A hybrid genetic–BP algorithm approach for thermal conductivity modeling of nanofluid containing silver nanoparticles coated with PVP
Since nanoparticles play a significant role in increasing the thermal conductivity of fluids, the present study aims to predict the thermal conductivity of silver nanofluid coated with polyvinylpyrrolidone (PVP) by the combinational model of multilayer perceptron artificial neural network and geneti...
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| Veröffentlicht in: | Journal of thermal analysis and calorimetry 2021-10, Vol.146 (1), p.17-30 |
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| creator | Paknezhad, B. Vakili, M. Bozorgi, M. Hajialibabaie, M. Yahyaei, M. |
| description | Since nanoparticles play a significant role in increasing the thermal conductivity of fluids, the present study aims to predict the thermal conductivity of silver nanofluid coated with polyvinylpyrrolidone (PVP) by the combinational model of multilayer perceptron artificial neural network and genetic algorithm. For modeling, the results of experimental measurements have been used for thermal conductivity of nanofluid containing PVP-coated silver nanoparticle-based deionized water at 25–55 °C in volume fraction of 250 ppm, 500 ppm and 1000 ppm. Henceforth, genetic algorithm is applied to improve learning process in the artificial neural network. It is in this way that the masses were chosen for each neuron’s communications as well as their bias happens according to optimization performed by the genetic algorithm. To evaluate the accuracy of the model in predicting thermal conductivity of nanofluid, mean absolute percentage error, root mean square error, coefficient of determination (
R
2
) and mean bias error have exerted indices which are 1.202, 0.345, 0.989 and − 0.016, respectively. The results of the indices and predictions, compared to the experimental results, show high accuracy and reliable combinational model of artificial neural network and genetic algorithm. |
| doi_str_mv | 10.1007/s10973-020-09989-x |
| format | Article |
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R
2
) and mean bias error have exerted indices which are 1.202, 0.345, 0.989 and − 0.016, respectively. The results of the indices and predictions, compared to the experimental results, show high accuracy and reliable combinational model of artificial neural network and genetic algorithm.</description><identifier>ISSN: 1388-6150</identifier><identifier>EISSN: 1588-2926</identifier><identifier>DOI: 10.1007/s10973-020-09989-x</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Algorithms ; Analytical Chemistry ; Artificial neural networks ; Bias ; Chemistry ; Chemistry and Materials Science ; Computational fluid dynamics ; Deionization ; Errors ; Genetic algorithms ; Heat conductivity ; Heat transfer ; Inorganic Chemistry ; Machine learning ; Measurement Science and Instrumentation ; Model accuracy ; Modelling ; Multilayer perceptrons ; Nanofluids ; Nanoparticles ; Neural networks ; Neurons ; Optimization ; Physical Chemistry ; Polymer Sciences ; Polyvinylpyrrolidone ; Povidone ; Silver ; Thermal conductivity</subject><ispartof>Journal of thermal analysis and calorimetry, 2021-10, Vol.146 (1), p.17-30</ispartof><rights>Akadémiai Kiadó, Budapest, Hungary 2020</rights><rights>COPYRIGHT 2021 Springer</rights><rights>Akadémiai Kiadó, Budapest, Hungary 2020.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c420t-6c3a6d667d1237af73d4f4f79154ee99b4bc7e327ea336fd925a9b15c67c7a433</citedby><cites>FETCH-LOGICAL-c420t-6c3a6d667d1237af73d4f4f79154ee99b4bc7e327ea336fd925a9b15c67c7a433</cites><orcidid>0000-0003-0024-4497</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10973-020-09989-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10973-020-09989-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Paknezhad, B.</creatorcontrib><creatorcontrib>Vakili, M.</creatorcontrib><creatorcontrib>Bozorgi, M.</creatorcontrib><creatorcontrib>Hajialibabaie, M.</creatorcontrib><creatorcontrib>Yahyaei, M.</creatorcontrib><title>A hybrid genetic–BP algorithm approach for thermal conductivity modeling of nanofluid containing silver nanoparticles coated with PVP</title><title>Journal of thermal analysis and calorimetry</title><addtitle>J Therm Anal Calorim</addtitle><description>Since nanoparticles play a significant role in increasing the thermal conductivity of fluids, the present study aims to predict the thermal conductivity of silver nanofluid coated with polyvinylpyrrolidone (PVP) by the combinational model of multilayer perceptron artificial neural network and genetic algorithm. For modeling, the results of experimental measurements have been used for thermal conductivity of nanofluid containing PVP-coated silver nanoparticle-based deionized water at 25–55 °C in volume fraction of 250 ppm, 500 ppm and 1000 ppm. Henceforth, genetic algorithm is applied to improve learning process in the artificial neural network. It is in this way that the masses were chosen for each neuron’s communications as well as their bias happens according to optimization performed by the genetic algorithm. To evaluate the accuracy of the model in predicting thermal conductivity of nanofluid, mean absolute percentage error, root mean square error, coefficient of determination (
R
2
) and mean bias error have exerted indices which are 1.202, 0.345, 0.989 and − 0.016, respectively. The results of the indices and predictions, compared to the experimental results, show high accuracy and reliable combinational model of artificial neural network and genetic algorithm.</description><subject>Algorithms</subject><subject>Analytical Chemistry</subject><subject>Artificial neural networks</subject><subject>Bias</subject><subject>Chemistry</subject><subject>Chemistry and Materials Science</subject><subject>Computational fluid dynamics</subject><subject>Deionization</subject><subject>Errors</subject><subject>Genetic algorithms</subject><subject>Heat conductivity</subject><subject>Heat transfer</subject><subject>Inorganic Chemistry</subject><subject>Machine learning</subject><subject>Measurement Science and Instrumentation</subject><subject>Model accuracy</subject><subject>Modelling</subject><subject>Multilayer perceptrons</subject><subject>Nanofluids</subject><subject>Nanoparticles</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>Optimization</subject><subject>Physical Chemistry</subject><subject>Polymer Sciences</subject><subject>Polyvinylpyrrolidone</subject><subject>Povidone</subject><subject>Silver</subject><subject>Thermal conductivity</subject><issn>1388-6150</issn><issn>1588-2926</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kc2KFDEQxxtRcF19AU8BTx56zVcnk-O4-LGw4ODXNWTSlZ4s3cmYpNeZmzcfwDf0SczsCMuASA4pqn7_Kqr-TfOc4AuCsXyVCVaStZjiFiu1UO3uQXNGusWipYqKhzVmNRakw4-bJznfYFwxTM6an0u02a-T79EAAYq3v3_8er1CZhxi8mUzIbPdpmjsBrmYUNlAmsyIbAz9bIu_9WWPptjD6MOAokPBhOjGubarSDE-HPLZj7eQ7mpbk-qMEXKtmwI9-l6HoNXX1dPmkTNjhmd___Pmy9s3ny_ft9cf3l1dLq9byykurbDMiF4I2RPKpHGS9dxxJxXpOIBSa762EhiVYBgTrle0M2pNOiuklYYzdt68OPatW32bIRd9E-cU6khNO8G45JzKe2owI2gfXCzJ2Mlnq5dCUtVhIUWlLv5B1dfD5Ov-4HzNnwhenggON4JdGcycs7769PGUpUfWpphzAqe3yU8m7TXB-uC5Pnquq-f6znO9qyJ2FOUKhwHS_Xb_Uf0BEwqxHA</recordid><startdate>20211001</startdate><enddate>20211001</enddate><creator>Paknezhad, B.</creator><creator>Vakili, M.</creator><creator>Bozorgi, M.</creator><creator>Hajialibabaie, M.</creator><creator>Yahyaei, M.</creator><general>Springer International Publishing</general><general>Springer</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><orcidid>https://orcid.org/0000-0003-0024-4497</orcidid></search><sort><creationdate>20211001</creationdate><title>A hybrid genetic–BP algorithm approach for thermal conductivity modeling of nanofluid containing silver nanoparticles coated with PVP</title><author>Paknezhad, B. ; Vakili, M. ; Bozorgi, M. ; Hajialibabaie, M. ; Yahyaei, M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c420t-6c3a6d667d1237af73d4f4f79154ee99b4bc7e327ea336fd925a9b15c67c7a433</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Analytical Chemistry</topic><topic>Artificial neural networks</topic><topic>Bias</topic><topic>Chemistry</topic><topic>Chemistry and Materials Science</topic><topic>Computational fluid dynamics</topic><topic>Deionization</topic><topic>Errors</topic><topic>Genetic algorithms</topic><topic>Heat conductivity</topic><topic>Heat transfer</topic><topic>Inorganic Chemistry</topic><topic>Machine learning</topic><topic>Measurement Science and Instrumentation</topic><topic>Model accuracy</topic><topic>Modelling</topic><topic>Multilayer perceptrons</topic><topic>Nanofluids</topic><topic>Nanoparticles</topic><topic>Neural networks</topic><topic>Neurons</topic><topic>Optimization</topic><topic>Physical Chemistry</topic><topic>Polymer Sciences</topic><topic>Polyvinylpyrrolidone</topic><topic>Povidone</topic><topic>Silver</topic><topic>Thermal conductivity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Paknezhad, B.</creatorcontrib><creatorcontrib>Vakili, M.</creatorcontrib><creatorcontrib>Bozorgi, M.</creatorcontrib><creatorcontrib>Hajialibabaie, M.</creatorcontrib><creatorcontrib>Yahyaei, M.</creatorcontrib><collection>CrossRef</collection><collection>Gale In Context: Science</collection><jtitle>Journal of thermal analysis and calorimetry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Paknezhad, B.</au><au>Vakili, M.</au><au>Bozorgi, M.</au><au>Hajialibabaie, M.</au><au>Yahyaei, M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A hybrid genetic–BP algorithm approach for thermal conductivity modeling of nanofluid containing silver nanoparticles coated with PVP</atitle><jtitle>Journal of thermal analysis and calorimetry</jtitle><stitle>J Therm Anal Calorim</stitle><date>2021-10-01</date><risdate>2021</risdate><volume>146</volume><issue>1</issue><spage>17</spage><epage>30</epage><pages>17-30</pages><issn>1388-6150</issn><eissn>1588-2926</eissn><abstract>Since nanoparticles play a significant role in increasing the thermal conductivity of fluids, the present study aims to predict the thermal conductivity of silver nanofluid coated with polyvinylpyrrolidone (PVP) by the combinational model of multilayer perceptron artificial neural network and genetic algorithm. For modeling, the results of experimental measurements have been used for thermal conductivity of nanofluid containing PVP-coated silver nanoparticle-based deionized water at 25–55 °C in volume fraction of 250 ppm, 500 ppm and 1000 ppm. Henceforth, genetic algorithm is applied to improve learning process in the artificial neural network. It is in this way that the masses were chosen for each neuron’s communications as well as their bias happens according to optimization performed by the genetic algorithm. To evaluate the accuracy of the model in predicting thermal conductivity of nanofluid, mean absolute percentage error, root mean square error, coefficient of determination (
R
2
) and mean bias error have exerted indices which are 1.202, 0.345, 0.989 and − 0.016, respectively. The results of the indices and predictions, compared to the experimental results, show high accuracy and reliable combinational model of artificial neural network and genetic algorithm.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><doi>10.1007/s10973-020-09989-x</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0003-0024-4497</orcidid></addata></record> |
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| source | SpringerNature Journals |
| subjects | Algorithms Analytical Chemistry Artificial neural networks Bias Chemistry Chemistry and Materials Science Computational fluid dynamics Deionization Errors Genetic algorithms Heat conductivity Heat transfer Inorganic Chemistry Machine learning Measurement Science and Instrumentation Model accuracy Modelling Multilayer perceptrons Nanofluids Nanoparticles Neural networks Neurons Optimization Physical Chemistry Polymer Sciences Polyvinylpyrrolidone Povidone Silver Thermal conductivity |
| title | A hybrid genetic–BP algorithm approach for thermal conductivity modeling of nanofluid containing silver nanoparticles coated with PVP |
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