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(444b) Exceptional Events Management for Continuous Pharmaceutical Manufacturing: Feeder, Blender, & Roller Compactor in Series

Hamdan, I. M., Purdue University
Reklaitis, G. V., Purdue University
Venkatasubramanian, V., Purdue University

The process analytical technology (PAT) initiative advanced by the FDA gives the pharmaceutical industry an opportunity to apply various systems engineering tools, such as fault detection and diagnosis that are commonplace in other industries. Fault detection and diagnosis, which deals with the timely remedy of process abnormalities, can potentially avoid the progression of exceptional events and thus reduce material and productivity loss while ensuring the safety and quality of the products. Additionally, fault detection and diagnosis, reduces the reliance of plant operation on human operators, which according to statistics results in seventy percent of industrial accidents [1].

We have previously presented our Exceptional Events Management (EEM) framework as applied to the operation of roller compaction [2] and its integration into an ontological framework and DeltaV [3]. In continuous manufacturing, an abnormality that arises in one process/equipment propagates throughout the downstream processes. Thus, we extend the application of our EEM framework for a continuous pharmaceutical line consisting of two feeders, a blender and roller compactor in series. We conduct experimental simulations of various exceptional events on the integrated pharmaceutical line to demonstrate the evolution and impact of exceptional events as they progress. Greater emphasis is placed on diagnosing incipient faults (in which a fault is identified as soon as it manifests) and corrective actions are explored to reduce and/or eliminate the effects of the exceptional events. Our approach involves the use of a low resolution qualitative method known as signed directed graphs (SDGs) to roughly detect and diagnose a set of possible candidate faults, followed by a higher resolution, more discriminatory method of trend analysis that determines the fault that is most likely occurring. Furthermore, we present a scenario in which the operator is alerted and informed of an exceptional event occurring and is presented with mitigation strategies that are suggested and/or automated as diagnosis is being made.

Though the application has been relatively unexplored in the pharmaceutical domain, the benefits of having a system that diagnoses an exceptional event and allows for corrective measures provide a strong incentive for its development and implementation [4].

References: 1. Venkatasubramanian, V., A review of process fault detection and diagnosis Part I: Quantitative model-based methods, Computers & Chemical Engineering. 2003. p. 293. 2. Hamdan, I.M., Anomaly Detection and Diagnosis in Continuous Pharmaceutical Manufacturing: An Ontological Approach, AIChE Annual Meeting. 2008. 3. Hamdan, I.M., Exceptional Events Management for Continuous Pharmaceutical Manufacturing: Application and Integration into POPE and DeltaV, AIChE Annual Meeting. 2009 4. Gudi, R., Monitoring Bioprocesses, Pharmaceutical Manufacturing. 2008.