Systematic Model-Based Steady State and Dynamic Optimization of Combined Cooling and Antisolvent Multistage Continuous Crystallization Processes

Currently, one of the key challenges in the pharmaceutical industry is the transformation of traditional batch production methods into robust continuous processes with the intention of reducing manufacturing costs and time and improving product quality. Crystallization is by far the most important purification technology in Pharma, as more than 80% of the active pharmaceutical ingredients (API) require at least one crystallization step. A successful crystallization process requires tight control over crystal size, shape and polymorphic purity. A rigorous and systematic methodology is presented to design and optimize multistage combined cooling and antisolvent continuous (mixed-suspension, mixed-product removal- MSMPR) crystallizers. The crystallization of acetylsalicylic acid (API) in ethanol (solvent) and water (anti-solvent) is used as a case study. A predictable and validated mathematical model of the system, which consists of a one-dimensional population balance model, was used to develop several optimizations strategies. Firstly, the attainable region of the mean particle size was determined for both minimum and maximum attainable crystal size. The method helped identify the most suitable number of stages and total residence time or volume for a cascade of continuous crystallizers. This was followed by a steady state optimization which helped determine the optimal operating temperatures and antisolvent flowrates. To minimize the startup time, a series of dynamic optimization strategies were implemented, assuming starting from empty vessels. The optimal dynamic profiles of the temperature and antisolvent flow rate, at different crystallization steps, were identified using a systematic and rigorous approach allowing a reduction in the startup time by 31%.