市政污水管道硫化氢生成影响因素及预测模型研究进展

远野; 高君; 张璐璐; 陈天明; 丁成

doi:10.13205/j.hjgc.202311013

市政污水管道硫化氢生成影响因素及预测模型研究进展

doi: 10.13205/j.hjgc.202311013

远野^1,2,
高君¹,
张璐璐³,
陈天明^1,2,
丁成^1,2, ,

1. 盐城工学院环境科学与工程学院, 江苏盐城 224051;
2. 江苏省环保装备智慧化工程研究中心, 江苏盐城 224051;
3. 盐城工学院科学技术处, 江苏盐城 224051

基金项目:

国家自然科学基金项目（52170054，51608467）

详细信息

作者简介:
远野(1984-),男,博士研究生,副教授,主要研究方向为水污染控制。yuanye_19840915@163.com

通讯作者:
丁成(1971-),男,教授,主要研究方向为污染防治技术。ycdingc@163.com

计量
- 文章访问数: 161
- HTML全文浏览量: 34
- PDF下载量: 5
- 被引次数: 0
出版历程
- 收稿日期: 2023-08-16
- 网络出版日期: 2023-12-25

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

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

摘要

摘要: 污水在市政污水管道的运输过程中，会释放大量的硫化氢（H₂S），易引发恶臭、中毒和管道腐蚀等问题。采用合理的预测模型对管道中H₂S的产生进行预测，可为后续采取相关的H₂S控制措施提供依据，对于污水管网的规划也具有重要意义。因此，首先分析了影响污水管道中H₂S生成的主要因素；其次将H₂S生成预测模型按照传统统计学和机器学习2类进行归类，并总结其研究进展；最后，探索了H₂S生成预测模型的潜在研究热点和难点，以期为市政污水管道H₂S预测模型的建立提供参考。
- 市政污水管道 /
- 硫化氢 /
- 影响因素 /
- 机器学习 /
- 预测模型
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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