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Middle-East Journal of Scientific Research 7 (1): 22-29, 2011

ISSN 1990-9233

© IDOSI Publications, 2011

Corresponding Author: Dr. Ghasem D. Najafpour, Biotechnology Research Center, Faculty of Chemical Engineering, Noushirvani

University of Technology, Babol, Iran, Tel/Fax: +98111-3210975, E-mail: najafpour8@yahoo.com.

22

Biodesulfurization of Natural Gas: Growth Kinetic Evaluation

Maryam Khavarpour, Ghasem D. Najafpour, Ali-Asghar Ghoreyshi,

1 1 1

Mohsen Jahanshahi and Bijan Bambai

2 3

Biotechnology Research Center, Faculty of Chemical Engineering,

1

Noushirvani University of Technology, Babol, Iran

Nanobiotechnology Lab., Faculty of Chemical Engineering,

2

Noushirvani University of Technology, Babol, Iran

Faculty of Biological Sciences Shahid Beheshti University, GC, Tehran, Iran

3

Abstract: The present study focused on evaluation of various kinetic models for hydrogen sulfur removal by

means of active microorganisms. The microorganisms used for the removal of hydrogen sulfide were isolated

from a local hot spring. The experiments were conducted with natural gas at initial pressures of 1 to 1.8atm.

Several kinetic models such as; Andrew, Contois, Logistic, Monod, Moser, Tessier and Verhulst models in a

batch culture were used to describe the microbial growth and substrate utilization. At low pressure (1atm), the

bacterial behavior were substrate related and growth dependent; thus, Monod and Tessier models were unable

to explain the microbial behavior. At gas pressure of 1.2atm, maximum cell dry weight of 3.136 and 1.724g.lG1

were obtained with Logistic and Verhulst models, respectively. The obtained regression values for Logistic

model were reasonably acceptable for all initial gas pressures. As the gas pressure was increased to 1.8atm, the

inhibition coefficient may be dominated in growth kinetic. Andrew’s equation was also able to predict inhibition

constant.

Key words: Hydrogen sulfide % Kinetic models % Substrate consumption rate % Microbial growth

% Gas pressures

INTRODUCTION methods are energy intensive, high chemical and capital

Hydrogen sulfide is a flammable gas that has acost of equipment, reliable and less polluting operation,

characteristic malodour of rotten eggs. Upon inhalation,biological treatment exhibit to be the most economical and

hydrogen sulfide reacts with enzymes in bloodstream and efficient alternative for the removal of hydrogen sulfide

inhibits cellular respiration which is resulting in[12-16]. These processes operate at ambient temperature

pulmonary paralysis, headache, dizziness, nausea, sudden and atmospheric pressure; thus eliminate high costs for

collapse, staggering, drowsiness [1]. The existence ofheat and pressure generation as required in a variety of

hydrogen sulfur in natural gas creates serious problemschemical processes. The biological routes are easily

which comprise the disposal of hazardous waste materials, applicable and minimize waste formation [7, 17-19]. Kinetic

corrosion during transmission and distribution, reducedmodels can be used to gain a better understanding of the

well production, decline gas combustion capacity, publicmicrobial growth and substrate consumption for process

nuisance, environmental pollution from emitted gas anddescription governed by the microorganisms [20-24]. The

high capital cost [2-7]. objective of present work described here is to investigate

Traditional physico-chemical methods such asseveral kinetic models for hydrogen sulfide removal by

incineration, adsorption, absorption, thermal and chemical the isolated microorganisms from a hot spring.

oxidation and alkanolamine processes have been usedExperimental data was fitted by kinetic models and kinetic

for the treatment of sour gas [8-10]. These conventionalparameters were determined.

cost and also associated with pollutions [11]. Based on

dx

X

dt

µ

=

Middle-East J. Sci. Res., 7 (1): 22-29, 2011

23

MATERIALS AND METHODS The gas and liquid samples were taken in a time

Microorganism and Growth Media: The sulfur oxidizing

bacteria used in this work has been isolated from Ramsar

hot spring (Ramsar, Iran). It was grown in an anaerobic

serum bottle media. The media was incubated at 30°C and

180 rpm. The serum bottles containined 50ml liquid media;

with media composition in grams per liter given as follow:

2.0 KH PO, 2.0 K HPO, 0.6 NH Cl, 0.4 MgCl .6H O, 8.0

2 2 4 2 2

Na S0.5H O, 2.0 yeast extract, 2ml vitamin solution and

2 2 3 2

1ml trace element solution. The trace element solution

consisted (g.lG) of 50 Na -EDTA, 11 ZnSO .7H O,

12 4 2

7.34 CaCl .2H O, 2.5 MnCl .4H O, 0.5 CoCl .6H O, 0.5

2 2 2 2 2 2

(NH)Mo O.4H O, 5.0 FeSO .7H O, 0.2 CuSO .5H O.

4 6 7 24 2 4 2 4 2

The pH of trace element solution was adjusted to 6.0

using 1 M NaOH solution. The vitamin solution contained

(mg.lG): 10 Thiamine-HCl.2H O, 20 Nicotinic acid, 20

12

Pyridoxine-HCl, 10 p-Aminobenzoic acid, 20 Riboflavin,

20 Ca-pantothenate, 1.0 Biotin, 1.0 Vitamin B . The pH of

12

Vitamin solution was adjusted to 7.0. Distilled water was

added to make 1-liter of broth solutions. The initial media

pH was 6.5 and monitored by pH meter (HANA, 211,

Romania). All chemicals used for the experiments were

analytical graded and supplied by Merck (Darmstadt,

Germany).

Analytical Methods: Batch experiments were carried

out in sealed serum bottles with a volume of 125ml.

The serum bottles contained liquid media which

was prepared according to growth media composition

discussed above. In each experiment, 50ml of fresh

media transferred into the serum bottles under

nitrogen gas. Gas impermeable rubber septum and

aluminum crimp seals were used to seal the bottles

for being used under various initial pressures.

The bottles with liquid media were sterilized at 121°C

for 15min. The sterilized serum bottles were inoculated

with 3ml of seed culture. The inoculated culture was

purged with a mixed gas from iron cylinder of compressed

gas through a two-stage stainless steel regulator

under variable initial pressures. The mixed gas comprises

of the components of HS, CO , Ar and CH gas. The

2 2 4

experiments were conducted with various initial

total pressures at 1 to 1.8atm with 0.2 intervals. The argon

was selected as internal standard for gas analysis.

The serum bottles were placed horizontally on an

orbital shaker (Stuart, S1500 and UK) set at agitation rate

of 180rpm and 30°C.

interval of 4 h. The liquid samples were analyzed for

optical density at a wavelength of 600nm using a

spectrophotometer (Unico, 2100, USA). According to

standard calibration curve, the cell dry weight

concentration was also determined based on turbidity of

the media by light absorbance as a function of cell dry

weight. A gas-tight syringe (SGE, Australia) was used to

take a 1ml of the gas sample for GC analysis. Gas

chromatograph (Agilent, 7890A, USA), equipped with a

thermal conductivity detector (TCD) was used for gas

analysis. A packed column (HayeSep Q) with 80/100 mesh

(Supelco, USA) was used to separate hydrogen sulfide,

argon, methane and carbon dioxide. The initial oven

temperature was 80°C. The oven temperature was

programmed with a rate of 10°C.minG until reached 140°C

1

and remained at that temperature for 1min. The injector

and detector temperatures were 100 and 250°C,

respectively. Helium gas was used as carrier gas at a flow

rate of 25 ml.minG. Several kinetics models such as;

1

Andrew, Contois Logistic, Monod, Moser, Tessier and

Verhulst models were used to describe the behavior of

microbial growth and substrate consumption by the

active microorganisms for natural gas within pressure

range of 1 to 1.8atm.

RESULTS AND DISCUSION

Hydrogen sulfide as an inorganic sulfur compound

for cultivation of the isolated bacteria in batch media was

used. One of the simple unstructured rate models for the

batch culture defined as Malthus law is expressed as

follows [25]:

(1)

Where X is cell concentration of bacteria (g.lG), t is

1

time (h) and µ is the specific growth rate (hG). This model

1

predicts unlimited growth with respect to incubation time;

while an inhibition term may provides limited growth

which is dependent on cell concentration.

Logistic Kinetic Model: This model incorporated

inhibition term; that means the model project inhibition

coefficient which is proportional to cell density [25].

Also, the specific growth rate may be inhibited by high

substrate concentration. In this case, the growth kinetics

of microorganism is determined with respecting to logistic

model. The specific growth rate for logistic model is

defined by the following equation:

1

m

m

x

x

µµ

=−

0

0

1(1)

mt

mt

m

Xe

XXe

X

µ

µ

=−

*2

*

2

mH

pH

CS

KCS

µ

µ=+

*

2

CS

H

*

2

CS

H

22

*

(

/)

HSgas

C

HSPH

=

2

*

111

p

mm

HS

K

C

µµµ

=+×

2

*

(1/),

C

HS

Middle-East J. Sci. Res., 7 (1): 22-29, 2011

24

Fig. 1: Microbial cell concentration grown at various

initial gas pressures obtained by logistic kinetic

model Fig. 2: Experimental data for microbial growth and

(2) Monod model

Where µ is the specific growth rate (hG), µ is the

1m

maximum specific growth rate (hG) and X is the maximumWhere µ and µ are the specific growth rate and

1m

cell dry weight (g.lG). The logistic model leads to a lagmaximum specific growth rate for H S, respectively. The

1

phase, an exponential initial growth rate and a stationaryterm is represents hydrogen sulfide concentration

growth concentration (X) which is described in thein gas phase in equilibrium with liquid phase and K is

m

following equation [25]: Monod constant for HS. The value of was

(3) Henry’ s law constant . The linearized

Equation (3) gives the cell density with respect to(5)

time. Figure 1 shows the cell dry weight of mixed culture

obtained in batch experiment with 5 initial gas pressureThe illustrated plot of (1/µ) verse

ranged 1 to 1.8atm. (Lineweaver-Burk plot), is shown in Figure 2.

A stepwise increase in gas pressure resulted inThe obtained kinetic parameters are shown in

direct proportional increase in hydrogen sulfideTable 1. Microbial growth and substrate consumption at

concentration as gaseous substrate. At 1.6 and 1.8atm aslow pressure such as 1atm did not follow Monod kinetic

the initial gas pressure did not show any influence on cell model. The regression value for the experimental data

dry weight concentrations. As the initial gas pressurefitting to Monod model at 1.2atm was unsatisfactory

increased from 1 to 1.2atm, there was also an increase in(R =0.88). However, the regression analysis and kinetic

cell dry weight, but as the gas pressure rose to 1.8atm, the parameters obtained at 1.4, 1.6 and 1.8atm were reasonably

cell concentration was decreased. The maximum cell dryacceptable. Thus, Monod kinetic model is capable to

weight was obtained with initial gas pressure of 1.2atm.describe the culture growth and substrate consumption

In the batch bioreactor, the exponential growth ratesbehavior at 1.4, 1.6 and 1.8atm.

were clearly observed with initial gas pressure in the

pressure range of 1 to 1.4atm. Contois Kinetic Model: Contois kinetic model is one of

Monod Kinetic Model: Monod kinetic model is consideredsubstrate and cell concentrations. The following

as one of the unstructured models which are dependentequations are the Non-linear and linear forms of Contois

to substrate concentration as follows [25]: model [25]:

substrate consumption at various gas pressures fitted to

(4)

m

2

p

2

calculated based on relationship between the partial

pressure of hydrogen sulfide and gas solubility known as

form of Monod model is expressed by the following

equation:

2

the unstructured models depends two terms such as

*2*

2

mHS

pHS

C

KXC

µ

µ=+

2

*

11 p

mm

HS

K

X

C

µµµ

=+×

*2

2

(/)

HS

XC

*2

*2

2

n

mHS

pHS

C

KC

µ

µ=+

2

*

11 p

n

mm

HS

K

X

C

µµµ

=+×

*2

2

(1/)

HS

C

Middle-East J. Sci. Res., 7 (1): 22-29, 2011

25

Table 1: Kinetic parameters at various pressures obtained by fitting the experimental data with different kinetic models

Pressure, atm

------------------------------------------------------------------------------------------------------------------------------------------------------------------

Kinetic models 11.2 1.4 1.6 1.8

Logestic

x(g,lG)0.0450 0.0450 0.0400 0.0250 0.0250

01

µ(hG)0.1250 0.1238 0.1408 0.0748 0.0813

m1

x(g,lG)1.0670 3.1360 1.0220 0.0781 0.0733

m1

R(–) 0.9984 0.9821 0.9637 0.9910 0.9865

2

Monod

µ(hG) - 0.0980 0.0949 0.0632 0.0621

max 1

K(mmol,lG) - 1.6540 2.0980 3.7440 5.4260

p1

R(–) -0.8865 0.9921 0.9958 0.9718

2

Contois

µ(hG)0.0930 0.0612 0.0680 0.0466 0.0440

max 1

K(mmol,gG)3.7570 1.1531 2.3215 38.510 48.870

p1

R(–) 0.9771 0.9243 0.9886 0.9829 0.9237

2

Moser

µ(hG)0.1870 0.0706 0.0714 0.0431 0.0448

max 1

K(mmol ,gG)4.5210 1.1960 2.2100 4.5650 13.058

p2 1

R(–) 0.9601 0.9104 0.9931 0.9957 0.9742

2

Tessier

µ(hG) - 0.0694 0.0716 0.0447 0.0465

max 1

K(mmol ,lG) - 1.3250 1.9240 2.9570 4.9540

p2 1

R(–) -0.9125 0.9968 0.9971 0.9630

2

Verhulst

µmax(hG)0.0915 0.0619 0.0683 0.0551 0.0475

1

x(g,lG)0.6580 1.7240 0.9350 0.1040 0.1380

m1

R(–) 0.9031 0.9015 0.9770 0.9302 0.8560

2

Andrew

K(atm) -3.9160 4.9200 7.0270 -

p

µmax(hG) - 0.1763 0.1719 0.1017 -

1

K(mmol ,lG) - 5.6930 8.3210 13.920 -

p2 1

R(–) -0.9900 0.9900 0.9900 -

2

(6) Moser Kinetic Model: Moser kinetic model is strictly

(7) [25]:

The linearized plot (1/F) verse illustrated in

Figure 3. The useful kinetic parameters (µ and K)(9)

m p

were determined. Summary of the regression values

and obtained kinetic parameters are reported in Table 1.Where µ and µ are the specific growth rate and

The obtained data for all initial gas pressures fitted tomaximum specific growth rate for H S, respectively. The

Contois model were quite promising. The slope ofterm K is saturation constant for H S and n is constant as

illustrated plot, (K/µ) was highly dependent on gasthe exponent of substrate concentration. The illustrated

pm

pressures. Figure 3 shows data plotted based on Contoisplots of (1/F) verse were obtained by Matlab

model; as the slope of the lines related to low gassoftware is shown in Figure 4. The slope, (K/µ ), the

pressure was insignificant; while the slope of the line was intercept, (1/µ) and the exponent of substrate

sharply increasing as the initial gas pressure increased.concentration for n = 2, the kinetic constants was

Therefore high K values were obtained at 1.6 and 1.8atm determined by Moser model. The kinetic parameters are

p

initial gas pressures. reported in Table 1.

related to substrate concentration. Equations 8 and 9

represent the Non-linear and linear form of Moser model

(8)

m

2

p 2

smax

max

m

mm

X

X

µ

µµ=−

2

*

(1)

HS

mp

C

eK

µµ −

=−

2

*

ln1

HS

mp

C

K

µ

µ

−=−

*

2

()

HS

C

Middle-East J. Sci. Res., 7 (1): 22-29, 2011

26

Fig. 3: Experimental data for microbial growth and

substrate consumption at various initial gas

pressures fitted to Contois model

Fig. 4: Experimental data for microbial growth and

substrate consumption at various initial gas

pressures fitted to Moser model

Figure 4 shows the regression values for the fitting

of experimental data achieved for cell growth and

substrate consumption in Moser kinetic model. The

obtained results were well fitted with the projected model.

Here, the best regression analysis was also obtained at

1.4, 1.6 and 1.8atm. In fact, at low pressures of 1 and

1.2atm, the bacterial behavior did not follow Monod and

Moser kinetic model; thus the unstructured models were

substrate related and growth dependent.

Verhulst Kinetic Model: Verhulst kinetic model depends

to cell concentration. This model has two kinetic

constants of maximum specific growth rate (µ) and

max

maximum cell concentration(X). Verhulst model is

m

expressed by the following equations [25]:

Fig. 5: Experimental data for microbial growth and

substrate consumption at various initial gas

pressures fitted to Verhulst model

(10)

The plots of (µ) verse (X) for all initial gas pressures

are depicted in Figure 5. The kinetic parameters

determined by the slope of (µ / X and intercept of

max m

(µ ) are summarized in Table 1.

max

The obtained regression values for linear

plot at pressures of 1 to 1.6atm were in acceptable

range, but the regression value at pressure of 1.8atm

was quite low (0.855). Since, Verhulst model is only

depend to cell concentration; where the value of 0.855

indicates that the bacterial behavior at high initial gas

pressure was related to both cell density and substrate

concentration.

Tessier Kinetic Model: Tessier model is another

unstructured model depends on substrate concentration.

Linear and Non-linear form of this model is given by the

following equations [25]:

(12)

(13)

Application of experimental data and Matlab

software for the plot of (µ) verse was illustrated in

Figure 6. The obtained kinetic constants are also

summarized in Table 1.

2

22

*

**2

()/

mHS

pHSHSt

C

KCCK

µ

µ=++

2

*

C

HS

(

)

2

*

**

zS

zSzS H

HH

p

mmmi

C

CC

K

K

µµµµ

=++

2

()

*2

C

HS

Middle-East J. Sci. Res., 7 (1): 22-29, 2011

27

Fig. 6: Experimental data for microbial growth and

substrate consumption at various initial gas

pressures fitted to Tessier model

Fig. 7: Experimental data for microbial growth and

substrate consumption at various initial pressures

fitted to Andrew model

The regression analysis for the linear plot of Tessier

model fitted with the cell concentration and substrate

consumption rate by the microorganisms was accepted

and quite satisfactory. The bacterial kinetic behavior at

low gas pressure (1atm) did not follow Tessier model

which was similar results obtained for Moser and Monod

Kinetic models. These results lead to conclusion that at

the low gas pressure of 1atm, the growth behavior did not

fit to any unstructured models which are directly related

to substrate concentration.

Andrew Kinetic Model: Andrew’s model is proposed the

following equation for the growth- dependent which

incorporate substrate inhibition [26, 27].

(14)

Where µ is the specific growth rate (hG), µ is the

1m

maximum specific growth rate for HS (hG), K is

2 p

1

hydrogen sulfide concentration in gas phase in

equilibrium with liquid phase (mmol.lG), is Monod

1

constant for HS (mmol.lG) and K is the inhibition

2 p

1

constant (mmol.lG). Equation (14) was modified to a new

1

expression as stated as follows:

(15)

Figure 7 shows the growth-dependent of HS by the

2

microorganism with initial gas pressures of 1.2 to 1.6atm.

The hydrogen sulfide flux has increased by augmentation

of hydrogen sulfide concentration as easily used

by the microorganisms in the culture media. The mixed

culture exhibited more HS inhibition in a batch process

2

under initial gas pressure of 1.6atm. This behavior may be

due to the toxicity of hydrogen sulfide which has

inhibited the activity of the microorganisms. The

inhibition constants for the total pressure of 1.2 and

1.6atm were 5.69 and 13.92 (mmol H S.lG), respectively.

21

The lowest value for inhibition coefficient was devoted to

the lowest pressure 1.2atm. As the pressure of gas

increased the inhibition coefficient was also increased.

The kinetic parameters for rate models with inhibition and

mass transfer coefficients are summarized in Table 1.

CONCLUSION

The removal of hydrogen sulfide from mixed gas was

successfully carried out in a batch bioreactor using

microorganisms isolated from hot spring. Experiments

were conducted with various initial gas pressures with

natural gas at 1 to 1.8atm, which comprise variable

hydrogen sulfide concentration. The experimental data

fitted to several kinetic models (Andrew, Contois,

Logistic, Monod, Moser, Tessier and Verhulst models)

were led to kinetic parameters under various initial gas

pressures. It was observed that maximum cell dry weight

of 3.136 and 1.724g.lG were obtained with Logistic and

1

Verhulst models, respectively. Logistic model described

the microbial growth and substrate utilization better than

the other projected models (R > 0.96). Andrew’s equation

2

Middle-East J. Sci. Res., 7 (1): 22-29, 2011

28

also predicted the inhibition constant; the maximum5. Buisman, C., G. Lettinga, C. Paasschens and

specific growth rate (F) for Andrew’s model wasL. Habets, 1991. Biotechnological sulphide

max

0.176hG. The low regression values (R =0.88) for theremoval from effluents. Water Sci Technol, for Ind

1 2

experimental data fitting to Monod model at 1.2atm wasWastewater, 24(3-4): 347-356.

unsatisfactory. It also concluded that the microorganism6. Oyarzun, P., C. Canales, F. Arancibia and G. Aroca,

isolated from a hot spring was capable of oxidizing sulfur 2003. Biofiltration of high concentration of hydrogen

compound and significant amount of the hydrogen sulfide sulphide using Thiobacillus thioparus. Process

from natural gas was removed. Biochemistry, 39(2): 165-170.

ACKNOEWLEDGMENT hydrogen sulfide 1. Design and operational

The authors are thankful for the facilities provided to Association, 44(7): 863-868.

carry out research in Biotechnology Research Laboratory8. Mannebeck, H., 1986. Covering Manure Storing

at Babol Noushirvani University of Technology. Tanks to Control Odour.

Nomenclature Biofiltration of nuisance sulfur gaseous odors from

Ar Argon

CEquilibrium concentration of hydrogen sulfide

*

(mmol.lG)

1

CH Methane

4

CO Carbon dioxide

2

HHenry’ s law constant (atm.l.mmolG)

1

HSHydrogen sulfide

2

KInhibition constant (mmol.lG)

i1

KMonod constant for H S (mmol.lG)

p 21

nExponent of concentration (-)

PPartial pressure of H S in the gas phase (atm)

H2s,gas 2

tTime (h)

TCD Thermal conductivity detector

XCell dry weight concentration (g.lG)

1

XInitial cell dry weight (g.lG)

01

XMaximum cell dry weight (g.lG)

m1

µSpecific growth rate (hG)

1

µMaximum specific growth rate (hG)

m1

REFERENCES

1. Syed, M., G. Soreanu, P. Falletta and M. Béland, 2006.

Removal of Hydrogen Sulfide from Gas Streams

Using Biological Processes: a REVIEW. Canadian

Biosystems Engineering, 48: 2.

2. Campbell, J., 1982. Gas processing in 1982. J.

Petroleum Technol., 34(3): 465-470.

3. Katz, D., D. Cornell, R. Kobayashi, F. Poettmann,

J. Vary, J. Elenbaas and C. Weinaug, 1959. Handbook

of natural gas engineering. New York: McGraw-Hill.

4. Fidler, B., K. Sublette, G. Jenneman and G. Bala, 2003.

A Novel Approach to Hydrogen Sulfide Removal

From Natural Gas.

7. Yang, Y. and E. Allen, 1994. Biofiltration control of

parameters. J. the Air & Waste Management

9. Shareefdeen, Z., B. Herner and S. Wilson, 2002.

a meat rendering plant. Journal of Chemical

Technology & Biotechnol., 77(12): 1296-1299.

10. Kim, B. and H. Chang, 1991. Removal of hydrogen

sulfide by Chlorobium thiosulfatophilum in

immobilized-cell and sulfur-settling free-cell recycle

reactors. Biotechnology Progress, 7(6): 495-500.

11. Gadre, R., 1989. Removal of hydrogen sulfide from

biogas by chemoautotrophic fixed film bioreactor.

Biotechnology and Bioengineering, 34(3): 410-414.

12. Sercu, B., D. Nunez, H. Van Langenhove, G. Aroca

and W. Verstraete, 2005. Operational and

microbiological aspects of a bioaugmented two stage

biotrickling filter removing hydrogen sulfide and

dimethyl sulfide. Biotechnology and Bioengineering,

90(2): 259-269.

13. Oyarzun, P., F. Arancibia, C. Canales and G. Aroca,

2003. Biofiltration of high concentration of hydrogen

sulphide using Thiobacillus thioparus. Process

Biochemistry, 39(2): 165-170.

14. Elvidge, A. and J. Blitz, 1992. Cost Benefits of

Biological Odour Control. Ind. Waste Manage,

3(1): 18.

15. Nagl, G., 1997. Controlling I-I S emission. Chemical

2

Engineering, 104: 125-128.

16. Ottengraf, S., 1986. Exhaust gas purification.

Biotechnology and Bioengineering, 8: 426-452.

17. Van Groenestijn, J. and P. Hesselink, 1993.

Biotechniques for air pollution control.

Biodegradation, 4(4): 283-301.

18. Gallup, D., 1996. "BIOX" Hydrogen Sulfide

Abatement Process-Application Analysis.

Transactions-geothermal Resources Council,

pp: 11-18.

Middle-East J. Sci. Res., 7 (1): 22-29, 2011

29

19. Wani, A., A. Lau and R. Branion, 1999. Biofiltration 23. Konishi, Y., S. Asai and N. Yoshida, 1995. Growth

control of pulping odors-hydrogen sulfide:kinetics of Thiobacillus thiooxidans on the surface

performance, macrokinetics and coexistence effectsof elemental sulfur. Applied and Environmental

of organo-sulfur species. J. Chemical Technology &Microbiol., 61(10): 3617.

Biotechnol., 74(1): 9-16. 24. Nemati, M., S. Harrison, G. Hansford and C. Webb,

20. Asadi, M., G. Najafpour, B. Hashemiyeh and 1998. Biological oxidation of ferrous sulphate by

M. Mohammadi, 2009. Removal of Acetone fromThiobacillus ferrooxidans: a review on the kinetic

Contaminated Air in Biofilter Using Pseudomonasaspects. Biochemical Engineering J., 1(3): 171-190.

Putida. 25. Bailey, J.E. and D.F. Ollis, 1986, Biochemical

21. Ardestani, F., S. Fatemi, B. Yakhchali, S. HosseyniEngineering Fundamentals. New York: McGraw-Hill.

and G. Najafpour, Evaluation of mycophenolic acid26. Andrew, J.F., 1968. A mathematical model for the

production by Penicillium brevicompactum MUCLcontinuous culture of microorganisms utilizing

19011 in batch and continuous submerged cultures.inhibitory substrate. Biotechnol. Bioeng, 10: 707-720.

Biochemical Engineering Journal. 27. Najafpour, G.D., 2007. Biochemical Engineering and

22. Gourdon, R. and N. Funtowicz, 1998. Kinetic model Biotechnology: Elsevier Science Ltd.

of elemental sulfur oxidation by Thiobacillus

thiooxidans in batch slurry reactors. Bioprocess and

Biosystems Engineering, 18(4): 241-249.