Dynamics and dynamic modelling of H2S production in sewer systems

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Dynamics and dynamic modelling of H2S production in sewer systems Keshab Raj Sharmaa,1, Zhiguo Yuana,, David de Haasa,2, Geoff Hamiltonb,3, Shaun Corrieb,4, Jurg Kellera,5 a

Advanced Water Management Centre, The University of Queensland, St. Lucia, 4072 Queensland, Australia Gold Coast Water, Gold Coast, Queensland, Australia

b

art i cle info

ab st rac t

Article history:

Accurate and reliable predictions of sulfide production in a sewer system greatly benefit

Received 8 November 2007

formulation of appropriate strategies for optimal sewer management. Sewer systems,

Received in revised form

rising main systems in particular, are highly dynamic in terms of both flow and wastewater

17 January 2008

composition. In order to get an insight in sulfide production as a response to the dynamic

Accepted 12 February 2008

changes in sewer conditions, several measurement campaigns were conducted in two

Available online 21 February 2008

rising mains in Gold Coast, Australia. The levels of various sulfur species and volatile fatty

Keywords: Dynamic modelling Rising main Sewage Sewer modelling Sewer management Sulfide control

acids (VFAs) were monitored through hourly sampling for periods ranging from 8 to 29 h. The results of these field studies showed large temporal as well as spatial variations in sulfide generation. A dynamic sewer model taking into account the hydraulics and the biochemical transformation processes was formulated and calibrated and validated using the data collected during the four measurement campaigns at the two sites. The model was demonstrated to reasonably well describe the temporal and spatial variations in sulfide, sulfate and VFA concentrations. Application of the model was illustrated with a case study aimed to optimize oxygen injection to one of the two mains studied, which is being used as a means to control sulfide production on this site. The model predicted that, moving the current oxygen injection point to a location close to the end of the sewer line could achieve the same degree of sulfide control with only 50% of the current oxygen use. This study highlighted that the location at which oxygen is injected plays a major role in sulfide control and a dynamic model could be used to make a proper choice of the location. & 2008 Elsevier Ltd. All rights reserved.

1.

Introduction

The production and emission of hydrogen sulfide has since long been identified as a major cause of corrosion and odor problems in a sewer system (Boon and Lister, 1975; Pomeroy

and Bowlus, 1946; Thistlethwayte, 1971; USEPA, 1974). The actual biological, physical and chemical processes responsible for H2S production are very complex and depend upon a number of factors (Joyce, 2001). Based on some extensive field studies in the USA and Australia, many factors have been

Corresponding author. Tel.: +61 7 3365 4374; fax: +61 7 3365 4726.

E-mail addresses: [email protected] (K.R. Sharma), [email protected] (Z. Yuan), [email protected] (D. de Haas), [email protected] (G. Hamilton), [email protected] (S. Corrie), [email protected] (J. Keller). 1 Tel.: 61 7 3346 7204; fax: +61 7 3365 4726. 2 Tel.: +61 7 3346 7205; fax: +61 7 3365 4726. 3 Tel.: +61 7 5582 8718; fax: +61 7 5582 8682. 4 Tel.: +61 7 5582 8159; fax: +61 7 5582 8682. 5 Tel.: +61 7 3365 4727; fax: +61 7 3365 4726. 0043-1354/$ - see front matter & 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2008.02.013

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Nomenclature e Zfe mH iSXN iSXS k1/2 kbio-oxi kchem-oxi kh kH2 S qfe qm A/V Bw CO2 FCOD H2S

efficiency constant for the biofilm biomass anaerobic hydrolysis reduction factor maximum specific growth rate for heterotrophic biomass, 1/d nitrogen content of biomass, g N/g COD sulfur content of biomass, g S/g COD half-order rate constant for oxygen consumption, g COD/(g O2)0.5 m0.5 d rate constant for biological sulfide oxidation, g S0.5 m/(g O2)0.5 d rate constant for chemical sulfide oxidation, m3/ g O2 d hydrolysis rate constant, g COD/g biomass COD d hydrogen sulfide production rate constant, g S/ m2 d maximum rate for fermentation, g COD/g biomass COD d maintenance energy requirement rate constant, 1/d ratio of biofilm area to bulk water volume, 1/m biomass in the bulk liquid carbon dioxide fermentable COD dissolved hydrogen sulfide

identified to influence sulfide production in sewers (Melbourne and Metropolitan Board of Works, 1989). These include (1) velocity of wastewater flow; (2) concentration of soluble electron donor (organic matters) in wastewater; (3) sulfate concentration in wastewater; (4) temperature; and (5) the presence of electron acceptors such as oxygen. Higher organic as well as the sulfate contents of the sewage, higher temperature and longer sewage age are conducive to sulfide generation. In addition, the conditions favouring septicity, slime growth and solids sedimentation affect the sulfide production significantly. Reliable prediction of sulfide formation during both the design phase and operation of sewers is important for planning engineering measures to mitigate the sulfiderelated problems. Several empirical models were developed in 1970s for the evaluation of H2S generation in sewer (Boon and Lister, 1975; Pomeroy and Parkhurst, 1977; Thistlethwayte, 1971). These models lumped several factors influencing H2S production into a single rate expression, and neglected many details of in-sewer biotransformations particularly related to the carbon cycles. Research on sulfatereducing bacteria (SRB) in the last few decades has revealed that the type of carbon sources has considerable impact on the activities of SRB (Nielsen and Hvitved-Jacobsen, 1988). In this regard, volatile fatty acids (VFAs) have been widely shown to be a preferred carbon source for sulfate reduction. To more properly account for the impact of carbon source on sulfate reduction, Hvitved-Jacobsen and co-workers developed the Wastewater Aerobic/Anaerobic Transformations in Sewers (WATS) model (Hvitved-Jacobsen, 2002), which describes both

Kfe KO2 KSf KSO4 KSW KX NH3 O2 SBCOD SO4 Sx VFA XBf XBw Xz YHf YHw

saturation constant for fermentation, mg COD/L saturation constant for dissolved oxygen, mg O2/L saturation constant for substrate for biofilm, mg COD/L saturation constant for sulfate, mg S/L saturation constant for substrate for bulk liquid, mg COD/L saturation constant for hydrolysis, mg COD/L ammonia dissolved oxygen slowly biodegradable COD sulfate concentration of soluble component x, mg/L or mol/L volatile fatty acids concentration of active heterotrophic biomass in biofilm, g COD/m2 concentration of active heterotrophic biomass in the water phase, g COD/m3 concentration of particulate component z, mg COD/L yield constant for heterotrophic biomass in biofilm (g biomass COD/g COD) yield constant for suspended heterotrophic biomass (g biomass COD/g COD)

the anaerobic and aerobic processes involving multiple carbon and sulfur species. This model is a major step forward, compared with earlier models based on empirical expressions (Boon and Lister, 1975; Pomeroy, 1959; Pomeroy and Parkhurst, 1977; Thistlethwayte, 1971) as more biological, chemical and physical processes have been included (Nielsen et al., 2005a, b; Yongsiri et al., 2004). Despite all these developments and efforts being made to investigate the dynamic changes occurring in sewer system (Gudjonsson et al., 2002), application of sewer models including the WATS model has generally been limited to sewer systems under steady-state conditions (Nielsen et al., 2005b; Mourato et al., 2003). The H2S concentration is predicted as a function of location with temporal variations completely ignored (Hvitved-Jacobsen et al., 1998; Tanaka and Hvitved-Jacobsen, 2001). In contrast, sewer systems are truly dynamic systems. Wastewater flows through sewer networks are known to vary greatly over time (during a day and between days), resulting in largely varying hydraulic retention time (HRT) of wastewater in sewers. In rising mains, the pumps are regularly turned on and off causing intermittent flow and also varying mixing conditions of wastewater, further increasing sewer dynamics (Thistlethwayte, 1971; Melbourne and Metropolitan Board of Works, 1989). These dynamic factors are expected to lead to large temporal variations in sulfide production, which cannot be predicted using the steady-state approach. Unfortunately, there is at present a lack of published data to support the calibration and validation of sewer models for the dynamic prediction of sulfide production in sewers. It could well be because of this,

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the wastewater industry is still accepting that steady-state models are adequate for predicting sulfide generation in sewer systems (Melbourne and Metropolitan Board of Works, 1989). The effects of the short-term H2S peaks on corrosion and odor problems are still a matter of speculation. Currently, several strategies are being employed by the wastewater industry to control sulfide production in sewer systems. These include, among others, injection of chemicals such as oxygen, nitrate or metal ions to either prevent sulfide formation or to remove sulfide from wastewater once formed (de Lomas et al., 2006; Hobson and Yang, 2000). The chemical dosing strategy is generally based on the steady-state predictions, which is not an ideal approach from both the cost and effectiveness point of view. A better control strategy could be formulated if exact variation in H2S production could be known. One of the options for this would be long-term monitoring of sewer system to have enough data to represent the dynamics. Collection of wastewater samples from an underground rising main has a lot of challenges as the site is not easily accessible, and tools to sample, analyze, monitor and evaluate H2S production are not well established. Tremendous efforts and investments would be needed to conduct such field studies, and consequently it is almost impossible to perform such studies on all sewer systems of concern. Modelling is clearly a much more economical means for obtaining information of the dynamic change in sulfide production. A dynamic sewer model able to predict the impact of temporal changes in hydraulic conditions and wastewater composition would serve as a valuable tool for optimal sewer management. This study aims to reveal the dynamics of sulfide production in sewer systems through comprehensive field measurements, and to investigate the feasibility of predicting both the spatial and temporal variations of sulfide in sewers using a

mathematical model. Field studies were conducted in two rising mains in Gold Coast, Australia. A dynamic rising main model, adapted from the WATS model, taking into consideration of the physical, chemical and biological changes was formulated, calibrated and validated using the field data. The calibrated model was then applied to assessing and optimizing oxygen injection to one of the two rising mains, which currently receives oxygen as a measure for controlling sulfide production.

2.

Materials and methods

2.1.

Field study/data collection

Field studies were carried out on two rising mains (UC09 and TH5) of the Gold Coast City Council, Australia. These were to reveal the spatial and temporal changes in H2S production along sewer lines and to produce data for calibration and validation of a sewer model under various sewer conditions. Major characteristics of the two test sewer systems namely UC09 and TH5 are listed in Table 1. The two mains were different in terms of both physical dimensions and hydraulic characteristics. Furthermore, no H2S control mitigation was in place for UC09, while oxygen injection was used for many years to control H2S production in TH5. Four measurement campaigns were conducted on the two systems. Each involved taking wastewater samples hourly for 8–29 h at multiple locations. Details of these campaigns can be found in Table 2. The oxygen injection in TH5 was turned off 2 months before Campaign 3 (oxygen off) was conducted to provide sufficient time for the biofilm to adapt to the changed redox conditions.

Table 1 – Major characteristics of the test sewer systems System 1 2

Rising main system

Length (m)

Pipe diameter (mm)

Daily average flow (m3/day)

H2S control strategy

UC09 TH5

1084 2030

150 525

123 2998

None Oxygen injection

Table 2 – Summary of the sampling campaigns Campaign

Rising main system

Oxygen injection

Sampling locations

Sample collection/analysis

1

UC09

No

2

TH5

Yes

3

TH5

No

4

TH5

Yes

5 (wet-well, 357, 547, 828 and 1002 m) 3 (wet-well, 751 and 1407 m) 3 (wet-well, 751 and 1407 m) 3 (wet-well, 751 and 1407 m)

Hourly samples for 29 h, sulfide, sulfate, sulfite, thiosulfate and VFA measurements Hourly samples for 8 h, sulfide, sulfate, sulfite and thiosulfate measurements Hourly samples for 18 h, sulfide, sulfate, sulfite and thiosulfate measurements Hourly samples for 18 h, sulfide, sulfate, sulfite, thiosulfate and VFA measurements

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Fig. 1 – Details of the sampling arrangement.

Fig. 2 – Wastewater slug profiles for UC09 demonstrating the determination of HRT (0 is the midnight, Ti is the time when a wastewater slug enters the pipe, T0 is the time when the same slug exits the pipe, and  shows the location and time where/ when the sludge was sampled).

2.2.

Sample collection, preservation and analysis

A special sampling arrangement consisting of a 16 mm diameter pipe connecting a sampling tap at the ground level to the tapping arrangement attached to the underground rising main was installed (Fig. 1). Samples were collected using these specifically designed tapping systems provided in each sampling location. The collected samples were analyzed for different dissolved sulfur species (sulfide, sulfate, sulfite and thiosulfate) and VFAs as listed in Table 2. The flow pattern in both rising mains was intermittent due to pump operation and varied significantly between the two pump stations.

A special sample collection and preservation protocol developed in Keller-Lehmann et al. (2006) was used to minimize oxidation and stripping of sulfide. The dissolved sulfide, sulfate, thiosulfate and sulfite concentrations were measured with ion chromatography (IC) using a Dionex ICS2000 system. The IC was equipped with an AG18 Dionex column (Dionex, Sunnyvale, USA). UV detector was used for sulfide measurement while suppressed conductivity detector was used for other parameters. The mobile phase was KOH at a flow rate of 1.0 mL/min. All the samples for IC measurements were filtered through a 0.20 mm filter (Millipore, USA) and then preserved in a sulfide anti-oxidant buffer solution (Keller-Lehmann et al., 2006). For VFA analysis, the samples

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were pre-treated with 0.1 M formic acid and then analyzed using gas chromatography. All the field samples were stored in an ice-box during transportation to the laboratory and were kept refrigerated till analysis was done.

1.19 and 1.93 h, respectively (marked as  in the graph). The HRT for the wastewater sampled at time Ts was then estimated as the difference between the time of sampling and the time of entry (Ts–Ti).

2.3.

2.4.

Pump operation data and HRT calculation

Actual pump operating times were obtained from the SCADA systems. The flow rates were calculated from the flow balance based on the pump operation data and the wet well dimensions. In order to establish a relationship between the actual dynamics of H2S generation and the factors causing it, the knowledge of the effect of pump operation on HRT of the wastewater slug being sampled is important. The HRT of a slug of wastewater sampled at a given sampling location at a given time was calculated as illustrated in Fig. 2. Each line in the figure represents the travel path of the head of each slug delivered by a pumping event. Taking the marked line as an example, the head of that slug entered the sewer pipe at time Ti and exited at time T0. This slug was sampled at locations 50, 357, 547 and 828 m at sampling times of 3.52, 2.23,

Model setup

A dynamic rising main model describing the anaerobic and aerobic carbon and sulfur transformation processes previously reported in literature for sewer systems (Freudenthal et al., 2005; Huisman, 2001; Hvitved-Jacobsen, 2002; Nielsen et al., 2005a) was formulated and implemented in MATLABs/ Simulinks. The key biological processes included and their kinetic expressions are shown in Table 3. The process kinetic expressions were mostly taken from Huisman (2001) and Hvitved-Jacobsen (2002) with exceptions for sulfide generation and sulfide oxidation. No respirometric experiment was conducted for the fractionation of COD. Instead, the COD fractions were obtained from the measured VFA, soluble, flocculated soluble and total COD concentrations using the approach proposed by Mamais et al. (1993) and Melcer (2004).

Table 3 – Summary of model processes and kinetic expressions Sl. no.

Process

Reaction

Reference component

1

Aerobic growth of biomass in bulk liquid

1 ð1  Y Hw Þ ðFCOD þ VFAÞ þ O2 YHw Y Hw ð1  Y Hw Þ 100 CO2 ! Bw þ Y Hw 32

XBw

2

Aerobic growth of biomass in biofilm

1 ð1  Y Hf Þ ðFCOD þ VFAÞ þ O2 YHf Y Hf ð1  Y Hf Þ 100 CO2 ! Bw þ Y Hf 32

XBw

3

Aerobic maintenance requirement

Bw þ O2 ! 100 32 CO2

XBw

4

Reaction rate

mH K

k1=2

A ðSFCOD þSVFA Þ Hf Þ V KSf þðSFCOD þSVFA Þ

SO

2

O2 þSO2

SBCODFCOD+iSXS  H2S+iSXN  NH3 SBCODFCOD+iSXS  H2S+iSXN  NH3

XSBCOD

6

Fermentation

FCOD-VFA

SFCOD

7

Sulfide generation using fermentable substrate

2FCOD+SO4-H2S

SH 2 S

kH2 S

8

Sulfide generation using VFA

2VFA+SO4-H2S

SH 2 S

kH2 S

9

Chemical sulfide oxidation

2O2+H2S-SO4

SSO4

10

Biological sulfide oxidation

2O2+H2S-SO4

SSO4

SBCOD

XBw

SO

SBCOD þXBw kh K XþX þX X

XSBCOD

XBw

pffiffiffiffiffiffiffiffi YHf SO2 ð1Y

qm K

Aerobic hydrolysis of slowly biodegradable substrate Anaerobic hydrolysis of slowly biodegradable substrate

5

S

O2 ðSFCOD þSVFA Þ Sw þðSFCOD þSVFA Þ KO2 þSO2

2

Bw KO2 þSO2



XBw þ XBf A V

SO 2 XSBCOD þ XBw  Zfe kh KX þ XSBCOD þ XBw KO2 þ SO2   A XBw þ XBf V K

O2 SFCOD fe þSFCOD KO2 þSO2

qfe K



XBw þ XBf A V

SSO4 ðSFCOD þ SVFA Þ KSf þ ðSFCOD þ SVFA Þ KSO4 þ SSO4 KO2 A SFCOD KO2 þ SO2 V SFCOD þ SVFA SSO4 ðSFCOD þ SVFA Þ KSf þ ðSFCOD þ SVFA Þ KSO4 þ SSO4 KO2 A SVFA KO2 þ SO2 V SFCOD þ SVFA kchem-oxi sO2 SH2 S pffiffiffiffiffiffiffiffipffiffiffiffiffiffiffiffiffiffi kbio-oxi sO2 SH2 S A V

Units: SFCOD, SVFA, SO2 , XBw and XSBCOD—mg COD/L; XBf—mg COD/m2; SCO2 —mol/L as CaCO3; SH2 S and SSO4 —mg S/L; SNH3 —mg N/L.





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Sulfide generation by the suspended biomass was ignored, while that by the biofilm was modelled using double Monod kinetics using VFA and sulfate as substrates (Freudenthal et al., 2005). The volumetric sulfide generation rate (g S/m3d) was calculated by multiplying the aerial sulfide generation rate (g S/m2d) with the biofilm area to water volume (A/V) ratio. Chemical oxidation of sulfide with oxygen was modelled using the first-order model (with respect to both oxygen and sulfide) proposed by O’Brien and Birkner (1977). A set of laboratory experiments were conducted to investigate the kinetics of sulfide oxidation and various models were tested (data not presented). It was found that the first-order kinetic model proposed by O’Brien and Birkner (1977) fitted the data best. The rate of oxidation of sulfide was determined by fitting the model predictions with the measured data. Most of the available sewer models assume sulfate as a non-limiting substrate for sulfide production. However, in sewers having long HRT, sulfate could be a limiting substrate. Sulfate limitation was therefore included in the kinetic expression for sulfide production. To highlight the diffusion limitation of substrate transfer to biofilm, a higher value of saturation constant was used. Biological sulfide oxidation was based on the model proposed by Nielsen et al. (2005a). Further, the pH change due to biological and chemical processes was modelled according to the Anaerobic Digestion Model No. 1 (Batstone et al., 2002). Also, the reduction in the

volumetric sulfide generation and VFA production rates described in Hydrogen Sulfide Control Manual (Melbourne and Metropolitan Board of Works, 1989) during non-mixing (no flow) periods was considered. The rising main was modelled as tanks-in-series to mimic plug flow in a real sewer pipe. Simulation was done with various number of tanks ranging from 25 to 100 for 1 km pipe (43–10 m length per tank), and the results did not show any remarkable differences. As a compromise between mimicking the plug flow in the pipe and the simulation speed, the length of each tank was chosen to be 25 m in all simulation studies. The total number of tanks used for UC09 model was 46, while that used for TH5 was 58.

2.5.

Model calibration and validation

Values previously established in literature (Hvitved-Jacobsen, 2002; Nielsen et al., 2005b) for most model parameters were used. However, several key model parameters were adjusted manually to produce the best fit between the model predictions and the field measurements. The Campaign 1 (UC09) and Campaign 2 (TH5) data sets were used for this purpose. The first data set was used to calibrate the model in general and the second set was used to calibrate particularly the aerobic components of the model as oxygen injection was employed. The calibrated model was then validated using the

Fig. 3 – (A) Variation of flow and HRT; (B) variations of sulfide and VFA concentrations in UC09, Campaign 1 (time 0 h is midnight).

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data obtained from Campaigns 3 and 4, and further employed to simulate the diurnal variation of H2S concentrations in TH5 under two differing conditions: one with and another without oxygen injection. The sewage temperature ranged from 24 to 27 1C during the study. Average temperature of 25 1C was therefore used in all the simulations.

3.

Results and discussion

3.1.

Dynamics of sulfide generation

As examples, the diurnal variation of the pump flow pattern measured during Campaign 1 and the estimated HRT at two locations of UC09 (357 and 828 m from the wet well, respectively) are presented in Fig. 3(A). The time interval between two consecutive pump runs (each run typically lasting for 2–4 min) varied from 15 min to 4 h showing a clear diurnal variation in flow pattern. Long quiescent periods were observed in early morning and in the afternoon. In addition to the variations in flow conditions, there were variations in VFA levels in the wastewater entering the rising main and SO2 4 (data not shown). The sulfide and VFA concentrations at these two locations over the 29 h sampling period are presented in Fig. 3(B) to show the large variations of these compounds with time and location. As will be presented later in the paper, large variations in these compounds as well as sulfate were

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observed at all sampling locations. A strong correlation between the HRT and the sulfide and VFA concentrations is observed in Fig. 3. A longer HRT was responsible for higher sulfide and VFA concentrations in the early morning and late afternoon hours. The sulfate concentration remained above 5 mg S/L throughout the sampling period. Also, sulfide concentrations at downstream locations were considerably higher than those at upstream ones at similar times. Similar trends were also observed for VFA. The variations of HRT and sulfide concentrations at the two sampling locations of TH5 (Campaign 3) are shown in Fig. 4. The flow rates were much higher and the duration of pumping in each pump run was longer as compared to the previous case (UC09). Pump-off periods were relatively longer during early morning hours as compared to those during the rest of the day. As a result, the HRT of the wastewater did not vary as much as that in UC09, suggesting that the hydraulic regime in TH5 was different from that in UC09. The maximum HRT in the entire sewer pipe was about 6 h (in the morning) and 2–3 h during the rest of the day. The variations in measured H2S concentrations at the two locations presented in Fig. 4(B) show much lower levels of H2S than in UC09. Since both the rising mains were receiving the domestic sewage, the difference in H2S concentration was likely the consequence of the short HRT and small biofilm surface area to volume (A/V) ratio. The A/V ratio for UC09 was 26.7 while that for TH5 was only 8.

Fig. 4 – (A) Variation of flow and HRT; (B) variations of sulfide concentrations in TH5, Campaign 3 (time 0 h is midnight).

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Considering the diurnal variations in sulfide concentration observed in this study, the current practice of flow paced chemical dosing strategy for H2S control will not be effective and economical. Since the chemical dosages are calculated based on H2S concentrations obtained using a steady-state model, the system would be substantially overdosed when the HRT is shorter, thus wasting the chemicals. The opposite would happen at longer HRT diminishing the controlling effect in this case. A good understanding of the diurnal variation of the sulfide generation in a rising main is therefore needed to design an efficient and economical sulfide control strategy.

3.2.

Dynamic modelling of sulfide generation

3.2.1.

Model calibration

Two sets of data, one collected from UC09 without oxygen injection (Campaign 1) and another from TH5 (Campaign 2)

with oxygen injection were used to determine the key model parameters to which the model was found sensitive through sensitivity analysis. These included the maximum sulfide production rate per biofilm surface area, the maximum fermentation rate and the first-order rate of chemical sulfide oxidation by oxygen. These parameters were manually adjusted to get a good fit between the simulated and the measured data. The key parameters related to sulfide production that were calibrated are listed in Table 4. Except the maximum fermentation rate, the calibrated parameters fall within the range of literature values. The fermentation rate reported in the literature was for a gravity sewer where the biofilm was exposed to low levels of oxygen due to surface aeration. It is reasonable to assume that the presence of oxygen in the bulk phase affects the growth of fermentative bacteria. However, in rising mains, the biofilm remains fully anaerobic and this causes a higher rate of fermentation. In anaerobic digestion systems, fermentation rates varying from

Table 4 – Calibrated key model parameters related to sulfide production Sl. no. 1 2 3

Parameter

Calibrated value

Literature value

Reference

Maximum sulfide production rate Maximum fermentation rate Chemical sulfide oxidation rate

10 g S/m2 d 24 g COD/g biomass COD d 0.05/(mg O2/L) h

6–26 g S/m2 d 3 g COD/g biomass COD d 0.04–0.08/(mg O2/L) h

Thistlethwayte (1971) Hvitved-Jacobsen (2002) O’Brien and Birkner (1977)

Fig. 5 – Model calibration resulting in good agreement between model predictions and measured sulfide and sulfate data at four locations along UC09, Campaign 1 (0 and 24 h are midnights). Simulate dissolved sulfide (solid line), simulate sulfate (dashed line), measured dissolved sulfide (&), measured sulfate ().

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6 g COD/(g biomass COD. d) for long-chain fatty acids to 50 g COD/(g biomass COD d) for amino acids have been reported (Batstone et al., 2002). The value of 24 g COD/(g biomass COD d) used in this model lies within the range of values used for anaerobic processes. The fit between the model-predicted and -measured sulfate and sulfide concentrations in UC09 at four locations

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(Campaign 1) resulting from model calibration is shown in Fig. 5. The model is able to reproduce the variations in sulfide and sulfate concentrations reasonably well. The first two sites (50 and 357 m from the wet well), in particular, showed excellent fit over the entire 29 h. The model predictions followed the measured trend reasonably well for the last two sections though some discrepancies were observed.

Fig. 6 – Model calibration resulting in good agreement between model predictions and measured VFA data at four locations along UC09 in Campaign (0 and 24 h are midnights). Simulate VFA (solid line), measured VFA ().

Fig. 7 – Fit between model predicted and measured (Campaign 2, oxygen on) sulfate and sulfide profiles in TH5 arising from model calibration (0 and 24 h are midnights). Simulate dissolved sulfide (thick solid line), simulate sulfate (dashed line), measured dissolved sulfide (&), measured sulfate ().

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Fig. 8 – Model validation: fit between model-predicted sulfide and sulfate concentrations with measured values at 1407 m in TH5: (A) results without oxygen injection in Campaign 3; (B) results with oxygen injection in Campaign 4 (0 and 24 h are midnights). Simulate dissolved sulfide (thick solid line), simulate sulfate (dashed line), measured dissolved sulfide (&), measured sulfate ().

The predicted sulfide concentrations were lower than the measured values in some periods, while in others the opposite was observed. The variation in the biofilm activity along the length of the sewer (Mohanakrishnan et al., 2007), which was not considered in this model, is likely the reason for the less ideal fit. There was an excellent fit between the measured and the model-predicted VFA concentrations for all four locations (Fig. 6). The fit between the predicted and measured sulfate and sulfide concentrations in TH5 with oxygen injection (Campaign 2) arising from model calibration with respect to the aerobic components of the model is shown in Fig. 7. Both sites showed excellent fit between the measured and predicted concentrations over the entire sampling period.

3.2.2.

Model validation

To confirm the validity of the calibrated model, results obtained from two monitoring events on TH5, one with oxygen injection (Campaign 3) and the other without oxygen injection (Campaign 4) were compared with the model predictions (Figs. 8(A and B), respectively). As it can be seen from the figures, the model was able to predict sulfide generation and sulfate consumption over the entire sampling period in both cases very well. These results confirmed the validity of the model. Compared to UC09, TH5 had a much larger diameter (525 mm versus 150 mm), and the average daily flow was also much higher (2998 m3/day versus 123 m3/day). A further major difference is that TH5 received long-term (years) oxygen injection prior to measurement Campaign 2. Despite these dissimilarities, the model was able to re-produce the measured data extremely well in both cases. The calibrated model is likely able to make reasonable predictions of the dynamics of H2S production in any rising mains receiving domestic wastewater, provided that the flow data and sewer dimensions are available. It would therefore be a potentially useful tool for the management of sewer system with respect to H2S control.

3.3.

Model application: assessment of oxygen injection

Different strategies are available for sulfide control in a sewer system and the use of pure oxygen is common. For example, this has been employed by the Gold Coast City Council for many years. The application of oxygen is thought to create aerobic environment in the sewer thereby preventing sulfate from being used as an electron acceptor. The presence of oxygen will also oxidize any sulfide that is produced prior to oxygen addition. A recent study performed by de Haas et al. (submitted) on a rising main network revealed that oxygen injection was the cheapest for this network among several options available for sulfide control including the addition of metal ions, nitrate and magnesium hydroxide. However, it has got some disadvantages. A major limitation of this approach is that the oxygen can only be injected when wastewater is flowing. Otherwise, only a small volume of wastewater will be aerated for an extended period resulting in wastage of oxygen. The added oxygen gets consumed quickly due to the activity of heterotrophic organisms and bacterial sludge is generated due to biomass growth. The oxygen injection has been found to be effective as long as it is dosed in such an amount that the oxygen is present in most portions of the sewer (Gutierrez et al., 2006). Since each slug of wastewater entering the system resides for a varying period depending upon the pumping pattern, the length of sewer that could be kept aerobic varies with the residence time. The effectiveness of oxygen injection therefore needs to be evaluated based on the dynamics of the rising main. One option would be the use of online measurements of liquid sulfide concentration as well as the flow rate and assess effectiveness in terms of sulfide load being discharged at that location. This approach can easily be used to determine optimal oxygen dosing, but determining the optimal injection location would be difficult as this would require extensive field trials. The dynamic model developed in this study could serve as a valuable supporting tool as this can be used for determining optimal location as well as the dosage. In

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Fig. 9 – Comparison of two oxygen injection scenarios for TH5 (0 and 24 h are midnights), Case I: oxygen injection at 0 m, oxygen supply ¼ 32 kg/day, H2S discharge ¼ 2.1 kg S/day and VFA discharge ¼ 48.8 kg COD/day; Case II: oxygen injection at 751 m, oxygen supply ¼ 16 kg/day, H2S discharge ¼ 2.5 kg S/day and VFA discharge ¼ 63.9 kg COD/day. Dissolved sulfide with O2 injection at 0 m (thick solid line) and at 751 m (thin solid line), VFA with O2 injection at 0 m (thick dashed line) and at 751 m (thin dashed line). The results presented here are at 1407 m.

addition, the model can be used to examine the effectiveness of chemical addition all along the sewer length. In order to demonstrate the application of the dynamic sewer model, two scenarios of TH5 were considered. Case I is the present oxygen injection scenario in which oxygen was injected at the beginning of the sewer pipe. The monitoring results indicate that the injection of oxygen was able to suppress the sulfide production at 751 m, but some H2S was still produced at 1407 m (Figs. 7 and 8). Case II is a hypothetical case, where the point of oxygen injection has been shifted to the middle of the sewer main (at 751 m). Since less oxygen would be required to keep the length of the sewer pipe aerobic, the amount of oxygen was cut to one-half of that in Case I for comparison. The model simulation results for these two cases are compared in Fig. 9. Since the rising main runs full all the time, only the point of concern as far as odor is concerned is the point where it discharges wastewater into a collection well. As such, the effectiveness could be evaluated based on the amount of H2S leaving the sewer system. In addition, amount of VFA consumed due to aerobic growth is another major concern due to its impact on the performance of biological nutrient removal. The results show that, similar effectiveness on H2S control can be achieved with only 50% of oxygen supply if the injection point is moved from the start of the sewer to the middle. This allows the sulfide produced in the first half of the pipe to be oxidized, and also keeps most of the remaining sewer length aerobic, thereby reducing the amount of oxygen used for aerobic growth. As a result, a significantly higher amount of VFA was available at the end of the pipe, which is expected to be beneficial to the downstream wastewater treatment plant performance. The location of oxygen injection is apparently important for achieving effective and

economical sulfide control using oxygen. The model can effectively deal with complex hydraulics prevailing in large sewer networks and could thus serve as a valuable tool in optimizing location of injection and dosage of oxygen to achieve better sulfide control.

3.4. Limitations of the model requiring further improvements Due to the progressive reduction of sulfate along the pipe, the biofilm located at a farther location is exposed to lower sulfate levels than that at a nearer location. Sulfate may run out completely in rising mains with long HRT, which is very common in Australia (Hutchinson and Hamilton, 2005). As a result, biofilms with different structures and sulfate reduction activities may develop at different locations along the mains (Mohanakrishnan et al., 2007). The current model does not take into account the spatial variation in biofilm activity and assumes uniform activity throughout the length of the pipe. This may indeed be responsible for poorer fit between the model predictions and field data at downstream locations. Further improvement of the model on this aspect is needed.

4.

Conclusions

The results of the field study showed large temporal and spatial variations in sulfide production in sewer systems. Optimal sulfide management in sewer systems requires the consideration of sewer dynamics. The dynamic sewer model attempted in this study predicted the changes in sulfide production as a response to the changes in wastewater composition and the sewer hydraulics reasonably well. This study demonstrated if it is feasible to predict the

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dynamic in-sewer biotransformation processes using mathematical models. It further showed that dynamic modelling is a highly valuable tool for sulfide management in sewers.

Acknowledgments The financial supports provided through the Australian Research Council (ARC) Linkage Project LP454182 (with Gold Coast Water and Sydney Water as industrial collaborators) and by Gold Coast City Council are gratefully acknowledged. R E F E R E N C E S

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