Model-Based Optimal Cooling Strategy for Batch Crystallization Processes

Abstract

A model-based optimization strategy is developed for optimal cooling profile of batch crystallization. Numerical method of lines is used to solve the batch crystallizer model by discretization of population density function over the size length. This discretization scheme is suitable to give a better physical insight about the dynamic behavior of the process for both the liquid phase and particles solid phase. The crystallization kinetic parameters are estimated via reconciling experimental data with model predictions. Here, two measurements, namely concentration, which describes the continuous liquid phase, and crystal size distribution, which describes the particles solid phase, are considered in the model validation. The obtained simulation model with the estimated parameters is utilized in optimizing the applied cooling rate strategy of the batch crystallizer. A new formulation of the objective function is presented to continuously compute the crystallizer temperature profile. The effectiveness of the proposed strategy is illustrated on a case study of potassium chloride-water system.