Sensor Network Robustness using Model-based Data Reconciliation for Continuous Tablet Manufacturing

Abstract Advances in continuous manufacturing in the pharmaceutical industry necessitate reliable process monitoring systems that are capable of handling measurement errors inherent to all sensor technologies, as well as detecting measurement outliers to ensure operational reliability. The purpose of this work is to demonstrate data reconciliation (DR) and gross error detection (GED) methods as real-time process management tools to accomplish robust process monitoring. DR mitigates the effects of random measurement errors while GED identifies non-random sensor malfunctions. DR is an established methodology in other industries (i.e., Oil and Gas) [1], and was recently investigated for use in drug product continuous manufacturing [2]. This work demonstrates the development and implementation of model-based steady-state data reconciliation (SSDR), on two different end-to-end continuous tableting lines: direct compression and dry granulation. These tableting lines involve different equipment and sensor configurations, with sensor network redundancy achieved using equipment embedded sensors and in-line process analytical technology (PAT) tools for the Critical Process Parameters (CPPs) and Critical Quality Attributes (CQAs). The nonlinearity of the process poses additional challenges to solve the SSDR optimization problem in real-time. At-line and off-line measurements were used to validate the framework results.