Dynamic simulation and model predictive control for gas antisolvent ecrystallization process

Crystallization processes are widely used in various applications such as polymers, dyes, pharmaceuticals, and explosives. Novel crystallization processes using supercritical fluids have recently attracted much attention due to the environmental advantage of using environmentally benign carbon dioxide as a solvent. Gas anti-solvent (GAS) process is one of the most common supercritical processes, which utilize the low solubility of the anti-solvent to produce particles. In this work, a mathematical model from a population balance model (PBM) is developed to describe the particle size distribution (PSD) of GAS process and it is numerically solved. The developed PBM involves a set of partial differentials equation with algebraic constraints, which requires effective numerical approaches. A solution scheme based on a high resolution method is proposed to solve the dynamic problem using MATLAB. In addition, the effect of the supercritical CO₂ addition rate is investigated. At last, we present the results of open-loop test for the system and propose a model predictive control (MPC) strategy to control the particle size distribution of the GAS process.