A New Data-Driven Modelling Framework for Moisture Content Prediction in Continuous Pharmaceutical Tablet Manufacturing

Continuous manufacturing of pharmaceutical tablets integrates multiple unit operations such as twin screw granulation and fluidized bed drying to transform powder into final dosage form such as tablets. However, complex process interactions can lead to variability in critical quality attributes including moisture content of the produced granules. This study presents an innovative multi-stage modelling framework to predict granule moisture content based on the twin screw granulator and the fluidized bed dryer process parameters. Machine learning techniques, including gradient boosting regression, and support vector regression were utilised to enhance predictive performance in ensemble method. Using data from a pilot-scale integrated continuous line, the stacking ensemble model achieved excellent accuracy with ( \(R^2\) ) of 91% for moisture content prediction. The Machine learning modelling framework demonstrates strong potential for advancing process knowledge, and optimization in continuous manufacturing of pharmaceutical tablets based on wet granultion.