The proposed research project aims to develop innovative control strategies to enhance the performance and efficiency of systems involving heat and mass transfer. The project is structured around four key objectives. First, it focuses on data-driven process identification to improve efficiency in complex industrial processes. Second, it involves the design of a multiple-model-based Model Predictive Control (MPC) system to address uncertainties and variations in system behavior. Third, robust MPC methods will be developed to mitigate the effects of plant-model mismatches and disturbances. Finally, the project will validate the performance of the proposed control strategies through real-time applications, ensuring practical applicability and effectiveness in industrial environments. This research will contribute to more efficient, reliable, and adaptable control systems in various process industries.