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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
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332
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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
S0960-8524(13)01181-4 http://dx.doi.org/10.1016/j.biortech.2013.07.113 BITE 12158
To appear in:
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
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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