A mechanism-data-driven hybrid model-based adaptive model predictive control method for alumina evaporation process

Precise control of the outlet density in the alumina evaporation process is of critical importance for ensuring stable system operation. However, due to the pronounced nonlinearity, strong coupling, and frequent operating condition fluctuations inherent in this process, outlet density control still faces significant challenges, including insufficient modeling accuracy, frequent operational disturbances, and difficulties in control parameter tuning. To address these issues, this paper proposes a mechanism-data-driven hybrid model-based adaptive model predictive control (MDH-AMPC) method for the alumina evaporation process. First, a hybrid model integrating heat and mass transfer mechanisms with data-driven approach is constructed to enhance the representation of the dynamic characteristics of the evaporation process, thereby providing a reliable prediction basis for model predictive control. Then, based on the hybrid model, a model predictive control framework is developed, in which a disturbance compensation mechanism is incorporated to improve the system's disturbance rejection capability. Furthermore, the JAYA algorithm is employed to adaptively adjust control parameters, enabling the controller to maintain stable operation and efficient response under practical operating conditions. Simulation results under high-concentration outlet section demonstrate that the proposed MDH-AMPC outperforms conventional MPC, PID, and manual operation in terms of control stability and response speed, thereby exhibiting its excellent comprehensive control performance and engineering application potential.