Application of multivariate latent variable modeling to pilot-scale spray drying monitoring and fault detection: Monitoring with fundamental knowledge

Abstract

This work describes the use of multivariate latent variable modeling (LVM) to enhance fundamental understanding of the operational space, the scale differences and the common-cause variability present in the operation of a pharmaceutical spray-dryer. LVM provided a real-time process monitoring and fault detection tool for continuous quality assurance. A latent variable model was built and tested using commercially available software in a pilot-scale facility at Bend Research Pharmaceutical Process Development Inc. (BRPPD) in Bend, OR. The key learning from the exercise at the pilot-scale helped identify and understand the normal variability of the commercial scale equipment. A key advantage of the LVM approach is that the variability that drives the process is easily understood in a fundamental way by interpreting the model parameters in light of fundamental engineering knowledge (e.g., transport phenomena, thermodynamics). The understanding of the common-cause variability enables the better understanding of the differences across scales for this unit. In monitoring the process, the faults are not only detected in a statistical way, but also understood in a fundamental way by using the model to track down the driving forces that were involved in detecting such fault (e.g., an abnormal behavior of the gas momentum across the unit).