Utilizing DeltaV Advanced Control Innovations to Improve Control Performance

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Many functions of the DeltaV system are unique in the process industry. In this presentation we explore and discuss innovative features of the DeltaV PID and embedded Advanced Control products that can be applied to improve control performance. In particular, PID options are addressed that enhance cascade and override applications and allow effective single loop control using a sampled or wireless measurement. Application examples are used to illustrate how MPC can be easily added and commissioned online with no changes in the existing control strategy. Also, continuous data analytics is used an example that illustrates how future tools will enable improvements to be made in plant operations.

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Utilizing DeltaV Advanced Control Innovations to Improve Control Performance

  1. 1. Utilizing DeltaV Innovations to Improve Control Performance
  2. 2. Presenters  Terry Blevins  Willy Wojsznis
  3. 3. Introduction The DeltaV control system includes many functions that are unique in the process industry. Significant value provides embedded into DCS advanced control functionality which effectiveness and ease of use was proven over many years and numerous applications, for example:  Insight – integrated with control loop tool for loop performance and loop state evaluation, loop auto and adaptive tuning and loop operation reporting  PredictPro – Model Predictive Control tools for process model identification, controller development and operation. The three functions blocks: MPC, MPCpro and MPCPlus support various configuration sizes and functionalities  Fuzzy Logic Control – function block and application for FLC controller development
  4. 4. Advanced Control Foundation    Published by ISA in 2012 Available through ISA web site or Amazon Addresses all the advanced control tools in DeltaV
  5. 5. Advanced Control Foundation Web Site
  6. 6. Advanced Control Foundation (Cont)
  7. 7. Advanced Control Foundation (Cont)
  8. 8. Advanced Control Foundation (Cont)
  9. 9. Advanced Control Foundation (Cont)
  10. 10. Advanced Control Foundation (Cont)
  11. 11. Control Loop Foundation    Published by ISA in 2010 Print and eBook version available through ISA web site or Amazon Addresses all the tools that have traditionally been used by a control engineer in the process industry
  12. 12. Control Loop Foundation Web Site
  13. 13. Control Loop Foundation Web Site (Cont)
  14. 14. Control Loop Foundation Web Site (Cont)
  15. 15. Control Loop Foundation Web Site (Cont)
  16. 16. Control Loop Foundation Web Site (Cont)
  17. 17. Control Loop Foundation Web Site (Cont)
  18. 18. Control Loop Foundation Web Site (Cont)
  19. 19. Control Loop Foundation Web Site (Cont)
  20. 20. Control Loop Foundation Web Site (Cont)
  21. 21. Control Loop Foundation Web Site (Cont)
  22. 22. Control Loop Foundation Web Site (Cont)
  23. 23. Control Loop Foundation Web Site (Cont)
  24. 24. Control Loop Foundation Web Site (Cont)
  25. 25. Control Loop Foundation Web Site (Cont)
  26. 26. Control Loop Foundation Web Site (Cont)
  27. 27. Control Loop Foundation Web Site (Cont)
  28. 28. Control Loop Foundation Web Site (Cont)
  29. 29. Control Loop Foundation Web Site (Cont)
  30. 30. Control Loop Foundation Web Site (Cont)
  31. 31. Agenda In this session we explore features that are less known, but they may be very effectively used to improve both traditional and advanced control applications and economic performance.  PID options that enhance cascade and override applications and allow effective single loop control using a sampled or wireless measurement  Adding and commissioning Model Predictive Control (MPC) on-line with no changes in the existing control strategy  Future products and tools that enable improvements in plant operations using data analytic techniques  Q&A
  32. 32. Background – DeltaV Reset SETPOINT + KP + - + PROCESS Academic Explanation SETPOINT + KP - + + PROCESS FILTER Industrial implementation  Automatically provides anti-reset windup protection  Required for preferred implementation of override and cascade control Industrial Implementation – DeltaV, Invensys, others
  33. 33. Background - DeltaV PID (Cont)  The reset component of the PID block is implemented with a positive feedback network  Reset windup is automatically prevented under limit conditions associated with process saturation conditions  Dynamic Reset Limit selection in FRSIPID_OPTS enables use of BKCAL_IN in the reset calculation
  34. 34. Cascade Control  Cascade control may be applied when a process is composed of two or more (sub)processes in series  Any change in the manipulated input to the first process in the series will impact the output of the other processes  The output of each process in the series is the controlled parameter of the PID associated with that process
  35. 35. Example – Boiler Steam Temperature    The temperature of steam supplied by utility boilers can have a large impact on process operation In an attemperator, steam is mixed with water to regulate the temperature of steam exiting the boiler The spray valve is used to adjust the flow rate of water introduced into the attemperator
  36. 36. Cascade Control Implementation    Cascade control may be implemented when a process is made up of a series of processes Also, one PID block is required for each process in the series. For normal operations, the master loop is maintained in Automatic mode and the slave loop is operated in Cascade mode Slave Cascade Mode Master Automatic Mode
  37. 37. Cascade Control – Use of External Reset  The PID block is designed to support dynamic reset limiting, also commonly know as external reset  The performance of cascade control loop may be improved by enabling this option in the primary loop  In the secondary loop the CONTROL_OPTS for Use PV for BKCAL_OUT should be selected
  38. 38. Cascade Control Implementation    Cascade control may be implemented when a process is made up of a series of processes Also, one PID block is required for each process in the series. For normal operations, the master loop is maintained in Automatic mode and the slave loop is operated in Cascade mode Slave Cascade Mode Master Automatic Mode
  39. 39. Override Control  The implementation of override control is often the most effective way to maintain the process within its operating constraint limits  In general, override control may be implemented using two or more PID blocks and a control selector block  Under normal operating conditions, the controlled parameter is maintained at setpoint by the selected PID. The override PID takes an active role if the value of the constraint variable approaches its setpoint.
  40. 40. Override Example – Compressor  In this example, a large natural gas compressor is powered by an electric motor. Under normal operating conditions, the gas flow to the compressor is regulated to maintain a constant discharge pressure  However, the load on the electric motor changes as the gas flow rate changes  To avoid the current exceeding some limit, the motor current is the constraint variable and the discharge pressure is the controlled parameter in the override control strategy
  41. 41. Override Control Implementation  The control selector block supports upstream and downstream back calculation connections  Numbered pairs of input and back calculation outputs of the control selector should be connected to the same PID  Dynamic reset should always be enabled in the PID blocks involved in the override control
  42. 42. Recovery from Process Saturation  A process saturation condition exists when the setpoint of a PID can not be maintained and the PID output is limited  When operating conditions change that allow the process to recover from a process saturation condition, then improved response is provided by enabling the FRSIPID_OPTS option for PIDPlus  The PIDPlus option in DeltaV v11 provides improved control response for recovery from process saturation
  43. 43. PIDPlus Feature of DeltaV PID  The PIDPlus feature of the DeltaV PID (DeltaV v11 and higher) is enabled through the FRSIPID_OPTS parameter  When PIDPlus is enabled then special behavior is provided to address: – Control using Wireless measurement or sampled inputs provided by an analyzer – Recovery from process saturation conditions.
  44. 44. Recovery From Process Saturation  The recovery of the PID from process saturation is critical in many continuous and batch applications  One way of addressing recover from process saturation is to incorporate preload switching to the PID.
  45. 45. Recovery From Process Saturation  PI Control PI Control with Variable Pre-load By utilizing a variable preload (enabled by the PIDPlus selection) when the PID output is limited for an extended period of time (process saturation), it is possible to minimize setpoint overshoot on recovery from saturation
  46. 46. PIDPlus for Recovery From Process Saturation  The PIDPlus option in DeltaV v11 provides improved control response for recovery from process saturation – PIDPlus option added in DeltaV v11.3 to improve control response for recovery from process saturation. – Anticipation action can be adjusted using the PID parameter RECOVERY_FILTR. Value of 1 = No anticipation, Value of 0 = full anticipation utilized to avoid SP overshoot when recovering from process saturation
  47. 47. Example – Steam Temperature Control using PIDPlus   SP Overshoot When boiler firing rate is reduced, then the spray value should be cut back as the outlet temperature drops  Standard PID If steam generation exceeds the attemperator capacity then the boiler outlet steam temperature will exceed the outlet setpoint with the spray valve fully open When the FRSIPID_OPTS for PIDPlus is enabled then the valve moves before PV reached DeltaV PIDPlus 50% Drop in steam generation SP – providing improved response
  48. 48. Example - Air Compressor Anti-Surge  The function of the surge control system is to detect the approach to surge and provide more flow to the compressor through opening the recycle valve to avoid surge  Opening of the vent valve provides more flow and reduces compressor head, to move the compressor away from its surge point
  49. 49. Control Response – PIDPlus Disabled 60% Reduction in Air Demand Surge Margin Surge Line Exceeded
  50. 50. Control Response – PIDPlus Enabled Surge Margin 60% Reduction in Air Demand Surge Margin Maintained
  51. 51. PIDPlus for Wireless Control The Challenge – Control Using Wireless  Transmitter power consumption is minimized by reducing the number of times the measurement value is communicated.  Conventional PID execution synchronizes the measurement value with control action, by over-sampling the measurement by a factor of 2-10X  The rule of thumb to minimize control variation is to have feedback control executed 4X to 10X times faster than the process response time (process time constant plus process delay)  The conventional PID design (i.e., difference equation and ztransform) assumes that a new measurement value is available at each execution and that control is executed on a periodic basis
  52. 52. Sampling of Wired Measurement
  53. 53. *WirelessHART Communication Window communication is the preferred method of communications for control applications. A new value will be communicated only if:  The magnitude of the difference between the new measurement value and the last communicated measurement value is greater that a specified trigger value  Or if the time since the last communication exceeds a maximum update period Thus, the measurement is communicated only as often as required to allow control action to correct for unmeasured disturbances or response to setpoint changes. For Windowed mode you must specify an update period, a maximum update period, and a trigger value. *HART 7 specification that has been adopted as an international standard, IEC 62591Ed. 1.0.
  54. 54. PID Modification for Wireless Control   To provide the best control for a non-periodic measurement, the PID must be modified to reflect the reset contribution for the expected process response since the last measurement update Control execution is set faster than measurement update. This permits immediate action on setpoint change and update in faceplate
  55. 55. PIDPlus Using Wireless Transmitter vs. Conventional PID and Wired Transmitter Lambda Tuning ʎ = 1.0 Communication Resolution = 1% Communication Refresh = 10sec Setpoint PIDPlus Control Measurement PID PIDPlus Control Output PID Unmeasured Disturbance
  56. 56. Control Performance Difference   Communications transmissions are reduced by over 96 % when window communication is utilized The impact of non-periodic measurement updates on control performance as measured by Integral of Absolute Error (IAE) is minimized through the PID modifications for wireless communication
  57. 57. PID Performance for Lost Communications   The Conventional PID provides poor dynamic response when wireless communications are lost The PID modified for wireless control provides improved dynamic response under these conditions
  58. 58. Wireless Communication Loss – During Setpoint Change Setpoint PIDPlus Control Measurement PID PIDPlus Control Output PID Communication Loss
  59. 59. Wireless Communication Loss – During Process Disturbance PIDPlus Setpoint Control Measurement PID PIDPlus Control Output PID Communication Loss
  60. 60. Example - Separations Research Program, University of Texas at Austin  The Separations Research Program was established at the J.J. Pickle Research Campus in 1984  This cooperative industry/university program performs fundamental research of interest to chemical, biotechnological, petroleum refining, gas processing, pharmaceutical, and food companies  CO2 removal from stack gas is a focus project for which WirelessHART transmitters were installed for pressure and steam flow control
  61. 61. PC215 On-line Column Pressure Control  The same dynamic control response was observed for SP changes  Original plant PID tuning was used for both wired and wireless control Wired Measurement Used in Control GAIN=2.5 Wireless Measurement Used in Control RESET=4 RATE=1
  62. 62. Control Performance – Wired vs Wireless   Test #1 Test #2 Comparable control as measured by IAE was achieved using WirelessHART Measurements and PIDPlus vs. control with wired measurements and PID The number of measurement samples with WirelessHART vs Wired transmitter was reduced by a factor of 10X for flow control and 6X for pressure control – accounting for differences in test duration
  63. 63. Model Predictive Control (MPC)  Model Predictive Control (MPC) was developed by Shell Oil in the 1970s to improve the control of large interactive processes such as refinery distillation columns – DeltaV Predict and PredictPro may be used to implement MPC and may be used to address control of single input-single output (SISO), as well as multiple input-multiple output (MIMO) processes – In the DeltaV system MPC runs in the DeltaV controller and may execute as fast as 1/sec – making it possible to apply MPC to small processes that have historically been controlled using multi-loop techniques – No license is required to implement MPC in DeltaV if only one (1) manipulated process input is utilized in the control strategy – MPC may be used to more effectively control processes that are dominated by deadtime and difficult dynamics such as inverse response than is possible with PID – The multi-variable constraint handling capability of model predictive control may often be used to increase a plant’s production rate
  64. 64. Model Predictive Control(MPC) in DeltaV Three versions of Model Predictive Control (MPC) are provided in DeltaV  DeltaV Predict (DeltaV v7 or later) – Addresses processes as large as 8x8. Pusher capability is provided to allow process throughput to be maximized by maintaining the process at its operating constraints. No cost if only one(1) manipulated parameter. Module runs in Controller or Application station  DeltaV PredictPro DeltaV v9 or later) - For larger, more complex process as large as 40x80 . Linear Program (LP) embedded to support process optimization based on user defined control objective. Module runs in Controller or Application station  DeltaV PedictPlus (DeltaV v12 or later)– Adds greater capability to address changing operating constraints and integrating processes. Module runs only in Application station
  65. 65. Addressing Difficult Dynamics  The control performance achieved may not be satisfactory when PID feedback control is applied to a deadtime-dominant process. In such cases, control performance may be improved by replacing PID feedback control with Model Predictive Control
  66. 66. Using MPC to Address Process Interactions  When a process is characterized by multiple manipulated process inputs and multiple controlled process outputs, there is a potential for process interaction  The interaction of the manipulated inputs and controlled outputs is automatically accounted for by MPC
  67. 67. Layering MPC onto an Existing Strategy  An easy way to initially learn about MPC and to gain experience commissioning MPC blocks is to layer MPC blocks on top of traditional PID-based control strategies  The RCAS_IN and RCAS_OUT of the Analog Output block allow the MPC block to be in control when the Analog Output block mode changes from Cas to RCas
  68. 68. Integrating Advanced Control Into a DCS  When advanced control is embedded in the distributed control system (DCS), the plant operator has a single window interface with consistent system interaction and single log-in and span of control  If the DCS does not support advanced control, then the advanced control applications must be layered onto the DCS. Several approaches may be taken depending on the DCS support for layered applications
  69. 69. Future Data Analytic Products and Tools Continuous List 1. Batch Data Analytic product in DeltaV v12 – presented and discussed in several workshops 2. Continuous Data Analytic prototype has been developed and tested in two plants: Lubrizol and Huntsman 3. Operator user interface can be common for both products as it was tested in the prototype Batch List 4. The focus in this presentation will be on continuous data analytic based on the field trial results
  70. 70. Continuous Data Analytic Functionality Quality prediction Fault detection Fault identification Fault diagnosis Continuous Data Analytic predicts on-line product quality and monitor process operation. Process operation faults are detected, identified and diagnosed
  71. 71. Data Analytics Workshops Learn more about continuous and batch data analytics by accessing workshop presentations at this year’s Emerson Exchange:  8-4775 Challenges and Solutions in Data Analytics Application for a Distillation Column  8-4342 How to install Batch Analytics on a non-V12 DeltaV system  8-4240 Application of On-line Data Analytics to a Continuous Process Polybutene Unit
  72. 72. Where To Get More Information  Terrence Blevins, Willy K. Wojsznis and Mark Nixon Advanced Control Foundation, ISA, 2013  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: 409-428
  73. 73. Thank You for Attending! Enjoy the rest of the conference.

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