Development of artificial neural network model for predicting dynamic viscosity and specific heat of MWCNT nanoparticle-enhanced ionic liquids with different [HMIM]-cation base agents
DC Field | Value | Language |
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dc.contributor.author | Boldoo, Tsogtbilegt | - |
dc.contributor.author | Lee, Minjung | - |
dc.contributor.author | Kang, Yong Tae | - |
dc.contributor.author | Cho, Honghyun | - |
dc.date.accessioned | 2022-02-14T20:40:57Z | - |
dc.date.available | 2022-02-14T20:40:57Z | - |
dc.date.created | 2022-02-08 | - |
dc.date.issued | 2021-11-01 | - |
dc.identifier.issn | 0167-7322 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/135783 | - |
dc.description.abstract | The specific heat and dynamic viscosity of various 1-hexyl-3-methylimidazolium [HMIM]-cation with multiwalled carbon nanotube (MWCNT) nanoparticles are measured and used to develop an artificial neural network (ANN) model. The specific heat values of [C12MIM][Tf2N], [HMIM][Tf2N], [HMIM][TfO], and [HMIM][Pf(6)] ionic-liquid-based MWCNT nanofluids decrease with increasing nanoparticle concentration and increase with temperature. Also, the dynamic viscosity of the MWCNT nanoparticle-enhanced ionic liquids decreases at low concentrations; however, it increases significantly when the concentration increases up to 1 wt%. A new ANN model for predicting the dynamic viscosity and specific heat is developed, and the predictive values agree with the experimental data with high accuracy. The mean square error and R-value of the proposed predictive ANN model are 0.001291 and 0.9985, respectively. The maximum margin of deviation of the proposed ANN model for dynamic viscosity and specific heat is 9.63% and 4.3%. (C) 2021 Elsevier B.V. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER | - |
dc.subject | ABSORPTION-REFRIGERATION SYSTEM | - |
dc.subject | THERMAL-CONDUCTIVITY | - |
dc.subject | ANTIFREEZE | - |
dc.subject | SOLVENTS | - |
dc.subject | DENSITY | - |
dc.title | Development of artificial neural network model for predicting dynamic viscosity and specific heat of MWCNT nanoparticle-enhanced ionic liquids with different [HMIM]-cation base agents | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kang, Yong Tae | - |
dc.identifier.doi | 10.1016/j.molliq.2021.117356 | - |
dc.identifier.scopusid | 2-s2.0-85114481276 | - |
dc.identifier.wosid | 000700306300081 | - |
dc.identifier.bibliographicCitation | JOURNAL OF MOLECULAR LIQUIDS, v.341 | - |
dc.relation.isPartOf | JOURNAL OF MOLECULAR LIQUIDS | - |
dc.citation.title | JOURNAL OF MOLECULAR LIQUIDS | - |
dc.citation.volume | 341 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Physical | - |
dc.relation.journalWebOfScienceCategory | Physics, Atomic, Molecular & Chemical | - |
dc.subject.keywordPlus | ABSORPTION-REFRIGERATION SYSTEM | - |
dc.subject.keywordPlus | ANTIFREEZE | - |
dc.subject.keywordPlus | DENSITY | - |
dc.subject.keywordPlus | SOLVENTS | - |
dc.subject.keywordPlus | THERMAL-CONDUCTIVITY | - |
dc.subject.keywordAuthor | Artificial neural network | - |
dc.subject.keywordAuthor | Dynamic viscosity | - |
dc.subject.keywordAuthor | Ionic liquid | - |
dc.subject.keywordAuthor | Multiwalled carbon nanotube | - |
dc.subject.keywordAuthor | Specific heat | - |
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