Abstract:
Crystallization processes have been widely used for separation in many fields to provide a high purity product. In this work, dynamic optimization and neural network (NN) have been applied to improve the quality of the product: citric acid. In the dynamic optimization, optimization problems maximizing both crystal yield and crystal size have been formulated. In this work, a neural network forward model has been designed to provide estimations of crystallizer temperature, concentration of solution and jacket temperature as well as a neural network inverse model has been developed to predict jacket temperature set point. The Levenberg Marquadt algorithm has been used to train the networks and optimal neural network architectures have been determined by a mean squared error (MSE) minimization technique. In controller design, neural network direct inverse control (NNDIC) and neural network model predictive control (NNMPC) strategies have been applied to control the crystallizer temperature. The simulation results have shown that the obtained crystal size from optimization problem is 19% and 30% larger than cooling and evaporation methods, respectively moreover yield increase more than 50%. Both neural network forward and inverse models show good accuracy for the prediction of the system. The robustness of controller is investigated with respect to parameters mismatch. The results have shown that the NNMPC controller provides superior control performance in all case studies.
