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Batch Process Analytics


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This presentation on batch process analytics was given at Emerson Exchange, 2010. A overview of batch data analytics is presented and information provided on a field trail of on-line batch data analytics at the Lubrizol, Rouen, France plant.

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Batch Process Analytics

  1. 1. Batch Process Analytics - update - Robert Wojewodka, Technology Manager and Statistician Terry Blevins, Principal Technologist Willy Wojsznis, Senior Technologist
  2. 2. 2 Presenters  Bob Wojewodka  Terry Blevins  Willy Wojsznis
  3. 3. 3 Introduction  Lubrizol and Emerson Process Management have worked together over the last three years to develop and install a beta version of Emerson’s on-line batch analytics  This new functionality is currently in operation after successful field trials at the Lubrizol, Rouen, France plant  In this session we will present the lessons learned in implementation of this technology in a running plant  We will also summarize the results achieved by the process operators and operations management using this new capability  We outline the basic principles and objectives of analytic application  We sketch some innovative analytic concepts which were validated at the field trial  Discuss current activities
  4. 4. 4 • Operators and engineers work in a highly complex, highly correlated and dynamic environment each day • Operators and engineers manage a large amount of data and information on a running unit • Operators and engineers need to avoid undesirable operating conditions • Operators an engineers need to reduce variation, improve throughput and improve quality yet maintain safety The Setting
  5. 5. 5  Jointly develop viable on-line multivariate batch process data analytics  The primary objectives of the field trial were: – Demonstrate on-line prediction of product quality – Evaluate different means of on-line process fault detection and identification; abnormal situations  Document the benefits of this technology  Learn from the field trial to update and improve these new and evolving modules Objectives of the Beta TestObjectives of the Beta Test
  6. 6. 6  Process holdups  Access to lab data  Variations in feedstock  Varying operating conditions  Concurrent batches  Assembly and organization of the data Challenges in Applying Online Data Analytics to Batch Processes
  7. 7. 7 Functionality of the Analytics Application  Take all inputs and process variables associated with a batch process and characterize “acceptable variation” and process relationships associated with “good” batches  Identify how these variables relate to each other and to end of batch product quality characteristics  Use the analytic techniques to identify typical process relationships and faults as current and future batches are running  Use the analytic techniques to predict end of batch quality characteristics at any point in time as a batch is evolving  Identify and diagnose faults and provide recommendations to operations personnel how to improve batch operation and product quality
  8. 8. 8 The “Golden Batch” comparison approach is plagued with problems  What is the “best” batch?  Only refers to ONE batch; but there are many “good” batches  Does not address fact that variation exists nor does it address defining an acceptable level of variability  May significantly miss direct resources  May significantly miss direct control emphasis  Does not promote process understanding nor does it promote identifying important process relationships; nothing is learned  The economics may be completely wrong  Does not promote identification and control of critical parameters and relationships with quality parameters (analytical, physicals, time cycle, yield, waste, economics, etc.)  …and the list goes on… Golden Batch Comparison
  9. 9. 9 Analytics Drive the Power of Information The Power of Information Raw Data Standard Reports Descriptive Modeling Predictive Modeling Data Information Knowledge Intelligence Optimization What happened? Why did it happen? What will happen? What is the Best that could happen? $$$ ROI $$$ ROI Adapted by Bob Wojewodka from slide courtesy of SAS Inst. Ad hoc Reports & OLAP
  10. 10. 10 services SAP Process Order & Recipe Consumption Data Firewall Resource Optimization and Planning Application Batch Exec & Campaign Mgr Historian & Recipe Exchange PRO+ Operator Interface Recipe Transfer via XML Consumption from Batch Historian event file via XML Control Network LZ Domain SAP Analysis server(s) Analysts Embedded analytics Device level analysis / diagnostics Device level analysis/ diagnostics Embedded analysis & diagnostic apps. Embedded analysis and diagnostic apps. Business & Process Analytics Business and process analytics Data Transfer via XML Pro+ .net Web services Batch exec. Consumption SAP® Data historian Operator interface Data transfer Analysis servers Analysts chemists engineers Embedded analysis XML Recipe + schedule DeltaV and SAP Integration With Data Analytics Statgraphics®
  11. 11. 11 Summary of Actual Field Trial Analyses  2 units / products  18 input variables  38 process variables  4 output variables (2 initially for the online)  All data at 1-minute time intervals for the analysis  Total of 172 historical batches used for analysis and model development across these two processes
  12. 12. 12  PCA – Principal Components Analysis – Provides a concise overview of a data set. It is powerful for recognizing patterns in data: outliers, trends, groups, relationships, etc.  PLS – Projections to Latent Structures – The aim is to establish relationships between input and output variables and developing predictive models of a process.  PLS-DA – PLS with Discriminant Analysis – When coupled, is powerful for classification. The aim is to create predictive models of the process but where one can accurately classify the material into a category. The Primary Multivariate Methods
  13. 13. 13 Results of the Off-line Modeling Work
  14. 14. 14 Product 1 Control screen Analytics screen Product 2 Control screen Operations personnel interact with the data analysis screen. Other people from other locations / sites may access the on-line analysis displays via their web browser. What Has Been Deployed
  15. 15. 15 What Has Been Deployed
  16. 16. 16 What Has Been Deployed
  17. 17. 17 3 What Has Been Deployed
  18. 18. 18 What Has Been Deployed 3 3 Stage 1 Stage 2 Stage 3
  19. 19. 19 Web-based Interface - There’s an App for that  Since the user interface is web-based it can be accessed from multiple sites over the intranet (or internet)  As will be demonstrated at the Rouen beta site, access is also available through an iPod Touch or iPhone.
  20. 20. 20 Summary of field trial results  Operators and engineers at Rouen are using these new tools for faults detection and quality parameter prediction.  The impact of the on-line analytic tools installed at Rouen on the plant operation have been evaluated over a 6 month period and since then the installation is in use beyond the initially planned period.  Examples of faults detected using this capability are provided in the presentation given at Emerson Exchange 2009 – see Benefits Achieved Using On-Line Data Analytics by Robert Wojewodka and Terry Blevins.
  21. 21. 21 Lessons Learned - Key concepts / approaches that have evolved from the beta work  Use of Stage in data analytics to define the major manufacturing steps  Selection and pre-processing of data used for model development and on-line analytics  On-line interface designed to meet operator’s requirements  Web based architecture for operator interface and data exchange  Development of a web based dynamic process simulation to enable effective operator training
  22. 22. 22 Current Activities  Emerson progressing with the commercialization of the batch analytics modules – Will be part of the DeltaV Version 12 release  Following process improvement design changes on the field trial units, models will be updated and redeployed  Completion of a Design of Experiments to further characterize the modeling process relative to differing process relationships
  23. 23. 23 Design Of Experiments  Examining more process relationships and impacts on the analysis methods  Results will be used to further refine the modeling approach  Results will be used for pre-assessment of candidate units for use of the analysis modules
  24. 24. 24 Added Work Prior to Commercial Release  Off-line modeling tool set with enhanced diagnostics to aid the process engineers during model development steps  Ability to simultaneously predict multiple “Y” output variables while on-line  On-line diagnostics of the “health” of the running models; alert when model errors deviate beyond initial levels when deployed  Additional functionality for being able to update and redeploy models quickly following processing changes
  25. 25. 25 Where to Get More Information  Interactive demonstration of data analytics applied to the saline process  Robert Wojewodka and Terry Blevins, “Data Analytics in Batch Operations,” Control, May 2008  Video: Robert Wojewodka, Philippe Moro, Terry Blevins Emerson - Lubrizol Beta:  Emerson Exchange 2010 Workshop – SAP to DeltaV integration using the DeltaV SOA Gateway and SAP Web Services – Philippe Moro, Joe Edwards, Chris Felts  Emerson Exchange 2009 Workshop – Benefits Achieved Using On-line Data Analytics - Robert Wojewodka, Terry Blevins  Emerson Exchange 2008 Short Course: 366 – The Application of Data Analytics in Batch Operations - Robert Wojewodka, Terry Blevins  Emerson Exchange 2008 Short Course: 364 – Process Analytics In Depth - Robert Wojewodka, Willy Wojsznis  Emerson Exchange 2008 Workshop: 367 – Tools for Online Analytics - Michel Lefrancois, Randy Reiss  Emerson Exchange 2008 Workshop: 412 – Integration of SAP® Software into DeltaV - Philippe Moro, Chris Worek  Emerson Exchange 2007 Workshop: 686 – Coupling Process Control Systems and Process Analytics to Improve Batch Operations – Bob Wojewodka, Philippe Moro, Terry Blevins
  26. 26. 26 Thank You Q & A Vision without action is merely a dream. Action without vision just passes the time. Vision with action can change the world. --- Joel Barker, Futurist