Abstract
Advances in continuous manufacturing in the pharmaceutical industry necessitate reliable process monitoring systems that are capable of handling measurement errors inherent in all sensor technologies and detecting measurement outliers to ensure operational reliability. The purpose of this work was to demonstrate data reconciliation (DR) and gross error detection methods as real-time process management tools to accomplish robust process monitoring. DR mitigates the effects of random measurement errors, while gross error detection identifies nonrandom sensor malfunctions. DR is an established methodology in other industries (i.e., oil and gas) and was recently investigated for use in drug product continuous manufacturing. This work demonstrates the development and implementation of model-based steady-state data reconciliation on 2 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 tools for the critical process parameters and critical quality attributes. The nonlinearity of the process poses additional challenges to solve the steady-state data reconciliation optimization problem in real time. At-line and off-line measurements were used to validate the framework results.