Modelling and optimization of seeded batch crystallizers

The existing model identification methods for estimating nucleation and crystal growth kinetic parameters are generally based upon simplified population balance models, such as moment equations, which contain insufficient information on the crystal size distribution (CSD). This paper deals with model identification and optimization of batch-seeded crystallizers. The final product CSD, temperature profile and concentration profile are used for identification. The reliability and precision of estimates are analyzed. Optimal temperature profile is determined such that an objective function pertinent to the final product qualities is minimized. The optimization algorithm finds the minimum of an objective function with respect to a parameter vector temperature input, subject to the mass balance and energy balance dynamics as well as the population balance equation (PBE). Simulation results for different objective functions are given to show the effectiveness of the proposed method. Compared with linear cooling profile, the optimal cooling profile is able to reduce the volumes of fines by 53.2% to improve product quality.