Master the Mystery and Marvels of DeltaV MPC


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Presented by Emerson's James Beall at the 2012 Emerson Exchange in Anaheim, California USA.

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  • Main Points: DeltaV PredictPro uses an embedded LP Optimization algorithm to find the most profitable operating point. In this example, the feasible region is shown in blue, which is bounded by both MV and CV limits. The LP optimizer will find the most profitable operating point which will always occur at the intersection of operating limits. In many process operations, the optimum may change based on operating conditions and economic objectives. For example, one week you may be throughput limited and want to minimize energy consumption. Another week you may need to catch up on throughput and you’re not so concerned with energy. Or you may want to maximize profit based on both energy and throughput. Transition: With DeltaV PredictPro you can define multiple Optimization Objectives or Modes like Minimum Energy, Maximum Throughput, or Maximum Profit. Let’s see how.
  • Main Points: In the DeltaV Engineering Environment it is easy to select MPC Variables to be included in the optimization calculations. For each variable selected, the user specifies that unit cost and whether the control should maximize or minimize that variable. Up to five Optimization Modes can be defined.
  • Main Points: The Optimization Mode is displayed from the Operations Display and may be changed using a drop down menu provided you have configuration privileges. Transition: You can also view the Optimization configuration details and current operating conditions from an Optimization Detail Display.
  • Master the Mystery and Marvels of DeltaV MPC

    1. 1. Master the Mystery andMarvels of DeltaV MPC James BeallPrincipal Process Control Consultant
    2. 2. Presenters  James Beall
    3. 3. Introduction  Acknowledgement  What is DeltaV MPC?  The MPC Dynamic Controller  The Optimizer  “Tuning” the Optimizer  “Tuning” the Dynamic Controller  Troubleshooting Poor MPC Performance  Summary
    4. 4. What is DeltaV MPC?  MPC= Multivariable, Model Predictive Controller  The MPCPro block has a “Dynamic” Controller and a linear Optimizer  The MPC block only has a “Dynamic” Controller
    5. 5. Model Predictive Control (MPC) Learns From History Learns From History To Predict The Future To Predict The Future Modeled Relationship Past Present Future 5
    6. 6. Types Of Process Variables  “Process” Inputs  Manipulated Variables (MV) – Valves or controller setpoints written to by the MPC.  Disturbance Variables (DV) - Measured variables which may also affect the value of controlled variables  “Process” Outputs  Controlled Variables (CV) - Process variables which are to be maintained at a specific value; i.e., the setpoint  Constraints (LV) - Variables which must be maintained within an operating range (a special type of CV)
    7. 7. Matrix Control - Background Top_Temp = Kp11*Steam + Kp12*Reflux Btm_Temp = Kp21*Steam + Kp22*Reflux Using Linear Algreba “Matrix” math, you can solve for the Steam and Reflux flow required to achieve the desired Top_Temp and Bottom Temp.
    8. 8. MPC Process Models Process Models“Process” Inputs “Process” OutputsMV’s & DV’s CV’s & LV’s Process models are derived from observed step tests of the variables. Model ID 8
    9. 9. MPC – Dynamic Controller MV – Hot Water Process Models CV-Temperature MV – Cold Water CV-Flow Rate CV-Temp CV-FlowV – Hot Water : 1 Turn Open = +1 Deg F. +1 GPV –Cold Water : 1 Turn Open = -1 Deg F. +1 GP Setpoint Changes MV Changes Temp Flow Hot Cold +1 F +1 GPM +1 T 0 T +1 F -1 GPM 0 T -1 T 0 F +1 GPM +1/2 T +1/2 T Etc.
    10. 10. Model Predictive Control Here is how it works: Predicts current control and constraint parameters based on past adjustments. Effect of measured disturbance parameters is incorporated into the control and constraint parameter predictions Learns From The Past automatically. Learns From The Past To Predict The Future To Predict The Future Modeled Controlled Predicted Errors Relationship setpoint reference trajectory Controlled prediction t 0 past future Manipulated t 0
    11. 11. Selecting Variables for the Dynamic ControllerPredictPro – Application to determine processmodels, setup and tune the MPCPro Block Automatically selects the variables to be in the Dynamic Controller
    12. 12. Selecting Variables for theDynamic Controller Uncheck this to manually select the variables to be in the Dynamic Controller Condition < 1000
    13. 13. Tuning the Dynamic Controller  CV and LV - Penalty on Error – Default 1.0 – Usually minor change like 0.8 to 1.2 – Integrating variables usually less than 0.5 – Some special optimization applications use ~0.1  MV – Penalty on Move – The Predict or PredictPro application sets the default – Usually move by 25-50% of current value
    14. 14. The Optimizer  Consider a cruise (speed) controller for your car that can manipulate BOTH the accelerator and the brake. This would be an MPC, 2- MV’s, 1 -CV.  So, to hold 50% speed, the MPC could… – Accelerator = 50%, Brake = 0% – Accelerator = 100%, Brake = 50% – Accelerator = 80%, Brake = 30% – Etc.  But, if we “Optimize” to “Minimize” Braking… – Accelerator = 50%, Brake = 0%
    15. 15. MPCPro - Built-in LP Optimization F deg 120 50 ps i 100% position Maximized 100% position Maximized Energy Minimized Profit 0% position 10 0 Throughput ps i eg F 80 d 0% position
    16. 16. The Economic Problem  Objectives:  Solution: – Process Dependent – Economic cost function – • Maximize throughput penalty factors • Maximize yield – Utilize all Degrees of • Minimize “giveaway” Freedom • Minimize energy • CVs – Min – Max – Target – None • Constraints – Min – Max – None • MVs – Min – Max – PSV – Equalize – None
    17. 17. Using Setranges AV CV MV
    18. 18. Objective Function Configuration Define multiple operating modes Select from list of controller variables Set Max/Min and Price Easy to set up and configure the built-in LP Optimizer
    19. 19. Operator Selects Mode Select from list of Optimization Modes
    20. 20. Optimizer and Dynamic Controller  Based on the selected Objective Function, the Optimizer first calculates the “Target Value” for the MV’s at the end of the Tss  Then, based on the Target Values for the MV’s, the Optimizer calculates the value of the CV’s and LV’s at the end of the Tss which are now the “Target Setpoints” for the CV’s and LV’s.  The Dynamic Controller moves the MV’s to achieve the Target SP for the CV’s and LV’s that are in Dynamic Controller
    21. 21. Optimizer and Dynamic Controller “Show me the money!” 1. Calculate Target MV’s 2. Calculate Target SP’s for all CV/LV 3. CV/LV in Dynamic Controller are controlled to Target SP
    22. 22. Troubleshoot MPCPro  Using the Optimizer Dialogue (“show me the money”), determine if the Optimizer is calculating: – Target MV’s moving in the correct direction (increasing or decreasing) – Target SP’s for the CV’s and LV’s that seem to be correct (within the CV Setpoint range, within the limits for LV’s, minimized or maximized, etc.)  If not, the Optimizer needs tuning for such things as Value/%, Priority, OptType, Min/Max
    23. 23. Troubleshoot MPCPro  If the Optimizer is giving reasonable Target MV’s and SP’s but MPC doesn’t control the CV/LV’s to the Target SP’s, then then Dynamic Controller needs tuning – Typically the MV’s Penalty on Move (POM) is too high. Reduced the POM for each MV 25-50%. – May need to adjust the Penalty on Error (POE) for one or more of the CV/LV’s that are in the Dynamic Controller. To get more aggressive control of a CV/LV, increase the POE to 1.1 or 1.2 (0.8 or 0.9 to reduce aggressiveness). – Generate and download for these changes. Can use MPCPro Simulate to test.
    24. 24. Business Results Achieved  Quickly pinpoint the reason your MPC application is not performing to expectations  These techniques will help you quickly tune your MPC applications and received benefits much sooner  There are many “small” MPC projects that be implemented easily with DeltaV embedded MPC technology that have a great ROI
    25. 25. Summary  DeltaV MPCPro has an Optimizer and a Dynamic Controller  To get the desired performance, tune the Optimizer first  Once the Optimizer provides the correct Target SP’s for CV/LV’s, tune the Dynamic Controller  Most MPC applications have a 1-6 month ROI  Questions?
    26. 26. Where To Get More Information  Other training sessions – 8-2242 – DeltaV MPC – Small Project Yields Big Benefits! – 8-2064 – PredictPro Tips – Exhibit area – APC Booth, Distillation Solutions Booth  Other information sources – Blevins, T. L., McMillan, G. K., Wojsznis, W. K. and Brown, M. W., Advanced Control Unleashed, – Emerson Education Services Courses  Consulting services – Emerson Process Management, Industry Solutions Group -