Application of LQR and MPC on Distillation and Batch Crystallization Process

As the widespread use of a batch crystallization process in many industries, finding an optimal operating condition and effective control strategy are significant for improving product quality and downstream processing. To achieve these, an accurate model is required to predict the process behavior and to design controller. However, due to unknown disturbances and batch-to batch variations, the kinetic parameters obtained from experimental study may not represent the real process resulting in poor control and estimation performances. In this work, improvement of batch crystallization control under uncertain kinetic parameters has been proposed. Model predictive controller (MPC) is used for optimal control of distillation and batch crystallization process. Feedback control strategy is found out using LQR technique. A Kalman filter has been designed to estimate uncertain parameters and immeasurable states. A MPC TOOLBOX in MATLAB software is used to obtain desired crystal size distribution (CSD).