Image Analysis for Crystallization Monitoring and Control
Image Analysis for inline particle size and shape characterization presents an emerging and feasible approach to measure, understand, and control crystallization. With a new generation of real time microcopy and advances in software, researchers apply image analysis in real time and make better decisions to optimize crystallization processes. This presentation describes recent advance in the use of image analysis for crystallization monitoring and control.
This talk provides an overview of recent advances of applications for in situ imaging and image analysis approaches for monitoring and control of crystallization systems. After introducting the role of image analysis in crystallization monitoring, Dr. Nagy describes four main application areas:
- An image analysis-based nucleation control approach which provides more robust control performance than the tradional laser backscattering measurement approach.
- A novel feedback approach using in situ imaging and imaging analysis to control crystal shape by controlling the addition of crystal growth modifiers into the crystallization system.
- Active polymorphic feedback control and the use of imaging and image analysis for polymorphic crystallization monitoring and control.
- A novel approach to correlate the measurements obtained from in situ imaging and image analysis with multi-dimensional population balance models. A mathematical model of the imaging sensor is developed and used to derive a correction factor between true and measured aspect ratio distribution. By applying this correction factor, crystal size and shape distribution measured by in situ image analysis can be closely correlated with the ideal size and shape distribution described by morphological population balance models.
Crystallization is an important unit in the pharmaceutical and fine chemical industries. For high performance control of crystallization, real time process data is required. Increasing attention needs to be given to crystal shape. Reliable shape information can be obtained exclusively from crystal images. Real time, in situ images coupled with image analysis transforms the imaging sensor from monitoring to high performance control instrument for DNC, crystal shape and even polymorphic processes.
Dr. Zoltan Nagy is a Professor of Chemical Engineering at Purdue University. He joined Purdue in fall 2012 from Loughborough University, UK, where he was a professor of process systems engineering and Director of the Departmental Pharmaceutical Engineering Research Centre.
Dr. Nagy has over 17 years of experience in advanced process control, process analytical technologies, crystallization modeling and control approaches and advanced control of particulate systems. His current research focuses on the application of systems approaches and tools in the design and robust control of batch and continuous crystallization systems, process analytical technologies and integrated particulate manufacturing processes. He has authored more than 150 journal papers and is the co-author of 2 books. He has given over 150 invited seminars at conferences, universities and companies worldwide.
Dr. Nagy is the Founding Editor of the Pharmaceutical Engineering Subject area of Chemical Engineering Research and Design, and associate editor of four other international journals in the area of process control. Dr. Nagy is member of the American Association for Crystallization Technologies, and the Crystallization Working Party of the European Federation of Chemical Engineers. He received awards and best paper prizes in the areas of crystallization and control.