Advanced Computational Models for Thermal System Optimization Using Machine Learning and Hybrid Techniques
Kathiravan M
Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India.
Department of Marketing and Human Resources, Loyola Institute of Business Administration, Chennai, Tamil Nadu, India, Department of Business Analytics, Loyola Institute of Business Administration, Chennai, Tamil Nadu, India.
Thermal systems are fundamental to a wide range of industrial applications, where performance and efficiency critically depend on precise and reliable modeling techniques. Traditional Artificial Neural Network (ANN)-based models, although widely used, often struggle with overfitting, limited generalization, and inadequate representation of the complex, nonlinear behavior inherent to thermal processes. These limitations restrict their deployment in real-time and dynamic operational settings. This study aims to enhance the predictive accuracy and robustness of thermal system modeling by integrating advanced machine learning (ML) techniques with hybrid optimization strategies. The research focuses on complex systems such as heat exchangers, gas-solid fluidized beds, and thermal energy storage units. A comprehensive methodology involving industrial data collection, preprocessing via normalization and feature selection, and model training using individual and hybrid ML algorithms is proposed. Performance is benchmarked using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R² metrics. Advanced methods like Deep Learning (DL), Support Vector Machines (SVM), Genetic Algorithms (GA), Ensemble Learning, Transfer Learning, and Evolutionary Optimization are employed to address shortcomings of conventional approaches. Results demonstrate that hybrid models outperform standalone ANN-based techniques in prediction accuracy and generalization.
Keywords
Hybrid Machine Learning, Thermal Systems, Evolutionary Optimization, Heat Transfer Modeling, Ensemble Learning, Deep Learning.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Kathiravan M, Joe Arun C S J, Kishore Kunal, Parthasarthy K, Mohamedyaseen A and Vairavel Madeshwaren;
Writing- Original Draft Preparation: Kathiravan M, Joe Arun C S J, Kishore Kunal, Parthasarthy K, Mohamedyaseen A and Vairavel Madeshwaren;
Visualization: Kathiravan M, Joe Arun C S J and Kishore Kunal;
Investigation: Parthasarthy K, Mohamedyaseen A and Vairavel Madeshwaren;
Supervision: Kathiravan M, Joe Arun C S J and Kishore Kunal;
Validation: Parthasarthy K, Mohamedyaseen A and Vairavel Madeshwaren;
Writing- Reviewing and Editing: Kathiravan M, Joe Arun C S J, Kishore Kunal, Parthasarthy K, Mohamedyaseen A and Vairavel Madeshwaren; All authors reviewed the results and approved the final version of the manuscript.
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Kathiravan M
Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India.
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Cite this article
Kathiravan M, Joe Arun C S J, Kishore Kunal, Parthasarthy K, Mohamedyaseen A and Vairavel Madeshwaren, “Advanced Computational Models for Thermal System Optimization Using Machine Learning and Hybrid Techniques”, Journal of Machine and Computing, vol.5, no.4, pp. 2361-2372, October 2025, doi: 10.53759/7669/jmc202505183.
