Minimizing pump energy in a wastewater processing plant
This paper discusses energy savings in wastewater processing plant pump operations and proposes a pump system scheduling model to generate operational schedules to reduce energy consumption. A neural network algorithm is utilized to model pump energy consumption and fluid flow rate after pumping. Th...
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| Veröffentlicht in: | Energy (Oxford) 2012-11, Vol.47 (1), p.505-514 |
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| container_title | Energy (Oxford) |
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| creator | Zhang, Zijun Zeng, Yaohui Kusiak, Andrew |
| description | This paper discusses energy savings in wastewater processing plant pump operations and proposes a pump system scheduling model to generate operational schedules to reduce energy consumption. A neural network algorithm is utilized to model pump energy consumption and fluid flow rate after pumping. The scheduling model is a mixed-integer nonlinear programming problem (MINLP). As solving a data-driven MINLP is challenging, a migrated particle swarm optimization algorithm is proposed. The modeling and optimization results show that the performance of the pump system can be significantly improved based on the computed schedules.
▸ Energy minimization of pumps is studied. ▸ Pump performance is measured with two parameters. ▸ A neural network algorithm is used to develop models. ▸ Pump configuration and control parameters are optimized. ▸ A migrated particle swarm optimization algorithm solves the model. |
| doi_str_mv | 10.1016/j.energy.2012.08.048 |
| format | Article |
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▸ Energy minimization of pumps is studied. ▸ Pump performance is measured with two parameters. ▸ A neural network algorithm is used to develop models. ▸ Pump configuration and control parameters are optimized. ▸ A migrated particle swarm optimization algorithm solves the model.</description><identifier>ISSN: 0360-5442</identifier><identifier>DOI: 10.1016/j.energy.2012.08.048</identifier><identifier>CODEN: ENEYDS</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>algorithms ; Applied sciences ; Data mining ; Energy ; energy conservation ; Energy saving ; Exact sciences and technology ; Mixed-integer nonlinear programming ; Neural networks ; Particle swarm optimization ; Pump control ; wastewater</subject><ispartof>Energy (Oxford), 2012-11, Vol.47 (1), p.505-514</ispartof><rights>2012 Elsevier Ltd</rights><rights>2014 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c393t-7ab23403d0a188e2e8e16500ce9581f3ddffb1b4b4279b9c3697ad979b6852a43</citedby><cites>FETCH-LOGICAL-c393t-7ab23403d0a188e2e8e16500ce9581f3ddffb1b4b4279b9c3697ad979b6852a43</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.energy.2012.08.048$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=26624533$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhang, Zijun</creatorcontrib><creatorcontrib>Zeng, Yaohui</creatorcontrib><creatorcontrib>Kusiak, Andrew</creatorcontrib><title>Minimizing pump energy in a wastewater processing plant</title><title>Energy (Oxford)</title><description>This paper discusses energy savings in wastewater processing plant pump operations and proposes a pump system scheduling model to generate operational schedules to reduce energy consumption. A neural network algorithm is utilized to model pump energy consumption and fluid flow rate after pumping. The scheduling model is a mixed-integer nonlinear programming problem (MINLP). As solving a data-driven MINLP is challenging, a migrated particle swarm optimization algorithm is proposed. The modeling and optimization results show that the performance of the pump system can be significantly improved based on the computed schedules.
▸ Energy minimization of pumps is studied. ▸ Pump performance is measured with two parameters. ▸ A neural network algorithm is used to develop models. ▸ Pump configuration and control parameters are optimized. ▸ A migrated particle swarm optimization algorithm solves the model.</description><subject>algorithms</subject><subject>Applied sciences</subject><subject>Data mining</subject><subject>Energy</subject><subject>energy conservation</subject><subject>Energy saving</subject><subject>Exact sciences and technology</subject><subject>Mixed-integer nonlinear programming</subject><subject>Neural networks</subject><subject>Particle swarm optimization</subject><subject>Pump control</subject><subject>wastewater</subject><issn>0360-5442</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><recordid>eNp9kMtOwzAQRbMAifL4AySyQWLTMH7EsTdIqOIlFbGAri3HmVSu8ih2SlW-HpdULFmNF-fOXJ8kuSSQESDidpVhh365yygQmoHMgMujZAJMwDTnnJ4kpyGsACCXSk2S4tV1rnXfrlum6027Tsd06rrUpFsTBtyaAX269r3FEH6xxnTDeXJcmybgxWGeJYvHh4_Z83T-9vQyu59PLVNsmBampIwDq8AQKZGiRCJyAIsql6RmVVXXJSl5yWmhSmWZUIWpVHwLmVPD2VlyM-6NBT43GAbdumCxiR2w3wRNaEELIiFXEeUjan0fgsdar71rjd9pAnrvRq_0-Du9d6NB6ugmxq4PF0ywpqm96awLf1kqBOU5Y5G7Grna9NosfWQW73GRiC4FZ5BH4m4kMAr5cuh1sA47i5XzaAdd9e7_Kj8VfIau</recordid><startdate>20121101</startdate><enddate>20121101</enddate><creator>Zhang, Zijun</creator><creator>Zeng, Yaohui</creator><creator>Kusiak, Andrew</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>FBQ</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7U6</scope><scope>C1K</scope></search><sort><creationdate>20121101</creationdate><title>Minimizing pump energy in a wastewater processing plant</title><author>Zhang, Zijun ; Zeng, Yaohui ; Kusiak, Andrew</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c393t-7ab23403d0a188e2e8e16500ce9581f3ddffb1b4b4279b9c3697ad979b6852a43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>algorithms</topic><topic>Applied sciences</topic><topic>Data mining</topic><topic>Energy</topic><topic>energy conservation</topic><topic>Energy saving</topic><topic>Exact sciences and technology</topic><topic>Mixed-integer nonlinear programming</topic><topic>Neural networks</topic><topic>Particle swarm optimization</topic><topic>Pump control</topic><topic>wastewater</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Zijun</creatorcontrib><creatorcontrib>Zeng, Yaohui</creatorcontrib><creatorcontrib>Kusiak, Andrew</creatorcontrib><collection>AGRIS</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Sustainability Science Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><jtitle>Energy (Oxford)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Zijun</au><au>Zeng, Yaohui</au><au>Kusiak, Andrew</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Minimizing pump energy in a wastewater processing plant</atitle><jtitle>Energy (Oxford)</jtitle><date>2012-11-01</date><risdate>2012</risdate><volume>47</volume><issue>1</issue><spage>505</spage><epage>514</epage><pages>505-514</pages><issn>0360-5442</issn><coden>ENEYDS</coden><abstract>This paper discusses energy savings in wastewater processing plant pump operations and proposes a pump system scheduling model to generate operational schedules to reduce energy consumption. A neural network algorithm is utilized to model pump energy consumption and fluid flow rate after pumping. The scheduling model is a mixed-integer nonlinear programming problem (MINLP). As solving a data-driven MINLP is challenging, a migrated particle swarm optimization algorithm is proposed. The modeling and optimization results show that the performance of the pump system can be significantly improved based on the computed schedules.
▸ Energy minimization of pumps is studied. ▸ Pump performance is measured with two parameters. ▸ A neural network algorithm is used to develop models. ▸ Pump configuration and control parameters are optimized. ▸ A migrated particle swarm optimization algorithm solves the model.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.energy.2012.08.048</doi><tpages>10</tpages></addata></record> |
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| language | eng |
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| source | ScienceDirect Journals (5 years ago - present) |
| subjects | algorithms Applied sciences Data mining Energy energy conservation Energy saving Exact sciences and technology Mixed-integer nonlinear programming Neural networks Particle swarm optimization Pump control wastewater |
| title | Minimizing pump energy in a wastewater processing plant |
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