Estimating the heat capacity of non-Newtonian ionanofluid systems using ANN, ANFIS, and SGB tree algorithms

dc.contributor.authorDaneshfar, Reza
dc.contributor.authorBemani, Amin
dc.contributor.authorHadipoor, Masoud
dc.contributor.authorSharifpur, Mohsen
dc.contributor.authorAli, Hafiz Muhammad
dc.contributor.authorMahariq, Ibrahim
dc.contributor.authorAbdeljawad, Thabet
dc.contributor.emailmohsen.sharifpur@up.ac.zaen_ZA
dc.date.accessioned2021-02-20T05:32:09Z
dc.date.available2021-02-20T05:32:09Z
dc.date.issued2020-09
dc.description.abstractThis work investigated the capability of multilayer perceptron artificial neural network (MLP–ANN), stochastic gradient boosting (SGB) tree, radial basis function artificial neural network (RBF–ANN), and adaptive neuro-fuzzy inference system (ANFIS) models to determine the heat capacity (Cp) of ionanofluids in terms of the nanoparticle concentration (x) and the critical temperature (Tc), operational temperature (T), acentric factor (ω), and molecular weight (Mw) of pure ionic liquids (ILs). To this end, a comprehensive database of literature reviews was searched. The results of the SGB model were more satisfactory than the other models. Furthermore, an analysis was done to determine the outlying bad data points. It showed that most of the experimental data points were located in a reliable zone for the development of the model. The mean squared error and R 2 were 0.00249 and 0.987, 0.0132 and 0.9434, 0.0320 and 0.8754, and 0.0201 and 0.9204 for the SGB, MLP–ANN, ANFIS, and RBF–ANN, respectively. According to this study, the ability of SGB for estimating the Cp of ionanofluids was shown to be greater than other models. By eliminating the need for conducting costly and time-consuming experiments, the SGB strategy showed its superiority compared with experimental measurements. Furthermore, the SGB displayed great generalizability because of the stochastic element. Therefore, it can be highly applicable to unseen conditions. Furthermore, it can help chemical engineers and chemists by providing a model with low parameters that yields satisfactory results for estimating the Cp of ionanofluids. Additionally, the sensitivity analysis showed that Cp is directly related to T, Mw, and Tc, and has an inverse relation with ω and x. Mw and Tc had the highest impact and ω had the lowest impact on Cp.en_ZA
dc.description.departmentMechanical and Aeronautical Engineeringen_ZA
dc.description.librarianpm2021en_ZA
dc.description.urihttps://www.mdpi.com/journal/applscien_ZA
dc.identifier.citationDaneshfar, R., Bemani. A., Hadipoor, M. et al. 2020, 'Estimating the heat capacity of non-Newtonian ionanofluid systems using ANN, ANFIS, and SGB tree algorithms', Applied Sciences (Switzerland), vol. 10, no. 18, art. 6432, pp. 1-20.en_ZA
dc.identifier.issn2076-3417 (online)
dc.identifier.other10.3390/app10186432
dc.identifier.urihttp://hdl.handle.net/2263/78780
dc.language.isoenen_ZA
dc.publisherMDPIen_ZA
dc.rights© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).en_ZA
dc.subjectIonic liquiden_ZA
dc.subjectNanofluiden_ZA
dc.subjectHeat capacityen_ZA
dc.subjectSoft computing modelsen_ZA
dc.subjectParticle swarm optimizationen_ZA
dc.subjectStochastic gradient boosting (SGB)en_ZA
dc.subjectRadial basis function artificial neural network (RBF–ANN)en_ZA
dc.subjectAdaptive neuro-fuzzy inference system (ANFIS)en_ZA
dc.subjectNon-Newtonian ionanofluid systemsen_ZA
dc.subject.otherEngineering, built environment and information technology articles SDG-04
dc.subject.otherSDG-04: Quality education
dc.subject.otherEngineering, built environment and information technology articles SDG-07
dc.subject.otherSDG-07: Affordable and clean energy
dc.subject.otherEngineering, built environment and information technology articles SDG-09
dc.subject.otherSDG-09: Industry, innovation and infrastructure
dc.subject.otherEngineering, built environment and information technology articles SDG-12
dc.subject.otherSDG-12: Responsible consumption and production
dc.subject.otherEngineering, built environment and information technology articles SDG-13
dc.subject.otherSDG-13: Climate action
dc.titleEstimating the heat capacity of non-Newtonian ionanofluid systems using ANN, ANFIS, and SGB tree algorithmsen_ZA
dc.typeArticleen_ZA

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