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Home Expertise Library Use of a Semi-Automated Crystallizer to Study Oiling-Out and Agglomeration
Seminar

Use of a Semi-Automated Crystallizer to Study Oiling-Out and Agglomeration

Industrial Crystallization Optimization

Program Overview

  • Study on challenging industrial crystallization prone to oiling-out and agglomeration
  • Use of semi-automated crystallizer to study the crystallization and identify failure modes
  • Incorporation of fiber optic probe to track turbidity and detect oiling-out
  • Use of turbidity monitoring as feedback mechanism to reverse oiling-out/resume crystal growth

In this presentation, "The Application of a Semi-Automated Crystallizer to Study Oiling-Out and Agglomeration", Xiaowen Zhao of Corteva presents a case study on a challenging industrial crystallization. The crystallization under investigation was prone to oiling-out, which led to agglomeration and crystallization stalling. A semi-automated crystallizer — designed and built iteratively to meet the changing requirements of the crystallization process — 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, turbidity was monitored to detect oiling-out and was subsequently used as a feedback mechanism to change the temperature profile to reverse oiling-out/resume crystal growth during crystallization. The automated sampler, coupled with offline 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 allowed for the expedient optimization of crystallization parameters, such as 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.  

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About the Presenter

Xiaowen Zhao

Xiaowen Zhao

Corteva

Xiaowen Zhao earned her PhD in chemical engineering from the University of Michigan. Her PhD research focused on heterogeneous catalysis, and she gained experience in catalyst synthesis/characterization and kinetic modeling. She then joined Corteva Agriscience as a process development engineer where her main responsibility is developing robust manufacturing processes to meet business needs. In this role, she has gained experience in utilizing PAT tools to optimize various processes, including crystallization.