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

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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

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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

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References(61)

References

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