SUMMARY
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. The neural networks have been developed to provide NN models to be used in the formulation of not only neural network inverse control (NNDIC) but also neural network model predictive control (NNMPC) strategies. The Levenberg Marquadt algorithm has been used to train the network and optimal neural network architectures have been determined by a mean squared error (MSE) minimization technique. In addition, a neural network model has been designed to provide estimates of the temperature and the concentration of the crystallizer. These estimates have been incorporated into the NNMPC controller. In the NNDIC controller, another neural network model has been applied to predict the set point of jacket temperature. The simulation results have shown that the obtained crystal size is increased by 19% and 30% compared to that by cooling and evaporation methods respectively and the obtained yield is increased more than 50%. The robustness of the proposed controller is investigated with respect to parameters mismatches. The results have shown that the NNMPC controller provides superior control performances in all case studies.