{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T10:56:15Z","timestamp":1784026575110,"version":"3.55.0"},"reference-count":28,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2021,8,25]],"date-time":"2021-08-25T00:00:00Z","timestamp":1629849600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Water Environment Research"],"published-print":{"date-parts":[[2021,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:label\/><jats:p>The objective of this study was to develop a machine learning (ML) application to determine the optimal dosage of sodium hypochlorite (NaOCl) to curtail corrosion and odor by H<jats:sub>2<\/jats:sub>S in the headworks of a water resource recovery facility (WRRF) without overly consuming volatile fatty acids (VFAs) that are essential for the enhanced biological phosphorus removal. Given the highly diverse datasets available, three subproblems were formulated, and three cascaded ML modules were developed accordingly. The final ML models, chosen based on performance, were able to predict various targeted variables. More specifically, in Module 1, a recurrent neural network (RNN) was designed to predict wastewater characteristics. In Module 2, a random forest (RF) classifier and a support vector machine (SVM) classifier were built with the information from Module 1 along with other datasets to predict the concentrations of VFAs and H<jats:sub>2<\/jats:sub>S, respectively. Finally, in Module 3, with the information obtained from Module 2, another RF classifier was developed to predict NaOCl dosage to reduce H<jats:sub>2<\/jats:sub>S but keeping VFAs within the target range. These efforts are relevant and informative for WRRFs that are considering developing Intelligent Water Systems to predict the wastewater characteristics to make operational improvements.<\/jats:p><\/jats:sec><jats:sec><jats:title>Practitioner Points<\/jats:title><jats:p>\n<jats:list list-type=\"bullet\">\n<jats:list-item><jats:p>A recurrent neural network (RNN) using long short\u2010term memory (LSTM) successfully predicted influent wastewater parameters.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>A support vector machine classifier predicted hydrogen sulfide (H<jats:sub>2<\/jats:sub>S) with 97.6% accuracy.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>The concentration of VFAs, an important parameter in EBPR, was predicted using a random forest classifier with 93.4% accuracy.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>The optimal NaOCl dosage for H<jats:sub>2<\/jats:sub>S control can be predicted with a random forest classifier using H<jats:sub>2<\/jats:sub>S, VFAs, and flow.<\/jats:p><\/jats:list-item>\n<\/jats:list><\/jats:p><\/jats:sec>","DOI":"10.1002\/wer.1618","type":"journal-article","created":{"date-parts":[[2021,7,30]],"date-time":"2021-07-30T13:16:26Z","timestamp":1627650986000},"page":"2346-2359","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Advanced machine learning application for odor and corrosion control at a water resource recovery facility"],"prefix":"10.1002","volume":"93","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3616-5865","authenticated-orcid":false,"given":"Fenghua","family":"Yang","sequence":"first","affiliation":[{"name":"Monitoring and Research Department Metropolitan Water Reclamation District of Greater Chicago  Chicago Illinois"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5851-9476","authenticated-orcid":false,"given":"Tha\u00eds Bremm","family":"Pluth","sequence":"additional","affiliation":[{"name":"Monitoring and Research Department Metropolitan Water Reclamation District of Greater Chicago  Chicago Illinois"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xing","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Information Technology Illinois State University  Normal Illinois"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyle Bradley","family":"Francq","sequence":"additional","affiliation":[{"name":"School of Information Technology Illinois State University  Normal Illinois"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew","family":"Jurjovec","sequence":"additional","affiliation":[{"name":"Maintenance and Operations Department Metropolitan Water Reclamation District of Greater Chicago  Des Plaines Illinois"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongning","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Information Technology Illinois State University  Normal Illinois"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,8,25]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_2_10_4_1","unstructured":"Ekster A. Carr J. Elliott T. Hagstrom J. Alchakov V. &Meleshin I.(2019).Model predictive control saves 47% of energy improves nutrient removal and reduces effluent TSS. WEFTEC 2019\u201092nd Annual Water Environment Federation's Technical Exhibition and Conference 954\u2013968."},{"key":"e_1_2_10_5_1","volume-title":"Hands\u2010on machine learning with Scikit\u2010learn, Keras, and TensorFlow","author":"G\u00e9ron A.","year":"2019"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.envsoft.2018.09.021"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0043-1354(00)00313-4"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.3390\/w9020105"},{"key":"e_1_2_10_9_1","volume-title":"Forecasting: Principles and practice","author":"Hyndman R. J.","year":"2018"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.watres.2014.07.026"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.3390\/atmos11080784"},{"issue":"1","key":"e_1_2_10_12_1","first-page":"67","article-title":"Robust data\u2010driven soft sensors for online monitoring of volatile fatty acids in anaerobic digestion processes","volume":"8","author":"Kazemi P.","year":"2020","journal-title":"Pro"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.48084\/etasr.2725"},{"issue":"10","key":"e_1_2_10_14_1","first-page":"1296","article-title":"Artificial neural network modelling in biological removal of organic carbon and nitrogen for the treatment of slaughterhouse wastewater in a batch reactor","volume":"35","author":"Kundu P.","year":"2014","journal-title":"Environmental Technology (United Kingdom)"},{"key":"e_1_2_10_15_1","first-page":"137","volume-title":"Machine learning for time series forecasting with Python\u00ae","author":"Lazzeri F.","year":"2021"},{"issue":"3","key":"e_1_2_10_16_1","first-page":"494","article-title":"Prediction model of ammonia nitrogen content based on BP neural network","volume":"8","author":"Liu C.","year":"2019","journal-title":"International Journal of Science, Environment and Technology"},{"key":"e_1_2_10_17_1","unstructured":"Long J.(2016). What every operator needs to know about collection system odor and corrosion control. WE&T.https:\/\/www.wef.org\/globalassets\/assets-wef\/direct-download-library\/public\/operator-essentials\/WET-Operator-Essentials-march-2016"},{"key":"e_1_2_10_18_1","unstructured":"Marks C. Newhart K. Cath T. Hering A. &Rauch\u2010Williams T.(2019).Use of Advanced Data Analytics and Model Predictive Control to Stabilize Ammonia\u2010Based Aeration Control. LIFT Intelligent Water System Challenge.https:\/\/www.waterrf.org\/sites\/default\/files\/file\/2019-10\/IWS19 \u2010 TEAM 9 \u2010 City of Boulder.pdf"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1002\/wer.1191"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.23919\/ChiCC.2018.8483168"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.envsoft.2006.07.003"},{"key":"e_1_2_10_22_1","volume-title":"Recent advances in recurrent neural networks","author":"Salehinejad H.","year":"2018"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0043-1354(00)00057-9"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2012.08.015"},{"key":"e_1_2_10_25_1","volume-title":"Minimization of odors and corrosion in collection systems (phase 1)","author":"WERF","year":"2007"},{"key":"e_1_2_10_26_1","doi-asserted-by":"publisher","DOI":"10.3390\/app9030523"},{"key":"e_1_2_10_27_1","doi-asserted-by":"publisher","DOI":"10.23919\/ChiCC.2017.8027983"},{"key":"e_1_2_10_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2019.101030"},{"key":"e_1_2_10_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apr.2019.07.013"}],"container-title":["Water Environment Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/wer.1618","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/wer.1618","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/wer.1618","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T02:52:17Z","timestamp":1693191137000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/wer.1618"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,25]]},"references-count":28,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2021,11]]}},"alternative-id":["10.1002\/wer.1618"],"URL":"https:\/\/doi.org\/10.1002\/wer.1618","archive":["Portico"],"relation":{},"ISSN":["1061-4303","1554-7531"],"issn-type":[{"value":"1061-4303","type":"print"},{"value":"1554-7531","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,25]]},"assertion":[{"value":"2021-01-27","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-07-22","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-08-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}