An application of deep learning to detect process upset during pharmaceutical manufacturing using passive acoustic emissions.

The multivariate nature of a fluidized bed system creates process complexity that increases the risk of production upset. This research explores the use of passive acoustic emissions monitoring paired with an artificial neural network to detect fluidized bed distributor plate blockage. In many cases, early process failure detection can allow for immediate intervention, thus lowering operation costs.

Inline acoustic monitoring to determine fluidized bed performance during pharmaceutical coating.

Fluidized beds are used by pharmaceutical manufacturers for multi-particulate drug coating. They provide effective mass and heat transfer; however, unit optimization can be difficult due to the multivariate nature of a fluidized bed system. This research explores the use of passive acoustic emissions monitoring as a method to improve temperature management during pellet coating.