RESEARCH PROGRESS ON INFLUENCING FACTORS AND THEIR PREDICTION MODELS OF HYDROGEN SULFIDE GENERATION IN MUNICIPAL SEWAGE PIPELINES

YUAN Ye; GAO Jun; ZHANG Lulu; CHEN Tianming; DING Cheng

doi:10.13205/j.hjgc.202311013

Volume 41 Issue 11

Nov. 2023

Turn off MathJax

Article Contents

Article Navigation > ENVIRONMENTAL ENGINEERING > 2023 > 41(11): 69-77

YUAN Ye, GAO Jun, ZHANG Lulu, CHEN Tianming, DING Cheng. RESEARCH PROGRESS ON INFLUENCING FACTORS AND THEIR PREDICTION MODELS OF HYDROGEN SULFIDE GENERATION IN MUNICIPAL SEWAGE PIPELINES[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(11): 69-77. doi: 10.13205/j.hjgc.202311013

Citation:

YUAN Ye, GAO Jun, ZHANG Lulu, CHEN Tianming, DING Cheng. RESEARCH PROGRESS ON INFLUENCING FACTORS AND THEIR PREDICTION MODELS OF HYDROGEN SULFIDE GENERATION IN MUNICIPAL SEWAGE PIPELINES[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(11): 69-77. doi: 10.13205/j.hjgc.202311013

Citation:

YUAN Ye, GAO Jun, ZHANG Lulu, CHEN Tianming, DING Cheng. RESEARCH PROGRESS ON INFLUENCING FACTORS AND THEIR PREDICTION MODELS OF HYDROGEN SULFIDE GENERATION IN MUNICIPAL SEWAGE PIPELINES[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(11): 69-77. doi: 10.13205/j.hjgc.202311013

PDF( 2533 KB)

RESEARCH PROGRESS ON INFLUENCING FACTORS AND THEIR PREDICTION MODELS OF HYDROGEN SULFIDE GENERATION IN MUNICIPAL SEWAGE PIPELINES

doi: 10.13205/j.hjgc.202311013

YUAN Ye^1,2,
GAO Jun¹,
ZHANG Lulu³,
CHEN Tianming^1,2,
DING Cheng^{1,2
,
,}

1. School of Environmental Science and Engineering, Yancheng Institute of Technology, Yancheng 224051, China;
2. Jiangsu Environmental Protection Equipment Intelligent Engineering Research Center, Yancheng 224051, China;
3. Science and Technology Department of Yancheng Institute of Technology, Yancheng 224051, China

Received Date: 2023-08-16
Available Online: 2023-12-25

Abstract

Abstract

When sewage is transported in municipal sewer pipes, a large amount of hydrogen sulfide (H₂S) will be released. This toxic and harmful gas is easy to cause odor, poisoning, and pipeline corrosion. Using a reasonable prediction model to predict the generation of H₂S in the pipeline can provide a basis for the subsequent adoption of relevant H₂S control measures, and has important practical significance for the planning of the sewage pipeline network. In this paper, the main factors affecting the generation of H₂S in the sewage pipeline are analyzed; H₂S generation prediction models are classified into two types of traditional statistics and machine learning, and their research progress is summarized; the potential research hotspots and difficulties of H₂S prediction model are explored to provide a reference for establishment of H₂S prediction model of municipal sewage pipeline.
- municipal sewage pipeline,
- H₂S,
- influencing factors,
- machine learning,
- prediction models

FullText(HTML)

References(61)

References

[1]	YONGSIRI C, VOLLERTSEN J, HVITVED-JACOBSEN T.Influence of wastewater constituents on hydrogen sulfide emission in sewer networks[J].Journal of environmental engineering, 2005, 131(12):1676-1683.
[2]	徐海明.间歇通气控制污水管道危害性气体应用探索研究[D].西安:西安建筑科技大学, 2018.
[3]	SALEHI R, CHAIPRAPAT S.Single-/triple-stage biotrickling filter treating a H₂S-rich biogas stream:statistical analysis of the effect of empty bed retention time and liquid recirculation velocity[J].Journal of the Air & Waste Management Association, 2019, 69(12):1429-1437.
[4]	WU L, HU C, LIU W V.The sustainability of concrete in sewer tunnel:a narrative review of acid corrosion in the city of Edmonton, Canada[J].Sustainability, 2018, 10(2):517.
[5]	FYTIANOS G, BALTIKAS V, LOUKOVITIS D, et al.Biocorrosion of concrete sewers in Greece:current practices and challenges[J].Sustainability, 2020, 12(7):2638.
[6]	GARCÍA J T, GARCÍA-GUERRERO J M, CARRILLO J M, et al.Sanitation network sulfide modeling as a tool for asset management.The case of the city of Murcia (Spain)[J].Sustainability, 2020, 12(18):7643.
[7]	KAEMPFER W, BERNDT M.Polymer modified mortar with high resistance to acid to corrosion by biogenic sulfuric acid[C].Proceedings of the IXth Icpic Congress, Bologna, Italy, 14th, 1998.
[8]	EL BRAHMI A, ABDERAFI S.Hydrogen sulfide production assessment based on sewage physicochemical properties using artificial neural network[J].Mater Today Proc, 2020.
[9]	邓丰, 王镇鑫, 许伟聪, 等.城市生活污水排水管道内硫化氢和甲烷产生机制综述[J].广东化工, 2012, 39:104-105.
[10]	ABBA S, HADI S J, SAMMEN S S, et al.Evolutionary computational intelligence algorithm coupled with self-tuning predictive model for water quality index determination[J].Journal of Hydrology, 2020, 587:124974.
[11]	ZUO Z, CHANG J, LU Z, et al.Hydrogen sulfide generation and emission in urban sanitary sewer in China:what factor plays the critical role?[J].Environmental Science:Water Research & Technology, 2019, 5(5):839-848.
[12]	NIELSEN A.Oxidation and precipitation of sulfide in sewer networks;Section of Environmental Engineering, Aalborg University[D].UNIPRINT, Aalborg University, 2005.
[13]	CARRERA L, SPRINGER F, LIPEME-KOUYI G, et al.A review of sulfide emissions in sewer networks:overall approach and systemic modelling[J].Water Science and Technology, 2016, 73(6):1231-1242.
[14]	任南琪, 王爱杰, 赵阳国.废水厌氧生物处理中硫酸盐还原菌的生态学研究[M].北京:科学出版社, 2009.
[15]	席劲瑛, 胡洪营, 罗彬, 等.城市污水处理厂主要恶臭源的排放规律研究[J].中国给水排水, 2006:99-103.
[16]	陈卫, 宋佩娣, 郑兴灿, 等.污水系统中导致硫化氢中毒的影响因素与控制措施[J].给水排水, 2006:15-19.
[17]	SENGUPTA A.Preliminary Hydrogen Sulfide Emission Factors and Emission Models for Wastewater Treatment Plant Headworks[D].University of New Orleans, 2014.
[18]	YONGSIRI C, VOLLERTSEN J, HVITVED-JACOBSEN T.Effect of temperature on air-water transfer of hydrogen sulfide[J].Journal of Environmental Engineering, 2004, 130(1):104-109.
[19]	GERAGHTY P.Ireland's environmental protection agency act 1992:an overview[J].European Environment, 1993, 3(4):10-13.
[20]	SHERIEF M, ALY HASSAN A.The Impact of Wastewater Quality and Flow Characteristics on H₂S Emissions Generation:Statistical Correlations and an Artificial Neural Network Model[J].Water, 2022, 14(5):791.
[21]	NIELSEN A H, VOLLERTSEN J, JENSEN H S, et al.Aerobic and anaerobic transformations of sulfide in a sewer system-field study and model simulations[J].Water Environment Research, 2008, 80(1):16-25.
[22]	王智超.城市污水处理厂硫化氢排放特征及释放模型研究[D].北京:清华大学, 2013.
[23]	JIANG G, SUN X, KELLER J, et al.Identification of controlling factors for the initiation of corrosion of fresh concrete sewers[J].Water research, 2015, 80:30-40.
[24]	GUISASOLA A, SHARMA K R, KELLER J, et al.Development of a model for assessing methane formation in rising main sewers[J].Water Research, 2009, 43(11):2874-2884.
[25]	GUTIERREZ O, PARK D, SHARMA K R, et al.Effects of long-term pH elevation on the sulfate-reducing and methanogenic activities of anaerobic sewer biofilms[J].Water research, 2009, 43(9):2549-2557.
[26]	HVITVED-JACOBSEN T.Sewer processes:microbial and chemical process engineering of sewer networks[M].CRC press, 2001.
[27]	SHARMA K, DE HAAS D W, CORRIE S, et al.Predicting hydrogen sulfide formation in sewers:a new model[J].Water, 2008, 35(2):132-137.
[28]	BORIES A, GUILLOT J-M, SIRE Y, et al.Prevention of volatile fatty acids production and limitation of odours from winery wastewaters by denitrification[J].Water research, 2007, 41(13):2987-2995.
[29]	邓绪伟, 陶敏, 张路, 等.洞庭湖水体异味物质及其与藻类和水质的关系[J].环境科学研究, 2013, 26:16-21.
[30]	DEVAI I, DELAUNE R D.Emissions of reduced gaseous sulfur compounds from wastewater sludge:redox effects[J].Environmental engineering science, 2000, 17(1):1-8.
[31]	李怀正, 张璐璇, 汤霞, 等.城市排水管道中硫化氢产气原因及影响因素分析[J].环境科学与管理, 2012, 37:95-97, 107.
[32]	BOON A G.Septicity in sewers:causes, consequences and containment[J].Water Science and Technology, 1995, 31(7):237-253.
[33]	JIANG G, KEATING A, CORRIE S, et al.Dosing free nitrous acid for sulfide control in sewers:results of field trials in Australia[J].Water research, 2013, 47(13):4331-4339.
[34]	DELGADO S, ALVAREZ M, RODRIGUEZ-GOMEZ L, et al.H₂S generation in a reclaimed urban wastewater pipe.Case study:tenerife (Spain)[J].Water Research, 1999, 33(2):539-547.
[35]	SANTRY I.Hydrogen sulfide in sewers[J].Journal (Water Pollution Control Federation), 1963:1580-1588.
[36]	GUAN S.Synergistic protection against microbiologically influenced corrosion using a 100% solids polyurethane incorporated with anti-microbial agents.(2000) Available at:http://madisonchemical.com/pdf_tech_papers[J].Synergistic_Protection_Against_MIC.pdf.(Accessed:16th April 2015).
[37]	吴迪.污水管道中硫化氢的形成实验及数学模型[D].西安:西安建筑科技大学, 2016.
[38]	FOLEY J, YUAN Z, LANT P.Dissolved methane in rising main sewer systems:field measurements and simple model development for estimating greenhouse gas emissions[J].Water Science and Technology, 2009, 60(11):2963-2971.
[39]	LAHAV O, SAGIV A, FRIEDLER E.A different approach for predicting H₂S (g) emission rates in gravity sewers[J].Water research, 2006, 40(2):259-266.
[40]	GARCÍA DE LOMAS J, CORZO A, GONZALEZ J M, et al.Nitrate promotes biological oxidation of sulfide in wastewaters:experiment at plant-scale[J].Biotechnology and bioengineering, 2006, 93(4):801-811.
[41]	SUN J, NI B-J, SHARMA K R, et al.Modelling the long-term effect of wastewater compositions on maximum sulfide and methane production rates of sewer biofilm[J].Water research, 2018, 129:58-65.
[42]	JOSEPH A P, KELLER J, BUSTAMANTE H, et al.Surface neutralization and H₂S oxidation at early stages of sewer corrosion:Influence of temperature, relative humidity and H₂S concentration[J].Water research, 2012, 46(13):4235-4245.
[43]	SHARMA K R, YUAN Z, DE HAAS D, et al.Dynamics and dynamic modelling of H₂S production in sewer systems[J].Water Research, 2008, 42(10/11):2527-2538.
[44]	KIM B, LEE J, JANG J, et al.Prediction on the seasonal behavior of hydrogen sulfide using a neural network model[J].TheScientificWorldJournal, 2011, 11:992-1004.
[45]	LI X, KHADEMI F, LIU Y, et al.Evaluation of data-driven models for predicting the service life of concrete sewer pipes subjected to corrosion[J].Journal of environmental management, 2019, 234:431-439.
[46]	JIANG G, KELLER J, BOND P L, et al.Predicting concrete corrosion of sewers using artificial neural network[J].Water research, 2016, 92:52-60.
[47]	POMEROY R D, PARKHURST J D.The forecasting of sulfide build-up rates in sewers[C].Eighth International Conference on Water Pollution Research, 1978:621-628.
[48]	LAHAV O, LU Y, SHAVIT U, et al.Modeling hydrogen sulfide emission rates in gravity sewage collection systems[J].Journal of environmental engineering, 2004, 130(11):1382-1389.
[49]	PARK K, LEE H, PHELAN S, et al.Mitigation strategies of hydrogen sulphide emission in sewer networks:a review[J].International Biodeterioration & Biodegradation, 2014, 95:251-261.
[50]	NIELSEN A H, HVITVED-JACOBSEN T, Vollertsen J.Kinetics and stoichiometry of sulfide oxidation by sewer biofilms[J].Water Research, 2005, 39(17):4119-4125.
[51]	MARLENI N, PARK K, LEE T, et al.A methodology for simulating hydrogen sulphide generation in sewer network using EPA SWMM[J].Desalination and Water Treatment, 2015, 54(4/5):1308-1317.
[52]	TIAN L, HAN C, ZHANG J, et al.Development of an H₂S emission model for wastewater treatment plants[J].Journal of the Air & Waste Management Association, 2021, 71(10):1303-1311.
[53]	SALEHI R, CHAIPRAPAT S.Predicting H₂S emission from gravity sewer using an adaptive neuro-fuzzy inference system[J].Water Quality Research Journal, 2022, 57(1):20-39.
[54]	ZHANG L, DE SCHRYVER P, De Gusseme B, et al.Chemical and biological technologies for hydrogen sulfide emission control in sewer systems:a review[J].Water research, 2008, 42(1/2):1-12.
[55]	芮栋妮, 马燕燕, 叶林.机器学习方法在污水处理系统中的应用[J].环境工程, 2022, 40(6):145-153.
[56]	YIN W, YUAN Y, CHEN F, et al.High-precision prediction of unionized hydrogen sulfide generation based on limited datasets and its impact on anaerobic digestion of sulfate-rich wastewater[J].Journal of Cleaner Production, 2022, 341:130875.
[57]	REGE M A, W.TOCK R.A simple neural network for estimating emission rates of hydrogen sulfide and ammonia from single point sources[J].Journal of the Air & Waste Management Association, 1996, 46(10):953-962.
[58]	MJALLI F S, AL-ASHEH S, ALFADALA H E.Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance[J].Journal of Environmental Management, 2007, 83(3):329-338.
[59]	NOURANI V, ELKIRAN G, ABBA S I.Wastewater treatment plant performance analysis using artificial intelligence:an ensemble approach[J].Water Science and Technology, 2018, 78(10):2064-2076.
[60]	XU R Z, CAO J S, WU Y, et al.An integrated approach based on virtual data augmentation and deep neural networks modeling for VFA production prediction in anaerobic fermentation process[J].Water Research, 2020, 184:116103.
[61]	ZAGHLOUL M S, IORHEMEN O T, HAMZA R A, et al.Development of an ensemble of machine learning algorithms to model aerobic granular sludge reactors[J].Water Research, 2021, 189:116657.