Application of online data analytics to a continuous process polybutene unit
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Application of online data analytics to a continuous process polybutene unit



Continuous data analytics may be used to provide an on-line prediction of quality parameters and enable on-line detection of fault conditions. In this workshop, we present the results achieved in ...

Continuous data analytics may be used to provide an on-line prediction of quality parameters and enable on-line detection of fault conditions. In this workshop, we present the results achieved in extending Lubrizol’s past work with on-line batch analytics to a continuous polybutene process. Information will be presented on how data analytics may be used to improve multiple quality and operational variables. The presentation will include a demonstration of the web interface used in the field trial and a summary of the operational benefits gained during the trial.



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Application of online data analytics to a continuous process polybutene unit Presentation Transcript

  • 1. Application of Online Data Analytics to a Continuous Process Polybutene Unit Regina Stone Process Improvement Engineer Robert Wojewodka Technology Manager and Statistician Efren Hernandez Process Control Superintendent Terry Blevins Principal Technologist1
  • 2. Presenters  Regina Stone  Robert Wojewodka  Efren Hernandez  Terry Blevins2
  • 3. Introduction Just as with batch processing, data analytics can be applied to continuous processes for on- line prediction of quality parameters and detection of fault conditions. In this workshop we present:  Background and example of continuous data analytics.  Field trial of continuous data analytics at Lubrizol, Deer Park, TX on a polybutene unit and refrigeration system.3
  • 4. The Lubrizol Corporation Segments Lubrizol Additives Lubrizol Advanced Materials The Right Mix of People, Ideas and Market Knowledge • Advanced chemical technology for global transportation, industrial and consumer markets • Unique, hard-to-duplicate formulations resulting in successful solutions for our customers • A talented and committed global work force delivering growth through skill, knowledge and imagination Growth. Innovation. People.4
  • 5. Lubrizol Leading Market Positions5
  • 6. Emerson and Lubrizol Roles Emerson Lubrizol  DeltaV modules knowledge  Process and analysis knowledge  Provide Lubrizol with a field trial  Apply software to a continuous tool for online quality parameter process and identify prediction and fault detection measurements  Provide technical support for  Build models and evaluate and difficulties experienced while validate the modeling software using software  Implement models into ongoing  Use Lubrizol’s feedback to unit operations further develop the Continuous  Collect feedback and report Data Analytics software package findings to Emerson Key Goal: Collaborate with Emerson to develop and test the Continuous Data Analytics software package.6
  • 7. The Setting Operators work in a highly complex, highly correlated and dynamic environment each day.  Any advanced warning of deviations is valuable. Operators manage a large amount of data and information on a continuously operating unit. Even with automation, only so much can be monitored and managed at one time.  Any help with continuous monitoring across many variables is valuable. The goal is to prevent the undesirable effects of an abnormal situation by early detection of precursor deviations and predict product quality real-time.7
  • 8. Background on Analytic Techniques  Analytic tools can be applied to both continuous and batch processes.  Application to continuous processes require special consideration such as: – Varying flow rates – Product grade transitions  For model development and on-line use it is necessary to allow real-time access to measurements and lab data associated with product quality and feedstock.8
  • 9. General Concepts – A Process PROCESS INPUTS OUTPUTS Very much like batch processing, continuous process applications can be simplified down to these major blocks of activityGeneric continuous process flow diagram.9
  • 10. General Concepts – A Process Initial Conditions Measurements reflecting operating Feed Stock Analysis conditions that impact product quality (X Parameters, In-Process Y Parameters) Lab Analysis of Product Quality (Y Parameter)Generic continuous process flow diagram.10
  • 11. Basic Concepts Univariate SPC Charts SPC Chart for Variable 1 98 UCL = 9 95 CTR = 9 LCL = 8 92 X 89 86 83 0 10 20 30 40 50 60 Observation …. Time ….11
  • 12. Basic Concepts Univariate SPC Charts SPC Chart for Variable 1 SPC Chart for Variable 2 98 12 UCL = 96.5239 10 95 CTR = 90.0907 LCL = 83.6576 8 92 6X X 89 4 86 2 83 0 0 10 20 30 40 50 60 0 10 20 30 40 50 60 Observation Observation Anything atypical Anything atypical with this point? with this point? 12
  • 13. CTR = 5.9 UCL = 11. LCL = 0.3 Control Ellipse Basic Concepts 14 60 SPC Chart for Variable 2 11 50 Observation Variable 2 40 Variable 2 8 30 5 20 2 10 -1 SPC Chart for Variable 1 0 98 82 86 90 94 98 12 10 8 6 4 2 0 UCL = 96.523 95 CTR = 90.090 X 92 Variable 1 LCL = 83.657 X 89 86 83 Variable 1 0 10 20 30 40 50 60 Observation13
  • 14. Basic Concepts Multivariate Control Chart Multivariate SPC Chart UCL = 10.77 24 20 16 T-Squared 12 8 4 0 0 10 20 30 40 50 60 Observation …. Time ….14
  • 15. The Nature of Continuous Data Process M1 M2 M3 M4 Q1 Quality Q2 Parameters from M5 Q3 Lab Online M6 ... Measurements M7 M8 M9 X - space Y - space .... Time Delays In a continuous process there can be a significant differences in the time required for each on-line measurement to impact processing or a measured quality parameter.15
  • 16. The Nature of Continuous Data The normal operating point of process measurement may change with process throughput. The parameter(s) that drive change in the process are known as state parameters (e.g. production rate). In this example, the state parameter is the fuel demand.16
  • 17. The Nature of Continuous Data Product grade can also be the state parameter in some cases. A change in the product grade being made is a change in the state parameter.17
  • 18. Online Data Analytics  Through the use of Principal Component Analysis (PCA) it is possible to detect abnormal operations resulting from both measured and unmeasured faults. – Measured disturbances – may be quantified through the application of Hotelling’s T2 statistic. • The T2 plot characterizes the amount of process variation that can be explained by the model and how it compares to “typical” operation. – Unmeasured disturbances – The Q statistic, also known as the Squared Prediction Error (SPE) or DMODX, may be used. • The Q plot characterizes the amount of process variation that cannot be explained by the model.  Projection to latent structures, also known as partial least squares (PLS) is used to provide operators with continuous prediction of quality parameters.18
  • 19. Preparation for the On-line Trial Capture team input using Collect lab data on Form a multi-discipline team quality parameters and that includes plant operations an “input-process-output” data matrix feedstock Survey Instrumentation, Conduct formal Enter lab data tune control loops operator trainingThis is the same approach that we took for our batch analytics trial several years ago.19
  • 20. Creating a Data Analytics ModelThe following steps are required to develop and deploy a data analytics model: • the process overview and identify the input, process, and output Define measurements • a module that contains a Continuous Data Analytics block and Create configure for measurements that may impact quality • the module that contains the CDA block and the continuous data Download historian and begin entering lab data Collect • process data over the full dynamic operating range • the selected historian data in the CDA application and perform a Analyze sensitivity analysis • a model by selecting the state parameter and method. Validate the Generate model for prediction accuracy using data then download the module • the web browser to view on-line fault detection and quality Launch parameter prediction; revalidate further once on-line20
  • 21. Define Scope of Lubrizol Field Trial 2+ Hours Product Bulk A prototype of a future Viscosity DeltaV capability for AT continuous process quality parameter Operation 1 Reaction Operation 3 Operation 4 prediction and fault detection is being tested Polybutene Unit in a field trial at Lubrizol on: Dynamic Compressor  Polybutene Unit Efficiency – Viscosity  Refrigeration System – Dynamic Compressor Efficiency Refrigeration System21
  • 22. Create & Configure Module1. Create module in DeltaVControl Studio2. Configure CDA block for measurementinputs that reflect conditions that impact quality 3. Download module and verify on-line that moduleis collecting and calculating data22
  • 23. Collect Process Data Wait for process data to be collected by the historian that reflect the normal process changes over the full dynamic operating range.23
  • 24. Analyze Historian Data24
  • 25. Generate Model25
  • 26. Validate Model  Model is fairly good for the higher grade of polymer, but not as good for the lower grade of polymer. Shared this information with Emerson, who then made some code changes in the software.26
  • 27. Validate Model Again  With the code changes, carefully excluding outliers, and the data time delay estimate enhanced, the model has been greatly improved. Model will be launched for the online trial after maintenance turnaround. Additional model development is ongoing.27
  • 28. Dynamic Compressor Efficiency  An on-line calculation of  dynamic compressor efficiency of both compressors was implemented in DeltaV.  PLS/PCA model for efficiency prediction and fault detection were trained using the on-line calculation of efficiency.28
  • 29. Compressor Efficiency Model29
  • 30. Model Verification – Compressor Efficiency Excellent Fit30
  • 31. Process Analytics Overview  In the Continuous Data Analytics Overview screen, Fault detection status and quality parameter prediction for deployed PCA/PLS model(s) are displayed.  A web browser can be used to access this overview if a station has Ethernet access to the field trial station.31
  • 32. Quality Parameter Prediction The impact of process variation on the quality parameters can be seen by selecting the quality parameter tab to view the predicted quality parameter. Predicted values over time can be obtained by clicking in the trend area. Under normal operating conditions, the predicted value should fall within the product specification range (green band).32
  • 33. Fault Detection  By clicking on a monitored process from the overview and selecting the fault detection tab, the calculated statistics are shown as Indicator 1 (T2) and Indicator 2 (Q).  A fault is indicated if either statistic exceeds an upper fault detection limit of 1.0.33
  • 34. Two Step Monitoring ProcedureIf a fault is indicted in the analytics overview screen, then select the associatedprocess and the Fault Detection Tab. – If either Fault Detection plot exceeds or approaches the upper fault detection limit of 1.0, click on that point in the trend and • Select the parameter(s) in the left pane of the screen that contributed to the fault • Evaluate the parameter trends from process operation standpoint • Take corrective action if necessary. – Inspect impact of the fault on quality prediction plot to find out how quality could be affected.34
  • 35. Good Start but More is Needed  Improve similarity between the Emerson on-line batch and continuous analytics offerings  Improve process analysis diagnostics  Support additional variables in the analysis  Support “vector data” types (e.g. IR, GC, MS)  Include Discriminant analysis in addition to PLS in both the batch and continuous offerings  Incorporate on-line monitoring of the model’s health  Implement adaptive updating of a model after initial deployment  Create the ability to handle select relevant variables when multiple processing paths may be utilized  Improve ability to make data available for additional analysis and model validation outside of DeltaV  Streamline network access to the online web interface35
  • 36. Installation and Network Setup36
  • 37. Summary  Lubrizol, Deer Park TX is testing a future DeltaV capability for quality parameter prediction and fault detection for continuous processes  Initial assessments indicate that the methodology will be applicable to continuous processes for: – Process monitoring and fault detection – On-line prediction of product quality – Application to “non traditional” settings such as equipment efficiency  Good starting point but more needs to be developed to have these modules applicable for use  We encourage Emerson to continue their development in this area to further develop the on-line continuous analytics module(s)37
  • 38. Data Analytics Workshops Learn more about continuous and batch data analytics by attending the following workshops at this year’s Emerson Exchange:  8-1322 Application of Online Data Analytics to a Continuous Process Polybutene Unit  8-2092 – Practical considerations for installing and using Batch Analytics  8-1965 Batch Analytics Applied to a Large Scale Nutrient Media Preparation Process  MTE-4021 Advanced Control Foundation – Tools & Techniques38
  • 39. Where To Get More Information  Dunia, R., Edgar, T., Blevins, T., Wojsznis, W., Multistate PLS for Continuous Process Monitoring, ACC, March, 2012  J.V. Kresta, J.F. MacGregor, and T.E. Marlin., Multivariate Statistical Monitoring of Process Operating Performance. Can. J. Chem.Eng. 1991; 69:35-47  Dunia, R., Edgar, T., Blevins, T., Wojsznis, W., Multistate Analytics for Continuous Processes, Journal of Process Control, 2012  MacGregor J.F., Kourti T., Statistical process control of multivariate processes. Control Engineering Practice 1995; 3:403-414  Kourti, T. Application of latent variable methods to process control and multivariate statistical process control in industry. International Journal of Adaptive Control and Signal Processing 2005; 19:213-246  Kourti T, MacGregor J.F. Multivariate SPC methods for process and product monitoring, Journal of Quality Technology 1996; 28: 409-42839