MODELING WITH PARAMETER IDENTIFICATION OF POLLUTANT ...

International Journal of Arts & Sciences,

CD-ROM. ISSN: 1944-6934 :: 08(08):347–374 (2015)

MODELING WITH PARAMETER IDENTIFICATION OF POLLUTANT

ADSORPTION ON NOVEL MODIFIED BIOMASS AS A MEANS OF

LAKE/RIVER WATER DECONTAMINATION

Fragiskos A. Batzias, Dimitrios K. Sidiras, Christina G. Siontorou, Ilias G. Konstantinou,

George N. Katsamas, Ioanna S. Salapa and Stavroula P. Zervopoulou

University of Piraeus, Greece

Adsorption is a physico-chemical process by which material accumulates mainly at the interface

between two phases. The couples of these phases may be liquid-liquid, solid-liquid, liquid-gas, and

solid-gas. In each case, the adsorbing phase is termed ‘adsorbent’, while the substance being adsorbed

is called ‘adsorbate’. The present work deals with the adsorption of substances from aquatic solutions

on novel modified (made of inexpensive waste biomass) adsorbents aiming at decontamination of

lake/river water after systematic or accidental pollution. More specifically, the adsorption models used

are either isotherms or rate equations, belonging to the domains of Thermodynamics or Chemical

Kinetics, respectively. Their parameters are identified by combining quantitative relations with

qualitative information (mainly surface topography images obtained through scanning electron

microscopy - SEM). The corresponding parameter values are estimated by using regression models

extracted from a Knowledge Base (KB) according to widely applied statistical methods in order to

obtain results comparable to the respective ones, reported in relevant publications. Implementation of

this procedure is presented in the cases of isolated and integrated river/lake environmental systems

contaminated by hydrocarbon releases. The superiority of adsorptive properties of the modified biomass

in comparison with the corresponding properties of the unmodified biomass was proved quantitatively

and relevant interpretation was achieved qualitatively, mainly by means of SEM and Fourier transform

infrared (FT-IR) spectroscopy. Last, an optimization methodology is presented in the discussion section

by combining physicochemical examination results with economic issues based on scenarions

concerning energy prices.

Keywords: Adsorbent, Acid hydrolysis, Diesel, Crude oil, Biomass.

Introduction

Oils can cause environmental pollution during production, transportation, storage, refining and use

(Srinivasan and Viraraghavan, 2008). Oil spills in marine aquatic environment may be due to releases of

oil from offshore platforms, drilling rigs, underwater pipeline raptures, routine oil tanker operations or

nautical accidents such as collisions, groundings, hull failures, fires and explosions. Oil spills cause great

damage to the coastal environment mainly in sensitive marine ecosystems and negative economical

impacts on tourism and fisheries (Angelova et al., 2011).

Chemical dispersion, in situ burning, mechanical containment (skimmers and booms) and oil

sorption by adsorbents are the generally cleanup methods to combat the oil pollution (Banerjee et al.,

347

348 Modeling with Parameter Identification of Pollutant ...

2006). Adsorbents concentrate and transform liquid oil to the semi solid or solid phase, which can then

be removed from the seawater, can be divided into three basic categories: inorganic mineral, organic

synthetic and natural organic products like waste lignocellulosic biomass or agro-industrial byproducts

(Husseien et al., 2009). The modification of such wastes can provide adsorbents with relatively high

sorption capacity, biodegradability and cost-effectiveness for the adsorption of dyes (Batzias et al., 2009),

heavy metals (Sidiras et al., 2011b; Sidiras et al., 2013a) and oil products (Sidiras and Konstantinou,

2012; Sidiras et al., 2014a).

Literature survey shows that numerous untreated and pretreated lignocellulosic materials can be used

as adsorbents for oil spill cleaning. Some of the untreated materials are: bagasse (Said et al., 2009), barley

straw (Witka-Jezewska et al., 2003; Husseien et al., 2009), cotton grass fiber (Suni et al. 2004), cotton

grass mats (Suni et al. 2004), garlic/onions peels (Sayed and Zayed, 2006), groundnut husks (Nwokoma

and Avene, 2010), peat (Suni et al. 2004; Viraraghavan and Mathavan, 1988), rice husk (Khan et al.,

2004), and walnut shell (Srinivasan and Viraraghavan, 2008). Some of the pretreated materials are:

acetylated wheat straw (Sun et al., 2004b), acetylated rice straw (Sun et al., 2002), acetylated sugarcane

bagasse (Sun et al., 2004a), carbonized fir fibers (Inagaki et al., 2002), carbonized pith bagasse (Hussein,

et al., 2008a), carbonized rice husk (Kumagai et al., 2007; Angelova et al., 2011), fatty acid grafted

sawdust (Banerjee et al., 2006), heated barley straw (Husseien et al., 2008b), NaOH-treated barley straw

(Ibrahim et al., 2009; Ibrahim et al., 2010), pretreated banana trunk fiber (Sathasivam and Harris, 2010)

and ferrofluid-modified plant-based magnetic materials (Safarik et al., 2005).

Among the above materials, straw is more often used during containment and cleanup of oil spills.

The surface properties of straw play a crucial role. A thin wax layer covering stalks and leaves of cereals

is composed of esters, long chain fatty acids and monohydroxy alcohols, therefore straw should favorably

adsorb hydrophobic liquids. Wax coverage making the straw surface hydrophobic, together with the

existing capillary forces, determines the efficiency of oil removal. Considering the phenomenon at a

deeper phenomenological or higher information granularity level, oil is mostly held due to capillary of

straw tissue and interior part of stalk, as well as to the existence of oil bridges between stalks

(Wisniewska et al., 2003; Witka-Jezewska et al., 2003).

This work is part of the “THALIS - University of Piraeus - Development of New Material from

Waste Biomass for Hydrocarbons Adsorption in Aquatic Environments” Project (relevant published work

by Batzias et al., 2012a; Batzias et al., 2012d; Sidiras et 2013b) and mainly deals with the adsorption of

substances from aquatic solutions on novel modified (made of inexpensive waste biomass) adsorbents

aiming at decontamination of lake/river water after systematic or accidental pollution. The lignocellulosic

waste adsorbents examined herein were selected by means of multicriteria analysis (Batzias et al., 2012b;

Batzias et al., 2012e) and subsequently studied with a view to applicable adsorption models, i.e.,

isotherms or rate equations, belonging to the domains of Thermodynamics or Chemical Kinetics,

respectively. Their parameters are identified through the methodological framework we have

designed/developed under the form of an algorithmic procedure, including 27 activity stages, 6 decision

nodes, and a KB (Batzias et al., 2012c; Batzias et al., 2014). Implementation of this procedure is

presented in the cases of isolated and integrated river/lake environmental systems contaminated by

hydrocarbon releases. The superiority of adsorptive properties of the modified biomass in comparison

with the corresponding properties of the unmodified biomass was proved experimentally by estimating

the respective models’ parameters while interpretation of results is given mainly by means of SEM and

FT-IR. Last, the economo-technical and the managerial/operational aspects are co-examined within a

multicriteria interdisciplinary optimization method, functioning also as an Inference Engine of the MBR

type incorporated into the KB mentioned above.

Experimental

The wheat straw used in this work was obtained from the Kapareli village, close to the Thiva city at the

Kopaida area in central Greece (harvesting year 2012), as a suitable source for full-scale industrial

applications. The moisture content of the material when received was 8.8% w/w; after screening, the

Fragiskos A. Batzias et al. 349

fraction with particle sizes between 14 and 24 cm was isolated. Part of the material was reduced to

particle sizes between 1 and 2 cm. The wheat straw chemical composition is presented in Table 1.

Table 1. Composition of wheat straw

Component Wheat straw % w/w

Cellulose 32.7

Hemicelluloses

24.5

Xylose 19,3

Arabinose 2,7

Acetyl groups 2,5

Klason lignin (acid insoluble)

16.8

Ash 4.7

Extractives 6.2

Other components 15.1

Figure 1. The wheat straw was pretreated by acid hydrolysis in a CHEMGLASS 20 L glass reactor.


The wheat straw acid hydrolysis pretreatment was performed in a CHEMGLASS 20 L glass reactor

(see Fig. 1) for the big particles and in a grass reactor 0.5 L as regards the small particles. In the case of

the 0.5 L glass reactor, the acid hydrolysis isothermal time was 0-4 h (not including the preheating time);

the reaction was catalyzed by sulfuric acid 0.06-1.8 M at a liquid-to-solid ratio of 10:1; the liquid phase

volume (water) was 400 mL and the solid material dose (wheat straw) was 40 g. The reaction ending

temperature was 100 °C reached after the 40 min preheating period. In the case of the 20 L

CHEMGLASS reactor, the acid hydrolysis isothermal time was 4 h (not including the preheating time);

the reaction was catalyzed by sulfuric acid 0.45 M at a liquid-to-solid ratio of 20:1; the liquid phase

volume (water) was 10 L and the solid material dose (wheat straw) was 500 g. The reaction ending

temperature was 100 °C reached after the 1 h and 50 min preheating period. The untreated and the

pretreated big wheat straw particles are shown in Fig. 2. The 20 L CHEMGLASS reactor experiments

temperature profile is given in Fig. 3. The average was 101.09 oC; the sulfuric acid concentration was

0.45 M; the liquid-to-solid ratio of 20:1, i.e., the liquid phase volume was 10 L water and the solid

material dose was 500 g wheat straw; the solid residue yield was 55.34% w/w on dry basis.

Figure 2. Untreated (left) and pretreated (right) wheat straw; particle size 14-24 cm

20

30

40

50

60

70

80

90

100

110

0 100 200 300 400

Time (min)

Tem

pera

ture

(oC

)

Figure 3. Wheat straw pretreatment temperature profile


Methylene Blue (MB) adsorption isotherms were derived from batch experiments. Following the

batch procedure, accurately weighed quantities of adsorbent (wheat or barley straw) were transferred into

0.8-L bottles, where 0.5 L of adsorbate solution were added. The sorbent weight was 0.5 g, the

temperature was 23 oC, the initial Methylene Blue (MERCK, C.I. 52015) concentration varied from 1.4

mg/L to 156 mg/L. The bottles were sealed and mechanically tumbled for a period of 7 days. This time

period was chosen after experimental studies (the time varied from 4 h to 14 days), to ensure that nearly

equilibrium conditions were achieved. The resulting solution concentrations were determined and the

equilibrium data from each bottle represented one point on the adsorption isotherm plots.

Methylene Blue adsorption kinetics batch experiments were conducted in a 2-L completely mixed

glass reactor fitted with a twisted blade-type stirrer, operating at 300 rpm for keeping the lignocellulosic

material in suspension. The reactor, containing 1 L aqueous dye solution, was placed into a water bath to

keep temperature constant at the desired level. The sorbent weight was 1 g, the temperature was 23 oC,

and the initial Methylene Blue concentration was approximately 14 mg/L.

The study of untreated and pretreated wheat straw samples by scanning electron microscopy, SEM,

was conducted at the Institute of Materials Science of the National Center for Scientific Research

‘Demokritos’ using an FEI INSPECT SEM equipped with an EDAX super ultra-thin window analyzer for

energy dispersive X-ray spectroscopy (EDS). The magnification was X750, X7,500 and X20,000.

The FT-IR spectra were conducted also at the Institute of Materials Science of the National Center

for Scientific Research ‘Demokritos’ using a Thermo ScientiÞc Nicolet 6700 FTIR with N2 purging

system. Spectra were acquired using a single reflection ATR (attenuated total reflection) SmartOrbit

accessory equipped with a single-bounce diamond crystal (spectral range: 10,000–55 cm 1, angle of

incidence: 45 ). A total of 32 scans were averaged for each sample and the resolution was 4 cm 1. The

spectra were obtained against a single-beam spectrum of the clean ATR crystal and converted into

absorbance units. Data were collected in the range 4000–400 cm 1.

Following the technique proposed by Saeman et al. (1945), the lignocellulosic materials were

hydrolyzed to glucose and reducing sugars in nearly quantitative yields; the filtrates were analyzed for

glucose and xylose using appropriate enzymatic tests. Based on these results the cellulose and

hemicelluloses content of the adsorbents were estimated. Finally, the acid-insoluble lignin (Klason lignin)

was determined according to the Tappi T222 om-88 method (1997).

The concentration of Methylene Blue in the solution was obtained by measuring O.D. at 663 nm,

using a HACH DR 6000™ UV VIS Spectrophotometer with RFID technology.

The water and oil adsorbency (defined as the ratio of water or oil adsorbed to dry adsorbent weight,

according to the ASTM F726-06 method, 2006) test was performed, following the procedure of this

standard method (Fig. 4), using diesel 10 PPM produced by Hellenic Petroleum SA and crude oil. In a 2 L

vessel we put 4 g wheat straw (untreated or pretreated) and 300 ml water or diesel or crude oil (see Fig.

4). In the cases of oil spills we put 250 mL and 50 ml diesel or crude oil to produce a 3 mm thickness

spill. After 17 min mild agitation the wheat straw was separated by sieves and weighted. Quality

specifications of diesel and crude oil are given in Table 2.

Table 2. Diesel and crude oil quality specifications

Properties Units Results

Diesel oil quality specifications

Density at 15 oC kg/m3 823.0

Color L0.5

% (v/v) Rec. at 250 oC %v/v 35.3

% (v/v) Rec. at 350 oC %v/v 94.6

95% (v/v) Recovered oC 359.0

Flash point oC 61.5

Sulfur content mg/kg 2.2

Copper strip corrosion (3 h at 50 oC) Class 1a


CFPP oC -17

Viscosity at 40 oC cST 2.772

Water content mg/kg 45

Ash content % m/m 0.003

Carbon residue (on 10% distill. residue) %m/m 0.01

Total contamination mg/kg 5.0

Oxidation stability g/m3 3.4

Polycyclic aromatic hydrocarbons %m/m 0.6

Lubricity, corrected (wsd1.4) at 60 oC m 435

Crude oil quality specifications

Density kg/m3 860 at 15oC

Water content mg/kg 250

Figure 4. Water and oil adsorbency tests (ASTM F726-06 method, 2005): In three 2 L vessels we put 4 g pretreated

wheat straw and 1000 mL water, 300 mL diesel and 300 mL crude oil, respectively (from the left to the right).

Diesel and crude oil spills were formed on tap water, stream water and lake water. As regards field-

simulation of oil spills cleaning, field-water sampling locations were selected (in cooperation with the

Hellenic Center for Marine Research - HCMR) as follows: one lake ( = Koumoundourou Lake), and

one stream (P6 = Pikrodafnis Stream). The map of these locations is presented bellow in Fig. 5. The

physical and chemical parameters of the stream water and the lake water samples are presented in

Tables 3 and 4. Their heavy metals composition is given in Table 5.

Figure 5. Field-water sampling locations (one lake and one stream):

= Koumoundourou Lake and = Pikrodafnis Stream


Table 3. Physical parameters of the water samples

Location Koumoundourou Lake Pikrodafnis Stream

pH 6.83 6.84

DO (mg/L) 8.68 6

Conductivity ( S/cm) 15,240 1,033

Temperature ( C) 29.1 24

Salinity (ppt) 11.47 0.68

Turbidity (NTU) 37.9 14

Table 4. Chemical parameters of the water samples


NO3- (mg/L) 0.4 17.81

2- (mg/L) 0.02 0.02

SiO2 (mg/L) - 17

PO43-(mg/L) 0.08 2.09

4+ (mg/L) 0.26 0.04

TotaL P (mg/L) 0.03 0.76

TiN (mg/L) 0.3 -

Table 5. Heavy metals concentration of the water samples.


Mn ( g/L) 10.72 0.62

Fe ( g/L) 11.62 5.59

Co ( g/L) 0.078 0.68

Ni ( g/L) 5.16 4.67

Cu ( g/L) 0.86 2.53

Zn ( g/L) 28.67 4.23

Cd ( g/L) 0.011 0.04

Pb ( g/L) 0.67 0.53

Results and Discussion

SEM migrographs and FT-IR peaks

The SEM surface topography for original/untreated (a, c, e) and modified/pretreated (b, d, f) (acid

hydrolysis 100 oC, 0.45 M H2SO4, 4 h) wheat straw are given in Figs. 6 (exterior), 7 (interior) and 8 (cross

section). The magnifications were X750, X7,500 and X20,000. The surface of the pretreated straw is

rougher than the surface of the untreated straw. The rougher surface favors the higher adsorptivity of dyes

and oil. The FT-IR peaks of untreated and modified wheat straw are given in Figs. 9-11 before and after

Methylene Blue (Fig. 9), diesel (Fig. 10) and crude oil (Fig. 11) adsorption.


Figure 6. SEM images of the untreated (a, c, e) and pretreated (b, d, f) (acid hydrolysis 100 oC, 0.45 M H2SO4, 4

h) wheat straw exterior. The magnifications are X750, X7,500, X20,000



h) wheat straw interior. The magnifications are X750, X7,500, X20,000



h) wheat straw cross section. The magnifications are X7,500, X20,000


40

50

60

70

80

90

100

110

400140024003400

Wavenumbers (cm-1

)

Tra

ns

mit

tan

ce

%

Pretreated MB

Untreated MB

Untreated

Pretreated

Figure 9. FT-IR peaks of untreated and modified wheat straw before and after Methylene Blue adsorption

30

40

50

60

70

80

90

100

110

120

400140024003400

Wavenumbers (cm-1

)

Tra

ns

mit

tan

ce

%

Untreated Diesel

Pretreated Diesel

Untreated

Pretreated

Figure 10. FT-IR peaks of untreated and modified wheat straw before and after Diesel adsorption

40

50

60

70

80

90

100

110

120

400140024003400

Wavenumbers (cm-1

)

Tra

ns

mit

tan

ce %

Untreated Crude

Pretreated Crude

Untreated

Pretreated

Figure 11. FT-IR peaks of untreated and modified wheat straw before and after crude oil adsorption


Isotherms

Nine isotherm models were applied to fit the experimental results. The Freundlich (Freundlich 1906)

isotherm is given by the following equation:

neF CKq

1

)( (1)

where q is the amount adsorbed per unit mass of the adsorbent (mg g-1), Ce is the equilibrium

concentration of the adsorbate (mg L-1) and KF, n are the Freundlich constants related to adsorption

capacity and intensity, respectively. Eq. (1) in logarithmic form gives:

eF Cn

Kq log1

loglog (2)

KF and n were estimated by non-linear regression analysis (NLRA) from the experimental adsorption data

obtained at 230C for MB, while the values of KF and n estimated by linear least squares regression

through eq. (2) were used as initial values for starting the algorithmic procedure of NLRA. From the

environmental point of view, parameter KF is the most important parameter representing the adsorption

capacity of the materials produced herein for low MB concentration Ce=1 mg L-1 .

The Langmuir isotherm (Langmuir 1916) is given by the following equation.

eL

emL

CK

CqKq

1 (3)

or

emLm CqKqq

1111 (4)

where KL is the Langmuir constant related to the energy of adsorption (L.mg-1) and qm the amount of MB

adsorbed (mg g-1) when saturation is attained. The parameters KL and qm can be obtained either by

plotting 1/q versus 1/Ce or by non-linear regression analysis. From the technical point of view, parameter

qm is the most important parameter representing the maximum adsorption capacity of the materials

produced herein. The characteristics of the Langmuir isotherm can be described by a dimensionless

constant called ‘equilibrium parameter’ or ‘separation factor’ RL:

01

1

CKR

L

L (5)

where C0 is the initial MB concentration (mg L-1).

The Sips (Langmuir – Freundlich) (Sips 1948) isotherm equation is

neL

neLm

CK

CKqq

/1

/1

1 or

n

em

n

m CqKqqL

/1

/1

1111 (6)

where KL and qm is the Langmuir constants, and n the Freundlich constant.

The Radke–Prausnitz (Radke 1972; Chern and Wu 2001) isotherm equation, is

neL

emL

CK

CqKq

/11

(7)


The Modified Radke – Prausnitz (Chern and Wu 2001) isotherm equation is

neL

emL

CK

CqKq

/11

(8)

The Tóth (Tóth, 2000) isotherm equation is

n/neL

em

CK/

Cqq

11

(9)

The UNILAN (Chern and Wu 2001) isotherm equation is

seL

seLm

eCK

eCK

s

qq

1

1ln

2 (10)

where s is a new constant.

The Temkin isotherm model (Temkin and Pyzhev, 1940) is

)ln( eT

T

CAb

RTq or )ln( eTT CABq or )ln( eLm CKqq (11)

where R=0.008314 kJ mol-1 K-1, T is the adsorption temperature in K, KL=AT in L mg-1 and qm=BT=RT/bT

in mg g-1. In linearized form Eq (11) is as follows

)ln( emT Cqaq (12)

where aT=qm ln(KL).

The Dubinin-Radushkevich (Dubinin and Radushkevich 1947) isotherm model is

})]1

1ln([exp{ 2

e

DDC

RTBqq or })]1

1[ln(exp{ 2

e

DDC

Aqq or

})]1

1[ln(exp{ 2

e

mC

nqq (13)

where qm=qD in mg g-1 and n=AD=BDR2T2 a dimensionless constant for T=constant.

The standard error of estimate (SEE) was calculated in each case by the following expression

'

1

2

, )''/()(n

i

theorii pnyySEE (14)

where: yi is the experimental value of the depended variable, yi,theor is the theoretical or estimated value of

the depended variable, n is the number of the experimental measurements and p is the number of

parameters (the difference n –p being the number of the degrees of freedom).

The Freundlich model isotherms of MB adsorption on untreated and acid hydrolysed (0.45 M H2SO4,

100 oC, 4 h) wheat straw are presented in linearized and normal form in Figs 12 and 13. Their parameters

are estimated in Table 6 using linear and nonlinear regression analysis, respectively (LRA and NLRA).

The SEE values are also estimated.


The Langmuir model isotherms of MB adsorption on untreated and acid hydrolysed wheat straw are

presented in linearized and normal form in Figs 14 and 15. Their parameters and SEE values are

estimated in Table 7 using LRA and NLRA, respectively.

The other models isotherms are presented in Figs 16-20. Their parameters and SEE values are

estimated in Tables 8-12 using NLRA. According to the SEE criterion the Tóth model isotherms have the

best fitting to the experimental data.

Figure 12. Linearized Freundlich model isotherms of MB adsorption on untreated and acid hydrolysed

(0.45 M H2SO4, 100oC, 4h) wheat straw

Figure 13. Freundlich model isotherms of of MB adsorption on untreated and acid hydrolysed

(0.45 M H2SO4 100oC, 4h) wheat straw


Table 6. Estimated Freundlich isotherm model parameter values for MB

adsorption on untreated and pretreated wheat straw

linear non-linear

KF n R KF n SEE

Untreated 14-24cm 1,373 1,611 0,950 2,527 2,220 2,093

Untreated 1-2cm 1,031 1,251 0,851 3,585 2,388 4,571

Pretreated 14-24cm 3,726 1,921 0,941 6,586 2,957 3,324

Pretreated 1-2cm 5,006 2,044 0,950 8,350 2,975 3,997

Figure 14. Linearized Langmuir model isotherms of of MB adsorption on untreated and acid hydrolysed


Figure 15. Langmuir model isotherms of of MB adsorption on untreated and acid hydrolysed

(0.45 M H2SO4 , 100oC, 4h) wheat straw


Table 7. Estimated Langmuir isotherm model parameter values for MB


linear non-linear

KL qm R KL qm SEE

Untreated 14-24cm 0,12 11,149 0,933 0,05 22,991 1,673

Untreated 1-2cm -0,03 -21,108 0,974 0,07 26,799 3,648

Pretreated 14-24cm 0,04 69,499 0,966 0,13 30,694 1,312

Pretreated1-2cm 0,29 24,660 0,988 0,15 37,968 1,314

Figure 16. Sips model isotherms of of MB adsorption on untreated and acid hydrolysed


Table 8. Estimated Sips isotherm model parameter values for MB


KL qm 1/n n SEE

Untreated 14-24 cm 0,043 24,246 0,928 1,077 1,756

Untreated 1-2 cm 0,119 21,865 3,194 0,313 2,582

Pretreated 14-24cm 0,134 30,719 0,998 1,002 1,383

Pretreated 1-2 cm 0,158 37,508 1,039 0,962 1,375

Figure 17. Radke-Prausnitz model isotherms of of MB adsorption on untreated and acid hydrolysed



Table 9. Estimated Radke-Prausnitz isotherm model parameter values for MB


KL qm n SEE

Untreated 14-24 cm 0,015 49,981 0,875 0,543

Untreated 1-2 cm 0,137 24,050 1,003 2,833

Pretreated 14-24 cm 0,043 61,529 0,872 0,971

Pretreated 1-2 cm 0,098 49,108 0,947 1,240

Figure 18. Modified Radke-Prausnitz model isotherms of of MB adsorption on untreated and acid hydrolysed


Table 10. Estimated Modified Radke-Prausnitz isotherm model parameter values for MB


KL qm n SEE

Untreated 14-24cm 0,023 36,109 0,822 0,559

Untreated 1-2cm 0,160 21,955 1,037 2,830

Pretreated 14-24cm 0,057 50,227 0,817 0,953

Pretreated 1-2cm 0,114 44,549 0,944 1,255

Figure 19. Tóth model isotherms of of MB adsorption on untreated and acid hydrolysed



Table 11. Estimated Tóth isotherm model parameter values for MB


KL qm n SEE

Untreated 14-24cm 0,004 21,103 0,623 0,504

Untreated 1-2 cm 1,5E-05 21,893 0,249 2,553

Pretreated 14-24cm 0,025 29,142 0,670 1,067

Pretreated 1-2cm 0,011 34,885 0,522 1,096

Figure 20. UNILAN model isotherms of of MB adsorption on untreated and acid hydrolysed


Table 12. Estimated UNILAN isotherm model parameter values for MB


KL qm s SEE

Untreated 14-24 cm 0,036 24,842 -0,000166 0,594

Untreated 1-2cm 0,133 24,436 1,134E-05 2,833

Pretreated 14-24cm 0,118 31,235 -5,49E-05 1,163

Pretreated 1-2cm 0,146 38,155 -2,22E-05 1,294

Kinetics

The kinetics of adsorption of MB on various materials has been extensively studied using four kinetic

equations. The widely used Lagergren equation (Lagergren 1898) is shown below:

tk

t eqqq (17)


where q and qt are the amounts of MB adsorbed per unit mass of the adsorbent (in mg g-1) at equilibrium

time ( t ) and adsorption time t, respectively, while k is the pseudo-first order rate constant for the

adsorption process (in min-1). Furthermore,

m/V)CC(q e0 and m/V)CC(qt 0 (18)

where C, C0 , Ce are the concentrations of MB in the bulk solution at time t, 0, and , respectively, while

m is the weight of the adsorbent used (in g), and V is the solution volume (in mL). Further modification of

eq. (18) in logarithmic form gives:

tkqln)qqln( t (19)

The -order kinetic model is

tqqkdtdq / (20)

Solving this differential eq. for 1, we obtain:

)1/(11 1 tkqqqt (21)

The commonly used second order kinetic model (Ho et al. 2000) is as follows

1

21 tkqqqt or

tkq

qqt

21

1 (22)

The possibility of intra-particle diffusion was explored by using the intra-particle diffusion model (Weber

and Morris 1963):

tkcq pt (23)

where qt is the amount of MB adsorbed at time t, c is a constant (mg g-1) and kp is the intra-particle

diffusion rate constant in mg g-1 min-0.5. For c=0 eq. (23) becomes as follows

tkq pt (24)

The Lagergen kinetic model curves of adsorption on untreated and acid hydrolysed (0.45 M H2SO4

100oC, 4h) wheat straw are presented in Fig. 21. Their parameters are estimated in Table 13 using NLRA.

The SEE values are also estimated.

The second order kinetic model curves are presented in Fig. 21. Their parameters and SEE values are

estimated in Table 14. The intrapartical diffusion kinetic model curves are presented in Fig. 22. Their

parameters and SEE values are estimated in Table 15. The intrapartical diffusion kinetic model for c=0

curves are presented in Fig. 23. Their parameters and SEE values are estimated in Table 16.

The second order kinetic model estimated values gave the best fitting to the experimental data. The

rate constants and the capacity are higher for the adsorption on pretreated wheat straw comparing to

the untreated one, but lower for the big particles (14-24 cm) comparing to the small ones (1-2 cm). On the

other hand, the big particles are more appropriate for scale up applications while they need no size

reduction and they form easier booms and pillows for oil spill adsorption using a net with big openings.


Figure 21. Lagergen kinetic model curves of adsorption on untreated and acid hydrolysed


Table 13. Lagergen kinetic model parameters of adsorption on untreated and acid hydrolysed


k (min-1) q (mg g-1) SEE

untreated 14-24cm 0,0161 3,70 0,1739

untreated 1-2cm 0,0178 3,93 0,2921

pretreated 14-24cm 0,0092 7,16 0,1785

pretreated 1-2cm 0,0100 9,63 0,2720

Figure 22. Second order kinetic model curves of adsorption on untreated and acid hydrolysed



Table 14. Second order kinetic model parameters of

adsorption on untreated and acid hydrolysed (0.45 M H2SO4 100oC, 4h) wheat straw

k (min-1 mg-1 g) q (mg g-1) SEE

Untreated 14-24 cm 0,00321 4,81 0,1359

Untreated 1-2 cm 0,00387 4,91 0,2320

Pretreated 14-24cm 0,00061 10,8 0,1780

Pretreated 1-2cm 0,00056 13,9 0,2234

Figure 23. Intrapartical diffusion kinetic model curves of adsorption on untreated and acid hydrolysed


Table 15. Intrapartical diffusion kinetic model parameters of

adsorption on untreated and acid hydrolysed (0.45 M H2SO4, 100oC, 4h) wheat straw

c (mg g-1) kp (mg g-1 min-0.5) SEE

Untreated 14-24cm 0,1230 0,2665 0,1029

Untreated 1-2cm 0,4487 0,2653 0,2853

Pretreated 14-24cm -0,7045 0,4893 0,2819

Pretreated 1-2cm -0,5292 0,6447 0,1962

Figure 24. Intrapartical diffusion kinetic model (with c=0) curves of adsorption on untreated and acid

hydrolysed (0.45 M H2SO4, 100oC, 4h) wheat straw


Table 16. Intrapartical diffusion kinetic model (with c=0) parameters of

adsorption on untreated and acid hydrolysed (0.45 M H2SO4, 100oC, 4h) wheat straw

kp (mg g-1 min-0.5) SEE

Untreated 14-24cm 0.2782 0.1106

Untreated 1-2cm 0.3080 0.3265

Pretreated 14-24cm 0.4223 0.3847

Pretreated 1-2cm 0.5943 0.2784

Water, Diesel and Crude Oil Adsorbencies

The results of the water, diesel, crude oil, diesel spill and crude oil spill adsorption on original and

modified (acid hydrolysis at 100 oC for 4 h with 0.45 M H2SO4) wheat straw is presented as follows:

In the case of the original/untreated wheat straw, the pure tap water, pure diesel, pure crude oil,

diesel oil spill and crude oil spill adsorbencies are presented (i) for straw particles 1-2 cm and (ii) for

straw particles 14-24 cm in Fig. 25. In the case of the modified/pretreated wheat straw, the pure tap water,

pure diesel, pure crude oil, diesel oil spill and crude oil spill adsorbencies are presented (i) for straw

particles 1-2 cm pretreated in a 0.5 L glass reactor and (ii) for straw particles 14-24 cm pretreated in a 20

L glass reactor in Fig. 26.

0,0

0,5

1,0

1,5

2,0

2,5

3,0

3,5

4,0

water diesel crude oil diesel spill crude oil

spil

ad

so

rbe

nc

y (

g/g

)

particle size 14-24 cm


Figure 25. Original wheat straw: Adsorbencies (i) for straw particles 1-2 cm and (ii) for straw particles 14-24 cm.

0

1

2

3

4

5

6

7

8

9


spil

ad

so

rben

cy

(g

/g)

Reactor 20 L, particle size 14-24 cm

Reactor 0.5 L, particle size1-2 cm

Figure 26. Modified wheat straw: Adsorbencies (i) for straw particles 1-2 cm pretreated in a

0.5 L glass reactor and (ii) for straw particles 14-24 cm pretreated in a 20 L glass reactor.


The effect of the pretreatment on the wheat straw pure tap water, pure diesel, pure crude oil, diesel

oil spill and crude oil spill adsorbencies is presented for straw particles 1-2 cm pretreated in a 0.5 L glass

reactor in Fig. 27. The effect of the pretreatment on the wheat straw pure tap water, pure diesel, pure

crude oil, diesel oil spill and crude oil spill adsorbencies is presented for straw particles 14-24 cm

pretreated in a 20 L glass reactor in Fig. 28.

0

1

2

3

4

5

6

7

8

9


spil

ad

so

rbe

ncy

(g

/g)

Reactor 0.5 L, particle size1-2 cm


Figure 27. Original and modified wheat straw: Adsorbencies for straw particles 1-2 cm;

a 0.5 L glass reactor was used

0

1

2

3

4

5

6

7


spil

ad

so

rben

cy (

g/g

)

Reactor 20 L, particle size 14-24 cm


Figure 28. Original and modified wheat straw: Adsorbencies for straw particles 14-24 cm;

a 20 L glass reactor was used

The effect of the river or lake water (comparing to the tap water) on the original and modified wheat

straw adsorbencies is given in Fig. 29-31 as regards water (Fig. 29), diesel oil spill (Fig. 30) and crude oil

spill (Fig. 31).

The adsorbencies of modified wheat straw are significantly higher compared to that of the original

material. The effect of the particle size of wheat straw and the kind of the water (river or lake) is not

significant as regards oil spills.


Figure 29. Original and modified wheat straw water adsorbencies

Figure 30. Diesel oil spill adsorbency on original and modified wheat straw

Figure 31. Crude oil spill adsorbency on original and modified wheat straw


The results mentioned above might contribute to determining the optimum adsorbent particles

dimension Dopt found at maximum benefit Bmax which is an equilibrium point of the tradeoff between two

rival partial benefits B1 and B2, both depended on D. The former is an increasing function of D (i.e.,

dB1/dD > 0), since the independent / explanatory variable increase implies (i) cutting (for adsorbent size

reduction) energy saving, and (ii) higher applicability, including avoidance of particles release through

the open spaces of the dense net-like material, constituting the envelope covering the adsorbent; the larger

these open spaces the higher the release probability, since particles size follows an apparent diameter

distribution, containing a percentage of fine particles of significantly lower size compared with the mean

value of them in the same distribution. On the other hand, the rate of change of B1 is a decreasing function

of D (i.e., d2B1 / dD2 < 0), because of the validity of the Law of Diminishing Returns (LDR).

The other partial benefit, B2, depended mainly on adsorption efficiency (rate and capacity,

representing Kinetics and Thermodynamics, respectively) is a decreasing function of D with a decreasing

algebraic or an increasing absolute rate (i.e., dB2 / dD < 0, d2B2 / dD2 < 0 or d|dB2 / dD| / dD > 0), since

adsorptivity is an increasing function of adsorbent specific surface, which disproportionally decreases as

adsorbent particles size increases. Evidently, Dopt is found at Bmax = (B1 + B2)max or MB1 = MB2, where

MB1 = dB1 / dD and MB2 = |dB2 / dD| are the marginal benefit values of the respective depended

variables.

In case of electric energy price decrease, the B1-curve is moving upwards, becoming also more flat,

since the corresponding partial benefit increase is more expressed in the region of lower D-values, where

more energy is required so that smaller adsorbent particles can be produced by cutting within the

appropriate electric machine for size reduction of lignocellulosic wastes; as a result, Dopt is shifting to

D opt, where D opt < Dopt, as shown in Fig. 32a. In the same case, the B2-curve is also moving upwards,

becoming steeper, since adsorptivity can be further enhanced through the thermochemical process

intensification in the advantageous region of lower D-values, that can be achieved more economically

under the regime of low energy prices; as a result, Dopt is shifting to D opt, where D opt < Dopt, as shown in

Fig. 32b. It is worthwhile noting that both implications due to energy prices decrease contribute to Bmax

increase and Dopt shifting to lower values, since the vectors (D opt - Dopt) and (D opt - Dopt) have the same

direction.

Ma

rgin

al

Ben

efit

,M

Adsorbent Particles Dimension, D

MB2

MB1

MB'2

DoptD''opt

Ben

efit

,

B1

B2

B'2

DoptD''opt

B1+B2

B1+B'2

(b)

Ben

efit

DoptD'opt

(a)

B'1+B2B1+B2

B1

B'1

B2

Ma

rgin

al

Ben

efit

M

Adsorbent Particles Dimension, D

DoptD'opt

MB2

MB'1MB1

Figure 32. Dependence of partial benefits B1 and B2 (based on the novel lignocellulosic adsorbent applicability and

efficiency, respectively) on adsorbent dimension D, and determination of the optimal value Dopt shifting in case of

energy prices decrease, because of implications due to (a) energy cost saving and (b) adsorption efficiency increase

due to thermochemical process intensification at lower operating cost.


Conclusions

The present work deals with the adsorption of substances from aquatic solutions on novel modified

adsorbents aiming at decontamination of lake or river water after systematic or accidental pollution. The

adsorption models used herein are nine isotherms (Freundlich, Langmuir, Sips, Radke–Prausnitz,

Modified Radke – Prausnitz, Tóth, UNILAN, Temkin, and Dubinin-Radushkevich) and three rate

equations (Lagergren, second order kinetics and intra-particle diffusion). The best fitting to the

experimental data was achieved by using the Tóth isotherm. Implementation of this procedure is

presented in the cases of river and lake environmental systems contaminated by dyes and hydrocarbon

releases. The superiority of adsorptive properties of the modified lignocellulosic biomass in comparison

with the corresponding properties of the unmodified biomass was proved experimentally by estimating

the respective models’ parameters while interpretation of results is given mainly by means of SEM and

FT-IR spectroscopy. The rate constants and the capacity are higher for the adsorption (used as an

index for sake of comparability with other adsorption data) on pretreated wheat straw as compared to the

untreated one depending also on the particle size of the innovative adsorbent examined herein. These

constants/parameters are lower for the large particles in comparison with the small ones. On the other

hand, the large particles are more appropriate for in situ applications since they need no size reduction and

form easier booms and pillows for oil spill adsorption using a net with big openings. More specifically,

the oil adsorbencies on modified wheat straw are significantly higher compared to those on the original

material, while the effect of (i) the particle size of wheat straw and (ii) the spill formation on river or lake

water is not significant. The combination of the technical aspect of adsorption efficiency with the energy

cost, implying differentiation of the thermochemical conversion conditions was proved to be capable in

determining the optimal value of adsorbent particles size.

Acknowledgments

The present work is part of a research project co-financed by the European Union (European Social

Fund - ESF) and Greek national funds through the Operational Program “Education and Lifelong

Learning” of the National Strategic Reference Framework (NSRF) - Research Funding Program:

THALIS - University of Piraeus - Development of New Material from Waste Biomass for Hydrocarbons

Adsorption in Aquatic Environments.

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