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Frontiers of Environmental Science & Engineering

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Front. Environ. Sci. Eng.    2019, Vol. 13 Issue (4) : 62    https://doi.org/10.1007/s11783-019-1144-1
RESEARCH ARTICLE
A comprehensive simulation approach for pollutant bio-transformation in the gravity sewer
Nan Zhao1, Huu Hao Ngo2, Yuyou Li3, Xiaochang Wang1, Lei Yang1, Pengkang Jin1(), Guangxi Sun4
1. School of Environmental and Municipal Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
2. Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology, Sydney, NSW 2007, Australia
3. Department of Civil and Environmental Engineering, Tohoku University, Sendai, Miyagi 980-8579, Japan
4. Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
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Abstract

A comprehensive pollutant transformation model for sewer systems is established.

The model comprises fermentation, sulfate reduction and ammonification processes.

Biochemical reactions related to distinct carbon sources are depicted in the model.

Pollutant transformation is attributed to different biochemical reaction processes.

Presently, several activated sludge models (ASMs) have been developed to describe a few biochemical processes. However, the commonly used ASM neither clearly describe the migratory transformation characteristics of fermentation nor depict the relationship between the carbon source and biochemical reactions. In addition, these models also do not describe both ammonification and the integrated metabolic processes in sewage transportation. In view of these limitations, we developed a new and comprehensive model that introduces anaerobic fermentation into the ASM and simulates the process of sulfate reduction, ammonification, hydrolysis, acidogenesis and methanogenesis in a gravity sewer. The model correctly predicts the transformation of organics including proteins, lipids, polysaccharides, etc. The simulation results show that the degradation of organics easily generates acetic acid in the sewer system and the high yield of acetic acid is closely linked to methanogenic metabolism. Moreover, propionic acid is the crucial substrate for sulfate reduction and ammonification tends to be affected by the concentration of amino acids. Our model provides a promising tool for simulating and predicting outcomes in response to variations in wastewater quality in sewers.
Keywords Gravity sewer      Modeling      Pollutant transformation      Biochemical reaction process     
Corresponding Author(s): Pengkang Jin   
Issue Date: 10 July 2019
 Cite this article:   
Nan Zhao,Huu Hao Ngo,Yuyou Li, et al. A comprehensive simulation approach for pollutant bio-transformation in the gravity sewer[J]. Front. Environ. Sci. Eng., 2019, 13(4): 62.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-019-1144-1
https://academic.hep.com.cn/fese/EN/Y2019/V13/I4/62
Fig.1  Schematic structure of the SWTM: hydrolysis (blue arrows); acidogenesis (red arrows); homoacetogenesis (black arrows); methanogenesis (purple arrows); sulfate reduction (green arrows) and ammonification (orange lines).
Fig.2  Simulation flow chart.
Fig.3  Simulation results for the main bacteria species along the sewer (lines represent the simulation result and points represent the measurement data).
Fig.4  The simulation results for: (a) organic matter transformation; (b) generation of gases.
Fig.5  The validation results for: (a) ammonification process, and (b) sulfate reduction process.
Fig.6  Functional analysis of the HAc biochemical reaction process in sewer: (a) methanogenesis; (b) sulfate reduction. The measurement data are derived from Song and Zhang (2011).
Reaction times (min) 0 10 20 30 40 50 60
Acetic acid measurement (mg/L) 34.60 38.20 38.70 40.00 42.30 45.00 45.90
Acetic acid Simulation (mg/L) 34.60 37.15 39.66 42.60 45.42 47.56 48.45
Butyric acid measurement (mg/L) 11.03 9.00 7.80 7.65 7.54 6.03 8.34
Butyric acid simulation (mg/L) 11.03 9.45 8.14 7.10 6.32 5.79 5.53
Methane measurement (mg/m3) 0.34 1.20 3.41 5.12 5.74 7.03 8.61
Methane simulation (mg/m3) 0.34 1.58 2.90 4.17 5.40 6.57 7.69
Sulfate measurement (mg/L) 83.23 79.01 73.41 69.98 65.70 62.03 61.83
Sulfate simulation (mg/L) 83.23 75.88 70.41 65.86 63.21 61.31 60.20
Tab.1  Model validation with fermentation data from Guisasola et al. (2009)
Parameters Meanings Unit
af The specific biofilm area m2/m3
HH2S Henry’s constant for H2S Pa·L/mg
fsubstrate Yield of substrate on sugar. kg COD-substrate/kg COD-sugar
fproducts,substrate Yield of products on substrates kg COD-products·kg/COD-substrates
Ji The flux into the biofilm g/(m2·d)
kH2S,i Specific maximum uptake rate for carbon i in sulfate reduction kg COD-substrate/(kg COD-carbon source·d)
km,substrate Specific maximum uptake rate of substrate kg COD-substrate/(kg COD-biomass·d)
km, homo Specific maximum uptake rate in homoacetogenesis kg COD-substrate/(kg COD-biomass·d)
ksubstrate Half-saturation coefficient of substrate kmol/m3
ks,homo Half saturation coefficient in homoacetogenisis kg COD/m3
kSO4,i Half saturation coefficient of carbon i in sulfate reduction kmol/m3
Lf Biofilm thickness m
rif Concentration generation (consumption) rate for substrate mg/(L·min)
PH2S The hydrogen sulfide pressure Pa
S Concentration mg/L
SCH4,substrate Methane generated from substrate mg/m3
Ssubstrate Concentration of substrate kg COD/m3
Si Carbon content of component i kg COD/m3
Sin The influent concentration of the substrate mg/L
Sout The effluent concentration of the substrate mg/L
Xalcohol Ethanol consuming microorganism copies/mL
Xhomo Homoacetogenic bacteria copies/mL
XSRB SRB copies/mL
Ysubstrate Biomass yield on substrate kg COD-biomass/kg COD-substrate
Yhomo Biomass yield on homoacetogenesis process kg COD-biomass/kg COD-substrate
hf,i The effectiveness factor
  
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