A combined support vector regression with a firefly algorithm for prediction of energy consumption in wastewater treatment plants
Wastewater treatment plants (WWTPs) comprise energy-intensive processes, serving as primary contributors to overall WWTP costs. This research study proposes a novel approach that integrates support vector regression (SVR) with the firefly algorithm (FFA) for the prediction of energy consumption in a...
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| Veröffentlicht in: | Water science and technology 2024-11, Vol.90 (10), p.2747-2763 |
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| creator | Achite, Mohammed Samadianfard, Saeed Elshaboury, Nehal Toubal, Kamel Abderezak Abdelkader, Eslam Mohammed Sharafi, Milad |
| description | Wastewater treatment plants (WWTPs) comprise energy-intensive processes, serving as primary contributors to overall WWTP costs. This research study proposes a novel approach that integrates support vector regression (SVR) with the firefly algorithm (FFA) for the prediction of energy consumption in a WWTP in Chlef City, Algeria. The database comprises a comprehensive set of 1,653 samples, capturing diverse information categories. It includes chemical and physical characteristics, encompassing chemical oxygen demand, 5-day biochemical oxygen demand, potential of hydrogen, water temperature, total suspended sediment in water and basin, influent N-NH
concentration, number of aerators, and operating time. Additionally, the hydraulic and energy-related parameters are represented by the flow entered at the station and the energy consumed by aerators, respectively. Finally, meteorological data, comprising rainfall, temperature, relative humidity, and the aridity index, are part of the dataset required for analysis. In this regard, 15 different models that correspond to 15 different combinations of input parameters are assessed in this study. The results show that the SVR-FFA-15 can render an improvement in the prediction accuracy of energy consumption in WWTPs. This study provides a useful tool for managing the energy consumption of wastewater treatment and makes insightful recommendations for future energy savings. |
| doi_str_mv | 10.2166/wst.2024.375 |
| format | Article |
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concentration, number of aerators, and operating time. Additionally, the hydraulic and energy-related parameters are represented by the flow entered at the station and the energy consumed by aerators, respectively. Finally, meteorological data, comprising rainfall, temperature, relative humidity, and the aridity index, are part of the dataset required for analysis. In this regard, 15 different models that correspond to 15 different combinations of input parameters are assessed in this study. The results show that the SVR-FFA-15 can render an improvement in the prediction accuracy of energy consumption in WWTPs. This study provides a useful tool for managing the energy consumption of wastewater treatment and makes insightful recommendations for future energy savings.</description><identifier>ISSN: 0273-1223</identifier><identifier>EISSN: 1996-9732</identifier><identifier>DOI: 10.2166/wst.2024.375</identifier><identifier>PMID: 39612172</identifier><language>eng</language><publisher>England: IWA Publishing</publisher><subject>Aeration ; Aerators ; Algorithms ; Alternative energy sources ; Ammonia ; Aridity ; Biochemical oxygen demand ; Chemical oxygen demand ; Climate change ; Cost control ; Deep learning ; Effluents ; Emission standards ; Energy ; Energy conservation ; Energy consumption ; Energy costs ; Energy efficiency ; Energy resources ; Feature selection ; Heuristic methods ; Influents ; Investigations ; Mean square errors ; Meteorological data ; Neural networks ; Oxygen ; Oxygen requirement ; Parameters ; Physical characteristics ; Physical properties ; Predictions ; Quality standards ; Rainfall ; Regression analysis ; Relative humidity ; Sludge ; Support Vector Machine ; Support vector machines ; Suspended sediments ; Sustainable development ; Temperature requirements ; Total oxygen demand ; Waste Disposal, Fluid - methods ; Wastewater ; Wastewater treatment ; Wastewater treatment plants ; Water Purification - methods ; Water quality ; Water temperature ; Water treatment</subject><ispartof>Water science and technology, 2024-11, Vol.90 (10), p.2747-2763</ispartof><rights>2024 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).</rights><rights>Copyright IWA Publishing 2024</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c206t-9f35f48b86d23ff370e8f673dc66f64365ca41e7138d7a7c88a0f52cbe2d27cb3</cites><orcidid>0000-0002-7531-4173</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,860,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39612172$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Achite, Mohammed</creatorcontrib><creatorcontrib>Samadianfard, Saeed</creatorcontrib><creatorcontrib>Elshaboury, Nehal</creatorcontrib><creatorcontrib>Toubal, Kamel Abderezak</creatorcontrib><creatorcontrib>Abdelkader, Eslam Mohammed</creatorcontrib><creatorcontrib>Sharafi, Milad</creatorcontrib><title>A combined support vector regression with a firefly algorithm for prediction of energy consumption in wastewater treatment plants</title><title>Water science and technology</title><addtitle>Water Sci Technol</addtitle><description>Wastewater treatment plants (WWTPs) comprise energy-intensive processes, serving as primary contributors to overall WWTP costs. This research study proposes a novel approach that integrates support vector regression (SVR) with the firefly algorithm (FFA) for the prediction of energy consumption in a WWTP in Chlef City, Algeria. The database comprises a comprehensive set of 1,653 samples, capturing diverse information categories. It includes chemical and physical characteristics, encompassing chemical oxygen demand, 5-day biochemical oxygen demand, potential of hydrogen, water temperature, total suspended sediment in water and basin, influent N-NH
concentration, number of aerators, and operating time. Additionally, the hydraulic and energy-related parameters are represented by the flow entered at the station and the energy consumed by aerators, respectively. Finally, meteorological data, comprising rainfall, temperature, relative humidity, and the aridity index, are part of the dataset required for analysis. In this regard, 15 different models that correspond to 15 different combinations of input parameters are assessed in this study. The results show that the SVR-FFA-15 can render an improvement in the prediction accuracy of energy consumption in WWTPs. This study provides a useful tool for managing the energy consumption of wastewater treatment and makes insightful recommendations for future energy savings.</description><subject>Aeration</subject><subject>Aerators</subject><subject>Algorithms</subject><subject>Alternative energy sources</subject><subject>Ammonia</subject><subject>Aridity</subject><subject>Biochemical oxygen demand</subject><subject>Chemical oxygen demand</subject><subject>Climate change</subject><subject>Cost control</subject><subject>Deep learning</subject><subject>Effluents</subject><subject>Emission standards</subject><subject>Energy</subject><subject>Energy conservation</subject><subject>Energy consumption</subject><subject>Energy costs</subject><subject>Energy efficiency</subject><subject>Energy resources</subject><subject>Feature selection</subject><subject>Heuristic methods</subject><subject>Influents</subject><subject>Investigations</subject><subject>Mean square errors</subject><subject>Meteorological data</subject><subject>Neural networks</subject><subject>Oxygen</subject><subject>Oxygen requirement</subject><subject>Parameters</subject><subject>Physical characteristics</subject><subject>Physical properties</subject><subject>Predictions</subject><subject>Quality standards</subject><subject>Rainfall</subject><subject>Regression analysis</subject><subject>Relative humidity</subject><subject>Sludge</subject><subject>Support Vector Machine</subject><subject>Support vector machines</subject><subject>Suspended sediments</subject><subject>Sustainable development</subject><subject>Temperature requirements</subject><subject>Total oxygen demand</subject><subject>Waste Disposal, Fluid - methods</subject><subject>Wastewater</subject><subject>Wastewater treatment</subject><subject>Wastewater treatment plants</subject><subject>Water Purification - methods</subject><subject>Water quality</subject><subject>Water temperature</subject><subject>Water treatment</subject><issn>0273-1223</issn><issn>1996-9732</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpdkTtPwzAUhS0EoqWwMSNLLAyk-JHYyVghXhISC8yR41yXVEkcbIeqI_8clxYGpisdfTo6uh9C55TMGRXiZu3DnBGWzrnMDtCUFoVICsnZIZoSJnlCGeMTdOL9ihAieUqO0YQXgjIq2RR9LbC2XdX0UGM_DoN1AX-CDtZhB0sH3je2x-smvGOFTePAtBus2qV1MeqwidzgoG502HLWYOjBLTextPdjN_ykTSxQPsBaBXA4OFChgz7goVV98KfoyKjWw9n-ztDb_d3r7WPy_PLwdLt4TjQjIiSF4ZlJ8yoXNePGcEkgN0LyWgthRMpFplVKQVKe11JJneeKmIzpCljNpK74DF3tegdnP0bwoewar6GNI8COvuQ0_kYUuSQRvfyHruzo-rguUikTLKM5j9T1jtLOeh8_Uw6u6ZTblJSUWzVlVFNu1ZRRTcQv9qVj1UH9B_-64N98nIy4</recordid><startdate>202411</startdate><enddate>202411</enddate><creator>Achite, Mohammed</creator><creator>Samadianfard, Saeed</creator><creator>Elshaboury, Nehal</creator><creator>Toubal, Kamel Abderezak</creator><creator>Abdelkader, Eslam Mohammed</creator><creator>Sharafi, Milad</creator><general>IWA Publishing</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>H97</scope><scope>K9.</scope><scope>L.G</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-7531-4173</orcidid></search><sort><creationdate>202411</creationdate><title>A combined support vector regression with a firefly algorithm for prediction of energy consumption in wastewater treatment plants</title><author>Achite, Mohammed ; Samadianfard, Saeed ; Elshaboury, Nehal ; Toubal, Kamel Abderezak ; Abdelkader, Eslam Mohammed ; Sharafi, Milad</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c206t-9f35f48b86d23ff370e8f673dc66f64365ca41e7138d7a7c88a0f52cbe2d27cb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Aeration</topic><topic>Aerators</topic><topic>Algorithms</topic><topic>Alternative energy sources</topic><topic>Ammonia</topic><topic>Aridity</topic><topic>Biochemical oxygen demand</topic><topic>Chemical oxygen demand</topic><topic>Climate change</topic><topic>Cost control</topic><topic>Deep learning</topic><topic>Effluents</topic><topic>Emission standards</topic><topic>Energy</topic><topic>Energy conservation</topic><topic>Energy consumption</topic><topic>Energy costs</topic><topic>Energy efficiency</topic><topic>Energy resources</topic><topic>Feature selection</topic><topic>Heuristic methods</topic><topic>Influents</topic><topic>Investigations</topic><topic>Mean square errors</topic><topic>Meteorological data</topic><topic>Neural networks</topic><topic>Oxygen</topic><topic>Oxygen requirement</topic><topic>Parameters</topic><topic>Physical characteristics</topic><topic>Physical properties</topic><topic>Predictions</topic><topic>Quality standards</topic><topic>Rainfall</topic><topic>Regression analysis</topic><topic>Relative humidity</topic><topic>Sludge</topic><topic>Support Vector Machine</topic><topic>Support vector machines</topic><topic>Suspended sediments</topic><topic>Sustainable development</topic><topic>Temperature requirements</topic><topic>Total oxygen demand</topic><topic>Waste Disposal, Fluid - methods</topic><topic>Wastewater</topic><topic>Wastewater treatment</topic><topic>Wastewater treatment plants</topic><topic>Water Purification - methods</topic><topic>Water quality</topic><topic>Water temperature</topic><topic>Water treatment</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Achite, Mohammed</creatorcontrib><creatorcontrib>Samadianfard, Saeed</creatorcontrib><creatorcontrib>Elshaboury, Nehal</creatorcontrib><creatorcontrib>Toubal, Kamel Abderezak</creatorcontrib><creatorcontrib>Abdelkader, Eslam Mohammed</creatorcontrib><creatorcontrib>Sharafi, Milad</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aqualine</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) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 3: Aquatic Pollution & Environmental Quality</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>MEDLINE - Academic</collection><jtitle>Water science and technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Achite, Mohammed</au><au>Samadianfard, Saeed</au><au>Elshaboury, Nehal</au><au>Toubal, Kamel Abderezak</au><au>Abdelkader, Eslam Mohammed</au><au>Sharafi, Milad</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A combined support vector regression with a firefly algorithm for prediction of energy consumption in wastewater treatment plants</atitle><jtitle>Water science and technology</jtitle><addtitle>Water Sci Technol</addtitle><date>2024-11</date><risdate>2024</risdate><volume>90</volume><issue>10</issue><spage>2747</spage><epage>2763</epage><pages>2747-2763</pages><issn>0273-1223</issn><eissn>1996-9732</eissn><abstract>Wastewater treatment plants (WWTPs) comprise energy-intensive processes, serving as primary contributors to overall WWTP costs. This research study proposes a novel approach that integrates support vector regression (SVR) with the firefly algorithm (FFA) for the prediction of energy consumption in a WWTP in Chlef City, Algeria. The database comprises a comprehensive set of 1,653 samples, capturing diverse information categories. It includes chemical and physical characteristics, encompassing chemical oxygen demand, 5-day biochemical oxygen demand, potential of hydrogen, water temperature, total suspended sediment in water and basin, influent N-NH
concentration, number of aerators, and operating time. Additionally, the hydraulic and energy-related parameters are represented by the flow entered at the station and the energy consumed by aerators, respectively. Finally, meteorological data, comprising rainfall, temperature, relative humidity, and the aridity index, are part of the dataset required for analysis. In this regard, 15 different models that correspond to 15 different combinations of input parameters are assessed in this study. The results show that the SVR-FFA-15 can render an improvement in the prediction accuracy of energy consumption in WWTPs. This study provides a useful tool for managing the energy consumption of wastewater treatment and makes insightful recommendations for future energy savings.</abstract><cop>England</cop><pub>IWA Publishing</pub><pmid>39612172</pmid><doi>10.2166/wst.2024.375</doi><tpages>17</tpages><orcidid>https://orcid.org/0000-0002-7531-4173</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0273-1223 |
| ispartof | Water science and technology, 2024-11, Vol.90 (10), p.2747-2763 |
| issn | 0273-1223 1996-9732 |
| language | eng |
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| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Aeration Aerators Algorithms Alternative energy sources Ammonia Aridity Biochemical oxygen demand Chemical oxygen demand Climate change Cost control Deep learning Effluents Emission standards Energy Energy conservation Energy consumption Energy costs Energy efficiency Energy resources Feature selection Heuristic methods Influents Investigations Mean square errors Meteorological data Neural networks Oxygen Oxygen requirement Parameters Physical characteristics Physical properties Predictions Quality standards Rainfall Regression analysis Relative humidity Sludge Support Vector Machine Support vector machines Suspended sediments Sustainable development Temperature requirements Total oxygen demand Waste Disposal, Fluid - methods Wastewater Wastewater treatment Wastewater treatment plants Water Purification - methods Water quality Water temperature Water treatment |
| title | A combined support vector regression with a firefly algorithm for prediction of energy consumption in wastewater treatment plants |
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