Optimization of biological sulfide removal in a CSTR bioreactor

ORIGINAL PAPER

Optimization of biological sulfide removal in a CSTR bioreactor

Aliakbar Roosta • Abdolhossein Jahanmiri •

Dariush Mowla • Ali Niazi • Hamidreza Sotoodeh

Received: 10 November 2011 / Accepted: 9 January 2012 / Published online: 18 January 2012

� Springer-Verlag 2012

Abstract In this study, biological sulfide removal from

natural gas in a continuous bioreactor is investigated for

estimation of the optimal operational parameters. According

to the carried out reactions, sulfide can be converted to ele-

mental sulfur, sulfate, thiosulfate, and polysulfide, of which

elemental sulfur is the desired product. A mathematical model

is developed and was used for investigation of the effect of

various parameters on elemental sulfur selectivity. The results

of the simulation show that elemental sulfur selectivity is a

function of dissolved oxygen, sulfide load, pH, and concen-

tration of bacteria. Optimal parameter values are calculated

for maximum elemental sulfur selectivity by using genetic

algorithm as an adaptive heuristic search. In the optimal

conditions, 87.76% of sulfide loaded to the bioreactor is

converted to elemental sulfur.

Keywords Sulfide removal � Bioreactor � Optimization �Genetic algorithms � Sulfur selectivity �Thiobacillus thioparus

List of symbols

A Constant of extended Debye–Huckel equation

(0.509 mol-� L�)

a Radius of the ion (m)

CB Optical density of bacteria

Fin Input flow rate (ml h-1)

I The ionic strength (mmol L-1)

k1 Reaction rate constant (mmol L-1 h-1)

k2 Reaction rate constant (mmol L-1)

k3 Reaction rate constant(mmol L-1)

k4 Reaction rate constant (mmol L-1 h-1)


k6 Reaction rate constant(mmol L-1)


k8 Reaction rate constant (mmol-0.59 L0.59 h-1)

pKx The acid dissociation constant

r Reaction rate (mmol L-1 h-1)

t Time (h)

V Volume of the broth in the bioreactor (L)

x Chain length of polysulfide ion

z The ionic charge of ion

b Constant of extended Debye–Huckel equation

(0.328 9 108 mol-1/2 L1/2 m-1)

c Activity coefficient

Introduction

Hydrogen sulfide is emitted by many industries, such as

petroleum refining, natural gas and petrochemical plants,

with very low odor-threshold value [1]. It is an extremely

toxic gas and has potential for injuring developing central

nervous systems at low-dose exposures [2]. The threshold

limit value for air 0.5–10 ppbv [3], natural gas 4 ppmv [4]

and for fresh or salty water fish is 0.5 ppm [5].

A. Roosta � A. Jahanmiri (&) � H. Sotoodeh

School of Chemical and Petroleum Engineering,

Shiraz University, Shiraz, Iran

e-mail: [email protected]

A. Roosta


H. Sotoodeh


D. Mowla

Environmental Research Center in Petroleum and Petrochemical

Industry, Shiraz University, Shiraz, Iran


A. Niazi

Biotechnology Research Center, Shiraz University, Shiraz, Iran


123

Bioprocess Biosyst Eng (2012) 35:1005–1010

DOI 10.1007/s00449-012-0685-5

The removal of hydrogen sulfide has been accomplished

using physical or chemical methods but, in the recent years

biological removal of hydrogen sulfide by microorganisms

at ambient temperatures and pressures is shown to be an

interesting alternative [6]. A review on bacteria of the

sulfur cycle was discussed by Tang et al. [7]. Also, a

review on removal of H2S from gas streams using bio-

logical processes was discussed by Sayed et al. [8]. In this

study, Thiobacillus thioparus (DSMZ 5368) was used (as

sulfur-oxidizing bacteria) for oxidation of hydrogen sulfide

into elemental sulfur. There are different types of biore-

actors, which could be used for biological sulfide removal,

the more common types are: bioscrubber, biotrickling fil-

ter, and biofilter. In the case of hydrogen sulfide removal

from natural gas, bioscrubber is more suitable than two

other processes. In a bioscrubber system (as shown in

Fig. 1), the sour gas is passed through a gas absorber where

H2S is washed from the gas stream by an alkaline such as

NaOH (Eqs. 1, 2), and then the rich alkaline solution is sent

to a bioreactor where the dissolved sulfide (HS-) is oxi-

dized to elemental sulfur or sulfate (Eqs. 3, 4) [9].

H2S gð Þ �! H2S aqð Þ ð1Þ

H2S aqð Þ þ OH� �! HS� þ H2O ð2Þ

HS� þ 1

2O2 �!

r1S0 þ OH� ð3Þ

S0 þ OH� þ 3

2O2 �!

r2SO2�

4 þ Hþ ð4Þ

In this system, in addition to the biological oxidation of

sulfide to sulfur and sulfate, undesirable abiotic reactions

occur in the bioreactor as shown in Eqs. 5, 6 [10]:

HS� þ ðx� 1ÞS0 $r3; r�3S2�

x þ Hþ ð5Þ

S2�x þ

3

2O2 �!

r4S2O2�

3 þ ðx� 2ÞS0 ð6Þ

According to these equations, dissolved sulfide can react

with produced S0 to producep polysulfide ions S2�x

� �; and

then S2�x ions are oxidized to S0 and S2O2�

3 .

The hydroxyl ions, consumed in the absorption of H2S,

are regenerated upon oxidation of sulfide to elemental

sulfur. This saves costs of dosing NaOH to the process. In

addition, elemental sulfur is easily separated from the

solution by sedimentation, and the produced elemental

sulfur can be used. When sulfate or thiosulfate is produced,

sodium hydroxide cannot be regenerated, and a stream of

hydroxide should be dosed to the bioreactor and a bleed

stream is necessary to prevent accumulation of sulfate and

thiosulfate. This leads to a considerable cost for the pro-

cess. Knowledge of the kinetics of the reactions helps to

prevent sulfate and thiosulfate formation. One of the most

important parameter which affects sulfate and thiosulfate

selectivity is the dissolved oxygen (DO) [11–13]. Ele-

mental sulfur selectivity increases by decrease of DO value

in the bioreactor. In addition, pH value, the amount of

sulfide load and concentration of bacteria have important

roles in sulfur selectivity.

The industrial sulfide removal process operates in

steady-state condition, and the operating parameters should

be at optimal values. Optimization as a major quantitative

tool is used in industrial decision making. In the present

study, the optimal condition of biological sulfide removal is

investigated. The goal of the optimization is the maximum

sulfur selectivity as shown in Eq. 7:

Sulfur selectivity ¼ ½S0�½HS��in

ð7Þ

Methods

A model was proposed and validated by Roosta et al. [14]

for biological sulfide removal in a fed-batch bioreactor

using the bacterium T. thioparus. The bacteria were pre-

served in a medium culture containing thiosulfate as an

energy source in a 5-day batch-inoculation period. After

inoculation with thiosulfate, the bacteria were adapted to

sulfide in a 36-h fed-batch period.

In their experiments, the bioreactor was operated in a

well-stirred condition, because a recirculating gas with a

flowrate of 15 L min-1 was spread by a diffuser; this

caused a good mixing of the broth. According to this fact,

they assumed that kinetic limitation is taken into account,

thus they ignored the interfacial mass transfer process of

gaseous oxygen as well as the dissolution of the solid

elemental sulfur. In addition, the density of broth was

assumed to be constant during the process. Also, theFig. 1 Schematic diagram of bioscrubber for biological sulfide

removal from natural

1006 Bioprocess Biosyst Eng (2012) 35:1005–1010

123

operating conditions of the bioreactor are listed in Table 1.

According to these assumptions and operational conditions,

the model proposed by Roosta et al. [14] is employed to

find the optimal steady-state operational conditions of the

process. The model equations are rewritten for steady-state

condition as follows:

½S2�x � ¼ 10�pKx

½HS��½Hþ�

cHS�

cHþcS2�x

ð8Þ

logci ¼ �ziA

ffiffiIp

1þ bai

ffiffiIp ð9Þ

I ¼ 1

2

Xz2

i ½i� ð10Þ

Finð½HS��in � ½HS��ÞV

� r1 � r4 ¼ 0 ð11Þ

�FinS0

Vþ r1 � r2 � r4 ¼ 0 ð12Þ

�Fin½SO2�4 �

Vþ r2 ¼ 0 ð13Þ

�Fin½S2O2�3 �

Vþ r4 ¼ 0 ð14Þ

r1 ¼CBk1½HS��k2 þ ½HS��

O2

k3 þ O2

ð15Þ

r2 ¼CBk4½S0�k5 þ ½S0�

O2

O2 þ k6

½OH��½OH�� þ k7

ð16Þ

r4 ¼ k8½S2�x �O0:59

2 ð17Þ

In this model, Eq. 5 assumed to be an equilibrium

reaction which the equilibrium is shown in Eqs. 8–10.

According to this assumption, by making material balances

on the present component in the bioreactor (HS-, S0,

SO42-, and S2O3

2-), Eqs. 11–14 are obtained for steady-

state condition and the reaction rates are given by Eqs. 15–

17. Gonz0alez-S0anchez and Revah [15] reported that the

inhibition of sulfide on microorganisms was detected at

sulfide concentration more than 3 mM; thus, the sulfide

inhibition is ignored in Eq. 15 due to low concentration of

sulfide in this study. Also, proposed models for sulfur and

sulfate production rates (Eqs. 15, 16) were validated for the

condition listed in Table 1; specially, pH dependence of

Eq. 16 is just true at pH values between 7.5 and 8.5, and the

elemental sulfur dependence of Eq. 16 is validated when the

particle sizes of elemental sulfur was less than 1 lm.

The model parameters obtained by Roosta et al. [14] are

listed in Table 2.

As mentioned before, the goal of the present study is the

maximum selectivity of elemental sulfur, which leads to

minimum hydroxide loss. In the following, the effect of

various parameters on sulfur selectivity is investigated to

find the optimum operational condition.

Results and discussion

Effect of sulfide load and DO on sulfur selectivity

According to Eqs. 3 and 4, sulfur is an intermediate in

oxidation of sulfide to sulfate. On the other words, these

equations are in series which the intermediate is the desired

reaction product. In this part, effects of sulfide load and DO

on sulfur selectivity are investigated. As illustrated in

Fig. 2, by increasing sulfide load, sulfur selectivity

increases, and passes a maximum, and then it decreases

with the increase of sulfide load. Furthermore, sulfur

selectivity decreases by increasing DO.

At low sulfide loads, the concentration of uneliminated

sulfide ([HS-]) is low as shown in Fig. 3; thus, rate of sulfur

production (r1), which is dependent on ([HS-] is low too.

However, rate of sulfate production (r2) is independent of

([HS-] and can be highly relative to r1; thus, large amount of

elemental sulfur is converted to sulfate. Consequently,

Table 1 Bioreactor operating

conditionsTemperature (�C) OD600 of bacteria DO (ppm) pH

30 ± 0.5 0.3–0.6 0.4–6 8 ± 0.5

Sulfide load (mmol L-1 h-1) Uneliminated sulfide (mM) Flowrate (ml h-1)

0.3–5.7 up to 1 1.5–23

Table 2 Parameters of model equations [14]

k1 (mmol L-1 h-1) k2 (mmol L-1) k3 (mmol L-1) k4 (mmol L-1 h-1) k5 (mmol L-1)

10.051 0.106 2.796 9 10-5 16.091 2.524

k6 (mmol L-1) k7 (mmol L-1) k8 (mmol-0.59 L0.591 h-1) pKx

0.203 5.593 9 10-4 39.751 8.96

Bioprocess Biosyst Eng (2012) 35:1005–1010 1007

123

sulfate selectivity is high at low sulfide loads as shown in

Fig. 4. Increase of [HS-] by increasing sulfide load leads to

increase of r1 and sulfur selectivity passes a maximum.

As the sulfide load is increased, the concentration of

polysulfide ions [Sx2-] increase according to Eq. 8 and it

leads to increase of thiosulfate production as shown in

Fig. 5. As thiosulfate production consumes elemental sul-

fur, increase of thiosulfate selectivity is the main reason for

the decrease of sulfur selectivity at higher sulfide loads.

The effect of DO on sulfur, sulfate, and thiosulfate

selectivity is shown in Figs. 2, 4 and 5.

In addition, by increasing DO value, the maximum

sulfur selectivity occurs at higher sulfide load as shown by

filled circles (•) in Fig. 2.

Effect of pH on sulfur selectivity

As shown in Fig. 6, by increasing pH value, sulfur selec-

tivity decreases, which is due to two mechanisms. Firstly,

increasing [OH-] leads to increase r2 according to Eq. 4;

thus, more elemental sulfur is oxidized to sulfate. Sec-

ondly, at high pH values, thiosulfate selectivity increases

(the concentration of thiosulfate increases due to increase

of polysulfide concentration), and more parts of produced

sulfur are converted to thiosulfate. The effect of pH on

thiosulfate selectivity is illustrated in Fig. 7.

Effect of bacteria OD on sulfur selectivity

Sulfur selectivity as a function of sulfide load at different

bacteria ODs is investigated and is shown in Fig. 8. For

low sulfide loads (below 2.4 mmol L-1 h-1), sulfur

selectivity decreases by increasing bacteria concentration

at a known sulfide load. At sulfide load about

2.4 mmol L-1 h-1, sulfur selectivity is almost independent

of bacteria concentration, and there is a direct relation

between sulfur selectivity and bacteria OD at higher sulfide

loads. At a known concentration of bacteria, sulfur

Fig. 2 Ratio of [S0] to [HS-]in as a function of [HS-] load at various

DO values. Filled circles represents peak of curves

Fig. 3 Uneliminated HS- concentration as a function of HS- load at

various DO values

Fig. 4 Ratio of [SO42-] to [HS-]in as a function of [HS-] load at

various DO values

Fig. 5 Ratio of [S2O32-] to [HS-]in as a function of [HS-] load at

various DO values


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selectivity increases with the increase of sulfide load, and

it passes through a maximum, and then it decreases with

the increase of sulfide load as discussed before. The

maximum sulfur selectivity occurs at higher sulfide load

for higher bacteria OD as shown by filled circles (•) in

Fig. 8.

Optimization of process using genetic algorithm (GA)

According to the above sections, various parameters can

affect sulfur selectivity such as: pH, DO value, OD of

bacteria, and sulfide load. In this part of study, the optimum

conditions of the bioreactor are estimated using genetic

algorithm optimization method. Genetic algorithms are

efficient in getting good solutions for difficult non-linear

optimization problems [16]. They are search methods that

simulate natural evolution patterns [17]. A simple GA was

first expressed by Holland [18] and further developed by

Goldberg [19]. Since then, several amendments were made

to turn the initial idea of genetic algorithms to more effi-

cient optimization algorithms, very useful information can

be found in Michalewicz [20]. In the recent years, GA

method has been applied to optimization of many of bio-

technology and biochemical engineering processes

[21–25].

As mentioned previously, the objective of the optimi-

zation is the maximum sulfur selectivity, which should be

maximized and the decision variables are considered as OD

of bacteria, DO value, sulfide load, and pH value. The

choices of GA parameters have a great effect on the speed

of convergence and success of the optimization (finding a

global optimum instead of local optimum). The exact set-

tings used for running the GA (number of individuals in the

population, mutation rate, migration fraction, and cross-

over fraction) are listed in Table 3. The best objective

function was achieved at less time with using these par-

ticular parameters.

The obtained optimal values of decision variables are

shown in Table 4, and the selectivity of the products at

optimal conditions are shown in Table 5. Sulfur selectivity

is in the maximum value (87.76%) in the optimal

condition.

Fig. 6 Effect of pH on sulfur selectivity at various DO values

Fig. 7 Effect of pH on thiosulfate selectivity at various DO values

Fig. 8 Effect of bacteria OD on sulfur selectivity, pH = 8,

DO = 0.8 ppm. Filled circles represents peak of curves

Table 3 Parameters of GAPopulation size Mutation rate Cross over Migration No. of generations

Function fraction Direction fraction

30 0.03 Scattered 0.75 Forward 0.2 1,000

Bioprocess Biosyst Eng (2012) 35:1005–1010 1009

123

Conclusion

In the present study, optimization of biological sulfide

removal from natural gas is successfully performed using

GA. The objective of the optimization is sulfur selectivity

and the decision variables are bacteria OD, dissolved

oxygen, pH, and sulfide load. Optimization results show

that at optimal condition, maximum 87.76% of sulfide

loaded to the bioreactor could be converted to elemental

sulfur and the remained is converted to sulfate and thio-

sulfate. At this condition, for each 100 mol of sulfide load,

12.24 mol hydroxide ions are spent due to sulfate and

thiosulfate production.

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Table 4 Optimal values of the process variables

OD of bacteria DO (ppm) Sulfide load (mmol L-1 h-1) pH

0.39 0.40 2.22 7.80

Table 5 Selectivity of products at optimal conditions

Sulfur particles Sulfate Thiosulfate Uneliminated sulfide

87.76% 8.53% 3.65% 0.06%


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Optimization of biological sulfide removal in a CSTR bioreactor