Integrating artificial Intelligence-Based metaheuristic optimization with Machine learning to enhance Nanomaterial-Containing latent heat thermal energy storage systems
Progress in artificial intelligence and machine learning has significantly improved the capability to accurately predict the properties of nano-enhanced phase change materials (NePCMs). Machine learning can be a reliable solution to avoid the cost and time of repetitive tests to measure thermophysic...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Energy Conversion and Management: X |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590174524003131 |
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| author | Ali Basem Hanaa Kadhim Abdulaali As’ad Alizadeh Pradeep Kumar Singh Komal Parashar Ali E. Anqi Husam Rajab Pancham Cajla H. Maleki |
| author_facet | Ali Basem Hanaa Kadhim Abdulaali As’ad Alizadeh Pradeep Kumar Singh Komal Parashar Ali E. Anqi Husam Rajab Pancham Cajla H. Maleki |
| author_sort | Ali Basem |
| collection | DOAJ |
| description | Progress in artificial intelligence and machine learning has significantly improved the capability to accurately predict the properties of nano-enhanced phase change materials (NePCMs). Machine learning can be a reliable solution to avoid the cost and time of repetitive tests to measure thermophysical characteristics. The present study proposes a novel strategy to provide optimal data-driven models for predicting the latent heat of NePCMs. In this regard, a database containing 22 laboratory studies with five input variables, including nanomaterials characteristics (mass fraction, density, and average diameter) and PCM properties (latent heat and density) is used. The proposed strategy combines machine learning algorithms, including multilayer perceptron neural network (MLPNN), generalized additive model (GAM), Gaussian kernel regression (GKR), support vector machine (SVM), and Gaussian process regression (GPR) with artificial intelligence-based metaheuristic optimization algorithms (PSO and GA) to optimize their structural/training parameters. Statistical criteria, margin of deviation plots, violin graphs, and external experimental datasets are utilized to assess the developed optimized models. Results showed a noticeable difference in the effectiveness of the GA and PSO algorithms in optimizing the structural and training parameters of various machine learning approaches. Both GA and PSO provided the same parameters for the MLPNN model, with an R-value of 0.9558 and a mean absolute percentage error of 3.7%, indicating the best accuracy among all models. The optimized models based on GAM and GPR were classified in the second accuracy level. On the other hand, the models generated using SVM and GKR were found to be the least accurate. |
| format | Article |
| id | doaj-art-ea5f565aff9643e7bac74b248065af9d |
| institution | Kabale University |
| issn | 2590-1745 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Conversion and Management: X |
| spelling | doaj-art-ea5f565aff9643e7bac74b248065af9d2025-01-06T04:08:56ZengElsevierEnergy Conversion and Management: X2590-17452025-01-0125100835Integrating artificial Intelligence-Based metaheuristic optimization with Machine learning to enhance Nanomaterial-Containing latent heat thermal energy storage systemsAli Basem0Hanaa Kadhim Abdulaali1As’ad Alizadeh2Pradeep Kumar Singh3Komal Parashar4Ali E. Anqi5Husam Rajab6Pancham Cajla7H. Maleki8Faculty of Engineering, Warith Al-Anbiyaa University, Karbala 56001, IraqDepartment of Chemical Engineering, University of Technology- Iraq, Baghdad, IraqDepartment of Civil Engineering, College of Engineering, Cihan University-Erbil, Erbil, IraqDepartment of Mechanical Engineering, Institute of Engineering and Technology, GLA University, Mathura (U.P.), IndiaCentre of Research Impact and Outcome, Chitkara University, Rajpura 140417, Punjab, IndiaDepartment of Mechanical Engineering, College of Engineering, King Khalid University, Abha 61421, Saudi ArabiaCollege of Engineering, Department of Mechanical Engineering, Najran University, King Abdulaziz Road, P.O Box 1988, Najran, Kingdom of Saudi ArabiaChitkara Centre for Research and Development, Chitkara University, Himachal Pradesh-174103, IndiaRenewable Energy Research Group, Isfahan, Iran; Corresponding author.Progress in artificial intelligence and machine learning has significantly improved the capability to accurately predict the properties of nano-enhanced phase change materials (NePCMs). Machine learning can be a reliable solution to avoid the cost and time of repetitive tests to measure thermophysical characteristics. The present study proposes a novel strategy to provide optimal data-driven models for predicting the latent heat of NePCMs. In this regard, a database containing 22 laboratory studies with five input variables, including nanomaterials characteristics (mass fraction, density, and average diameter) and PCM properties (latent heat and density) is used. The proposed strategy combines machine learning algorithms, including multilayer perceptron neural network (MLPNN), generalized additive model (GAM), Gaussian kernel regression (GKR), support vector machine (SVM), and Gaussian process regression (GPR) with artificial intelligence-based metaheuristic optimization algorithms (PSO and GA) to optimize their structural/training parameters. Statistical criteria, margin of deviation plots, violin graphs, and external experimental datasets are utilized to assess the developed optimized models. Results showed a noticeable difference in the effectiveness of the GA and PSO algorithms in optimizing the structural and training parameters of various machine learning approaches. Both GA and PSO provided the same parameters for the MLPNN model, with an R-value of 0.9558 and a mean absolute percentage error of 3.7%, indicating the best accuracy among all models. The optimized models based on GAM and GPR were classified in the second accuracy level. On the other hand, the models generated using SVM and GKR were found to be the least accurate.http://www.sciencedirect.com/science/article/pii/S2590174524003131Machine learningArtificial intelligenceMultilayer perceptron neural networkPhase change materialThermal energy storageLatent heat |
| spellingShingle | Ali Basem Hanaa Kadhim Abdulaali As’ad Alizadeh Pradeep Kumar Singh Komal Parashar Ali E. Anqi Husam Rajab Pancham Cajla H. Maleki Integrating artificial Intelligence-Based metaheuristic optimization with Machine learning to enhance Nanomaterial-Containing latent heat thermal energy storage systems Energy Conversion and Management: X Machine learning Artificial intelligence Multilayer perceptron neural network Phase change material Thermal energy storage Latent heat |
| title | Integrating artificial Intelligence-Based metaheuristic optimization with Machine learning to enhance Nanomaterial-Containing latent heat thermal energy storage systems |
| title_full | Integrating artificial Intelligence-Based metaheuristic optimization with Machine learning to enhance Nanomaterial-Containing latent heat thermal energy storage systems |
| title_fullStr | Integrating artificial Intelligence-Based metaheuristic optimization with Machine learning to enhance Nanomaterial-Containing latent heat thermal energy storage systems |
| title_full_unstemmed | Integrating artificial Intelligence-Based metaheuristic optimization with Machine learning to enhance Nanomaterial-Containing latent heat thermal energy storage systems |
| title_short | Integrating artificial Intelligence-Based metaheuristic optimization with Machine learning to enhance Nanomaterial-Containing latent heat thermal energy storage systems |
| title_sort | integrating artificial intelligence based metaheuristic optimization with machine learning to enhance nanomaterial containing latent heat thermal energy storage systems |
| topic | Machine learning Artificial intelligence Multilayer perceptron neural network Phase change material Thermal energy storage Latent heat |
| url | http://www.sciencedirect.com/science/article/pii/S2590174524003131 |
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