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
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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
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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