(120e) Developing Dynamic Models of Pharmaceutical Drying Operations Using On-Line Mass Spectroscopy

Gopi, A. - Presenter, University of Massachusetts, Amherst

Drying is a critical downstream process in pharmaceutical manufacturing. This type of liquid-solid separation is subject to stringent constraints because the active pharmaceutical ingredient (API) can degrade if the drying temperature is too high while long batch times can result if the temperature is too low. Because pharmaceutical manufacturing is typically accomplished through batch operation, overall process productivity is strongly affected by the batch times of the individual unit operations. Drying often represents a process bottleneck because the moisture content of the wet cake is difficult to measure in real time. As a result, the drying process may be run far longer than necessary to ensure that the moisture content drops to an acceptable level. We believe that the combination of on-line moisture content analysis and dynamic process models offer the potential to reduce and ultimately minimize dryer batch times.

The objective of this study was to use on-line mass spectroscopy to develop dynamic models capable of predicting cake moisture content in a vacuum tray dryer. To demonstrate generality of the approach, we considered both diffusion-type models and combined heat-mass transfer models. Unlike many simpler models available in the literature, these models allowed the prediction of both the constant and falling rate drying regimes without resorting to artificial switching between individual models. As a first step towards pharmaceutical applications, we first developed models for a simple experimental system consisting of glass beads as the solid matrix and different solvents. We studied a wide range of particle sizes (10-200 microns) and explored the effects of particle size, initial moisture content, cake depth, drying temperature and solvent type on drying behavior with moisture content profiles resolved on a second time scale by on-line mass spectroscopy. The data was used to develop and validate diffusion and heat-mass transfer models for predicting cake moisture content from gas phase moisture measurements. We believe that this technology holds considerable promise for monitoring and optimizing drying operations in the pharmaceutical industry.