated

SCB

Pre-treated

SCBInoculum

TS 721 913 872 828 819 19

VS TS 807 909 925 894 951 722

COD gl 7487 11598 12099 7939 9156 211

TSVS - 12 11 11 11 11 14

CN ratio - 124 831 953 1465 1919 49

Glucan ND 307 463 351 428 ND

Xylan ND 168 117 20 146 ND


Lignin ND 298 283 315 282 ND

TKP gPkg 82 11 07 05 06 312

TKN gNkg 836 97 108 512 71 1113







66



SCT

Pre-treated

SCT

Untreated

SCB

Pre-treated

SCBInoculum

Al mgg 103 123 099 084 024 817

As mgg 000 000 000 000 000 000

B mgg 003 000 000 000 000 004

Ba mgg 004 002 002 001 001 019

Ca mgg 1687 312 167 025 015 1973

Cd mgg 000 000 000 000 000 000

Co mgg 000 000 000 000 000 001

Cr mgg 004 001 001 001 001 005

Cu mgg 011 000 000 000 000 056

Fe mgg 107 092 019 086 026 1972

K mgg 1977 601 283 293 076 1582

Mg mgg 461 188 138 036 028 666

Mn mgg 036 010 006 005 003 017

Mo mgg 001 000 000 000 000 003

Na mgg 171 007 034 001 004 1644

Ni mgg 002 000 000 000 000 005

P mgg 796 101 041 015 010 2735

Pb mgg 001 000 000 000 000 003

S mgg 321 082 809 018 524 1469

Se mgg 001 000 001 000 001 000

Zn mgg 027 000 000 000 000 067









67





CM













al 2015)







68




Untreated SCT














69



























70


















71



























72



digested with CM




CM








73






















74

















75















untreated SCB












76
















mono-digestion


77
















78



























79




Pre-treated SCBCM






2015)












80




Untreated

SCTCM

1000 5552 1444 2018 215 353 1960 28

7525 5444 1307 1791 247 307 1671 67

5050 5336 1178 1582 248 308 1642 -38

2575 5228 1074 1413 244 270 1413 00

Pre-treated

SCTCM

1000 5073 1625 2075a 265 410 2082 -03

7525 5085 1614 2065a 252 391 1988 37

5050 5097 1318 1691 235 319 1628 37

2575 5109 1106 1422 190 255 1302 85

Untreated

SCBCM

1000 4364 1812 1990 262 421 1839 76

7525 4553 1615 1850 237 391 1780 38

5050 4742 1429 1706 232 350 1660 27

2575 4931 1302 1615 179 301 1486 80

Pre-treated

SCBCM

1000 2882 3110 2256 251 715 2061 86

7525 3442 2304 1995 220 538 1852 72

5050 4000 1729 1741 235 414 1656 49

2575 4561 1357 1558 194 348 1586 -18

CM 1000 5121 895 1153 216 206 1056 84


81



























82












83




84


























85




136 mgL


CM


mgL mgL mgL mgL

Untreated

SCTCM

1000 0 548 16160 1535

7525 0 554 16160 1465

5050 0 550 16867 1444

2575 0 553 17271 1404

Pre-treated

SCTCM

1000 0 556 15655 1636

7525 0 547 16059 1596

5050 0 551 16362 1505

2575 0 550 16867 1414

Untreated

SCBCM

1000 0 548 16362 1646

7525 0 561 16564 1586

5050 0 535 17069 1515

2575 0 551 17271 1444

Pre-treated

SCBCM

1000 0 364 15756 1566

7525 0 549 16665 1576

5050 0 543 16968 1505

2575 0 552 17271 1444

CM 1000 0 553 17473 1364






86
























modelled data


87


SubstratesFeed


B B0 Rm λR2



USCTCM 100 0 20175 19347 712 264 09942 20753 005 266 09988

75 25 17909 16507 623 128 09772 17679 005 183 09956

50 50 15820 14442 648 122 09701 15343 006 188 09911

25 75 14128 12653 858 198 09727 13238 010 255 09898

PSCTCM 100 0 20748 19853 2142 168 09927 20397 015 185 09867

75 25 20650 19748 1819 123 09905 20331 014 167 09905

50 50 16910 16360 1766 142 09920 16782 016 174 09896

25 75 14224 13498 1521 157 09887 13889 016 178 09881

USCBCM 100 0 19900 19394 797 521 09972 20561 006 538 09889

75 25 18503 17700 528 197 09864 19472 004 197 09989

50 50 17056 16131 492 045 09734 17440 004 119 09940

25 75 16154 14420 767 109 09691 15218 003 178 09897

PSCBCM 100 0 22557 21982 702 154 09815 23570 004 188 09934

75 25 19955 19203 660 007 09787 20400 005 089 09941

50 50 17407 16241 718 015 09745 17090 007 110 09928

25 75 15577 14114 806 040 09624 14869 008 119 09837

CM 100 0 11532 10787 1237 280 09858 11135 016 299 09919


88





















89






90



























91






92







1000 7525 5050 2575




















93



production


94

1


95



96


97





























98





























99



SCTSCB with CM





100

101

APPENDIX 1






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean


Upper

Bound

100USCT +

0CM

3 2017333 223010 128755 1961935 2072732 19920 20340

75USCT +

25CM

3 1791000 34641 20000 1782395 1799605 17890 17950

50USCT +

50CM

3 1581667 85049 49103 1560539 1602794 15730 15900

25USCT +

75CM

3 1413000 338674 195533 1328869 1497131 13750 14400

0USCT +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1591267 3092455 798468 1420012 1762521 11460 20340





102


adjusted df

938 4 4174 521


ANOVA

Methane Yields


Between Groups 13352489 4 3338122 924688 000


Total 13388589 14




Mean

Difference (I-

J) Std Error Sig


Lower

Bound

Upper

Bound

Tukey

HSD

100USCT

+ 0CM

75USCT +

25CM

2263333 155134 000 175277 277389

50USCT +

50CM

4356667 155134 000 384611 486723

25USCT +

75CM

6043333 155134 000 553277 655389

0USCT +

100CM

8640000 155134 000 812944 915056

75USCT +

25CM

100USCT +

0CM

-2263333 155134 000 -277389 -175277

50USCT +

50CM

2093333 155134 000 158277 260389

25USCT +

75CM

3780000 155134 000 326944 429056

0USCT +

100CM

6376667 155134 000 586611 688723

103

50USCT +

50CM

100USCT +

0CM

-4356667 155134 000 -486723 -384611

75USCT +

25CM

-2093333 155134 000 -260389 -158277

25USCT +

75CM

1686667 155134 000 117611 219723

0USCT +

100CM

4283333 155134 000 377277 479389

25USCT +

75CM

100USCT +

0CM

-6043333 155134 000 -655389 -553277

75USCT +

25CM

-3780000 155134 000 -429056 -326944

50USCT +

50CM

-1686667 155134 000 -219723 -117611

0USCT +

100CM

2596667 155134 000 208611 310723

0USCT +

100CM

100USCT +

0CM

-8640000 155134 000 -915056 -812944

75USCT +

25CM

-6376667 155134 000 -688723 -586611

50USCT +

50CM

-4283333 155134 000 -479389 -377277

25USCT +

75CM

-2596667 155134 000 -310723 -208611

LSD 100USCT

+ 0CM

75USCT +

25CM

2263333 155134 000 191767 260899

50USCT +

50CM

4356667 155134 000 401101 470233

25USCT +

75CM

6043333 155134 000 569767 638899

0USCT +

100CM

8640000 155134 000 829434 898566

75USCT +

25CM

100USCT +

0CM

-2263333 155134 000 -260899 -191767

104

50USCT +

50CM

2093333 155134 000 174767 243899

25USCT +

75CM

3780000 155134 000 343434 412566

0USCT +

100CM

6376667 155134 000 603101 672233

50USCT +

50CM

100USCT +

0CM

-4356667 155134 000 -470233 -401101

75USCT +

25CM

-2093333 155134 000 -243899 -174767

25USCT +

75CM

1686667 155134 000 134101 203233

0USCT +

100CM

4283333 155134 000 393767 462899

25USCT +

75CM

100USCT +

0CM

-6043333 155134 000 -638899 -569767

75USCT +

25CM

-3780000 155134 000 -412566 -343434

50USCT +

50CM

-1686667 155134 000 -203233 -134101

0USCT +

100CM

2596667 155134 000 225101 294233

0USCT +

100CM

100USCT +

0CM

-8640000 155134 000 -898566 -829434

75USCT +

25CM

-6376667 155134 000 -672233 -603101

50USCT +

50CM

-4283333 155134 000 -462899 -393767

25USCT +

75CM

-2596667 155134 000 -294233 -225101

Dunnett

t (2-sided)b

100USCT

+ 0CM

0USCT +

100CM

8640000 155134 000 819159 908841

75USCT +

25CM

0USCT +

100CM

6376667 155134 000 592825 682508

105

50USCT +

50CM

0USCT +

100CM

4283333 155134 000 383492 473175

25USCT +

75CM

0USCT +

100CM

2596667 155134 000 214825 304508



106

APPENDIX2






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100PSCT +

0CM

3 2075000 629603 363501 1918598 2231402 20050 21270

75PSCT +

25CM

3 2065000 580259 335012 1920856 2209144 20310 21320

50PSCT +

50CM

3 1691000 272213 157162 1623379 1758621 16600 17110

25PSCT +

75CM

3 1422667 250067 144376 1360547 1484787 13980 14480

0PSCT +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1681400 3743018 966443 1474119 1888681 11460 21320





107


adjusted df

501 4 4789 739


ANOVA

Methane Yields


Between Groups 19438783 4 4859696 276948 000


Total 19614256 14




Mean

Difference (I-

J) Std Error Sig


Lower

Bound

Upper

Bound

Tukey

HSD

100PSCT

+ 0CM

75PSCT +

25CM

100000 342027 998 -102564 122564

50PSCT +

50CM

3840000 342027 000 271436 496564

25PSCT +

75CM

6523333 342027 000 539770 764897

0PSCT +

100CM

9216667 342027 000 809103 1034230

75PSCT +

25CM

100PSCT +

0CM

-100000 342027 998 -122564 102564

50PSCT +

50CM

3740000 342027 000 261436 486564

25PSCT +

75CM

6423333 342027 000 529770 754897

108

0PSCT +

100CM

9116667 342027 000 799103 1024230

50PSCT +

50CM

100PSCT +

0CM

-3840000 342027 000 -496564 -271436

75PSCT +

25CM

-3740000 342027 000 -486564 -261436

25PSCT +

75CM

2683333 342027 000 155770 380897

0PSCT +

100CM

5376667 342027 000 425103 650230

25PSCT +

75CM

100PSCT +

0CM

-6523333 342027 000 -764897 -539770

75PSCT +

25CM

-6423333 342027 000 -754897 -529770

50PSCT +

50CM

-2683333 342027 000 -380897 -155770

0PSCT +

100CM

2693333 342027 000 156770 381897

0PSCT +

100CM

100PSCT +

0CM

-9216667 342027 000 -1034230 -809103

75PSCT +

25CM

-9116667 342027 000 -1024230 -799103

50PSCT +

50CM

-5376667 342027 000 -650230 -425103

25PSCT +

75CM

-2693333 342027 000 -381897 -156770

LSD 100PSCT

+ 0CM

75PSCT +

25CM

100000 342027 776 -66208 86208

50PSCT +

50CM

3840000 342027 000 307792 460208

25PSCT +

75CM

6523333 342027 000 576125 728542

0PSCT +

100CM

9216667 342027 000 845458 997875

109

75PSCT +

25CM

100PSCT +

0CM

-100000 342027 776 -86208 66208

50PSCT +

50CM

3740000 342027 000 297792 450208

25PSCT +

75CM

6423333 342027 000 566125 718542

0PSCT +

100CM

9116667 342027 000 835458 987875

50PSCT +

50CM

100PSCT +

0CM

-3840000 342027 000 -460208 -307792

75PSCT +

25CM

-3740000 342027 000 -450208 -297792

25PSCT +

75CM

2683333 342027 000 192125 344542

0PSCT +

100CM

5376667 342027 000 461458 613875

25PSCT +

75CM

100PSCT +

0CM

-6523333 342027 000 -728542 -576125

75PSCT +

25CM

-6423333 342027 000 -718542 -566125

50PSCT +

50CM

-2683333 342027 000 -344542 -192125

0PSCT +

100CM

2693333 342027 000 193125 345542

0PSCT +

100CM

100PSCT +

0CM

-9216667 342027 000 -997875 -845458

75PSCT +

25CM

-9116667 342027 000 -987875 -835458

50PSCT +

50CM

-5376667 342027 000 -613875 -461458

25PSCT +

75CM

-2693333 342027 000 -345542 -193125

100PSCT

+ 0CM

0PSCT +

100CM

9216667 342027 000 822805 1020529

110

Dunnett

t (2-

sided)b

75PSCT +

25CM

0PSCT +

100CM

9116667 342027 000 812805 1010529

50PSCT +

50CM

0PSCT +

100CM

5376667 342027 000 438805 636529

25PSCT +

75CM

0PSCT +

100CM

2693333 342027 000 170471 368195



111

APPENDIX 3






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100USCB +

0CM

3 1990000 481248 277849 1870451 2109549 19400 20360

75USCB +

25CM

3 1850333 221209 127715 1795382 1905285 18270 18710

50USCB +

50CM

3 1705667 51316 29627 1692919 1718414 17000 17100

25USCB +

75CM

3 1615333 187172 108064 1568837 1661829 15940 16290

0USCB +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1662933 2957005 763495 1499180 1826687 11460 20360





112

Based on Median and

with adjusted df

1518 4 4354 339


ANOVA

Methane Yields


Between Groups 12176263 4 3044066 467120 000


Total 12241429 14




Mean



Lower

Bound

Upper

Bound

Tukey

HSD

100USCB

+ 0CM

75USCB +

25CM

1396667 208433 000 71070 208264

50USCB +

50CM

2843333 208433 000 215736 352930

25USCB +

75CM

3746667 208433 000 306070 443264

0USCB +

100CM

8366667 208433 000 768070 905264

75USCB +

25CM

100USCB +

0CM

-1396667 208433 000 -208264 -71070

50USCB +

50CM

1446667 208433 000 76070 213264

25USCB +

75CM

2350000 208433 000 166403 303597

113

0USCB +

100CM

6970000 208433 000 628403 765597

50USCB +

50CM

100USCB +

0CM

-2843333 208433 000 -352930 -215736

75USCB +

25CM

-1446667 208433 000 -213264 -76070

25USCB +

75CM

903333 208433 000 21736 158930

0USCB +

100CM

5523333 208433 000 483736 620930

25USCB +

75CM

100USCB +

0CM

-3746667 208433 000 -443264 -306070

75USCB +

25CM

-2350000 208433 000 -303597 -166403

50USCB +

50CM

-903333 208433 000 -158930 -21736

0USCB +

100CM

4620000 208433 000 393403 530597

0USCB +

100CM

100USCB +

0CM

-8366667 208433 000 -905264 -768070

75USCB +

25CM

-6970000 208433 000 -765597 -628403

50USCB +

50CM

-5523333 208433 000 -620930 -483736

25USCB +

75CM

-4620000 208433 000 -530597 -393403

LSD 100USCB

+ 0CM

75USCB +

25CM

1396667 208433 000 93225 186109

50USCB +

50CM

2843333 208433 000 237891 330775

25USCB +

75CM

3746667 208433 000 328225 421109

0USCB +

100CM

8366667 208433 000 790225 883109

114

75USCB +

25CM

100USCB +

0CM

-1396667 208433 000 -186109 -93225

50USCB +

50CM

1446667 208433 000 98225 191109

25USCB +

75CM

2350000 208433 000 188558 281442

0USCB +

100CM

6970000 208433 000 650558 743442

50USCB +

50CM

100USCB +

0CM

-2843333 208433 000 -330775 -237891

75USCB +

25CM

-1446667 208433 000 -191109 -98225

25USCB +

75CM

903333 208433 000 43891 136775

0USCB +

100CM

5523333 208433 000 505891 598775

25USCB +

75CM

100USCB +

0CM

-3746667 208433 000 -421109 -328225

75USCB +

25CM

-2350000 208433 000 -281442 -188558

50USCB +

50CM

-903333 208433 000 -136775 -43891

0USCB +

100CM

4620000 208433 000 415558 508442

0USCB +

100CM

100USCB +

0CM

-8366667 208433 000 -883109 -790225

75USCB +

25CM

-6970000 208433 000 -743442 -650558

50USCB +

50CM

-5523333 208433 000 -598775 -505891

25USCB +

75CM

-4620000 208433 000 -508442 -415558

100USCB

+ 0CM

0USCB +

100CM

8366667 208433 000 776419 896914

115

Dunnett

t (2-

sided)b

75USCB +

25CM

0USCB +

100CM

6970000 208433 000 636753 757247

50USCB +

50CM

0USCB +

100CM

5523333 208433 000 492086 612581

25USCB +

75CM

0USCB +

100CM

4620000 208433 000 401753 522247



116

APPENDIX 4






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100PSCB +

0CM

3 2255667 539290 311359 2121700 2389634 21940 22940

75PSCB +

25CM

3 1995667 427239 246667 1889535 2101799 19470 20270

50PSCB +

50CM

3 1740333 234592 135442 1682057 1798609 17140 17590

25PSCB +

75CM

3 1557667 281129 162310 1487830 1627503 15390 15900

0PSCB +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1740533 3908233 1009101 1524103 1956964 11460 22940





117


adjusted df

407 4 5808 798


ANOVA

Methane Yields


Between Groups 21260984 4 5315246 432087 000


Total 21383997 14




Mean



Lower

Bound

Upper

Bound

Tukey

HSD

100PSCB

+ 0CM

75PSCB +

25CM

2600000 286372 000 165753 354247

50PSCB +

50CM

5153333 286372 000 421086 609581

25PSCB +

75CM

6980000 286372 000 603753 792247

0PSCB +

100CM

11023333 286372 000 1008086 1196581

75PSCB +

25CM

100PSCB

+ 0CM

-2600000 286372 000 -354247 -165753

50PSCB +

50CM

2553333 286372 000 161086 349581

25PSCB +

75CM

4380000 286372 000 343753 532247

118

0PSCB +

100CM

8423333 286372 000 748086 936581

50PSCB +

50CM

100PSCB

+ 0CM

-5153333 286372 000 -609581 -421086

75PSCB +

25CM

-2553333 286372 000 -349581 -161086

25PSCB +

75CM

1826667 286372 000 88419 276914

0PSCB +

100CM

5870000 286372 000 492753 681247

25PSCB +

75CM

100PSCB

+ 0CM

-6980000 286372 000 -792247 -603753

75PSCB +

25CM

-4380000 286372 000 -532247 -343753

50PSCB +

50CM

-1826667 286372 000 -276914 -88419

0PSCB +

100CM

4043333 286372 000 310086 498581

0PSCB +

100CM

100PSCB

+ 0CM

-11023333 286372 000 -1196581 -1008086

75PSCB +

25CM

-8423333 286372 000 -936581 -748086

50PSCB +

50CM

-5870000 286372 000 -681247 -492753

25PSCB +

75CM

-4043333 286372 000 -498581 -310086

LSD 100PSCB

+ 0CM

75PSCB +

25CM

2600000 286372 000 196192 323808

50PSCB +

50CM

5153333 286372 000 451526 579141

25PSCB +

75CM

6980000 286372 000 634192 761808

0PSCB +

100CM

11023333 286372 000 1038526 1166141

119

75PSCB +

25CM

100PSCB

+ 0CM

-2600000 286372 000 -323808 -196192

50PSCB +

50CM

2553333 286372 000 191526 319141

25PSCB +

75CM

4380000 286372 000 374192 501808

0PSCB +

100CM

8423333 286372 000 778526 906141

50PSCB +

50CM

100PSCB

+ 0CM

-5153333 286372 000 -579141 -451526

75PSCB +

25CM

-2553333 286372 000 -319141 -191526

25PSCB +

75CM

1826667 286372 000 118859 246474

0PSCB +

100CM

5870000 286372 000 523192 650808

25PSCB +

75CM

100PSCB

+ 0CM

-6980000 286372 000 -761808 -634192

75PSCB +

25CM

-4380000 286372 000 -501808 -374192

50PSCB +

50CM

-1826667 286372 000 -246474 -118859

0PSCB +

100CM

4043333 286372 000 340526 468141

0PSCB +

100CM

100PSCB

+ 0CM

-11023333 286372 000 -1166141 -1038526

75PSCB +

25CM

-8423333 286372 000 -906141 -778526

50PSCB +

50CM

-5870000 286372 000 -650808 -523192

25PSCB +

75CM

-4043333 286372 000 -468141 -340526

100PSCB

+ 0CM

0PSCB +

100CM

11023333 286372 000 1019558 1185109

120

Dunnett

t (2-

sided)b

75PSCB +

25CM

0PSCB +

100CM

8423333 286372 000 759558 925109

50PSCB +

50CM

0PSCB +

100CM

5870000 286372 000 504225 669775

25PSCB +

75CM

0PSCB +

100CM

4043333 286372 000 321558 487109



121

REFERENCES











122












123













124












125













126












127












128












129













130












131













132












133














134













135













136




ABSTRACT


LIST OF FIGURES

LIST OF TABLES


ACKNOWLEDGMENTS

APPENDIX 1

APPENDIX 3

APPENDIX 4

REFERENCES

viii








31 Substrates 45


312 Manure 45

32 Inoculum 45

















ix









and Inoculum 63


with CM 67








with CM 72











x










References 121

xi

LIST OF FIGURES




the SCT 14












Figure 31 (e) CM 47








for 83 days 68


xii







for 83 days 74












xiii

LIST OF TABLES


(2012-2017) 10





previous studies 26













with CM 85



xiv






BD Biodegradability




CM Chicken Manure















xv



SCT Sugarcane Trash





TS Total Solids



UV Ultra-Violet



VS Volatile Solids

xvi

ACKNOWLEDGMENTS











researcher












xvii
















AMAL BABU PUTHUMANA

18


1



2


3

11 Background


























4



























5












chicken manure



13 Thesis outline




project




6
















7



8


9
























Barbieri 2013)


10








Source (FAO 2018)

Crop


2012 2013 2014 2015 2016 2017

Maize 696 786 524 596 533 678

Rice 1031 1136 767 697 266 822

Cotton 5965 444 3901 197 2804 NA

Soybean 381 411 37 301 292 300

Sugarcane 3386 3293 3752 3765 4472 4534

Sunflower 402 303 27 295 23 27

Wheat 139021 129793 126132 123837 112822 1219115











11





12




















13









14



forming the SCB


2018)


15


















(Jain et al 2014)









16
















al 2015)










17













Table 22


18






























19









C6H10O4 + 2H2O C6H12O6 + H2 Eq21







et al 2019)





20













21






CO2 + 4H2 CH4 + 2H2O Eq28

HCO-3 + 4H2 + H+ CH4 + 3H2O Eq29















22





2017)











Table 23








23






More stable

Less energy input









important


24

272 pH













Cheon 2008)


and Stetter 1982)





Jones 2008)


2008)







25



























26


studies




























27





Stroeve 2009)












28



























29





















Zeeman 2009)







30





31


Sl

No

Pre-treatment

method

Lignocellulosic

substrates

Pre-treatment

condition

CH4 production

after pre-

treatment

(mLCH4gVSadded)

Increase in

CH4 ()

References




Balsari 2012)








2016)


Olive mill solid

waste




Borja 2016)


32


Morken 2012)


MHz 1600 W



2011)


MHz 1600 W





and Carrere 2012)



2012)





33



























34



























35



























36












AD process






37


wastes

Sl

No


conditions

Feed Ratios

tested


1 Sugarcane leaves

+ Diary cow

manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 7525

5050 2575

0100 (VS basis)


LCH4kgVSadded)

(Mokomele et al

2019)

2 Sugarcane

Bagasse + Diary

cow manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 5050

0100 (VS basis)


LCH4kgVSadded)

(Mokomele et al

2019)

3 Sugarcane leaves

+ Diary cow

manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 7525

5050 2575

0100 (VS basis)

NA 7525 (292

LCH4kgVSadded)

(Mokomele et al

2019)

4 Switchgrass +

Diary manure

500 mL Mesophilic

temperature ISR =

067 30 days

1000 7525

5050 2575

0100 (TS basis)



mono-digestion of

switchgrass

(Zheng et al 2015)

5 Salix viminalis +

Cow manure

1125 mL

mesophilic

temperature ISR =

067 57 days

1000 8020

7030 6040

5050 4060

3070 2080

0100 (VS basis)


mLCH4gVS)


mLgVS) has 23



38

improvement over

untreated salix

6 Corn Stover + Pig

Manure

250 mL Mesophilic

temperature ISR =

026 70 days

1000 8020

6733 5050

3367 2080

0100 (VS basis)

NA 0100 (889

mLCH4gVS)

(Li et al 2018b)

7 Cotton stalk +

swine manure

500 mL Mesophilic

temperature ISR =

025 45 days

1000 7525

5050 2575 (TS

basis)


in yield when

compared to control

(102 mLCH4gVS)

(Cheng and Zhong

2014)

8 Apple waste +

Chicken manure

250 mL Mesophilic

temperature ISR =

026 70 days

1000 8020

6733 5050

3367 2080

0100 (VS basis)

NA 3367 (827

mLCH4gVS)

(Li et al 2018b)

9 Whey + cow

manure

400 mL Mesophilic

temperature 55 days

1000 8515

7525 5050

2575 1585

0100 (VS basis)

NA 8515 (266

mLCH4gVS)

(Vivekanand Mulat

Eijsink and Horn

2018)


39




















al 2017)







40







al 2017)


41







2 Grass Cow manure














6

Organic fraction of








42


















with CM


43



44


45

31 Substrates












312 Manure




32 Inoculum








46



























47





Sample Untreated

SCT ()

Pre-treated

SCT ()

Untreated

SCB ()

Pre-treated

SCB ()

CM

()

















CM 0 0 0 0 100


48









49























Batstone 2015)


50

























51


(PO43-)

















52











53










54

37 Data Processing

371 Theoretical BMP







Fdz-Polanco 2015)


tttEq31





ᆒt =ᆒ썈υ

Eq32









55


using equation 33










Sung 2012)


Eq34


Eq35









56







Eq36
















tt Eq37


57



υ =

tt Eq38










Eq310









58






al 2014)

= t 㢐t) Eq311













= t

t

Eq312





59












60

1


61



62


63


inoculum

























64

















study










65






Bolantildeos 2007)



SCT

Pre-treated

SCT

Untreated

SCB

Pre-treated

SCBInoculum

TS 721 913 872 828 819 19

VS TS 807 909 925 894 951 722

COD gl 7487 11598 12099 7939 9156 211

TSVS - 12 11 11 11 11 14

CN ratio - 124 831 953 1465 1919 49

Glucan ND 307 463 351 428 ND

Xylan ND 168 117 20 146 ND


Lignin ND 298 283 315 282 ND

TKP gPkg 82 11 07 05 06 312

TKN gNkg 836 97 108 512 71 1113







66



SCT

Pre-treated

SCT

Untreated

SCB

Pre-treated

SCBInoculum

Al mgg 103 123 099 084 024 817

As mgg 000 000 000 000 000 000

B mgg 003 000 000 000 000 004

Ba mgg 004 002 002 001 001 019

Ca mgg 1687 312 167 025 015 1973

Cd mgg 000 000 000 000 000 000

Co mgg 000 000 000 000 000 001

Cr mgg 004 001 001 001 001 005

Cu mgg 011 000 000 000 000 056

Fe mgg 107 092 019 086 026 1972

K mgg 1977 601 283 293 076 1582

Mg mgg 461 188 138 036 028 666

Mn mgg 036 010 006 005 003 017

Mo mgg 001 000 000 000 000 003

Na mgg 171 007 034 001 004 1644

Ni mgg 002 000 000 000 000 005

P mgg 796 101 041 015 010 2735

Pb mgg 001 000 000 000 000 003

S mgg 321 082 809 018 524 1469

Se mgg 001 000 001 000 001 000

Zn mgg 027 000 000 000 000 067









67





CM













al 2015)







68




Untreated SCT














69



























70


















71



























72



digested with CM




CM








73






















74

















75















untreated SCB












76
















mono-digestion


77
















78



























79




Pre-treated SCBCM






2015)












80




Untreated

SCTCM

1000 5552 1444 2018 215 353 1960 28

7525 5444 1307 1791 247 307 1671 67

5050 5336 1178 1582 248 308 1642 -38

2575 5228 1074 1413 244 270 1413 00

Pre-treated

SCTCM

1000 5073 1625 2075a 265 410 2082 -03

7525 5085 1614 2065a 252 391 1988 37

5050 5097 1318 1691 235 319 1628 37

2575 5109 1106 1422 190 255 1302 85

Untreated

SCBCM

1000 4364 1812 1990 262 421 1839 76

7525 4553 1615 1850 237 391 1780 38

5050 4742 1429 1706 232 350 1660 27

2575 4931 1302 1615 179 301 1486 80

Pre-treated

SCBCM

1000 2882 3110 2256 251 715 2061 86

7525 3442 2304 1995 220 538 1852 72

5050 4000 1729 1741 235 414 1656 49

2575 4561 1357 1558 194 348 1586 -18

CM 1000 5121 895 1153 216 206 1056 84


81



























82












83




84


























85




136 mgL


CM


mgL mgL mgL mgL

Untreated

SCTCM

1000 0 548 16160 1535

7525 0 554 16160 1465

5050 0 550 16867 1444

2575 0 553 17271 1404

Pre-treated

SCTCM

1000 0 556 15655 1636

7525 0 547 16059 1596

5050 0 551 16362 1505

2575 0 550 16867 1414

Untreated

SCBCM

1000 0 548 16362 1646

7525 0 561 16564 1586

5050 0 535 17069 1515

2575 0 551 17271 1444

Pre-treated

SCBCM

1000 0 364 15756 1566

7525 0 549 16665 1576

5050 0 543 16968 1505

2575 0 552 17271 1444

CM 1000 0 553 17473 1364






86
























modelled data


87


SubstratesFeed


B B0 Rm λR2



USCTCM 100 0 20175 19347 712 264 09942 20753 005 266 09988

75 25 17909 16507 623 128 09772 17679 005 183 09956

50 50 15820 14442 648 122 09701 15343 006 188 09911

25 75 14128 12653 858 198 09727 13238 010 255 09898

PSCTCM 100 0 20748 19853 2142 168 09927 20397 015 185 09867

75 25 20650 19748 1819 123 09905 20331 014 167 09905

50 50 16910 16360 1766 142 09920 16782 016 174 09896

25 75 14224 13498 1521 157 09887 13889 016 178 09881

USCBCM 100 0 19900 19394 797 521 09972 20561 006 538 09889

75 25 18503 17700 528 197 09864 19472 004 197 09989

50 50 17056 16131 492 045 09734 17440 004 119 09940

25 75 16154 14420 767 109 09691 15218 003 178 09897

PSCBCM 100 0 22557 21982 702 154 09815 23570 004 188 09934

75 25 19955 19203 660 007 09787 20400 005 089 09941

50 50 17407 16241 718 015 09745 17090 007 110 09928

25 75 15577 14114 806 040 09624 14869 008 119 09837

CM 100 0 11532 10787 1237 280 09858 11135 016 299 09919


88





















89






90



























91






92







1000 7525 5050 2575




















93



production


94

1


95



96


97





























98





























99



SCTSCB with CM





100

101

APPENDIX 1






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean


Upper

Bound

100USCT +

0CM

3 2017333 223010 128755 1961935 2072732 19920 20340

75USCT +

25CM

3 1791000 34641 20000 1782395 1799605 17890 17950

50USCT +

50CM

3 1581667 85049 49103 1560539 1602794 15730 15900

25USCT +

75CM

3 1413000 338674 195533 1328869 1497131 13750 14400

0USCT +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1591267 3092455 798468 1420012 1762521 11460 20340





102


adjusted df

938 4 4174 521


ANOVA

Methane Yields


Between Groups 13352489 4 3338122 924688 000


Total 13388589 14




Mean

Difference (I-

J) Std Error Sig


Lower

Bound

Upper

Bound

Tukey

HSD

100USCT

+ 0CM

75USCT +

25CM

2263333 155134 000 175277 277389

50USCT +

50CM

4356667 155134 000 384611 486723

25USCT +

75CM

6043333 155134 000 553277 655389

0USCT +

100CM

8640000 155134 000 812944 915056

75USCT +

25CM

100USCT +

0CM

-2263333 155134 000 -277389 -175277

50USCT +

50CM

2093333 155134 000 158277 260389

25USCT +

75CM

3780000 155134 000 326944 429056

0USCT +

100CM

6376667 155134 000 586611 688723

103

50USCT +

50CM

100USCT +

0CM

-4356667 155134 000 -486723 -384611

75USCT +

25CM

-2093333 155134 000 -260389 -158277

25USCT +

75CM

1686667 155134 000 117611 219723

0USCT +

100CM

4283333 155134 000 377277 479389

25USCT +

75CM

100USCT +

0CM

-6043333 155134 000 -655389 -553277

75USCT +

25CM

-3780000 155134 000 -429056 -326944

50USCT +

50CM

-1686667 155134 000 -219723 -117611

0USCT +

100CM

2596667 155134 000 208611 310723

0USCT +

100CM

100USCT +

0CM

-8640000 155134 000 -915056 -812944

75USCT +

25CM

-6376667 155134 000 -688723 -586611

50USCT +

50CM

-4283333 155134 000 -479389 -377277

25USCT +

75CM

-2596667 155134 000 -310723 -208611

LSD 100USCT

+ 0CM

75USCT +

25CM

2263333 155134 000 191767 260899

50USCT +

50CM

4356667 155134 000 401101 470233

25USCT +

75CM

6043333 155134 000 569767 638899

0USCT +

100CM

8640000 155134 000 829434 898566

75USCT +

25CM

100USCT +

0CM

-2263333 155134 000 -260899 -191767

104

50USCT +

50CM

2093333 155134 000 174767 243899

25USCT +

75CM

3780000 155134 000 343434 412566

0USCT +

100CM

6376667 155134 000 603101 672233

50USCT +

50CM

100USCT +

0CM

-4356667 155134 000 -470233 -401101

75USCT +

25CM

-2093333 155134 000 -243899 -174767

25USCT +

75CM

1686667 155134 000 134101 203233

0USCT +

100CM

4283333 155134 000 393767 462899

25USCT +

75CM

100USCT +

0CM

-6043333 155134 000 -638899 -569767

75USCT +

25CM

-3780000 155134 000 -412566 -343434

50USCT +

50CM

-1686667 155134 000 -203233 -134101

0USCT +

100CM

2596667 155134 000 225101 294233

0USCT +

100CM

100USCT +

0CM

-8640000 155134 000 -898566 -829434

75USCT +

25CM

-6376667 155134 000 -672233 -603101

50USCT +

50CM

-4283333 155134 000 -462899 -393767

25USCT +

75CM

-2596667 155134 000 -294233 -225101

Dunnett

t (2-sided)b

100USCT

+ 0CM

0USCT +

100CM

8640000 155134 000 819159 908841

75USCT +

25CM

0USCT +

100CM

6376667 155134 000 592825 682508

105

50USCT +

50CM

0USCT +

100CM

4283333 155134 000 383492 473175

25USCT +

75CM

0USCT +

100CM

2596667 155134 000 214825 304508



106

APPENDIX2






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100PSCT +

0CM

3 2075000 629603 363501 1918598 2231402 20050 21270

75PSCT +

25CM

3 2065000 580259 335012 1920856 2209144 20310 21320

50PSCT +

50CM

3 1691000 272213 157162 1623379 1758621 16600 17110

25PSCT +

75CM

3 1422667 250067 144376 1360547 1484787 13980 14480

0PSCT +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1681400 3743018 966443 1474119 1888681 11460 21320





107


adjusted df

501 4 4789 739


ANOVA

Methane Yields


Between Groups 19438783 4 4859696 276948 000


Total 19614256 14




Mean

Difference (I-

J) Std Error Sig


Lower

Bound

Upper

Bound

Tukey

HSD

100PSCT

+ 0CM

75PSCT +

25CM

100000 342027 998 -102564 122564

50PSCT +

50CM

3840000 342027 000 271436 496564

25PSCT +

75CM

6523333 342027 000 539770 764897

0PSCT +

100CM

9216667 342027 000 809103 1034230

75PSCT +

25CM

100PSCT +

0CM

-100000 342027 998 -122564 102564

50PSCT +

50CM

3740000 342027 000 261436 486564

25PSCT +

75CM

6423333 342027 000 529770 754897

108

0PSCT +

100CM

9116667 342027 000 799103 1024230

50PSCT +

50CM

100PSCT +

0CM

-3840000 342027 000 -496564 -271436

75PSCT +

25CM

-3740000 342027 000 -486564 -261436

25PSCT +

75CM

2683333 342027 000 155770 380897

0PSCT +

100CM

5376667 342027 000 425103 650230

25PSCT +

75CM

100PSCT +

0CM

-6523333 342027 000 -764897 -539770

75PSCT +

25CM

-6423333 342027 000 -754897 -529770

50PSCT +

50CM

-2683333 342027 000 -380897 -155770

0PSCT +

100CM

2693333 342027 000 156770 381897

0PSCT +

100CM

100PSCT +

0CM

-9216667 342027 000 -1034230 -809103

75PSCT +

25CM

-9116667 342027 000 -1024230 -799103

50PSCT +

50CM

-5376667 342027 000 -650230 -425103

25PSCT +

75CM

-2693333 342027 000 -381897 -156770

LSD 100PSCT

+ 0CM

75PSCT +

25CM

100000 342027 776 -66208 86208

50PSCT +

50CM

3840000 342027 000 307792 460208

25PSCT +

75CM

6523333 342027 000 576125 728542

0PSCT +

100CM

9216667 342027 000 845458 997875

109

75PSCT +

25CM

100PSCT +

0CM

-100000 342027 776 -86208 66208

50PSCT +

50CM

3740000 342027 000 297792 450208

25PSCT +

75CM

6423333 342027 000 566125 718542

0PSCT +

100CM

9116667 342027 000 835458 987875

50PSCT +

50CM

100PSCT +

0CM

-3840000 342027 000 -460208 -307792

75PSCT +

25CM

-3740000 342027 000 -450208 -297792

25PSCT +

75CM

2683333 342027 000 192125 344542

0PSCT +

100CM

5376667 342027 000 461458 613875

25PSCT +

75CM

100PSCT +

0CM

-6523333 342027 000 -728542 -576125

75PSCT +

25CM

-6423333 342027 000 -718542 -566125

50PSCT +

50CM

-2683333 342027 000 -344542 -192125

0PSCT +

100CM

2693333 342027 000 193125 345542

0PSCT +

100CM

100PSCT +

0CM

-9216667 342027 000 -997875 -845458

75PSCT +

25CM

-9116667 342027 000 -987875 -835458

50PSCT +

50CM

-5376667 342027 000 -613875 -461458

25PSCT +

75CM

-2693333 342027 000 -345542 -193125

100PSCT

+ 0CM

0PSCT +

100CM

9216667 342027 000 822805 1020529

110

Dunnett

t (2-

sided)b

75PSCT +

25CM

0PSCT +

100CM

9116667 342027 000 812805 1010529

50PSCT +

50CM

0PSCT +

100CM

5376667 342027 000 438805 636529

25PSCT +

75CM

0PSCT +

100CM

2693333 342027 000 170471 368195



111

APPENDIX 3






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100USCB +

0CM

3 1990000 481248 277849 1870451 2109549 19400 20360

75USCB +

25CM

3 1850333 221209 127715 1795382 1905285 18270 18710

50USCB +

50CM

3 1705667 51316 29627 1692919 1718414 17000 17100

25USCB +

75CM

3 1615333 187172 108064 1568837 1661829 15940 16290

0USCB +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1662933 2957005 763495 1499180 1826687 11460 20360





112

Based on Median and

with adjusted df

1518 4 4354 339


ANOVA

Methane Yields


Between Groups 12176263 4 3044066 467120 000


Total 12241429 14




Mean



Lower

Bound

Upper

Bound

Tukey

HSD

100USCB

+ 0CM

75USCB +

25CM

1396667 208433 000 71070 208264

50USCB +

50CM

2843333 208433 000 215736 352930

25USCB +

75CM

3746667 208433 000 306070 443264

0USCB +

100CM

8366667 208433 000 768070 905264

75USCB +

25CM

100USCB +

0CM

-1396667 208433 000 -208264 -71070

50USCB +

50CM

1446667 208433 000 76070 213264

25USCB +

75CM

2350000 208433 000 166403 303597

113

0USCB +

100CM

6970000 208433 000 628403 765597

50USCB +

50CM

100USCB +

0CM

-2843333 208433 000 -352930 -215736

75USCB +

25CM

-1446667 208433 000 -213264 -76070

25USCB +

75CM

903333 208433 000 21736 158930

0USCB +

100CM

5523333 208433 000 483736 620930

25USCB +

75CM

100USCB +

0CM

-3746667 208433 000 -443264 -306070

75USCB +

25CM

-2350000 208433 000 -303597 -166403

50USCB +

50CM

-903333 208433 000 -158930 -21736

0USCB +

100CM

4620000 208433 000 393403 530597

0USCB +

100CM

100USCB +

0CM

-8366667 208433 000 -905264 -768070

75USCB +

25CM

-6970000 208433 000 -765597 -628403

50USCB +

50CM

-5523333 208433 000 -620930 -483736

25USCB +

75CM

-4620000 208433 000 -530597 -393403

LSD 100USCB

+ 0CM

75USCB +

25CM

1396667 208433 000 93225 186109

50USCB +

50CM

2843333 208433 000 237891 330775

25USCB +

75CM

3746667 208433 000 328225 421109

0USCB +

100CM

8366667 208433 000 790225 883109

114

75USCB +

25CM

100USCB +

0CM

-1396667 208433 000 -186109 -93225

50USCB +

50CM

1446667 208433 000 98225 191109

25USCB +

75CM

2350000 208433 000 188558 281442

0USCB +

100CM

6970000 208433 000 650558 743442

50USCB +

50CM

100USCB +

0CM

-2843333 208433 000 -330775 -237891

75USCB +

25CM

-1446667 208433 000 -191109 -98225

25USCB +

75CM

903333 208433 000 43891 136775

0USCB +

100CM

5523333 208433 000 505891 598775

25USCB +

75CM

100USCB +

0CM

-3746667 208433 000 -421109 -328225

75USCB +

25CM

-2350000 208433 000 -281442 -188558

50USCB +

50CM

-903333 208433 000 -136775 -43891

0USCB +

100CM

4620000 208433 000 415558 508442

0USCB +

100CM

100USCB +

0CM

-8366667 208433 000 -883109 -790225

75USCB +

25CM

-6970000 208433 000 -743442 -650558

50USCB +

50CM

-5523333 208433 000 -598775 -505891

25USCB +

75CM

-4620000 208433 000 -508442 -415558

100USCB

+ 0CM

0USCB +

100CM

8366667 208433 000 776419 896914

115

Dunnett

t (2-

sided)b

75USCB +

25CM

0USCB +

100CM

6970000 208433 000 636753 757247

50USCB +

50CM

0USCB +

100CM

5523333 208433 000 492086 612581

25USCB +

75CM

0USCB +

100CM

4620000 208433 000 401753 522247



116

APPENDIX 4






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100PSCB +

0CM

3 2255667 539290 311359 2121700 2389634 21940 22940

75PSCB +

25CM

3 1995667 427239 246667 1889535 2101799 19470 20270

50PSCB +

50CM

3 1740333 234592 135442 1682057 1798609 17140 17590

25PSCB +

75CM

3 1557667 281129 162310 1487830 1627503 15390 15900

0PSCB +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1740533 3908233 1009101 1524103 1956964 11460 22940





117


adjusted df

407 4 5808 798


ANOVA

Methane Yields


Between Groups 21260984 4 5315246 432087 000


Total 21383997 14




Mean



Lower

Bound

Upper

Bound

Tukey

HSD

100PSCB

+ 0CM

75PSCB +

25CM

2600000 286372 000 165753 354247

50PSCB +

50CM

5153333 286372 000 421086 609581

25PSCB +

75CM

6980000 286372 000 603753 792247

0PSCB +

100CM

11023333 286372 000 1008086 1196581

75PSCB +

25CM

100PSCB

+ 0CM

-2600000 286372 000 -354247 -165753

50PSCB +

50CM

2553333 286372 000 161086 349581

25PSCB +

75CM

4380000 286372 000 343753 532247

118

0PSCB +

100CM

8423333 286372 000 748086 936581

50PSCB +

50CM

100PSCB

+ 0CM

-5153333 286372 000 -609581 -421086

75PSCB +

25CM

-2553333 286372 000 -349581 -161086

25PSCB +

75CM

1826667 286372 000 88419 276914

0PSCB +

100CM

5870000 286372 000 492753 681247

25PSCB +

75CM

100PSCB

+ 0CM

-6980000 286372 000 -792247 -603753

75PSCB +

25CM

-4380000 286372 000 -532247 -343753

50PSCB +

50CM

-1826667 286372 000 -276914 -88419

0PSCB +

100CM

4043333 286372 000 310086 498581

0PSCB +

100CM

100PSCB

+ 0CM

-11023333 286372 000 -1196581 -1008086

75PSCB +

25CM

-8423333 286372 000 -936581 -748086

50PSCB +

50CM

-5870000 286372 000 -681247 -492753

25PSCB +

75CM

-4043333 286372 000 -498581 -310086

LSD 100PSCB

+ 0CM

75PSCB +

25CM

2600000 286372 000 196192 323808

50PSCB +

50CM

5153333 286372 000 451526 579141

25PSCB +

75CM

6980000 286372 000 634192 761808

0PSCB +

100CM

11023333 286372 000 1038526 1166141

119

75PSCB +

25CM

100PSCB

+ 0CM

-2600000 286372 000 -323808 -196192

50PSCB +

50CM

2553333 286372 000 191526 319141

25PSCB +

75CM

4380000 286372 000 374192 501808

0PSCB +

100CM

8423333 286372 000 778526 906141

50PSCB +

50CM

100PSCB

+ 0CM

-5153333 286372 000 -579141 -451526

75PSCB +

25CM

-2553333 286372 000 -319141 -191526

25PSCB +

75CM

1826667 286372 000 118859 246474

0PSCB +

100CM

5870000 286372 000 523192 650808

25PSCB +

75CM

100PSCB

+ 0CM

-6980000 286372 000 -761808 -634192

75PSCB +

25CM

-4380000 286372 000 -501808 -374192

50PSCB +

50CM

-1826667 286372 000 -246474 -118859

0PSCB +

100CM

4043333 286372 000 340526 468141

0PSCB +

100CM

100PSCB

+ 0CM

-11023333 286372 000 -1166141 -1038526

75PSCB +

25CM

-8423333 286372 000 -906141 -778526

50PSCB +

50CM

-5870000 286372 000 -650808 -523192

25PSCB +

75CM

-4043333 286372 000 -468141 -340526

100PSCB

+ 0CM

0PSCB +

100CM

11023333 286372 000 1019558 1185109

120

Dunnett

t (2-

sided)b

75PSCB +

25CM

0PSCB +

100CM

8423333 286372 000 759558 925109

50PSCB +

50CM

0PSCB +

100CM

5870000 286372 000 504225 669775

25PSCB +

75CM

0PSCB +

100CM

4043333 286372 000 321558 487109



121

REFERENCES











122












123













124












125













126












127












128












129













130












131













132












133














134













135













136




ABSTRACT


LIST OF FIGURES

LIST OF TABLES


ACKNOWLEDGMENTS

APPENDIX 1

APPENDIX 3

APPENDIX 4

REFERENCES

ix









and Inoculum 63


with CM 67








with CM 72











x










References 121

xi

LIST OF FIGURES




the SCT 14












Figure 31 (e) CM 47








for 83 days 68


xii







for 83 days 74












xiii

LIST OF TABLES


(2012-2017) 10





previous studies 26













with CM 85



xiv






BD Biodegradability




CM Chicken Manure















xv



SCT Sugarcane Trash





TS Total Solids



UV Ultra-Violet



VS Volatile Solids

xvi

ACKNOWLEDGMENTS











researcher












xvii
















AMAL BABU PUTHUMANA

18


1



2


3

11 Background


























4



























5












chicken manure



13 Thesis outline




project




6
















7



8


9
























Barbieri 2013)


10








Source (FAO 2018)

Crop


2012 2013 2014 2015 2016 2017

Maize 696 786 524 596 533 678

Rice 1031 1136 767 697 266 822

Cotton 5965 444 3901 197 2804 NA

Soybean 381 411 37 301 292 300

Sugarcane 3386 3293 3752 3765 4472 4534

Sunflower 402 303 27 295 23 27

Wheat 139021 129793 126132 123837 112822 1219115











11





12




















13









14



forming the SCB


2018)


15


















(Jain et al 2014)









16
















al 2015)










17













Table 22


18






























19









C6H10O4 + 2H2O C6H12O6 + H2 Eq21







et al 2019)





20













21






CO2 + 4H2 CH4 + 2H2O Eq28

HCO-3 + 4H2 + H+ CH4 + 3H2O Eq29















22





2017)











Table 23








23






More stable

Less energy input









important


24

272 pH













Cheon 2008)


and Stetter 1982)





Jones 2008)


2008)







25



























26


studies




























27





Stroeve 2009)












28



























29





















Zeeman 2009)







30





31


Sl

No

Pre-treatment

method

Lignocellulosic

substrates

Pre-treatment

condition

CH4 production

after pre-

treatment

(mLCH4gVSadded)

Increase in

CH4 ()

References




Balsari 2012)








2016)


Olive mill solid

waste




Borja 2016)


32


Morken 2012)


MHz 1600 W



2011)


MHz 1600 W





and Carrere 2012)



2012)





33



























34



























35



























36












AD process






37


wastes

Sl

No


conditions

Feed Ratios

tested


1 Sugarcane leaves

+ Diary cow

manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 7525

5050 2575

0100 (VS basis)


LCH4kgVSadded)

(Mokomele et al

2019)

2 Sugarcane

Bagasse + Diary

cow manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 5050

0100 (VS basis)


LCH4kgVSadded)

(Mokomele et al

2019)

3 Sugarcane leaves

+ Diary cow

manure

100 mL Mesophilic

temperature ISR =

04 55 days

1000 7525

5050 2575

0100 (VS basis)

NA 7525 (292

LCH4kgVSadded)

(Mokomele et al

2019)

4 Switchgrass +

Diary manure

500 mL Mesophilic

temperature ISR =

067 30 days

1000 7525

5050 2575

0100 (TS basis)



mono-digestion of

switchgrass

(Zheng et al 2015)

5 Salix viminalis +

Cow manure

1125 mL

mesophilic

temperature ISR =

067 57 days

1000 8020

7030 6040

5050 4060

3070 2080

0100 (VS basis)


mLCH4gVS)


mLgVS) has 23



38

improvement over

untreated salix

6 Corn Stover + Pig

Manure

250 mL Mesophilic

temperature ISR =

026 70 days

1000 8020

6733 5050

3367 2080

0100 (VS basis)

NA 0100 (889

mLCH4gVS)

(Li et al 2018b)

7 Cotton stalk +

swine manure

500 mL Mesophilic

temperature ISR =

025 45 days

1000 7525

5050 2575 (TS

basis)


in yield when

compared to control

(102 mLCH4gVS)

(Cheng and Zhong

2014)

8 Apple waste +

Chicken manure

250 mL Mesophilic

temperature ISR =

026 70 days

1000 8020

6733 5050

3367 2080

0100 (VS basis)

NA 3367 (827

mLCH4gVS)

(Li et al 2018b)

9 Whey + cow

manure

400 mL Mesophilic

temperature 55 days

1000 8515

7525 5050

2575 1585

0100 (VS basis)

NA 8515 (266

mLCH4gVS)

(Vivekanand Mulat

Eijsink and Horn

2018)


39




















al 2017)







40







al 2017)


41







2 Grass Cow manure














6

Organic fraction of








42


















with CM


43



44


45

31 Substrates












312 Manure




32 Inoculum








46



























47





Sample Untreated

SCT ()

Pre-treated

SCT ()

Untreated

SCB ()

Pre-treated

SCB ()

CM

()

















CM 0 0 0 0 100


48









49























Batstone 2015)


50

























51


(PO43-)

















52











53










54

37 Data Processing

371 Theoretical BMP







Fdz-Polanco 2015)


tttEq31





ᆒt =ᆒ썈υ

Eq32









55


using equation 33










Sung 2012)


Eq34


Eq35









56







Eq36
















tt Eq37


57



υ =

tt Eq38










Eq310









58






al 2014)

= t 㢐t) Eq311













= t

t

Eq312





59












60

1


61



62


63


inoculum

























64

















study










65






Bolantildeos 2007)



SCT

Pre-treated

SCT

Untreated

SCB

Pre-treated

SCBInoculum

TS 721 913 872 828 819 19

VS TS 807 909 925 894 951 722

COD gl 7487 11598 12099 7939 9156 211

TSVS - 12 11 11 11 11 14

CN ratio - 124 831 953 1465 1919 49

Glucan ND 307 463 351 428 ND

Xylan ND 168 117 20 146 ND


Lignin ND 298 283 315 282 ND

TKP gPkg 82 11 07 05 06 312

TKN gNkg 836 97 108 512 71 1113







66



SCT

Pre-treated

SCT

Untreated

SCB

Pre-treated

SCBInoculum

Al mgg 103 123 099 084 024 817

As mgg 000 000 000 000 000 000

B mgg 003 000 000 000 000 004

Ba mgg 004 002 002 001 001 019

Ca mgg 1687 312 167 025 015 1973

Cd mgg 000 000 000 000 000 000

Co mgg 000 000 000 000 000 001

Cr mgg 004 001 001 001 001 005

Cu mgg 011 000 000 000 000 056

Fe mgg 107 092 019 086 026 1972

K mgg 1977 601 283 293 076 1582

Mg mgg 461 188 138 036 028 666

Mn mgg 036 010 006 005 003 017

Mo mgg 001 000 000 000 000 003

Na mgg 171 007 034 001 004 1644

Ni mgg 002 000 000 000 000 005

P mgg 796 101 041 015 010 2735

Pb mgg 001 000 000 000 000 003

S mgg 321 082 809 018 524 1469

Se mgg 001 000 001 000 001 000

Zn mgg 027 000 000 000 000 067









67





CM













al 2015)







68




Untreated SCT














69



























70


















71



























72



digested with CM




CM








73






















74

















75















untreated SCB












76
















mono-digestion


77
















78



























79




Pre-treated SCBCM






2015)












80




Untreated

SCTCM

1000 5552 1444 2018 215 353 1960 28

7525 5444 1307 1791 247 307 1671 67

5050 5336 1178 1582 248 308 1642 -38

2575 5228 1074 1413 244 270 1413 00

Pre-treated

SCTCM

1000 5073 1625 2075a 265 410 2082 -03

7525 5085 1614 2065a 252 391 1988 37

5050 5097 1318 1691 235 319 1628 37

2575 5109 1106 1422 190 255 1302 85

Untreated

SCBCM

1000 4364 1812 1990 262 421 1839 76

7525 4553 1615 1850 237 391 1780 38

5050 4742 1429 1706 232 350 1660 27

2575 4931 1302 1615 179 301 1486 80

Pre-treated

SCBCM

1000 2882 3110 2256 251 715 2061 86

7525 3442 2304 1995 220 538 1852 72

5050 4000 1729 1741 235 414 1656 49

2575 4561 1357 1558 194 348 1586 -18

CM 1000 5121 895 1153 216 206 1056 84


81



























82












83




84


























85




136 mgL


CM


mgL mgL mgL mgL

Untreated

SCTCM

1000 0 548 16160 1535

7525 0 554 16160 1465

5050 0 550 16867 1444

2575 0 553 17271 1404

Pre-treated

SCTCM

1000 0 556 15655 1636

7525 0 547 16059 1596

5050 0 551 16362 1505

2575 0 550 16867 1414

Untreated

SCBCM

1000 0 548 16362 1646

7525 0 561 16564 1586

5050 0 535 17069 1515

2575 0 551 17271 1444

Pre-treated

SCBCM

1000 0 364 15756 1566

7525 0 549 16665 1576

5050 0 543 16968 1505

2575 0 552 17271 1444

CM 1000 0 553 17473 1364






86
























modelled data


87


SubstratesFeed


B B0 Rm λR2



USCTCM 100 0 20175 19347 712 264 09942 20753 005 266 09988

75 25 17909 16507 623 128 09772 17679 005 183 09956

50 50 15820 14442 648 122 09701 15343 006 188 09911

25 75 14128 12653 858 198 09727 13238 010 255 09898

PSCTCM 100 0 20748 19853 2142 168 09927 20397 015 185 09867

75 25 20650 19748 1819 123 09905 20331 014 167 09905

50 50 16910 16360 1766 142 09920 16782 016 174 09896

25 75 14224 13498 1521 157 09887 13889 016 178 09881

USCBCM 100 0 19900 19394 797 521 09972 20561 006 538 09889

75 25 18503 17700 528 197 09864 19472 004 197 09989

50 50 17056 16131 492 045 09734 17440 004 119 09940

25 75 16154 14420 767 109 09691 15218 003 178 09897

PSCBCM 100 0 22557 21982 702 154 09815 23570 004 188 09934

75 25 19955 19203 660 007 09787 20400 005 089 09941

50 50 17407 16241 718 015 09745 17090 007 110 09928

25 75 15577 14114 806 040 09624 14869 008 119 09837

CM 100 0 11532 10787 1237 280 09858 11135 016 299 09919


88





















89






90



























91






92







1000 7525 5050 2575




















93



production


94

1


95



96


97





























98





























99



SCTSCB with CM





100

101

APPENDIX 1






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean


Upper

Bound

100USCT +

0CM

3 2017333 223010 128755 1961935 2072732 19920 20340

75USCT +

25CM

3 1791000 34641 20000 1782395 1799605 17890 17950

50USCT +

50CM

3 1581667 85049 49103 1560539 1602794 15730 15900

25USCT +

75CM

3 1413000 338674 195533 1328869 1497131 13750 14400

0USCT +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1591267 3092455 798468 1420012 1762521 11460 20340





102


adjusted df

938 4 4174 521


ANOVA

Methane Yields


Between Groups 13352489 4 3338122 924688 000


Total 13388589 14




Mean

Difference (I-

J) Std Error Sig


Lower

Bound

Upper

Bound

Tukey

HSD

100USCT

+ 0CM

75USCT +

25CM

2263333 155134 000 175277 277389

50USCT +

50CM

4356667 155134 000 384611 486723

25USCT +

75CM

6043333 155134 000 553277 655389

0USCT +

100CM

8640000 155134 000 812944 915056

75USCT +

25CM

100USCT +

0CM

-2263333 155134 000 -277389 -175277

50USCT +

50CM

2093333 155134 000 158277 260389

25USCT +

75CM

3780000 155134 000 326944 429056

0USCT +

100CM

6376667 155134 000 586611 688723

103

50USCT +

50CM

100USCT +

0CM

-4356667 155134 000 -486723 -384611

75USCT +

25CM

-2093333 155134 000 -260389 -158277

25USCT +

75CM

1686667 155134 000 117611 219723

0USCT +

100CM

4283333 155134 000 377277 479389

25USCT +

75CM

100USCT +

0CM

-6043333 155134 000 -655389 -553277

75USCT +

25CM

-3780000 155134 000 -429056 -326944

50USCT +

50CM

-1686667 155134 000 -219723 -117611

0USCT +

100CM

2596667 155134 000 208611 310723

0USCT +

100CM

100USCT +

0CM

-8640000 155134 000 -915056 -812944

75USCT +

25CM

-6376667 155134 000 -688723 -586611

50USCT +

50CM

-4283333 155134 000 -479389 -377277

25USCT +

75CM

-2596667 155134 000 -310723 -208611

LSD 100USCT

+ 0CM

75USCT +

25CM

2263333 155134 000 191767 260899

50USCT +

50CM

4356667 155134 000 401101 470233

25USCT +

75CM

6043333 155134 000 569767 638899

0USCT +

100CM

8640000 155134 000 829434 898566

75USCT +

25CM

100USCT +

0CM

-2263333 155134 000 -260899 -191767

104

50USCT +

50CM

2093333 155134 000 174767 243899

25USCT +

75CM

3780000 155134 000 343434 412566

0USCT +

100CM

6376667 155134 000 603101 672233

50USCT +

50CM

100USCT +

0CM

-4356667 155134 000 -470233 -401101

75USCT +

25CM

-2093333 155134 000 -243899 -174767

25USCT +

75CM

1686667 155134 000 134101 203233

0USCT +

100CM

4283333 155134 000 393767 462899

25USCT +

75CM

100USCT +

0CM

-6043333 155134 000 -638899 -569767

75USCT +

25CM

-3780000 155134 000 -412566 -343434

50USCT +

50CM

-1686667 155134 000 -203233 -134101

0USCT +

100CM

2596667 155134 000 225101 294233

0USCT +

100CM

100USCT +

0CM

-8640000 155134 000 -898566 -829434

75USCT +

25CM

-6376667 155134 000 -672233 -603101

50USCT +

50CM

-4283333 155134 000 -462899 -393767

25USCT +

75CM

-2596667 155134 000 -294233 -225101

Dunnett

t (2-sided)b

100USCT

+ 0CM

0USCT +

100CM

8640000 155134 000 819159 908841

75USCT +

25CM

0USCT +

100CM

6376667 155134 000 592825 682508

105

50USCT +

50CM

0USCT +

100CM

4283333 155134 000 383492 473175

25USCT +

75CM

0USCT +

100CM

2596667 155134 000 214825 304508



106

APPENDIX2






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100PSCT +

0CM

3 2075000 629603 363501 1918598 2231402 20050 21270

75PSCT +

25CM

3 2065000 580259 335012 1920856 2209144 20310 21320

50PSCT +

50CM

3 1691000 272213 157162 1623379 1758621 16600 17110

25PSCT +

75CM

3 1422667 250067 144376 1360547 1484787 13980 14480

0PSCT +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1681400 3743018 966443 1474119 1888681 11460 21320





107


adjusted df

501 4 4789 739


ANOVA

Methane Yields


Between Groups 19438783 4 4859696 276948 000


Total 19614256 14




Mean

Difference (I-

J) Std Error Sig


Lower

Bound

Upper

Bound

Tukey

HSD

100PSCT

+ 0CM

75PSCT +

25CM

100000 342027 998 -102564 122564

50PSCT +

50CM

3840000 342027 000 271436 496564

25PSCT +

75CM

6523333 342027 000 539770 764897

0PSCT +

100CM

9216667 342027 000 809103 1034230

75PSCT +

25CM

100PSCT +

0CM

-100000 342027 998 -122564 102564

50PSCT +

50CM

3740000 342027 000 261436 486564

25PSCT +

75CM

6423333 342027 000 529770 754897

108

0PSCT +

100CM

9116667 342027 000 799103 1024230

50PSCT +

50CM

100PSCT +

0CM

-3840000 342027 000 -496564 -271436

75PSCT +

25CM

-3740000 342027 000 -486564 -261436

25PSCT +

75CM

2683333 342027 000 155770 380897

0PSCT +

100CM

5376667 342027 000 425103 650230

25PSCT +

75CM

100PSCT +

0CM

-6523333 342027 000 -764897 -539770

75PSCT +

25CM

-6423333 342027 000 -754897 -529770

50PSCT +

50CM

-2683333 342027 000 -380897 -155770

0PSCT +

100CM

2693333 342027 000 156770 381897

0PSCT +

100CM

100PSCT +

0CM

-9216667 342027 000 -1034230 -809103

75PSCT +

25CM

-9116667 342027 000 -1024230 -799103

50PSCT +

50CM

-5376667 342027 000 -650230 -425103

25PSCT +

75CM

-2693333 342027 000 -381897 -156770

LSD 100PSCT

+ 0CM

75PSCT +

25CM

100000 342027 776 -66208 86208

50PSCT +

50CM

3840000 342027 000 307792 460208

25PSCT +

75CM

6523333 342027 000 576125 728542

0PSCT +

100CM

9216667 342027 000 845458 997875

109

75PSCT +

25CM

100PSCT +

0CM

-100000 342027 776 -86208 66208

50PSCT +

50CM

3740000 342027 000 297792 450208

25PSCT +

75CM

6423333 342027 000 566125 718542

0PSCT +

100CM

9116667 342027 000 835458 987875

50PSCT +

50CM

100PSCT +

0CM

-3840000 342027 000 -460208 -307792

75PSCT +

25CM

-3740000 342027 000 -450208 -297792

25PSCT +

75CM

2683333 342027 000 192125 344542

0PSCT +

100CM

5376667 342027 000 461458 613875

25PSCT +

75CM

100PSCT +

0CM

-6523333 342027 000 -728542 -576125

75PSCT +

25CM

-6423333 342027 000 -718542 -566125

50PSCT +

50CM

-2683333 342027 000 -344542 -192125

0PSCT +

100CM

2693333 342027 000 193125 345542

0PSCT +

100CM

100PSCT +

0CM

-9216667 342027 000 -997875 -845458

75PSCT +

25CM

-9116667 342027 000 -987875 -835458

50PSCT +

50CM

-5376667 342027 000 -613875 -461458

25PSCT +

75CM

-2693333 342027 000 -345542 -193125

100PSCT

+ 0CM

0PSCT +

100CM

9216667 342027 000 822805 1020529

110

Dunnett

t (2-

sided)b

75PSCT +

25CM

0PSCT +

100CM

9116667 342027 000 812805 1010529

50PSCT +

50CM

0PSCT +

100CM

5376667 342027 000 438805 636529

25PSCT +

75CM

0PSCT +

100CM

2693333 342027 000 170471 368195



111

APPENDIX 3






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100USCB +

0CM

3 1990000 481248 277849 1870451 2109549 19400 20360

75USCB +

25CM

3 1850333 221209 127715 1795382 1905285 18270 18710

50USCB +

50CM

3 1705667 51316 29627 1692919 1718414 17000 17100

25USCB +

75CM

3 1615333 187172 108064 1568837 1661829 15940 16290

0USCB +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1662933 2957005 763495 1499180 1826687 11460 20360





112

Based on Median and

with adjusted df

1518 4 4354 339


ANOVA

Methane Yields


Between Groups 12176263 4 3044066 467120 000


Total 12241429 14




Mean



Lower

Bound

Upper

Bound

Tukey

HSD

100USCB

+ 0CM

75USCB +

25CM

1396667 208433 000 71070 208264

50USCB +

50CM

2843333 208433 000 215736 352930

25USCB +

75CM

3746667 208433 000 306070 443264

0USCB +

100CM

8366667 208433 000 768070 905264

75USCB +

25CM

100USCB +

0CM

-1396667 208433 000 -208264 -71070

50USCB +

50CM

1446667 208433 000 76070 213264

25USCB +

75CM

2350000 208433 000 166403 303597

113

0USCB +

100CM

6970000 208433 000 628403 765597

50USCB +

50CM

100USCB +

0CM

-2843333 208433 000 -352930 -215736

75USCB +

25CM

-1446667 208433 000 -213264 -76070

25USCB +

75CM

903333 208433 000 21736 158930

0USCB +

100CM

5523333 208433 000 483736 620930

25USCB +

75CM

100USCB +

0CM

-3746667 208433 000 -443264 -306070

75USCB +

25CM

-2350000 208433 000 -303597 -166403

50USCB +

50CM

-903333 208433 000 -158930 -21736

0USCB +

100CM

4620000 208433 000 393403 530597

0USCB +

100CM

100USCB +

0CM

-8366667 208433 000 -905264 -768070

75USCB +

25CM

-6970000 208433 000 -765597 -628403

50USCB +

50CM

-5523333 208433 000 -620930 -483736

25USCB +

75CM

-4620000 208433 000 -530597 -393403

LSD 100USCB

+ 0CM

75USCB +

25CM

1396667 208433 000 93225 186109

50USCB +

50CM

2843333 208433 000 237891 330775

25USCB +

75CM

3746667 208433 000 328225 421109

0USCB +

100CM

8366667 208433 000 790225 883109

114

75USCB +

25CM

100USCB +

0CM

-1396667 208433 000 -186109 -93225

50USCB +

50CM

1446667 208433 000 98225 191109

25USCB +

75CM

2350000 208433 000 188558 281442

0USCB +

100CM

6970000 208433 000 650558 743442

50USCB +

50CM

100USCB +

0CM

-2843333 208433 000 -330775 -237891

75USCB +

25CM

-1446667 208433 000 -191109 -98225

25USCB +

75CM

903333 208433 000 43891 136775

0USCB +

100CM

5523333 208433 000 505891 598775

25USCB +

75CM

100USCB +

0CM

-3746667 208433 000 -421109 -328225

75USCB +

25CM

-2350000 208433 000 -281442 -188558

50USCB +

50CM

-903333 208433 000 -136775 -43891

0USCB +

100CM

4620000 208433 000 415558 508442

0USCB +

100CM

100USCB +

0CM

-8366667 208433 000 -883109 -790225

75USCB +

25CM

-6970000 208433 000 -743442 -650558

50USCB +

50CM

-5523333 208433 000 -598775 -505891

25USCB +

75CM

-4620000 208433 000 -508442 -415558

100USCB

+ 0CM

0USCB +

100CM

8366667 208433 000 776419 896914

115

Dunnett

t (2-

sided)b

75USCB +

25CM

0USCB +

100CM

6970000 208433 000 636753 757247

50USCB +

50CM

0USCB +

100CM

5523333 208433 000 492086 612581

25USCB +

75CM

0USCB +

100CM

4620000 208433 000 401753 522247



116

APPENDIX 4






Descriptives

Methane Yields

N Mean

Std

Deviation

Std

Error


for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

100PSCB +

0CM

3 2255667 539290 311359 2121700 2389634 21940 22940

75PSCB +

25CM

3 1995667 427239 246667 1889535 2101799 19470 20270

50PSCB +

50CM

3 1740333 234592 135442 1682057 1798609 17140 17590

25PSCB +

75CM

3 1557667 281129 162310 1487830 1627503 15390 15900

0PSCB +

100CM

3 1153333 87369 50442 1131630 1175037 11460 11630

Total 15 1740533 3908233 1009101 1524103 1956964 11460 22940





117


adjusted df

407 4 5808 798


ANOVA

Methane Yields


Between Groups 21260984 4 5315246 432087 000


Total 21383997 14




Mean



Lower

Bound

Upper

Bound

Tukey

HSD

100PSCB

+ 0CM

75PSCB +

25CM

2600000 286372 000 165753 354247

50PSCB +

50CM

5153333 286372 000 421086 609581

25PSCB +

75CM

6980000 286372 000 603753 792247

0PSCB +

100CM

11023333 286372 000 1008086 1196581

75PSCB +

25CM

100PSCB

+ 0CM

-2600000 286372 000 -354247 -165753

50PSCB +

50CM

2553333 286372 000 161086 349581

25PSCB +

75CM

4380000 286372 000 343753 532247

118

0PSCB +

100CM

8423333 286372 000 748086 936581

50PSCB +

50CM

100PSCB

+ 0CM

-5153333 286372 000 -609581 -421086

75PSCB +

25CM

-2553333 286372 000 -349581 -161086

25PSCB +

75CM

1826667 286372 000 88419 276914

0PSCB +

100CM

5870000 286372 000 492753 681247

25PSCB +

75CM

100PSCB

+ 0CM

-6980000 286372 000 -792247 -603753

75PSCB +

25CM

-4380000 286372 000 -532247 -343753

50PSCB +

50CM

-1826667 286372 000 -276914 -88419

0PSCB +

100CM

4043333 286372 000 310086 498581

0PSCB +

100CM

100PSCB

+ 0CM

-11023333 286372 000 -1196581 -1008086

75PSCB +

25CM

-8423333 286372 000 -936581 -748086

50PSCB +

50CM

-5870000 286372 000 -681247 -492753

25PSCB +

75CM

-4043333 286372 000 -498581 -310086

LSD 100PSCB

+ 0CM

75PSCB +

25CM

2600000 286372 000 196192 323808

50PSCB +

50CM

5153333 286372 000 451526 579141

25PSCB +

75CM

6980000 286372 000 634192 761808

0PSCB +

100CM

11023333 286372 000 1038526 1166141

119

75PSCB +

25CM

100PSCB

+ 0CM

-2600000 286372 000 -323808 -196192

50PSCB +

50CM

2553333 286372 000 191526 319141

25PSCB +

75CM

4380000 286372 000 374192 501808

0PSCB +

100CM

8423333 286372 000 778526 906141

50PSCB +

50CM

100PSCB

+ 0CM

-5153333 286372 000 -579141 -451526

75PSCB +

25CM

-2553333 286372 000 -319141 -191526

25PSCB +

75CM

1826667 286372 000 118859 246474

0PSCB +

100CM

5870000 286372 000 523192 650808

25PSCB +

75CM

100PSCB

+ 0CM

-6980000 286372 000 -761808 -634192

75PSCB +

25CM

-4380000 286372 000 -501808 -374192

50PSCB +

50CM

-1826667 286372 000 -246474 -118859

0PSCB +

100CM

4043333 286372 000 340526 468141

0PSCB +

100CM

100PSCB

+ 0CM

-11023333 286372 000 -1166141 -1038526

75PSCB +

25CM

-8423333 286372 000 -906141 -778526

50PSCB +

50CM

-5870000 286372 000 -650808 -523192

25PSCB +

75CM

-4043333 286372 000 -468141 -340526

100PSCB

+ 0CM

0PSCB +

100CM

11023333 286372 000 1019558 1185109

120

Dunnett

t (2-

sided)b

75PSCB +

25CM

0PSCB +

100CM

8423333 286372 000 759558 925109

50PSCB +

50CM

0PSCB +

100CM

5870000 286372 000 504225 669775

25PSCB +

75CM

0PSCB +

100CM

4043333 286372 000 321558 487109



121

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ABSTRACT


LIST OF FIGURES

LIST OF TABLES


ACKNOWLEDGMENTS

APPENDIX 1

APPENDIX 3

APPENDIX 4

REFERENCES

AmalBabuPuthumana - Griffith University

Documents

Transcript of AmalBabuPuthumana - Griffith University