Prediction of sludge settleability through artificial neural networks with optimized input variables
Sludge bulking is a major problem in activated sludge processes. It is of great practically useful to predict the sludge settleability through water quality (influent and effluent) and the operation patterns. In this study, the artificial neural network (ANN) to predict the variation of sludge settl...
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| Veröffentlicht in: | Water and environment journal : WEJ 2022-11, Vol.36 (4), p.694-703 |
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| description | Sludge bulking is a major problem in activated sludge processes. It is of great practically useful to predict the sludge settleability through water quality (influent and effluent) and the operation patterns. In this study, the artificial neural network (ANN) to predict the variation of sludge settleability was established with MLSS, organic loading rate and NH4+‐N loading rate as basic input variables. Additional input variables were optimized through comparing the performance of multilayer perceptron artificial neural network (MLPANN) with different combinations. The results showed that excellent nitrification will improve sludge settleability, and the famine phase would promote the growth of filamentous bacteria. Furthermore, the model performance of MLPANN and long short‐term memory networks (LSTM) were compared by using optimized input variables. The results indicated the MLPANN performed better than LSTM with optimized inputs. This study provided a reference of optimizing variables to predict the variation of sludge settleability in activated sludge process. This study will provide a reference for selecting appropriate variables to predict sludge settleability in activated sludge processes.
In this study, the artificial neural network (ANN) to predict the variation of sludge settleability was established with MLSS, organic loading rate and NH4+‐N loading rate as basic input variables. Additional input variables were optimized through comparing the performance of multilayer perceptron artificial neural network (MLPANN) with different combinations. The results showed that excellent nitrification will improve sludge settleability and the famine phase would promote the growth of filamentous bacteria. This study will provide a reference for selecting appropriate variables to predict sludge settleability in activated sludge processes. |
| doi_str_mv | 10.1111/wej.12808 |
| format | Article |
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In this study, the artificial neural network (ANN) to predict the variation of sludge settleability was established with MLSS, organic loading rate and NH4+‐N loading rate as basic input variables. Additional input variables were optimized through comparing the performance of multilayer perceptron artificial neural network (MLPANN) with different combinations. The results showed that excellent nitrification will improve sludge settleability and the famine phase would promote the growth of filamentous bacteria. This study will provide a reference for selecting appropriate variables to predict sludge settleability in activated sludge processes.</description><identifier>ISSN: 1747-6585</identifier><identifier>EISSN: 1747-6593</identifier><identifier>DOI: 10.1111/wej.12808</identifier><language>eng</language><publisher>London: Wiley Subscription Services, Inc</publisher><subject>Activated sludge ; Activated sludge process ; Artificial neural networks ; Bacteria ; Bulking sludge ; Famine ; Filamentous bacteria ; Influents ; Load distribution ; Loading rate ; LSTM ; MLPANN ; Multilayer perceptrons ; Neural networks ; Nitrification ; Organic loading ; sensitivity analysis ; Sludge ; sludge bulking ; variable optimization ; Water quality</subject><ispartof>Water and environment journal : WEJ, 2022-11, Vol.36 (4), p.694-703</ispartof><rights>2022 CIWEM</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2278-890a9533f1130484f53867fd8c4d4bd6153ddab806fbb53bb2c0e630fddd7d483</citedby><cites>FETCH-LOGICAL-c2278-890a9533f1130484f53867fd8c4d4bd6153ddab806fbb53bb2c0e630fddd7d483</cites><orcidid>0000-0002-7189-0819</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fwej.12808$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fwej.12808$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids></links><search><creatorcontrib>Zheng, Yue</creatorcontrib><creatorcontrib>Peng, Zhaoxu</creatorcontrib><creatorcontrib>Xia, Houbing</creatorcontrib><creatorcontrib>Zhang, Wangcheng</creatorcontrib><title>Prediction of sludge settleability through artificial neural networks with optimized input variables</title><title>Water and environment journal : WEJ</title><description>Sludge bulking is a major problem in activated sludge processes. It is of great practically useful to predict the sludge settleability through water quality (influent and effluent) and the operation patterns. In this study, the artificial neural network (ANN) to predict the variation of sludge settleability was established with MLSS, organic loading rate and NH4+‐N loading rate as basic input variables. Additional input variables were optimized through comparing the performance of multilayer perceptron artificial neural network (MLPANN) with different combinations. The results showed that excellent nitrification will improve sludge settleability, and the famine phase would promote the growth of filamentous bacteria. Furthermore, the model performance of MLPANN and long short‐term memory networks (LSTM) were compared by using optimized input variables. The results indicated the MLPANN performed better than LSTM with optimized inputs. This study provided a reference of optimizing variables to predict the variation of sludge settleability in activated sludge process. This study will provide a reference for selecting appropriate variables to predict sludge settleability in activated sludge processes.
In this study, the artificial neural network (ANN) to predict the variation of sludge settleability was established with MLSS, organic loading rate and NH4+‐N loading rate as basic input variables. Additional input variables were optimized through comparing the performance of multilayer perceptron artificial neural network (MLPANN) with different combinations. The results showed that excellent nitrification will improve sludge settleability and the famine phase would promote the growth of filamentous bacteria. This study will provide a reference for selecting appropriate variables to predict sludge settleability in activated sludge processes.</description><subject>Activated sludge</subject><subject>Activated sludge process</subject><subject>Artificial neural networks</subject><subject>Bacteria</subject><subject>Bulking sludge</subject><subject>Famine</subject><subject>Filamentous bacteria</subject><subject>Influents</subject><subject>Load distribution</subject><subject>Loading rate</subject><subject>LSTM</subject><subject>MLPANN</subject><subject>Multilayer perceptrons</subject><subject>Neural networks</subject><subject>Nitrification</subject><subject>Organic loading</subject><subject>sensitivity analysis</subject><subject>Sludge</subject><subject>sludge bulking</subject><subject>variable optimization</subject><subject>Water quality</subject><issn>1747-6585</issn><issn>1747-6593</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp1kL1OwzAURi0EEqUw8AaWmBjS2rGTOCOqyp8qwQBitOzYbl3SONgOUXl6QoPYuMt3h_PdKx0ALjGa4WHmvd7OcMoQOwITXNAiybOSHP_tLDsFZyFsEaJFmecToJ69VraK1jXQGRjqTq01DDrGWgtpaxv3MG6869YbKHy0xlZW1LDRnT9E7J1_D7C3cQNdG-3OfmkFbdN2EX4Kb4WsdTgHJ0bUQV_85hS83i5fFvfJ6unuYXGzSqo0LVjCSiTKjBCDMUGUUZMRlhdGsYoqKlWOM6KUkAzlRsqMSJlWSOcEGaVUoSgjU3A13m29--h0iHzrOt8ML3laEIwIS1M6UNcjVXkXgteGt97uhN9zjPiPRD5I5AeJAzsf2d7Wev8_yN-Wj2PjG4sNdbA</recordid><startdate>202211</startdate><enddate>202211</enddate><creator>Zheng, Yue</creator><creator>Peng, Zhaoxu</creator><creator>Xia, Houbing</creator><creator>Zhang, Wangcheng</creator><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7ST</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H97</scope><scope>L.G</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0002-7189-0819</orcidid></search><sort><creationdate>202211</creationdate><title>Prediction of sludge settleability through artificial neural networks with optimized input variables</title><author>Zheng, Yue ; Peng, Zhaoxu ; Xia, Houbing ; Zhang, Wangcheng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2278-890a9533f1130484f53867fd8c4d4bd6153ddab806fbb53bb2c0e630fddd7d483</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Activated sludge</topic><topic>Activated sludge process</topic><topic>Artificial neural networks</topic><topic>Bacteria</topic><topic>Bulking sludge</topic><topic>Famine</topic><topic>Filamentous bacteria</topic><topic>Influents</topic><topic>Load distribution</topic><topic>Loading rate</topic><topic>LSTM</topic><topic>MLPANN</topic><topic>Multilayer perceptrons</topic><topic>Neural networks</topic><topic>Nitrification</topic><topic>Organic loading</topic><topic>sensitivity analysis</topic><topic>Sludge</topic><topic>sludge bulking</topic><topic>variable optimization</topic><topic>Water quality</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zheng, Yue</creatorcontrib><creatorcontrib>Peng, Zhaoxu</creatorcontrib><creatorcontrib>Xia, Houbing</creatorcontrib><creatorcontrib>Zhang, Wangcheng</creatorcontrib><collection>CrossRef</collection><collection>Aqualine</collection><collection>Environment Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 3: Aquatic Pollution & Environmental Quality</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Environment Abstracts</collection><jtitle>Water and environment journal : WEJ</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zheng, Yue</au><au>Peng, Zhaoxu</au><au>Xia, Houbing</au><au>Zhang, Wangcheng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of sludge settleability through artificial neural networks with optimized input variables</atitle><jtitle>Water and environment journal : WEJ</jtitle><date>2022-11</date><risdate>2022</risdate><volume>36</volume><issue>4</issue><spage>694</spage><epage>703</epage><pages>694-703</pages><issn>1747-6585</issn><eissn>1747-6593</eissn><abstract>Sludge bulking is a major problem in activated sludge processes. It is of great practically useful to predict the sludge settleability through water quality (influent and effluent) and the operation patterns. In this study, the artificial neural network (ANN) to predict the variation of sludge settleability was established with MLSS, organic loading rate and NH4+‐N loading rate as basic input variables. Additional input variables were optimized through comparing the performance of multilayer perceptron artificial neural network (MLPANN) with different combinations. The results showed that excellent nitrification will improve sludge settleability, and the famine phase would promote the growth of filamentous bacteria. Furthermore, the model performance of MLPANN and long short‐term memory networks (LSTM) were compared by using optimized input variables. The results indicated the MLPANN performed better than LSTM with optimized inputs. This study provided a reference of optimizing variables to predict the variation of sludge settleability in activated sludge process. This study will provide a reference for selecting appropriate variables to predict sludge settleability in activated sludge processes.
In this study, the artificial neural network (ANN) to predict the variation of sludge settleability was established with MLSS, organic loading rate and NH4+‐N loading rate as basic input variables. Additional input variables were optimized through comparing the performance of multilayer perceptron artificial neural network (MLPANN) with different combinations. The results showed that excellent nitrification will improve sludge settleability and the famine phase would promote the growth of filamentous bacteria. This study will provide a reference for selecting appropriate variables to predict sludge settleability in activated sludge processes.</abstract><cop>London</cop><pub>Wiley Subscription Services, Inc</pub><doi>10.1111/wej.12808</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-7189-0819</orcidid></addata></record> |
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| subjects | Activated sludge Activated sludge process Artificial neural networks Bacteria Bulking sludge Famine Filamentous bacteria Influents Load distribution Loading rate LSTM MLPANN Multilayer perceptrons Neural networks Nitrification Organic loading sensitivity analysis Sludge sludge bulking variable optimization Water quality |
| title | Prediction of sludge settleability through artificial neural networks with optimized input variables |
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