exceptional events management applied to roller compaction of pharmaceutical powders

this study focuses on the development and implementation of an exceptional events management (eem) framework that detects, diagnoses, and mitigates exceptional events inherent to particulate processes that are prevalent in the pharmaceutical industry.

methods

the eem framework combines several methods, including signed directed graph (sdg), qualitative trend analysis (qta), and fast fourier transform analysis.

results

we discuss the efficacy of the eem framework in detecting and diagnosing abnormal events, and demonstrate its application on roller compaction. we demonstrate that various commonly occurring exceptional events such as “no powder entering the roll region” (which includes “jamming in the nip region”), “caking of powder on rolls” and “varying moisture in powder” can be detected and diagnosed using the eem framework. in addition, we show that the sdg-qta approach is useful in determining the type and location of sensors that would enable better monitoring thereby improving the detection and diagnostic capabilities of our eem framework.

conclusion

the developed eem framework shows potential for facilitating the pharmaceutical transition from batch to continuous manufacturing.