Predicting the heat capacity of strontium-praseodymium oxysilicate SrPr4(SiO4)3O using machine learning, deep learning, and hybrid models
A thorough examination of the specific heat capacity of strontium-praseodymium oxysilicate, which serves as a vital thermophysical parameter was investigated. This parameter is important in enhancing the performance and efficiency of heat transfer-based equipment, as well as applications in catalysi...
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Elsevier
2025-03-01
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| Series: | Chemical Thermodynamics and Thermal Analysis |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667312624000270 |
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| author | Amir Hossein Sheikhshoaei Ali Khoshsima Davood Zabihzadeh |
| author_facet | Amir Hossein Sheikhshoaei Ali Khoshsima Davood Zabihzadeh |
| author_sort | Amir Hossein Sheikhshoaei |
| collection | DOAJ |
| description | A thorough examination of the specific heat capacity of strontium-praseodymium oxysilicate, which serves as a vital thermophysical parameter was investigated. This parameter is important in enhancing the performance and efficiency of heat transfer-based equipment, as well as applications in catalysis, insulation materials, and advanced ceramics. Machine learning (ML) offers a potent solution for forecasting diverse processes using both data-driven and knowledge-based methods. In this study, the capability of five advanced machine learning models, including Random Forest (RF), Gradient Boosting (GBoost), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Decision Tree (DT) models, and three deep learning models, TabNet, Deep Belief Network (DBN), and Deep Neural Network (DNN) was investigated. Our analysis indicates that the Random Forest and Deep Belief Network models outperform all other competing models. Additionally, we introduce a hybrid model combining these two models, which enhances the accuracy of predicting the heat capacity of strontium-praseodymium oxysilicate. Specifically, the Hybrid model achieved an AAPRE of 0.42213, an RMSE of 0.38914, and a near-perfect R2 value of 0.9999. The analysis indicated that the Hybrid model could accurately anticipate how the heat capacity of strontium-praseodymium oxysilicate would change. In conclusion, outlier detection was performed using the Leverage method to identify any anomalous data points, thereby illustrating the effective range of the proposed Hybrid model. |
| format | Article |
| id | doaj-art-ea8036e026734396b962d29354afeb12 |
| institution | Kabale University |
| issn | 2667-3126 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Chemical Thermodynamics and Thermal Analysis |
| spelling | doaj-art-ea8036e026734396b962d29354afeb122025-02-10T04:35:26ZengElsevierChemical Thermodynamics and Thermal Analysis2667-31262025-03-0117100154Predicting the heat capacity of strontium-praseodymium oxysilicate SrPr4(SiO4)3O using machine learning, deep learning, and hybrid modelsAmir Hossein Sheikhshoaei0Ali Khoshsima1Davood Zabihzadeh2School of Petroleum and Chemical Engineering, Hakim Sabzevari University, Sabzevar, IranSchool of Petroleum and Chemical Engineering, Hakim Sabzevari University, Sabzevar, Iran; Corresponding author.Computer Engineering Department, Hakim Sabzevari University, Sabzevar, IranA thorough examination of the specific heat capacity of strontium-praseodymium oxysilicate, which serves as a vital thermophysical parameter was investigated. This parameter is important in enhancing the performance and efficiency of heat transfer-based equipment, as well as applications in catalysis, insulation materials, and advanced ceramics. Machine learning (ML) offers a potent solution for forecasting diverse processes using both data-driven and knowledge-based methods. In this study, the capability of five advanced machine learning models, including Random Forest (RF), Gradient Boosting (GBoost), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Decision Tree (DT) models, and three deep learning models, TabNet, Deep Belief Network (DBN), and Deep Neural Network (DNN) was investigated. Our analysis indicates that the Random Forest and Deep Belief Network models outperform all other competing models. Additionally, we introduce a hybrid model combining these two models, which enhances the accuracy of predicting the heat capacity of strontium-praseodymium oxysilicate. Specifically, the Hybrid model achieved an AAPRE of 0.42213, an RMSE of 0.38914, and a near-perfect R2 value of 0.9999. The analysis indicated that the Hybrid model could accurately anticipate how the heat capacity of strontium-praseodymium oxysilicate would change. In conclusion, outlier detection was performed using the Leverage method to identify any anomalous data points, thereby illustrating the effective range of the proposed Hybrid model.http://www.sciencedirect.com/science/article/pii/S2667312624000270Machine learningHeat capacityApatiteDeep learningHybrid models |
| spellingShingle | Amir Hossein Sheikhshoaei Ali Khoshsima Davood Zabihzadeh Predicting the heat capacity of strontium-praseodymium oxysilicate SrPr4(SiO4)3O using machine learning, deep learning, and hybrid models Chemical Thermodynamics and Thermal Analysis Machine learning Heat capacity Apatite Deep learning Hybrid models |
| title | Predicting the heat capacity of strontium-praseodymium oxysilicate SrPr4(SiO4)3O using machine learning, deep learning, and hybrid models |
| title_full | Predicting the heat capacity of strontium-praseodymium oxysilicate SrPr4(SiO4)3O using machine learning, deep learning, and hybrid models |
| title_fullStr | Predicting the heat capacity of strontium-praseodymium oxysilicate SrPr4(SiO4)3O using machine learning, deep learning, and hybrid models |
| title_full_unstemmed | Predicting the heat capacity of strontium-praseodymium oxysilicate SrPr4(SiO4)3O using machine learning, deep learning, and hybrid models |
| title_short | Predicting the heat capacity of strontium-praseodymium oxysilicate SrPr4(SiO4)3O using machine learning, deep learning, and hybrid models |
| title_sort | predicting the heat capacity of strontium praseodymium oxysilicate srpr4 sio4 3o using machine learning deep learning and hybrid models |
| topic | Machine learning Heat capacity Apatite Deep learning Hybrid models |
| url | http://www.sciencedirect.com/science/article/pii/S2667312624000270 |
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