A Workflow for Crystallization Process Design with Simultaneous Process Optimization and Solvent Selection based on the Perturbed‐Chain Statistical Associating Fluid Theory

Abstract

Solvent selection is a critical part of crystallization process design and is inherently intertwined with optimization of the operating conditions. Computer-aided tools can greatly assist in solving these two problems simultaneously. However, the integration of predictive thermodynamic models and process optimization tools is often complicated, which may hamper industry adoption. This work presents a workflow for simultaneous solvent selection and process optimization for solution crystallization processes based on the perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state. The workflow is provided with readily executable computational tools and aims to strike a balance between the resources needed to obtain experimental input data and good prediction performance. The use of the workflow is demonstrated through a case study involving aspirin crystallization, which shows that the workflow can provide suitable solvents and operating conditions for the crystallization process based on either cooling, antisolvent, or evaporative crystallization.