Advanced Computational Models for Thermal System Optimization Using Machine Learning and Hybrid Techniques

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.