Modeling and prediction of heat and mass transfer in industrial systems via ML

Heat and mass transfer phenomena are at the core of numerous industrial operations, ranging from energy conversion and chemical processing to drying, cooling, and separation technologies. Accurate modeling and prediction of these processes are essential for improving efficiency, reducing costs, and enabling sustainable solutions. Conventional numerical methods, such as finite element or computational fluid dynamics approaches, often require extensive computational resources and are sensitive to uncertainties in boundary conditions and material properties. In recent years, machine learning (ML) algorithms have emerged as a powerful tool for analyzing nonlinear, multiscale, and high-dimensional data. This study explores the potential of ML techniques for modeling and predicting industrial heat and mass transfer processes. By employing supervised learning methods such as artificial neural networks, support vector regression, and ensemble models, we establish predictive frameworks trained on both experimental datasets and simulated results. The proposed models capture complex transport mechanisms with improved accuracy while reducing computational cost compared to traditional approaches. The findings highlight that machine learning-based predictive models not only enhance process understanding but also provide a foundation for real-time monitoring and control in industrial systems. These results suggest a promising direction for integrating data-driven models into digital twins and advanced process optimization strategies.

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