Abstract
Reactive crystallization (RC) is a chemical process in which the reaction yields a crystalline product. It is used in various industries such as pharmaceutical manufacturing or water purification. In some cases, RC is the only feasible process pathway, such as the precipitation of certain ionic solids from solution. In other cases, a reaction can become a RC by changing the reaction environment to a solvent with low product-solubility. Despite the utility and prevalence of RC, it is not often emphasized in process design software. There are RC models that simulate the inner reactions and dynamics of a RC, but each has limiting assumptions, and are difficult to integrate with the rest of a process-line simulation. This modeling gap complicates RC process design and limits the exploration of the possible benefits to using RC as well as the ability to optimize a system that relies on it. To fill this gap, we built an open-source, customizable model that can be integrated with other unit operations in the Python process simulator package PharmaPy. This model focuses on the reaction-crystallization interactions and dynamics to predict reaction yield and crystal critical quality attributes given inlet streams and reactor conditions. In this way, RC can be integrated with other unit operations to capture the effects RC has on the process overall. The model and assumptions are described in this work. The model space, limitations, and capabilities are explored. Finally, the potential benefits of the RC system are shown using three example cases.