ABSTRACT

SHARMA, RAJAT. Novel Pretreatment Methods of Switchgrass for Fermentable Sugar

Generation. (Under the direction of Dr Ratna Sharma-Shivappa).

Lignocellulosic biomass has proven to be a good alternative for starch based biomass for

biofuel generation. However, due to it high liginin content, pretreatment of lignocellulosc

biomass has been studied extensively for high reducing sugar generation. Two techniques for

pretreatment of switchgrass to generate reducing sugars were tested during this study: a)

Chemical pretreatment – potassium hydroxide (KOH), b) physical pretreatment -

ultrasonication.

Chemical pretreatment was aimed at studying the potential of potassium hydroxide as a

viable alternative alkaline reagent for lignocellulosic pretreatment based on its different

reactivity patterns compared to NaOH (Raymundo-Piñero et al., 2005). Performer

switchgrass was pretreated at KOH concentrations of 0.5-2% for varying treatment times at

21, 50 and 121oC. The pretreatments resulted in delignification up to 55.4% at 2% KOH,

121oC, 1h and the highest retention of reducing sugar content at 99.26% at 0.5%, 21

oC

, 12h.

Six sets of pretreatment combinations were selected for subsequent enzymatic hydrolysis

with Cellic CTec2® for sugar generation. The pretreatment combination of 0.5% KOH, 12 h,

21oC was determined to be the most effective pretreatment combination (p <0.5 ) as it

utilized the least amount of KOH while generating 582.4 mg sugar/ g raw biomass for a

corresponding % conversion ( based on reducing sugars ) of 91.8%.

The physical pretreatment technique, ultrasonication, was aimed at exploring a refinement

technique that did not involve the addition of a chemical agent. The mechanism of

ultrasonication as a mode of irradiation on biomass particles in liquid medium is cavitation,

which involves the creation of localized high temperature and pressure zones due to

collapsing of bubbles. A Hieschler UID 1000, which generated ultrasonic sound waves up to

a maximum intensity of 20 kHz and amplitude 170 micron was used for batch sonication of

the biomass. Switchgrass was ultrasonicated at 50-100% amplitude for 5-60 min in glass and

stainless steel vessels at atmospheric pressure. Treatments in stainless steel vessels were

performed with and without temperature control. Compositional analyses including acid

insoluble lignin and reducing sugars content of all sonicated samples, structural changes in

biomass structure, and enzymatic hydrolysis for reducing sugar generation from select

ultrasonicated samples was performed. Average lignin degradation of approximately 20%

and up to 85% sugar retention across all pretreatment sets was observed. The lignin and sugar

content of pretreated samples was not significantly (p > 0.05) impacted by the treatment

parameters. Based on visual evidence of disintegration from scanning electron microscopy

images and compositional analyses pretreatment conditions, two different enzyme loadings

were selected for subsequent enzymatic hydrolysis. The combination of temp controlled, 60

min sonication at 100% amplitude gave the highest sugar conversions of 84.6 and 84.7 % for

H1 (Cellic Ctec® 2) and H2 (Dyadic Alternafuel 200L) loadings, respectively.

Novel Pretreatment Methods of Switchgrass for Fermentable Sugar generation

by

Rajat Sharma

A thesis submitted to the graduate faculty of

North Carolina State University

in partial fulfillment of the

requirements for the degree of

Master of Science

Biological & Agricultural Engineering

Raleigh, North Carolina

2012

APPROVED BY:

Dr Michael D. Boyette Dr Larry F. Skyleather

Dr Ratna Sharma-Shivappa

Chair of Advisory committee

ii

BIOGRAPHY

Rajat Sharma was born on the 2nd

of December 1985 in Gwalior, Madhya Pradesh, India. He

was born in a middle class Indian family to parents who serve in the government insurance

and banking sector in India.

He completed his higher secondary education from St. Paul’s college, morar, Gwalior and

went on to pursue an undergraduate course in biotechnology engineering from Madhav

Institute of Technology and Science, Gwalior, M.P., India with honours.

He was brought up in a joint family consisting of 12 members and had a happy and endearing

childhood, in an environment filled with Hindi music and a love for cricket. At a very young

age Rajat developed a keen analytical interest in the game of cricket and that passion still

continues as he writes blogs and debates on the current cricket scenario in the world.

He realized a penchant for singing Hindustani music during his school days and has been

trying to hone his skills, though on a non professional level. He has performed in various

charity events at NC State University and is a part of a two member band, named Jugal

brandy along with fellow State college colleague, Suman Basu who plays the guitar.

His other interests include a passion for studying religion and its impact on culture, media

and politics across the world having gained a rich experience of a multi-layered, rich

complex religious history of India. He constantly video blogs and writes on modern standings

and impacts of various religious beliefs of the world, though personally remaining an

agnostic, having spiritual leaning towards pantheism.

During his undergraduate stint as a biotechnology engineer, Rajat was keenly interested in

the concept of sustainable industrial development through the use of biomass and bioproducts

iii

and went to present a review study on the use of enzymes for industrial chemical processes,

ISTE, 2005. This led to series of planned courses and a quest for advanced knowledge of

bioprocessing which made him apply to the United States for an MS program in biological

and agricultural engineering. His admission in NC State University was one of the biggest

high points of his life and he has enjoyed a challenging and rewarding stay as a master’s of

science candidate under the guidance of his advisor Dr Ratna Sharma-Shivappa.

The keen and inspiring guidance of Dr Ratna Sharma-Shivappa helped Rajat put his goals

into perspective and gradually develop key analytical abilities to put research and data into

perspective and a structured approach to interpretation of results. Through the guidance of Dr

Sharma-Shivappa and further interest in research Rajat is aiming to attain a PhD position in

the field of bio-processing after the successful completion of his master’s degree.

iv

ACKNOWLEDGEMENTS

In this wonderful and challenging journey I would foremost like to acknowledge the

contribution of my parents, Shri Ashit Kumar Sharma & Smt Neelam Sharma. I am indebted

to them to have provided me the opportunity to attain a platform as wonderful as North

Carolina State University. My late grandfather Shri Satya Pal Solanki and my sister Akshita

Sharma along with my entire family in India who have been the backbone of my life and any

mentions of them stop short of the magnitude of their contribution.

I would like to acknowledge the most important contribution towards shaping my master’s

degree program and my research project of my graduate advisor and chair of my committee

Dr Ratna Sharma Shivappa. I would like to acknowledge and appreciate the tremendous

patience she has shown in guiding me and providing direction to my work and personal life. I

would feel no shame in admitting my lack of of personal management skills and I consider

myself extremely lucky to have had a guide like Dr Sharma, whose unending support always

brought me back on my toes whenever I saw my prospects of successfully completing my

masters program dwindling. I have had the privilege of learning an unfailing sense of focus

and self motivation from her and most importantly I have learnt a very calm sense of

professionalism from her. I hope that someday, I make her proud and exhibit some sense of

imbibing the same virtues I have admired in her. Any future successes of mine will have a

huge contribution of Dr Sharma’s guidance and her determination to help me despite my

drawbacks. I would like to thank my graduate committee members, Dr Michael Boyette and

Dr Larry Stikeleather for providing me honest and quick feedback on the progress of my

research and the trust that they have invested in me to undertake such a challenging project.

v

I would like to extend a note gratitude to my colleagues in my lab who have been through my

thick and thin and provided much needed encouragement. In order of having met them I

would acknowledge the help Anusha Devi Panneerselvam gave me to settle in the lab 270B

when I first arrived at NCSU, I would gratefully acknowledge the patience has she displayed

in teaching me the nitty-gritty’s of lab work . Dr Ziyu Wang, Sneha Athalye & Bingqing

Wang’s contribution towards making me understand the discipline of life science laboratory

procedures has had a strong impact on my attitude towards laboratory research, Ximing

Zang, for always helping me share the lab equipment and being a model of perseverance and

hard work. I would also like to thank John Long for helping me become adept at handling the

Ultrasonicator. Dr Dhana Savithri and Dr Debby Clare for helping me learn HPLC in their

lab in flex laboratories, Rachel Huie for helping me with lab equipment, whenever I needed

and managing the 270A lab wonderfully, Barry Lineberger and David Buffalo for providing

technical expertise.

The acknowledgments cannot be complete without a list of teachers and faculty members

who have taught me, worked alongside me in India and these two years at NCSU. I would

like to thank, Dr Todd Klaenhammer, Dr Jay Cheng, Dr Jason Osborne, Dr Gary Gilleskie

and Dr Balaji Rao to have taught me the graduate level courses that built the foundation of

my research. I would like to extend a special mention for Dr Rodney Huffman and Dr Gary

Roberson, whom I had the privilege of working as a teaching assistant and having had

wonderful conversations ranging from, politics and science to culture and technology. I will

always cherish the wisdom they have imparted on me. I would also like to make a special

mention of Dr Nand K Sah, ex head of department, department of biotechnology, Madhav

vi

Institute of Technology, Gwalior, M.P., India to have encouraged me to study in the US and

recommending as an applicant for a master’s of science program at the BAE department at

the program at NCSU, I have rarely met a man of such knowledge and humility, Mr K K

Bakshi and Mr Alok Sajwan for being true mentors and building confidence in me and most

importantly providing a global vision.

I would like to mention my friends who have been pillars of strength and having stood by me

in times of ill health and low self belief, back in India, Vinay Haswani, Srikant Sundaran,

Honey Ramani and Naved Khan. A special mention of thanks to Lalitendu Das for being a

friend, philosopher and guide with his dual roles as an apartment mate and lab colleague, he

has been an inspiration and will always be a pivot of guidance in the future. A special word

of thanks to my roommates; Aditya Gandhi, Abhijit Sipani and Christopher Cyril Sandeep

for being younger brothers possessing better wisdom. Last but not the least, Sonali Pandey

for being my closest friend and support, without which I would never have cleared any

obstacles.

vii

TABLE OF CONTENTS

LIST OF TABLES ......................................................................................................... x

LIST OF FIGURES………………………………………………….. ............................. xi

CHAPTER 1 LITERATURE REVIEW………………………….. ............................... 1

1.1 Introduction………………………………………..... ....................................... 3

1.2 What are biofuels ............................................................................................. 5

1.2.1 Bioalcohols ........................................................................................ 5

1.2.2 Bioethanol………………………………............ ................................ 5

1.2.3 Structure of lignocellulose .................................................................. 6

1.2.4 Switchgrass as a Lignocellulosic Resource and its Advantages

in Biofuel Production ......................................................................... 7

1.2.5 Conversion of lignocellulosic feedstocks to bioethanol ....................... 8

1.3 Pretreatment ................................................................................................... 9

1.3.1 Goals of pretreatment………………………......................................... 9

1.3.2 Physical pretreatment..... ..................................................................... 9 1.3.2.1 Mechanical Communition ................................................................... 9

1.3.2.2 Pyrolisis ......................................................................................... ...10

1.3.2.3 Steam explosion………………….................... .................................. 10

1.3.2.4 Ammonia fiber explosion .................................................................. 11

1.3.2.5 Ultrasonication .................................................................................. 12

1.3.2.6 Major components of the ultrasonicator ............................................. 15

1.3.3 Chemical pretreatment ............................................................................... 16

1.3.3.1 Acid Hydrolysis ................................................................................ 16

1.3.3.2 Alkaline Hydrolysis ........................................................................... 14

1.3.3.3 KOH pretreatement ........................................................................... 20

1.3.3.4 Ozonolysis ........................................................................................ 20

1.4 Hydrolysis ............................................................................................................... 21

1.5 Objectives................................................................................................................ 22

1.6 References ................................................................................................................ 23

CHAPTER 2 POTENTIAL OF POTASSIUM HYDROXIDE PRETREATMENT OF

SWITCHGRASS FOR FERMENTABLE SUGAR PRODUCTION ............................ 28

2.1 Abstract.......................................................................................................... 28

2.2 Introduction..................................................................................................... 29

2.3 Materials and method ...................................................................................... 32

2.3.1 Biomass ............................................................................................ 32

viii

2.3.2 Pretreatment ...................................................................................... 32

2.3.3 Hydrolysis ........................................................................................ 34

2.3.4 Analytical methods ........................................................................... 34

2.3.5 Statistical analysis ............................................................................. 35

2.4 Results and discussion .................................................................................... 36

2.4.1 Composition of switchgrass .............................................................. 36

2.4.2 Effect of pretreatment conditions ..................................................... 37

2.4.3 Solid recovery ................................................................................. 37

2.4.4 Lignin reduction................................................................................ 37

2.4.5 Reducing sugar content ..................................................................... 38

2.4.6 Selection of optimal pretreatment conditions .................................... 39

2.5 Hydrolysis .................................................................................................... 40

2.6 Conclusion ................................................................................................... 41

2.7 Acknowledgements ...................................................................................... 42

2.8 References.................................................................................................... 42

CHAPTER 3 EFFECTS OF ULTRASONICATION OF SWITCHGRASS ON

FERMENTABLE SUGAR GENERATION AND STRUCTURE ABSTRACT ............ 56

3.1 Abstract........................................................................... .................................. 56

3.2 Introduction..................................................................... .................................. 57

3.3 Materials and methods ................................................................................... 59

3.3.1 Biomass preparation .......................................................................... 60

3.3.2 Compositional analysis ..................................................................... 60

3.3.3 Scanning electron microscopy ........................................................... 61

3.3.4 Pretreatment ...................................................................................... 61

3.3.5 Enzymatic hydrolysis ........................................................................ 63

3.3.6 Statistical analysis ............................................................................. 64

3.4 Results and discussion .................................................................................... 64

3.4.1 Effect of ultrasonication on switchgrass composition ................................. 64

3.4.1.1 Solid recovery .......................................................................... 65

3.4.1.2 Acid insoluble lignin ................................................................ 65

3.4.1.3 Total reducing sugars ................................................................ 66

3.4.2 Scanning electron microscopy ........................................................... 67

3.4.3 Sugar yield after enzymatic hydrolysis .............................................. 69

3.5 Conclusions .................................................................................................... 70

3.6 Acknowledgements ......................................................................................... 71

3.7 References ...................................................................................................... 72

ix

CHAPTER 4 CONCLUSIONS AND SCOPE OF FUTURE WORK ...................... 84

REFERENCES...................................................................................... ........................... 86

APPENDICES .............................................................................................................. 87

Appendix 1 Scanning electro microscopy for chapter 3 .................................... 88

Appendix 2 Statistical analysis tables and codes for

orthogonal decomposition for Chapter2 ........................................ 98

Appendix 3 SAS code for enzymatic hydrolysis data for chapter 2 .................. 131

Appendix 4 SAS code for compositional analysis for variables AIL

and sugars for chapter 3 .............................................................. 135

Appendix 5 SAS code for enzymatic hydrolysis data for chapter 3 ................... 148

x

LIST OF TABLES

Table 2.1 Conditions selected for KOH pretreatment ................................................ 48

Table 2.2 Chemical composition of performer switchgrass ....................................... 49

Table 2.3 Solid recoveries after KOH pretreatment ................................................... 50

Table 2.4 Sugar yields and % conversion for washed samples with 0% and

30% enzyme loading ................................................................................. 51

Table 2.5 Sugar yields and % conversion for dilute washed samples with 0%

and 30% enzyme loading........................................................................... 52

Table 2.6 Orthogonal decomposition of sugars variable ........................................... 53

Table 3.1 Treatment parameters investigated during ultrasonication ........................ 56

Table 3.2 Solid recoveries of ultrasonicated samples................................................ 76

Table 3.3 Sugar yields and % conversion for samples with Novozyme

Cellic® Ctec2 loadings & 0% loadings .................................................... 77

Table 3.4 Sugar loadings and % conversion for samples with Dyadic

Alternafuel 200L loadings ........................................................................ 78

xi

LIST OF FIGURES

Chapter 1

Figure 1 The photographic image of the ultrasonic instrument and

its basic embodiment .......................................................................... 16

Chapter 2

Figure 1 Percent reducing sugars in the three fixed temperature

sets, 21oC, 50

oC, 121

oC ..................................................................... 54

Figure 2 Percent Acid soluble lignin in the three fixed temperature

sets, 21oC, 50

oC, 121

oC .................................................................... 55

Chapter 3

Figure 1 Temperature and power dissipation profile during

ultrasonication of switchgrass in glass reaction

vessel ............................................................................................... 80

Figure 2 SEM images of untreated and pretreated

switchgrass 100X, 250X, 500X, magnification ................................. 81

Figure 3 Percent acid insoluble lignin content of ultrasonicated

switchgrass sample ............................................................................ 82

Figure 4 Percent reducing sugars content of ultrasonicated

switchgrass sample ............................................................................ 83

1

CHAPTER 1

Literature review

1.1 Introduction

The imminent energy crisis has led to a new found interest in exploration and development of

renewable sources of energy that are clean, efficient and safe for the environment. One such

highly acknowledged realm of cleaner fuels is bio-fuel. Bio-fuels are an inexhaustible source

of energy that offers a competent alternative to fossil fuels.

The choice of feedstock is central to the controversy surrounding bio-fuels today,

with current technologies associated with the use of food as fuel and large-scale changes in

land use. For bio-fuels to have any meaningful impact on energy, biomass feedstock must be

widely available at low cost and without negative environmental impact. Lignocelluloses -

the non-food component of plants fit this description (Mousdale, 2008). Switchgrass offers a

potential lignocellulosic alternative as it obviates the problem of food security, being a non-

edible plant source. It is available abundantly and unlike fossil fuels, which release more and

more of the CO2, energy crops like switchgrass "recycle" CO2 with each year's cycle of

growth and use and are thereby emerging as a sustainable development model (Keshwani and

Cheng, 2009).

Lignocelluloses have 3 main components: lignin, cellulose and hemicellulose. Pretreatment

of lignocelluloses is done to break down the lignin structure and disrupt the crystalline

structure of cellulose, so that enzymes can easily access and hydrolyze the cellulose for

production of fermentable sugars. A variety of pretreatments have been investigated by

several researchers, with the most common being physical and chemical pretreatments.

2

Physical pretreatments include mechanical communition, pyrolyisis, steam explosion, and

ammonia fiber explosion. These methods are energy intensive and therefore lack overall

efficiency and are not environmentally viable (Galbe and Zacchi, 2007, Kilzer and Broido,

1965).

Chemical pretreatments involve reagents such as acid and alkali. These techniques involve

treating biomass with chemicals that degrade the lignin by oxidation or hydrolysis of the

bonds in the lignocellulosic feedstock. The limitations of these techniques are production of

undesirable toxic substances that effect biofuel yield and cost effectiveness due to use of

costly non-renewable reagents (Quesada et al., 1999; Sun and Cheng, 2000).

In this review we analyze two novel pretreatment techniques, which hitherto have been

relatively unexplored for the specific need of pretreatment of lignocelluloses. The first

technique involves pretreatment of switchgrass with dilute potassium hydroxide (KOH).

KOH is a relatively unexplored chemical treatment method for lignocelluloses, primarily

because of its higher cost of purchase compared to NaOH. We opted for KOH on the basis of

studies evaluating its effect on the structure of carbon nano fibres, which suggested that KOH

degrades ordered structures in a more effective manner than NaOH (Raymundo-Piñero et al,

2005). The choice of an alkali pretreatment was also made due to higher retention of

reducing sugars in the pretreated solids (Xu et al., 2010).

The other novel technique we studied is a refined physical pretreatment process -

ultrasonication. Ultrasonication (or sonication) uses ultra high frequency sound waves to alter

the molecular structure of biomass. It is commonly applied in biological processes for

disruption of cell membranes and release cellular enzymes, also known as sonoporation

3

(Zhou et al 2008). This method was chosen for our study as it offers simple solutions to the

problems associated with conventional chemical pretreatment methods. The process uses

water as the primary reagent which is relatively more abundant compared to other reagents.

As there are no chemical reactions involved during the process there is minimal chance of

production of toxic wastes. Below we analyze results from other works/researchers using

ultrasonication (plant treatment, microbial decontamination, organic matter) and propose a

process for ultrasonic pretreatment of lignocellulose.

1.2 What are biofuels?

Biofuels are fuels that derive their energy from solid, liquid and gaseous biomass sources that

fix carbon biologically. These biomass sources are renewable plant and animal organic

materials. The most common biomass sources for fuel production being perennial grasses,

corn, algae, waste oil (Demiribas, 2009). Most contemporary fuels are biological in nature

but what sets biofuels apart is their minimal impact on the accumulation of total carbon

dioxide in the atmosphere (Demiribas, 2009). The aspect that sets biofuels apart from fossil

fuels is the sheer time scale of their development compared to fossil fuels, which have taken

thousands of years for their formation (Shrestha and Paudel, 2008). Another beneficial aspect

of biofuels is their net negative contribution to carbon emissions after burning, thus being

termed “CO2 neutral”.

Parameters that determine the viability of any biofuel including bioalcohols, biodiesel,

bioethers, biogas, syngas and solid biofuels are governed by the availability of feedstock and

4

how efficiently its energy content can be utilized (Shrestha and Paudel, 2008). The

chronological analysis of biofuel development is an interesting exploration of the sources of

biomass utilized (feedstock) and the methods utilized for their conversion. First generation

biofuels can be characterized as fuels produced from sources such as sugar, starch, and

vegetable oils or animal fats to produce fuels like bioethanol and biodiesel. The use of such

feedstock sources, especially starch, for energy production has however led to a steep rise in

food prices as most of these sources such as wheat, corn and sugarcane are major food

industry inputs and have raised apprehensions in the land usage for their production. Also,

the agricultural inputs for production of starch-based feedstocks are very high thus making

them economically unviable and the fuel generation through biological sources more

expensive than conventional sources (Keshwani and Cheng, 2009).

Biofuels derived from conventionally used starch based food sources such as corn, wheat

and sugarcane were categorized as first generation biofuels.Biofuels derived from cellulose

rich sources that are non-edible and have a greater regeneration capability are classified as

second-generation biofuels( European biofuels, 2011).This category mainly includes

resources such as corn stover, switchgrass, miscanthus, woodchips, and the byproducts of

lawn and tree maintenance which are broadly termed as lignocelluloses.

Lignocelluloses like switchgrass are perennial vegetations that have evolved over many years

of harsh sunlight and heat which have led them to adapt to harsher conditions and use the

available ground water more efficiently (Keshwani and Cheng, 2009). A disadvantage of

lignocellulosic material as compared to starch based sources is difficulty in hydrolysis.

Starch based sources such as corn can be easily hydrolyzed by enzymes or chemical reagents

5

to generate sugars for fermentation whereas due to the lignin protected carbohydrate

framework, lignocellulosic sources need to be pretreated for saccharification to be effective.

The shortcoming of lignocellulosics is however far outweighed by their benefits as they

provide environmental friendly feedstocks that would require innovative processing

technology to generate fuel that competes commercially with conventional sources to emerge

as the foundation for energy security and environment protection.

1.2.1Bioalcohols

Alcohols obtained from biological sources are known as bio-alcohols. Bio-alcohols are fast

emerging as effective alternatives to fossil fuel. Some of the advantages include high octane

numbers and comparable energy densities of bioalcohols like butanol to those of fossil fuels.

Aliphatic alcohols, being able to be synthesized biologically provide cleaner and greener

alternative to fossil fuels. (Chen et al., 2007)

1.2.2Bioethanol

“The principle fuel used as a petrol substitute for road transport vehicles is bioethanol.

Bioethanol is mainly produced by the sugar fermentation process, although it can also be

manufactured by the chemical process of reacting ethylene with steam” (what is bioethanol,

2012).It is a high-octane fuel and has replaced lead as an octane enhancer in petrol. It is a

clear, colorless, clean burning liquid fuel, which is biodegradable and does not pollute the

environment after burning (Grous et al., 1986). Bioethanol has emerged as a successful

model since ethanol-gasoline blends (E10 with 10% ethanol and 90% petrol) are being

6

commercially utilized in countries such as the United States. Sugarcane and corn are

currently the primary feedstocks for commercial bioethanol production in Brazil and US,

respectively. As mentioned previously, the drawbacks of such feedstocks have led to a surge

in research and development of techniques, methods and materials to enhance quality of

blends and reduce cost of production (Grous et al., 1986) with a significant effort being

directed towards lignocellulose conversion.

1.2.3 Structure of Lignocellulose

Lignocelluloses consist of three main components: Cellulose, Hemicellulose and Lignin.

Cellulose and hemicellulose are polymers of monosaccharides joined together by glycosidic

linkages whereas lignin is an aromatic polymer synthesized from phenylpropanoid

precursors. Cellulose makes up 45% of the biomass and it is composed of D-glucose

subunits joined together by -1,4, glycosidic linkage, which form long elemental fibrils that

are linked together by hydrogen bonds and Vander Val’s forces. Hemicelluloses and lignin

cover the microfibrils that are made by elemental fibrils. Microfibrils constitute the cellulose

fiber, which is present primarily in a crystalline form and sometimes in an amorphous form

that is relatively easily hydrolyzed (Kuhad et al., 1997). Hemicellulose is the second major

component of lignocellulose and forms about 25-30% of total dry wood weight. It consists of

all the D- pentose sugars (D-xylose, D-mannose, D-galactose, D-glucose, D-arabinose) with

D-xylose present in the largest amoint. It occasionally consists of some L-sugars as well with

small amounts of glucornic and mannuronic acids. Sugars are linked together by β-1, 4- and

occasionally β-1, 3-glycosidic bonds. Hemicellulose is more easily hydrolysed as compared

7

to celluose as it consists of branched chain of mono saccharides that often contains acetyl

groups, like hetroxylan. These do not form aggregates even when they are co-crystallized

with cellulose (Kuhad et al., 1997)

Lignin is the third major component of lignocellulose and it is the most abundant polymer

found in nature. It is found in the cell wall of plants and provides the plant with structural

support, impermeability, and resistance against microbial attack and oxidative stress. It is an

amorphous heteropolymer, insoluble in water and optically inactive consisting of Phenyl

propane subunits linked randomly thorough various types of linkages. The synthesis of

lignin constitutes the peroxidase-mediated dehydrogenation of three propionic alcohols that

leads to free radical generation. The three propionic alcohols being: guaiacyl (propanol),

coumaryl alcohol (p-hydroxyphenylpropanol), and sinapyl alcohol (syringylpropanol). The

polymerization of lignin is characterized by hetrogneous C-C and aryl-ether linkages forming

monomeric units of aryl-glycerol β aryl ether (Sánchez et al., 2009).

1.2.4 Switchgrass as a lignocellulosic resource and its advantages in biofuel production.

Switchgrass is a promising feedstock for biofuels production due to its high productivity, and

need for relatively low agricultural inputs. It is an excellent renewable source that has

multifarious environmental benefits such as, carbon sequestration, nutrient recovery from

runoff, soil remediation and provision of habitats for grassland birds. Switchgrass, on

average, consists of 45% cellulose and 35.1% hemicelluloses, making it rich in reducing

sugars. Pretreatment of switchgrass is however required to improve the yields of fermentable

sugars, as switchgrass being lignocellulosic contains a relatively high average lignin content

8

of 19% (Wiselogel et al., 1996). Based on the type of pretreatment, glucose conversion yields

from switchgrass ranged from 70% to 90% and xylose yields ranged from 70% to 100% after

hydrolysis. Following pretreatment and hydrolysis, ethanol yields in the range of 72% to

92% of the theoretical maximum have been reported (Wood and Saddler, 1988; Chum et

al.1988; Wyman et al.; 1992).

The characteristics that make switchgrass a viable biofuel feedstock option are its ability to

convert a large amount of solar energy into cellulose, which is the target molecule for

bioethanol production. It also has excellent water usage capacity as its roots dig deep into the

soil and extract ground water (Keshwani and Cheng, 2009). Switchgrass’ has the ability to

add organic matter by expanding deep into the soil and its complex underground network of

stems and roots help it to retain the soil content on the cultivated land and obviate a major

environmental concern of soil erosion and runoff. Besides helping slow runoff and anchor

soil, switchgrass can also filter runoff from fields planted with traditional row crops. Buffer

strips of switchgrass, planted along stream banks and around wetlands, could remove soil

particles, pesticides, and fertilizer residues from surface water before it reaches groundwater

or streams and could also provide energy.

1.2.5 Conversion of lignocellulosic feedstocks to bioethanol

Lignocellulosic feedstocks such as switchgrass with their high reducing sugar content have

emerged as effective bio-fuel generation sources. The basic scheme of fuel generation from

lignocellulosic feedstocks involves the following steps: a) harvest and storage b)

pretreatment c) hydrolysis and d) fermentation. Lignocellulosic sources such as switchgrass

9

requir a process of pretreatment which is aimed at making the biomass conducive for

enzymatic activity. The pretreatment step is followed by enzymatic hydrolysis and the sugars

generated from hydrolysis are then fermented to biofuels.

1.3 Pretreatment

1.3.1 Goal of pretreatment

The goals of any lignocellulose pretreatment are to breakdown lignin and/or hemicellulose or

the scaffolding of cellulose microfibrils and de-crystallize it to increase porosity of the

lignocellulosic material (Kumar et al., 2009). An effective preatment strategy must meet the

following requirements: (1) retain the carbohydrate content (2) minimize production of by-

products that affect ethanol yield, (3) improve generation of sugars after enzymatic

hydrolysis, and (4) be cost-effective (Kumar et al., 2009). A variety of pretreatment methods

have been investigated on various feedstocks.

1.3.2 Physical pretreatment

It is a technique that involves application of specific physical and mechanical stress for

disrupting the lignocellulosic structure. The various types of commonly used methods are as

follows:

1.3.2.1 Mechanical communition

Communition of lignocelluosic particles is done to achieve decrystallization of cellulose.

During this process, size of the particles is brought down to 0−30 mm after chipping and

10

0.2−2 mm after milling or grinding (Kumar et al., 2009).It has been proposed that if the final

particle size is held in the range of 3−6 mm, the energy input for comminution could be kept

below 30 kWh per ton of biomass. The energy input required for the process was however

found to be higher than the theoretical energy content available in the biomass in most cases

(Cadoche and Lopez, 1989) Irradiation of cellulose by γ-rays, which leads to cleavage of β-1,

4-glycosidic bonds and gives a larger surface area and a lower crystallinity, has also been

tested (Takacs et al., 2000). This method was deemed to be extremely cost ineffective (Galbe

and Zacchi, 2007).

1.3.2.2 Pyrolysis

Pyrolysis of biomass leads to decomposition of cellulose to gaseous products and residual

char when it is treated at temperatures greater than 300 °C (Mousdale, 2008; Keshwani and

Cheng, 2009). At lower temperatures, decomposition is much slower, and the products

formed are less volatile (Kilzer and Broido, 1965). Fan et al.(1987) reported that mild acid

hydrolysis (1 N H2SO4, 97 °C, 2.5 h) of the products from pyrolysis pretreatment resulted in

80−85% conversion of cellulose to reducing sugars with more than 50% glucose. The process

has been shown to be cost ineffective.

1.3.2.3 Steam explosion

Sudden high pressure and low-pressure steam currents bring about explosive decompression

of biomass. Steam explosion is typically initiated at a temperature of 160−260 °C

(corresponding pressure, 0.69−4.83 MPa) for several seconds to a few minutes before the

11

material is exposed to atmospheric pressure (McMillan, 1994). This process causes

hemicellulose degradation and lignin transformation due to high temperature (Sun and

Cheng, 2002). Removal of hemicelluloses from the microfibrils is believed to expose the

cellulose surface and increase enzyme accessibility to the cellulose microfibrils (Grou et al.,

1986).Rapid flashing to atmospheric pressure and turbulent flow of the material cause

fragmentation of the material, thereby increasing the accessible surface area (Li et al., 2007).

Steam explosion coupled with a catalyst is the closest to commercialization as it enjoys the

benefit of being cost and energy effective (Holtzapple et al., 1989). The limitations of this

method include the destruction of a part of the xylan fraction, incomplete disruption of the

lignin−carbohydrate matrix, and generation of compounds that might be inhibitory to

microorganisms used in downstream processes (Mackie et al., 1985).

1.3.2.4 Ammonia fiber explosion

During the ammonia fiber explosion process biomass is exposed to liquid ammonia at high

temperature and pressure and then the pressure is suddenly released. In a typical AFEX

process the dosage of liquid ammonia is 1−2 kg of ammonia/kg of dry biomass, the

temperature is 90 °C, and the residence time is 30 min (Mackie et al., 1985). AFEX has

particularly proved effective for herbaceous materials and crops. The various lignocellulosic

materials pretreated effectively by AFEX are alfalfa, wheat straw, and wheat chaff (Alizadeh

et al., 2005). During AFEX pretreatment only a small percentage of solid material is

solubilized and no hemicellulose and lignin are removed. Hemicellulose is broken down into

oligomeric sugars and deacetylated. The structure of the material is changed in the process

12

and the water holding capacity and digestibility is increased (Gollapalli et al., 2002). Over

90% hydrolysis of cellulose and hemicellulose was obtained after AFEX pretreatment of

bermudagrass (approximately 5% lignin) and bagasse (15% lignin) (Galbe and Zacchi,

2007).

1.3.2.5 Ultrasonication

Ultrasound vibrations are disturbances caused by sound waves at frequencies above the

audible range of humans at frequencies above 20 kHz (Feng et al., 20011). The acoustics

from an ultrasound irradiated system in an ultrasonic span in liquids have been shown to

effect particles in the range of 0.15mm to 100mm. There is a non linear effect of the acoustic

phenomena which depends on cavitation, which is defined as the growth and implosive

collapse of bubbles in a liquid irradiated by ultrasound. The creation of positive and negative

compression zones in the liquid by sound waves leads to the rise and recompression of

bubbles formed by the solute, solvent vapour and previously dissolved gases (Suslick et al.,

1991, 1994).

Asymmetrical collapse of the bubble leads to a jet of liquid directed at the surface (Suslick et

al., 1991, 1994). Tip jet velocities which, are generated have been measured to be greater

than 100 ms-1

. The impingement of these jets can create localized erosion (and even melting),

surface pitting, and ultrasonic cleaning. A second contribution to erosion created by

cavitation involves the impact of shock waves generated by cavitational collapse. The

magnitude of such shock waves is thought to be as high as 104

bar, which can easily produce

plastic deformation of malleable metals (Preece and Hannson, 1981).

13

Ultrasonication is a relatively less explored physical refinement pretreatment technique for

biomass. It is a method that involves the treatment of biomass through ultrasonic waves in a

liquid medium. The principle behind such a technique is the transmission of waves leading to

growth and implosive collapse of bubbles in a liquid which further leads to cavity hot spots at

temperatures of roughly 5300 K, pressures of about 1720 bar, and heating and cooling rates

above 109 K/s (Suslick et al., 1991, 1994). Recent studies on the effect of ultrasonic

irradiation of biomass have shown removal of the cellulosic fibers from the lignocellulosic

framework and release of lignin and hemicellulose from biomass particles (Zhang et al.,

2007; Xia et al.; 2004, Gronroos et al., 2004).

Zhang et al. (2007) while working on developing cellulose fibers for use as support in

composites showed that cellulose nano fibres could be extracted from lignocellulose by the

application of high intensity ultrasonication. They showed that cellulose could be treated

with ultra high frequency sound waves to produce small fibrils at nano and micro scales.

They proposed that hydrodynamic forces of ultrasound produce very strong oscillating

mechanical power, which may lead to the separation of cellulose microfibrils from the

cellulose fibre. This work indicates critically that ultrasound acoustic waves do impact the

complex lignocellulosic matrix and there is scope for more refined work in the area (Zhang et

al., 2007).

Sun et al. (2004) investigated the extractability of the hemicelluloses from bagasse obtained

by ultrasound-assisted extraction and found that ultrasonic treatment and sequential

extractions with alkali and alkaline peroxide under the conditions given led to a release of

over 90% of the original hemicelluloses and lignin. They went on to observe that

14

ultrasonication attacked the integrity of cell walls, cleaved the ether linkages between lignin

and hemicelluloses, and increased accessibility and extractability of the hemicelluloses. The

hemicellulosic fractions obtained after ultrasonic extracxtion contained relatively low

amounts of associated lignins, ranging between 0.41% and 7.36%, which was lower than

those of the corresponding hemicellulosic preparations obtained without ultrasound. The low

content of chemically linked lignin in hemicelluloses showed that the α-benzyl ether linkages

between lignin and hemicelluloses in the cell walls of bagasse were substantially cleaved

during ultrasonic irradiation (Sun et al., 2004).

Mao et al. (2007) in their work on influence of ultrasonication on anaerobic bioconversion of

sludge showed that hydrolysis rates of biomass increased considerably by pretreatment with

ultrasonication (Mao et al., 2007). Particle disruption was effected by low-frequency

ultrasound treatment, which was shown evident by a significant reduction in bioparticle size,

from 47.5 to 18.5 µm, and more than 160% increase in soluble substances. First-order

hydrolysis rates increased from 0.0384 on day 21 in the control digester to 0.0456, 0.0576,

and 0.0672 W/mL on day 21 in the digesters fed with sludge sonicated at densities of 0.18,

0.33, and 0.52 W/mL, respectively (Mao et al., 2007 ).

Wong et al. (2009) in their work on bacterial and plant cellulose modification using

ultrasound irradiation showed that depolymerization of plant (PC) and bacterial (BC)

celluloses could be achieved by employing suitable ultrasonication settings intensities.

During this study they observed a decrease in the average molecular weight of the plant

samples due to the scission of β-d-(1 → 4) glycosidic linkages after being pretreated with

ultrasonication (Wong et al., 2009).

15

They also went on to observe that a reduction in the polydispersity index (PI), which is an

indication of the segmental size distribution of a particular polymer defined as the ratio of

weight to number average molecular weight had decreased. It was thus inferred meant that

prolonged sonication yielded chain segments that could not be further degraded, an outcome

which tended to create homogeneous systems with a relatively narrow molecular weight

(Wong et al., 2009).

1.3.2.6 Major Components of an ultrasonicator

An ultrasonicator consists of the following parts:

Transducer: This part of the instrument converts the electrical energy from the power

source and converts it into mechanical oscillations of the range of 20KHz, which are

in the ultrasonic vibration range. It is usually made of a metallic material capable of

generating heavy oscillations.

Booster: This is a mechanical embodiment, which is responsible for increasing the

amplitude of the waves that are applied on the liquid medium.

Sonotrode: This tool which is usually made up of Tungsten is the tool that transfers

the oscillations on the medium and remains in physical contact with the medium

Continuous Flow Cell: This part contains the medium that is pretreated and usually is

made of inert material that could withstand moisture and heavy pressure and

temperature changes.

Amplitude Control Unit (not in picture): This unit is a separate entity connected to the

16

instrument and its function is modulating the amplitude of the ultrasonic irradiation,

Figure 1. The photographic image of the ultrasonic instrument and its basic embodiments.

Referenced from Hielsher.com (image authorized for use)

1.3.3 Chemical Pretreatment:

These techniques involve the use of chemical reagents to hydrolyze and depolymerize the

lignocellulosic framework. Some of the most commonly used chemical pretreatments are

reviewed below:

17

1.3.3.1 Acid Hydrolysis

In this process, dilute acid is mixed with biomass to hydrolyze hemicellulose to xylose and

other sugars. Further breakage of xylose into furfural can also occur at high temperatures

(Mosier et al., 2005). This leads to an increase in the reaction rates, which improves the

cellulose hydrolysis (Esteghlalian et al., 1997). Dilute acid effectively removes and recovers

most of the hemicellulose as dissolved sugars, and glucose yields from cellulose increase

with hemicellulose removal to almost 100% following complete hemicellulose hydrolysis.

Hemicellulose is removed when sulfuric acid H2SO4 is added and this enhances digestibility

of cellulose in the residual solids (Mosier et al., 2005). High temperature has been observed

to improve acid hydrolysis and hemicelluose breakdown (Hinman et al., 1992). As xylan

accounts for one-third of the total lignocellulose carbohydrate content, high xylan to xylose

conversion is desirable to pretreatments as has been observed in dilute acid pretreatments

(Hinman et al., 1992). Two types of dilute-acid pretreatment processes are typically used: a

high-temperature (T > 160 oC), continuous-flow process for low solid loadings (weight of

substrate/weight of reaction mixture) 5-10%) (Brennan et al. 1986, Converse et al., 1989) and

a low-temperature (T < 160oC), batch process for high solid loadings (10-40%) (Esteghlalian

et al., 1997). The most widely used and tested approaches are based on dilute sulfuric acid.

However, nitric acid (Brink, 1993), hydrochloric acid (Israilides et al. 1978, Goldstein et al.,

1983), and phosphoric acid (Israilides et al., 1978) have also been tested. Recently, acid

pretreatment has been used on a wide variety of feedstocks ranging from hardwoods to

grasses and agricultural residues (Ishizawa et al., 2007). Cara et al., (2008) performed acid

pretreatment at 0.2%, 0.6%, 1.0%, and 1.4% (w/w) sulfuric acid concentrations, and the

18

temperature varied in the range of 170-210 oC for olive tree biomass. Sugar recoveries in

both the liquid fraction from pretreatment (prehydrolysate) and the water-insoluble solid

were taken into consideration. A maximum of 83% of hemicellulosic sugars in the raw

material were recovered in the prehydrolysate obtained at 170 oC and 1% H2SO4

concentration, but the enzyme accessibility of the corresponding pretreated solid was not

very high. A maximum enzymatic hydrolysis yield of 76.5% was obtained from solid

pretreated at 210 oC and 1.4% acid concentration. r.. The maximum value of 36.3 g of

sugar/100 g of raw material (75%) was obtained from olive-tree biomass pretreated at 180 oC

and 1% H2SO4concentration. Dilute-acid pretreatment improved enzymatic hydrolysis

compared to water pretreatment (Cara et al., 2008). Selig et al., 2007 reported the formation

of spherical droplets on the surface of residual corn stover following dilute-acid pretreatment

at high temperature. They suggested that the droplets formed were composed of lignins and

possible lignin-carbohydrate complexes. It was demonstrated that these droplets were

produced from corn stover during pretreatment under neutral and acidic pH at and above 130

oC and that they can deposit onto the surface of residual biomass. The deposition of droplets

produced under certain pretreatment conditions (acidic pH, T > 150 oC) and captured on pure

cellulose was shown to have a negative effect on enzymatic saccharification of the substrate.

Additional disadvantages of acid pretreatment reported over the years have turned out to be

the costly materials of construction, high pressures, need for neutralization and conditioning

of hydrolysate prior to biological steps, slow cellulose digestion by enzymes, and non-

productive binding of enzymes to lignin (Wyman et al., 2005).

19

1.3.3.2 Alkaline Hydrolysis

Alkali pretreatment processes utilize lower temperatures and pressures than other

pretreatment technologies (Mosier et al., 2005). Alkali pretreatment can be carried out at

ambient conditions, but the treatment time frame can be of the order of hours and days ( Fan

and Gharpuray, 2007; Alizadeh et al., 2005). Alkaline processes have been shown to produce

less sugar degradation, and many of the caustic salts have been recovered. Sodium,

potassium, calcium, and ammonium hydroxides are suitable alkaline pretreatment agents. Of

these four, sodium hydroxide (NaOH) has been studied the most (Elshafei et al., 1991; Soto

et al., 1994; Fox et al., 1989). However, calcium hydroxide (slake lime) has been shown to

be an effective pretreatment agent and is the least expensive per kilogram of hydroxide. Lime

pretreatment affects structural features of biomass (Kim and Holtzapple, 2006) due to the

combined effects of acetylation, lignification, and crystallization. Lime pretreatment removes

amorphous substances (e.g., lignin), which decreases the crystallinity. Chang et al. (2010)

reported correlations between enzymatic digestibility and three structural factors: lignin

content, crystallinity, and acetyl content. They concluded that (1) extensive delignification is

sufficient to obtain high digestibility regardless of acetyl content and crystallinity, (2)

delignification and deacetylation remove parallel barriers to enzymatic hydrolysis; and (3)

crystallinity significantly affects initial hydrolysis rates but has less effect on ultimate sugar

yields. These results indicate that an effective lignocellulose treatment process should

remove all of the acetyl groups and reduce the lignin content to about 10% in the treated

biomass. Dilute NaOH treatment of lignocellulosic materials results in swelling, leading to

an increase in internal surface area, a decrease in the degree of polymerization, a decrease in

20

crystallinity, separation of structural linkages between lignin and carbohydrates, and

disruption of the lignin structure (Chang and Holtzapple, 2000). The digestibility of

Ca(OH)2-treated hardwood was reported to increase from 14% to 55% with a delignification

of 55% (Chang and Holtzapple, 2000).

1.3.3.3 KOH pretreatment

In a study conducted by Raymundo-Piñero et al. (2005) the structural pattern of carbon

activation was studied with potassium hydroxide (KOH) and NaOH on carbon nano tubes. It

was observed that NaOH could degrade the tubular structure of disoriented structures,

whereas KOH on the other hand degraded highly ordered tubular structures (Wood and

Saddler, 1998). Based on the difference in its reactivity with carbon nano fibres and carbon

nano structures it is believed that KOH can be effective in modifying the lignin-carbohydrate

complex structure for enhanced enzymatic accessibility. This can be further supported by a

study conducted by Ong et al., 2010 on a comparison of simultaneous saccharification of rice

straw through alkali pretreatment, where they compared the effectiveness of pretreatment

between KOH and NaOH, the KOH treated samples at the same concentration/g biomass

gave higher yield as compared to NaOH pretreated samples.

1.3.3.4 Ozonolysis

Ozonolysis is utilized as a pretreatment technique to breakdown the lignin and some

hemicellulose content of biomass. It has proven to be an effective in-vitro method to degrade

the lignin content without producing any chemical waste and toxic residues. (Kumar et al.,

2009) One key aspect of ozonolysis as a pretreatment method is that it affects mainly the

21

lignin and does not affect cellulose at all, the effect on hemicellulose also being very small.

This method has been applied to biomass materials such as wheat straw, bagasse (Ben-

Ghedalia and Miron, 1984) green hay, peanut, pine,( Neely, 1984 ) and poplar sawdust(Vidal

and Molinier, 1988). The notable advantage of this process is that it can be carried out at

room temperature and pressure. Ozone can be easily decomposed using a catalytic bed

thereby minimizing environmental pollution (Vidal and Molinier, 1988). Since ozone is

required in a large amount coupled with the need for on-site generation, this process has

proven to be expensive (Quesada et al., 1998) though an extensive economic analysis is

required to compare the associated operating costs of ozonolysis with the operating and

waste disposal and treatment costs of conventional chemical pretreatments.

The study of biomass structure and different pretreatment techniques highlights the need for

a better understanding of chemical and structural changes that take place during pretreatment.

The need for novel pretreatment techniques that are less intensive and more effective in terms

of sugar retention and sugar yield led us to study two different pretreatment methods. First

being a chemical method involving the use of KOH as pretreating agent as an alternative to

other chemicals and the second a physical refinement technique, ultrasonication that did not

involve any chemical addition and presented a new mechanism for alteration of biomass

structure for higher sugar yield.

1.4 Hydrolysis

The dissolution of chemical compounds through a reaction with water is known as

hydrolysis. Hydrolysis is conducted to extract fermentable sugars from the pretreated

22

biomass for subsequent fermentation for value added products. It is essentially the action of

either an enzyme or a chemical agent aimed at dissolving and depolymerizing

polysaccharides such as cellulose and hemicellulose to simpler monomeric or dimeric sugars

such as glucose and xylose to facilitate fermentation for valuable products. The cellullotytic

enzymes are most commonly utilized for hydrolysis of cellulosic biomass (Gray et al., 2006).

The cellulase complex mainly consists of three categories of enzymes: a) endoglucanase-

these hydrolyze internal β-1,4-glucosidic bonds of polysaccharides; b) exoglucanases, - these

cleave the reducing and non-reducing ends of cellulose chains and generate short-chain

cello-oligosaccharides and c) β-glucosidases- that eventually yield glucose from the cello-

oligosaccharides units (Gray et al., 2006). These glucose units can then be utilized for

fermentation to produce valuable energy and products such as bio-ethanol, bio-chemicals and

antibiotics.

1.5 Objectives

This research aimed at analyzing the pretreatment effectiveness of two novel techniques;

ultrasonication and potassium hydroxide (KOH) and ultrasonication pretreatment on

lignocellulosic biomass as represented by ground switchgrass. The effect of reagent

concentration, treatment time and temperature on enzyme hydrolysis efficiency was

investigated for KOH pretreatment. The key aim of this study was to assess the extent of

lignin degradation and reducing sugar retention.

During ultrasonication, the effect of amplitude, treatment time and operation mode on the

23

proximate composition of switchgrass were investigated. The structural changes that take

place in switchgrass particles after ultrasonication pretreatment were also studied through

scanning electron microscopy (SEM).

The primary response parameters for both pretreatment techniques were carbohydrate

recovery after pretreatment, lignin content (acid soluble and acid insoluble lignin) in the

pretreated biomass as compared to untreated biomass and reducing sugars generated per g

pretreated biomass pretreated by these two methods through enzymatic hydrolysis

1.6 References

1 A. Gronroos, P. Pirkonen and O. Ruppert, Ultrasonic depolymerization of aqueous

carboxymethylcellulose, Ultrasonics Sonochemistry 11 (1) (2004), pp. 9–12.

2 Alizadeh, H., Teymouri, F., Gilbert, T. I., Dale, B. E,Pretreatment of switchgrass by

ammonia fiber explosion (AFEX) Appl. Biochem. Biotechnol, 2005, 121-123, 1133–

1141.

3 Ben-Ghedalia, D., Miron, J., The effect of combined chemical and enzyme

treatment on the saccharification and in vitro digestion rate of wheat straw.

Biotechnol. Bioeng. 1981, 23, 823–831.

4 "Biofuel Production". European Biofuels Technology Platform. Retrieved ,17 May

2011.

5 Cadoche, L,, Lopez, G. D., Assessment of size reduction as a preliminary step in the

production of ethanol from lignocellulosic wastes Biol. Wastes 1989, 30, 153– 157.

24

6 Chen, Y., Sharma-Shivappa, R.R., Keshwani, D. And Chen, C. Potential of

agricultural residues and hey for bioethanol production. Appl. Biochem. Biotechnol.

2007, 142: 276-290

7 Elshafei, A. M., Vega, J. L., Klasson, K. T., Clausen, E. C., Gaddy, J. L., The

saccharification of corn stover by cellulase from Penicillin funiculosum. Bioresour.

Technol. 1991, 35, 73–80.

8 Fan, L. T., Gharpuray, M. M., Lee, Y.-H, Cellulose Hydrolysis; Biotechnology

Monographs; Springer: Berlin; Feb 2007, Vol. 3, p 57.Feb 2007, p 58.

9 Feng. H., Barbosa-Canovas, G., Weiss, J., Ultrasound technologies for food and

bioprocessing, 2011, 2011, X11, 666p.

10 Galbe, M., Zacchi, G., Pretreatment of lignocellulosic materials for efficient

bioethanol production Adv. Biochem. Eng./Biotechnol. 2007, 108, 41– 65.

11 Gollapalli, L. E., Dale, B. E., Rivers, D. M,Predicting digestibility of ammonia fiber

explosion (AFEX)-treated rice straw Appl. Biochem. Biotechnol. 2002, 98, 23– 35.

12 Grous, W. R., Converse, A. O., Grethlein, H. E.Effect of steam explosion

pretreatment on pore size and enzymatic hydrolysis of poplar Enzyme Microb.

Technol. 1986, 8, 274– 280.Li, J., Henriksson, G., Gellerstedt, G., Lignin

depolymerization/repolymerization and its critical role for delignification of aspen

wood by steam explosion Bioresour. Technol. 2007, 98, 3061– 3068.

13 Holtzapple, M. T., Humphrey, A. E., Taylor, J. D.Energy requirements for the size

reduction of poplar and aspen wood Biotechnol. Bioeng. 1989, 33, 207– 210.

25

14 Ishizawa, C. I., Davis, M. F., Schell, D. F.,Hohnson, D. K. Porosity and its effect on

the digestibility of dilute sulfuric acid pretreated corn stover.J, Agric. Food Chem.

2007, 55, 2575–2581.

15 Keshwani, D.R., Cheng, J.J., Switchgrass for bioethanol and other value-added

applications: a review, Bioresour Technol 2009 Feb;100(4):1515-23. Epub 2008.

16 Kilzer, F. J., Broido, A.Speculations on the nature of cellulose pyrolysis

Pyrodynamics 1965, 2, 151– 163. Shafizadeh, F., Bradbury, A. G, W.Thermal

degradation of cellulose in air and nitrogen at low temperatures J. Appl. Polym. Sci.

1979, 23, 1431– 1442.

17 Kumar, P., Diane, M.B., ,Delwiche, M. J., Stroeve , P., Methods for Pretreatment of

Lignocellulosic Biomass for Efficient Hydrolysis and Biofuel Production, Ind. Eng.

Chem. Res., 2009, 48 (8), pp 3713–3729.

18 Mackie, K. L.; Brownell, H. H., West, K. L.,Saddler, J,N.Effect of sulphur dioxide

and sulphuric acid on steam explosion of aspenwood J. Wood Chem. Technol. 1985

19 Mao, T., Show, K.Y., Influence of Ultrasonication on Anaerobic Bioconversion of

Sludge, Water Environ Res. 2007 Apr; 79(4):436-41.

20 McMillan, J. D, Pretreatment of lignocellulosic biomass. In Enzymatic Conversion of

Biomass for Fuels Production; Himmel, M. E.; Baker, J. O.; Overend, R. P., Eds.;

American Chemical Society: Washington, DC, 1994; pp 292− 324

21 McMillan, J. D., Pretreatment of lignocellulosic biomass. In Enzymatic ConVersion

of Biomass for Fuels Production; Himmel, M. E., Baker, J. O., Overend, R. P., Eds.;

American Chemical Society: Washington, DC, 1994; pp 292-324.

26

22 Mosier, N. S., Wyman, C., Dale, B., Elander, R., Lee, Y. Y., Holtzapple, M.,

Ladisch, M. R. Features of promising technologies for pretreatment of lignocellulosic

biomass. Bioresour. Technol. 2005, 96, 673-686

23 Mousdale, David M, Biofuels,biotechnology, chemistry, and sustainable development

Boca Raton : CRC Press, c2008.

24 Neely, W. C, Factors affecting the pretreatment of biomass with gaseous ozone,

Biotechnol. Bioeng. 1984, 26, 59–65.

25 Preece, C.M., Hannson, I.L, Adv. Mech. Phys. Surf., 1981, 1, 199.

26 Quesada, J., Rubio, M., Gomez, D, Ozonation of Lignin Rich Solid Fractifrom Corn

Stalks. J. Wood Chem. Technol. 1999, 19, 115–137.

27 Sun, J., Sun, R., Sun, X., Su, Y., Fractional and physico-chemical characterization of

hemicelluloses from ultrasonic irradiated sugarcane bagasse , Volume 339, Issue 2,

22 January 2004, Pages 291-300.

28 Sun, Y., Cheng, J., Hydrolysis of lignocellulosic materials for ethanol production: A

review Bioresour. Technol. 2002, 83, 1– 11.

29 Suslick, K. S., Cline, Jr., R. E., Hammerton, D. A, "The Sonochemical Hot Spot," J.

Am. Chem. Soc. 1986, 108, 5641-5642.

30 Suslick, K.S., Kemper. K.A., in Bubble Dynamics and Interface Phenomem (Ed.: J.

R. Blake, N. Thomas) Kluwer, Dordrecht, 1994, pp. 31 I-320.

31 Takacs, E., Wojnarovits, L., Foldvary, C., Hargittai, P., Borsa, J.,Sajo, I, Effect of

combined gamma-irradiation and alkali treatment on cotton-cellulose Radiat. Phys.

Chem. 2000, 57, 399– 403.

27

32 Vidal, P. F., Molinier, J., Ozonolysis of lignin Improvement of in vitro digestibility of

poplar sawdust. Biomass 1988, 16, 1–17.

33 Wong,S.S, Kasapis, S., Yanfang, M.T., “Bacterral and plant cellulose modification

using ultrasonic irradiation, Carbohydrate polymersm, Volume 77, Issue 2, 10 June

2009, Pages 280-287.

34 Zhang, H., Wang, Z.G., Zhang, Z.N., Wu, J., Zhang , J., He, J.S., Regenerated-

Cellulose/Multiwalled- Carbon-Nanotube Composite Fibers with Enhanced

Mechanical Properties Prepared with the Ionic Liquid 1-Allyl-3-methylimidazolium

Chloride, Advanced Materials Volume 19 Issue 5, Pages 698 – 704, Feb 2007.

35 http://www.esru.strath.ac.uk/EandE/Web_sites/02-03/biofuels/what_bioethanol.htm,

what is bioethanol, retrieved, April 2012.

28

CHAPTER 2

Potential of potassium hydroxide pretreatment for fermentable sugar production

2.1 Abstract

Chemical pretreatment of lignocellulosic biomass has proven to be an effective method for

sugar generation and subsequent fuel production. Alkaline pretreatment has emerged for use

as a successful chemical pretreatment method and most of the studies thus far have utilized

NaOH for dissolution of lignocellulosic biomass for sugar generation and have emphasized

its ability to generate substantial sugars after enzymatic hydrolysis (Xu et al., 2010a). This

study was aimed at studying the potential of potassium hydroxide as a viable alternative

alkaline reagent for lignocellulosic pretreatment based on its different reactivity patterns

compared to NaOH (Raymundo-Piñero et al., 2005). Performer switchgrass was pretreated at

KOH concentrations of 0.5-2% for varying treatment times at 21, 50 and 121oC The

pretreatments resulted in delignification up to 55.4% at 2%KOH, 121oC, 1h and the highest

percent sugar content retention of 99.26% at 0.5%, 21oC, 12 h. Six sets of pretreatment

combinations were selected for subsequent enzymatic hydrolysis with Cellic CTec2® for

sugar generation. The pretreatment combination of 0.5% KOH, 24 H, 21oC was determined

to be the most effective pretreatment combination as it utilized the least amount of KOH

while generating 582.4mg sugar/ g raw biomass for a corresponding % released sugar

conversion of 91.8%.

Key words: switchgrass, lignocelluloses, KOH, enzymatic hydrolysis, AIL, sugars.

29

2.2 Introduction

Lignocellulose-to-ethanol production technology has been investigated intensively around

the world over the last two decades. Lignocellulosic biomass is a complex substrate that

typically contains 50%-80% (dry basis) carbohydrates that are polymers of 5C and 6C sugar

units. The two types of polysaccharides, cellulose (~45% of dry weight) and hemicellulose

(~25% of dry weight), are bound together by a third component lignin (~25% of dry weight),

which is a complex three-dimensional polyaromatic matrix. Lignin is partly covalently

associated with hemicellulose, thus preventing hydrolytic enzymes and acids from accessing

some regions of the holocellulose and releasing the sugar units (Carlo et al., 2005)

Of the various lignocellulosic feedstocks available, switchgrass (Panicum virgatum L.), a

perennial warm-season grass native to North America (Dale, 2012), has received

considerable attention for ethanol production because of its excellent growth in various soil

and climatic conditions and its low requirements of agricultural inputs (Keshwani et al.,

2009). According to the study by Schmer et al., switchgrass is capable of producing 5.4 times

more renewable energy in the form of ethanol and other value added products than non

renewable energy consumed, while greenhouse gas emissions from switchgrass-based

ethanol are 94% less than those from gasoline (Schmer et al., 2008).

The process of ethanol production from lignoellulosic biomass constitutes three stages: a)

pretreatment of biomass to reduce lignin content and cellulose crystallinity b) hydrolysis of

pretreated biomass for sugar generation and c) fermentation of sugars into ethanol.

Pretreatment of biomass has been found to change its macromolecular structure and increase

surface area and pore size, making it conducive for hydrolytic enzymes to attach themselves

30

to the carbohydrate matrix for generating sugars which are subsequently converted to ethanol

through bacterial or yeast fermentation (Awolu and Ibileke, 2011).

Pretreatment can be divided into three main categories: a) physical b) chemical and c)

biological. Physical pretreatment processes have proven to be energetically unviable and

biological pretreatment methods can be expensive and time consuming (Belkacemi et al.

1998; Chang et al., 2001; Chen et al., 2007; Xu et al., 2010). Chemical pretreatment

techniques on the other hands have been the most widely studied and alkaline pretreatment in

particular has seen considerable success. Silverstein et al. (2007) investigated chemical

pretreatment of cotton stalks and reported that, among four pretreatment methods (NaOH,

H2SO4, H2O2 and ozone pretreatments), NaOH pretreatment resulted in the highest level of

delignification (65.63% at 2% NaOH, 90 min, 121 °C) with cellulose conversion of 60.8%

(Silverstein et al., 2007) . Xu et al. (2010)b

investigated sodium hydroxide pretreatment of

switchgrass for ethanol production and reported that at the best pretreatment condition (50

°C, 12 h and 1.0% NaOH), the yield of total reducing sugars was 453.4 mg/g raw biomass,

which was 3.78 times that from untreated biomass. The maximum lignin reductions were

85.8% at 121 °C, 77.8% at 50 °C and 62.9% at 21 °C, all of which were obtained at the

combinations of the longest residence times and the highest NaOH concentrations (Xu et alb.,

2010). Sodium hydroxide pretreatment of lignocellulosic materials results not only in

significant lignin reduction but also excellent retention of the total reducing sugar content per

g of biomass treated ( Xu et ala., 2010). Although NaOH is the most commonly investigated

alkali reagent, other alkalis like calcium hydroxide (Ca(OH)2) (Kaar et al., 2000, Xu et ala.,

2010) ,have been investigated and achieved a maximum sugar yield of 433-462 mg/g raw

31

biomass. Potassium hydroxide (KOH) pretreatment of rice straw and poplar woodhave also

been researched (Chang et al., 2000, Ong et al., 2010)

Potassium hydroxide is a relatively less explored pretreatment (Ong et al., 2010) agent but

could potentially be used for lignocellulose pretreatment due to its reported reactivity with

carbon nano fibres and carbon nano structures (Chang et al., 2000) and its ability to

deacetylate biomass. In a study conducted by Raymundo-Piñero et al.(2005) , the structural

pattern of carbon activation on carbon nano tubes was studied with KOH and NaOH as the

carbon activating agents and it was found that NaOH could degrade the tubular structure of

disoriented structures, whereas KOH on the other hand could degrade highly ordered tubular

(Raymundo-Piñero et al., 2005). One of the key aspects for attaining a good yield of sugars

after enzymatic hydrolysis of pretreated biomass is low cellulose crystallinity and lignin

content. However if the lignin content is sufficiently low, crystallinity index and acetyl

content do not have a significant impact on enzyme digestibility (Chang et al., 2000). Ong et

al. (2010) in their study on a comparision between NaOH and KOH pretreatment of rice

straw showed that at equal enzyme loading,, the KOH treatment sugar yield was significantly

higher sugars than the NaOH treatment at similar conditions (Ong et al., 2010 ). Hence with

this background, an attempt was made to study the effect of KOH during pretreatment and

subsequent hydrolysis of switchgrass. A comparison between pretreatment effectiveness at

high and low treatment temperatures was made to better understand the mechanism of KOH

in modifying lignocellulose structure. Various combinations of residence times and KOH

concentrations at each temperature were also investigated. Samples with the greatest

32

delignification and carbohydrate availability after pretreatment were hydrolyzed to estimate

reducing sugar generation.

2.3 Materials and Methods

2.3.1 Biomass

“Performer” switchgrass was used as feedstock and was obtained from the Central Crops

Research station at Clayton, NC (Burns et al., 2008). This switchgrass variety has been found

to possess high nutritional value and digestibility, providing a dry matter yield of

approximately 13450 kg/ha. The switchgrass plants harvested up to 6 inch stubble in July

2007 were put into cloth bags and dried at 70oC in a forced air oven, ground to pass a 2 mm

sieve in a Wiley fitted mill and stored at room temperature in zip locked plastic bags at the

Biological and Agricultural Engineering department at NC State University, Raleigh, NC for

use in various studies.

2.3.2 Pretreatment

Switchgrass samples were pretreated at three different temperatures: 121 °C, 50°C, 21°C.

Constant temperature for the 121°C batch was maintained in an autoclave at 15 psi,

corresponding with treatment times of 15 min, 30 min and 60 min. The 50 °C treatments

were performed in a water bath for 6h, 12h and 24h while the 21 °C pretreatments were

performed at room temperature (maintained through a thermostat) for 6h, 12h, 24h and 48 h.

All the temperature-time pretreatment combinations were performed at KOH concentrations

of 0.5%, 1%, 2% (w/v) in a factorial experiment design. Longer residence times were applied

33

at lower temperatures to offset the impact of reduced chemical reaction rates and provide a

comparison between pretreatment effectiveness at low and high temperatures. The

pretreatment conditions selected for the study are summarized in Table 1.

Five g of biomass sample and 50 ml of KOH solution for the desired treatment combination

were mixed in a serum bottle using a glass rod forming slurry at a solid/liquid ratio of 1:10.

All serum bottles were sealed and crimped before pretreatment. Pretreated solids were

carefully transferred to 250 ml plastic centrifuge bottles for separation of the prehydrolysate.

The samples were washed using 2 strategies: dilute washed and washed, to remove any

residual alkali and dissolved by-products that might inhibit enzymes during subsequent

hydrolysis. For ‘dilute-washed’ samples, after transferring bulk of the biomass-KOH slurry

to the centrifuge bottle, the serum bottle was rinsed with 50 ml DI water to recover any

residual solids. The wash water was transferred to the centrifuge bottle and the total volume

made up to 200 ml. The bottles were centrifuged at 4000 rpm for 10 min, decanted and the

supernatant filtered through a Buchner funnel and flask assembly by vacuum filtration to

recover all solids. The ‘washed’ samples were prepared by transferring the pretreated solids-

KOH slurry to the centrifuge bottle, centrifuging at 4000 rpm for 10 min, and decanting the

supernatant in the vacuum filtration assembly. The solids remaining were washed by adding

the wash water from the serum bottle (approx. 50 ml) and an additional 50 ml DI water and

centrifuged again. The supernatant was filtered as described previously. All solids

accumulated on the filter paper in the filtration set up were quantified by oven drying and the

value used for solid recovery calculations. Approximately 5 g of wet biomass was drawn

from each pretreated sample and kept for oven drying at 105 °C for the estimation of the

34

solid recovery. A similar amount was placed for vacuum drying at 40 °C to obtain sample for

composition analysis.

2.3.3 Hydrolysis

Select pretreated samples equivalent to 1.6 g (dry basis) in 20 ml volume (8% w/v solid

loading) made up by 0.05M citrate buffer (pH 5.0), 40 µg/mL tetracycline and Cellic® Ctec2

cellulase enzyme complex (Novozymes North America, Franklinton) at a loading of 30% (g

enzyme protein /g biomass) were hydrolyzed in conical tubes for generation of reducing

sugars. To generate enough biomass for hydrolysis at the various conditions, pretreatments

were performed in 6 replicates and 2 consequtive replicates from the 6 replicates were

combined randomly to generate one larger replicate. This was done to avoid the impact of

any scale changes during pretreatment of larger amounts. Untreated samples with equivalent

enzyme loading were also hydrolyzed as control. Pretreated and untreated samples with no

enzyme were prepared to determine the effect of soaking. Hydrolysis was performed for 72 h

at 500 C in a shaking water bath at 50 rpm. Upon termination of hydrolysis, the samples tubes

were centrifuged at 4000 rpm for 10 min and the filtrate was collected for sugar analysis. The

retentate was placed in the 105 oC oven for estimation of residual solids after hydrolysis.

2.3.4 Analytical methods

The chemical composition of switchgrass samples before and after pretreatment was

analyzed using standard procedures given in National Renewable Energy Laboratory’s

(NREL) Laboratory Analytical Procedures (LAP) (Sluiter et al., 2005a, 2005

b, and 2008) for

35

the measurement of total solids, acid soluble lignin (ASL) and acid insoluble lignin (AIL).

Briefly, AIL was measured by a 2 step sulfuric acid hydrolysis and the filtrate from the AIL

acid hydrolysis was utilized for the estimation of ASL and total sugars in untreated biomass

and solids recovered after pretreatment. ASL was estimated through absorbance

measurements at 205 nm in a UV-Vis spectrophotometer (Shimadzu Pharmaspec UV-

1700).Total reducing sugars in the AIL filtrate and enzyme hydrolysate were estimated by

the 3,5-dinitrosalycylic acid (DNS) method(Miller, 1959, Ghose, 1987).

2.3.5 Statistical Analysis

All the treatments in this study were conducted in triplicates. SAS 9.2 Software (Cary, NC)

was used for all data analysis. The experimental design was balanced and completely

randomized, but with a rather complex factorial structure. There were a total of 31 different

experimental conditions. These conditions were comprised of 30 combinations of 3 factors

plus an untreated control. These 30 combinations can be broken down into three “design

sectors”. The “short treatment times” sector comprised of 9 design points: treatment times of

0.25, 0.5 and 1h crossed with the three concentrations at the higher temperature of 121oC in a

complete 3×3 layout. In the “intermediate times” sector, 18 design points came from a

complete, crossed three-factor layout (3 × 2 × 3), with the three factors concentrations (0.5, 1

and 2%), temperatures (22 and 50oC) and treatment times (6, 12 and 24 h). In a “long

treatment time” sector, a treatment time of 48 h with temperature fixed at 21oC was observed

across the three concentrations. Lastly, an untreated control was used, for a total of 31

conditions. In the subsequent analysis of variance, an orthogonal decomposition of the

36

treatment sum of squares on 30 degrees of freedom was obtained to investigate variability

due to time, concentration and temperature, separately within these sectors, while pooling

information about variability across the entire experiment. There were n = 3 replicates per

treatment combination for a total of 31 × (n − 1) = 62 degrees of freedom.

The statistical anaylsis codes and ouputs and ANOVA table for sugars and AIL is given in

the appendix (2). The decomposition of the treatment sum of squares for sugars and AIL into

orthogonal components is given in the appendix (2).

2.4 Results and Discussion

2.4.1Composition of Switchgrass

The initial composition (dry basis) of “Performer” switchgrass used in this study is presented

in Table 2. The carbohydrate portion (represented by total reducing sugars) of the

switchgrass feedstock was estimated to be 67.3%. Total lignin (including acid insoluble

lignin and acid soluble lignin), which is the major non-carbohydrate component, was

estimated at 24.77 % of which ASL was 1.2% The lignin content of 24.77% was comparable

to typical lignin contents of herbaceous species and agricultural residues(McMillan, 1994).

The ash content of 3.6 % was also in conjunction with the study of Xu et al, 2010a. Other

undefined components are believed to be mainly non-structural compounds including

protein, waxes, fats, resins and chlorophyll (Kuhad and Singh, 1993, Sluiter et al., 2005c).

37

2.4.2 Effect of Pretreatment Conditions

Pretreatment conditions had varying effects on solid recovery, lignin reduction and sugar

availability in the biomass. The severity of the treatment increased with increasing KOH

concentration and treatment temperature. The most severe treatment sets were the

combination of the most extreme ranges of both the parameters.

2.4.3 Solid recovery

Table 3 shows the total percent biomass recovered after pretreatment per g of untreated

biomass used for pretreatment at the various conditions. On average, solid recoveries ranged

between 46%-79% at 121oC, 65%-79% at 50

oC and 66%-85% at 21

oC. It was observed that

lesser solids were recovered as the severity of the pretreatment increased. Statistically, the

main effect of both time and concentration had significant (p< 0.05) impact on solid

recovery, the interaction effect between temperature and concentration had a significant

(p<0.05) impact on loss of solids. The NaOH pretreatment conducted by Xu et al., 2010b

follows the same pattern of solid loss where higher intensity treatments in terms of

temperature and high concentration decrease the solid recovery, overall they report less

solid recovery than the KOH pretreated samples, with the highest solid recovery being 80% .

2.4.4 Lignin reduction

Lignin is a three-dimensional complex aromatic that acts as a strong barrier for the release of

sugars from lignocellulosic biomass. This makes it imperative to degrade lignin without

major disruption of the reducing sugars needed for bio-conversion into fuel (Fan et al., 1987).

38

Statistical analysis indicated that at 121 and 50 °C, residence time had a significant impact

(p<0.05) on lignin reduction at all three KOH concentrations and the maximum lignin

reductions at 121 and 50 °C were 55.56 and 38.7 % respectively, which were obtained at 1

h, 2.0% KOH and 24 h 2.0% KOH, respectively. At 21 °C, residence time had significant

impact (p<0.05) on lignin reduction at higher concentrations and the highest lignin reduction

of 28.47 % was obtained at 48 h, 2.0% KOH. The maximum lignin reductions at different

temperatures were all obtained at the combinations of the highest KOH concentration and the

longest treatment times, which indicate a close relationship between pretreatment severity

and lignin reduction. However, It was observed that delignification in the NaOH pretreated

switchgrass (Xu et al, 2010b) is much more pronounced than the KOH pretreated samples

,with NaOH samples observing a highest delignification of 85%. Since increasing

pretreatment intensity does not necessarily lead to higher sugar recovery due to greater

biomass solubilization, lignin reduction alone may not be an appropriate indicator for overall

pretreatment effectiveness. Delignification though important, isn’t the only parameter that

determines a high sugar yield.

2.4.5 Reducing Sugar content

Carbohydrate (cellulose and hemicellulose), which is the key component in pretreated

biomass for generation of fermentable sugars during hydrolysis was estimated in this study

through reducing sugar measurement. The carbohydrate availability in pretreated biomass (as

represented by total reducing sugar content) decreased with increase in the severity of

pretreatment conditions (concentration, temperature and residence times) (Fig 1). The main

39

effect of concentration and the interaction effect of temperature and concentration had a

significant impact (p<0.05), whereas time did not have either a significant main effect

(p<0.05) or a significant (P<0.05) interaction effect with concentration on sugar retention. It

was observed that 0.9 – 42.7% of the original untreated reducing sugar content was lost

during various combinations of pretreatments. The maximum sugar retention in the 21°C set

was observed to be 66.62% at 0.5% KOH (w/v) for 12 h, while the highest retention of

reducing sugars in the 50°C set was observed at 0.5% KOH (w/v), 24 h at 64.1% and highest

sugars for 121oC was observed at 0.5% KOH (w/v) for 1 h at 60.59%.

2.4.6 Selection of optimal pretreatment conditions

Pairwise comparisons at a confidence interval of 95% were made among all treatment

combinations for a specific temperature by the LS means procedure (SAS 9.2) (appendix 2).

Tukey method was used to provide a conservative estimate of significant differences among

mean pairs. The comparisons were made between a manually chosen extreme response value

to all other treatment means in the temperature set to pick optimal values for subsequent

enzymatic hydrolysis. Selections were based relative to delignification and carbohydrate

(reducing sugar) recovery. The combinations chosen relative to highest sugar values were a)

0.5 % KOH, 12 h, 21oC, b) 0.5% KOH, 24 hr 50

oC, c) 1% KOH, 1 h 21

oC and the

combinations chosen for highest delignification were a) 2% KOH, 48 h, 21oC, b) 2% KOH,

24 h , 50oC and c) 2% KOH, 1 h,121

oC.

40

2.4.7 Hydrolysis

Tables 4 and 5 represent the sugar yields and % conversion efficiency for samples obtained

from the 2 washing strategies at the selected pretreatment conditions. A significant difference

(p<0.05) was observed between the % conversions and sugar yields (g/g) for the 30%

enzyme loading hydrolyzed samples from the two washing strategies. This may be attributed

to lack of sufficient washing in the dilute washed samples leading to retention of KOH on the

biomass thus leading to the formation of some inhibitory compounds which potentially

decreased enzymatic activity. The pH of pretreated biomass-citrate buffer mixture prior to

initiation of hydrolysis was however not significantly different from 5.0 (pH of citrate buffer)

for both the dilute - washed and washed samples. A pH of 5.0 has been reported to be

optimized for Cellic® Ctec2 by the manufacturers. Based on the analysis of reducing sugars

generated after hydrolysis of the various pretreated samples, the optimal pretreatment

providing significantly higher (p < 0.05) % conversions was observed to be 2% KOH, 48 h,

21 oC, washed set with a conversion of 101.%±13.2 with 30% (g enzyme protein/g dry

biomass) enzyme loading. The highest yield of 582.4 mg/g untreated biomass was observed

with 0.5% KOH, 12h, 21C, washed. There was no significant difference ( p< 0.05) in the %

conversion values of the dilute washed samples and the no significant difference in the %

conversion values of the samples hydrolyzed at 0% enzyme loading. A higher yield from the

30% enzyme loading washed samples as compared to those from NaOH pretreatment studies

from Xu et al., 2010b could be attributed to the reactivity patterns of KOH and NaOH on the

biomass structure. Some variations may have also arisen from differences in the enzymes and

41

enzyme loadings used. It was observed that on average 35% of the original untreated biomass

was left after hydrolysis across all pretreated samples.

2.5 Conclusions

The treatment of ground switchgrass with KOH has shown promising reducing sugar

conversion values on an average of 85% after hydrolysis. High sugar yield with the 0.5%

KOH, 12 h, 21 oC treatments showed that even very low concentrations of KOH can be

effective in generating high sugars during hydrolysis. Xu et al. (2010) in their study on lime

pretreatment and NaOH pretreatment of switchgrass observed best yields of 433mg/g raw

biomass and 453 mg/g raw biomass respectively. Kaar and Holtzapple (2010) reported the

best yield of 462 mg/g raw biomass in their study on lime pretreatment of corn stover

compared to these studies, the highest yield KOH pretreatment was numerically higher for

our best yield of 586.2 mg/g raw biomass (Xu et al., 2010a, 2010b, Kaar and Holtzapple,

2000). A theoretical ethanol yield of 96 gallon/ton of switchgrass can be estimated with this

novel method (www1.eere.energy.gov). There is a clear indication of a requirement for a post

pretreatment washing step for generation of maximum sugars from the pretreated biomass.

The high enzyme loading (30% g of enzyme protein/g biomass, based on 210 mg/ml protein

content in the enzyme solution of Cellic ® CTec2 , however has also shown to have

generated high amount of sugars just from the untreated switchgrass samples compared to

previous studies (Xu et al, 2010) which have utilized lesser loadings. This seems to suggest

higher efficacy of the enzyme and its ability to generate considerable sugars for the untreated

biomass. However this aspect needs further exploration. Overall, the high theoretical ethanol

42

yield from the mild KOH pretreated samples suggests that this alkaline pretreatment reagent

has considerable potential but needs to be extensively investigated for comprehensive cost

analysis keeping in mind its higher initial cost of purchase.

2.6 Acknowledgements

The authors appreciate the input by Dr. Sanjeev Tyagi, Principal Scientist, Central Institute

of Post Harvest Engineering and Technology (CIPHET), Ludhiana, Punjab, India during the

initiation of this study. Partial funding for this study was provided by the Indian Council for

Agricultural Research (ICAR) under the World Bank’s National Agricultural Innovation

Project (NAIP).

2.7 References

1 Awolu, Olugbenga O.*, Ibileke, Ibiyemi O, Bioethanol production from brewer’s

spent grain, bread wastes and corn fiber , African Journal of Food Science Vol. 5(3),

pp. 148-155, March 2011.

2 Belkacemi, K., Turcotte, G., de Halleux, D., Savoie, P., Ethanol production from

AFEX-treatedforages and agricultural residues. Appl. Biochem. Biotechnol. 1998, 70-

72: 441-462

3 Burns, J.C., Godshalk, E.B., Timothy, D.H. Registration of “Performer” switchgrass.

J. Plant Registrations. 2008, 2(1): 29-30.

43

4 Carlo N. H., Geertje V.H., Andre, PC. Ethanol from lignocellulosic biomass: Techno-

economic performance in short-middle and long term. Biomass and Bioenergy. 2005,

28: 384-410.

5 Chang, V.S., Nagwani, M., Kim, C.H., Holtzapple, M.T. Oxidative lime pretreatment

of high-lignin biomass: Poplar Wood and newspaper. Appl. Biochem. Biotechnol.

2001, 94: 1-28.

6 Chang,V., Holtzapple, M.T. Fundamental factors affecting biomass enzymatic

reactivity,

7 Chen, Y., Sharma-Shivappa, R.R., Keshwani, D. And Chen, C. Potential of

agricultural residues and hey for bioethanol production. Appl. Biochem. Biotechnol.

2007, 142: 276-290.

8 Dale, B.E., Biofuels, Bioproducts and Biorefining, 13/160 Biotechnology and

Applied Microbiology

9 DOE. Biomass Feedstock Composition and Property Database. Available online at

http://www.afdc.energy.gov/biomass/progs/search1. cgi, 2004.

10 Fan, L.T., Gharpuray, M.M., Lee, Y.H. Cellulose Hydrolysis. In Biotechnology

Moniographs, Springer, Berlin, Germany, 1987; p8.

11 Gharpuray, M. M., Lee, Y.-H., and Fan, L. T. (1983), Biotechnol. Bioeng. 25, 157–

172

12 Ghose, T.K. Measurement of Cellulase activities. Pure Appl. Chem. 1987, 59 (2),

257-268.

44

13 Keshwani, D.R. and Cheng, J.J. Switchgrass for bioethanol and other value-added

applications:a review. Bioresource technology. 2009, 100: 1515-1523.

14 Kong, R., Engler, C. R., and Soltes, E. J. (1992), Appl. Biochem. Biotechnol. 34, 23–

35

15 Kuhad, R.C., Singh, A. Lignocellulose biotechnology: current and future prospectus.

Crit. Rev. Biotechnol. 1993, 13, 151-172.

16 McMillan, J.D. Pretreatment of lignocellulosic biomass. In Enzymatic Conversion of

Biomass for Fuel Production. American Chemical Society: Washington, DC, 1994;

pp 292-324.

17 Miller, G.L. Use of dinitrosalicylic acid reagent for determination of reducing sugars.

Anal. Chem. 1959, 31, 426-428.

18 NREL, theoretical ethanol yield calculator. www1.eere.energy.gov, last accessed, 03-

12-2012.

19 Raymundo-Piñero, P. Azaïs, T. Cacciaguerra, D. Cazorla-Amorós

, A. Linares-

Solano, F. Béguin, KOH and NaOH activation mechanisms of multiwalled carbon

nanotubes with different structural organization, Volume 43, Issue 4, 2005, Pages

786–795.

20 Schmer, M.R., Vogel, K.P., Mitchel, R.B. and Perrin, R.K. Net energy of cellulosic

ethanol from switchgrass. Proc. Natl. Acad. Sci. U.S.A. 2008, 105:464-469.

21 Silverstein, R.A., Chen, Y., Sharma-Shivappa, R.R., Boyette, M.D. and Osborne, J. A

comparison of chemical pretreatment methods for improving saccharification of

cotton stalks. Bioresource Technology. 2007, 98: 3000-3011.

45

22 Sluiter, A., Hames, B., Hyman, D., Payne, C., Ruiz, R., Scarlata, C., Sluiter, J.,

Templeton, D., Wolfe, J. Determination of total solids in biomass and total dissolved

solids in liquid process samples. In Laboratory Analytical Procedure (LAP); National

Renewable Energy Laboratory: Golden, CO, 2005.

23 Sluiter, A., Hames, B., Ruiz, R., Scarlata, C., Sluiter, J., Templeton, D. Determination

of ash in biomass. In Laboratory Analytical Procedure (LAP); National Renewable

Energy Laboratory: Golden, CO, 2005. (a)

24 Sluiter, A., Ruiz, R., Scarlata, C., Sluiter, J., Templeton, D. Determination of

extractives in biomass. In Laboratory Analytical Procedure (LAP); National

Renewable Energy Laboratory: Golden, CO, 2005. (b)

25 Sluiter, A., Hames, B., Ruiz, R., Scarlata, C., Sluiter, J., Templeton, D. Determination

of structural carbohydrates and lignin in biomass. In Laboratory Analytical

Procedure (LAP); National Renewable Energy Laboratory: Golden, CO, 2008.

26 Tarkow, H. and Feist, W. C. (1969), in Cellulases and Their Applications, Gould, R.

F.,ed., American Chemical Society, Washington, DC, pp. 197–218

27 Xu, J., Cheng, J.J., Sharma-Shivappa, R.R. and Burns, J.C. Lime pretreatment of

switchgrass at mild temperatures for ethanol production. Bioresource Technology.

2010, 101: 2900-2903.(a)

28 Xu, J., Cheng, J.J., Sharma-Shivappa, R.R. and Burns, J.C. Sodium hydroxide

pretreatment of switchgrass for ethanol production. Energy Fuels. 2010, 24: 2113-

2119.(b)

46

Figure Legend (Sharma et al.)

Figure 1. Percent reducing sugars content of switchgrass pretreated with 0.5%-2% KOH

(w/v) at A) 21oC, B) 50

oC and C) 121

oC.

Figure 2. Percent acid insoluble content of switchgrass pretreated with 0.5%-2% KOH (w/v)

at A) 21oC, B) 50

oC and C) 121

oC

47

Table 2.1 Conditions for KOH pretreatment of biomass

Temperature (°C ) Residence times (h) KOH Concentration (%)

121 0.25, 0.5, 1.0

0.5, 1.0, 2.0

50 6, 12, 24

21 6, 12, 24, 48

48

Table 2.2 Chemical composition of “Performer” switchgrass

Component Dry weight (%)

Acid Insolube Lignin * 20.9±0.3

Acid soluble Lignin 3.8±0.1

Carbohydrates (Sugar) 67.7±1.

Ash 3.6±0.3

Other 4.0

49

Table 2.3 Solid recovery after KOH pretreatment

Solid recovery (%)

Temperature

(°C)

Time (h) KOH concentration (%)

0.5 1.0 2.0

121 0.25 74.9±2.9

72.9±5.0

59.7±3.5

121 0.5 74.1±2.2

61.9±1.0

54.1± 3.7

121

1.0 76.0±3.6 68.7±4.3 48.7±4.7

50 6 79.2±4.9

77.5±3.2

66.0±2.8

50 12 79.1±5.1

71.9±2.0

65.9 ±1.9

50

24 78.6±2.6 69.8±0.8 69.9±12.9

21 6

80.2±1.3

78.0±0.7

73.0±1.7

21 12

82.5±1.7

84.6±1.5

67.4±6.5

21 24

79.1±1.0

71.3±0.1

71.4±5.6

21

48 76.0±0.3 71.6±1.2 66.6±1.6

50

Table 2.4 Sugar yields and % conversion for washed samples hydrolyzed with 0% and 30%

enzyme loading

Pretreatment set Sugar yield (mg sugar/gm

biomass )

% conversions

0% 30% 0% 30%

UNTREATED 44.1 ±2.4 374.7±0.7 5.6±0.6 55.3±1.3

2% KOH, 48Hr, 21C 29.5±0.7 542.5±60.3 5.1±0.2 102.0±1.1

0.5 % KOH, 12 hr, 21 C 32.5 ±1.5 582.4±61.1 5.58±0.2 91.8±9.3

0.5% KOH , 24 hr,50 C 35.8 ± 2.2 566.9±17.2 5.4±0.1 88.3±0.5

2% KOH, 24 hr , 50 C 28.4± 1.0 514.0±23.7 5.3±0.1 98.3±3.1

1% KOH, 1 hr 21 C 28.7± 1.2 471.5±54.2 4.8±0.01 86.3±4.5

2% KOH, 1 hr, 121 C 23.9± 0.4 444.2±45.3 5.6±0.6 89.2±7.5

51

Table 2.5 Sugar yields and % conversion for dilute washed samples hydrlyzed with 0% and

30% enzyme loading

Pretreatment set Sugar yield (mg sugar/gm

biomass )

% conversions

0% 30% 0% 30%

UNTREATED 44.1±2.4 374.7±0.8 7.5±0.002 55.3±1.3

2% KOH, 48Hr, 21oC 4.6±0.8 286.0±22.7 0.83±0.46 48.42±0.4

0.5 % KOH, 12 hr, 21oC 5.9±0.3 326.1±4.9 0.93±1.5 48.70±1.5

0.5% KOH , 24 hr,50oC 4.8±1.3 278.4±12.3 0.86±3.0 50.19±3.0

2% KOH, 24 hr , 50oC 3.1±0.4 208.9±14.2 0.61±1.3 58.60±1.3

1% KOH, 1 hr 21oC 4.1±0.2 213.2±14.9 0.83±2.7 56.58±2.7

2% KOH, 1 hr, 121oC 3.6±0.7 201.6±13.6 0.80±0.6 54.71±0.6

52

Table 2.6 Orthogonal decomposition of sugars variable

Source Df sum of

squares

mean

square

F p-value

Model 30 2980.108052 99.336935 3.88 < .0001

temp×conc×time(sect) 27 1991.315699 73.752433 2.88 0.0003

Sector 3 988.7923526 329.5974509 12.86 < .0001

short treatment times

Short 8 650.711724 81.338966 3.17 0.0044

conc(sector) 2 507.7 253.9 9.91 0.0002

time(sector) 2 13.9 6.96 0.27 0.7631

conc × time(sector) 4 129.1 32.3 1.26 0.29

intermediate treatment times

intermediate 17 1294.1 76 2.34 0.0157

conc(sector) 2 168.132761 84.066380 3.28 0.0442

time(sector) 2 74.058298 37.029149 1.45 0.2435

temp(sector) 1 108. 335569 108.335569 4.23 0.044

conc × time(sector) 4 127.06 32.3 1.26 0.2942

temp × conc(sector) 2 145.704119 72.852060 2.84 0.0659

temp × time(sector) 2 423.948239 211.974120 8.27 0.0007

temp × conc ×

time(sect)

4 246.897445 61.724361 2.41 0.0587

Long treatment times

conc(sector) 2 46.472387 23.236194 0.91 0.4091

53

Figure 1 Percent reducing sugars content of KOH pretreated switchgrass

0.00

20.00

40.00

60.00

80.00

100.00

6 12 24 48

SUG

AR

S %

( w

/w)

Time (h)

A

0.5

1

2

% KOH

0.00

20.00

40.00

60.00

80.00

100.00

6 12 24

SUG

AR

S% (w

/w)

Time (h)

B

0.5

1

2

% KOH

0.00

20.00

40.00

60.00

80.00

100.00

0.25 0.5 1

SUG

AR

S %

( w

/w)

Time (h)

C

0.5

1

2

% KOH

54

Figure 2 Percent acid insoluble lignin content of KOH pretreated switchgrass

0.00

5.00

10.00

15.00

20.00

25.00

6 12 24 48

AIL

%(w

/w)

Time (h)

A

0.5

1

2

% KOH

0.00

5.00

10.00

15.00

20.00

25.00

12 24 48

AIL

%(w

/w)

Time (h)

0.5

1

2

B

% KOH

0.00

5.00

10.00

15.00

20.00

25.00

0.25 0.5 1

AIL

%(w

/w)

Time (h)

C

0.5

1

2

% KOH

55

CHAPTER 3

Effects of ultrasonication of switchgrass on fermentable sugar generation and structure

3.1 Abstract

Pretreatment of biomass for effective sugar yield has been studied extensively in the area of

bio-fuel research. The conventional methods employed to preprocess biomass for making it

conducive to better sugar yield through enzymatic hydrolysis have been hampered by some

key issues like poor energy efficiency and production of undesirable bio-products.

Ultrasonication is a method that involves the treatment of biomass through ultrasonic waves

in a liquid medium without additional chemicals. In this study the effects of ultrasonic

irradiation on switchgrass, a potential feedstock for bio ethanol production due to it high

cellulosic content were investigated.was observed. Scanning electron microscopy was

conducted to assess structural changes in pretreated samples and based on visual evidence of

disintegration and compositional analyses, samples from select pretreatment combinations

were chosen for enzymatic hydrolysis at two different enzyme loading to assess sugar

generation. Temperature controlled ultrasonication for 60 min, 100% amplitude in stainless

steel vessel , gave the highest sugar conversions of 84.6 and 84.7 % for 30% Cellic® Ctec2

and 30% Dyadic Alternafuel 200L loadings repectively. Based on the overall results, it is

inferred that ultrasonication alone is not suitable to improving sugar generation from

switchgrass and further investigation is needed.

Keywords: ultrasonication, Panicum virgatumis, amplitude, enzymatic hydrolysis, acid

insoluble lignin, scanning electron microscopy.

56

3.2 Introduction

The choice of feedstock is central to the controversy surrounding biofuels today.

Current technologies associated with the use of food as fuel and large-scale changes in land

usage have raised numerous concerns, For biofuels to have any meaningful impact on

energy, biomass feedstock must be widely available at low cost and without negative

environmental impact. Lignocelluloses - the non-food component of plants fits this

description (Mousdale, 2008). Switchgrass offers a potential alternative for lignocellulosic

biomass feedstock due to its high renewability and sugar content (Keshwani and Cheng,

2008). It however presents a need for pretreatment due to break down the lignin and disrupt

the crystalline structure of cellulose, so that enzymes can easily access and hydrolyze it

(Cadoche and Lopez, 1989; Kumar et al., 2009).

Physical pretreatments such as mechanical communiton, pyrolyisis, and steam explosion,

ammonia fibre explosion can be effective in mechanical disruption of cell wall and lignin

bonds but have proven to be energy intensive (Galbe and Zacchi, 2007, Kilzer and Broido,

1965). Chemical pretreatments involve techniques such as ozonolysis, acid hydrolysis and

alkali hydrolysis dissolve, hydrolyze or oxidize the lignin bonds to expose the carbohydrates

but are hampered by undesirable inhibitor/production and require the extensice use of

recovery agents such as water for washing and hence have proven to be cost ineffective (Sun

and Cheng, 2002; Quesada et al., 1999).Ultrasonication is a physical technique that uses ultra

high frequency sound waves to alter the molecular structure of biomass.These ultra high

frequency waves travel in a viscous flow pattern in liquid medium and create pressure

vibrations, which determine the intensity The intensity of these waves is dependent on

57

temperature of the medium, and it has been observed that increase in the temperature of the

medium decreased the intensity of sonication (Feng et al., 2011). Ultrasonication has also

been applied in biological processes for disruption of cell membranes and release of cellular

enzymes also known as sonoporation. The acoustics from an ultrasound irradiated system in

liquids have been shown to effect particles in the range of 0.15 to 100 to mm (Suslick et al.,

1991 and 1994). There is a non-linear effect of the acoustic phenomena which depends on

cavitation, defined as the growth and implosive collapse of bubbles in a liquid irradiated by

ultrasound. These implosive cavity hot spots created by the collapsing bubbles have been

reported to have temperatures of roughly 5300K, pressures of about 1720 bar, and heating

and cooling rates above 109 Ks-1

(Suslick et al., 1991 and 1994). Sun et al. (2004)

investigated the extractability of hemicelluloses from bagasse by ultrasound-assisted

extraction and found that ultrasonic treatment and sequential extractions with alkali and

alkaline peroxide under the conditions led to a release of over 90% of the original

hemicelluloses and lignin. The hemicellulosic fractions obtained after ultrasonic extraction

contained relatively low amounts of associated lignins, ranging between 0.41% and 7.36%

(Sun et al., 2004). Zhang et al. (2007) while working on developing cellulose fiber for use in

composites showed that cellulose nano fibers could be extracted from lignocellulose by the

application of high intensity ultrasonication. They showed that cellulose could be treated

with ultra high frequency sound waves to produce small fibrils at nano and micro scales.

They proposed that ultrasonic hydrodynamic forces of ultrasound produce very strong

oscillating mechanical power, which may lead to the separation of cellulose microfibrils.

This indicates that ultrasound waves impact the complex lignocellulosic matrix and there is

58

potential for more refined work in the area. (Zhang et al, 2007) An added benefit of

ultrasonication is the elimination of a toxic waste stream typically generated by conventional

chemical methods. Hence this study was undertaken, as proof-of-concept, to determine the

impact of reaction vessel construction material, treatment time, amplitude and temperature

control during ultrasonication of switchgrass on its lignin content and sugar generation

potential. The structural changes in switchgrass subjected to ultrasonication were examined

by scanning electron microscopy (SEM).

3.3 Materials and methods

3.3.1 Biomass preparation

Switchgrass for all treatments in the stainless steel beaker were obtained through an

intercropping sustainability study by Weyerhaeuser Inc., investigating cultivation of loblolly

pine (Pinus taeda L.) silviculture for solid wood products intercropped with switchgrass

(Panicum virgatum L.) for biofuels production near Dover, NC was harvested in January

2011. The biomass was dried in cloth bags at 50 oC for 48 h in a convection oven at

Metabolism Education Unit, department of Animal Science, North Carolina State University,

Raleigh, NC. Dry biomass was ground to pass a 2 mm sieve in a Willey mill. The ground

switchgrass was stored in zip-locked bags at room temperature until used. Since the

composition of the two switcgrass batches were not significantly different the results reported

here are not distinguished on the basis of batch

59

3.3.2 Compositional Analysis

Acid soluble lignin (ASL), acid insoluble lignin (AIL) in untreated and sonicated samples

was determined by methods of Sluiter et al. (2008). Briefly, a 2 step acid hydrolysis was

conducted with 72% and 4 % sulfuric acid. The solids recovered after acid hydrolysis was

used to determine AIL and the hydrolysate were used for determination of ASL and reducing

sugars. Reducing sugar analysis for untreated sonicated, and enzyme hydrolysate samples

were conducted using the dinitrotrisalicylic acid (DNS) assay (Miller, 1959, Ghose, 1987).

Ash content in untreated biomass was determined as described by standard procedure given

by National Renewable Energy Laboratory Laboratory Analytical Procedures (LAP)

established by National Renewable Energy Laboratory (Sluiter et al., 2005a).

3.3.3 Scanning electron microscopy

Scanning electron microscopy (SEM) of selected ultrasonicated samples was performed for

comparative visual analyses. The samples analyzed were i) untreated sample, ii) temperature

controlled, 10 min, stainless steel 100 % amplitude sonicated and temperature controlled, 1h

min, stainless steel, 100% amplitude combination. The analysis was conducted with Hitachi

S-3200 N SEM equipment at the Analytical Instrumental Facility (AIF), NC State University.

Two g each of the select amples were vacuum dried at 40oC for 48 h. Each sample was then

dried to 0% moisture in a liquid nitrogen drying assembly before being sputter gold coated to

be visualized through the SEM.

60

3.3.4 Pretreatment

Two different batches of Alamo switchgrass were ultrasonicated by the Hielscher 1000 hd.

Hielscher 1000 hd consisted of the following parts 1) transducer 2) booster 3) sonotorode

fuel cell 4) amplitude control unit. The epuipment generates ultrasonic frequency of 20 kHz

at 2 kW, and maximum amplitude of 170 micron. Switchgrass slurry was sonicated using the

sonotrode which is the part of the equipment that transfers the oscillations created in the

transducer. The transducer being the part where electrical signals are converted into

mechanical oscillationsPreliminary studies on ultrasonication of switchgrass were conducted

in a 150 ml glass beaker at a biomass loading of 10% in 100ml deionized water without

temperature control and stirring. This resulted in accumulation of a thick layer of biomass

around the sonotrode of the ultrasonicator leading to non-uniform effect of the ultrasonic

irradiation on the biomass. The height of the sonotrode base from the bottom of the beaker

also impacted biomass di stribution during sonication. Hence stirring of the biomass slurry

inside the glass beaker to maintain uniform effect of irradiation was introduced and the

height of the sonotrode adjusted to be closer to the bottom of the beaker. Based upon studies

on ultrasonication it was observed that with increase in temperature in the biomass dissolving

medium, the intensity of the ultrasonication waves decreased. Hence treatment with

temperature control was also included in the study (Feng et al, 2011). Experiments were

conducted to determine the impact of reaction vessel construction, treatment time, and

amplitude and temperature control during sonication on biomass composition and structure

(Table 3.1).

61

Ground switchgrass slurry at 10% solid loading in 100 ml deionized water was prepared in

150 ml glass or stainless steel beakers for sonication. A magnetic stir bar was placed in the

beaker and the beaker was placed over a magnetic stirrer. The magnetic stirrer was then

placed over a “jiffy jack” such that it was exactly below the sonotrode of the ultrasonicator

(long vertical rod suspended from the top of the instrument). Constant stirring at 150 rpm

was maintained to ensure homogeneity during various treatments. Temperature of the slurry

and power dissipated during sonication was monitored every 30 s for representative samples

using a thermocouple and power gauge, respectively. Sonication without temperature control

in the glass beaker was carried out for 5, 7.5, and 10 min at 50, 75 and 100% amplitude

(corresponding with 75, 112.5, 150 µm at the face of the sonotrode) without temperature

control. Sonication treatments in a stainless steel beaker were carried out for 5, 10 and 60

min at 50, 75 and 100% amplitude. The effect of cooling the reaction vessel was investigated

by placing the stainless steel beaker in an ice bath during sonication. The temperature during

sonication was thus maintained on average at 50oC. After treatment the jiffy Jack was

lowered and the beaker with treated sample removed. Biomass stuck to the sontrode was

recovered in a clean beaker by spraying 100ml-distilled water on it. The wash water and

recovered biomass were filtered in a Buchner funnel and flask assembly by vacuum filtration

and solids collected for determination of solid recovery via moisture content measurement of

a sub-sample at 105 oC. Five g of wet recovered biomass per replicate was dried at 40

oC in a

vacuum oven for compositional analyses.

62

3.3.5 Enzymatic hydrolysis

Enzymatic hydrolysis was carried out on select pretreated biomass to determine the effect (if

any) of structural changes in ultrasonicated switchgrass on generation of fermentable sugars.

Samples from two treatments, a) highest amplitude and longest time period and b) lowest

amplitude and shortest time period were selected for hydrolysis. Cellic® Ctec2 provided by

Novozymes NA, Franklinton and AlternaFuel 200L from Dyadic International Inc., Jupiter,

Florida were used for hydrolysis. Both enzyme cocktails are mixtures of cellulase,

hemicellulase and -Glucosidase. The samples were treated with 2 enzyme loadings each to

establish the range of sugar production. A high loading of 0.3g enzyme protein/ g dry

biomass for Cellic® Ctec2 (H1) and Dyadic AlternaFuel 200L (H2) and a low enzyme

loading corresponding with 0.1 g enzyme protein / g dry biomass for Cellic® Ctec2 (L1) and

0.05 g enzyme protein / g dry biomass for Dyadic AlternaFuel 200 L (L2). Pretreated

samples with no enzyme addition (0 g enzyme protein/ g dry biomass) and untreated samples

with the low and high enzyme loadings were hydrolyzed as controls. Hydrolysis was

conducted at a solid loading of 8% in a 20 ml solution volume made up (apart from enzyme

volume) by sodium citrate buffer as the (pH 4.9) and tetracycline hydrochloride solution

equivalent to 40µg/ml to prevent contamination of the hydrolyzate.

3.3.6 Statistical analysis

All experiments in this study were conducted in triplicate. Statistical analyses using PROC

GLM procedure for a balanced factorial design was conducted in SAS 9.2© (Cary, NC) to

determine the significance of results for the two response variables (acid insoluble lignin and

63

reducing sugar content in the treated and untreated samples) over two independent variables

(amplitude and treatment time). Results from treatments in the three reaction vessel

construction materials a) stirred glass batch, b) stainless steel batch without temperature

control and c) stainless steel batch with temperature control were also compared. The

enzymatic hydrolysis data for % conversion of carbohydrates ( represented by reducing

sugars) in the pretreated or untreated biomass to fermentable sugars in the hydrolyzate was

also statistically analyzed using one way ANOVA, with % conversion as the response

variable.

3.4 Results and discussion

First batch of untreated switchgreass used in the study contained 23.5 % AIL, 4.7 % ASL,

64.5 % total reducing sugars and 1.6% ash. The second batch of untreated switchgrass used

in this study contained 24.3 % AIL, 1.9% ASL, 69.7% total reducing sugars and 1.2% ash.

3.4.1 Effect of ultrasonication on switchgrass composition

The effect of ultrasonciation on switchgrass composition was characterized by three

parameters for the various pretreatment conditions, namely, solid recovery, acid insoluble

lignin (AIL), and total reducing sugars.

3.4.1.1 Solid recovery

Since there was no chemical degradation during ultrasonication, high solid recoveries were

observed across all pretreatement combinations. The highest solid recovery of 94% was

64

observed to for 10 min, 75% amplitude batch treatment in and the lowest was observed to be

76% for the 7.5 min, 75% amplitude combination. An average solid recovery across all

pretreatements was estimated to be 88.3%. Statistical analysis of solid recovery showed that

neither time, nor amplitude had any significant effect (p< 0.05) on solid loss.

3.4.2.2 Acid insoluble lignin

The highest average acid insoluble lignin content of 25.4 % for samples sonicated in a glass

beaker with no temperature control was observed for the 10 min, 75% amplitude treatment

and the lowest acid insoluble lignin content was observed for the 10 min, 50% amplitude

treatment resulting in 18.6 % AIL.

The highest acid insoluble lignin content for temperature controlled, sonication in the

stainless steel beaker was 22.4% observed for the 10 min 100% amplitude treatment

combination. The lowest acid insoluble lignin values for this set were observed for the

combination 10 min, 50% amplitude at 20.04%.

Statistical analysis of the data from the 3 ultrasonication strategies namely a) glass with no

temperature control, b) stainless steel without temperature control and c) stainless steel with

temperature control did not show a statistically significant (p > 0.05) difference in lignin

content among the pretreated samples. In a study conducted by Gadhe et al.,( 2006) while

utilizing a sonochemical reactor at an ultrasonic frequency of 600 kHz to generate free

radicals coupled with 4 drops of 1 N NaOH on hydrolytic lignin for 5h an increase in non

conjugated carbonyl groups and a decrease in conjugated carbonyl groups was observed.

This was indicative of degradation of the lignin polymer. These results indicated that to

65

achieve degradation of lignin extremely high ultrasonic frequencies coupled with chemical

additives and long pretreatment time are required. Hence ultrasonication of switchgrass in

this study, which utilized a maximum ultrasonic frequency of 20 kHz with no chemical

additives, did not show any significant change in the composition of samples.

3.4.1.3 Total reducing sugars

The lowest reducing sugar values for the no temperature control, batch, glass beaker

combination set, were observed for the combination ranged between 7.5 min 75% amplitude

at 46.6% and the highest reducing sugar values were observed for 10 min 75% amplitude at

70.1%.

The lowest total reducing sugar values for the no temperature control, batch, metallic beaker

set were observed for the combination, 60 min, 75% amplitude at 59.0% and the highest

reducing sugar values were observed for 5 min 50% amplitude at 67.2%.

The lowest total reducing sugar values for the no temperature control, batch, metallic beaker

set were observed for the combination 5 min 75% amplitude at 60.7% and the highest total

reducing sugar values for this set was observed for the combination, 10 min, 100% amplitude

at 67.2%.

Statistical analysis of the data from the 3 ultrasonication strategies namely a) glass with no

temperature control, b) stainless steel without temperature control and c) stainless steel with

temperature control showed a significant drop (p<0.05) in sugar content of samples from

various pretreatment combinations compared to untreated samples.

66

The loss of sugars could be explained by the dissolution effect caused by disruption of the

lignocelluloses matrix. It can be inferred that solid loss, which ranged between recoveries of

76.3%-94.6% during sonication, was primarily due to loss of reducing sugars since the

average sugar retention of the original content observed for various pretreated samples was

85%.

3.4.2 Scanning electron microscopy

To better understand and comprehend the structural changes that occurred during

ultrasonication, select switchgrass samples were observed by scanning electron microscopy

(SEM). Figure 1A depicts untreated switchgrass which shows integrity and robustness of

particles. It shows a clear and intact outer core and solid edges. In Fig 1B, which represent

samples from temperature controlled sonication in the metallic beaker for 10 minutes, it was

observed that at shorter time intervals a slight peeling away and disruption of the outer core

occurred possibly due to the collapsing of bubbles and pressure variations.When

ultrasonication was conducted for a longer time interval of 1 h in the temperature controlled

stainless steel beaker, a extensive disruption of the outer sheath and rupturing of the inner

core occured (Figs 1 C and D). These samples also show a tendency of crack formation on

the outer layer on some of the particles visualized which suggest mechanical disruption of the

outer layer through the force exerted on the particles.

These trends of mechanical disruption were in accordance with earlier studies conducted on

SEM analyses of physically pretreated lignocellulosic materials (Behera et al., 1996). There

was no evidence of pore formation or any solubulization effect on the biomass structure in

67

the ultrasonicated samples confirming the compositional analysis trends of insignificant

lignin degradation. The mechanical disruption however might have led to an increase in the

surface area of the particles rendering them effective for enzymatic hydrolysis.

3.4.3 Sugar yield after enzymatic hydrolysis

As there were no significant(p<0.05) differences in the composition of samples from various

sonication conditions, samples for hydrolysis were selected on the basis of treatment

intensity and its impact on structure which could be deduced from visual analyses through

SEM. Two samples each from both the temperature controlled and no temperature controlled

stainless steel beaker sets with one being the least intense, i.e. 50% amplitude, 5 min, stirred

and the other most intense, i.e. 100% amplitude, 1 h , stirred were chosen for enzymatic

hydrolysis.

Reducing sugars in the hydrolysates from various treatment combinations and enzyme

loadings (Table 3.3 & 3.4) investigated ranged between 467 mg/g and 511 mg/g dry

untreated biomass for H1 loading and between 455mg/g and 511mg/g for H2. H1 for the

chosen combination sets led to sugar conversion of 84.6% in the temperature controlled 60

min, 100% amplitude, stainless steel beaker sonication. The conversion of untreated

switchgrass with H1 was observed to be 73.4%. H2 led to the highest (p< 0.05) sugar

conversion of 84.7% in the temperature controlled 60 min, 100% amplitude, stainless steel

beaker and the conversion of untreated switchgrass with H2 30% loading was observed to be

81.2 %. At lower enzyme loadings of L1 and L2, conversion values of 32.1% and 10.5 %

were observed for the higher intensity sonication treatment and 25.9% and 8.9% for lower

68

intensity treatments respectively. The 0% enzyme loading yielded negligible amount of

sugars in the range of 17-22 mg/g of dry untreated biomass across all pretreatment

combinations.

One way anova analysis indicated that with H1 loading, samples drawn from temperature

controlled 60 min, 100% amplitude, stainless steel beaker combination had significantly

higher conversion values (p < 0.05) than untreated switchgrass and samples from other

treatment combinations. The statistical analysis of H1 loading samples when compared with

H2 loading showed that H1 samples the untreated samples showed significantly low

conversion value than the highest converstion treatment, whereas H2 loading samples

showed that the untreated was not significantly different (p<0.05) from the highest

conversion treatment value but was significantly higher (p<0.05) than the rest of the

treatment combinations. The L1 and L2 loadings showed the same pattern with the highest

conversion observed for the temperature controlled 60 min, 100% amplitude combination

was significantly higher (p<0.05) from all the other sets.

The pretreatment effectiveness of temperature controlled ultrasonication at higher residence

time and high amplitude showed that temperature of the liquid medium had an effect on

mechanical disruption of the lignocellulosic matrix confirming the earlier studies on the

inverse relationship between intensity of ultrasonication and temperature of medium (Feng et

al, 2010). This enhanced mechanical disruption seemed to have increased the hydrolysis

efficiency of the treatment combination.

The total sugar yields however were less than the untreated for all the treatement

combinations in both the temperature sets possibly due to loss of some sugars during

69

ultrasonication. This suggested that ultrasonication alone did not alter the structure enough to

significantly (p< 0.5) produce higher yields of reducing sugars. This observation was in

accordance with studies conducted, where a chemical additve was required to assist

ultrasonication for significantly high yields (Mazzoccoli and Paul, 2010; Montalbo-Lomboy

et al., 2010; Younus et al., 2010).

3.5 Conclusions

Based on the results on this study it can be concluded that sonication treatment at 170 micron

(100%) amplitude, for 60 min in a stainless steel beaker with temperature control was the

most effective pretreatment strategy among all the conditions tested. The high conversion

values of the untreated samples at high enzyme loading H1 and H2, need to be investigated

further. The tendency of higher enzymatic loadings in this study to generate considerably

high sugar conversion in untreated switchgrass samples presents a new direction relative to

economic viability of pretreatment and hydrolysis processes.

However ultrasonication alone did not prove to be an effective pretreatment technique

overall. The reported range of particle size that can be affected by ultrasonication (0.15 – 100

mm) is large and therefore higher particle sizes need to be explored to determine the true

potential of this technique.

Considering that ultrasonication does not provide significant change in chemical composition

of biomass, it could be utilized as preliminary pretreatment step to decrease the severity of a

primary pretreatment method. In this study a tendency of the opening up of the biomass

70

structure through mechanical disruption and this could be utilized to assist biological

treatments which have longer residence times (Yu et al., 2009).

3.6 Acknowledgements

This material is based upon work supported by the Department of Energy under Award

Number GO88053.

The authors would like to thank Dr. Joe Burns, Department of Crop Science, NCSU and Dr.

Zakiya H. Leggett, Weyerhaeuser Company (Southern Timberlands RandD) for assistance in

obtaining the switchgrass.

Disclaimer: This report was prepared as an account of work sponsored by an agency of the

United States Government. Neither the United States Government nor any agency thereof,

nor any of their employees, makes any warranty, express or implied, or assumes any legal

liability or responsibility for the accuracy, completeness, or usefulness of any information,

apparatus, product, or process disclosed, or represents that its use would not infringe

privately owned rights. Reference herein to any specific commercial product, process, or

service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute

or imply its endorsement, recommendation, or favoring by the United States Government or

any agency thereof. The views and opinions of authors expressed herein do not necessarily

state or reflect those of the United States Government or any agency thereof.

71

3.7 References

1. Cadoche, L., Lopez, G. D., Assessment of size reduction as a preliminary step in the

production of ethanol from lignocellulosic wastes Biol. Wastes 1989, 30, 153– 157.

2. Feng. H., Barbosa-Canovas, G., Weiss, J., Ultrasound technologies for food and

bioprocessing, 2011, 2011, X11, 666p.

3. Galbe, M., Zacchi, G., Pretreatment of lignocellulosic materials for efficient

bioethanol production Adv. Biochem. Eng./Biotechnol. 2007, 108, 41– 65

4. Ghose, T.K. Measurement of Cellulase activities. Pure Appl. Chem. 1987, 59 (2),

257-268.

5. Keshwani, D.R., Cheng, J.J., Switchgrass for bioethanol and other value-added

applications: a review, Bioresour Technol 2009 Feb;100(4):1515-23. Epub 2008.

6. Kilzer, F. J., Broido, A.Speculations on the nature of cellulose pyrolysis

Pyrodynamics 1965, 2, 151– 163. Shafizadeh, F., Bradbury, A. G, W.Thermal

degradation of cellulose in air and nitrogen at low temperatures J. Appl. Polym. Sci.

1979, 23, 1431– 1442.

7. Kumar, P., Diane, M.B., ,Delwiche, M. J., Stroeve , P., Methods for Pretreatment of

Lignocellulosic Biomass for Efficient Hydrolysis and Biofuel Production, Ind. Eng.

Chem. Res., 2009, 48 (8), pp 3713–3729.

8. Miller, G.L. Use of dinitrosalicylic acid reagent for determination of reducing sugars.

Anal. Chem. 1959, 31, 426-428

72

9. Mousdale, David M, Biofuels, biotechnology, chemistry, and sustainable

development Boca Raton : CRC Press, c2008.

10. Quesada, J., Rubio, M., Gomez, D, Ozonation of Lignin Rich Solid Fractifrom Corn

Stalks. J. Wood Chem. Technol. 1999, 19, 115–137.

11. Schmer, M.R., Vogel, K.P., Mitchel, R.B. and Perrin, R.K. Net energy of cellulosic

ethanol from switchgrass. Proc. Natl. Acad. Sci. U.S.A. 2008, 105:464-469.

12. Sluiter, A., Hames, B., Hyman, D., Payne, C., Ruiz, R., Scarlata, C., Sluiter, J.,

Templeton, D., Wolfe, J. Determination of total solids in biomass and total dissolved

solids in liquid process samples. In Laboratory Analytical Procedure (LAP); National

Renewable Energy Laboratory: Golden, CO, 2005.

13. Sluiter, A., Hames, B., Ruiz, R., Scarlata, C., Sluiter, J., Templeton, D. Determination

of ash in biomass. In Laboratory Analytical Procedure (LAP); National Renewable

Energy Laboratory: Golden, CO, 2005 (a)

14. Sluiter, A., Hames, B., Ruiz, R., Scarlata, C., Sluiter, J., Templeton, D. Determination

of structural carbohydrates and lignin in biomass. In Laboratory Analytical

Procedure (LAP); National Renewable Energy Laboratory: Golden, CO, 2008.

15. Sluiter, A., Ruiz, R., Scarlata, C., Sluiter, J., Templeton, D. Determination of

extractives in biomass. In Laboratory Analytical Procedure (LAP); National

Renewable Energy Laboratory: Golden, CO, 2005(b)

16. Sun, J., Sun, R., Sun, X., Su, Y., Fractional and physico-chemical characterization of

hemicelluloses from ultrasonic irradiated sugarcane bagasse , Volume 339, Issue 2,

22 January 2004, Pages 291-300

73

17. Sun, Y., Cheng, J., Hydrolysis of lignocellulosic materials for ethanol production: A

review Bioresour. Technol. 2002, 83, 1– 11.

18. Suslick, K.S., Hammerton, D.A., Cline Jr., R.E., Amer, J., Chem,S.K., 1986,108,

SMl., E. B. Flint, K. S. Suslick, Science 1991,253, 1397

19. Suslick, K.S., Kemper. K.A., in Bubble Dynamics and Interface Phenomem (Ed.: J.

R. Blake, N. Thomas) Kluwer, Dordrecht, 1994, pp. 31 I-320.

20. Zhang, H., Wang, Z.G., Zhang, Z.N., Wu, J., Zhang , J., He, J.S., Regenerated-

Cellulose/Multiwalled- Carbon-Nanotube Composite Fibers with Enhanced

Mechanical Properties Prepared with the Ionic Liquid 1-Allyl-3-methylimidazolium

Chloride, Advanced Materials Volume 19 Issue 5, Pages 698 – 704, Feb 2007

74

Figure Legend (Sharma et al.)

Figure 1. Temperature and power dissipation profile during ultrasonication of switchgrass in

glass reaction vessel (A) 5 min, 100% amplitude, glass beaker, stirred, (B) 7.5 min,

100% amplitude, glass beaker, stirred (C) 10 min, 100% amplitude, glass beaker,

stirred.

Figure 2. SEM images of untreated and pretreated switchgrass at 500X magnification (A),

switchgrass pretreated at 100% amplitude for 10 min in a stainless steel beaker with

temperature control at 500X magnification (B), switchgrass pretreated at 100%

amplitude for 60 min in a stainless steel beaker with temperature control at location

1, 250X magnification (C), and switchgrass pretreated at 100% amplitude for 60

min in a stainless steel beaker with ntemperature control at location 2, 500X

magnification (D).

Figure 3. Percent acid insoluble lignin of switchgrass samples ultrasonicated in A) glass

beaker with no temperature control B) stainless steel beaker with no temperature

control and C) stainless steel beaker with temperature control.

Figure 4 Percent sugars of switchgrass samples ultrasonicated in A) glass beaker with no

temperature control B) stainless steel beaker with no temperature control and C) stainless

steel beaker with temperature control.

75

Table 3.1 Treatment parameters investigated during ultrasonication

Amplitude (%) Treatment time (min) Treatment condition

50, 75, 100 5, 7.5, 10 Glass vessel, no temperature control

50, 75, 100 5, 10, 60 Stainless steel vessel, no temperature control

50, 75, 100 5, 10, 60 Stainless steel vessel, temperature control

76

Table 3.2 Solid recoveries for ultrasonicated samplesTable

Treatment Time(min) Amplitude

%

50 75 100

Glass stirred 5 84.9±1.2

78.4±8.5

88.5±3.0

Glass stirred 7.5 87.7±1.7

76.0±9.0

89.7±1.8

Glass stirred 10 87.10±2.7 94.0±2.8 83.1±0.9

S. Steel, without

temperature control

5

89.7±1.1

90.5±6.0

88.7±1.9

S. Steel, without

temperature control

10 93.7±1.3

90.3±4.2

91.2±4.1

S. Steel, without

temperature control

60 91.2±2.8 90.9±2.5 90.2±1.5

S. Steel, with

temperature control

5

90.0±2.3

88.4±1.0

88.0±2.8

S. Steel, with

temperature control

10 89.4±4.9

89.7±0.9

88.6±4.0

S. Steel, with

temperature control

60 91.0±0.3

90.0±2.1

88.0±4.1

77

Table 3.3 Sugar yields and % conversion for samples with Novozyme Cellic® Ctec2

loadings (H1, L1,) & 0% loading

Pretreatment set

Sugar yield (mg sugar/gm biomass)

% conversions

0% L1 H1 0% L1 H1

UNTREATED 18.5±2.0 194.0±6.0 509.4±4.1 2.7±0.3 28.0±0.5 73.4±2.1

US NTC 100 % 1Hr 15.4±3.8 158.8±3.5 466.8±12.3 2.6±0.6 26.0±1.0 78.2±1.8

US NTC 50 % 5 min 20.5±0.9 178.5±28.2 491.9±32.4 3.1±0.2 27.2±3.5 75.2±2.1

US TC 100% 1hr 22.2±2.0 189.0±7.9 497.9±12.9 3.8±0.3 32.1±0.9 84.6±2.1

US TC 50% 5 min 17.4±0.9 167.7±16.5 510.6±22.7 2.7±0.2 25.9±3.5 78.6±5.1

78

Table 3.4 Sugar yields and % conversion for samples with Dyadic Alterna fuel 200L

loadings (H2, L2)

Pretreatment

set

Sugar yield (mg

sugar/gm biomass )

% conversions

L2

H2 L2 H2

UNTREATED 77.5±8.0 563.7±23.1 11.2±1.0 81.2±4.3

US NTC 100 % 1Hr 25.5±7.1 454.6±11.6 4.3±1.2 76.1±1.6

US NTC 50 % 5 min 62.5±5.1 500.1±25.4 9.6±1.0 76.5±3.1

US TC 100% 1hr 61.6±5.1 498.5±23.1 10.5±1.0 84.7±3.2

US TC 50% 5 min 58.2±3.1 510.5±10.8 8.9±0.4 78.5±5.1

79

Figure 1 Temperature and power dissipation profiles during ultrasonication of switchgrass in

glass reaction vessel.

0

100

200

300

400

0

20

40

60

80

100

0 100 200 300 400

Tem

p (

°C)

Time (s)

A

Temp Power( watt)

0

100

200

300

400

0

50

100

150

0 200 400 600

Tem

p (

°C)

Time (s)

B

Temp Power( watt)

0

100

200

300

400

0

20

40

60

80

100

120

0 200 400 600

Tem

p (

°C)

Time (s)

Temp Power( watt)

80

Figure 2 SEM images of untreated and pretreated switchgrass

A B

D C

81

Figure 3 Percent acid insoluble lignin in ultrasonicated switchgrass samples.

0.00

10.00

20.00

30.00

40.00

100% 75% 50%

A

I

L

%

AMPLITUDE

5 min

7.5 min

10 min

A

0.00

10.00

20.00

30.00

40.00

100% 75% 50%

A

I

L

%

AMPLITUDE

5 min

10 min

60 min

B

0.00

10.00

20.00

30.00

40.00

100% 75% 50%

A

I

L

%

AMPLITUDE

5 min

10 min

60 min

C

82

Figure 4 Percent reducing sugars in ultrasonicated switchgrass samples

0.00

20.00

40.00

60.00

80.00

100.00

100% 75% 50%

S

U

G

A

R

S

%

AMPLITUDE

5 min

7.5 min

10 min

A

0.00

20.00

40.00

60.00

80.00

100.00

100% 75% 50%

S

U

G

A

R

S

%

AMPLITUDE

5 min

10 min

60 min

B

0.00

20.00

40.00

60.00

80.00

100.00

100% 75% 50%

S

U

G

A

R

S

%

AMPLITUDE

5 min

10 min

60 min

C

83

CHAPTER 4

Conclusions and future scope of work

With the ever increasing demand for alternative and renewable sources of energy, conversion

of starch or sugar based biomass feedstocks for sugar generation has played an important role

in the realm of bio-fuels. However, these feedstocks also cannot meet all our fuel needs.

Hence, lignocellulosic feedstocks have been explored for conversion to sugar for

fermentation. Such feedstocks require an additional pretreatment step to make them

accessible to hydrolytic enzymes.

With constant advances in the usage of different physical, chemical and biological methods

to treat carbohydrate rich lignocellulosic biomass, the challenge now is to come up with

refined methods to circumvent issues like generation of significant waste streams.

In this study of a physical and a chemical pretreatment method, relatively less utilized modes

of affecting biomass structure for sugar generation were explored.

The key conclusions of the study were:

(1) All KOH pretreatment combinations resulted in an average of 85% released

conversion of carbohydrates present in the biomass.

(2) The highest carbohydrate conversion (based on reducing sugar contents) was

observed for the combination of 2% KOH, 12 h, and 21 C. However, the 0.5% KOH,

24 h, 21 C combination proved to be the most effective utilizing the least amount of

KOH and giving a sugar yield of 582.4 mg/g with a corresponding released sugar

conversion of 91.8% , with a high enzymatic loading of 0.3 Cellic® Ctec 2 g enzyme

/ g biomass.

84

(3) The 0.5% KOH, 12 h, 21 C pretreatment that yielded 582.4 mg/g reducing sugars when

compared to (Ca(OH)2 and NaOH pretreatment presents a strong alternative to these

extensively used alkaline solutions. Xu et al. (2010a, 2010

b) in their study on lime

pretreatment and NaOH pretreatment of switchgrass observed best yields of 433mg/g raw

biomass and 453 mg/g raw biomass. Kaar and Holtzapple (2010) reported best yield of

462 mg/g raw biomass in their study on lime pretreatment of corn stover. These values

are lower than those observed in this study on KOH.

In the second study on a physical refinement pretreatment technique, ultrasonication, no

chemical additives were utilized for altering the biomass. In our study we concluded that

(1) A delignification of 20% of the total acid insoluble lignin content relative to the

untreated biomass was observed on an average for all the pretreatment combinations

employed is this study. This however was statistically insignificant (p > 0.05) in

terms of differences in delignification capability among the pretreatment methods

utilized. This could be explained by the lack of any dissolution of the lignin structure

and due to no chemical activity. An average of 12% solid loss was observed across all

pretreatment combinations. A range of 68.2%-98.7% sugar retention was observed

across all pretreatment combinations suggesting high sugar retention.

(2) The pretreatment was with most potential was the temperature controlled, 60 min

sonication in a stainless steel container at 100 % amplitude. It was observed that 86%

carbohydrate conversion occurred with the two high enzyme loadings H1 and H2

85

(0.3g enzyme protein/ g dry biomass of Cellc® Ctec2 and Dyadic Alternafuel 200L

respectively).

(3) It must be noted that high carbohydrate conversions occurred during hydrolysis of

untreated carbohydrate biomass potentially due to the high enzyme loading of 0.3 g

enzyme protein/ g dry biomass. The sugar yields were observed to be 375.5 for

performer switchgrass and 509.9 for Alamo.

References

1. Kaar, W.E., Holtzapple, M.T., using lime pretreatment to facilitate the enzymatic

hydrolysis of corn stover, Biomass and Bioenergy 18 (2000) 189-99.

2. Xu, J., Cheng, J.J., Sharma-Shivappa, R.R. and Burns, J.C. Lime pretreatment of

switchgrass at mild temperatures for ethanol production. Bioresource Technology.

2010,101,2900-290.

86

APPENDICES

87

APPENDIX 1

Scanning electron microscopy for Chapter 3

Introduction

The compositional analyses of ultrasonicated samples and the subsequent statistical analyses

showed that ultrasonic irradiation did not significantly degrade the ligno-cellulosic

molecules. This can be explained as ultrasonication is a purely physical treatment, thus the

possibility of any chemical reactions taking place, when the solvent is double de-ionized

distilled water are minimal. This led to the examination of any structural changes that may

have taken place relative to increase in surface area, disruption of the biomass and breaking

or loosening off of the outer crust of the biomass structure. Hence scanning electron

microscopy analysis was performed on select samples.

Material and methods

SEM analysis was conducted with Hitachi S-3200 N SEM equipment at the Analytical

Instrumental Facility (AIF), NC State University. Two g each of the selected samples for

analysis was vacuum dried at 40oC for 48 h. Each sample was then dried to 0% moisture with

liquid nitrogen drying apparatus before being sputter gold coated to be visualized through the

SEM.

The samples were drawn from the following treatment combinations:

88

1. Untreated Panicum virgatum L sample: This category of images that analyzes untreated

switchgrass in fig (a) clearly depicts integrity and robustness of particles. When

magnified to 100x and 500X it shows a clear and intact outer core and solid edges

2. Temperature controlled batch, stirred sample slurry in DIW at 10% solid loading: Fig b

and c indicate that at lower time intervals we see slight peeling away of the outer core

and at hollow tubular exposure at the edges, as we move to a longer time interval of 1 h

we see complete disruption of the outer sheath and rupturing of the inner core. These

samples also show a tendency of physical disruption on the surface.

3. No temperature control,stirred continuous recirculated sample slurry in DIW with 3%

solid loading: These category of images (fig d), suggest similar patterns like fig b & c,

with peeling away at the edges and irregular crack formation but less profound which

suggest that direct exposure and higer loading may have impacted uniform action of the

ultrasonication irradiation.

4. No temperature control,stirred continuous recirculated sample slurry in 0.5% NaOH

(w/v) solution with 3% solid loading (fig e) show clear signs of degradation of the outer

layer by a chemical dissolution effect but in very mild proportions as most of the particles

seem to retain their integrity. This may be due to disintegration of NaOH during

sonication and thus limited attack on biomass.

5. Unsonicated, stirred slurry in 0.5% NaOH (w/v) with 10% solid loading show an

increased chemical treatment effect where the outer layers seem to have been degraded

and clear regular shaped pore formation is observed. This could be attributed to higher

89

loading and direct impact of the stirring for a prolonged period where NaOH seemed to

have uniformly impacted the biomass.

Scanning electron microscopy provided insight into the structural changes that may have

taken place during the pretreatment process. It supplemented compositional analyses data

which could not provide significant information on the bio-chemical or physical changes

taking place in the biomass. SEM images helped to deduce clear outer layer degradation and

inner core disruption in some of the treatment sets not involving any chemical addition,

suggesting that such visible disruption may have lead to an increase in the surface area of

particles as well some loss in crystallinity of the cellulosic material but not enough to

significantly enhance enzymatic attachment and subsequent digestion to generate sugars.

The various SEM image acquired for switchgrass samples treated at various ultrasonication

conditions are provided below:

Figure A) untreated alamo switchgrass at magnification 100x and 500x

Figure B) Temperature controlled batch in stainless steel container at 10% solid

loading sonicated for 10 min with 100% amplitude 100x and 500x

magnification.

Figure C) 1 hr, 100% amplitude, batch temperature controlled, stainless steel at

50x and 100x magnification.

Figure D) 1 hr, 100% amplitude, 3% loading, recirculated, diw slurry ,100x and

500x magnification

90

Figure E) No temepeature control., 0.5 % NaOH , 1 hr, 100% amplitude, 3% loading,

recirculated, 30x, 100x,

Figure F) 0.5 % NaOH, no sonication, 1 hr, stirred, no temperature control at

100 x, and 500x .

91

Figure A SEM image untreated alamo switchgrass

92

Figure B SEM image of temperature controlled batch in stainless steel container at 10% solid

loading sonicated for 10 min with 100% amplitude

93

Figure C SEM image of 1 hr, 100% amplitude, batch temperature controlled,

stainless steel at

94

Figure D SEM image of 1 hr, 100% amplitude, batch temperature controlled, stainless steel

95

Figure E SEM image of no temperature control, 0.5 % NaOH , 1 hr, 100% amplitude, 3%

loading, recirculated,

96

Figure F SEM image of 0.5 % NaOH, no sonication, 1 hr, stirred, no temperature control

97

APPENDIX 2

Statistical analysis tables and orthogonal decomposition for Chapter2

Summary of statistical output for decomposition tables

A2.1 Anova and decomposition tables

Table 1. Anova Table for Sugars

Source DF Sum of Squares Mean Square F Value Pr > F

Model 30 2980.108052 99.336935 3.88 <.0001

Error 62 1588.679109 25.623857

Corrected Total 92 4568.787160

A2.2 Anova Table for AIL

Source DF Sum of Squares Mean Square F Value Pr > F

Model 30 1111.296060 37.043202 28.91 <.0001

Error 62 79.455438 1.281539

Corrected Total 92 1190.7514

98

A2.3 Orthogonal decomposition of sugars variable

Source df sum of squares mean square F p-value

Model 30 2980.108052 99.336935 3.88 < .0001

temp×conc×time(sect) 27 1991.315699 73.752433 2.88 0.0003

sector 3 988.7923526 329.5974509 12.86 < .0001

short treatment times

short 8 650.711724 81.338966 3.17 0.0044

conc(sector) 2 507.7 253.9 9.91 0.0002

time(sector) 2 13.9 6.96 0.27 0.7631

conc × time(sector) 4 129.1 32.3 1.26 0.29

intermediate treatment times

intermediate 17 1294.1 76 2.34 0.0157

conc(sector) 2 168.132761 84.066380 3.28 0.0442

time(sector) 2 74.058298 37.029149 1.45 0.2435

temp(sector) 1 108.335569 108.335569 4.23 0.044

conc × time(sector) 4 127.06 32.3 1.26 0.2942

temp × conc(sector) 2 145.704119 72.852060 2.84 0.0659

temp × time(sector) 2 423.948239 211.974120 8.27 0.0007

temp × conc × time(sect) 4 246.897445 61.724361 2.41 0.0587

long treatment times

conc(sector) 2 46.472387 23.236194 0.91 0.4091

* orthogonal decomposition of the treatment sum of squares of sugars on 91 degrees of freedom

99

A2.4 Orthogonal decomposition of AIL variable

Source df sum of squares mean square F p-value

Model 30 1111.296060 37.0432 28.91 < .0001

temp×conc×time(sect) 27 136.7973329 45.5991 35.58 <.0001

sector 3 974.498 36.0925 28.16 < .0001

short treatment times

short 8 506.4228 63.3025 49.40 <.0001

conc(sector) 2 469.1168 234.5584 183.03 <.0001

time(sector) 2 16.3148 8.15741 6.37 0.0031

conc × time(sector) 4 21.00 5.25 4.1 0.0052

intermediate treatment times

intermediate 17 383.105 22.5356 31.95 <0.0001

conc 2 191.4099 95.7049 74.68 <0.0001

temp 1 91.5423 91.5423 71.43 <0.0001

time 2 45.0976 22.5488 17.60 <0.0001

conc × time 4 3.4793 0.8696 16.96 <0.0001

temp ×conc 2 43.4360 21.7315 8.27 0.0007

temp ×time 2 6.5584 3.279 2.56 0.0855

temp × conc × time(sect) 4 1.5544 0.3886 0.30 0.8747

long treatment times

conc(sector) 2 84.9706 48.48532 228.45 <0.0001

* orthogonal decomposition of the treatment sum of squares of AIL on 91 degrees of freedom

100

A2.5 SAS 9.2© Code for statistical analyses for an orthogonal decomposition design of

the impact of concentration, time and temp on Acid Insoluble lignin for KOH

pretreatment

options ls=110 ps=1000 formdlim="+" nocenter;

data sugars;

length sector $7;

input temp conc time Solid_Recovery AIL ASL Sugars;

if temp in (22,50) and time in (6,12,24) and conc in (0.5,1,2) then

sector="full";

else if temp=121 and conc>0 then sector="temp121";

else if temp=22 and time=48 then sector="time48";

else if conc=0 then sector="conc0";

if conc=0 and temp < 121 then delete;

cards;

121 0 0 100 21.33 3.85 68.4197

121 0 0 100 20.71 3.92 65.7604

121 0 0 100 20.77 3.74 68.9896

121 0.5 0.25 74.13 17.07 2.76 60.12

121 0.5 0.25 73.33 18.40 2.89 59.06

121 0.5 0.25 77.38 17.93 2.75 57.96

121 0.5 0.5 73.16 17.43 2.75 59.33

121 0.5 0.5 72.48 18.47 2.66 59.47

121 0.5 0.5 76.67 17.93 2.86 62.18

121 0.5 1 76.56 18.00 2.77 59.36

121 0.5 1 72.27 17.60 2.77 58.62

121 0.5 1 79.39 17.23 2.91 63.79

121 1 0.25 67.53 14.90 2.23 54.20

121 1 0.25 77.33 15.60 2.38 63.09

121 1 0.25 74.08 13.63 2.31 53.02

121 1 0.5 61.40 13.00 1.57 51.09

121 1 0.5 63.10 13.40 1.71 64.19

121 1 0.5 61.23 14.00 1.72 52.74

121 1 1 70.34 21.67 1.99 48.05

121 1 1 63.79 15.37 2.05 42.36

121 1 1 71.94 16.93 1.72 40.51

121 2 0.25 56.83 10.33 1.61 42.93

121 2 0.25 63.64 9.67 1.67 50.36

121 2 0.25 58.65 9.43 1.73 51.73

121 2 0.5 57.61 12.37 1.37 55.42

121 2 0.5 54.66 10.90 1.41 52.99

121 2 0.5 50.26 9.43 1.37 49.01

121 2 1 54.02 7.90 1.13 48.05

121 2 1 47.41 12.50 1.06 42.36

121 2 1 44.87 7.63 1.19 40.51

50 0 0 100 21.33 3.85 68.4197

50 0 0 100 20.71 3.92 65.7604

50 0 0 100 20.77 3.74 68.9896

101

50 0.5 6 74.32 20.27 2.85 55.23

50 0.5 6 79.17 20.33 2.93 62.69

50 0.5 6 84.22 20.30 2.90 70.93

50 0.5 12 76.62 18.37 2.51 58.71

50 0.5 12 84.99 21.17 2.75 72.24

50 0.5 12 75.75 21.77 3.05 57.38

50 0.5 24 77.30 19.80 2.36 59.75

50 0.5 24 81.56 19.43 2.86 66.52

50 0.5 24 77.02 20.30 2.63 59.32

50 1 6 77.19 16.27 2.23 59.58

50 1 6 74.53 16.57 2.32 55.55

50 1 6 80.89 16.43 2.24 65.43

50 1 12 35.02 14.97 1.76 12.26

50 1 12 70.44 16.97 2.24 49.62

50 1 12 73.35 17.33 2.10 53.80

50 1 24 68.85 15.80 1.81 47.40

50 1 24 70.09 15.10 1.69 49.12

50 1 24 70.49 15.13 1.71 49.69

50 2 6 66.86 13.87 2.23 44.70

50 2 6 68.32 14.13 2.32 46.68

50 2 6 62.93 16.23 2.24 39.61

50 2 12 68.12 13.03 1.84 46.40

50 2 12 64.40 13.33 1.78 41.47

50 2 12 65.27 12.13 1.82 42.60

50 2 24 76.77 12.67 1.59 58.94

50 2 24 78.02 12.20 1.63 60.88

50 2 24 54.97 11.77 1.56 30.22

22 0 0 100 21.33 3.85 68.4197

22 0 0 100 20.71 3.92 65.7604

22 0 0 100 20.77 3.74 68.9896

22 0.5 6 80.76 21.73 3.06 66.42

22 0.5 6 78.78 20.53 2.95 60.95

22 0.5 6 81.27 22.97 2.98 59.71

22 0.5 12 84.49 23.40 2.95 65.53

22 0.5 12 81.52 21.67 3.02 68.63

22 0.5 12 81.71 21.53 3.08 64.49

22 0.5 24 78.00 21.57 3.10 63.23

22 0.5 24 79.45 21.93 3.06 63.17

22 0.5 24 79.91 20.90 3.11 64.94

22 0.5 48 76.22 19.30 2.94 58.23

22 0.5 48 76.25 18.85 2.97 59.14

22 0.5 48 75.59 19.31 2.92 58.34

22 1 6 79.06 22.23 2.71 59.48

22 1 6 78.37 20.97 2.66 59.87

22 1 6 79.88 21.07 2.78 59.62

22 1 12 83.36 21.20 2.58 65.14

22 1 12 86.24 21.90 2.67 66.89

22 1 12 84.32 21.70 2.51 64.41

22 1 24 71.30 18.43 2.18 59.33

22 1 24 71.11 17.10 2.55 57.93

22 1 24 71.43 17.33 2.26 58.60

22 1 48 70.4862971 18.70 2.19 59.95

102

22 1 48 72.94462268 18.83 2.31 61.32

22 1 48 71.2683857 19.50 2.25 58.26

22 2 6 73.58 20.90 2.18 58.22

22 2 6 74.42 19.53 2.24 60.04

22 2 6 71.19 18.97 2.07 56.60

22 2 12 72.03 17.27 2.01 59.10

22 2 12 69.81 18.13 2.11 56.05

22 2 12 60.33 17.67 2.23 56.20

22 2 24 77.05 16.53 2.19 62.76

22 2 24 71.39 16.93 2.12 57.74

22 2 24 65.93 17.30 2.13 53.96

22 2 48 64.74 15.33 2.07 52.87

22 2 48 67.52 14.93 2.05 55.66

22 2 48 67.52 14.67 1.84 56.32

;

run;

/*

proc sort;

by conc;

run;

proc freq;

by conc;

*tables time*temp;

tables temp*time/nopercent norow nocol;

run;

proc sort;

by sector conc temp time;

run;

proc print;run;

proc glm data=sugars;

class temp conc time sector;

model Sugars = conc(sector) temp(sector) conc*temp(sector) time(sector)

conc*time(sector) time*temp(sector) time*conc*temp(sector) sector;

*lsmeans conc(sector) temp(sector) conc*temp(sector) time(sector)

conc*time(sector) time*temp(sector) time*conc*temp(sector)

sector/slice=sector pdiff;

*output out=two r=r p=p;

run;

*/

ods trace on;

proc mixed data=sugars method=type3;

class temp conc time sector;

model ail = conc(sector) temp(sector) conc*temp(sector) time(sector)

conc*time(sector) time*temp(sector) time*conc*temp(sector) sector;

lsmeans conc(sector) temp(sector) conc*temp(sector) time(sector)

conc*time(sector) time*temp(sector) time*conc*temp(sector)

sector/slice=sector diffs adj=tukey;

ods output diffs=diffs lsmeans=lsm;

run;

103

proc sort data=lsm;

by temp descending estimate;

run;

proc print data=lsm;

where temp >= 0 and time >= 0 and conc >= 0 ;

title "lsm";

run;

proc print data=lsm;

title "controls";

where conc = 0 ;

run;

proc print data=diffs;

title "diffs";

title2 "first, we'll do temp=22 and compare conditions with the

observed best, which was";

title3 "temp=22, conc=2, time=48";

*where (temp = 22 and temp=_temp and conc>0 and _conc>0 and time>0 and

_time > 0) and ((conc=1 and time=12) or (_conc=1 and _time=12));

where (temp = 22) and (temp=_temp) and ((conc=2 and time=48) or

(_conc=2 and _time=48));

run;

proc print data=diffs;

title "diffs";

title2 "next, we'll do temp=50 and compare conditions with the observed

best, which was";

title3 "temp=50, conc=2, time=24";

where (temp = 50) and (temp=_temp) and ((conc=2 and time=24) or

(_conc=2 and _time=24));

run;

proc print data=diffs;

title "diffs";

title2 "third, we'll do temp=121 and compare conditions with the

observed best, which was";

title3 "temp=121, conc=2, time=1";

where (temp = 121) and (temp=_temp) and ((conc=2 and time=1) or

(_conc=2 and _time=1));

run;

proc sort data=diffs;

by sector descending estimate;

run;

proc print data=diffs;

title "comparison involving control";

*where (temp=121 and sector="conc0" and time=0 and conc=0) and

(_temp=22 and _conc=1 and _time=12);

*where (temp=121 and sector="conc0" and time=0 and conc=0) and (_temp>0

and _conc=>0 and _time=>0);

where (temp=121 and sector="conc0" and time=0 and conc=0);

run;

/*

104

proc print data=diffs;

title "comparison involving control";

where (temp=121 and sector="conc0" and time=0 and conc=0) and (_temp=50

and _conc=.5 and _time=24);

run;

proc print data=diffs;

title "comparison involving control";

where (temp=121 and sector="conc0" and time=0 and conc=0) and

(_temp=121 and _conc=.5 and _time=.5);

run;

*/

105

A2.6 SAS 9.2© Output for statistical analyses of the impact of concentration, time and

temp on Acid Insoluble lignin for KOH pretreatment

The SAS System 06:00

Tuesday, October 25, 2011 1

The Mixed Procedure

Model Information

Data Set WORK.SUGARS

Dependent Variable AIL

Covariance Structure Diagonal

Estimation Method Type 3

Residual Variance Method Factor

Fixed Effects SE Method Model-Based

Degrees of Freedom Method Residual

Class Level Information

Class Levels Values

temp 3 22 50 121

conc 4 0 0.5 1 2

time 8 0 0.25 0.5 1 6 12 24 48

sector 4 conc0 full temp121 time48

Dimensions

Covariance Parameters 1

Columns in X 105

Columns in Z 0

Subjects 1

Max Obs Per Subject 93

Number of Observations

Number of Observations Read 93

Number of Observations Used 93

Number of Observations Not Used 0

Type 3 Analysis of Variance

Sum of

Source DF Squares Mean Square Expected Mean Square

Error Term

conc(sector) 6 571.691974 95.281996 Var(Residual) +

MS(Residual)

Q(conc(sector),temp*conc(sector),

conc*time(sector),temp*

106

conc*time(sect))

temp(sector) 1 169.672563 169.672563 Var(Residual) +

MS(Residual)

Q(temp(sector),temp*conc(sector),

temp*time(sector),temp*

conc*time(sect))

temp*conc(sector) 2 26.455581 13.227791 Var(Residual) +

MS(Residual)

Q(temp*conc(sector),temp*

conc*time(sect))

time(sector) 4 36.704852 9.176213 Var(Residual) +

MS(Residual)

Q(time(sector),conc*time(sector),

temp*time(sector),temp*

conc*time(sect))

conc*time(sector) 8 49.811704 6.226463 Var(Residual) +

MS(Residual)

Q(conc*time(sector),temp*

conc*time(sect))

temp*time(sector) 2 3.109793 1.554896 Var(Residual) +

MS(Residual)

Q(temp*time(sector),temp*

conc*time(sect))

temp*conc*time(sect) 4 4.988630 1.247157 Var(Residual) +

MS(Residual)

Q(temp*conc*time(sect))

sector 3 319.216753 106.405584 Var(Residual) + Q(sector)

MS(Residual)

Residual 62 72.159467 1.163862 Var(Residual)

.

Type 3 Analysis of Variance

Error

Source DF F Value Pr > F

conc(sector) 62 81.87 <.0001

temp(sector) 62 145.78 <.0001

temp*conc(sector) 62 11.37 <.0001

time(sector) 62 7.88 <.0001

conc*time(sector) 62 5.35 <.0001

temp*time(sector) 62 1.34 0.2704

temp*conc*time(sect) 62 1.07 0.3783

sector 62 91.42 <.0001

Residual . . .

Covariance Parameter

Estimates

Cov Parm Estimate

Residual 1.1639

Fit Statistics

107

-2 Res Log Likelihood 219.4

AIC (smaller is better) 221.4

AICC (smaller is better) 221.5

BIC (smaller is better) 223.5

Type 3 Tests of Fixed Effects

Num Den

Effect DF DF F Value Pr > F

conc(sector) 6 62 81.87 <.0001

temp(sector) 1 62 145.78 <.0001

temp*conc(sector) 2 62 11.37 <.0001

time(sector) 4 62 7.88 <.0001

conc*time(sector) 8 62 5.35 <.0001

temp*time(sector) 2 62 1.34 0.2704

temp*conc*time(sect) 4 62 1.07 0.3783

sector 3 62 91.42 <.0001

lsm 06:00

Tuesday, October 25, 2011 5

Obs Effect temp conc time sector Estimate StdErr

DF tValue Probt

45 temp*conc*time(sect) 22 0.5 12 full 22.2000 0.6229

62 35.64 <.0001

47 temp*conc*time(sect) 22 0.5 6 full 21.7433 0.6229

62 34.91 <.0001

48 temp*conc*time(sect) 22 1 12 full 21.6000 0.6229

62 34.68 <.0001

49 temp*conc*time(sect) 22 0.5 24 full 21.4667 0.6229

62 34.46 <.0001

50 temp*conc*time(sect) 22 1 6 full 21.4233 0.6229

62 34.40 <.0001

55 temp*conc*time(sect) 22 2 6 full 19.8000 0.6229

62 31.79 <.0001

57 temp*conc*time(sect) 22 0.5 48 time48 19.1533 0.6229

62 30.75 <.0001

59 temp*conc*time(sect) 22 1 48 time48 19.0100 0.6229

62 30.52 <.0001

64 temp*conc*time(sect) 22 2 12 full 17.6900 0.6229

62 28.40 <.0001

65 temp*conc*time(sect) 22 1 24 full 17.6200 0.6229

62 28.29 <.0001

66 temp*conc*time(sect) 22 2 24 full 16.9200 0.6229

62 27.17 <.0001

68 temp*conc*time(sect) 22 2 48 time48 14.9767 0.6229

62 24.05 <.0001

69 temp*conc*time(sect) 50 0.5 12 full 20.4367 0.6229

62 32.81 <.0001

70 temp*conc*time(sect) 50 0.5 6 full 20.3000 0.6229

62 32.59 <.0001

72 temp*conc*time(sect) 50 0.5 24 full 19.8433 0.6229

62 31.86 <.0001

108

76 temp*conc*time(sect) 50 1 12 full 16.4233 0.6229

62 26.37 <.0001

77 temp*conc*time(sect) 50 1 6 full 16.4233 0.6229

62 26.37 <.0001

80 temp*conc*time(sect) 50 1 24 full 15.3433 0.6229

62 24.63 <.0001

81 temp*conc*time(sect) 50 2 6 full 14.7433 0.6229

62 23.67 <.0001

83 temp*conc*time(sect) 50 2 12 full 12.8300 0.6229

62 20.60 <.0001

84 temp*conc*time(sect) 50 2 24 full 12.2133 0.6229

62 19.61 <.0001

88 temp*conc*time(sect) 121 0 0 conc0 20.9367 0.6229

62 33.61 <.0001

89 temp*conc*time(sect) 121 1 1 temp121 17.9900 0.6229

62 28.88 <.0001

90 temp*conc*time(sect) 121 0.5 0.5 temp121 17.9433 0.6229

62 28.81 <.0001

91 temp*conc*time(sect) 121 0.5 0.25 temp121 17.8000 0.6229

62 28.58 <.0001

93 temp*conc*time(sect) 121 0.5 1 temp121 17.6100 0.6229

62 28.27 <.0001

96 temp*conc*time(sect) 121 1 0.25 temp121 14.7100 0.6229

62 23.62 <.0001

100 temp*conc*time(sect) 121 1 0.5 temp121 13.4667 0.6229

62 21.62 <.0001

101 temp*conc*time(sect) 121 2 0.5 temp121 10.9000 0.6229

62 17.50 <.0001

103 temp*conc*time(sect) 121 2 0.25 temp121 9.8100 0.6229

62 15.75 <.0001

104 temp*conc*time(sect) 121 2 1 temp121 9.3433 0.6229

62 15.00 <.0001

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

+++++++++++++++++++++++++++

controls 06:00

Tuesday, October 25, 2011 6

Obs Effect temp conc time sector Estimate StdErr

DF tValue Probt

4 conc(sector) _ 0 _ conc0 20.9367 0.6229

62 33.61 <.0001

6 conc*time(sector) _ 0 0 conc0 20.9367 0.6229

62 33.61 <.0001

86 temp*conc(sector) 121 0 _ conc0 20.9367 0.6229

62 33.61 <.0001

88 temp*conc*time(sect) 121 0 0 conc0 20.9367 0.6229

62 33.61 <.0001

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

+++++++++++++++++++++++++++

diffs 06:00

Tuesday, October 25, 2011 7

first, we'll do temp=22 and compare conditions with the observed best, which was

temp=22, conc=2, time=48

109

A

d

E

j

_ s

u

E s s t S

t s

f e _ _ _ e i t

V P t

f t c t c t c t c m d

a r m A

O e e o i t e o i t a E

l o e d

b c m n m o m n m o t r D

u b n j

s t p c e r p c e r e r F

e t t p

506 temp*conc*time(sect) 22 0.5 6 full 22 2 48 time48 6.7667 0.8809 62

7.68 <.0001 Tukey <.0001

534 temp*conc*time(sect) 22 0.5 12 full 22 2 48 time48 7.2233 0.8809 62

8.20 <.0001 Tukey <.0001

561 temp*conc*time(sect) 22 0.5 24 full 22 2 48 time48 6.4900 0.8809 62

7.37 <.0001 Tukey <.0001

587 temp*conc*time(sect) 22 1 6 full 22 2 48 time48 6.4467 0.8809 62

7.32 <.0001 Tukey <.0001

612 temp*conc*time(sect) 22 1 12 full 22 2 48 time48 6.6233 0.8809 62

7.52 <.0001 Tukey <.0001

636 temp*conc*time(sect) 22 1 24 full 22 2 48 time48 2.6433 0.8809 62

3.00 0.0039 Tukey 0.4247

659 temp*conc*time(sect) 22 2 6 full 22 2 48 time48 4.8233 0.8809 62

5.48 <.0001 Tukey 0.0003

681 temp*conc*time(sect) 22 2 12 full 22 2 48 time48 2.7133 0.8809 62

3.08 0.0031 Tukey 0.3716

702 temp*conc*time(sect) 22 2 24 full 22 2 48 time48 1.9433 0.8809 62

2.21 0.0311 Tukey 0.9213

911 temp*conc*time(sect) 22 0.5 48 time48 22 2 48 time48 4.1767 0.8809 62

4.74 <.0001 Tukey 0.0043

912 temp*conc*time(sect) 22 1 48 time48 22 2 48 time48 4.0333 0.8809 62

4.58 <.0001 Tukey 0.0073

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

+++++++++++++++++++++++++++

diffs 06:00

Tuesday, October 25, 2011 8

next, we'll do temp=50 and compare conditions with the observed best, which was

temp=50, conc=2, time=24

A

d

E

110

j

_ s

u

E s s t S t

s

f e _ _ _ e i t V

P t

f t c t c t c t c m d a

r m A

O e e o i t e o i t a E l

o e d

b c m n m o m n m o t r D u

b n j

s t p c e r p c e r e r F e

t t p

710 temp*conc*time(sect) 50 0.5 6 full 50 2 24 full 8.0867 0.8809 62 9.18

<.0001 Tukey <.0001

729 temp*conc*time(sect) 50 0.5 12 full 50 2 24 full 8.2233 0.8809 62 9.34

<.0001 Tukey <.0001

747 temp*conc*time(sect) 50 0.5 24 full 50 2 24 full 7.6300 0.8809 62 8.66

<.0001 Tukey <.0001

764 temp*conc*time(sect) 50 1 6 full 50 2 24 full 4.2100 0.8809 62 4.78

<.0001 Tukey 0.0038

780 temp*conc*time(sect) 50 1 12 full 50 2 24 full 4.2100 0.8809 62 4.78

<.0001 Tukey 0.0038

795 temp*conc*time(sect) 50 1 24 full 50 2 24 full 3.1300 0.8809 62 3.55

0.0007 Tukey 0.1392

809 temp*conc*time(sect) 50 2 6 full 50 2 24 full 2.5300 0.8809 62 2.87

0.0056 Tukey 0.5162

822 temp*conc*time(sect) 50 2 12 full 50 2 24 full 0.6167 0.8809 62 0.70

0.4865 Tukey 1.0000

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

+++++++++++++++++++++++++++

diffs 06:00

Tuesday, October 25, 2011 9

third, we'll do temp=121 and compare conditions with the observed best, which was

temp=121, conc=2, time=1

Obs Effect temp conc time sector _temp _conc

_time _sector

474 temp*conc*time(sect) 121 0 0 conc0 121 2

1 temp121

854 temp*conc*time(sect) 121 0.5 0.25 temp121 121 2

1 temp121

864 temp*conc*time(sect) 121 0.5 0.5 temp121 121 2

1 temp121

873 temp*conc*time(sect) 121 0.5 1 temp121 121 2

1 temp121

881 temp*conc*time(sect) 121 1 0.25 temp121 121 2

1 temp121

888 temp*conc*time(sect) 121 1 0.5 temp121 121 2

1 temp121

894 temp*conc*time(sect) 121 1 1 temp121 121 2

1 temp121

111

899 temp*conc*time(sect) 121 2 0.25 temp121 121 2

1 temp121

903 temp*conc*time(sect) 121 2 0.5 temp121 121 2

1 temp121

Obs Estimate StdErr DF tValue Probt Adjustment Adjp

474 11.5933 0.8809 62 13.16 <.0001 Tukey <.0001

854 8.4567 0.8809 62 9.60 <.0001 Tukey <.0001

864 8.6000 0.8809 62 9.76 <.0001 Tukey <.0001

873 8.2667 0.8809 62 9.38 <.0001 Tukey <.0001

881 5.3667 0.8809 62 6.09 <.0001 Tukey <.0001

888 4.1233 0.8809 62 4.68 <.0001 Tukey 0.0052

894 8.6467 0.8809 62 9.82 <.0001 Tukey <.0001

899 0.4667 0.8809 62 0.53 0.5982 Tukey 1.0000

903 1.5567 0.8809 62 1.77 0.0821 Tukey 0.9944

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

+++++++++++++++++++++++++++

comparison involving control 06:00

Tuesday, October 25, 2011 10

A

d

E

j

_ s

u

E s s t S

t s

f e _ _ _ e i t

V P t

f t c t c t c t c m d

a r m A

O e e o i t e o i t a E

l o e d

b c m n m o m n m o t r D

u b n j

s t p c e r p c e r e r F

e t t p

2 temp*conc*time(sect) 121 0 0 conc0 121 2 1 temp121 11.5933 0.8809 62

13.16 <.0001 Tukey <.0001

4 temp*conc*time(sect) 121 0 0 conc0 121 2 0.25 temp121 11.1267 0.8809 62

12.63 <.0001 Tukey <.0001

8 temp*conc*time(sect) 121 0 0 conc0 121 2 0.5 temp121 10.0367 0.8809 62

11.39 <.0001 Tukey <.0001

9 temp*conc*time(sect) 121 0 0 conc0 50 2 24 full 8.7233 0.8809 62

9.90 <.0001 Tukey <.0001

10 temp*conc*time(sect) 121 0 0 conc0 50 2 12 full 8.1067 0.8809 62

9.20 <.0001 Tukey <.0001

13 temp*conc*time(sect) 121 0 0 conc0 121 1 0.5 temp121 7.4700 0.8809 62

8.48 <.0001 Tukey <.0001

22 temp*conc*time(sect) 121 0 0 conc0 121 1 0.25 temp121 6.2267 0.8809 62

7.07 <.0001 Tukey <.0001

112

23 temp*conc*time(sect) 121 0 0 conc0 50 2 6 full 6.1933 0.8809 62

7.03 <.0001 Tukey <.0001

27 temp*conc*time(sect) 121 0 0 conc0 22 2 48 time48 5.9600 0.8809 62

6.77 <.0001 Tukey <.0001

31 temp*conc*time(sect) 121 0 0 conc0 50 1 24 full 5.5933 0.8809 62

6.35 <.0001 Tukey <.0001

37 temp*conc*time(sect) 121 0 0 conc0 50 1 6 full 4.5133 0.8809 62

5.12 <.0001 Tukey 0.0011

38 temp*conc*time(sect) 121 0 0 conc0 50 1 12 full 4.5133 0.8809 62

5.12 <.0001 Tukey 0.0011

42 temp*conc*time(sect) 121 0 0 conc0 22 2 24 full 4.0167 0.8809 62

4.56 <.0001 Tukey 0.0078

47 temp*conc*time(sect) 121 0 0 conc0 121 0.5 1 temp121 3.3267 0.8809 62

3.78 0.0004 Tukey 0.0796

48 temp*conc*time(sect) 121 0 0 conc0 22 1 24 full 3.3167 0.8809 62

3.77 0.0004 Tukey 0.0820

49 temp*conc*time(sect) 121 0 0 conc0 22 2 12 full 3.2467 0.8809 62

3.69 0.0005 Tukey 0.1005

57 temp*conc*time(sect) 121 0 0 conc0 121 0.5 0.25 temp121 3.1367 0.8809 62

3.56 0.0007 Tukey 0.1367

59 temp*conc*time(sect) 121 0 0 conc0 121 0.5 0.5 temp121 2.9933 0.8809 62

3.40 0.0012 Tukey 0.1985

61 temp*conc*time(sect) 121 0 0 conc0 121 1 1 temp121 2.9467 0.8809 62

3.35 0.0014 Tukey 0.2225

71 temp*conc*time(sect) 121 0 0 conc0 22 1 48 time48 1.9267 0.8809 62

2.19 0.0325 Tukey 0.9276

77 temp*conc*time(sect) 121 0 0 conc0 22 0.5 48 time48 1.7833 0.8809 62

2.02 0.0472 Tukey 0.9681

78 temp*conc*time(sect) 121 0 0 conc0 22 2 6 full 1.1367 0.8809 62

1.29 0.2017 Tukey 1.0000

79 temp*conc*time(sect) 121 0 0 conc0 50 0.5 24 full 1.0933 0.8809 62

1.24 0.2192 Tukey 1.0000

83 temp*conc*time(sect) 121 0 0 conc0 50 0.5 6 full 0.6367 0.8809 62

0.72 0.4725 Tukey 1.0000

84 temp*conc*time(sect) 121 0 0 conc0 50 0.5 12 full 0.5000 0.8809 62

0.57 0.5723 Tukey 1.0000

91 temp*conc*time(sect) 121 0 0 conc0 22 1 6 full -0.4867 0.8809 62

-0.55 0.5826 Tukey 1.0000

92 temp*conc*time(sect) 121 0 0 conc0 22 0.5 24 full -0.5300 0.8809 62

-0.60 0.5496 Tukey 1.0000

93 temp*conc*time(sect) 121 0 0 conc0 22 1 12 full -0.6633 0.8809 62

-0.75 0.4543 Tukey 1.0000

94 temp*conc*time(sect) 121 0 0 conc0 22 0.5 6 full -0.8067 0.8809 62

-0.92 0.3633 Tukey 1.0000

96 temp*conc*time(sect) 121 0 0 conc0 22 0.5 12 full -1.2633 0.8809 62

-1.43 0.1565 Tukey 0.9998

A2.7 Code for statistical analyses of the impact of concentration, time and temp on

reducing sugars for KOH pretreatment

options ls=110 ps=1000 formdlim="+" nocenter;

data sugars;

113

length sector $7;

input temp conc time Solid_Recovery AIL ASL Sugars;

if temp in (22,50) and time in (6,12,24) and conc in (0.5,1,2) then

sector="full";

else if temp=121 and conc>0 then sector="temp121";

else if temp=22 and time=48 then sector="time48";

else if conc=0 then sector="conc0";

if conc=0 and temp < 121 then delete;

cards;

121 0 0 100 21.33 3.85 68.4197

121 0 0 100 20.71 3.92 65.7604

121 0 0 100 20.77 3.74 68.9896

121 0.5 0.25 74.13 17.07 2.76 60.12

121 0.5 0.25 73.33 18.40 2.89 59.06

121 0.5 0.25 77.38 17.93 2.75 57.96

121 0.5 0.5 73.16 17.43 2.75 59.33

121 0.5 0.5 72.48 18.47 2.66 59.47

121 0.5 0.5 76.67 17.93 2.86 62.18

121 0.5 1 76.56 18.00 2.77 59.36

121 0.5 1 72.27 17.60 2.77 58.62

121 0.5 1 79.39 17.23 2.91 63.79

121 1 0.25 67.53 14.90 2.23 54.20

121 1 0.25 77.33 15.60 2.38 63.09

121 1 0.25 74.08 13.63 2.31 53.02

121 1 0.5 61.40 13.00 1.57 51.09

121 1 0.5 63.10 13.40 1.71 64.19

121 1 0.5 61.23 14.00 1.72 52.74

121 1 1 70.34 21.67 1.99 48.05

121 1 1 63.79 15.37 2.05 42.36

121 1 1 71.94 16.93 1.72 40.51

121 2 0.25 56.83 10.33 1.61 42.93

121 2 0.25 63.64 9.67 1.67 50.36

121 2 0.25 58.65 9.43 1.73 51.73

121 2 0.5 57.61 12.37 1.37 55.42

121 2 0.5 54.66 10.90 1.41 52.99

121 2 0.5 50.26 9.43 1.37 49.01

121 2 1 54.02 7.90 1.13 48.05

121 2 1 47.41 12.50 1.06 42.36

121 2 1 44.87 7.63 1.19 40.51

50 0 0 100 21.33 3.85 68.4197

50 0 0 100 20.71 3.92 65.7604

50 0 0 100 20.77 3.74 68.9896

50 0.5 6 74.32 20.27 2.85 55.23

50 0.5 6 79.17 20.33 2.93 62.69

50 0.5 6 84.22 20.30 2.90 70.93

50 0.5 12 76.62 18.37 2.51 58.71

50 0.5 12 84.99 21.17 2.75 72.24

50 0.5 12 75.75 21.77 3.05 57.38

50 0.5 24 77.30 19.80 2.36 59.75

50 0.5 24 81.56 19.43 2.86 66.52

50 0.5 24 77.02 20.30 2.63 59.32

114

50 1 6 77.19 16.27 2.23 59.58

50 1 6 74.53 16.57 2.32 55.55

50 1 6 80.89 16.43 2.24 65.43

50 1 12 35.02 14.97 1.76 12.26

50 1 12 70.44 16.97 2.24 49.62

50 1 12 73.35 17.33 2.10 53.80

50 1 24 68.85 15.80 1.81 47.40

50 1 24 70.09 15.10 1.69 49.12

50 1 24 70.49 15.13 1.71 49.69

50 2 6 66.86 13.87 2.23 44.70

50 2 6 68.32 14.13 2.32 46.68

50 2 6 62.93 16.23 2.24 39.61

50 2 12 68.12 13.03 1.84 46.40

50 2 12 64.40 13.33 1.78 41.47

50 2 12 65.27 12.13 1.82 42.60

50 2 24 76.77 12.67 1.59 58.94

50 2 24 78.02 12.20 1.63 60.88

50 2 24 54.97 11.77 1.56 30.22

22 0 0 100 21.33 3.85 68.4197

22 0 0 100 20.71 3.92 65.7604

22 0 0 100 20.77 3.74 68.9896

22 0.5 6 80.76 21.73 3.06 66.42

22 0.5 6 78.78 20.53 2.95 60.95

22 0.5 6 81.27 22.97 2.98 59.71

22 0.5 12 84.49 23.40 2.95 65.53

22 0.5 12 81.52 21.67 3.02 68.63

22 0.5 12 81.71 21.53 3.08 64.49

22 0.5 24 78.00 21.57 3.10 63.23

22 0.5 24 79.45 21.93 3.06 63.17

22 0.5 24 79.91 20.90 3.11 64.94

22 0.5 48 76.22 19.30 2.94 58.23

22 0.5 48 76.25 18.85 2.97 59.14

22 0.5 48 75.59 19.31 2.92 58.34

22 1 6 79.06 22.23 2.71 59.48

22 1 6 78.37 20.97 2.66 59.87

22 1 6 79.88 21.07 2.78 59.62

22 1 12 83.36 21.20 2.58 65.14

22 1 12 86.24 21.90 2.67 66.89

22 1 12 84.32 21.70 2.51 64.41

22 1 24 71.30 18.43 2.18 59.33

22 1 24 71.11 17.10 2.55 57.93

22 1 24 71.43 17.33 2.26 58.60

22 1 48 70.4862971 18.70 2.19 59.95

22 1 48 72.94462268 18.83 2.31 61.32

22 1 48 71.2683857 19.50 2.25 58.26

22 2 6 73.58 20.90 2.18 58.22

22 2 6 74.42 19.53 2.24 60.04

22 2 6 71.19 18.97 2.07 56.60

22 2 12 72.03 17.27 2.01 59.10

22 2 12 69.81 18.13 2.11 56.05

22 2 12 60.33 17.67 2.23 56.20

22 2 24 77.05 16.53 2.19 62.76

115

22 2 24 71.39 16.93 2.12 57.74

22 2 24 65.93 17.30 2.13 53.96

22 2 48 64.74 15.33 2.07 52.87

22 2 48 67.52 14.93 2.05 55.66

22 2 48 67.52 14.67 1.84 56.32

;

run;

proc sort;

by conc;

run;

proc freq;

by conc;

*tables time*temp;

tables temp*time/nopercent norow nocol;

run;

proc sort;

by sector conc temp time;

run;

/**/

proc print;run;

proc glm data=sugars;

class temp conc time sector;

model Sugars = conc(sector) temp(sector) conc*temp(sector) time(sector)

conc*time(sector) time*temp(sector) time*conc*temp(sector) sector;

*lsmeans conc(sector) temp(sector) conc*temp(sector) time(sector)

conc*time(sector) time*temp(sector) time*conc*temp(sector)

sector/slice=sector pdiff;

*output out=two r=r p=p;

run;

/**/

ods trace on;

proc mixed data=sugars method=type3;

class temp conc time sector;

model Sugars = conc(sector) temp(sector) conc*temp(sector) time(sector)

conc*time(sector) time*temp(sector) time*conc*temp(sector) sector;

lsmeans conc(sector) temp(sector) conc*temp(sector) time(sector)

conc*time(sector) time*temp(sector) time*conc*temp(sector)

sector/slice=sector diffs adj=tukey;

ods output diffs=diffs lsmeans=lsm;

run;

proc sort data=lsm;

by temp descending estimate;

run;

proc print data=lsm;

where temp >= 0 and time >= 0 and conc >= 0 ;

title "lsm";

run;

proc print data=lsm;

title "controls";

where conc = 0 ;

116

run;

proc print data=diffs;

title "diffs";

title2 "first, we'll do temp=22 and compare conditions with the

observed best, which was";

title3 "temp=22, conc=0.5, time=12";

*where (temp = 22 and temp=_temp and conc>0 and _conc>0 and time>0 and

_time > 0) and ((conc=1 and time=12) or (_conc=1 and _time=12));

where (temp = 22) and (temp=_temp) and ((conc=.5 and time=12) or

(_conc=.5 and _time=12));

run;

proc print data=diffs;

title "diffs";

title2 "next, we'll do temp=50 and compare conditions with the observed

best, which was";

title3 "temp=50, conc=0.5, time=6";

where (temp = 50) and (temp=_temp) and ((conc=0.5 and time=6) or

(_conc=0.5 and _time=6));

run;

proc print data=diffs;

title "diffs";

title2 "third, we'll do temp=121 and compare conditions with the

observed best, which was";

title3 "temp=121, conc=0.5, time=1";

where (temp = 121) and (temp=_temp) and ((conc=0.5 and time=1) or

(_conc=0.5 and _time=1));

run;

proc sort data=diffs;

by sector descending estimate;

run;

proc print data=diffs;

title "comparison involving control";

*where (temp=121 and sector="conc0" and time=0 and conc=0) and

(_temp=22 and _conc=1 and _time=12);

*where (temp=121 and sector="conc0" and time=0 and conc=0) and (_temp>0

and _conc=>0 and _time=>0);

where (temp=121 and sector="conc0" and time=0 and conc=0);

run;

proc print data=diffs;

title "comparison involving control";

where (temp=121 and sector="conc0" and time=0 and conc=0) and (_temp=50

and _conc=.5 and _time=24);

run;

proc print data=diffs;

title "comparison involving control";

where (temp=121 and sector="conc0" and time=0 and conc=0) and

(_temp=121 and _conc=.5 and _time=.5);

run;

117

A2.8 SAS 9.2 © code for statistical analyses for an orthogonal decomposition design of

the impact of concentration, time and temp on Acid Insoluble lignin for KOH

pretreatment

Class Level Information

Class Levels Values

temp 3 22 50 121

conc 4 0 0.5 1 2

time 8 0 0.25 0.5 1 6 12 24 48

sector 4 conc0 full temp121 time48

Number of Observations Read 93

Number of Observations Used 93

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

+++++++++++++++++++++++++++

The SAS System 19:30

Monday, October 24, 2011 7

The GLM Procedure

Dependent Variable: Sugars

Sum of

Source DF Squares Mean Square F Value Pr > F

Model 30 5275.209965 175.840332 4.46 <.0001

Error 62 2441.999532 39.387089

Corrected Total 92 7717.209497

R-Square Coeff Var Root MSE Sugars Mean

0.683564 11.15667 6.275913 56.25258

Source DF Type I SS Mean Square F Value Pr > F

conc(sector) 9 2601.984426 289.109381 7.34 <.0001

temp(sector) 1 999.234150 999.234150 25.37 <.0001

temp*conc(sector) 2 332.119811 166.059906 4.22 0.0192

time(sector) 4 287.266685 71.816671 1.82 0.1356

conc*time(sector) 8 429.523237 53.690405 1.36 0.2305

temp*time(sector) 2 258.151078 129.075539 3.28 0.0444

temp*conc*time(sect) 4 366.930578 91.732644 2.33 0.0659

118

sector 0 0.000000 . . .

Source DF Type III SS Mean Square F Value Pr > F

conc(sector) 6 1955.703285 325.950548 8.28 <.0001

temp(sector) 1 999.234150 999.234150 25.37 <.0001

temp*conc(sector) 2 332.119811 166.059906 4.22 0.0192

time(sector) 4 287.266685 71.816671 1.82 0.1356

conc*time(sector) 8 429.523237 53.690405 1.36 0.2305

temp*time(sector) 2 258.151078 129.075539 3.28 0.0444

temp*conc*time(sect) 4 366.930578 91.732644 2.33 0.0659

sector 3 646.281141 215.427047 5.47 0.0021

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

+++++++++++++++++++++++++++

The SAS System 19:30

Monday, October 24, 2011 8

The Mixed Procedure

Model Information

Data Set WORK.SUGARS

Dependent Variable Sugars

Covariance Structure Diagonal

Estimation Method Type 3

Residual Variance Method Factor

Fixed Effects SE Method Model-Based

Degrees of Freedom Method Residual

Class Level Information

Class Levels Values

temp 3 22 50 121

conc 4 0 0.5 1 2

time 8 0 0.25 0.5 1 6 12 24 48

sector 4 conc0 full temp121 time48

Dimensions

Covariance Parameters 1

Columns in X 105

Columns in Z 0

Subjects 1

Max Obs Per Subject 93

Number of Observations

Number of Observations Read 93

Number of Observations Used 93

Number of Observations Not Used 0

119

Type 3 Analysis of Variance

Sum of

Source DF Squares Mean Square Expected Mean Square

Error Term

conc(sector) 6 1955.703285 325.950548 Var(Residual) +

MS(Residual)

Q(conc(sector),temp*conc(sector),

conc*time(sector),temp*

conc*time(sect))

temp(sector) 1 999.234150 999.234150 Var(Residual) +

MS(Residual)

Q(temp(sector),temp*conc(sector),

temp*time(sector),temp*

conc*time(sect))

temp*conc(sector) 2 332.119811 166.059906 Var(Residual) +

MS(Residual)

Q(temp*conc(sector),temp*

conc*time(sect))

time(sector) 4 287.266685 71.816671 Var(Residual) +

MS(Residual)

Q(time(sector),conc*time(sector),

temp*time(sector),temp*

conc*time(sect))

conc*time(sector) 8 429.523237 53.690405 Var(Residual) +

MS(Residual)

Q(conc*time(sector),temp*

conc*time(sect))

temp*time(sector) 2 258.151078 129.075539 Var(Residual) +

MS(Residual)

Q(temp*time(sector),temp*

conc*time(sect))

temp*conc*time(sect) 4 366.930578 91.732644 Var(Residual) +

MS(Residual)

Q(temp*conc*time(sect))

sector 3 646.281141 215.427047 Var(Residual) + Q(sector)

MS(Residual)

Residual 62 2441.999532 39.387089 Var(Residual)

.

Type 3 Analysis of Variance

Error

Source DF F Value Pr > F

conc(sector) 62 8.28 <.0001

temp(sector) 62 25.37 <.0001

temp*conc(sector) 62 4.22 0.0192

time(sector) 62 1.82 0.1356

conc*time(sector) 62 1.36 0.2305

temp*time(sector) 62 3.28 0.0444

temp*conc*time(sect) 62 2.33 0.0659

sector 62 5.47 0.0021

120

Residual . . .

Covariance Parameter

Estimates

Cov Parm Estimate

Residual 39.3871

Fit Statistics

-2 Res Log Likelihood 437.8

AIC (smaller is better) 439.8

AICC (smaller is better) 439.8

BIC (smaller is better) 441.9

Standard

Effect sector temp conc time _sector _temp _conc _time

Estimate Error DF

temp*conc*time(sect) temp121 121 2 0.25 time48 22 1 48 -

11.5033 5.1243 62

temp*conc*time(sect) temp121 121 2 0.25 time48 22 2 48 -

6.6100 5.1243 62

temp*conc*time(sect) temp121 121 2 0.5 temp121 121 2 1

8.8333 5.1243 62

temp*conc*time(sect) temp121 121 2 0.5 time48 22 0.5 48 -

6.0967 5.1243 62

temp*conc*time(sect) temp121 121 2 0.5 time48 22 1 48 -

7.3700 5.1243 62

temp*conc*time(sect) temp121 121 2 0.5 time48 22 2 48 -

2.4767 5.1243 62

temp*conc*time(sect) temp121 121 2 1 time48 22 0.5 48 -

14.9300 5.1243 62

temp*conc*time(sect) temp121 121 2 1 time48 22 1 48 -

16.2033 5.1243 62

temp*conc*time(sect) temp121 121 2 1 time48 22 2 48 -

11.3100 5.1243 62

temp*conc*time(sect) time48 22 0.5 48 time48 22 1 48 -

1.2733 5.1243 62

temp*conc*time(sect) time48 22 0.5 48 time48 22 2 48

3.6200 5.1243 62

temp*conc*time(sect) time48 22 1 48 time48 22 2 48

4.8933 5.1243 62

sector conc0 full

10.9505 3.7227 62

sector conc0 temp121

14.2973 3.8194 62

sector conc0 time48

9.9355 4.1839 62

121

sector full temp121

3.3469 1.4792 62

sector full time48 -

1.0150 2.2596 62

sector temp121 time48 -

4.3619 2.4156 62

Differences of Least Squares Means

Effect sector temp conc time _sector _temp _conc _time t

Value Pr > |t| Adjustment

temp*conc*time(sect) temp121 121 2 0.25 time48 22 1 48 -

2.24 0.0284 Tukey

temp*conc*time(sect) temp121 121 2 0.25 time48 22 2 48 -

1.29 0.2019 Tukey

temp*conc*time(sect) temp121 121 2 0.5 temp121 121 2 1

1.72 0.0897 Tukey

temp*conc*time(sect) temp121 121 2 0.5 time48 22 0.5 48 -

1.19 0.2387 Tukey

temp*conc*time(sect) temp121 121 2 0.5 time48 22 1 48 -

1.44 0.1554 Tukey

temp*conc*time(sect) temp121 121 2 0.5 time48 22 2 48 -

0.48 0.6306 Tukey

temp*conc*time(sect) temp121 121 2 1 time48 22 0.5 48 -

2.91 0.0050 Tukey

temp*conc*time(sect) temp121 121 2 1 time48 22 1 48 -

3.16 0.0024 Tukey

temp*conc*time(sect) temp121 121 2 1 time48 22 2 48 -

2.21 0.0310 Tukey

temp*conc*time(sect) time48 22 0.5 48 time48 22 1 48 -

0.25 0.8046 Tukey

temp*conc*time(sect) time48 22 0.5 48 time48 22 2 48

0.71 0.4826 Tukey

temp*conc*time(sect) time48 22 1 48 time48 22 2 48

0.95 0.3433 Tukey

sector conc0 full

2.94 0.0046 Tukey-Kramer

sector conc0 temp121

3.74 0.0004 Tukey-Kramer

sector conc0 time48

2.37 0.0207 Tukey-Kramer

sector full temp121

2.26 0.0272 Tukey-Kramer

sector full time48 -

0.45 0.6549 Tukey-Kramer

sector temp121 time48 -

1.81 0.0758 Tukey-Kramer

Differences of Least Squares Means

Effect sector temp conc time _sector _temp _conc _time Adj

P

temp*conc*time(sect) temp121 121 2 0.25 time48 22 1 48

0.9073

temp*conc*time(sect) temp121 121 2 0.25 time48 22 2 48

1.0000

122

temp*conc*time(sect) temp121 121 2 0.5 temp121 121 2 1

0.9961

temp*conc*time(sect) temp121 121 2 0.5 time48 22 0.5 48

1.0000

temp*conc*time(sect) temp121 121 2 0.5 time48 22 1 48

0.9998

temp*conc*time(sect) temp121 121 2 0.5 time48 22 2 48

1.0000

temp*conc*time(sect) temp121 121 2 1 time48 22 0.5 48

0.4862

temp*conc*time(sect) temp121 121 2 1 time48 22 1 48

0.3206

temp*conc*time(sect) temp121 121 2 1 time48 22 2 48

0.9210

temp*conc*time(sect) time48 22 0.5 48 time48 22 1 48

1.0000

temp*conc*time(sect) time48 22 0.5 48 time48 22 2 48

1.0000

temp*conc*time(sect) time48 22 1 48 time48 22 2 48

1.0000

sector conc0 full

0.0232

sector conc0 temp121

0.0022

sector conc0 time48

0.0927

sector full temp121

0.1181

sector full time48

0.9695

sector temp121 time48

0.2805

Tests of Effect Slices

Num Den

Effect sector DF DF F Value Pr > F

conc(sector) conc0 0 . . .

conc(sector) full 2 62 16.05 <.0001

conc(sector) temp121 2 62 8.29 0.0006

conc(sector) time48 2 62 0.49 0.6144

temp(sector) conc0 0 . . .

temp(sector) full 1 62 25.37 <.0001

temp(sector) temp121 0 . . .

temp(sector) time48 0 . . .

temp*conc(sector) conc0 0 . . .

temp*conc(sector) full 5 62 13.18 <.0001

temp*conc(sector) temp121 2 62 8.29 0.0006

temp*conc(sector) time48 2 62 0.49 0.6144

time(sector) conc0 0 . . .

time(sector) full 2 62 0.58 0.5641

time(sector) temp121 2 62 3.07 0.0536

time(sector) time48 0 . . .

conc*time(sector) conc0 0 . . .

conc*time(sector) full 8 62 4.87 0.0001

conc*time(sector) temp121 8 62 3.49 0.0022

123

conc*time(sector) time48 2 62 0.49 0.6144

temp*time(sector) conc0 0 . . .

temp*time(sector) full 5 62 6.62 <.0001

temp*time(sector) temp121 2 62 3.07 0.0536

temp*time(sector) time48 0 . . .

temp*conc*time(sect) conc0 0 . . .

temp*conc*time(sect) full 17 62 5.21 <.0001

temp*conc*time(sect) temp121 8 62 3.49 0.0022

temp*conc*time(sect) time48 2 62 0.49 0.6144

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

+++++++++++++++++++++++++++

lsm 19:30

Monday, October 24, 2011 67

Obs Effect temp conc time sector Estimate StdErr

DF tValue Probt

45 temp*conc*time(sect) 22 0.5 12 full 66.2167 3.6234

62 18.27 <.0001

46 temp*conc*time(sect) 22 1 12 full 65.4800 3.6234

62 18.07 <.0001

48 temp*conc*time(sect) 22 0.5 24 full 63.7800 3.6234

62 17.60 <.0001

50 temp*conc*time(sect) 22 0.5 6 full 62.3600 3.6234

62 17.21 <.0001

56 temp*conc*time(sect) 22 1 48 time48 59.8433 3.6234

62 16.52 <.0001

57 temp*conc*time(sect) 22 1 6 full 59.6567 3.6234

62 16.46 <.0001

58 temp*conc*time(sect) 22 1 24 full 58.6200 3.6234

62 16.18 <.0001

60 temp*conc*time(sect) 22 0.5 48 time48 58.5700 3.6234

62 16.16 <.0001

61 temp*conc*time(sect) 22 2 6 full 58.2867 3.6234

62 16.09 <.0001

62 temp*conc*time(sect) 22 2 24 full 58.1533 3.6234

62 16.05 <.0001

66 temp*conc*time(sect) 22 2 12 full 57.1167 3.6234

62 15.76 <.0001

68 temp*conc*time(sect) 22 2 48 time48 54.9500 3.6234

62 15.17 <.0001

69 temp*conc*time(sect) 50 0.5 6 full 62.9500 3.6234

62 17.37 <.0001

70 temp*conc*time(sect) 50 0.5 12 full 62.7767 3.6234

62 17.33 <.0001

72 temp*conc*time(sect) 50 0.5 24 full 61.8633 3.6234

62 17.07 <.0001

73 temp*conc*time(sect) 50 1 6 full 60.1867 3.6234

62 16.61 <.0001

77 temp*conc*time(sect) 50 2 24 full 50.0133 3.6234

62 13.80 <.0001

79 temp*conc*time(sect) 50 1 24 full 48.7367 3.6234

62 13.45 <.0001

82 temp*conc*time(sect) 50 2 6 full 43.6633 3.6234

62 12.05 <.0001

124

83 temp*conc*time(sect) 50 2 12 full 43.4900 3.6234

62 12.00 <.0001

84 temp*conc*time(sect) 50 1 12 full 38.5600 3.6234

62 10.64 <.0001

88 temp*conc*time(sect) 121 0 0 conc0 67.7232 3.6234

62 18.69 <.0001

89 temp*conc*time(sect) 121 0.5 1 temp121 60.5900 3.6234

62 16.72 <.0001

90 temp*conc*time(sect) 121 0.5 0.5 temp121 60.3267 3.6234

62 16.65 <.0001

92 temp*conc*time(sect) 121 0.5 0.25 temp121 59.0467 3.6234

62 16.30 <.0001

93 temp*conc*time(sect) 121 1 0.25 temp121 56.7700 3.6234

62 15.67 <.0001

95 temp*conc*time(sect) 121 1 0.5 temp121 56.0067 3.6234

62 15.46 <.0001

98 temp*conc*time(sect) 121 2 0.5 temp121 52.4733 3.6234

62 14.48 <.0001

101 temp*conc*time(sect) 121 2 0.25 temp121 48.3400 3.6234

62 13.34 <.0001

103 temp*conc*time(sect) 121 1 1 temp121 43.6400 3.6234

62 12.04 <.0001

104 temp*conc*time(sect) 121 2 1 temp121 43.6400 3.6234

62 12.04 <.0001

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

+++++++++++++++++++++++++++

controls 19:30

Monday, October 24, 2011 68

Obs Effect temp conc time sector Estimate StdErr

DF tValue Probt

1 conc(sector) _ 0 _ conc0 67.7232 3.6234

62 18.69 <.0001

3 conc*time(sector) _ 0 0 conc0 67.7232 3.6234

62 18.69 <.0001

86 temp*conc(sector) 121 0 _ conc0 67.7232 3.6234

62 18.69 <.0001

88 temp*conc*time(sect) 121 0 0 conc0 67.7232 3.6234

62 18.69 <.0001

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

+++++++++++++++++++++++++++

diffs 19:30

Monday, October 24, 2011 69

first, we'll do temp=22 and compare conditions with the observed best, which was

temp=22, conc=0.5, time=12

A

d

E

j

125

_ s

u

E s s t S

t s

f e _ _ _ e i t

V P t

f t c t c t c t c m d

a r m A

O e e o i t e o i t a E

l o e d

b c m n m o m n m o t r D

u b n j

s t p c e r p c e r e r F

e t t p

478 temp*conc*time(sect) 22 0.5 6 full 22 0.5 12 full -3.8567 5.1243 62 -

0.75 0.4545 Tukey 1.0000

507 temp*conc*time(sect) 22 0.5 12 full 22 0.5 24 full 2.4367 5.1243 62

0.48 0.6361 Tukey 1.0000

508 temp*conc*time(sect) 22 0.5 12 full 22 1 6 full 6.5600 5.1243 62

1.28 0.2052 Tukey 1.0000

509 temp*conc*time(sect) 22 0.5 12 full 22 1 12 full 0.7367 5.1243 62

0.14 0.8862 Tukey 1.0000

510 temp*conc*time(sect) 22 0.5 12 full 22 1 24 full 7.5967 5.1243 62

1.48 0.1433 Tukey 0.9997

511 temp*conc*time(sect) 22 0.5 12 full 22 2 6 full 7.9300 5.1243 62

1.55 0.1268 Tukey 0.9993

512 temp*conc*time(sect) 22 0.5 12 full 22 2 12 full 9.1000 5.1243 62

1.78 0.0807 Tukey 0.9940

513 temp*conc*time(sect) 22 0.5 12 full 22 2 24 full 8.0633 5.1243 62

1.57 0.1207 Tukey 0.9991

532 temp*conc*time(sect) 22 0.5 12 full 22 0.5 48 time48 7.6467 5.1243 62

1.49 0.1407 Tukey 0.9996

533 temp*conc*time(sect) 22 0.5 12 full 22 1 48 time48 6.3733 5.1243 62

1.24 0.2183 Tukey 1.0000

534 temp*conc*time(sect) 22 0.5 12 full 22 2 48 time48 11.2667 5.1243 62

2.20 0.0316 Tukey 0.9238

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

+++++++++++++++++++++++++++

diffs 19:30

Monday, October 24, 2011 70

next, we'll do temp=50 and compare conditions with the observed best, which was

temp=50, conc=0.5, time=6

A

d

E

j

_ s

u

E s s t S

t s

f e _ _ _ e i t

V P t

126

f t c t c t c t c m d

a r m A

O e e o i t e o i t a E

l o e d

b c m n m o m n m o t r D

u b n j

s t p c e r p c e r e r F

e t t p

703 temp*conc*time(sect) 50 0.5 6 full 50 0.5 12 full 0.1733 5.1243 62

0.03 0.9731 Tukey 1.0000

704 temp*conc*time(sect) 50 0.5 6 full 50 0.5 24 full 1.0867 5.1243 62

0.21 0.8328 Tukey 1.0000

705 temp*conc*time(sect) 50 0.5 6 full 50 1 6 full 2.7633 5.1243 62

0.54 0.5916 Tukey 1.0000

706 temp*conc*time(sect) 50 0.5 6 full 50 1 12 full 24.3900 5.1243 62

4.76 <.0001 Tukey 0.0040

707 temp*conc*time(sect) 50 0.5 6 full 50 1 24 full 14.2133 5.1243 62

2.77 0.0073 Tukey 0.5885

708 temp*conc*time(sect) 50 0.5 6 full 50 2 6 full 19.2867 5.1243 62

3.76 0.0004 Tukey 0.0823

709 temp*conc*time(sect) 50 0.5 6 full 50 2 12 full 19.4600 5.1243 62

3.80 0.0003 Tukey 0.0753

710 temp*conc*time(sect) 50 0.5 6 full 50 2 24 full 12.9367 5.1243 62

2.52 0.0142 Tukey 0.7629

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

+++++++++++++++++++++++++++

diffs 19:30

Monday, October 24, 2011 71

third, we'll do temp=121 and compare conditions with the observed best, which was

temp=121, conc=0.5, time=1

Obs Effect temp conc time sector _temp _conc

_time _sector

468 temp*conc*time(sect) 121 0 0 conc0 121 0.5

1 temp121

848 temp*conc*time(sect) 121 0.5 0.25 temp121 121 0.5

1 temp121

858 temp*conc*time(sect) 121 0.5 0.5 temp121 121 0.5

1 temp121

868 temp*conc*time(sect) 121 0.5 1 temp121 121 1

0.25 temp121

869 temp*conc*time(sect) 121 0.5 1 temp121 121 1

0.5 temp121

870 temp*conc*time(sect) 121 0.5 1 temp121 121 1

1 temp121

871 temp*conc*time(sect) 121 0.5 1 temp121 121 2

0.25 temp121

872 temp*conc*time(sect) 121 0.5 1 temp121 121 2

0.5 temp121

873 temp*conc*time(sect) 121 0.5 1 temp121 121 2

1 temp121

Obs Estimate StdErr DF tValue Probt Adjustment Adjp

127

468 7.1332 5.1243 62 1.39 0.1689 Tukey 0.9999

848 -1.5433 5.1243 62 -0.30 0.7643 Tukey 1.0000

858 -0.2633 5.1243 62 -0.05 0.9592 Tukey 1.0000

868 3.8200 5.1243 62 0.75 0.4588 Tukey 1.0000

869 4.5833 5.1243 62 0.89 0.3745 Tukey 1.0000

870 16.9500 5.1243 62 3.31 0.0016 Tukey 0.2407

871 12.2500 5.1243 62 2.39 0.0199 Tukey 0.8412

872 8.1167 5.1243 62 1.58 0.1183 Tukey 0.9990

873 16.9500 5.1243 62 3.31 0.0016 Tukey 0.2407

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

+++++++++++++++++++++++++++

comparison involving control 19:30

Monday, October 24, 2011 72

A

d

E

j

_ s

u

E s s t S

t s

f e _ _ _ e i t

V P t

f t c t c t c t c m d

a r m A

O e e o i t e o i t a E

l o e d

b c m n m o m n m o t r D

u b n j

s t p c e r p c e r e r F

e t t p

1 temp*conc*time(sect) 121 0 0 conc0 50 1 12 full 29.1632 5.1243 62

5.69 <.0001 Tukey 0.0001

2 temp*conc*time(sect) 121 0 0 conc0 50 2 12 full 24.2332 5.1243 62

4.73 <.0001 Tukey 0.0045

4 temp*conc*time(sect) 121 0 0 conc0 121 2 1 temp121 24.0832 5.1243 62

4.70 <.0001 Tukey 0.0049

6 temp*conc*time(sect) 121 0 0 conc0 121 1 1 temp121 24.0832 5.1243 62

4.70 <.0001 Tukey 0.0049

7 temp*conc*time(sect) 121 0 0 conc0 50 2 6 full 24.0599 5.1243 62

4.70 <.0001 Tukey 0.0050

13 temp*conc*time(sect) 121 0 0 conc0 121 2 0.25 temp121 19.3832 5.1243 62

3.78 0.0004 Tukey 0.0783

14 temp*conc*time(sect) 121 0 0 conc0 50 1 24 full 18.9866 5.1243 62

3.71 0.0005 Tukey 0.0957

18 temp*conc*time(sect) 121 0 0 conc0 50 2 24 full 17.7099 5.1243 62

3.46 0.0010 Tukey 0.1745

27 temp*conc*time(sect) 121 0 0 conc0 121 2 0.5 temp121 15.2499 5.1243 62

2.98 0.0042 Tukey 0.4419

38 temp*conc*time(sect) 121 0 0 conc0 22 2 48 time48 12.7732 5.1243 62

2.49 0.0154 Tukey 0.7829

128

43 temp*conc*time(sect) 121 0 0 conc0 121 1 0.5 temp121 11.7166 5.1243 62

2.29 0.0257 Tukey 0.8906

47 temp*conc*time(sect) 121 0 0 conc0 121 1 0.25 temp121 10.9532 5.1243 62

2.14 0.0365 Tukey 0.9425

50 temp*conc*time(sect) 121 0 0 conc0 22 2 12 full 10.6066 5.1243 62

2.07 0.0426 Tukey 0.9591

57 temp*conc*time(sect) 121 0 0 conc0 22 2 24 full 9.5699 5.1243 62

1.87 0.0666 Tukey 0.9881

58 temp*conc*time(sect) 121 0 0 conc0 22 2 6 full 9.4366 5.1243 62

1.84 0.0703 Tukey 0.9902

62 temp*conc*time(sect) 121 0 0 conc0 22 0.5 48 time48 9.1532 5.1243 62

1.79 0.0789 Tukey 0.9935

63 temp*conc*time(sect) 121 0 0 conc0 22 1 24 full 9.1032 5.1243 62

1.78 0.0806 Tukey 0.9940

65 temp*conc*time(sect) 121 0 0 conc0 121 0.5 0.25 temp121 8.6766 5.1243 62

1.69 0.0954 Tukey 0.9970

66 temp*conc*time(sect) 121 0 0 conc0 22 1 6 full 8.0666 5.1243 62

1.57 0.1205 Tukey 0.9991

70 temp*conc*time(sect) 121 0 0 conc0 22 1 48 time48 7.8799 5.1243 62

1.54 0.1292 Tukey 0.9994

76 temp*conc*time(sect) 121 0 0 conc0 50 1 6 full 7.5366 5.1243 62

1.47 0.1464 Tukey 0.9997

78 temp*conc*time(sect) 121 0 0 conc0 121 0.5 0.5 temp121 7.3966 5.1243 62

1.44 0.1539 Tukey 0.9998

80 temp*conc*time(sect) 121 0 0 conc0 121 0.5 1 temp121 7.1332 5.1243 62

1.39 0.1689 Tukey 0.9999

83 temp*conc*time(sect) 121 0 0 conc0 50 0.5 24 full 5.8599 5.1243 62

1.14 0.2572 Tukey 1.0000

84 temp*conc*time(sect) 121 0 0 conc0 22 0.5 6 full 5.3632 5.1243 62

1.05 0.2993 Tukey 1.0000

87 temp*conc*time(sect) 121 0 0 conc0 50 0.5 12 full 4.9466 5.1243 62

0.97 0.3381 Tukey 1.0000

90 temp*conc*time(sect) 121 0 0 conc0 50 0.5 6 full 4.7732 5.1243 62

0.93 0.3552 Tukey 1.0000

92 temp*conc*time(sect) 121 0 0 conc0 22 0.5 24 full 3.9432 5.1243 62

0.77 0.4445 Tukey 1.0000

95 temp*conc*time(sect) 121 0 0 conc0 22 1 12 full 2.2432 5.1243 62

0.44 0.6631 Tukey 1.0000

96 temp*conc*time(sect) 121 0 0 conc0 22 0.5 12 full 1.5066 5.1243 62

0.29 0.7697 Tukey 1.0000

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

+++++++++++++++++++++++++++

comparison involving control 19:30

Monday, October 24, 2011 73

A

d

E

j

_ s

u

E s s t S t

s

129

f e _ _ _ e i t V

P t

f t c t c t c t c m d a

r m A

O e e o i t e o i t a E l

o e d

b c m n m o m n m o t r D u

b n j

s t p c e r p c e r e r F e

t t p

90 temp*conc*time(sect) 121 0 0 conc0 50 0.5 6 full 4.7732 5.1243 62 0.93

0.3552 Tukey 1.0000

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

+++++++++++++++++++++++++++

comparison involving control 19:30

Monday, October 24, 2011 74

A

d

E

j

_ s

u

E s s t S

t s

f e _ _ _ e i t

V P t

f t c t c t c t c m d

a r m A

O e e o i t e o i t a E

l o e d

b c m n m o m n m o t r D

u b n j

s t p c e r p c e r e r F

e t t p

80 temp*conc*time(sect) 121 0 0 conc0 121 0.5 1 temp121 7.1332 5.1243 62

1.39 0.1689 Tukey 0.9999

130

APPENDIX 3

SAS 9.2© Code for analyses of sugar yields in hydrolysis of selected pretreatment

combinations for chapter 2

data Hydrolysis;

input treatment $ sugars;

cards;

UNTREATED 0.375

UNTREATED 0.374

UNTREATED 0.375

KOH2%48Hr21C 0.473

KOH2%48Hr21C 0.572

KOH2%48Hr21C 0.582

KOH0.5%12Hr21C 0.579

KOH0.5%12Hr21C 0.523

KOH0.5%12Hr21C 0.645

KOH0.5%24Hr50C 0.560

KOH0.5%24Hr50C 0.554

KOH0.5%24Hr50C 0.586

KOH2%24Hr50C 0.489

KOH2%24Hr50C 0.536

KOH2%24Hr50C 0.517

KOH1%1Hr121C 0.417

KOH1%1Hr121C 0.526

KOH1%1Hr121C 0.471

KOH2%1Hr121C 0.434

KOH2%1Hr121C 0.494

KOH2%1Hr121C 0.405

;

proc glm;

class treatment;

model sugars=treatment;

lsmeans treatment/stderr pdiff;

means treatment/duncan alpha=.05;

run;

131

SAS 9.2© Output for analyses of sugar yields in hydrolysis of selected pretreatment

combinations for chapter 2

The SAS System 04:22

Saturday, March 17, 2012 1

The GLM Procedure

Class Level Information

Class Levels Values

treatment 7 KOH0.5%1 KOH0.5%2 KOH1%1Hr KOH2%1Hr

KOH2%24H KOH2%48H UNTREATE

Number of Observations Read 21

Number of Observations Used 21

The SAS System 04:22

Saturday, March 17, 2012 2

The GLM Procedure

Dependent Variable: sugars

Sum of

Source DF Squares Mean Square

F Value Pr > F

Model 6 0.09851295 0.01641883

8.68 0.0005

Error 14 0.02647800 0.00189129

Corrected Total 20 0.12499095

R-Square Coeff Var Root MSE sugars

Mean

0.788161 8.708565 0.043489

0.499381

Source DF Type I SS Mean Square

F Value Pr > F

treatment 6 0.09851295 0.01641883

8.68 0.0005

132

Source DF Type III SS Mean Square

F Value Pr > F

treatment 6 0.09851295 0.01641883

8.68 0.0005

The SAS System 04:22

Saturday, March 17, 2012 3

The GLM Procedure

Least Squares Means

sugars Standard

LSMEAN

treatment LSMEAN Error Pr > |t|

Number

KOH0.5%1 0.58233333 0.02510834 <.0001

1

KOH0.5%2 0.56666667 0.02510834 <.0001

2

KOH1%1Hr 0.47133333 0.02510834 <.0001

3

KOH2%1Hr 0.44433333 0.02510834 <.0001

4

KOH2%24H 0.51400000 0.02510834 <.0001

5

KOH2%48H 0.54233333 0.02510834 <.0001

6

UNTREATE 0.37466667 0.02510834 <.0001

7

Least Squares Means for effect treatment

Pr > |t| for H0: LSMean(i)=LSMean(j)

Dependent Variable: sugars

i/j 1 2 3 4 5

6 7

1 0.6658 0.0074 0.0016 0.0749

0.2789 <.0001

2 0.6658 0.0178 0.0039 0.1602

0.5044 <.0001

3 0.0074 0.0178 0.4596 0.2495

0.0653 0.0165

4 0.0016 0.0039 0.4596 0.0700

0.0153 0.0700

5 0.0749 0.1602 0.2495 0.0700

0.4382 0.0015

133

6 0.2789 0.5044 0.0653 0.0153 0.4382

0.0003

7 <.0001 <.0001 0.0165 0.0700 0.0015

0.0003

NOTE: To ensure overall protection level, only probabilities associated

with pre-planned comparisons

should be used.

The SAS System 04:22

Saturday, March 17, 2012 4

The GLM Procedure

Duncan's Multiple Range Test for sugars

NOTE: This test controls the Type I comparisonwise error rate, not the

experimentwise error rate.

Alpha 0.05

Error Degrees of Freedom 14

Error Mean Square 0.001891

Number of Means 2 3 4 5

6 7

Critical Range .07616 .07980 .08205 .08357

.08465 .08544

Means with the same letter are not significantly

different.

Duncan Grouping Mean N treatment

A 0.58233 3 KOH0.5%1

A

A 0.56667 3 KOH0.5%2

A

B A 0.54233 3 KOH2%48H

B A

B A C 0.51400 3 KOH2%24H

B C

B C 0.47133 3 KOH1%1Hr

C

D C 0.44433 3 KOH2%1Hr

D

D 0.37467 3 UNTREATE

134

135

APPENDIX 4

SAS 9.2© Code for statistical analyses of impact of amplitude and time on SR, AIL and

Sugars for chapter 3.

data ultrasonication;

input time amp temp$ SR AIL ASL sugars;

datalines;

0 0 100 25.07 1.99 71.13

0 0 100 24.27 2.01 68.37

0 0 100 23.70 1.92 68.72

5 100 TC 88.98 21.77 2.73 67.43

5 100 TC 90.28 21.20 2.66 67.82

5 100 TC 84.79 19.58 2.69 61.43

10 100 TC 90.54 22.57 2.62 70.98

10 100 TC 90.56 23.03 2.51 60.95

10 100 TC 84.79 21.50 2.58 69.81

60 100 TC 75.82 19.03 2.57 70.39

60 100 TC 88.67 22.00 2.58 66.07

60 100 TC 87.25 22.43 2.55 62.91

5 75 TC 87.84 20.77 3.18 61.90

5 75 TC 87.85 19.63 3.09 63.80

5 75 TC 89.63 21.59 3.02 61.92

10 75 TC 88.93 21.50 2.94 62.66

10 75 TC 90.72 20.50 2.69 59.29

10 75 TC 89.49 20.20 2.64 60.07

60 75 TC 88.90 20.43 2.75 60.72

60 75 TC 88.71 19.50 2.91 62.68

60 75 TC 92.45 21.30 2.95 60.42

5 50 TC 92.38 22.67 2.94 62.19

5 50 TC 90.63 22.00 2.92 62.26

5 50 TC 87.72 19.67 2.79 63.70

10 50 TC 95.05 19.90 2.99 69.59

10 50 TC 85.79 22.17 2.86 64.15

10 50 TC 87.42 18.07 2.92 64.52

60 50 TC 90.84 20.33 2.71 62.04

60 50 TC 92.44 20.33 3.11 65.32

60 50 TC 89.79 21.73 1.84 59.92

5 100 NTC 90.65 21.73 1.84 58.00

5 100 NTC 88.64 18.37 1.94 64.54

5 100 NTC 86.79 18.53 1.83 64.90

10 100 NTC 94.76 21.60 2.02 65.47

10 100 NTC 86.75 20.30 2.51 58.23

10 100 NTC 92.17 22.70 2.53 68.92

60 100 NTC 88.55 16.30 1.99 59.44

60 100 NTC 91.52 19.06 1.97 63.23

60 100 NTC 90.68 17.07 1.97 62.83

5 75 NTC 84.70 19.97 2.59 65.17

5 75 NTC 96.70 17.30 2.58 71.17

5 75 NTC 90.08 20.97 2.62 69.31

10 75 NTC 89.15 20.30 2.81 55.12

136

10 75 NTC 86.77 22.03 2.71 66.76

10 75 NTC 94.89 20.43 2.79 68.72

60 75 NTC 88.04 21.03 2.51 57.02

60 75 NTC 92.10 19.94 2.20 61.46

60 75 NTC 92.59 19.80 1.91 58.52

5 50 NTC 89.21 20.20 2.71 68.64

5 50 NTC 88.82 20.30 2.77 66.42

5 50 NTC 90.98 19.30 2.76 66.61

10 50 NTC 94.59 20.37 2.51 63.68

10 50 NTC 92.49 19.60 2.74 57.18

10 50 NTC 92.14 20.57 2.83 66.91

60 50 NTC 89.95 21.57 2.44 59.67

60 50 NTC 93.23 21.50 2.36 63.68

60 50 NTC 87.66 19.66 2.43 61.94

;

proc glm;

class time amp temp;

model SR=time|amp|temp;

lsmeans time|amp|temp / pdiff adjust=tukey;

run;

proc glm;

class time amp temp;

model AIL=time|amp|temp;

lsmeans time|amp|temp / pdiff adjust=tukey;

run;

proc glm;

class time amp temp;

model ASL=time|amp|temp;

lsmeans time|amp|temp / pdiff adjust=tukey;

run;

proc glm;

class time amp temp;

model sugars=time|amp|temp;

lsmeans time|amp|temp / pdiff adjust=tukey;

run;

SAS 9.2© Output for statistical analyses of impact of amplitude and time on SR, AIL

and Sugars for chapter 3

The SAS System 04:37

Saturday, March 17, 2012 1

The GLM Procedure

Class Level Information

Class Levels Values

137

time 4 0 5 10 60

amp 4 0 50 75 100

temp 3 100 NTC TC

Number of Observations Read 55

Number of Observations Used 55

The SAS System 04:37

Saturday, March 17, 2012 2

The GLM Procedure

Dependent Variable: SR

Sum of

Source DF Squares Mean Square

F Value Pr > F

Model 18 8398.850021 466.602779

41.94 <.0001

Error 36 400.557233 11.126590

Corrected Total 54 8799.407255

R-Square Coeff Var Root MSE SR

Mean

0.954479 3.819995 3.335654

87.32091

Source DF Type I SS Mean Square

F Value Pr > F

time 3 8234.020521 2744.673507

246.68 <.0001

amp 2 45.327928 22.663964

2.04 0.1452

time*amp 4 18.228073 4.557018

0.41 0.8005

temp 1 41.997502 41.997502

3.77 0.0599

time*temp 2 5.287540 2.643770

0.24 0.7897

amp*temp 2 18.049351 9.024675

0.81 0.4523

138

time*amp*temp 4 35.939107 8.984777

0.81 0.5286

Source DF Type III SS Mean Square

F Value Pr > F

time 2 14.62282383 7.31141192

0.66 0.5245

amp 2 45.71267295 22.85633648

2.05 0.1430

time*amp 4 17.90776069 4.47694017

0.40 0.8056

temp 1 41.14245450 41.14245450

3.70 0.0624

time*temp 2 5.09562383 2.54781192

0.23 0.7965

amp*temp 2 16.58782383 8.29391192

0.75 0.4817

time*amp*temp 4 35.93910736 8.98477684

0.81 0.5286

The SAS System 04:37

Saturday, March 17, 2012 3

The GLM Procedure

Least Squares Means

Adjustment for Multiple Comparisons: Tukey

LSMEAN

time SR LSMEAN Number

0 Non-est 1

5 Non-est 2

10 Non-est 3

60 Non-est 4

Least Squares Means for effect time

Pr > |t| for H0: LSMean(i)=LSMean(j)

Dependent Variable: SR

i/j 1 2 3

4

1 . .

.

2 . 0.5410

0.9795

3 . 0.5410

0.6501

139

4 . 0.9795 0.6501

The SAS System 04:37

Saturday, March 17, 2012 4

The GLM Procedure

Least Squares Means

Adjustment for Multiple Comparisons: Tukey

LSMEAN

amp SR LSMEAN Number

0 Non-est 1

50 Non-est 2

75 Non-est 3

100 Non-est 4

Dependent Variable: SR

i/j 1 2 3

4

1 . .

.

2 . 0.5410

0.9795

3 . 0.5410

0.6501

4 . 0.9795 0.6501

The SAS System 04:37

Saturday, March 17, 2012 4

The GLM Procedure

Least Squares Means

Adjustment for Multiple Comparisons: Tukey

LSMEAN

amp SR LSMEAN Number

0 Non-est 1

50 Non-est 2

75 Non-est 3

100 Non-est 4

Least Squares Means for effect amp

Pr > |t| for H0: LSMean(i)=LSMean(j)

Dependent Variable: SR

i/j 1 2 3

4

140

1 . .

.

2 . 0.8321

0.1325

3 . 0.8321

0.3467

4 . 0.1325 0.3467

The SAS System 04:37

Saturday, March 17, 2012 5

The GLM Procedure

Least Squares Means

Adjustment for Multiple Comparisons: Tukey

LSMEAN

time amp SR LSMEAN Number

0 0 Non-est 1

5 50 Non-est 2

5 75 Non-est 3

5 100 Non-est 4

10 50 Non-est 5

10 75 Non-est 6

10 100 Non-est 7

60 50 Non-est 8

60 75 Non-est 9

60 100 Non-est 10

Least Squares Means for effect time*amp

Pr > |t| for H0: LSMean(i)=LSMean(j)

Dependent Variable: SR

i/j 1 2 3 4 5 6 7

8 9 10

1 . . . . . .

. . .

2 . 1.0000 0.9915 0.9989 1.0000 1.0000

1.0000 1.0000 0.8515

3 . 1.0000 0.9990 0.9900 1.0000 1.0000

0.9994 0.9998 0.9420

4 . 0.9915 0.9990 0.8323 0.9903 0.9923

0.9411 0.9616 0.9999

5 . 0.9989 0.9900 0.8323 0.9991 0.9987

1.0000 1.0000 0.4498

6 . 1.0000 1.0000 0.9903 0.9991 1.0000

1.0000 1.0000 0.8431

7 . 1.0000 1.0000 0.9923 0.9987 1.0000

141

1.0000 1.0000 0.8581

8 . 1.0000 0.9994 0.9411 1.0000 1.0000 1.0000

1.0000 0.6478

9 . 1.0000 0.9998 0.9616 1.0000 1.0000 1.0000

1.0000 0.7085

10 . 0.8515 0.9420 0.9999 0.4498 0.8431 0.8581

0.6478 0.7085

Dependent Variable: AIL

Sum of

Source DF Squares Mean Square

F Value Pr > F

Model 18 714.5129270 39.6951626

25.45 <.0001

Error 36 56.1595167 1.5599866

Corrected Total 54 770.6724436

R-Square Coeff Var Root MSE AIL

Mean

0.927129 6.313617 1.248994

19.78255

Source DF Type I SS Mean Square

F Value Pr > F

time 3 666.6913266 222.2304422

142.46 <.0001

amp 2 0.2795613 0.1397807

0.09 0.9145

time*amp 4 19.4508590 4.8627148

3.12 0.0267

temp 1 10.0293967 10.0293967

6.43 0.0157

time*temp 2 3.3510504 1.6755252

1.07 0.3523

amp*temp 2 6.1494507 3.0747254

1.97 0.1541

time*amp*temp 4 8.5612822 2.1403206

1.37 0.2630

Source DF Type III SS Mean Square

F Value Pr > F

time 2 6.96940015 3.48470007

142

2.23 0.1218

amp 2 0.25851243 0.12925621

0.08 0.9207

time*amp 4 19.21448444 4.80362111

3.08 0.0280

temp 1 9.96739459 9.96739459

6.39 0.0160

time*temp 2 3.27113348 1.63556674

1.05 0.3609

amp*temp 2 5.34795921 2.67397961

1.71 0.1945

time*amp*temp 4 8.56128222 2.14032056

1.37 0.2630

The GLM Procedure

Least Squares Means

Adjustment for Multiple Comparisons: Tukey

LSMEAN

time amp AIL LSMEAN Number

0 0 Non-est 1

5 50 Non-est 2

5 75 Non-est 3

5 100 Non-est 4

10 50 Non-est 5

10 75 Non-est 6

10 100 Non-est 7

60 50 Non-est 8

60 75 Non-est 9

60 100 Non-est 10

Least Squares Means for effect time*amp

Pr > |t| for H0: LSMean(i)=LSMean(j)

Dependent Variable: AIL

i/j 1 2 3 4 5 6 7

8 9 10

1 . . . . . .

. . .

2 . 0.9913 0.9884 0.9962 1.0000 0.7143

1.0000 0.9999 0.6137

3 . 0.9913 1.0000 1.0000 0.9716 0.2018

0.9654 1.0000 0.9832

4 . 0.9884 1.0000 1.0000 0.9664 0.2248

0.9600 0.9999 0.9941

5 . 0.9962 1.0000 1.0000 0.9846 0.2444

0.9806 1.0000 0.9694

6 . 1.0000 0.9716 0.9664 0.9846 0.8203

1.0000 0.9987 0.4914

143

7 . 0.7143 0.2018 0.2248 0.2444 0.8203

0.8384 0.4021 0.0205

8 . 1.0000 0.9654 0.9600 0.9806 1.0000 0.8384

0.9981 0.4681

9 . 0.9999 1.0000 0.9999 1.0000 0.9987 0.4021

0.9981 0.8858

10 . 0.6137 0.9832 0.9941 0.9694 0.4914 0.0205

0.4681 0.8858

The SAS System 04:37

Saturday, March 17,

i/j 1 2 3 4 5 6 7

8 9 10

1 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

<.0001 <.0001 <.0001

2 <.0001 0.9895 1.0000 1.0000 1.0000 1.0000

1.0000 1.0000 0.9999

3 <.0001 0.9895 0.8698 1.0000 0.9188 1.0000

0.9986 0.9954 1.0000

4 <.0001 1.0000 0.8698 0.9988 1.0000 1.0000

1.0000 1.0000 0.9900

5 <.0001 1.0000 1.0000 0.9988 0.9997 1.0000

1.0000 1.0000 1.0000

6 <.0001 1.0000 0.9188 1.0000 0.9997 1.0000

1.0000 1.0000 0.9962

7 <.0001 1.0000 1.0000 1.0000 1.0000 1.0000

1.0000 1.0000 1.0000

8 <.0001 1.0000 0.9986 1.0000 1.0000 1.0000 1.0000

1.0000 1.0000

9 <.0001 1.0000 0.9954 1.0000 1.0000 1.0000 1.0000

1.0000 1.0000

10 <.0001 0.9999 1.0000 0.9900 1.0000 0.9962 1.0000

1.0000 1.0000

11 <.0001 1.0000 1.0000 0.9977 1.0000 0.9993 1.0000

1.0000 1.0000 1.0000

12 <.0001 0.9818 1.0000 0.8295 1.0000 0.8877 0.9999

0.9969 0.9913 1.0000

13 <.0001 0.6450 1.0000 0.3216 0.9675 0.3946 0.9550

0.7948 0.7171 0.9935

14 <.0001 1.0000 1.0000 0.9907 1.0000 0.9965 1.0000

1.0000 1.0000 1.0000

15 <.0001 1.0000 1.0000 0.9960 1.0000 0.9987 1.0000

1.0000 1.0000 1.0000

16 <.0001 1.0000 0.9993 1.0000 1.0000 1.0000 1.0000

1.0000 1.0000 1.0000

17 <.0001 1.0000 0.9999 0.9999 1.0000 1.0000 1.0000

1.0000 1.0000 1.0000

18 <.0001 0.6297 0.0382 0.9076 0.2129 0.8550 0.5315

0.4684 0.5550 0.1276

19 <.0001 0.9991 1.0000 0.9608 1.0000 0.9807 1.0000

1.0000 0.9997 1.0000

144

The SAS System 04:37

Saturday, March 17, 2012 20

The GLM Procedure

Least Squares Means

Adjustment for Multiple Comparisons: Tukey-

Kramer

Least Squares Means for effect time*amp*temp

Pr > |t| for H0: LSMean(i)=LSMean(j)

Dependent Variable: AIL

i/j 11 12 13 14 15 16

17 18 19

1 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

<.0001 <.0001 <.0001

2 1.0000 0.9818 0.6450 1.0000 1.0000 1.0000

1.0000 0.6297 0.9991

3 1.0000 1.0000 1.0000 1.0000 1.0000 0.9993

0.9999 0.0382 1.0000

4 0.9977 0.8295 0.3216 0.9907 0.9960 1.0000

0.9999 0.9076 0.9608

5 1.0000 1.0000 0.9675 1.0000 1.0000 1.0000

1.0000 0.2129 1.0000

6 0.9993 0.8877 0.3946 0.9965 0.9987 1.0000

1.0000 0.8550 0.9807

7 1.0000 0.9999 0.9550 1.0000 1.0000 1.0000

1.0000 0.5315 1.0000

8 1.0000 0.9969 0.7948 1.0000 1.0000 1.0000

1.0000 0.4684 1.0000

9 1.0000 0.9913 0.7171 1.0000 1.0000 1.0000

1.0000 0.5550 0.9997

10 1.0000 1.0000 0.9935 1.0000 1.0000 1.0000

1.0000 0.1276 1.0000

11 1.0000 0.9778 1.0000 1.0000 1.0000

1.0000 0.1862 1.0000

12 1.0000 1.0000 1.0000 1.0000 0.9984

0.9997 0.0309 1.0000

13 0.9778 1.0000 0.9930 0.9849 0.8345

0.9004 0.0034 0.9992

14 1.0000 1.0000 0.9930 1.0000 1.0000

1.0000 0.1303 1.0000

15 1.0000 1.0000 0.9849 1.0000 1.0000

1.0000 0.1643 1.0000

16 1.0000 0.9984 0.8345 1.0000 1.0000

1.0000 0.4206 1.0000

17 1.0000 0.9997 0.9004 1.0000 1.0000 1.0000

0.3323 1.0000

18 0.1862 0.0309 0.0034 0.1303 0.1643 0.4206

0.3323 0.0765

145

19 1.0000 1.0000 0.9992 1.0000 1.0000 1.0000

1.0000 0.0765

The GLM Procedure

Least Squares Means

Adjustment for Multiple Comparisons: Tukey-

Kramer

sugars LSMEAN

time amp temp LSMEAN Number

0 0 100 2.5000000 1

5 50 NTC 67.2233333 2

5 50 TC 62.7166667 3

5 75 NTC 68.5500000 4

5 75 TC 62.5400000 5

5 100 NTC 62.4800000 6

5 100 TC 64.6250000 7

10 50 NTC 62.5900000 8

10 50 TC 66.0866667 9

10 75 NTC 63.5333333 10

10 75 TC 60.6733333 11

10 100 NTC 64.2066667 12

10 100 TC 67.2466667 13

60 50 NTC 61.7633333 14

60 50 TC 62.4266667 15

60 75 NTC 59.0000000 16

60 75 TC 61.2733333 17

60 100 NTC 61.8333333 18

60 100 TC 66.4566667 19

Least Squares Means for effect time*amp*temp

Pr > |t| for H0: LSMean(i)=LSMean(j)

Dependent Variable: sugars

i/j 1 2 3 4 5 6 7

8 9 10

1 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

<.0001 <.0001 <.0001

2 <.0001 0.9850 1.0000 0.9782 0.9755 1.0000

0.9803 1.0000 0.9983

3 <.0001 0.9850 0.8709 1.0000 1.0000 1.0000

1.0000 0.9994 1.0000

4 <.0001 1.0000 0.8709 0.8431 0.8330 0.9991

0.8513 1.0000 0.9595

5 <.0001 0.9782 1.0000 0.8431 1.0000 1.0000

1.0000 0.9990 1.0000

6 <.0001 0.9755 1.0000 0.8330 1.0000 1.0000

1.0000 0.9987 1.0000

7 <.0001 1.0000 1.0000 0.9991 1.0000 1.0000

146

1.0000 1.0000 1.0000

8 <.0001 0.9803 1.0000 0.8513 1.0000 1.0000 1.0000

0.9991 1.0000

9 <.0001 1.0000 0.9994 1.0000 0.9990 0.9987 1.0000

0.9991 1.0000

10 <.0001 0.9983 1.0000 0.9595 1.0000 1.0000 1.0000

1.0000 1.0000

11 <.0001 0.7412 1.0000 0.4440 1.0000 1.0000 0.9990

1.0000 0.9247 0.9999

12 <.0001 0.9999 1.0000 0.9896 1.0000 1.0000 1.0000

1.0000 1.0000 1.0000

13 <.0001 1.0000 0.9842 1.0000 0.9772 0.9743 1.0000

0.9794 1.0000 0.9982

14 <.0001 0.9196 1.0000 0.6904 1.0000 1.0000 1.0000

1.0000 0.9901 1.0000

15 <.0001 0.9728 1.0000 0.8237 1.0000 1.0000 1.0000

1.0000 0.9985 1.0000

16 <.0001 0.3716 0.9982 0.1628 0.9990 0.9992 0.9585

0.9988 0.6229 0.9841

17 <.0001 0.8529 1.0000 0.5793 1.0000 1.0000 0.9999

1.0000 0.9719 1.0000

18 <.0001 0.9272 1.0000 0.7058 1.0000 1.0000 1.0000

1.0000 0.9917 1.0000

19 <.0001 1.0000 0.9980 1.0000 0.9966 0.9960 1.0000

0.9971 1.0000 0.9999

The SAS System 04:37

Saturday, March 17, 2012 40

The GLM Procedure

Least Squares Means

Adjustment for Multiple Comparisons: Tukey-

Kramer

Least Squares Means for effect time*amp*temp

Pr > |t| for H0: LSMean(i)=LSMean(j)

Dependent Variable: sugars

i/j 11 12 13 14 15 16

17 18 19

1 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

<.0001 <.0001 <.0001

2 0.7412 0.9999 1.0000 0.9196 0.9728 0.3716

0.8529 0.9272 1.0000

3 1.0000 1.0000 0.9842 1.0000 1.0000 0.9982

1.0000 1.0000 0.9980

4 0.4440 0.9896 1.0000 0.6904 0.8237 0.1628

0.5793 0.7058 1.0000

5 1.0000 1.0000 0.9772 1.0000 1.0000 0.9990

1.0000 1.0000 0.9966

147

6 1.0000 1.0000 0.9743 1.0000 1.0000 0.9992

1.0000 1.0000 0.9960

7 0.9990 1.0000 1.0000 1.0000 1.0000 0.9585

0.9999 1.0000 1.0000

8 1.0000 1.0000 0.9794 1.0000 1.0000 0.9988

1.0000 1.0000 0.9971

9 0.9247 1.0000 1.0000 0.9901 0.9985 0.6229

0.9719 0.9917 1.0000

10 0.9999 1.0000 0.9982 1.0000 1.0000 0.9841

1.0000 1.0000 0.9999

11 0.9990 0.7364 1.0000 1.0000 1.0000

1.0000 1.0000 0.8783

12 0.9990 0.9999 1.0000 1.0000 0.9447

0.9999 1.0000 1.0000

13 0.7364 0.9999 0.9170 0.9716 0.3670

0.8491 0.9247 1.0000

14 1.0000 1.0000 0.9170 1.0000 1.0000

1.0000 1.0000 0.9778

15 1.0000 1.0000 0.9716 1.0000 0.9993

1.0000 1.0000 0.9953

16 1.0000 0.9447 0.3670 1.0000 0.9993

1.0000 0.9999 0.5380

17 1.0000 0.9999 0.8491 1.0000 1.0000 1.0000

1.0000 0.9467

18 1.0000 1.0000 0.9247 1.0000 1.0000 0.9999

1.0000 0.9807

19 0.8783 1.0000 1.0000 0.9778 0.9953 0.5380

0.9467 0.9807

148

APPENDIX 5

SAS 9.2 © Code for statistical analyses of reducing suagr yields for enzymatic

hydrolysis of selected pretreatement samples for chapter 3

Code for Dyadic Alternafuel 200 L hydrolysis data analyses

data Hydrolysis;

input treatment $ sugars;

cards;

UNTREATED 78.93

UNTREATED 78.53

UNTREATED 86.25

USNTC1001Hr 78.01

USNTC1001Hr 74.94

USNTC1001Hr 75.47

USNTC505min 72.89

USNTC505min 78.28

USNTC505min 78.31

USTC100%1hr 81.44

USTC100%1hr 84.71

USTC100%1hr 87.94

USTC50%5min 77.41

USTC50%5min 84.04

USTC50%5min 74.12

;

proc glm;

class treatment;

model sugars=treatment;

lsmeans treatment/stderr pdiff;

means treatment/duncan alpha=.05;

run;

Output for Dyadic Alternafuel 200 L hydrolysis data analyses

The SAS System 02:31

Friday, March 16, 2012 9

The GLM Procedure

Class Level Information

Class Levels Values

treatment 5 UNTREATE USNTC100 USNTC505

USTC100% USTC50%5

149

Number of Observations Read 15

Number of Observations Used 15

The SAS System 02:31

Friday, March 16, 2012 10

The GLM Procedure

Dependent Variable: sugars

Sum of

Source DF Squares Mean Square

F Value Pr > F

Model 4 153.8137733 38.4534433

2.85 0.0815

Error 10 134.8302667 13.4830267

Corrected Total 14 288.6440400

R-Square Coeff Var Root MSE sugars

Mean

0.532884 4.623541 3.671924

79.41800

Source DF Type I SS Mean Square

F Value Pr > F

treatment 4 153.8137733 38.4534433

2.85 0.0815

Source DF Type III SS Mean Square

F Value Pr > F

treatment 4 153.8137733 38.4534433

2.85 0.0815

The SAS System 02:31

Friday, March 16, 2012 11

The GLM Procedure

Least Squares Means

sugars Standard

LSMEAN

150

treatment LSMEAN Error Pr > |t|

Number

UNTREATE 81.2366667 2.1199864 <.0001

1

USNTC100 76.1400000 2.1199864 <.0001

2

USNTC505 76.4933333 2.1199864 <.0001

3

USTC100% 84.6966667 2.1199864 <.0001

4

USTC50%5 78.5233333 2.1199864 <.0001

5

Least Squares Means for effect treatment

Pr > |t| for H0: LSMean(i)=LSMean(j)

Dependent Variable: sugars

i/j 1 2 3 4

5

1 0.1200 0.1447 0.2753

0.3867

2 0.1200 0.9085 0.0171

0.4451

3 0.1447 0.9085 0.0210

0.5137

4 0.2753 0.0171 0.0210

0.0665

5 0.3867 0.4451 0.5137 0.0665

NOTE: To ensure overall protection level, only probabilities associated

with pre-planned

comparisons should be used.

The SAS System 02:31

Friday, March 16, 2012 12

The GLM Procedure

Duncan's Multiple Range Test for sugars

NOTE: This test controls the Type I comparisonwise error rate, not the

experimentwise error rate.

Alpha 0.05

Error Degrees of Freedom 10

151

Error Mean Square 13.48303

Number of Means 2 3 4

5

Critical Range 6.680 6.981 7.158

7.271

Means with the same letter are not significantly

different.

Duncan Grouping Mean N treatment

A 84.697 3 USTC100%

A

B A 81.237 3 UNTREATE

B A

B A 78.523 3 USTC50%5

B

B 76.493 3 USNTC505

B

B 76.140 3 USNTC100

Code for Novozymes Cellic Ctec2© hydrolysis data analyses

data Hydrolysis;

input treatment $ sugars;

cards;

UNTREATED 71.06

UNTREATED 75.13

UNTREATED 74.10

USNTC1001Hr 80.19

USNTC1001Hr 76.89

USNTC1001Hr 77.49

USNTC505min 74.83

USNTC505min 73.24

USNTC505min 77.45

USTC100%1hr 83.69

USTC100%1hr 83.16

USTC100%1hr 87.01

USTC50%5min 75.42

USTC50%5min 84.50

USTC50%5min 75.84

;

proc glm;

class treatment;

model sugars=treatment;

152

lsmeans treatment/stderr pdiff;

means treatment/duncan alpha=.05;

run;

Code for Novozymes Cellic Ctec2© hydrolysis data analyses

The SAS System 02:31

Friday, March 16, 2012 1

The GLM Procedure

Class Level Information

Class Levels Values

treatment 5 UNTREATE USNTC100 USNTC505

USTC100% USTC50%5

Number of Observations Read 15

Number of Observations Used 15

The SAS System 02:31

Friday, March 16, 2012 2

The GLM Procedure

Dependent Variable: sugars

Sum of

Source DF Squares Mean Square

F Value Pr > F

Model 4 219.2388667 54.8097167

6.42 0.0080

Error 10 85.4227333 8.5422733

Corrected Total 14 304.6616000

R-Square Coeff Var Root MSE sugars

Mean

0.719614 3.747073 2.922717

78.00000

153

Source DF Type I SS Mean Square

F Value Pr > F

treatment 4 219.2388667 54.8097167

6.42 0.0080

Source DF Type III SS Mean Square

F Value Pr > F

treatment 4 219.2388667 54.8097167

6.42 0.0080

The SAS System 02:31

Friday, March 16, 2012 3

The GLM Procedure

Least Squares Means

sugars Standard

LSMEAN

treatment LSMEAN Error Pr > |t|

Number

UNTREATE 73.4300000 1.6874313 <.0001

1

USNTC100 78.1900000 1.6874313 <.0001

2

USNTC505 75.1733333 1.6874313 <.0001

3

USTC100% 84.6200000 1.6874313 <.0001

4

USTC50%5 78.5866667 1.6874313 <.0001

5

Least Squares Means for effect treatment

Pr > |t| for H0: LSMean(i)=LSMean(j)

Dependent Variable: sugars

i/j 1 2 3 4

5

1 0.0740 0.4818 0.0009

0.0560

2 0.0740 0.2349 0.0225

0.8713

3 0.4818 0.2349 0.0027

0.1831

154

4 0.0009 0.0225 0.0027

0.0300

5 0.0560 0.8713 0.1831 0.0300

NOTE: To ensure overall protection level, only probabilities associated

with pre-planned

comparisons should be used.

The SAS System 02:31

Friday, March 16, 2012 4

The GLM Procedure

Duncan's Multiple Range Test for sugars

NOTE: This test controls the Type I comparisonwise error rate, not the

experimentwise error rate.

Alpha 0.05

Error Degrees of Freedom 10

Error Mean Square 8.542273

Number of Means 2 3 4

5

Critical Range 5.317 5.556 5.697

5.787

Means with the same letter are not significantly

different.

Duncan Grouping Mean N treatment

A 84.620 3 USTC100%

B 78.587 3 USTC50%5

B

B 78.190 3 USNTC100

B

B 75.173 3 USNTC505

B

B 73.430 3 UNTREATE