modeling with parameter identification of pollutant
TRANSCRIPT
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.
350 Modeling with Parameter Identification of Pollutant ...
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
Fragiskos A. Batzias et al. 351
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
352 Modeling with Parameter Identification of Pollutant ...
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
Fragiskos A. Batzias et al. 353
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
Location Koumoundourou Lake Pikrodafnis Stream
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.
Location Koumoundourou Lake Pikrodafnis Stream
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.
354 Modeling with Parameter Identification of Pollutant ...
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
Fragiskos A. Batzias et al. 355
Figure 7. 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 interior. The magnifications are X750, X7,500, X20,000
356 Modeling with Parameter Identification of Pollutant ...
Figure 8. 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 cross section. The magnifications are X7,500, X20,000
Fragiskos A. Batzias et al. 357
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
358 Modeling with Parameter Identification of Pollutant ...
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)
Fragiskos A. Batzias et al. 359
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
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.
360 Modeling with Parameter Identification of Pollutant ...
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
Fragiskos A. Batzias et al. 361
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
(0.45 M H2SO4, 100oC, 4h) wheat straw
Figure 15. Langmuir model isotherms of of MB adsorption on untreated and acid hydrolysed
(0.45 M H2SO4 , 100oC, 4h) wheat straw
362 Modeling with Parameter Identification of Pollutant ...
Table 7. Estimated Langmuir isotherm model parameter values for MB
adsorption on untreated and pretreated wheat straw
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
(0.45 M H2SO4, 100oC, 4h) wheat straw
Table 8. Estimated Sips isotherm model parameter values for MB
adsorption on untreated and pretreated wheat straw
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
(0.45 M H2SO4, 100oC, 4h) wheat straw
Fragiskos A. Batzias et al. 363
Table 9. Estimated Radke-Prausnitz isotherm model parameter values for MB
adsorption on untreated and pretreated wheat straw
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
(0.45 M H2SO4 100oC, 4h) wheat straw
Table 10. Estimated Modified Radke-Prausnitz isotherm model parameter values for MB
adsorption on untreated and pretreated wheat straw
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
(0.45 M H2SO4 100oC, 4h) wheat straw
364 Modeling with Parameter Identification of Pollutant ...
Table 11. Estimated Tóth isotherm model parameter values for MB
adsorption on untreated and pretreated wheat straw
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
(0.45 M H2SO4, 100oC, 4h) wheat straw
Table 12. Estimated UNILAN isotherm model parameter values for MB
adsorption on untreated and pretreated wheat straw
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)
Fragiskos A. Batzias et al. 365
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.
366 Modeling with Parameter Identification of Pollutant ...
Figure 21. Lagergen kinetic model curves of adsorption on untreated and acid hydrolysed
(0.45 M H2SO4 100oC, 4h) wheat straw
Table 13. Lagergen kinetic model parameters of adsorption on untreated and acid hydrolysed
(0.45 M H2SO4 100oC, 4h) wheat straw
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
(0.45 M H2SO4 100oC, 4h) wheat straw
Fragiskos A. Batzias et al. 367
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
(0.45 M H2SO4, 100oC, 4h) wheat straw
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
368 Modeling with Parameter Identification of Pollutant ...
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
particle size 1-2 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
water diesel crude oil diesel spill crude oil
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.
Fragiskos A. Batzias et al. 369
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
water diesel crude oil diesel spill crude oil
spil
ad
so
rbe
ncy
(g
/g)
Reactor 0.5 L, particle size1-2 cm
particle size 1-2 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
water diesel crude oil diesel spill crude oil
spil
ad
so
rben
cy (
g/g
)
Reactor 20 L, particle size 14-24 cm
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.
370 Modeling with Parameter Identification of Pollutant ...
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
Fragiskos A. Batzias et al. 371
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.
372 Modeling with Parameter Identification of Pollutant ...
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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