is the implementation of intelligent systems that can assist human operators
in making supervisory control decisions, instead of simply sounding an alarm
when process variables go out of range. Operator failures to exercise the appropriate
mitigation actions often have an adverse effect on product quality,
process safety, occupational health and environmental impact. The economic
effect of such exceptional events is immense; an estimated $20billion/year losses
in petrochemical industry have been reported. The challenges and opportunities
for improvements are even larger in the pharmaceutical manufacturing domain
because so much of the processing involves particulate and granular systems
whose characteristics tends to be more problematic than that of fluids.
The development and manufacturing of pharmaceutical products are governed
by strict safety regulations but with the advent of Process Analytical
Technology (PAT) initiative advanced by the FDA; gives the pharmaceutical
industry an opportunity to apply various systems engineering tools. Early detection
and diagnosis of process faults while the plant is still operating in a controllable
region can help avoid abnormal event progression, production disruptions
and productivity losses. An EEM framework has been developed to deal with
fault detection, diagnosis and mitigation of conditions that result from process
anomalies. The framework developed uses a combination of Principal Component
Analysis (PCA), Signed Digraphs (SDG) and Qualitative Trend Analysis
(QTA) in applications involving continuous pharmaceutical product manufacturing
line. An ontological database has been created to maintain records of the
signatures of a number of exceptional events typical of a dry granulation line
and their associated mitigation strategies. In trials on pilot scale equipment we
have found that the EEM framework was able to detect and diagnose several
types of faults within a few seconds of their inception and to provide mitigation
advisories to the operator.
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