Adaptive Decoupling Switching Control of the Forced-Circulation Evaporation System Using Neural Networks

For the forced-circulation evaporation process of alumina production, the control objectives include maintaining the liquid level and fast tracking of the product density with respect to its setpoint. However, with the strong coupling between the level control and product density control loops and the process exhibits strong nonlinearities, conventional control strategies, such as proportional-integral-derivative and other linear control design, cannot achieve satisfactory control performance and meet production demands. By augmenting the forced-circulation evaporation system model with dynamics of its operating valves, a nonlinear adaptive decoupling switching control strategy is proposed. This control strategy includes a linear adaptive decoupling controller, a neural-network-based nonlinear adaptive decoupling controller, and a switching mechanism. The linear adaptive decoupling controller is used to reduce the coupling between the two control loops. The neural-network-based nonlinear adaptive decoupling controller is employed to improve the transient performance and reduce the effects of the nonlinearities on the system, and the switching mechanism is introduced to guarantee the input-output stability of the closed-loop system. Simulation results show that the proposed method can decouple the loops effectively for the forced-circulation evaporation system and thus improve the evaporation efficiency.