Master the Mystery and Marvels of DeltaV

MPC

James Beall

Principal Process Control Consultant

Presenters

James Beall

Introduction

Acknowledgement What is DeltaV MPC? The MPC Dynamic Controller The Optimizer “Tuning” the Optimizer “Tuning” the Dynamic Controller Troubleshooting Poor MPC Performance Summary

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

Model Predictive Control (MPC)

Learns From History

To Predict The Future

Learns From History

To Predict The Future

Past Present Future

Modeled

Relationship

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)

Matrix Control - Background

Top_Temp = Kp11*Steam + Kp12*RefluxBtm_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

MPC Process Models

Process Models

“Process” InputsMV’s & DV’s

“Process” OutputsCV’s & LV’s

Process models are derived from observed step tests of the variables.

Model ID

MPC – Dynamic Controller

Process ModelsMV – Hot Water CV-Temperature

MV – Cold Water CV-Flow Rate

MV – Hot Water : 1 Turn Open = +1 Deg F. +1 GPM

MV –Cold Water : 1 Turn Open = -1 Deg F. +1 GPM

CV-Temp CV-Flow

Setpoint ChangesTemp Flow+1 F +1 GPM+1 F -1 GPM 0 F +1 GPM Etc.

MV ChangesHot Cold+1 T 0 T 0 T -1 T+1/2 T +1/2 T

Model Predictive Control Here is how it works:

Learns From The Past

To Predict The Future

Learns From The Past

To Predict The Future

Modeled

Relationship

Predicts current control and constraint parameters based on past adjustments. Effect of measured

disturbance parameters is incorporated into the control and constraint parameter predictions

automatically.

0

setpoint

reference trajectory

t

Controlled

0

t

Manipulated futurepast

Controlled prediction

Predicted Errors

Selecting Variables for the Dynamic Controller

PredictPro – Application to determine process models, setup and tune the MPCPro Block

Automatically selects the variables to be in the Dynamic Controller

Selecting Variables for the Dynamic Controller

Uncheck this to manually select the variables to be in the Dynamic Controller

Condition < 1000

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

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%

MPCPro - Built-in LP Optimization

100% position

0% position

0% p

ositi

on

100%

pos

ition

80 deg F

120 deg F

50 psi

100 psi

Maximized

ThroughputMaximized ProfitMinimized Energy

The Economic Problem Objectives:

– Process Dependent• Maximize throughput• Maximize yield• Minimize “giveaway”• Minimize energy

Solution:– Economic cost function –

penalty factors– Utilize all Degrees of Freedom

• CVs– Min– Max– Target– None

• Constraints– Min– Max– None

• MVs– Min– Max– PSV– Equalize– None

Using Setranges

AV

CV

MV

Objective Function Configuration

Select from list of controller variables

Set Max/Min and Price

Define multiple operating

modes

Easy to set up and configure the built-in LP Optimizer

Operator Selects Mode

Select from list of Optimization Modes

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

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

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

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.

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

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?

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 - http://www2.emersonprocess.com/en-US/brands/processautomation/consultingservices/Pages/ConsultingServices.aspx