1. Coupling Process Control Systems and Process Analytics Robert Wojewodka –Technology Manager Philippe Moro –IS Manager Terry Blevins – Principal Technologist

2. PresentersPresenters Robert Wojewodka Philippe Moro Terry Blevins

3. IntroductionIntroduction The Lubrizol Corporation and Emerson are partnering to explore the technical challenges of applying online analytics in a batch operation. In this session, we will present: –The role Principal Component Analysis (PCA) and Projection to Latent Structures (PLS) can play in fault detection and prediction of end-point quality parameters. –Prototype tools that Emerson developed for this study –The approach used in testing on-line analytics on a batch process at a Lubrizol plant. –Continuing collaboration to address on-line data analytics with DeltaV.

4. PAT Framework The PAT framework defines the following tool categories: Multivariate data acquisition and analysis tools Modern process analyzers and process analytical chemistry tools Process and endpoint monitoring and control tools Continuous improvement and knowledge management tools. An appropriate combination of some, or all, of these tools may be applicable to a single unit operation or to an entire manufacturing process and its quality assurance.

5. Three aspects of data analytics Routine & data access Off-Line On-Line Data Analytics Clients Applications Routine analyses Routine reports Routine graphical summaries Routine metrics & KPIs Vehicle for data selection by user Vehicle to deliver data to the user On-line visualization Add hoc analyses Model development Process studies Lab studies Business studies Troubleshooting Process improvement Interactive analyses …etc. People do their own analyses using the analysis tools Real-time analytics Deployment of models ASP analytics Process analytics Monitoring, feedback, control, alerts Link back into PlantWeb Web interface for the display Etc. Clients Via a Web Page

6. Process Analytics Emerson Process Management established a research project at University of Texas, Austin in September, 2005 to investigate advanced process analytics. The primary objective of this project is to explore the on- line application of Analytics for prediction and fault detection and identification in batch operations. Beta installation to demonstrate this technology is targeted to be on-line in mid-2007 timeframe. The research grant given to UT is funding the work of a PhD graduate student, Yang Zhang, under the supervision of Professor Tom Edgar.

7. Summary of Research Much of this funded research is summarized in chapter 8 of the book “New Directions in Bioprocess Modeling and Control: Maximizing Process Analytical Technology Benefits”

8. Detection of Abnormal Operation Measured Disturbances – The Score space associated with Principal Component Analysis, PCA, captures contributions that can be associated with process measurements. Deviations in the principal component subspace score may be quantified through the application of Hotelling’s T2 statistic. Unmeasured Disturbances – The Residual space that is not captured by the score space reflects changes in unmeasured disturbances that impact the operation. The Q statistic, Squared Prediction Error (SPE), is a measure of deviations in process operation that are captured by the residual subspace. Identification of the primary measurements that contribute to a process deviation will be done using contribution plots.

9. Quality Parameter Estimation The detection of deviations in quality parameters will be addressed through the use of Projection to Latent Structures, PLS. Through this technique, it is possible to maximize the covariance (between the predictor (independent) variables X and the predicted (dependent) Y parameters. Where the objective is to classify the operation results, then discriminate PLS, PLS-DA, will be applied. The fault detection, identification, techniques that may be used with PCA can be applied in exactly the same way for PLS e.g. Q and T2 statistics, contribution plots.

10. Planned On-line Analytic Support Three function blocks will be developed for beta testing of on-line process analytics –PCA Block –PLS Block –Analyzer Block Each block supports the models identified for the associated process unit.

11. Example – PCA Block Function

12. The Challenge of Batch Operation The wide range of operating conditions presents challenge in the design and commissioning.

13. Analytics for Batch Processes

14. Models Vary with Product/phase and Unit

15. Batch Analytics To support the development of PCA/PLS/PLS-DA models for the units making up a batch process, the following information must be collected by the control system historian: Product Identifier Operation phase/state of the unit Available process measurements for the unit Identifier of shared resources Lab analysis associated with unit inputs or outputs. Historic data targeted for model development should be saved as periodic samples with data compression turned off.

16. Added Support Is Required for Lab Results Module Lab Results Analytic Block Controller DeltaV Historian Operator Station Use of Lab Results in DeltaV ProPlus Off-line Modeling

17. Model Development - Processing Data Analysis is based on one minute average samples – minimizing impact of noise.

18. Model Development – Aligning Batches Data for different length of Batches is aligned using dynamic time warping The aligned data is processed using hybrid unfolding before using this to train the multi-way PCA or batch-wise unfolding for PLS/PLS-DA model.

19. Model Testing/Verification High fidelity, dynamic simulation models of the target process will utilized in the beta test to support PCA/PLS development and testing For off-line testing, the beta station will act as a Virtual Plant in which the mathematical simulation of the process runs with identical control loops and the same tuning parameters as an actual plant.

20. Virtual Plant Used For Initial Checkout

21. Operator Interface – Fault Detection Plots of the T2 and Q statistics will be provided in this interface. Using the slew button, it will be possible to view the operation of previous batches processed by this unit. By clicking in the trend area associated with the current batch, the operator may view a score contribution plot to determine the parameter(s) that caused the deviation

22. Operator Interface By clicking in the trend area associated with the T2 and Q plots for current batch, the operator may view a score contribution plot to determine the parameter(s) that caused the deviation

23. Prediction of Key Quality Parameters When the dynamo for the PLS/PLS- DA function block is reference by the operator, then the following view will be provided to examine the predicted quality parameter.

24. Planned Beta Installation Demonstrate on-line prediction of quality and economic parameters Evaluate different means of on-line fault detection and identification i.e. multiway-PCA/PLS. Show value of high fidelity process models for testing fault detection and alternate control strategies.

25. Beta Installation

26. SummarySummary On-line process analytics can play in batch processes through fault detection and projection of quality parameters. Plans are in place to do beta testing of on-line analytics at a Lubrizol plant Results of the beta will be presented at Emerson Exchange, 2008.

27. Where To Get More Information “New Directions in Bioprocess Modeling and Control: Maximizing Process Analytical Technology Benefits”