MAR-APR 2019

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INTECH MARCH/APRIL 2019 19 PROCESS AUTOMATION performing the data analysis. Appli- cations that connect to and present data from a multitude of disparate data sources, including process his- torians, should be a core element of the PAT implementation strategy. l Ease of data analytics and visualiza- tion: The ability to view data from both the process instrumentation and the process analytical instru - mentation together in one place during select times of operation is crucial for performing investigations and monitoring, and for develop - ing accurate models. Analytics tech- niques, including multivariate- and first-principles-based modeling, only work well when the PAT methodology provides: — a centralized, single location to overlay multiple experiments or multiple conditions from within an experiment —automated file transfer from off-line instruments to eliminate cutting and pasting various data files into spreadsheets — handling samples from different time intervals, including analy- sis when a sample may have been missed or skipped — easy batch definition with the ability to search by key metadata about the experiments — automated templates to apply standard data views and calcula- tions for quick routine analysis, tied in with report generation Figure 3 illustrates how engineers can selectively query data to identify stages of operation. For example, when an analytical instrument is being tested or recalibrated, the data signal no lon - ger represents the actual process. To develop a robust model, the engineer should be able to exclude these periods of bad signals from the analysis. Fur- ther, the best data analytics application can support the engineer in develop- ing a model to predict what the values should have been if the sensor had been operating properly. l Ensuring a dynamic culture that is ready to embrace change: Re- alizing the full potential of the PAT methodology often requires a fundamental change in company mindset from the top down and the bottom up. This is especially true in groups that have not already imple- mented agile data collection and an analysis methodology in their man- ufacturing process. In these cases, the group needs to review work- flows to understand the existing obstacles for collecting and access- ing the right data, and then use the right data analytic software. This software must be able to perform the required analysis and be imple- mented so subject-matter experts can use it, as opposed to only data scientists. A successful PAT meth- odology is evidenced by a system that the full organization can use (figure 4), but this often requires the adoption of new workflows. Advanced analytics in action For robust, quality products, facilities operations and product manufacturing processes must both run smoothly. On the facilities operations side, examples of unit operations include water purifi- cation, filtration, heating, and cooling. On the product manufacturing side, examples of pharmaceutical unit op- erations include: l crystallization in reactors and pro- tein expression in bioreactors l filtration and purification (e.g., chro- matography) l lyophilization l feeding and blending l granulation l tableting l tablet coating Both areas are subject to the inherent challenge of enabling engineers to ap- ply their domain expertise to optimize the operation of equipment such as pumps, valves, compressors, and heat exchangers. The following case studies illus- trate the effective implementation of a complete PAT methodology. These case studies showcase both the facility operations and active pharmaceutical manufacturing processes required to produce quality products. For each case study, the PAT methodology being fol- lowed employs the following aspects: l Proactive data type selection: Install- ing instrumentation to deliver the re- quired data Figure 4. Leveraging an effective PAT methodology for continuous improvement benefits SMEs and the entire organization. Leveraging PAT throughout an organization to enable continuous improvement

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