Modeling and nonlinear control of a continuous KCl-NaCl crystallizer

A controller is developed by combining the extended linear quadratic matrix control (EQDMC) and neural network algorithms. The dynamic neural network scheme is used to identify the process and generate a nonlinear model. The control. algorithm is applied to a multi-input multi-output (MIMO) evaporative cooling KCl-NaCl-H2O crystallizer. Closed loop responses of the system using the proposed algorithm and those of PID controllers are compared. It is shown that in all cases, the response of the proposed controller to step changes in setpoints is faster than the PID controllers.