Predicting particle size during fluid bed granulation using process measurement data.

In this study, a new concept for particle size prediction during the fluid bed granulation is presented. Using the process measurements data obtained from a design of experimental study, predictive partial least squares models were developed for spraying and drying phases. Measured and calculated process parameters from an instrumented fluid bed granulation environment were used as explaining factors, whereas an in-line particle size data determined by spatial filtering technique were used as response. Modeling was carried out by testing all possible combinations of two to six process parameters (factors) of the total of 41 parameters. Eleven batches were used for model development and four batches for model testing. The selected models predicted particle size (d50) well, especially during the spraying phase (Q2=0.86). While the measured in-line d50 data were markedly influenced by different process failures, e.g., impaired fluidization activity, the predicted data remained more consistent. This introduced concept can be applied in fluid bed granulation processes if the granulation environment is soundly instrumented and if reliable real-time particle size data from the design of experiment batches are retrieved for the model development.