Using deepest regression method for optimization of fluidized bed granulation on semi-full scale

Abstract

This study applied the deepest regression method to estimate the granule size of unsuccessful fluidized bed granulation runs. This study uses data from a previous study [Int. J. Pharm. 220 (2001) 149] on optimization of fluidized granulation process, wherein 8 of the 30 runs did not succeeded due to overwetting of the powder bed. The “complete data” (the observed and the estimated granule size by the depth regression method) were used to develop two regression models for the granule size: an empirical model based on the process variables (inlet air temperature, inlet airflow rate, spray rate, and inlet air humidity) and a fundamental model based on the powder bed moisture content and the relative droplet size. The regression models based on the incomplete data from the previous study and the regression models of the “complete data” were comparable in the sense that the contour plots based on the respective models and the predicted granule size were comparable.