{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T21:04:44Z","timestamp":1790197484006,"version":"4.1.0"},"reference-count":28,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,6,16]],"date-time":"2024-06-16T00:00:00Z","timestamp":1718496000000},"content-version":"vor","delay-in-days":46,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100017691","name":"Guangxi Key Research and Development Program","doi-asserted-by":"publisher","award":["2023B03J0007"],"award-info":[{"award-number":["2023B03J0007"]}],"id":[{"id":"10.13039\/501100017691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012245","name":"Science and Technology Planning Project of Guangdong Province","doi-asserted-by":"publisher","award":["2021A0505020010"],"award-info":[{"award-number":["2021A0505020010"]}],"id":[{"id":"10.13039\/501100012245","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52300124"],"award-info":[{"award-number":["52300124"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2023M734002"],"award-info":[{"award-number":["2023M734002"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Water Research X"],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1016\/j.wroa.2024.100230","type":"journal-article","created":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T16:50:06Z","timestamp":1718643006000},"page":"100230","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":6,"special_numbering":"C","title":["Integrating machine learning algorithm with sewer process model to realize swift prediction and real-time control of H2S pollution in sewer systems"],"prefix":"10.1016","volume":"23","author":[{"given":"Zhensheng","family":"Liang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenlang","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"11","key":"10.1016\/j.wroa.2024.100230_bib0001","doi-asserted-by":"crossref","first-page":"2391","DOI":"10.2166\/wst.2020.298","article-title":"Real-time model predictive control of a wastewater treatment plant based on machine learning","volume":"81","author":"Bernardelli","year":"2020","journal-title":"Water Sci. Technol."},{"key":"10.1016\/j.wroa.2024.100230_bib0002","article-title":"Control and soft sensing strategies for a wastewater treatment plant using a neuro-genetic approach","volume":"144","author":"Canete","year":"2021","journal-title":"Comput. Chem. Eng."},{"issue":"5","key":"10.1016\/j.wroa.2024.100230_bib0003","doi-asserted-by":"crossref","first-page":"1761","DOI":"10.1109\/TFUZZ.2014.2379252","article-title":"Implementation of an evolving fuzzy model (eFuMo) in a monitoring system for a waste-water treatment process","volume":"23","author":"Dovzan","year":"2015","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"12","key":"10.1016\/j.wroa.2024.100230_bib0004","doi-asserted-by":"crossref","first-page":"2681","DOI":"10.2166\/wst.2020.311","article-title":"Data analytics in control and operation of municipal wastewater treatment plants: qualitative analysis of needs and barriers","volume":"82","author":"Eerikinen","year":"2020","journal-title":"Water Sci. Technol."},{"issue":"14","key":"10.1016\/j.wroa.2024.100230_bib0005","doi-asserted-by":"crossref","first-page":"3139","DOI":"10.3390\/s19143139","article-title":"Machine learning weather soft-sensor for advanced control of wastewater treatment plants","volume":"19","author":"F\u00e9lix","year":"2019","journal-title":"Sensors"},{"issue":"2","key":"10.1016\/j.wroa.2024.100230_bib0006","first-page":"105","article-title":"Machine learning algorithms for the forecasting of wastewater quality indicators","volume":"9","author":"Francesco","year":"2017","journal-title":"Water (Basel)"},{"issue":"1","key":"10.1016\/j.wroa.2024.100230_bib0007","doi-asserted-by":"crossref","first-page":"15","DOI":"10.3390\/computation8010015","article-title":"Machine-learning methods for computational science and engineering","volume":"8","author":"Frank","year":"2020","journal-title":"Computation"},{"issue":"6","key":"10.1016\/j.wroa.2024.100230_bib0008","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1177\/1091581810384882","article-title":"Hydrogen sulfide: advances in understanding human toxicity","volume":"29","author":"Guidotti","year":"2010","journal-title":"Int. J. Toxicol."},{"key":"10.1016\/j.wroa.2024.100230_bib0009","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.jes.2015.01.007","article-title":"Prediction of effluent concentration in a wastewater treatment plant using machine learning models","volume":"32","author":"Guo","year":"2015","journal-title":"J. Environ. Sci."},{"issue":"3","key":"10.1016\/j.wroa.2024.100230_bib0010","doi-asserted-by":"crossref","first-page":"651","DOI":"10.2166\/wst.2010.901","article-title":"Simultaneous online measurement of sulfide and nitrate in sewers for nitrate dosage optimisation","volume":"61","author":"Gutierrez","year":"2010","journal-title":"Water Sci. Technol."},{"issue":"6","key":"10.1016\/j.wroa.2024.100230_bib0011","doi-asserted-by":"crossref","first-page":"51","DOI":"10.2166\/wst.2002.0093","article-title":"Modelling wastewater transformation in sewers based on ASM3","volume":"45","author":"Huisman","year":"2002","journal-title":"Water Sci. Technol."},{"key":"10.1016\/j.wroa.2024.100230_bib0012","series-title":"Sewer Processes: Microbial and Chemical Process Engineering of Sewer Networks","first-page":"p221","author":"Hvitved-Jacobsen","year":"2013"},{"key":"10.1016\/j.wroa.2024.100230_bib0013","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.copbio.2015.03.007","article-title":"Corrosion and odor management in sewer systems","volume":"33","author":"Jiang","year":"2015","journal-title":"Curr. Opin. Biotechnol."},{"issue":"3","key":"10.1016\/j.wroa.2024.100230_bib0014","doi-asserted-by":"crossref","first-page":"402","DOI":"10.3390\/su9030402","article-title":"Evaluation of sulfide control by air-injection in sewer force mains: field and laboratory study","volume":"9","author":"Juan","year":"2017","journal-title":"Sustainability"},{"key":"10.1016\/j.wroa.2024.100230_bib0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2021.117797","article-title":"Data-driven method based on deep learning algorithm for detecting fat, oil, and grease (FOG) of sewer networks in urban commercial areas","volume":"207","author":"Jiang","year":"2021","journal-title":"Water Res."},{"key":"10.1016\/j.wroa.2024.100230_bib0016","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1016\/j.watres.2016.05.036","article-title":"Indirect sulfur reduction via polysulfide contributes to serious odor problem in a sewer receiving nitrate dosage","volume":"100","author":"Liang","year":"2016","journal-title":"Water Res."},{"key":"10.1016\/j.wroa.2024.100230_bib0017","doi-asserted-by":"crossref","DOI":"10.1016\/j.envint.2019.105011","article-title":"Experimental and modelling evaluations of sulfide formation in a mega-sized deep tunnel sewer system and implications for sewer management","volume":"131","author":"Liang","year":"2019","journal-title":"Environ. Int."},{"key":"10.1016\/j.wroa.2024.100230_bib0018","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1016\/j.jes.2021.12.038","article-title":"Experimental and modeling investigations on the unexpected hydrogen sulfide rebound in a sewer receiving nitrate addition: mathematical and solution","volume":"125","author":"Liang","year":"2023","journal-title":"J. Environ. Sci."},{"key":"10.1016\/j.wroa.2024.100230_bib0019","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.watres.2019.01.033","article-title":"Systematic evaluation of a dynamic sewer process model for prediction of odor formation and mitigation in large-scale pressurized sewers in Hong Kong","volume":"154","author":"Liang","year":"2019","journal-title":"Water Res."},{"key":"10.1016\/j.wroa.2024.100230_bib0020","series-title":"Time Lags in Biological Models","first-page":"27","author":"MacDonald","year":"2013"},{"issue":"10","key":"10.1016\/j.wroa.2024.100230_bib0021","first-page":"193","article-title":"Advanced monitoring platform for industrial wastewater treatment: multivariable approach using the self-organizing map","volume":"48","author":"Mika","year":"2013","journal-title":"Environ. Modell. Soft."},{"key":"10.1016\/j.wroa.2024.100230_bib0022","series-title":"A Quick Review of Machine Learning algorithms. 2019 International conference On Machine learning, Big data, Cloud and Parallel Computing (COMITCon)","first-page":"35","author":"Ray","year":"2019"},{"issue":"10\u201311","key":"10.1016\/j.wroa.2024.100230_bib0023","doi-asserted-by":"crossref","first-page":"2527","DOI":"10.1016\/j.watres.2008.02.013","article-title":"Dynamics and dynamic modelling of H2S production in sewer systems","volume":"42","author":"Sharma","year":"2008","journal-title":"Water Res."},{"issue":"1","key":"10.1016\/j.wroa.2024.100230_bib0024","doi-asserted-by":"crossref","first-page":"221","DOI":"10.2166\/hydro.2017.175","article-title":"Urban flash flood forecast using support vector machine and numerical simulation","volume":"20","author":"Yan","year":"2018","journal-title":"J. Hydroinf."},{"issue":"1","key":"10.1016\/j.wroa.2024.100230_bib0025","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.watres.2007.07.013","article-title":"Chemical and biological technologies for hydrogen sulfide emission control in sewer systems: a review","volume":"42","author":"Zhang","year":"2008","journal-title":"Water Res."},{"key":"10.1016\/j.wroa.2024.100230_bib0026","doi-asserted-by":"crossref","DOI":"10.1016\/j.cej.2023.143483","article-title":"Deep learning based data-driven model for detecting time-delay water quality indicators of wastewater treatment plant influent","volume":"467","author":"Zhang","year":"2023","journal-title":"Chem. Eng. J."},{"key":"10.1016\/j.wroa.2024.100230_bib0027","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2023.120046","article-title":"Hydrogen sulfide control in sewer systems: a critical review of recent progress","volume":"240","author":"Zhang","year":"2023","journal-title":"Water Res."},{"key":"10.1016\/j.wroa.2024.100230_bib0028","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.eehl.2022.06.001","article-title":"A review of the application of machine learning in water quality evaluation","volume":"1","author":"Zhu","year":"2022","journal-title":"Eco-Environ. Health"}],"container-title":["Water Research X"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2589914724000203?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2589914724000203?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T19:40:12Z","timestamp":1722973212000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2589914724000203"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5]]},"references-count":28,"alternative-id":["S2589914724000203"],"URL":"https:\/\/doi.org\/10.1016\/j.wroa.2024.100230","relation":{},"ISSN":["2589-9147"],"issn-type":[{"value":"2589-9147","type":"print"}],"subject":[],"published":{"date-parts":[[2024,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Integrating machine learning algorithm with sewer process model to realize swift prediction and real-time control of H2S pollution in sewer systems","name":"articletitle","label":"Article Title"},{"value":"Water Research X","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.wroa.2024.100230","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"100230"}}