Prediction of the thermal behavior of multi-walled carbon nanotubes-CuO-CeO2 (20-40-40)/water hybrid nanofluid using different types of regressors and evolutionary algorithms for designing the best artificial neural network modeling
For conducting an analysis of the experimental data, it is imperative to establish a mathematical correlation between the input and output variables. This entails executing a curve fitting or regression procedure on the data, for which numerous methodologies exist. Within the scope of present invest...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016823009705 |
| _version_ | 1797401731512926208 |
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| author | Reza Rostamzadeh-Renani Mohammadreza Baghoolizadeh S. Mohammad Sajadi Mostafa Pirmoradian Mohammad Rostamzadeh-Renani Sh. Baghaei Soheil Salahshour |
| author_facet | Reza Rostamzadeh-Renani Mohammadreza Baghoolizadeh S. Mohammad Sajadi Mostafa Pirmoradian Mohammad Rostamzadeh-Renani Sh. Baghaei Soheil Salahshour |
| author_sort | Reza Rostamzadeh-Renani |
| collection | DOAJ |
| description | For conducting an analysis of the experimental data, it is imperative to establish a mathematical correlation between the input and output variables. This entails executing a curve fitting or regression procedure on the data, for which numerous methodologies exist. Within the scope of present investigation, the design variables encompass the solid volume fraction (φ) and temperature. Thermal conductivity (TC) of MWCNT-CuO-CeO2 (20-40-40)/water hybrid nanofluid (HNF) is also the objective function. Ten different types of regressors are utilized for regression operations which are Multiple Linear Regression (MLR), Decision Tree (D-Tree), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Extreme Learning Machine (ELM), Radial Basis Function (RBF), Adaptive Neuro-Fuzzy Inference System (ANFIS), Gaussian Process Regression (GPR), Multivariate Polynomial Regression (MPR) and Group Method of Data Handling (GMDH). Once the governing equations linking the design variables and the objective functions have been established, these equations can be employed to forecast the simulation data. By substituting the above input values into the equations, we can calculate the corresponding output values for the TC of the HNF. The results obtained from the MPR algorithm are compared to the experimental data. For the GPR, MLR, D-Tree, ELM, MPR, MLP, RBF, SVM, ANFIS, and GMDH algorithms, the maximum margin of error is found to be 0.031, 0.02579, 0.028946, 0.033889, 0.01568, 0.02515, 0.03485, 0.03, 0.0385, and 0.0178, respectively. Moreover, the kernel density estimation diagram indicates the gap between experimental data and data predicted by regression algorithms. Finally, it is evident that the MPR algorithm demonstrates to have a reduced residual dispersion, with the residuals approaching zero. |
| first_indexed | 2024-03-09T02:15:29Z |
| format | Article |
| id | doaj.art-0cd920bc47ae4c76919f2516f55ea1ba |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-03-09T02:15:29Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-0cd920bc47ae4c76919f2516f55ea1ba2023-12-07T05:27:55ZengElsevierAlexandria Engineering Journal1110-01682023-12-0184184203Prediction of the thermal behavior of multi-walled carbon nanotubes-CuO-CeO2 (20-40-40)/water hybrid nanofluid using different types of regressors and evolutionary algorithms for designing the best artificial neural network modelingReza Rostamzadeh-Renani0Mohammadreza Baghoolizadeh1S. Mohammad Sajadi2Mostafa Pirmoradian3Mohammad Rostamzadeh-Renani4Sh. Baghaei5Soheil Salahshour6Energy Department, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, ItalyDepartment of Mechanical Engineering, Shahrekord University, Shahrekord 88186-34141, IranDepartment of Nutrition, Cihan University-Erbil, Kurdistan Region, IraqDepartment of Mechanical Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr, IranEnergy Department, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, ItalyDepartment of Mechanical Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr, IranFaculty of Engineering and Natural Sciences, Istanbul Okan University, Istanbul, Turkey; Faculty of Engineering and Natural Sciences, Bahcesehir University, Istanbul, Turkey; Department of Computer Science and Mathematics, Lebanese American University, Beirut, LebanonFor conducting an analysis of the experimental data, it is imperative to establish a mathematical correlation between the input and output variables. This entails executing a curve fitting or regression procedure on the data, for which numerous methodologies exist. Within the scope of present investigation, the design variables encompass the solid volume fraction (φ) and temperature. Thermal conductivity (TC) of MWCNT-CuO-CeO2 (20-40-40)/water hybrid nanofluid (HNF) is also the objective function. Ten different types of regressors are utilized for regression operations which are Multiple Linear Regression (MLR), Decision Tree (D-Tree), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Extreme Learning Machine (ELM), Radial Basis Function (RBF), Adaptive Neuro-Fuzzy Inference System (ANFIS), Gaussian Process Regression (GPR), Multivariate Polynomial Regression (MPR) and Group Method of Data Handling (GMDH). Once the governing equations linking the design variables and the objective functions have been established, these equations can be employed to forecast the simulation data. By substituting the above input values into the equations, we can calculate the corresponding output values for the TC of the HNF. The results obtained from the MPR algorithm are compared to the experimental data. For the GPR, MLR, D-Tree, ELM, MPR, MLP, RBF, SVM, ANFIS, and GMDH algorithms, the maximum margin of error is found to be 0.031, 0.02579, 0.028946, 0.033889, 0.01568, 0.02515, 0.03485, 0.03, 0.0385, and 0.0178, respectively. Moreover, the kernel density estimation diagram indicates the gap between experimental data and data predicted by regression algorithms. Finally, it is evident that the MPR algorithm demonstrates to have a reduced residual dispersion, with the residuals approaching zero.http://www.sciencedirect.com/science/article/pii/S1110016823009705Thermal behaviorHybrid nanofluidRegressorsEvolutionary algorithmsArtificial neural network modeling |
| spellingShingle | Reza Rostamzadeh-Renani Mohammadreza Baghoolizadeh S. Mohammad Sajadi Mostafa Pirmoradian Mohammad Rostamzadeh-Renani Sh. Baghaei Soheil Salahshour Prediction of the thermal behavior of multi-walled carbon nanotubes-CuO-CeO2 (20-40-40)/water hybrid nanofluid using different types of regressors and evolutionary algorithms for designing the best artificial neural network modeling Alexandria Engineering Journal Thermal behavior Hybrid nanofluid Regressors Evolutionary algorithms Artificial neural network modeling |
| title | Prediction of the thermal behavior of multi-walled carbon nanotubes-CuO-CeO2 (20-40-40)/water hybrid nanofluid using different types of regressors and evolutionary algorithms for designing the best artificial neural network modeling |
| title_full | Prediction of the thermal behavior of multi-walled carbon nanotubes-CuO-CeO2 (20-40-40)/water hybrid nanofluid using different types of regressors and evolutionary algorithms for designing the best artificial neural network modeling |
| title_fullStr | Prediction of the thermal behavior of multi-walled carbon nanotubes-CuO-CeO2 (20-40-40)/water hybrid nanofluid using different types of regressors and evolutionary algorithms for designing the best artificial neural network modeling |
| title_full_unstemmed | Prediction of the thermal behavior of multi-walled carbon nanotubes-CuO-CeO2 (20-40-40)/water hybrid nanofluid using different types of regressors and evolutionary algorithms for designing the best artificial neural network modeling |
| title_short | Prediction of the thermal behavior of multi-walled carbon nanotubes-CuO-CeO2 (20-40-40)/water hybrid nanofluid using different types of regressors and evolutionary algorithms for designing the best artificial neural network modeling |
| title_sort | prediction of the thermal behavior of multi walled carbon nanotubes cuo ceo2 20 40 40 water hybrid nanofluid using different types of regressors and evolutionary algorithms for designing the best artificial neural network modeling |
| topic | Thermal behavior Hybrid nanofluid Regressors Evolutionary algorithms Artificial neural network modeling |
| url | http://www.sciencedirect.com/science/article/pii/S1110016823009705 |
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