Granule Moisture Content Prediction During Fluid Bed Drying

The quality attributes of pharmaceutical products have been affected by the duration of manufacturing processes; therefore, manufacturers use various endpoint determination approaches such as time- and property-defined endpoints. In this study, two methods were proposed for determining the endpoint of the drying process of wet granules via experiments on a commercial-scale fluidized bed dryer (FBD) with a production setting. In particular, a near-infrared (NIR) spectroscope and humidity-temperature probes were used to determine their feasibility for alternatively monitoring the moisture content (MC) of granules. Wet granules were prepared using a high-shear wet granulator (HSWG) and subsequently dried in a 250-kg FBD equipped with moisture sensor probes at the inlet and outlet air ducts; these probes automatically recorded data throughout the drying process. NIR spectra were manually recorded using a fiber optic probe placed at the viewing port of the FBD chamber. Data from the NIR spectroscope and sensor probes were processed to develop prediction models for determining MC, and their predictive accuracies were evaluated using the root mean square error (RMSE). Results indicated that the probe model achieved higher predictive accuracy (RMSE = 0.11) compared to the NIR model (RMSE = 0.29). Thus, sensors-based MC monitoring is a precise and practical solution for endpoint determination in commercial-scale FBD.