An artificial neural network‐based optimization of reverse electrodialysis power generating cells using CFD and genetic algorithm
Summary Reverse electrodialysis (RED) is a renewable energy production method that employs salinity gradient to produce electricity. The salinity gradient between the rejected brine of desalination process and river water/seawater is a reliable source of energy, particularly for desalination plants...
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| Veröffentlicht in: | International journal of energy research 2022-12, Vol.46 (15), p.21217-21233 |
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| creator | Faghihi, Parsa Jalali, Alireza |
| description | Summary
Reverse electrodialysis (RED) is a renewable energy production method that employs salinity gradient to produce electricity. The salinity gradient between the rejected brine of desalination process and river water/seawater is a reliable source of energy, particularly for desalination plants located in susceptible areas. In this study, the performance of RED is predicted using computational fluid dynamics and an artificial neural network. This approach reduces the computational costs of optimization, and more importantly, networks can be updated by more data in the future. Since geometric, hydrodynamic, and electrochemical variables affect the performance of these cells, ignoring any of them will influence the final design. We can consider all of these factors through deep learning. Performance parameters such as Sherwood number, Power number, and concentration polarization coefficient are evaluated in this study. Mass transport and pressure drop are optimized using genetic algorithm, and accessible electrical power is obtained for the optimized cases that help designers make final decisions. Using predictors and a set of optimized cases provide an efficient tool for the design. Based on our results, RED cells can produce net power density of 2.4 W m−2 by using rejected brine of desalination and river water as the two solutions. In addition, Sherwood number of 80 and Power number of 5248 show a good balance between the amount of mass transfer and pressure drop in RED cells.
Reverse electrodialysis (RED) is a renewable energy production method that employs salinity gradient to produce electricity. The salinity gradient between the rejected brine of desalination process and river water/seawater is a reliable source of energy, particularly for desalination plants located in susceptible areas. In this study, the performance of reverse electrodialysis is predicted using computational fluid dynamics and an artificial neural network. |
| doi_str_mv | 10.1002/er.8379 |
| format | Article |
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Reverse electrodialysis (RED) is a renewable energy production method that employs salinity gradient to produce electricity. The salinity gradient between the rejected brine of desalination process and river water/seawater is a reliable source of energy, particularly for desalination plants located in susceptible areas. In this study, the performance of RED is predicted using computational fluid dynamics and an artificial neural network. This approach reduces the computational costs of optimization, and more importantly, networks can be updated by more data in the future. Since geometric, hydrodynamic, and electrochemical variables affect the performance of these cells, ignoring any of them will influence the final design. We can consider all of these factors through deep learning. Performance parameters such as Sherwood number, Power number, and concentration polarization coefficient are evaluated in this study. Mass transport and pressure drop are optimized using genetic algorithm, and accessible electrical power is obtained for the optimized cases that help designers make final decisions. Using predictors and a set of optimized cases provide an efficient tool for the design. Based on our results, RED cells can produce net power density of 2.4 W m−2 by using rejected brine of desalination and river water as the two solutions. In addition, Sherwood number of 80 and Power number of 5248 show a good balance between the amount of mass transfer and pressure drop in RED cells.
Reverse electrodialysis (RED) is a renewable energy production method that employs salinity gradient to produce electricity. The salinity gradient between the rejected brine of desalination process and river water/seawater is a reliable source of energy, particularly for desalination plants located in susceptible areas. In this study, the performance of reverse electrodialysis is predicted using computational fluid dynamics and an artificial neural network.</description><identifier>ISSN: 0363-907X</identifier><identifier>EISSN: 1099-114X</identifier><identifier>DOI: 10.1002/er.8379</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Inc</publisher><subject>Algorithms ; Artificial neural networks ; Brines ; Cells ; CFD ; Computational fluid dynamics ; Computer applications ; Computing costs ; Deep learning ; Desalination ; Desalination plants ; Design ; Electric power ; Electrochemistry ; Electrodialysis ; Energy sources ; Fluid dynamics ; Genetic algorithms ; Hydrodynamics ; ion exchange membranes ; Machine learning ; Mass transfer ; Mass transport ; neural network ; Neural networks ; Optimization ; Pareto front ; Pressure drop ; Production methods ; Renewable energy ; reverse electrodialysis ; River water ; Rivers ; Salinity ; Salinity effects ; Salinity gradients ; Seawater ; Water desalting</subject><ispartof>International journal of energy research, 2022-12, Vol.46 (15), p.21217-21233</ispartof><rights>2022 John Wiley & Sons Ltd.</rights><rights>2022 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2199-cfd79e1d86f9cb7435c4d7746d80b3c2da749fcd9ce895453a10c80b13e875ce3</citedby><cites>FETCH-LOGICAL-c2199-cfd79e1d86f9cb7435c4d7746d80b3c2da749fcd9ce895453a10c80b13e875ce3</cites><orcidid>0000-0001-5345-2279</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fer.8379$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fer.8379$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27903,27904,45553,45554</link.rule.ids></links><search><creatorcontrib>Faghihi, Parsa</creatorcontrib><creatorcontrib>Jalali, Alireza</creatorcontrib><title>An artificial neural network‐based optimization of reverse electrodialysis power generating cells using CFD and genetic algorithm</title><title>International journal of energy research</title><description>Summary
Reverse electrodialysis (RED) is a renewable energy production method that employs salinity gradient to produce electricity. The salinity gradient between the rejected brine of desalination process and river water/seawater is a reliable source of energy, particularly for desalination plants located in susceptible areas. In this study, the performance of RED is predicted using computational fluid dynamics and an artificial neural network. This approach reduces the computational costs of optimization, and more importantly, networks can be updated by more data in the future. Since geometric, hydrodynamic, and electrochemical variables affect the performance of these cells, ignoring any of them will influence the final design. We can consider all of these factors through deep learning. Performance parameters such as Sherwood number, Power number, and concentration polarization coefficient are evaluated in this study. Mass transport and pressure drop are optimized using genetic algorithm, and accessible electrical power is obtained for the optimized cases that help designers make final decisions. Using predictors and a set of optimized cases provide an efficient tool for the design. Based on our results, RED cells can produce net power density of 2.4 W m−2 by using rejected brine of desalination and river water as the two solutions. In addition, Sherwood number of 80 and Power number of 5248 show a good balance between the amount of mass transfer and pressure drop in RED cells.
Reverse electrodialysis (RED) is a renewable energy production method that employs salinity gradient to produce electricity. The salinity gradient between the rejected brine of desalination process and river water/seawater is a reliable source of energy, particularly for desalination plants located in susceptible areas. In this study, the performance of reverse electrodialysis is predicted using computational fluid dynamics and an artificial neural network.</description><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Brines</subject><subject>Cells</subject><subject>CFD</subject><subject>Computational fluid dynamics</subject><subject>Computer applications</subject><subject>Computing costs</subject><subject>Deep learning</subject><subject>Desalination</subject><subject>Desalination plants</subject><subject>Design</subject><subject>Electric power</subject><subject>Electrochemistry</subject><subject>Electrodialysis</subject><subject>Energy sources</subject><subject>Fluid dynamics</subject><subject>Genetic algorithms</subject><subject>Hydrodynamics</subject><subject>ion exchange membranes</subject><subject>Machine learning</subject><subject>Mass transfer</subject><subject>Mass transport</subject><subject>neural network</subject><subject>Neural networks</subject><subject>Optimization</subject><subject>Pareto front</subject><subject>Pressure drop</subject><subject>Production methods</subject><subject>Renewable energy</subject><subject>reverse electrodialysis</subject><subject>River water</subject><subject>Rivers</subject><subject>Salinity</subject><subject>Salinity effects</subject><subject>Salinity gradients</subject><subject>Seawater</subject><subject>Water desalting</subject><issn>0363-907X</issn><issn>1099-114X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp1kMFKAzEQhoMoWKv4CgEPHmRr0uxuNsdSWxUKgij0tqTJbE3dbtbJ1lJPgi_gM_okbluvnv6B_5sZ_p-Qc856nLH-NWAvE1IdkA5nSkWcx9ND0mEiFZFicnpMTkJYMNZ6XHbI16CiGhtXOON0SStY4U6atcfXn8_vmQ5gqa8bt3QfunG-or6gCO-AASiUYBr0tl3dBBdo7deAdA4VYMtWc2qgLANdhe08HN9QXdmd3ThDdTn36JqX5Sk5KnQZ4OxPu-R5PHoa3kWTh9v74WASmT5vk5jCSgXcZmmhzEzGIjGxlTJObcZmwvStlrEqjFUGMpXEidCcmdbiAjKZGBBdcrG_W6N_W0Fo8oVfYdW-zPsyyVIhuMha6nJPGfQhIBR5jW6pcZNzlm8bzgHzbcMtebUn166EzX9YPnrc0b8eUX_Q</recordid><startdate>202212</startdate><enddate>202212</enddate><creator>Faghihi, Parsa</creator><creator>Jalali, Alireza</creator><general>John Wiley & Sons, Inc</general><general>Hindawi Limited</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7ST</scope><scope>7TB</scope><scope>7TN</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>F28</scope><scope>FR3</scope><scope>H96</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0001-5345-2279</orcidid></search><sort><creationdate>202212</creationdate><title>An artificial neural network‐based optimization of reverse electrodialysis power generating cells using CFD and genetic algorithm</title><author>Faghihi, Parsa ; Jalali, Alireza</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2199-cfd79e1d86f9cb7435c4d7746d80b3c2da749fcd9ce895453a10c80b13e875ce3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Artificial neural networks</topic><topic>Brines</topic><topic>Cells</topic><topic>CFD</topic><topic>Computational fluid dynamics</topic><topic>Computer applications</topic><topic>Computing costs</topic><topic>Deep learning</topic><topic>Desalination</topic><topic>Desalination plants</topic><topic>Design</topic><topic>Electric power</topic><topic>Electrochemistry</topic><topic>Electrodialysis</topic><topic>Energy sources</topic><topic>Fluid dynamics</topic><topic>Genetic algorithms</topic><topic>Hydrodynamics</topic><topic>ion exchange membranes</topic><topic>Machine learning</topic><topic>Mass transfer</topic><topic>Mass transport</topic><topic>neural network</topic><topic>Neural networks</topic><topic>Optimization</topic><topic>Pareto front</topic><topic>Pressure drop</topic><topic>Production methods</topic><topic>Renewable energy</topic><topic>reverse electrodialysis</topic><topic>River water</topic><topic>Rivers</topic><topic>Salinity</topic><topic>Salinity effects</topic><topic>Salinity gradients</topic><topic>Seawater</topic><topic>Water desalting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Faghihi, Parsa</creatorcontrib><creatorcontrib>Jalali, Alireza</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Environment Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Oceanic Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Environment Abstracts</collection><jtitle>International journal of energy research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Faghihi, Parsa</au><au>Jalali, Alireza</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An artificial neural network‐based optimization of reverse electrodialysis power generating cells using CFD and genetic algorithm</atitle><jtitle>International journal of energy research</jtitle><date>2022-12</date><risdate>2022</risdate><volume>46</volume><issue>15</issue><spage>21217</spage><epage>21233</epage><pages>21217-21233</pages><issn>0363-907X</issn><eissn>1099-114X</eissn><abstract>Summary
Reverse electrodialysis (RED) is a renewable energy production method that employs salinity gradient to produce electricity. The salinity gradient between the rejected brine of desalination process and river water/seawater is a reliable source of energy, particularly for desalination plants located in susceptible areas. In this study, the performance of RED is predicted using computational fluid dynamics and an artificial neural network. This approach reduces the computational costs of optimization, and more importantly, networks can be updated by more data in the future. Since geometric, hydrodynamic, and electrochemical variables affect the performance of these cells, ignoring any of them will influence the final design. We can consider all of these factors through deep learning. Performance parameters such as Sherwood number, Power number, and concentration polarization coefficient are evaluated in this study. Mass transport and pressure drop are optimized using genetic algorithm, and accessible electrical power is obtained for the optimized cases that help designers make final decisions. Using predictors and a set of optimized cases provide an efficient tool for the design. Based on our results, RED cells can produce net power density of 2.4 W m−2 by using rejected brine of desalination and river water as the two solutions. In addition, Sherwood number of 80 and Power number of 5248 show a good balance between the amount of mass transfer and pressure drop in RED cells.
Reverse electrodialysis (RED) is a renewable energy production method that employs salinity gradient to produce electricity. The salinity gradient between the rejected brine of desalination process and river water/seawater is a reliable source of energy, particularly for desalination plants located in susceptible areas. In this study, the performance of reverse electrodialysis is predicted using computational fluid dynamics and an artificial neural network.</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1002/er.8379</doi><tpages>17</tpages><orcidid>https://orcid.org/0000-0001-5345-2279</orcidid></addata></record> |
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| subjects | Algorithms Artificial neural networks Brines Cells CFD Computational fluid dynamics Computer applications Computing costs Deep learning Desalination Desalination plants Design Electric power Electrochemistry Electrodialysis Energy sources Fluid dynamics Genetic algorithms Hydrodynamics ion exchange membranes Machine learning Mass transfer Mass transport neural network Neural networks Optimization Pareto front Pressure drop Production methods Renewable energy reverse electrodialysis River water Rivers Salinity Salinity effects Salinity gradients Seawater Water desalting |
| title | An artificial neural network‐based optimization of reverse electrodialysis power generating cells using CFD and genetic algorithm |
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