Sulfate-reduction, sulfide-oxidation and elemental sulfur bioreduction process: Modeling and experimental validation

Accepted Manuscript Sulfate-reduction, Sulfide-oxidation and Elemental Sulfur Bioreduction Proc‐ ess: Modeling and Experimental Validation Xijun Xu, Chuan Chen, Duu-Jong Lee, Aijie Wang, Wanqian Guo, Xu Zhou, Hongliang Guo, Ye Yuan, Nanqi Ren, Jo-Shu Chang PII: DOI: Reference:

S0960-8524(13)01181-4 http://dx.doi.org/10.1016/j.biortech.2013.07.113 BITE 12158

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Bioresource Technology

Received Date: Revised Date: Accepted Date:

22 June 2013 21 July 2013 24 July 2013

Please cite this article as: Xu, X., Chen, C., Lee, D-J., Wang, A., Guo, W., Zhou, X., Guo, H., Yuan, Y., Ren, N., Chang, J-S., Sulfate-reduction, Sulfide-oxidation and Elemental Sulfur Bioreduction Process: Modeling and Experimental Validation, Bioresource Technology (2013), doi: http://dx.doi.org/10.1016/j.biortech.2013.07.113

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Sulfate-reduction, Sulfide-oxidation and Elemental Sulfur Bioreduction Process: Modeling and Experimental Validation

1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16 

Xijun Xu1, Chuan Chen1, Duu-Jong Lee1,2,3*, Aijie Wang1, Wanqian Guo1, Xu Zhou1, Hongliang Guo1, Ye Yuan1, Nanqi Ren1*, Jo-Shu Chang4 1 State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China 2 Department of Chemical Engineering, National Taiwan University, Taipei 106, Taiwan 3 Department of Chemical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan 4 Research Center for Energy Technology and Strategy, National Cheng Kung University, Tainan, Taiwan * Corresponding authors: [email protected] (DJL); [email protected] (RNQ)

ABSTRACT

17 

This study describes the sulfate-reducing (SR) and sulfide-oxidizing (SO) process

18 

using Monod-type model with best-fit model parameters both being reported and

19 

estimated. The molar ratio of oxygen to sulfide (ROS) significantly affects the kinetics

20 

of the SR+SO process. The S0 is produced by SO step but is later consumed by

21 

sulfur-reducing bacteria to lead to “rebound” in sulfide concentration. The model

22 

correlated well all experimental data in the present SR+SO tests and the validity of

23 

this approach was confirmed by independent sulfur bioreduction tests in four

24 

denitrifying sulfide removal (DSR) systems. Modeling results confirm that the ratio of

25 

oxygen to sulfide is a key factor for controlling S0 formation and its bioreduction.

26 

Overlooking S0 bioreduction step would overestimate the yield of S0.

27 

Keywords: Kinetic model; sulfur bioreduction; microaeration; data fitting

28  29 

1. INTRODUCTION

30 

Sulfate-bearing wastewaters are produced by pulp and paper manufacturers,

31 

petrochemical plants, mineral processes and acid mine drainage from mining

32 

activities (Knobel and Lewis, 2002). Under anaerobic environment with the presence

33 

of chemical oxygen demand (COD), the sulfate-reducing bacteria (SRB) can convert 1   

34 

sulfate in wastewaters to sulfide, which is toxic to living being and is corrosive to

35 

metals. Biological conversion of sulfide to elemental sulfur (S0) using

36 

sulfide-oxidizing bacteria (SOB) is a promising process (Xu et al., 2012;

37 

Lohwacharin and Annachhatre, 2010) since the formed S0 can be recovered as a

38 

renewable resource for fertilizer industries, sulfuric acid production and as substrates

39 

for bioleaching processes (Celis-Garcia et al., 2008). Integration

40 

of

the

sulfate-to-sulfide

conversion

step

by

SRB

and

41 

sulfide-to-S0conversion by SOB into one single reactor is of practical interest (van der

42 

Zee et al., 2007). The level of dissolved oxygen (DO) has been proposed as an

43 

effective process parameter to regulate the activities of SRB and SOB (Okabe et al.,

44 

2005). To have SRB+SOB working in the same reactor faced difficulty of low S0

45 

conversion. Xu et al. (2012) showed that the activities of SOB were enhanced by

46 

limited oxygen to peak recovery of S0 from sulfate. The sulfide was oxidized by free

47 

oxygen at increased DO so conversion of S0 declined. At high DO concentration the

48 

activities of SRB were inhibited so the sulfate reducing (SR) + sulfide oxidizing (SO)

49 

reactor failed. A few SRB strains can utilized the formed S0 as electron acceptor at the

50 

expense of COD in the wastewaters (Chen et al., 2008a, 2008b). Certain

51 

methanogenic bacteria could also form sulfide from oxidized states of sulfur via

52 

dissimilatory sulfur reduction pathway (Zhou et al., 2011). The excess assimilatory

53 

sulfur metabolism was claimed for reduced S0 yield in the SR+SO process (Thauer et

54 

al., 1977).

55 

Quantitative determination of the formed S0 was mainly by mass balance

56 

calculation (Chen et al., 2009) or by sulfite method (Jiang et al., 2009). Kinetic

57 

models can assist development and optimization of SR+SO reactor with maximum

58 

sulfur recovery. This paper describes a mathematical model for SR+SO reactions on 2   

59 

microaerophilic treatment of sulfate-bearing wastewaters. In particular, the process

60 

parameter, ROS (molar ratio of oxygen to sulfide), is used for process optimization.

61 

Experiments were conducted to validate the model outputs.

62  63 

2. MATERIALS AND METHODS

64 

2.1 Experimental setup

65 

Activated sludge was collected as inoculum from an anaerobic reactor for sulfur and

66 

nitrogen-containing wastewater (Chen et al., 2008a). All batch tests were performed

67 

in 250 mL anaerobic media bottles sealed with butyl rubber stoppers. The medium

68 

consisted of 600 mg L-1 sulfate and 2000 mg COD L-1 with sodium lactate as carbon

69 

source. Macro-nutrients were added in the following amounts (g L-1): NH4Cl, 0.575;

70 

CaCl2, 0.070; MgSO4.7H2O, 0.100; K2HPO4, 0.22. 1 mL L-1 of trace solution was

71 

added as described elsewhere (Chen et al., 2008b). The pH of suspensions was

72 

adjusted to 8.0 using bicarbonate. Before experiments, nitrogen was sparged into the

73 

bottles for 5 min to remove oxygen from both the aqueous phase and the headspace.

74 

Experiments,

75 

microaerophilic sulfate-reduction, sulfide-oxidation and elemental sulfur bioreduction,

76 

were carried out. Pure oxygen was added depend on the molar ratios of oxygen to

77 

sulfide (ROS) indicated in Table 2 and the volume of oxygen to be added to the

78 

headspace of each bottle was calculated according to Johnston and Voordouw (2012).

79 

Assuming the concentration of gaseous oxygen at 23 oC and 1 atm being 41.2 mM,

80 

the volume (V) of 100% (v/v) oxygen to add was calculated as

81 

V = (mM O2 wanted in solution 50 mL headspace)/ 41.2 mM

82 

The concentration of gas phase H2S was ignored in the calculation of ROS. For the

83 

abiotic experiments, the inoculums were autoclaved and cooled before oxygen was

investigating

elemental

sulfur

3   

production

through

coupling

84 

added.

85 

In order to calibrate the model and its parameter values, independent experiments

86 

were conducted with initial conditions as follows. 23.0 mL oxygen were added to the

87 

headspace of anaerobic media bottle to generate ROS=1.0. For model evaluation, six

88 

more experiments (Table 2) were carried out. All tests were run in triplicate and the

89 

averaged results were reported.

90  91 

2.2 Analysis

92 

An ion chromatography (Dionex ICS-3000) measured the concentration of sulfate

93 

(SO42-), thiosulfate (S2O32-) in the collected liquor samples following 0.45-µm

94 

filtration. Sample separation and elution were performed using an IonPac AG4A

95 

AS4A-SC 4mm analytic column with carbonate/bicarbonate eluent (1.8 mmolL-3

96 

Na2CO3/1.7 mmolL-1 NaHCO3 at 1 mL min-1) and a sulfuric regeneration (H2SO4, 25

97 

mmolL-1 at 5 mL min-1). Sulfide concentration (including H2S, HS- and S2-) was

98 

determined according to the methylene blue method (Truper and Schlegel, 1964).

99 

Both volatile suspended solids and suspended solids were measured according to

100 

Standard Methods. The dissolved oxygen in liquid samples was measured by DO

101 

meter (pH/Oxi 340i, WTW, Germany) and the oxygen in the headspace was

102 

determined by gas chromatography (GC-6890, Agilent, Foster City, CA, USA).

103 

Elemental sulfur production was calculated according to:

104 

[S0] = [Influent S]-[SO42-]-2*[S2O32-]-[HS-]

105  106 

2.3 Modeling approach

107 

The total S0 production is combination of sulfate-reduction, sulfide-oxidation and

108 

elemental sulfur bioreduction (eq 1) 4   

dS S 0

net

109 

dt

=

dS S 2− ox

dt

+

dS S 0

re

dt

  

 

 

 

 

 

 

 

 

 

 

 

(1)

110 

Four main processes associated with sulfur-species cycle were incorporated in this

111 

model with all parameters listing in Table 1. The Monod-type kinetics for substrate

112 

utilization is adopted. Si stands for the concentration of component i; XSRB, XSOB and

113 

XS0 are biomass responsible for SR, SO and S0 bioreduction process respectively. We

114 

consider excess COD presented in the suspension so its effect on the sulfate reducing

115 

process is ignored. The pH and H2S inhibition effects were included in the ADM1

116 

model (Batstone et al., 2002), but are not considered in this model since the

117 

suspension pH was at around 8.0.

118 

The Process 1 considers SO42- reduction to S2- (R1), mediated by SRB,

119 

consuming SO42- and COD (electron source) and yielding biomass (eq. S1). Its kinetic

120 

expression is stated as follows:

121 

SO42− + 8 H + + 8e − → S 2− + 4 H 2O     dSSO 2− SSO2− µ 4 X SRB     =- SRB SRB 4 dt YSRB K SO 2− +SSO2−

122 

4

 

 

 

 

 

 

 

 

 

(S1)

 

 

 

 

 

 

 

 

 

(2)

4

123 

The Process 2 considers S2- oxidation to S0 (R2), mediated by SOB, consuming S2-

124 

and O2 and yield biomass (eq. S2). The kinetic expressions for this process for S2- and

125 

O2 are as follows:

126 

2 HS − + O2 → 2S 0 + 2OH −         dS S 2 - µ SO2 S 2− ox X SOB   =- SOB SOBS SOB dt YSOB K S 2− +SS 2− KO2 +SO2

127 

dSOSOB 2

128 

dt

 

 

 

 

 

 

 

 

(S2)

 

 

 

 

 

 

 

 

(3)

SO2 S 2− 1 µ =- ⋅ SOB SOBS X SOB   2 YSOB K S 2− +SS 2− K OSOB +SO2 2

 

 

 

 

 

 

 

(4)

129 

The Process 3 considers S0 reduction to S2- (R3), mediated by sulfur bioreduction

130 

bacteria, consuming S0 and COD and yield biomass (eq. S3). The kinetic expression 5   

131 

for S3 is eq. 5.

132 

S 0 + 2e − → S 2 −    

dS S 0

re

133 

dt

=-

µeSRE

 

 

SS 0

YeSRE K

eSRE S0

+ SS 0

X S0

 

 

 

KO2

 

K O2 + S S 0

 

(S3)

 

 

 

 

 

 

 

 

 

 

 

              (5)

134 

The Process 4 considers aerobic oxidation of COD by heterotrophs, consuming

135 

oxygen and COD in eq. S4. The corresponding kinetic expression is stated in eq 6.

136 

O2 +4e − + 2 H + → 2OH −    

dS

137 

H O2

=-

dt

µX

H

YX H K

SO2 XH O2

+ SO2

XH  

 

 

 

 

 

 

 

 

 

 

(S4) 

 

 

 

 

 

 

 

 

 

 

(6)

138 

The Process 5 takes into account the biomass for SO42--reduction, S2--oxidation and S0

139 

bioreduction, yield and decay. The kinetic expressions for the biomass as follows:

140 

S0 0 dX bS =(µeSRE eSRES -kdeSRE )X bS   dt K S 0 + SS 0

141 

SSO 2− dX SRB =（µ SRB SRB 4 -k dSRB ) X SRB     dt K SO 2− +SSO 2−

0

4

 

 

 

 

 

 

 

(7)

                                                  (8)

4

SO2 S 2− dX SOB =（µ SOB SOBS -k dSOB ) X SOB       SOB dt K S 2− +SS 2− K O2 +SO2

142 

 

(9)

143 

Since aerobic COD oxidation by facultative organisms can consume part of

144 

oxygen and since S0 is the main end-product of sulfide oxidation under

145 

limited-oxygen condition (Janssen et al., 1997), S2- oxidation to SO42- step with O2 as

146 

electron acceptor is ignored. (This assumption is justified in experimental findings

147 

reported latter). The competition of chemical oxidation on bioreduction of elemental

148 

sulfur is described by the inhibition function of O2 (eq 4). Equation 5 is the kinetic

149 

equation of oxygen consumption for sulfide oxidation. The kinetics of oxygen

150 

consumption for aerobic COD oxidation by heterotrophs is described by eq. 6 and the

151 

parameters for this equation are taken from the published literature (Koch et al., 2000). 6   

152 

Equation 7 presents the kinetics of active biomass. According to Zhou et al. (2011),

153 

we assume that methanogenic bacteria were responsible for sulfur bioreduction

154 

process and so when modeling the sulfur bioreduction process we applied a new

155 

parameter XS0 for sulfur bioreduction biomass. The input values for XSRB, XSOB and

156 

XH were estimated based on the results of experiments using the baseline endogenous

157 

OUR level prior to substrate addition (Ni et al., 2012). In the present work, the initial

158 

concentrations of total active biomass, SRB, SOB and heterotrophic biomass in the

159 

batch tests were measured as 32000, 10000, 6000 and 15000 mg L-1, respectively, and

160 

thus the initial concentration of active sulfur bioreduction biomass XS0 was calculated

161 

to be 1000 mg L-1.

162 

The model parameters in eqs. 2–9 were estimated. For sulfate reduction process,

163 

the parameters were extracted from Moosa et al. (2002). For sulfide oxidation process,

164 

we estimated YSOB, µSOB, KS2-SOB, kdSOB and KO2SOB by fitting eqs. 3, 5, 6 and 9 with

165 

the S2- and O2 data provided in this study. For sulfur bioreduction process, YeSRE, µeSRE,

166 

KS0eSRE, KO2, kdeSRE were estimated by fitting eqs. 4 and 7 with the experimental data

167 

for S2-. The sulfide rebound phenomenon (as shown latter in the experimental section)

168 

was considered to be attributed to sulfur bioreduction. Since the kdSOB is low

169 

(10-5–10-6 h-1) in value, this parameter was ignored in model fitting.

170 

The weighted nonlinear least-squares analysis was applied to determine the

171 

kinetic parameters by fitting the experimental data using criterion in eq. 10 (Ni et al.,

172 

2012):

173 

SSWE = ∑ ( Si0 measured − Si0 predicted )2    

n

 

 

 

 

 

 

 

 

(10)

i =1

174 

where Si0measuredand Si0predicted are the i-th measured and predicted concentrations of

175 

specific substrates listed in eqs. 2–7, respectively. Modeling and simulations were 7   

176 

performed using the software package AQUASIM (Reichert, 1998).

177  178 

3. RESULTS AND DISCUSSION

179 

3.1 Elemental sulfur production at different ROS

180 

At ROS=0 (anaerobic environment), the dosed SO42- was converted to S2- in 2 hr (Fig.

181 

1). The sulfate reduction data at ROS=0.25–2.5 were also shown in Fig. 1 for

182 

comparison sake. The DO level investigated had minimal effects on sulfate reduction,

183 

correlating with the findings of Xu et al. (2012). Experimental data for concentrations

184 

of S2-, S0 and O2 at ROS=0.25–2.5 are shown in Fig. 2. No SO42- was detected after its

185 

exhaustion at 2 hr and onward (Fig. 1), hence supporting the assumption that S2-

186 

oxidation to SO42- step with O2 as electron acceptor is negligible in the modeling steps.

187 

Both SO42- and S2O32- concentrations were low in the batch tests. These observations

188 

supported the assumption that S0 was the main oxidation product in the SO process

189 

and chemical sulfide oxidation could be ignored during the present work based on the

190 

fact that thiosulfate was the main product of chemical sulfide oxidation (Nielsen et al.,

191 

2005; Chen et al., 2012). Meanwhile, the variation of substrates (sulfate, oxygen and

192 

COD) was less than 5% under abiotic conditions, which showed that the degradation

193 

of substrates was mostly attributed to biotic function.

194 

Three phases of S2- profiles were observed. The first phase was related to the S2-

195 

production governed by simultaneous SO42- reduction and S2- oxidation. The second

196 

phase represented the S2- oxidation only, which was followed by a slow S2- oxidation

197 

lag by S2- utilization and O2 uptake by both SOB and heterotrophs. The third phase of

198 

S2- profile was associated with the S0 bioreduction, which emerged right after the

199 

depletion of O2. The impact of different oxygen concentrations (ROS) on methanogenic activity

200 

8   

201 

was depicted (data not shown) and no obvious difference was observed. Although

202 

methanogens were related to extremely oxygen-sensitive organisms, they could create

203 

their own anaerobic environment to survive and oxygen, which was harmful to these

204 

strict anaerobes, could be removed from their biotopes by non-enzymatic reduction of

205 

O2 by H2S formed by the sulfate-reducing bacteria; this might have contributed to the

206 

extensive distribution of the methanogens in nature (Stetter and Gaag, 1983).

207  208 

3.2 Model fitting

209 

The model parameters in eqs. 2–9 were estimated. For SR process, the parameters

210 

were extracted from (Moosa et al., 2002). For SO process, parameters YSOB, µSOB,

211 

KS2-SOB, kdSOB and KO2SOB were fitted with eqs. 3, 5, 6 and 9 using the S2- and O2 data

212 

provided in this study. For sulfur bioreduction process, YeSRE, µeSRE, KS0eSRE, KO2, kdeSRE

213 

were estimated by fitting eqs. 4 and 7 with the experimental data for S2-. The sulfide

214 

rebound phenomenon (as shown latter in the experimental section) was considered to

215 

be attributed to sulfur bioreduction. Since the kdSOB is low in value (10-5–10-6 h-1),

216 

this parameter was ignored in model fitting.

217 

As the maximum specific growth rate (µSRB), decay coefficient and yield

218 

coefficient (YSRB) in SR process were not dependent on the SO42- concentration, and

219 

the half-saturation constant (KSO42-SRB) showed a linear increase with increase in initial

220 

substrate concentration, their values were obtained as 0.061 h-1, 0.035 h-1, 0.584

221 

g-bacteria/g-SO42- and 0.02 kg m-3, respectively.

222 

The experimental data at ROS=1.0 for S0 utilization for bioreduction were used to

223 

fit eq. 5 for estimating maximum specific growth rate (µeSRE), half saturation constant

224 

(

225 

and KO2 for S0 bioreduction were estimated by fitting eq. 5 to the S0 production.

), decay coefficient (

) and yield coefficient (YeSRE). The rate coefficients

9   

Table 1 lists the values and units of the kinetic and stoichiometric parameters

226  227 

used in the present model.

228  229 

3.3 Model validation

230 

The model predictions based on the so-fitted data were used to calculate the time

231 

course curves for SO42-, S2-, S0 and O2 (solid curves in Figs. 1 and 2). The model

232 

predictions correlate well with experimental results with no systematic deviations.

233 

The proposed model was further validated using the experimental results by Zhou et

234 

al. (2011) which also observed and studied sulfur bioreduction process in denitrifying

235 

sulfide removal system and by Chen et al. (2010). The inoculums by Zhou et al. (2011)

236 

were denitrifying sulfide granules and the media were: 200 mg L-1 S2-, 240 mg L-1

237 

acetate, and 194, 387.5, 542.5, or 620 mg L-1 nitrate giving sulfide/nitrate molar ratios

238 

of 5/2.5, 5/5, 5/7, 5/8. Figure 3 shows that the simulation results agree well with the

239 

measured S0 concentrations, and sulfide and oxygen consumption profiles. The

240 

capability of the present model with the best-fit parameters using SR+SO data to

241 

describe kinetic behaviors of sulfur bioreduction in a denitrifying sulfide removal

242 

system confirmed the validity of the proposed model.

243  244 

3.4 Parametric sensitivity

245 

Uncertainty analysis was performed according to Ni et al. (2012). High

246 

sensitivity of certain parameter suggests that this parameter is easy to be assigned by a

247 

unique value. The surface plots of the objective functions (SSWE in eq. 8) for the

248 

levels of correlation between parameters were evaluated (Fig. 4). The surface plots of

249 

the objective function for maximum growth rate (µeSRE) versus half saturation constant

250 

(

), and maximum growth rate (µeSRE) versus decay coefficient ( 10 

 

) show a

251 

well-defined valley, with the fitted values of these parameters residing in. These

252 

best-fitted parameters are regarded well defined as listed in Table 1. Conversely, the

253 

sensitivity of µeSRE to the model is higher than that of

254 

change in SSWE on the µeSRE compared with the

255 

of model parameters for S0 bioreduction were shown in Fig. 5. Effects of maximum

256 

growth rate of bacteria, µeSRE (h-1), on the substrate profiles were shown in Fig. 5a. At

257 

high µeSRE, its effect on S0 bioreduction is minimal. As µeSRE was decreased, the S0

258 

bioreductionrate was significantly changed. The sensitivity analyses of affinity

259 

constant Ks and yield coefficient YH were performed. The bioreduction rates of S0

260 

were increased with reducing Ks and YH (Figs. 5b and 5c).

. There is a greater

axis. The output sensitivities

261  262 

SR+SO processes

263 

An integrated model including SR+SO and S0 bioreduction processes was

264 

established. The production of S0 depended greatly on ROS (Fig. 2). The S0

265 

concentrations was increased to 79, 109, 194 mg L-1 at around 7 hr at ROS=1.0, 1.5

266 

and 2.0, respectively. The formed S0 was reduced by MPB to S2- in the latter stage of

267 

all tests, leading to low S0 yield for the SR+SO process (Belyakova et al., 2006;

268 

Mogensen et al., 2005; Nakagawa et al., 2005; Thabet et al., 2004). The “rebound” in

269 

S2- concentration was in agreement with those reported by Chen et al. (2008b).

270 

From experimental data and model simulation, the peak points of S2- profiles

271 

correspond to complete removal of SO42- and the bending point of S0 profiles

272 

correspond to the exhaustion of O2 for S2- oxidation and occurrence of S0 bioreduction.

273 

Thus, the time course of S2- indicates the transition from S0 production to

274 

bioreduction. In DSR studies the excess organic substances stimulated the activities of

275 

11   

276 

Methanobacterium sp. to lead to reduction of S0 (Zhou et al., 2011). In the present

277 

study, the formed S0 was also consumed by bioreduction; however, the consumption

278 

rate was reduced in the presence of trace oxygen. This observation is attributable to

279 

the fact that SRB (Belyakova et al., 2006; Mogensen et al., 2005) and methanogenic

280 

bacteria (Stetter and Gaag, 1983) have DO-sensitive activities. Although S2- could

281 

also be produced abiologically by disproportionation of S0, since no SO42- or S2O32-

282 

was accumulated, the S0 noted in the present study should be formed via biological

283 

pathway and S2- production was exponential rather than linear shape.

284 

To the authors’ best knowledge, no kinetic parameters are available in the

285 

literature for bioreduction of S0. The parameters reported herein are hence valuable

286 

for model development involving S0 kinetics. The maximum specific growth rate

287 

(µeSRB) of 0.035 h-1 determined for S0 bioreduction was close to those obtained by

288 

Zavarzinaet al. (2000) and Escobar et al. (2007), although these authors worked with

289 

pure culture. The YeSRE estimated herein, 0.712g biomass/g S0, is close to that by

290 

Escobar et al. (2007). The half-saturation constant and decay coefficient for S0

291 

bioreduction were 0.024 kg m-3 and 5.75× 10-6 h-1, respectively.

292 

The µSOB for SOB was low (0.028 h-1) when compared with those reported in

293 

other studies (Alcantara et al., 2004; Gadekar et al., 2006). The value of

294 

half-saturation constant for SOB, KSOB (0.011 kg m-3), estimated in this work is

295 

generally in accord with those reported in (Alcantara et al., 2004; Gadekar et al.,

296 

2006). The YSOB estimated in this study, 0.0029g biomass/mmol sulfide, is

297 

significantly lower than those reported by Gadekar et al. (2006) and McComas and

298 

Sublette (2001). In general, the present SR+SO system has lower growth rates and

299 

yields than the literature results considering only SR or SO systems. Since S0 bioreduction takes place in the SR+SO process, overlooking S0

300 

12   

301 

bioreduction step would overestimate the yield of S0. On the other hand, S0

302 

bioreduction is a primitive means of energy conservation and for mesophilic bacteria

303 

at moderate temperatures, S0 appears to be an unfavorable oxidant due to the

304 

relatively few energy yield in its reduction (Stetter and Gaag, 1983; Thauer et al.,

305 

1977; Belkin et al., 1985). However, the presence of S0 could enhance final cell yield

306 

by a factor of up to 4 (Belkin et al., 1985). The present model indicates that the S2-/S0

307 

cycles by SRB and SOB contribute to this increased cell yield, with the side benefits

308 

of consuming excess COD in the pathways using S0 as a mediator. So the proposed

309 

model in this work including sulfate-reduction, sulfide-oxidation and elemental

310 

sulfur-bioreduction would help us better understand the procedure of elemental sulfur

311 

formation and enhance its production by minimizing the likelihood of S0 bioreduction

312 

reaction.

313  314 

4. CONCLUSIONS

315 

The SR+SO process considering the formation and bioreduction of S0 was

316 

modeled. The Monod-type kinetic equations were used to fit the model parameter

317 

with experimental data. The validity of the best-fit parameters was confirmed using

318 

the present SR+SO data and the DSR data from literature. The kinetic parameters for

319 

bioreduction of S0 were for the first time reported. The effects of DO on the dynamic

320 

behavior of the studied SR+SO process were presented. The present model can be

321 

applied for process design and optimization that involve biological sulfur (SO42-/S2-)

322 

cycle.

323  324 

ACKNOWLEDGEMENTS

325 

This research was supported by the National Natural Science Foundation of China 13   

326 

(Grant No.51176037), National High-tech R&D Program of China (863 Program,

327 

Grant No.2011AA060904), Project 51121062 (National Creative Research Groups),

328 

the State Key Laboratory of Urban Water Resource and Environment (2012DX06)

329 

and NSC 102-3113-P-110-016.

330  331 

REFERENCES

332 

1. Alcantara, S., Velasco, A., Munoz, A., Cid, J., Revah, S., Razo-Flores, E. 2004.

333 

Hydrogen sulfide oxidation by a microbial consortium in a recirculation reactor

334 

system: sulfur formation under oxygen limitation and removal of phenols.

335 

Environ. Sci. Technol. 38, 918–923.

336 

2. Batstone, D.J., Keller, J., Angelidaki, I., Kalyuzhnyi, S.V., Pavlostathis, S.G.,

337 

Rozzi, A., Sanders, W.T.M., Siegrist, H., Vavilin, V.A. 2002. The IWA anaerobic

338 

digestion model no 1 (ADM1). Water Sci. Technol. 45, 65–73.

339 

3. Belkin, S., Wirsen, C.O., Jannasch, H.W. 1985. Biological and abiological sulfur reduction at high temperatures. Appl. Environ. Microbiol. 49, 1057–1061.

340  341 

4. Belyakova, E.V., Rozanova, E.P., Borzenkov, I.A., Tourova, TP., Pusheva, M.A.,

342 

Lysenko,

A.M.,

Kolganova,

343 

chemolithoautotrophic,

344 

Desulfover miculushalophilus gen. nov., sp. nov., isolated from an oil field.

345 

Microbiol. 75, 161–171.

moderately

T.V.

2006.

halophilic,

The

new

sulfate-reducing

facultatively bacterium

346 

5. Celis-Garcia, L.B., Gonzalez-Blanco, G., Meraz, M. 2008. Removal of sulfur

347 

inorganic compounds by a biofilm of sulfate reducing and sulfide oxidizing

348 

bacteria in a down-flow fluidized bed reactor. J. Chem. Technol. Biotechnol. 83,

349 

260–268.

350 

6. Chen, C., Ren, N.Q., Wang, A.J., Yu, Z.G., Lee, D.J. 2008a. Microbial community 14   

351 

of granules in expanded sludge bed reactor for simultaneous biological removal

352 

of sulfate, nitrate and lactate. Appl. Microbiol. Biotechnol. 79, 1071–1077.

353 

7. Chen, C., Ren, N.Q., Wang, A.J., Yu, Z.G., Lee, D.J. 2008b. Simultaneous

354 

biological removal of sulfur, nitrogen and carbon using EGSB reactor. Appl.

355 

Microbial. Biotechnol. 78, 1071–1077.

356 

8. Chen, C., Wang, A.J., Ren, R.Q., Lee, D.J., Lai, J.Y. 2009. High-rate denitrifying

357 

sulfide removal process in expanded granular sludge bed reactor. Bioresour.

358 

Technol. 100, 2316–2319.

359 

9. Chen, C., Ren, N.Q., Wang, A.J., Liu, L.H., Lee, D.J. 2010. Enhanced

360 

performance of denitrifying sulfide removal process under micro-aerobic

361 

condition. J. Hazard. Mater. 179, 1147-1151.

362 

10. Chen, C., Zhou, C., Wang, A.J., Wu, D.H., Liu, L.H., Ren, N.Q., Lee, D.J. 2012.

363 

Elementary sulfur in effluent from denitrifying sulfide removal process as

364 

adsorbent for Zinc(II). Bioresour. Technol. 121, 441–444.

365 

11. Escobar, C., Bravo, L., Hernandez, J., Herrera, L. 2007. Hydrogen sulfide

366 

production from elemental sulfur by Desulfovibrio desulfuricans in an anaerobic

367 

bioreactor. Biotechnol. Bioeng. 98, 569–577.

368 

12. Gadekar, S., Nemati, M., Hill, G.A. 2006. Batch and continuous biooxidation of

369 

sulfide by Thiomicrospira sp. CVO: reaction kinetics and stoichiometry. Water

370 

Res. 40, 2436–2446.

371 

13. Jiang, G.M., Sharma, K.R., Guisasola, A., Keller, J., Yuan, Z.G. 2009. Sulfur

372 

transformation in rising main sewers receiving nitrate dosage. Water Res. 43,

373 

4430–4440.

374 

14. Johnston,

S.L.,

Voordouw,

G.

2012.

Sulfate-reducing

bacteria

lower

sulfur-mediated pitting corrosion under conditions of oxygen ingress. Environ.

375 

15   

Sci. Technol. 46, 9183–9190.

376  377 

15. Klok, J.B.M., van den Bosch, P.L.F., Buisman, C.J.N., Stams, A.J.M., Keesman,

378 

K.J., Janssen, A.J.H. 2012. Pathways of sulfide oxidation by haloalkaliphilic

379 

bacteria in limited-oxygen gas lift reactors. Environ. Sci. Technol. 46,

380 

7581–7586.

381 

16. Knobel, A.N.; Lewis, A.E. 2002, A mathematical model of a high sulfate wastewater anaerobic treatment system. Water Res. 36, 257–265.

382  383 

17. Koch, G., Egli, K., van der Meer, J.R., Siegrist, H. 2000. Mathematical modeling

384 

of autotrophic denitrification in a nitrifying biofilm of a rotating biological

385 

contactor. Water Sci. Technol. 41, 191–198.

386 

18. Lohwacharin, J., Annachhatre, A.P., 2010. Biological sulfide oxidation in an airlift bioreactor. Bioresour. Technol. 101, 2114–2120.

387  388 

19. McComas, C., Sublette, K.L. 2001. Characterization of a novel biocatalyst for sulfide oxidation. Biotechnol. Progr. 17, 439–446.

389  390 

20. Mogensen, G.L., Kjeldsen, K.U., Ingvorsen, K. 2005. Desulfovibrioaerotolerans

391 

sp. nov., an oxygen tolerant sulphate reducing bacterium isolated from activated

392 

sludge. Anaerobe 11, 339–349.

393 

21. Moosa, S., Nemati, M., Harrison, S.T.L. 2002. A kinetic study on anaerobic

394 

reduction of sulfate, part I: effect of sulfate concentration. Chem. Eng. Sci. 57,

395 

2773–2780.

396 

22. Nakagawa, S., Inagaki, F., Takai, K., Horikoshi, K., Sako, Y. 2005.

397 

Thioreductormicantisoli gen. nov., sp. nov., a novel mesophilic, sulfur-reducing

398 

chemolithoautroph within the ε-proteobacteria isolated from hydrothermal

399 

sediments in the Mid-Okinawa tough. Int. J. Syst. Evol. Microbiol. 55, 599–605.

400 

23. Ni, B.J., Fang, F., Xie, W.M., Xu, J., Yu, H.Q. 2012. Formation of distinct soluble 16   

401 

microbial products by activated sludge: kinetic analysis and quantitative

402 

determination. Environ. Sci. Technol. 46, 1667–1674.

403 

24. Nielsen, A.H., Hvitved-Jacobsen, T., Vollertsen, J. 2005. Kinetics and stoichiometry of sulfide oxidation by sewer biofilms. Water Res. 39, 4119–4125.

404  405 

25. Nielsen, A.H., Vollertsen, J., Hvitved-Jacobsen, T. 2003. Determination of

406 

kinetics and stoichiometry of chemical sulfide oxidation in wastewater of sewer

407 

networks. Environ. Sci. Technol. 37, 3853–3858

408 

26. Okabe, S., Ito, T., Sugita, K., Satoh, H. 2005. Succession of internal sulfur cycles

409 

and sulfur-oxidizing bacterial communities in microaerophilic wastewater

410 

biofilms. Appl. Environ. Microbiol. 71, 2520–2529.

411 

27. Reichert, P. 1998. AQUASIM 2.0-User manual, computer program for the

412 

identification and simulation of aquatic systems. Dubendorf, Switzerland: Swiss

413 

Federal Institute for Environmental Science and Technology (EAWAG).

414 

28. Stetter, K.O., Gaag, G. 1983. Reduction of molecular sulfur by methangenic bacteria. Nature, 305, 309–311.

415  416 

29. Thabet, O.B., Fardeau, M.L., Joulian, C., Thomas, P., Hamdi, M., Garcia, J.L.,

417 

Ollivier, B. 2004. Clostridium tunisiense sp. nov., a new proteolytic,

418 

sulfur-reducing bacterium isolated from an olive mill wastewater contaminated

419 

by phosphogypse. Anaerobe, 10, 185–190.

420 

30. Thauer, R.K., Jungermann, K. Decker, K. 1977. Energy conservation in chemotrophic anaerobic bacteria. Bacteriol. Rev. 41, 100–180.

421  422 

31. Truper, H.G., Schlegel, H.G. 1964. Sulfur metabolism in Thiorhodaceae:

423 

Quantitative

424 

AntonieLeeuwnhoek 30, 225–238.

425 

measurements

on

growing

cells

of

Chromatiumokenii.

32. van der Zee, F.P., Villaverde, S., Garcia, P.A., Fdz.-Polanco, F. 2007. Sulfide 17   

426 

removal by moderate oxygenaion of anaerobic sludge environments. Bioresour.

427 

Technol. 98, 518–524.

428 

33. Xu, X.J., Chen, C., Wang, A.J., Fang, N., Yuan, Y., Ren, N.Q., Lee, D.J. 2012.

429 

Enhanced

elementary

sulfur

recovery

in

integrated

sulfate-reducing,

430 

sulfur-producing reactor under micro-aerobic condition. Bioresour. Technol. 116,

431 

517–521.

432 

34. Zavarzina, D.G., Zhilina, T.N., Tourova, T.P., Kuznetsov, B.B., Kostrikina, N.A,

433 

Bonch-Osmolovskaya, E.A. 2000, Thermanaerovibriovelox sp. nov., a new

434 

anaerobic, thermophilic, organotrophic bacterium that reduces elemental sulfur,

435 

and emended description of the genus Thermanaerovibrio. Int. J. Syst. Evol.

436 

Mocrobiol. 50, 1287–1295.

437 

35. Zhou, X., Liu, L.H., Chen, C., Ren, N.Q., Wang, A.J., Lee, D.J. 2011. Reduction

438 

of produced elemental sulfur in denitrifying sulfide removal process. Appl.

439 

Microbiol. Biotechnol. 90, 1129–1136.

440  441 

18   

Table 1. Kinetic and stoichiometric parameters of the model Parameter Definition Kinetic parameters µ SRB   Maximum specific growth rate of SRB  SRB K SO2−   Sulfate affinity constant for SRB  4

Unit

0.061

h-1 

0.02

kg m-3 

k dSRB   µ SOB  

Endogenous decay rate of SRB 

0.035

h-1 

Maximum specific growth rate of SOB 

0.028

h-1 

K SSOB 2−  

Sulfide affinity constant for SOB

0.011

kg m-3 

KOSOB   2

Oxygen affinity constant for SOB 

0.2

kg m-3 

µeSRE  

Maximum specific growth rate of sulfur reduction bacteria 

0.035

h-1 

K SeSRE   0

Elemental sulfur affinity constant for sulfur-reduction 

0.024

kg m-3 

kdeSRE   K O2  

Endogenous decay rate of sulfur-reduction bacteria

5.75 × 10-6

h-1 

0.0016

kg m-3 

0.584

g VSS g-1 SO42- 

Oxygen inhibiting coefficient constant for sulfur-reduction  Stoichiometric parameters YSRB Yield coefficient for SRB 

YSOB

Yield coefficient for SOB 

0.090

g VSS g-1 S2- 

YeSRE  

Yield coefficient for sulfur bioreduction bacteria

0.712

g VSS g-1 S0

19   

Values

Table 2. Initial conditions of the eight experiments for model evaluations Condition I II III VI V Sulfate concentration 600 600 600 600 600 (mg L-1) Oxygen injected 0 5.75 11.5 23.0 34.5 volume (ml) ROS 0 0.25 0.5 1.0 1.5

20   

VI

VII

600

600

46.0

57.5

2.0

2.5

Figure Captions

FIGURE 1. Model fitting results of the sulfate reduction equations to the substrate utilization data at all ROS and sulfide production data at ROS=0. The model (solid line) was fitted to data at ROS=1.0, which resulted in the parameter values. Model curves obtained with the same parameter values were shown for other six data sets for comparison. FIGURE 2. Modeling fitting results of sulfide oxidation, elemental sulfur bioreduction and oxygen consumption equations to the experimental data at different ROS (except ROS=0). The model (solid line) was fitted to data at ROS=1.0, which resulted in the parameter values. Model curves obtained with the same parameter values were shown for other six data sets for comparison. FIGURE 3. Comparison between the model simulations and the experimental data from Zhou et al. (2011) (A) and Chen et al. (2010) (B~C) for the verification of the approach. (A) Different initial S0 concentrations; (B) 740 mg L-1 initial S2concentration with S/N=5:6; (C) 540mg L-1 initial S2- concentration with S/N=5:6. FIGURE 4. Surface plots of the objective function used for elemental sulfur bioreduction parameter estimation (SSWE) as a function of different parameter combinations: kd vs Ks; µm vs kd; µm vs Ks; Y vs kd; Y vs Ks; Y vs µm. The plots were drawn using the optimal parameters (Table 1) as midpoint of intervals with 1 order of magnitude change (except Y, which was always lower than 1) on both sides of intervals. The detailed information could be seen in Ni et al. [27]. FIGURE 5. Output sensitivity of parameters to elemental sulfur bioreduction: (A) µeSRE ; (B) K SeSRE ; (C) YeSRE . 0

21   

0

2

4

6

8

600

600

Sulfate

450

(mg/l)

10

450

300

300

Sulfide at Ros=0 150

150

0

0

0

2

4

6

8

10

Time (h)

FIGURE 1. Model fitting results of the sulfate reduction equations to the substrate utilization data at all ROS and sulfide production data at ROS=0. The model (solid line) was fitted to data at ROS=1.0, which resulted in the parameter values. Model curves obtained with the same parameter values were shown for other six data sets for comparison.

22   

0

10

20

30

40

50 400

400

300

0

10

20

30

40

50 400

Ros=2.0

Ros=2.5 250 300

O2

300

2-

O2

0

300

S

0

S

200

2-

S

S

200

200

100

100

150

200

100 100

50

0

0

0

0

10

250

20

0

10

30 20

40 30

50 40

50 400

Ros=1.5

2-

0

0

10

20

30

40

50

0

10

20

30

40

50 400

Ros=1.0

200

S

200

O2

2-

S

300

300

150

0

(mg/l)

150

S

200

200

O2

100

100

0

S 100

50

0

0

0

0

10

10

20

30

20

30

40

40

100

50

0

0

50

0

10

20

30

40

50

50

0

10

20

30

40

50

400

Ros=0.5

200

 

400

200

S

2-

S

300

150

2-

Ros=0.25

300

150

200

100

200

100

O2

50

100

S

O2

0 10

20

30

40

S

0

0

0

0

100

50

0

50

0

0

10

20

30

40

50

Time (h)

Time (h)

FIGURE 2.Modeling fitting results of sulfide oxidation and elemental sulfur bioreduction equations to the experimental data at different ROS (except ROS=0). The method for estimating parameter values was the same as described in Figure 1.

23   

  160

0

10

20

30

40

50 160

750

0

10

20

30

40

50

60

40

600 120

Concentration(mg/L)

Elemental Sulfur (mg/l)

0 -1 S/N=5/2.5, S =145 mg L 0 -1 S/N=5:5, S =100 mg L 0 -1 S/N=5:7, S =61.6 mg L 0 -1 S/N=5:8, S = 6.2 mg L

80

80

40

0 10

20

30

40

2S O 2

450

600

400

150

200

0

0

50

800

300

0

0

0

80 1000

(B)

(A) 120

70

10

20

30

40

50

60

70

80

Time(h)

Time (h)

600

0

10

20

30

40

50 1000

(C) Concentration(mg/L)

500

2S

800

O 2

400

600

300 400

200 200

100

0

0

0

10

20

30

40

50

Time(h)

 

FIGURE 3. Comparison between the model simulations and the experimental data from Zhou et al. (2011) (A) and Chen et al. (2010) (B~C) for the verification of the approach. (A) Different initial S0 concentrations; (B) 740 mg L-1 initial S2- concentration with S/N=5:6; (C) 540mg L-1 initial S2- concentration with S/N=5:6..

24   

x 10

4

12 10 8 6 4 2 0 250 200

6 5

150

4

100

3 2

50

-5

0

Ks

x 10

x 10

1 0

kd

5

2.5

2

1.5

1

0.5

0 6 5 4 3 x 10

-5

2 1 0

0.05

0

kd

x 10

0.15

0.1

0.2

0.25

0.35

0.3

u

5

2.5

2

1.5

1

0.5

0 250 200 150 100

0.15 50 0

0.05

0

Ks

u

25   

0.1

0.2

0.25

0.3

0.35

x 10

5

3.5 3 2.5 2 1.5 1 0.5 0 6 5 4 3 x 10

-5

2 1 0

0.1

0

0.2

0.3

kd

x 10

0.4

0.5

0.6

0.7

0.8

0.9

1

Y

5

4 3.5 3 2.5 2 1.5 1 0.5 0 250 200 150 100 50 0

0.1

0

0.2

0.3

Ks

x 10

0.4

0.5

0.6

0.7

0.8

0.9

1

Y

5

5

4

3

2

1

0 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0

0.1

0

u

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Y

FIGURE 4. Surface plots of the objective function used for elemental sulfur bioreduction parameter estimation (SSWE) as a function of different parameter combinations: kd vs Ks; µm vs kd; µm vs Ks; Y vs kd; Y vs Ks; Y vs µm. The plots were drawn using the optimal parameters (Table 1) as midpoint of intervals with one order of magnitude change (except Y, which was always lower than 1) on both sides of intervals. The detailed information could be seen in Ni et al. [27]. 26   

200

180

Elemental Sulfur (mg/l)

160

u

140

0.025

120

100

80

60

40

0.075 20

0

0

5

10

15

Time(h)

200

180

Elemental Sulfur (mg/l)

160

Ks

140

120

60.22 100

80

60

40

0.22 20

0

0

1

2

3

4

5

6

7

8

9

10

6

7

8

9

10

Time (h)

200

180

Elemental Sulfur (mg/l)

160

Y

140

120

0.911

100

80

60

40

0.411

20

0

0

1

2

3

4

5

Time (h)

Figure 5. Output sensitivity of parameters to elemental sulfur bioreduction: (A) µeSRE ; eSRE

(B) K S 0

; (C) YeSRE .

27   

>The sulfate-reducing (SR) and sulfide-oxidizing (SO) process was modeled. >The Monod-type model was used to get best-fit kinetic parameters. >The molar ratio of oxygen to sulfide significantly affects the SR+SO process. >Overlooking S0 bioreduction step would overestimate the yield of S0.  

28