Application of a Semiautomated Crystallizer to Study Oiling-Out and Agglomeration Events-A Case Study in Industrial Crystallization Optimization

A case study on a challenging industrial crystallization is presented. The crystallization under investigation was prone to oiling-out, which led to agglomeration and stalling of crystallization. A semiautomated crystallizer-designed and built iteratively as the crystallization needs changed-was used to study the crystallization and identify the failure modes. A fiber optic probe was included in this setup to track turbidity, which was found to drop in signal whenever an oiling-out/agglomeration event was observed with in situ video microscopy. With this knowledge, the turbidity was monitored to detect oiling-out and was subsequently used as a feedback mechanism to change the temperature profile to reverse oiling-out and resume crystal growth during the crystallization. The automated sampler, coupled with off-line HPLC analysis, provided valuable insight about the behavior of impurities during oiling-out and identified one specific impurity that influenced the formation of the second liquid phase and crystallization rate. The data-rich experimentation performed enabled the expedient optimization of crystallization parameters such as the temperature profile, seed loading, and cycle time. The final crystallization was robust, predictable, and capable of self-correction should a potential failure mode be encountered.

Automated summary

This study investigated a challenging industrial crystallization process that was prone to oiling-out and agglomeration. By using a semi-automated crystallizer with a fiber optic probe for real-time turbidity monitoring and in situ video microscopy, failure modes were identified and resolved efficiently. The combination of online monitoring and offline analysis provided valuable insights into impurity behavior, leading to the optimization of crystallization parameters for robust and predictable final crystallization.