Calibration transfer in pharmaceutical process engineering

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My research program focuses on the development of methods to monitor and optimize industrial processes. Specifically, I work on Process Analytical Technologies (PAT), such as near infrared probes (NIR), to provide real-time information on the products. These probes are critical to proper monitoring, understanding and control. To do so, they require multivariate mathematical models to convert the raw data into a variable of interest (e.g. a NIR spectrum into composition). However, PAT implementation is limited by the time and cost to calibrate the models at large scales. It is much simpler and cost efficient to calibrate models on small systems and apply them to industrial ones. However, this transfer is not easily done. Even minor differences in acquisition conditions (e.g. lighting intensity) and material properties (e.g. sample density) may be enough to prevent direct model transfer (CT: calibration transfer). The development of versatile CT is therefore critical to industrial scale up and modeling. I propose to develop better methods for CT from small scale applications to larger ones. This is critical to the improvement of manufacturing to improve process understanding, safety and efficiency. It is also critical to the development of new production methods, such as the replacing batch processes by continuous ones. I specialize in process engineering and on-line monitoring through the use of multivariate data analysis. My short-term objectives seek to develop PAT to monitor pharmaceutical processes. My project contains 2 inter-related themes: - Theme 1 centers on calibration transfer for tablet press units used to produce pharmaceutical tablets. The novelty lies in the use of the spatial information present in the flowing in the press powder (such as segregation) to improve calibration transfer - Theme 2 centers on calibration transfer for fluid bed coating units used for taste masking in the pharmaceutical products. The novelty lies in the use of the temporal information of the batch process (its dynamics) to improve calibration transfer While tailored to a pharmaceutical context, this project pertains to any manufacturing sector. Beyond this, improved process monitoring methods, especially those available at low cost, are liable to impact any industrial production. This project seeks to put students in a position to grasp advanced concepts of quality control, process monitoring and applied statistics. These key notions are critical to the well-being of our industries and the next generation of engineers must be trained to master them.