Application of artificial intelligence to wastewater treatment: A bibliometric analysis and systematic review of technology, economy, management, and wastewater reuse
[Display omitted] •Bibliometric analysis and systematic review of AI applied to wastewater treatment.•Wastewater treatment technology, economy, management, and reuse were discussed.•Prediction accuracy of AI technologies on pollutant removal ranged 0.64–1.00.•Application of AI technology could reduc...
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Veröffentlicht in: | Process safety and environmental protection 2020-01, Vol.133, p.169-182 |
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creator | Zhao, Lin Dai, Tianjiao Qiao, Zhi Sun, Peizhe Hao, Jianye Yang, Yongkui |
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•Bibliometric analysis and systematic review of AI applied to wastewater treatment.•Wastewater treatment technology, economy, management, and reuse were discussed.•Prediction accuracy of AI technologies on pollutant removal ranged 0.64–1.00.•Application of AI technology could reduce operational costs by up to 30 %.•Combined AI methods could provide higher accuracy and lower error.
Wastewater treatment is an important step for pollutant reduction and the promotion of water environment quality. The complexity of natural conditions, influent shock, and wastewater treatment technology result in uncertainty and variation in the wastewater treatment system. These uncertainties result in fluctuations in effluent water quality and operation costs, as well as the environmental risk of receiving waters. Artificial intelligence has become a powerful tool for minimizing the complexities and complications in wastewater treatment. In this study, we examine the literature from 1995 to 2019 to conduct a large-scale bibliometric analysis of trends in the application of artificial intelligence technology to wastewater treatment. Furthermore, we present a systematic review of four aspects of the application of artificial intelligence to wastewater treatment: technology, economy, management, and wastewater reuse. Finally, we provide perspectives on the potential future directions of new research frontiers in the utilization of artificial intelligence in wastewater treatment plants that simultaneously address pollutant removal, cost reduction, water reuse, and management challenges in complex practical applications. |
doi_str_mv | 10.1016/j.psep.2019.11.014 |
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•Bibliometric analysis and systematic review of AI applied to wastewater treatment.•Wastewater treatment technology, economy, management, and reuse were discussed.•Prediction accuracy of AI technologies on pollutant removal ranged 0.64–1.00.•Application of AI technology could reduce operational costs by up to 30 %.•Combined AI methods could provide higher accuracy and lower error.
Wastewater treatment is an important step for pollutant reduction and the promotion of water environment quality. The complexity of natural conditions, influent shock, and wastewater treatment technology result in uncertainty and variation in the wastewater treatment system. These uncertainties result in fluctuations in effluent water quality and operation costs, as well as the environmental risk of receiving waters. Artificial intelligence has become a powerful tool for minimizing the complexities and complications in wastewater treatment. In this study, we examine the literature from 1995 to 2019 to conduct a large-scale bibliometric analysis of trends in the application of artificial intelligence technology to wastewater treatment. Furthermore, we present a systematic review of four aspects of the application of artificial intelligence to wastewater treatment: technology, economy, management, and wastewater reuse. Finally, we provide perspectives on the potential future directions of new research frontiers in the utilization of artificial intelligence in wastewater treatment plants that simultaneously address pollutant removal, cost reduction, water reuse, and management challenges in complex practical applications.</description><identifier>ISSN: 0957-5820</identifier><identifier>EISSN: 1744-3598</identifier><identifier>DOI: 10.1016/j.psep.2019.11.014</identifier><language>eng</language><publisher>Rugby: Elsevier B.V</publisher><subject>Artificial intelligence ; Bibliometric analysis ; Bibliometrics ; Complexity ; Complications ; Cost ; Environmental risk ; Management ; Pollutant removal ; Pollutants ; Pollution control ; Receiving waters ; Systematic review ; Technology ; Uncertainty ; Waste management ; Wastewater analysis ; Wastewater management ; Wastewater pollution ; Wastewater reuse ; Wastewater treatment ; Wastewater treatment plants ; Water pollution ; Water quality ; Water reuse ; Water treatment</subject><ispartof>Process safety and environmental protection, 2020-01, Vol.133, p.169-182</ispartof><rights>2019 Institution of Chemical Engineers</rights><rights>Copyright Elsevier Science Ltd. Jan 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c328t-58e91a72ea1b4888dcd4aa9fde006e6a8330f3380eef546c29ee844e45a3360d3</citedby><cites>FETCH-LOGICAL-c328t-58e91a72ea1b4888dcd4aa9fde006e6a8330f3380eef546c29ee844e45a3360d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.psep.2019.11.014$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,777,781,3537,27905,27906,45976</link.rule.ids></links><search><creatorcontrib>Zhao, Lin</creatorcontrib><creatorcontrib>Dai, Tianjiao</creatorcontrib><creatorcontrib>Qiao, Zhi</creatorcontrib><creatorcontrib>Sun, Peizhe</creatorcontrib><creatorcontrib>Hao, Jianye</creatorcontrib><creatorcontrib>Yang, Yongkui</creatorcontrib><title>Application of artificial intelligence to wastewater treatment: A bibliometric analysis and systematic review of technology, economy, management, and wastewater reuse</title><title>Process safety and environmental protection</title><description>[Display omitted]
•Bibliometric analysis and systematic review of AI applied to wastewater treatment.•Wastewater treatment technology, economy, management, and reuse were discussed.•Prediction accuracy of AI technologies on pollutant removal ranged 0.64–1.00.•Application of AI technology could reduce operational costs by up to 30 %.•Combined AI methods could provide higher accuracy and lower error.
Wastewater treatment is an important step for pollutant reduction and the promotion of water environment quality. The complexity of natural conditions, influent shock, and wastewater treatment technology result in uncertainty and variation in the wastewater treatment system. These uncertainties result in fluctuations in effluent water quality and operation costs, as well as the environmental risk of receiving waters. Artificial intelligence has become a powerful tool for minimizing the complexities and complications in wastewater treatment. In this study, we examine the literature from 1995 to 2019 to conduct a large-scale bibliometric analysis of trends in the application of artificial intelligence technology to wastewater treatment. Furthermore, we present a systematic review of four aspects of the application of artificial intelligence to wastewater treatment: technology, economy, management, and wastewater reuse. Finally, we provide perspectives on the potential future directions of new research frontiers in the utilization of artificial intelligence in wastewater treatment plants that simultaneously address pollutant removal, cost reduction, water reuse, and management challenges in complex practical applications.</description><subject>Artificial intelligence</subject><subject>Bibliometric analysis</subject><subject>Bibliometrics</subject><subject>Complexity</subject><subject>Complications</subject><subject>Cost</subject><subject>Environmental risk</subject><subject>Management</subject><subject>Pollutant removal</subject><subject>Pollutants</subject><subject>Pollution control</subject><subject>Receiving waters</subject><subject>Systematic review</subject><subject>Technology</subject><subject>Uncertainty</subject><subject>Waste management</subject><subject>Wastewater analysis</subject><subject>Wastewater management</subject><subject>Wastewater pollution</subject><subject>Wastewater reuse</subject><subject>Wastewater treatment</subject><subject>Wastewater treatment plants</subject><subject>Water pollution</subject><subject>Water quality</subject><subject>Water reuse</subject><subject>Water treatment</subject><issn>0957-5820</issn><issn>1744-3598</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kctOwzAQRS0EEuXxA6wssaXBEzupg9hUiJdUiQ2sLdeZFFdJHGyXqj_Ed-JQFqxYzSzm3Jk7l5ALYBkwKK_X2RBwyHIGVQaQMRAHZAIzIaa8qOQhmbCqmE0LmbNjchLCmjEG-Qwm5Gs-DK01OlrXU9dQ7aNtrLG6pbaP2LZ2hb1BGh3d6hBxqyN6Gj3q2GEfb-icLu2yta7D6K2hutftLtiQmpqGXSK6pG2ox0-L23FDRPPeu9atdlcUjetdl5oucSscFa9-yD-7PG4CnpGjRrcBz3_rKXl7uH-9e5ouXh6f7-aLqeG5jMkgVqBnOWpYCillbWqhddXUyFiJpZacs4ZzyRCbQpQmrxClECgKzXnJan5KLve6g3cfGwxRrd3GJ09B5bwQEpgUkKby_ZTxLgSPjRq87bTfKWBqzEOt1ZiHGvNQACrlkaDbPYTp_vQMr4Kx429r69FEVTv7H_4NCHeYtw</recordid><startdate>202001</startdate><enddate>202001</enddate><creator>Zhao, Lin</creator><creator>Dai, Tianjiao</creator><creator>Qiao, Zhi</creator><creator>Sun, Peizhe</creator><creator>Hao, Jianye</creator><creator>Yang, Yongkui</creator><general>Elsevier B.V</general><general>Elsevier Science Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7TB</scope><scope>7U7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>KR7</scope><scope>SOI</scope></search><sort><creationdate>202001</creationdate><title>Application of artificial intelligence to wastewater treatment: A bibliometric analysis and systematic review of technology, economy, management, and wastewater reuse</title><author>Zhao, Lin ; Dai, Tianjiao ; Qiao, Zhi ; Sun, Peizhe ; Hao, Jianye ; Yang, Yongkui</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c328t-58e91a72ea1b4888dcd4aa9fde006e6a8330f3380eef546c29ee844e45a3360d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Artificial intelligence</topic><topic>Bibliometric analysis</topic><topic>Bibliometrics</topic><topic>Complexity</topic><topic>Complications</topic><topic>Cost</topic><topic>Environmental risk</topic><topic>Management</topic><topic>Pollutant removal</topic><topic>Pollutants</topic><topic>Pollution control</topic><topic>Receiving waters</topic><topic>Systematic review</topic><topic>Technology</topic><topic>Uncertainty</topic><topic>Waste management</topic><topic>Wastewater analysis</topic><topic>Wastewater management</topic><topic>Wastewater pollution</topic><topic>Wastewater reuse</topic><topic>Wastewater treatment</topic><topic>Wastewater treatment plants</topic><topic>Water pollution</topic><topic>Water quality</topic><topic>Water reuse</topic><topic>Water treatment</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhao, Lin</creatorcontrib><creatorcontrib>Dai, Tianjiao</creatorcontrib><creatorcontrib>Qiao, Zhi</creatorcontrib><creatorcontrib>Sun, Peizhe</creatorcontrib><creatorcontrib>Hao, Jianye</creatorcontrib><creatorcontrib>Yang, Yongkui</creatorcontrib><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Environment Abstracts</collection><jtitle>Process safety and environmental protection</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhao, Lin</au><au>Dai, Tianjiao</au><au>Qiao, Zhi</au><au>Sun, Peizhe</au><au>Hao, Jianye</au><au>Yang, Yongkui</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Application of artificial intelligence to wastewater treatment: A bibliometric analysis and systematic review of technology, economy, management, and wastewater reuse</atitle><jtitle>Process safety and environmental protection</jtitle><date>2020-01</date><risdate>2020</risdate><volume>133</volume><spage>169</spage><epage>182</epage><pages>169-182</pages><issn>0957-5820</issn><eissn>1744-3598</eissn><abstract>[Display omitted]
•Bibliometric analysis and systematic review of AI applied to wastewater treatment.•Wastewater treatment technology, economy, management, and reuse were discussed.•Prediction accuracy of AI technologies on pollutant removal ranged 0.64–1.00.•Application of AI technology could reduce operational costs by up to 30 %.•Combined AI methods could provide higher accuracy and lower error.
Wastewater treatment is an important step for pollutant reduction and the promotion of water environment quality. The complexity of natural conditions, influent shock, and wastewater treatment technology result in uncertainty and variation in the wastewater treatment system. These uncertainties result in fluctuations in effluent water quality and operation costs, as well as the environmental risk of receiving waters. Artificial intelligence has become a powerful tool for minimizing the complexities and complications in wastewater treatment. In this study, we examine the literature from 1995 to 2019 to conduct a large-scale bibliometric analysis of trends in the application of artificial intelligence technology to wastewater treatment. Furthermore, we present a systematic review of four aspects of the application of artificial intelligence to wastewater treatment: technology, economy, management, and wastewater reuse. Finally, we provide perspectives on the potential future directions of new research frontiers in the utilization of artificial intelligence in wastewater treatment plants that simultaneously address pollutant removal, cost reduction, water reuse, and management challenges in complex practical applications.</abstract><cop>Rugby</cop><pub>Elsevier B.V</pub><doi>10.1016/j.psep.2019.11.014</doi><tpages>14</tpages></addata></record> |
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subjects | Artificial intelligence Bibliometric analysis Bibliometrics Complexity Complications Cost Environmental risk Management Pollutant removal Pollutants Pollution control Receiving waters Systematic review Technology Uncertainty Waste management Wastewater analysis Wastewater management Wastewater pollution Wastewater reuse Wastewater treatment Wastewater treatment plants Water pollution Water quality Water reuse Water treatment |
title | Application of artificial intelligence to wastewater treatment: A bibliometric analysis and systematic review of technology, economy, management, and wastewater reuse |
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