Optimizing wastewater pumping system with data-driven models and a greedy electromagnetism-like algorithm
Optimizing a pumping system in the wastewater treatment process by improving its operational schedules is presented. The energy consumption and outflow rate of the pumping system are modeled by a data-driven approach. A mixed-integer nonlinear programming (MINLP) model containing data-driven compone...
Gespeichert in:
| Veröffentlicht in: | Stochastic environmental research and risk assessment 2016-04, Vol.30 (4), p.1263-1275 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1275 |
|---|---|
| container_issue | 4 |
| container_start_page | 1263 |
| container_title | Stochastic environmental research and risk assessment |
| container_volume | 30 |
| creator | Zeng, Yaohui Zhang, Zijun Kusiak, Andrew Tang, Fan Wei, Xiupeng |
| description | Optimizing a pumping system in the wastewater treatment process by improving its operational schedules is presented. The energy consumption and outflow rate of the pumping system are modeled by a data-driven approach. A mixed-integer nonlinear programming (MINLP) model containing data-driven components and pump operational constraints is developed to minimize the energy consumption of the pumping system while maintaining the required pumping workload. A greedy electromagnetism-like (GEM) algorithm is designed to solve the MINLP model for optimized operational schedules and pump speeds. Three computational cases are studied to demonstrate the effectiveness of the proposed data-driven modeling and GEM algorithm. The computational results show that significant energy saving can be obtained. |
| doi_str_mv | 10.1007/s00477-015-1115-4 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1877850303</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1780524790</sourcerecordid><originalsourceid>FETCH-LOGICAL-c406t-40c1ad000a1b354c0657ae93c4e43088153421b13cd20ccc3304c939a537aa823</originalsourceid><addsrcrecordid>eNqFkU2LFDEQhhtRcNndH-DJgBcvrVX56HQfZfELFvagew416Zo22l8mGYfx15uhRWQPekmF4nmKKt6qeobwCgHs6wSgra0BTY1YHv2oukCtmlpJ0z3-89fwtLpOKeyKY1TXIVxU4W7NYQo_wzyII6XMR8ocxXqY1nMrnUprEseQv4ieMtV9DD94FtPS85gEzb0gMUTm_iR4ZJ_jMtEwcw5pqsfwjQWNwxKLPl1VT_Y0Jr7-XS-r-3dvP998qG_v3n-8eXNbew1NrjV4pB4ACHfKaA-NscSd8pq1grZFo7TEHSrfS_DeKwXad6ojoyxRK9Vl9XKbu8bl-4FTdlNInseRZl4OyWFrbWtAgfo_alswUtsOCvriAfp1OcS5HFIo28hOotaFwo3ycUkp8t6tMUwUTw7BnaNyW1SuROXOUbmzIzcnFXYeOP41-R_S803a0-JoiCG5-08SsAFAhWUb9Qvqkp8L</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1776292144</pqid></control><display><type>article</type><title>Optimizing wastewater pumping system with data-driven models and a greedy electromagnetism-like algorithm</title><source>Springer Nature - Complete Springer Journals</source><creator>Zeng, Yaohui ; Zhang, Zijun ; Kusiak, Andrew ; Tang, Fan ; Wei, Xiupeng</creator><creatorcontrib>Zeng, Yaohui ; Zhang, Zijun ; Kusiak, Andrew ; Tang, Fan ; Wei, Xiupeng</creatorcontrib><description>Optimizing a pumping system in the wastewater treatment process by improving its operational schedules is presented. The energy consumption and outflow rate of the pumping system are modeled by a data-driven approach. A mixed-integer nonlinear programming (MINLP) model containing data-driven components and pump operational constraints is developed to minimize the energy consumption of the pumping system while maintaining the required pumping workload. A greedy electromagnetism-like (GEM) algorithm is designed to solve the MINLP model for optimized operational schedules and pump speeds. Three computational cases are studied to demonstrate the effectiveness of the proposed data-driven modeling and GEM algorithm. The computational results show that significant energy saving can be obtained.</description><identifier>ISSN: 1436-3240</identifier><identifier>EISSN: 1436-3259</identifier><identifier>DOI: 10.1007/s00477-015-1115-4</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Aquatic Pollution ; Chemistry and Earth Sciences ; Computation ; Computational Intelligence ; Computer Science ; Earth and Environmental Science ; Earth Sciences ; energy ; Energy conservation ; Energy consumption ; Energy efficiency ; Environment ; Greedy algorithms ; Math. Appl. in Environmental Science ; Mathematical models ; Nonlinear programming ; Original Paper ; Physics ; Probability Theory and Stochastic Processes ; Pumping ; Pumps ; Schedules ; Statistics for Engineering ; Waste Water Technology ; wastewater ; Wastewater treatment ; Water Management ; Water Pollution Control ; Water treatment</subject><ispartof>Stochastic environmental research and risk assessment, 2016-04, Vol.30 (4), p.1263-1275</ispartof><rights>Springer-Verlag Berlin Heidelberg 2015</rights><rights>Springer-Verlag Berlin Heidelberg 2016</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c406t-40c1ad000a1b354c0657ae93c4e43088153421b13cd20ccc3304c939a537aa823</citedby><cites>FETCH-LOGICAL-c406t-40c1ad000a1b354c0657ae93c4e43088153421b13cd20ccc3304c939a537aa823</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00477-015-1115-4$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00477-015-1115-4$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Zeng, Yaohui</creatorcontrib><creatorcontrib>Zhang, Zijun</creatorcontrib><creatorcontrib>Kusiak, Andrew</creatorcontrib><creatorcontrib>Tang, Fan</creatorcontrib><creatorcontrib>Wei, Xiupeng</creatorcontrib><title>Optimizing wastewater pumping system with data-driven models and a greedy electromagnetism-like algorithm</title><title>Stochastic environmental research and risk assessment</title><addtitle>Stoch Environ Res Risk Assess</addtitle><description>Optimizing a pumping system in the wastewater treatment process by improving its operational schedules is presented. The energy consumption and outflow rate of the pumping system are modeled by a data-driven approach. A mixed-integer nonlinear programming (MINLP) model containing data-driven components and pump operational constraints is developed to minimize the energy consumption of the pumping system while maintaining the required pumping workload. A greedy electromagnetism-like (GEM) algorithm is designed to solve the MINLP model for optimized operational schedules and pump speeds. Three computational cases are studied to demonstrate the effectiveness of the proposed data-driven modeling and GEM algorithm. The computational results show that significant energy saving can be obtained.</description><subject>Algorithms</subject><subject>Aquatic Pollution</subject><subject>Chemistry and Earth Sciences</subject><subject>Computation</subject><subject>Computational Intelligence</subject><subject>Computer Science</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>energy</subject><subject>Energy conservation</subject><subject>Energy consumption</subject><subject>Energy efficiency</subject><subject>Environment</subject><subject>Greedy algorithms</subject><subject>Math. Appl. in Environmental Science</subject><subject>Mathematical models</subject><subject>Nonlinear programming</subject><subject>Original Paper</subject><subject>Physics</subject><subject>Probability Theory and Stochastic Processes</subject><subject>Pumping</subject><subject>Pumps</subject><subject>Schedules</subject><subject>Statistics for Engineering</subject><subject>Waste Water Technology</subject><subject>wastewater</subject><subject>Wastewater treatment</subject><subject>Water Management</subject><subject>Water Pollution Control</subject><subject>Water treatment</subject><issn>1436-3240</issn><issn>1436-3259</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNqFkU2LFDEQhhtRcNndH-DJgBcvrVX56HQfZfELFvagew416Zo22l8mGYfx15uhRWQPekmF4nmKKt6qeobwCgHs6wSgra0BTY1YHv2oukCtmlpJ0z3-89fwtLpOKeyKY1TXIVxU4W7NYQo_wzyII6XMR8ocxXqY1nMrnUprEseQv4ieMtV9DD94FtPS85gEzb0gMUTm_iR4ZJ_jMtEwcw5pqsfwjQWNwxKLPl1VT_Y0Jr7-XS-r-3dvP998qG_v3n-8eXNbew1NrjV4pB4ACHfKaA-NscSd8pq1grZFo7TEHSrfS_DeKwXad6ojoyxRK9Vl9XKbu8bl-4FTdlNInseRZl4OyWFrbWtAgfo_alswUtsOCvriAfp1OcS5HFIo28hOotaFwo3ycUkp8t6tMUwUTw7BnaNyW1SuROXOUbmzIzcnFXYeOP41-R_S803a0-JoiCG5-08SsAFAhWUb9Qvqkp8L</recordid><startdate>20160401</startdate><enddate>20160401</enddate><creator>Zeng, Yaohui</creator><creator>Zhang, Zijun</creator><creator>Kusiak, Andrew</creator><creator>Tang, Fan</creator><creator>Wei, Xiupeng</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>FBQ</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7ST</scope><scope>7XB</scope><scope>88I</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>KR7</scope><scope>L6V</scope><scope>M2P</scope><scope>M7S</scope><scope>PATMY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>S0W</scope><scope>SOI</scope><scope>7SU</scope><scope>7TA</scope><scope>JG9</scope></search><sort><creationdate>20160401</creationdate><title>Optimizing wastewater pumping system with data-driven models and a greedy electromagnetism-like algorithm</title><author>Zeng, Yaohui ; Zhang, Zijun ; Kusiak, Andrew ; Tang, Fan ; Wei, Xiupeng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c406t-40c1ad000a1b354c0657ae93c4e43088153421b13cd20ccc3304c939a537aa823</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Algorithms</topic><topic>Aquatic Pollution</topic><topic>Chemistry and Earth Sciences</topic><topic>Computation</topic><topic>Computational Intelligence</topic><topic>Computer Science</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>energy</topic><topic>Energy conservation</topic><topic>Energy consumption</topic><topic>Energy efficiency</topic><topic>Environment</topic><topic>Greedy algorithms</topic><topic>Math. Appl. in Environmental Science</topic><topic>Mathematical models</topic><topic>Nonlinear programming</topic><topic>Original Paper</topic><topic>Physics</topic><topic>Probability Theory and Stochastic Processes</topic><topic>Pumping</topic><topic>Pumps</topic><topic>Schedules</topic><topic>Statistics for Engineering</topic><topic>Waste Water Technology</topic><topic>wastewater</topic><topic>Wastewater treatment</topic><topic>Water Management</topic><topic>Water Pollution Control</topic><topic>Water treatment</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zeng, Yaohui</creatorcontrib><creatorcontrib>Zhang, Zijun</creatorcontrib><creatorcontrib>Kusiak, Andrew</creatorcontrib><creatorcontrib>Tang, Fan</creatorcontrib><creatorcontrib>Wei, Xiupeng</creatorcontrib><collection>AGRIS</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Environment Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>Environmental Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>DELNET Engineering & Technology Collection</collection><collection>Environment Abstracts</collection><collection>Environmental Engineering Abstracts</collection><collection>Materials Business File</collection><collection>Materials Research Database</collection><jtitle>Stochastic environmental research and risk assessment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zeng, Yaohui</au><au>Zhang, Zijun</au><au>Kusiak, Andrew</au><au>Tang, Fan</au><au>Wei, Xiupeng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimizing wastewater pumping system with data-driven models and a greedy electromagnetism-like algorithm</atitle><jtitle>Stochastic environmental research and risk assessment</jtitle><stitle>Stoch Environ Res Risk Assess</stitle><date>2016-04-01</date><risdate>2016</risdate><volume>30</volume><issue>4</issue><spage>1263</spage><epage>1275</epage><pages>1263-1275</pages><issn>1436-3240</issn><eissn>1436-3259</eissn><abstract>Optimizing a pumping system in the wastewater treatment process by improving its operational schedules is presented. The energy consumption and outflow rate of the pumping system are modeled by a data-driven approach. A mixed-integer nonlinear programming (MINLP) model containing data-driven components and pump operational constraints is developed to minimize the energy consumption of the pumping system while maintaining the required pumping workload. A greedy electromagnetism-like (GEM) algorithm is designed to solve the MINLP model for optimized operational schedules and pump speeds. Three computational cases are studied to demonstrate the effectiveness of the proposed data-driven modeling and GEM algorithm. The computational results show that significant energy saving can be obtained.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s00477-015-1115-4</doi><tpages>13</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1436-3240 |
| ispartof | Stochastic environmental research and risk assessment, 2016-04, Vol.30 (4), p.1263-1275 |
| issn | 1436-3240 1436-3259 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1877850303 |
| source | Springer Nature - Complete Springer Journals |
| subjects | Algorithms Aquatic Pollution Chemistry and Earth Sciences Computation Computational Intelligence Computer Science Earth and Environmental Science Earth Sciences energy Energy conservation Energy consumption Energy efficiency Environment Greedy algorithms Math. Appl. in Environmental Science Mathematical models Nonlinear programming Original Paper Physics Probability Theory and Stochastic Processes Pumping Pumps Schedules Statistics for Engineering Waste Water Technology wastewater Wastewater treatment Water Management Water Pollution Control Water treatment |
| title | Optimizing wastewater pumping system with data-driven models and a greedy electromagnetism-like algorithm |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-01T04%3A22%3A17IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Optimizing%20wastewater%20pumping%20system%20with%20data-driven%20models%20and%20a%20greedy%20electromagnetism-like%20algorithm&rft.jtitle=Stochastic%20environmental%20research%20and%20risk%20assessment&rft.au=Zeng,%20Yaohui&rft.date=2016-04-01&rft.volume=30&rft.issue=4&rft.spage=1263&rft.epage=1275&rft.pages=1263-1275&rft.issn=1436-3240&rft.eissn=1436-3259&rft_id=info:doi/10.1007/s00477-015-1115-4&rft_dat=%3Cproquest_cross%3E1780524790%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1776292144&rft_id=info:pmid/&rfr_iscdi=true |