The formation of crystals from solutions plays a key role in various industrial applications. In this study, a new approach is presented into the optimal control of batch cooling crystallizers through a genetic algorithm. The Population balance is formularized for a typical batch crystallizer. The objective functions considered here are related to quality of products at the end of the batch. These functions are objective function of maximum mean weight size, closeness to the specified value and minimum coefficient of variation. By using an optimization algorithm (genetic algorithm), the minimum and maximum values of the objective function the input temperature parameter are obtained. The obtained results show that various trajectories can be used for cooling batch crystallizer based on objective functions. This method is applied for the potassium-nitrate system.
Amini, Y., Gerdroodbary, M. B., Pishvaie, M. R., Moradi, R., & Monfared, S. M. (2016). Optimal control of batch cooling crystallizers by using genetic algorithm. Case Studies in Thermal Engineering, 8, 300–310. https://doi.org/10.1016/j.csite.2016.09.001