Environmental pollution, high cost and high energy consumption associated with thermal regeneration of activated carbon polluted with hydrocarbon necessitated the search for a better way of regenerating activated carbon, bioregeneration. Spent granular activated carbon was regenerated having been initially characterized using cultured Pseudomonas Putida. The rate of bioregeneration was studied by varying the volume of bacteria from 10ml, 20ml, 30ml and 40ml. The regeneration temperature was also varied from 25oC to ambient temperature of 27oC, 35oC and further at 40 and 45oC over a period of 21 days. The experimental results showed clear correlation when validated using the Langmuir-Hinshelwood kinetic model. The experiment at ambient temperature showed a negative correlation due to the fluctuation in the ambient temperature unlike all other experiment where temperature was controlled in an autoclave machine.
Application of Langmuir-Hinshelwood Model to Bioregeneration of Activated Carbon Contaminated With Hydrocarbons
1. IOSR Journal of Mathematics (IOSR-JM)
e-ISSN: 2278-5728,p-ISSN: 2319-765X, Volume 6, Issue 2 (Mar. - Apr. 2013), PP 70-83
www.iosrjournals.org
www.iosrjournals.org 70 | Page
Application of Langmuir-Hinshelwood Model to Bioregeneration
of Activated Carbon Contaminated With Hydrocarbons
1
Ameh, C.U., 2
Jimoh, A., 3
Abdulkareem, A.S. and 4
Otaru, A.J.
1
(Chevron Nigeria Limited, 2, Chevron Drive, Lekki, Lagos, Nigeria).
2,3&4
(Department of Chemical Engineering, Federal University of Technology, Minna, Nigeria)
Abstract: Environmental pollution, high cost and high energy consumption associated with thermal
regeneration of activated carbon polluted with hydrocarbon necessitated the search for a better way of
regenerating activated carbon, bioregeneration. Spent granular activated carbon was regenerated having been
initially characterized using cultured Pseudomonas Putida. The rate of bioregeneration was studied by varying
the volume of bacteria from 10ml, 20ml, 30ml and 40ml. The regeneration temperature was also varied from
25o
C to ambient temperature of 27o
C, 35o
C and further at 40 and 45o
C over a period of 21 days. The
experimental results showed clear correlation when validated using the Langmuir-Hinshelwood kinetic model.
The experiment at ambient temperature showed a negative correlation due to the fluctuation in the ambient
temperature unlike all other experiment where temperature was controlled in an autoclave machine.
Keywords: Bioregeneration, GAC, Model, Nigeria and Pollution.
I. Introduction
Nigeria, like any other developing countries has engaged in extensive oil exploration activities (being
the major source of revenue) to stimulate her economic growth since the discovery of crude oil about 55 years
ago (Nwankwo and Ifeadi, 1988). The dependence of the nation on crude oil exploitation has been attributed to
the degree of economic benefits that can be derived and subsequently channelled towards development, growth
and sustainability (Sanusi, 2010). For instance, as at 1976, oil export was reported to have accounted for about
14% of the Gross Domestic Product (GDP), 95% of total export (Nwankwo and Ifeadi, 1988) and about 80% of
government annual revenue (Nwankwo and Ifeadi, 1988). The trend remains the same even though crude oil is a
non-renewable source of wealth that may varnish with time. All attempts by government to diversify the
economy and reduce over dependency on oil exploitation as major source of revenue ends up as rhetoric, which
implies that oil is still the mainstay of the Nigerian economy (Sanusi, 2010).
Production and consumption of oil and petroleum products are increasing worldwide, and the risk of
oil pollution is increasing accordingly. The movement of petroleum from the oil polluted site is still rising. The
movement of petroleum from the oil fields to the consumer involves as many as 10 to 15 transfers between
many different modes of transportation, including tanks, pipelines, railcars, and trucks (Fingas, 2011). Accidents
can occur during any of these transportation steps or storage times. An important part of protecting the
environment is ensuring that there are as few spills as possible. Both government and industry in developed
countries are working to reduce the risk of oil spills by introducing strict new legislation and stringent operating
codes. In Nigeria, the much dependence on the exploration of crude petroleum has hampered the
implementation of her decree. The low penalty cost even encouraged the abrogation of the decree by the
companies (Ayaegbunami, 1998). As human and environment respond to environmental pollution, the
environmental engineer faces the rather daunting task of elucidating evidence relating cause and effects. This
calls the attention to finding a better way of remediating petroleum polluted site using adsorbent and of
economic benefit, regeneration of used adsorbent.
Adsorbent like activated carbon (AC) have the capacity to remove contaminants up to an allowable
concentration and subsequently loses its sorption capacity after been saturated (Amer and Hussein, 2006). It is
important to regenerate such AC so as to regain most of its sorption capacity and be available for reuse. This
became necessary due to the expensive nature of most of the commercial available AC in use (Amer and
Hussein, 2006). Thermal regeneration which is another option actually consumes money and energy as the
temperature of reactivation alone is about 600 β 900o
C (Bagreev et al, 2000). Carbon losses (Moreno-Castilla,
1995) will be present too due to burnout when using heat to regenerate. There is also the issue of environmental
pollution inherent in the use of thermal regeneration (Dehdashti, 2010).
Efficiency, cost and convenience are of major importance. Mathematical model presents a realistic way
of addressing experimental results. Mathematical models can provide valuable information to analyze and
predict the performance of bioregeneration of activated carbon. It is important to gain an understanding of
operations where time-variant influent concentrations and multiple substrates are encountered (Speitel et al.,
1987). The bioregeneration model requires the mathematical description of two distinct processes (Speitel et al.,
2. Application Of Langmuir-Hinshelwood Model To Bioregeneration Of Activated Carbon
www.iosrjournals.org 71 | Page
1987), the kinetics of adsorption/desorption in the activated carbon column and kinetics of microbial growth and
solute degradation in the activation column. This research therefore looks into the application of Langmuir-
Hinshelwood equation on an experiment results on bioregeneration of activated carbon contaminated with
hydrocarbon. The Langmuir-Hinshelwood model is established from Monod equation.
II. Research methodology
Extracted used activated carbon was treated with pseudomonas putida bacteria culture. This treatment
take place in a Bioreactor set up in a laboratory. The rate of hydrocarbon degeneration was measured at intervals
of 24 hours for 21 days by collecting samples and testing for hydrocarbon content and concentration. Evidence
of activated carbon regeneration occurred due to the reduction in total hydrocarbon content in the sample over
the 21 days. These values were validated using the Langmuir- Hinshelwood equation (Kumar et al, 2008)
established from Monod equation. Also, comparison between the experimental results and modelled results were
correlated using the correlation coefficient function in Microsoft Excel.
III. Working Model
Kinetics of Microbial Growth and Solute Degradation
The performance of the Biological Activated Carbon system is a simple combination of adsorption and
biodegradation. Bio-film development is described by the Monod model leading to substrate utilization
increasing exponentially. Eventually, the thickness of the active bio-film becomes limited by substrate
penetration, oxygen penetration or hydrodynamic shear, and it is assumed that the rate of substrate utilization
becomes constant at its maximum value (Walker & Weatherley, 1997). The growth of microorganisms can be
modelled by Monod equation.
π =
π πππ₯ π
πΎ π +π
(1)
Where ΞΌ is the specific growth rate, ΞΌm is the maximum specific growth rate, Ks is the half saturation
coefficient and S is the substrate concentration.
The pathways of substrates after entering the bio-film are biodegradation and metabolism-dependent
processes such as bio-sorption (Aksu and Tunc, 2005). Similar type of equation was proposed by Lin and Leu
(2008) to describe the simultaneous adsorptive decolourization and degradation of azo-dye by Pseudomonas
luteola in a biological activated carbon process. Goeddertz et al., (1988) used Haldane type biodegradation
kinetics to model the bioregeneration of granular activated carbon saturated with phenol. The rate of
biodegradation, r1, for an inhibitory substance can be modelled using Haldane expression:
π1 = β
π
π
(2)
π =
π πππ₯ πΆ
πΎ π +πΆ+
πΆ2
πΎ π
(3)
Where, X is the biomass concentration, Y yield coefficient and Ks, Ki are the Haldane constants. The
model successfully predicted the bulk liquid substrate concentrations when phenol was the substrate, as well as
the extent of bioregeneration.
Langmuir-Hinshelwood model
Just as the term Michelis-Mentin kinetics is used to describe the kinetics of enzyme-catalyzed reactions
that follow one simple type of reaction mechanism, the term Langmuir Hinshelwood kinetics generally refers to
heterogeneous catalytic reaction kinetics that can be described by a simple mechanistic model. In Langmuir-
Hinshelwood models, the surface of the catalyst is modeled as being energetically uniform, and it is assumed
that there is no energetic interaction between species adsorbed on the surface. These are the same assumptions
that Langmuir used in deriving his isotherm to model surface adsorption processes. Each reactant is assumed to
adsorb on a surface site. Following surface reaction between adsorbed reactants to generate surface products, the
products desorbed from the surface.
Model equation for validating experimental results
Regeneration usually involves the adsorbed contaminants from the activated carbon using temperatures
or processes that drive the contaminants from the activated carbon but do not destroy the contaminants or the
activated carbon. The growth of microorganisms can well be explained by Langmuir β Hinshelwood equation
which can be formulated from Monod equation as in equation (1).
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π =
π πππ₯ π
πΎ π +π
Where ΞΌ is the specific growth rate, ΞΌm is the maximum specific growth rate, Ks is the half saturation coefficient
and S is the substrate concentration.
π =
π πππ₯ π
πΎπ + π
π =
π πππ₯ . π
πΎπ (
πΎπ
πΎπ
+
1
πΎπ
. π)
π =
π πππ₯
πΎπ
. π
(1 +
1
πΎπ
. π)
Let πΎβ
=
π πππ₯
πΎπ
πΆπ΄= S, and πΎπ΄=
1
πΎ π
Hence,
Ι€ π΄=
πΎβ πΆ π΄
1+π π΄ πΆ π΄
(Langmuir-Hinshelwood equation) (4)
Where Ι€ π΄= adsorption rate (g/hr), πΆπ΄ is the adsorbed concentration (grams), the constant πΎβ
and π π΄ are
equilibrium constants and can be best obtained using the least mean square method (LMSM) presented below.
Least Mean Square Method (LMSM)
Using the formulated Langmuir Hinshelwood equation
Ι€ π΄=
πΎβ
πΆπ΄
1 + π π΄ πΆ π΄
let R =
1+π π΄ πΆ π΄
πΎβ =
1
πΎβ +
π π΄
π π΄
πΆπ΄
let a =
1
πΎβ πππ π =
π π΄
πΎβ
R = a + bπΆπ΄ and Ι€ π΄=
πΆ π΄
π
π =
πΆ π΄
Ι€ π΄
= a + bπΆπ΄ (5)
Since R = a + bπΆπ΄ is a linear equation, a and b can be determined by method of LMSM (least mean square
method).
To find a we multiply equation (5) by the coefficient variable of a and taking the summation of both LHS and
RHS of the equation.
β (1) R = β (1) * a + β (1) πΆπ΄
βR = βa + bβπΆπ΄ = na + bβπΆ π΄
βR β bβπΆ π΄ = a
π
(6)
To find b we multiply equation (5) by the coefficient variable of b (i.e.πΆπ΄ ) and take the summation sign.
βRπΆ π΄ = βaπΆπ΄ + βbπΆπ΄
2
βR. πΆπ΄ = aβπΆπ΄ + βbπΆπ΄
2
=
βR β bβπΆ π΄
π
βπΆ π΄ + bβπΆπ΄
2
4. Application Of Langmuir-Hinshelwood Model To Bioregeneration Of Activated Carbon
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βR. πΆπ΄ =
βR.βπΆ π΄ β b(βπΆπ΄ )2
π
+ bβπΆπ΄
2
n. βπ . πΆπ΄ = βR. βπΆ π΄ - b βπΆ π΄
2
- b.n βπΆπ΄
2
n. βR. πΆπ΄ - βR. βπΆ π΄ = b π. βπΆπ΄
2
β βπΆ π΄
2
b =
n.βR.πΆ π΄ β βR.βπΆ π΄
π.βπΆ π΄
2β βπΆ π΄
2
b =
βR.πΆ π΄ β βR.βπΆ π΄ /π
βπΆ π΄
2β βπΆ π΄
2/π
(7)
In summary
R = a + bπΆπ΄
Ι€ π΄=
πΆ π΄
π
as π =
πΆ π΄
Ι€ π΄
a = βR β bβπΆ π΄ /π
b =
βR.πΆ π΄ β βR.βπΆ π΄ /π
βπΆ π΄
2β βπΆ π΄
2/π
a =
1
πΎβ = πΎβ
=
1
π
b =
πΎ π΄
πΎβ
Ι€ π΄=
πΎβ
πΆπ΄
1 + πΎπ΄ πΆ π΄
The adsorption rate Ι€ π΄ is defined mathematically above. Also, comparison between the experimental
results and modelled results were correlated using the correlation coefficient function in Microsoft Excel.
IV Results and Discussions
Figure 1 compared the adsorption rates obtained using experimental parameters and that simulated using
Langmuir-Hinshelwood equation when 10 ml volume of bacteria was used to treat used GAC. It can be seen that
both curves plotted against time (t) depict the behaviour indicating decrease in adsorption rate with time for the
first few days, followed by an almost constant adsorption rate for most of the experimental duration. The curves
show an increase in the rate of adsorption towards the end of the experiment.
The value of correlation coefficient for both set of data was calculated as 0.78 for the entire experiment
duration. However, when the set of data was considered from the 1st day of the experiment to the 18th day, the
correlation coefficient significantly improved to 0.97 which shows that there is a very good agreement between
experimental results obtained in the current study and the simulated results obtained using Langmuir-
Hinshelwood equation for the first 18 days of the experiment..
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Figure 1: Validation of experimental result for 10ml bacteria
Using the Langmuir-Hinshelwood equation (Kumar et al, 2008), simulation results obtained for adsorption
rates were compared with adsorption rates calculated from experimental results obtained for GAC treated with
20 ml bacteria. The graphical behaviour of both set of data is as presented in Figure 2. The correlation
coefficient was determined using Microsoft Excel program to be 0.35 when the entire experimental results for
the 21 days were considered. However, considering the experimental result and the modelled result for the
initial 18 days also, the correlation coefficient significantly improved to 0.81.
Figure 2: Validation of Result for 20ml bacteria
The simulation results obtained for adsorption rates were also compared with adsorption rates calculated
from experimental results obtained for GAC treated with 30 ml bacteria. The graphical behaviour of both set of
data is as presented in Figure 3. The correlation coefficient was determined using Microsoft Excel program to be
0.07 when the result for the entire 21 days was considered. However, just like in the 10 and 20ml experimental
result validation, the correlation when the initial 18 days was considered was 0.98 giving an almost perfect fit.
0
0.02
0.04
0.06
0.08
0.1
0.12
0 100 200 300 400 500 600
AdsorptionRate(g/hr)
Tme (hr)
rA
rm
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0 100 200 300 400 500 600
AdsorptionRate(g/hr)
Time (hr)
rA
rm
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Figure 3: Validation of Result for 30ml bacteria
Figure 4 also shows the plot for the validation of the experimental result against the modelled result for
the experiment using 40ml bacteria volume. The correlation coefficient for the entire 21 days experimental
results gave 0.17 but when the initial 18 days results were considered; the correlation coefficient significantly
improved giving a near perfect fit of 0.98.
Figure 4: Validation of Result for 40ml bacteria
Taking a critical look at the graphs on Figures 1, 2, 3 and 4, the behaviour indicates the same phenomenon
and the adsorption rate can be seen to reduce gradually for the first few days only to stabilise for most of the
experiment period. It is important to note that the curves are similar and that the correlation for both results for
the four graphs indicates a perfect fit until the 19th day of the experiment. Irrespective of the increase in bacteria
volume, this behaviour remains the same for all the samples.
Figure 5 shows the plot for the validation of the experimental result for the experiment at 25o
C. This
experiment was conducted below the prevailing atmospheric temperature of 27o
C at the time of the experiment.
The plot indicates a degree of correlation with the coefficient of 0.65 when the entire experimental result was
compared with the modelled result. The coefficient of correlation increased however to 0.69 when considered
from the 3rd to the 21st day.
-0.25
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0 100 200 300 400 500 600
AdsorptionRate(g/hr)
Time (hr)
rA
rm
-0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0 100 200 300 400 500 600
AdsorptionRate(g/hr)
Time (hr)
rA
rm
7. Application Of Langmuir-Hinshelwood Model To Bioregeneration Of Activated Carbon
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Figure 5 Validation of Result for 25o
C temperature
Figure 5 shows the plot for the experimental result against the simulated results using the Langmuir-
Hinshelwood kinetic equation (Kumar et al., 2008) for the experiment at atmospheric temperature of 27o
C. The
plot showed a negative correlation of -0.06 when the entire experimental duration was considered. However, the
experimental result and the modelled result showed a good fit of 0.85 when the results from the 1st day to the
16th day was considered. This is attributed to the impact of atmospheric temperature variation.
Figure 5 Validation of Result for 27o
C temperature
Figure 6 shows the plot for the experimental result at 35o
C against the result obtained using the kinetic
model used in the prior validations above. The correlation between the modelled and experimental result for the
entire experiment duration gave a poor fit of 0.33. When the results from the 3rd day to the 21st day was
considered also, there was a poor fit of 0.35.
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0 100 200 300 400 500 600
Adsorptionrateg/hr
Time ( hr )
Plot for rA and rm vs time for 25oC
rA
rm
-0.3
-0.25
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0 100 200 300 400 500 600
Adsorptionrate,g/hr
Time ( hr )
Plot for rA and rm Vs time for 27oC
rA
rm
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Fig 6: Validation of Result for 35o
C temperature
Figure 7 shows the plot for the experimental result at 40o
C against the result obtained using the kinetic model
used in the prior validations as above. . The correlation between both results for the entire experiment duration
gave a negative fit of -0.6. When the results from the 2nd day to the 21st day were considered, there was a poor
fit of -0.3.
Figure 7 Validation of Result for 40o
C temperature
Figure 8 shows the plot for the experimental result at 45o
C against the result obtained using the kinetic
model used in the prior validations. The correlation between both results for the entire experiment duration gave
an excellent fit of 0.93. When the entire results from the 1st day to the 21st day was considered. There was even
a better fit of 0.98 when the results from the 1st to the 20th day was considered.
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0 100 200 300 400 500 600
Adsorptionrate(g/hr)
Time (hr)
Plot of rA and rm Vs time for 35o C
rA
rm
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0 100 200 300 400 500 600
Adsorptionrate,g/hr
Time ( hr )
Plot of rA and rm Vs time for 40oC
rA
rm
9. Application Of Langmuir-Hinshelwood Model To Bioregeneration Of Activated Carbon
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Figure 8: Validation of Result for 45o
C temperature
Taking a look at the regeneration efficiency for the temperatures of 25, 27, 35, 40 and 45o
C as considered
above, results obtain were 96.8, 97.4, 93.7, 90.8 and 91.5% respectively. This clearly showed that the
experiment at 27o
C which was the room temperature was the most efficient regeneration temperature. It implies
that the bioregeneration efficiency did not improve with increase in temperature above the room temperature
(Delage, 1999).
V. Conclusions
Bioregeneration is very effective in recovering spent granulated activated carbon (GAC) for reuse
considering the quality of the regenerated GAC in comparison to a virgin sample. Temperature plays an
important role in bioregeneration efficiency and increasing the temperature improved the efficiency in as much
as it is beyond the temperature that will incapacitate the bacteria colony. Effective bioregeneration was
achieved at 40o
C as such it is concluded that increasing the temperature of bioregeneration to 45o
C was not
cost effective. Also, increasing the volume of bacteria increased the rate of bioregeneration. The validation of
the experimental result also leads to the conclusion that there is clear correlation between the experimental
results and the Langmuir-Hinshelwood kinetic model.
Acknowledgements
I wish to express my appreciation to Mr. OTARU, Abdulrazak and all my course mates of the 2010/11
M.Eng students in the Department of Chemical Engineering, Federal University of Technology Minna, Nigeria
for the support and comradeship while the program lasted.
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0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0 100 200 300 400 500 600
Adsorptionrate,g/hr
Time (hr )
Plot of rA and rm Vs time for 45oC
rA
rm