1. © ABB - 1 - cpmPlus Loop Performance Manager 3.2 Introduction to LPM

2. © ABB - 2 Presentation Outline Introduction / Motivation cpmPlus LPM Features Tuning Control Performance Monitoring Supporting Utilities cpmPlus LPM Plant-wide Disturbance Analysis

3. © ABB - 3 cpmPlus Loop Performance Manager 1. Introduction/Motivation

4. © ABB - 4 Why Loop Performance Monitoring? “Does my plant run optimally?” If not, how much can be accounted to the process automation, especially the control loops?” We should use available measurement data instead of just storing it. Normal operation does not necessarily mean optimal operation Loop optimization saves money without new capital investments

5. © ABB - 5 Real world performance is suboptimal!

6. © ABB - 6 An investment that has to pay off! Typical control loop as a $25,000 asset Half of it is lost 50 % well tuned 25 % uneffective control 25 % decrease performance Half time of good performance = 6 months 2 – 4 hours to investigate and improve control performance Typical process contains 2000 – 4000 control loops Only few people with appropriate know-how Average process engineer in charge of 400 control loops 25 % of 4000 loops do harm, this means…

7. © ABB - 7 Analysts start to get the message Quotes: “…while process equipment is an integral part of AM [asset management] programs, control loops … often don’t receive the same attention.” “Performance of control loops … degrades slowly over time with little fanfare.” “Without properly tuned control loops to minimize variability, … substantial benefits are lost.” “… even a slight degradation in process control can result in millions of dollars in lost profitability.” “Identifying the high-payback control loops requires evaluating all control loops, which would be an insurmountable task without the aid of control loop performance monitoring and analysis software.” “When first installed, advanced process control typically provides substantial benefits. Sustaining those benefits due to changing conditions, however, has been a problem.” “… it’s a good time to ensure control systems are part of your AM efforts.” Recent issue: “Include control loops in asset management” Les A. Kane, Editor

8. © ABB - 8 Benefits of Tuning and Auditing Maintains control system at its peak Loop Tuning Enables the plant engineers to reach loops optimum performance with significant time savings (vs. manual tuning) Loop Auditing Provides timely indication of equipment/automation/process problems. In this way it easy to keep the loop at their, allowing to stay at the optimal performance Also, it provides stable foundation for multivariable/advanced control

9. © ABB - 9 cpmPlus Loop Performance Manager – What is it? Loop Tuning  Challenge  Optimal PID Tuning is critical to efficient process operation  Loop Tuning is a time consuming activity  Typically, only expert engineers can perform Tuning  Solution  LPM Tuning makes definition of optimal PID parameters an easy, reliable & manageable task Loop Auditing  Challenge  Loop optimization is frustrating, because after few months all results seem lost due to the process variability  Plant engineers have to look at hundreds of signals and among them detect possible problems  Solution  Once Loop Optimization is performed, LPM Auditing monitors loops and allows the process engineer to immediately address problem areas

10. © ABB - 10 Cost of bad control High Low Time Dream Cost High Low Time Reality Cost High Low Time Realistic dream with Auditing Cost Loop Tuning Execution

11. © ABB - 11 cpmPlus Loop Performance Manager 2. LPM Features

12. © ABB - 12 LPM Tuning – Workflow Which step to tune a Loop? Configure Collect Model Tune Log

13. © ABB - 13 LPM Features – Data Collection Configure database by loops Simultaneous data collection for multiple loops OPC connectivity Direct connection for Infi90/Symphony Data collections stored as object on navigation tree for future retrieval Possibility to exploit auditing automatic data collection for tuning purposes

14. © ABB - 14 LPM Tuning - Identification BASIC for not experts and ADVANCED with fully scalable complexity for expert control engineers BASIC ADVANCED Manual or Automatic structure selection by best fit Parameters specified - up to 4 th order Identification also with Process in Close Loop Validation Model simulated with another data set Evaluation Ideal step response Bode diagram

15. © ABB - 15 LPM Tuning 5 Tuning methods available Time domain analysis Frequency analysis Support many vendor specific PID controller types Ability to model, tune, and analyze Feedforward control loops. Considers feedback tuning. Special treatment of Cascade control loops

16. © ABB - 16 LPM Advanced Tuning Features New Tuning values can be assessed on model different from the ones used to obtain the tuning set (Simulate Mode) Data pre-processing functionalities Advanced Feedforward Loop Tuning Management HTML-based and information-richer Tuning Logs Advanced Cascade Loop Tuning Management

17. © ABB - 17 LPM Tuning – Advantage  State of the art Tuning Algorithm, but with user- friendly tool to make Advanced Control Theory accessible to every Plant Engineers  Ready for every DCS  OPC connection  Calculated PID parameters (K p, T i, T d ) with the definition of your DCS  Identification also with Loop in normal Close Loop Mode  Not only basic PIDs, but also FeedForward and Cascade Loop Control Tuning becomes easy, fast, profitable

18. © ABB - 18 Performance Assessment: Tuning vs. Auditing Tuning - Design stage Assessment stage Reasonable design Slightly tight design ? Is this good control? If not: why?

19. © ABB - 19 Control loop monitoring – non-invasive! indices

20. © ABB - 20 LPM Auditing - General concept based on available signals only(SetPoint, PV, CO) available information can be incorporated performance indices, measures inference engine suggest remedies know how Info Hypothesis, Diagnosis know how I 1, I 2, I 3, …

21. © ABB - 21 Kinds of Performance Indices in LPM Basic statistics Data Validity Control loop modes Tuning Performance indices Oscillation indices Valve indices Measurement “PV” Measurement “PV” Target “SP” Target “SP” Controller Output “CO” Controller Output “CO” Nonlinearity indices Property indices Housekeeping Special indices Continuous indices

22. © ABB - 22 Kinds of Diagnoses in LPM Performance indices Auditing Rules + Maintenance Diagnoses Indices plus know-how organized in a Root-Cause analysis elaborate Maintenance Suggestions Diagnoses dealt with problems in: Tuning, Actuators and Sensors, External disturbance

23. © ABB - 23 Overall Performance Acceptable performance index Harris index Acceptable setpoint crossings index Setpoint crossing index (not for Level Control) Variability random Oscillation index of control error Controller output within range Saturation index Loop automatic Automatic mode index Acceptable cascade tracking Cascade tracking index (if in cascade) Acceptable response speed ACF to horizon index Acceptable performance index Harris index Acceptable setpoint crossings index Setpoint crossing index (not for Level Control) Variability random Oscillation index of control error Controller output within range Saturation index Loop automatic Automatic mode index Acceptable cascade tracking Cascade tracking index (if in cascade) Acceptable response speed ACF to horizon index Acceptable Overall performance excellent good fair poor PRECONDITIONS

24. © ABB - 24 Auditing workflow Loop configuration Assign TAG connection Signal ranges Loop Type Auditing configuration Assign Data collection schedule Batch / continuous auditing Loop category configuration Assign Sampling rate Batch duration Report configuration Assign Report layout Configuration file Configuration Indices Reports Excel, HTML Diagnoses Report Excel, HTML Indices Trend Plot Output Periodical reports Maintenance Operator Repair device Tuning Process Engineer Investigate Problem Activate Maintenance Maintenance Start auditing Database Data collection Indices calculation Setpoint CO,PV

25. © ABB - 25 Example oscillation investigation... F FC static friction cycling load tight tuning Diagnoses  Verify overall Performance  Detect oscillation  Decide among the 3 causes Indices  Oscillation details (period, amplitude…)  Amount of problem for every causes  Trend plot for every index

26. © ABB - 26 LPM Auditing - KPI Reporting & Analysis Reporting Pre-defined report templates Both numerical and chart-based assessment

27. © ABB - 27 Advanced Auditing Features Advanced Indices & Diagnosis trend facility (on multiple even non consecutive periods) User-defined Indices Enhanced KPI and Diagnosis set Server Status Monitor to supervise all the auditing functions “What Is Changed” report to immediately eye-catch recently developed events Possibility to generate a “Detailed Loop” Report, with in-depth charts and numerical figures

28. © ABB - 28 Detailed Report Time domain view (PV,SP,CO) Power spectrum view (PV) Statistical view (PV, CE) CE vs. CO, during oscillation becomes a ring. From the shape it is possible to detect stiction Impulse response of Disturbance Rejection Sensitivity study for Prediction horizon (good situation when lines is increasing with steps)

29. © ABB - 29 And More … Operation-Sensitive Reports: allow to monitor control loops according their operating region(s) Examples: production campaign types, loads, … Capability to extract and utilize for Tuning purposes data automatically collected during Auditing normal operation

30. © ABB - 30 Bulk Database Import for quick DB Configuration  Allows to import tag configuration details from Excel spreadsheets  Results in Relevant Manpower Savings

31. © ABB - 31 Infi90/AC800F Bulk Import Tool Available as an add-on to standard LPM Functions

32. © ABB - 32 LPM auditing - Everything also by Web Facility to get and manage all LPM information from any location in the net From the LPM Home Page it is possible to navigate to … … Reports Configuration … … Reports Retrieval … … Tuning Logs

33. © ABB - 33 LPM Auditing: Advantages Automatic data-collection enable actual continuous loop performance assessment rather than “sporadic sampling”, maximizing the chance to identify and correct insurgent production- related problems Simple, straightforward diagnostic indications are made available for the basic user or for quick assessment Diagnostic results are based on sophisticated indices which are able to provide explanations or in depth analysis for advanced user or when needed Both Diagnosis and Indices are saved and stored in user-configurable Reports so to not require continuous attention from plant crew and to provide a comprehensive “plant history” track record

34. © ABB - 34 cpmPlus Loop Performance Manager 3. Plantwide Disturbance Analysis

35. © ABB - 35 Plant-wide disturbance analysis - intro Analysis process data off-line Searches for data pattern in time (oscillations) and frequency (specra) to identify Oscillations Interactions Identifies most likely root-cause (with no info on plant topology/interconnections) Integrated in LPM, could use auditing data or external data (e.g. plant historian)

36. © ABB - 36 Plant-wide disturbance analysis - intro

37. © ABB - 37 PDA Application – Case 1 Cascaded Distillation Columns:

38. © ABB - 38 PDA Application – Case 1: Dataset Details Primary cycle Column 1 level through column 2 distillate Cause is LC2 valve movement problem Many variables cycling together Secondary cycle Top of column 1 (distillate FC2 and temperatures) Cause is FC2 valve movement problem 96 hours total data, sample time = 30 sec Dataset window chosen

39. © ABB - 39 PDA Application – Case 1: Clustering Three main Clusters detected: Two Oscillation Clusters One PCA Cluster A few tags have been added to clusters due to process considerations Oscillation Clustering: manually added 1 related tag to grouping (primary cycle)

40. © ABB - 40 PDA Application – Case 1: Clustering Three main Clusters detected: Two Oscillation Clusters One PCA Cluster A few tags have been added to clusters due to process considerations Default grouping: secondary cycle, had to add ti2.pv and ti3.pv tags manually

41. © ABB - 41 PDA Application – Case 1: Clustering Three main Clusters detected: Two Oscillation Clusters One PCA Cluster A few tags have been added to clusters due to process considerations PCA cluster default grouping, manually added 2 related tags to grouping (primary cycle)

42. © ABB - 42 FC LC FC 22 32 10 TI FC LC 19 4 FC Internal Condenser Column 1 20 TI PI TI TC Steam Column 2 PI Internal Condenser PDI TC TI PI PDI Decanter LC TC TI TC Steam 1 3 1 1 5 4 3 1 2 1 1 2 39 1 2 2 3 4 2 6 3 7 8 4 7 5 9 3 2 6 16 PDA Application – Case 1: Main Clustered Disturbances

43. © ABB - 43 Good default results for non-linearity analysis (primary cycle) (ranks LC2 as highest non-linearity) PDA Application – Case 1: Root Cause Analysis

44. © ABB - 44 FC2 cycle (secondary cycle) analysis: non-linearity correctly identifies FC2 PDA Application – Case 1: Root Cause Analysis

45. © ABB - 45 FC LC FC 22 32 10 TI FC LC 19 4 FC Internal Condenser Column 1 20 TI PI TI TC Steam Column 2 PI Internal Condenser PDI TC TI PI PDI Decanter LC TC TI TC Steam 1 3 1 1 5 4 3 1 2 1 1 2 39 1 2 2 3 4 2 6 3 7 8 4 7 5 9 3 2 6 16 PDA Application – Case 1: Disturbance Propagation Cluster 1 Cluster 2

46. © ABB - 46 LC Liquid LC PC Liquid LC FC Liquid Steam LC FC Liquid AB DC Steam 7 PI 7 7 2 2 5 5 9 9 9 Vapor Header PC 1 SP PC PDA Application – Case 2 Vaporizer System:

47. © ABB - 47 Cycle of interest Two main Clusters detected: One Oscillation Clusters One PCA Cluster A few tags have been added to clusters due to process considerations PDA Application – Case 2: Clustering

48. © ABB - 48 Good results for non-linearity, clearly identifies LC2 as root cause PDA Application – Case 2: Root Cause Analysis Ref. to: “Peak Performance: Root Cause Analysis of Plant- wide Disturbances”, ABB Review 1/2007

49. © ABB - 49 cpmPlus - LPM Conclusions Tuning With LPM Process Engineers (also non expert in control theory) can optimize Loop behavior Benefits: increase process profit, more stable working condition, more safety operations PDA Very valuable insight on process corrrelations, oscillations and root causes with a few points and click Could use your historian data (with reasonable data compression) Complementary to tuning and auditing Auditing Control Performance Monitoring is non-invasive, simple to perform and very efficient LPM detects automatically problem at the beginning of their occurrence Performance monitoring nowadays answers the most important questions to help the plant personnel to pinpoint and remove problems The right information to the right people