Estimating the Heat Capacity of Non-Newtonian Ionanofluid Systems Using ANN, ANFIS, and SGB Tree Algorithms
This 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 (<i>C&...
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MDPI AG
2020-09-01
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| author | Reza Daneshfar Amin Bemani Masoud Hadipoor Mohsen Sharifpur Hafiz Muhammad Ali Ibrahim Mahariq Thabet Abdeljawad |
| author_facet | Reza Daneshfar Amin Bemani Masoud Hadipoor Mohsen Sharifpur Hafiz Muhammad Ali Ibrahim Mahariq Thabet Abdeljawad |
| author_sort | Reza Daneshfar |
| collection | DOAJ |
| description | This 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 (<i>C<sub>p</sub></i>) of ionanofluids in terms of the nanoparticle concentration (<i>x</i>) and the critical temperature (<i>T<sub>c</sub></i>), operational temperature (<i>T</i>), acentric factor (<i>ω</i>), and molecular weight (<i>M<sub>w</sub></i>) 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 <i>R</i><sup>2</sup> 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 <i>C<sub>p</sub></i> 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 <i>C<sub>p</sub></i> of ionanofluids. Additionally, the sensitivity analysis showed that <i>C<sub>p</sub></i> is directly related to <i>T</i>, <i>M<sub>w</sub></i>, and <i>T<sub>c</sub></i>, and has an inverse relation with <i>ω</i> and <i>x</i>. <i>M<sub>w</sub></i> and <i>T<sub>c</sub></i> had the highest impact and <i>ω</i> had the lowest impact on <i>C<sub>p</sub></i>. |
| first_indexed | 2024-03-10T16:18:50Z |
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| institution | Directory Open Access Journal |
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| language | English |
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| publishDate | 2020-09-01 |
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| spelling | doaj.art-a137957940fc4f05b2adc9bf29a87cf12023-11-20T13:51:28ZengMDPI AGApplied Sciences2076-34172020-09-011018643210.3390/app10186432Estimating the Heat Capacity of Non-Newtonian Ionanofluid Systems Using ANN, ANFIS, and SGB Tree AlgorithmsReza Daneshfar0Amin Bemani1Masoud Hadipoor2Mohsen Sharifpur3Hafiz Muhammad Ali4Ibrahim Mahariq5Thabet Abdeljawad6Department of Petroleum Engineering, Ahwaz Faculty of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz P.O. Box 63431, IranDepartment of Petroleum Engineering, Ahwaz Faculty of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz P.O. Box 63431, IranDepartment of Petroleum Engineering, Ahwaz Faculty of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz P.O. Box 63431, IranInstitute of Research and Development, Duy Tan University, Da Nang 550000, VietnamMechancial Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi ArabiaCollege of Engineering and Technology, American University of the Middle East, Al-Eqaila 54200, KuwaitDepartment of Mathematics and General Sciences, Prince Sultan University, P.O. Box 66833, Riyadh 11586, Saudi ArabiaThis 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 (<i>C<sub>p</sub></i>) of ionanofluids in terms of the nanoparticle concentration (<i>x</i>) and the critical temperature (<i>T<sub>c</sub></i>), operational temperature (<i>T</i>), acentric factor (<i>ω</i>), and molecular weight (<i>M<sub>w</sub></i>) 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 <i>R</i><sup>2</sup> 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 <i>C<sub>p</sub></i> 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 <i>C<sub>p</sub></i> of ionanofluids. Additionally, the sensitivity analysis showed that <i>C<sub>p</sub></i> is directly related to <i>T</i>, <i>M<sub>w</sub></i>, and <i>T<sub>c</sub></i>, and has an inverse relation with <i>ω</i> and <i>x</i>. <i>M<sub>w</sub></i> and <i>T<sub>c</sub></i> had the highest impact and <i>ω</i> had the lowest impact on <i>C<sub>p</sub></i>.https://www.mdpi.com/2076-3417/10/18/6432ionic liquidnanofluidheat capacitysoft computing modelsparticle swarm optimization |
| spellingShingle | Reza Daneshfar Amin Bemani Masoud Hadipoor Mohsen Sharifpur Hafiz Muhammad Ali Ibrahim Mahariq Thabet Abdeljawad Estimating the Heat Capacity of Non-Newtonian Ionanofluid Systems Using ANN, ANFIS, and SGB Tree Algorithms Applied Sciences ionic liquid nanofluid heat capacity soft computing models particle swarm optimization |
| title | Estimating the Heat Capacity of Non-Newtonian Ionanofluid Systems Using ANN, ANFIS, and SGB Tree Algorithms |
| title_full | Estimating the Heat Capacity of Non-Newtonian Ionanofluid Systems Using ANN, ANFIS, and SGB Tree Algorithms |
| title_fullStr | Estimating the Heat Capacity of Non-Newtonian Ionanofluid Systems Using ANN, ANFIS, and SGB Tree Algorithms |
| title_full_unstemmed | Estimating the Heat Capacity of Non-Newtonian Ionanofluid Systems Using ANN, ANFIS, and SGB Tree Algorithms |
| title_short | Estimating the Heat Capacity of Non-Newtonian Ionanofluid Systems Using ANN, ANFIS, and SGB Tree Algorithms |
| title_sort | estimating the heat capacity of non newtonian ionanofluid systems using ann anfis and sgb tree algorithms |
| topic | ionic liquid nanofluid heat capacity soft computing models particle swarm optimization |
| url | https://www.mdpi.com/2076-3417/10/18/6432 |
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