Performance Monitoring and the Process Analytical Technology Initiative

I caught a sneak preview of draft article that ModelingAndControl.com‘s Terry Blevins who collaborated with James Beall whom you may recall from earlier posts.

The draft explores the initial steps Pharmaceutical and Biotech manufacturers should consider when preparing to implement the U.S. Food and Drug Administration’s Process Analytical Technology (PAT) initiative. For those unfamiliar with the PAT guidelines, they were established to encourage innovation in development and implementation of manufacturing processes to improve product quality. The existing regulation designed to achieve quality through rigorous design and documentation actually served to discourage improvements due to the time-consuming nature of the revalidation of any changes.

Terry and James offer some guidance on some initial steps that Life Sciences manufacturers can take. Since most of their manufacturing processes are batch-based, it can be trickier to apply some of the advanced process control technologies more often found in continuous processes found in the chemical, petrochemical, and oil and gas industries. They recommend starting by looking at ongoing performance monitoring. This software has typically layered on top of the automation systems but has begun to become embedded in the automation system. DeltaV Insight is a good example of this type of performance monitoring software embedded in the DeltaV system. This performance monitoring can be keyed to the phases within the running batch to account for the changing process conditions. The dynamics of the process are learned as changes in the process are made.

Terry points out that these performance monitoring tools can help manufacturers spot issues like excessive process variability which can have direct impact on product quality. Other conditions this software can help detect include control-limited conditions, bad/unreliable data coming from intelligent field devices, and control loops operating in modes other than those intended. All of these conditions can contribute to quality issues in the final product.

He notes that the ability of intelligent field devices to provide status of the goodness of the data is a key part of performance monitoring so that the control strategies, history collection, and analytical tools have a clear picture of what is really happening in the process.

I look forward to seeing the finished article!

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