profit controller concepts reference guide

7/23/2019 Profit Controller Concepts Reference Guide

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Honeywell Process Solutions

Advanced Process Control

Profit Controller

Concepts Reference Guide

RM09-400

R320

11/08

Release 320


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ii Advanced Process Control Profit Controller Concepts Reference Guide R32011/08

Notices and Trademarks

Copyright 2008 by Honeywell International Inc.Release 320 November, 2008

While this information is presented in good faith and believed to be accurate, Honeywell disclaimsthe implied warranties of merchantability and fitness for a particular purpose and makes noexpress warranties except as may be stated in its written agreement with and for its customers.

In no event is Honeywell liable to anyone for any indirect, special or consequential damages. Theinformation and specifications in this document are subject to change without notice.

Honeywell, PlantScape, Experion PKS, and TotalPlantare registered trademarks of HoneywellInternational Inc.

Other brand or product names are trademarks of their respective owners.

Honeywell International

Process Solutions

2500 West Union Hills

Phoenix, AZ 85027

1-800 343-0228


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R320 Advanced Process Control Profit Controller Concepts Reference Guide iii11/08

About This Publication

Statement of Work

The following table describes the audience, purpose, and scope of this document:

Purpose This book provides a technical though broadly pennedorientation to Profit Controller (RMPCT) concepts andfunctionality.

Audience Process and control engineersField technicians

For Product Release All Profit Controller (RMPCT) releases

LCN releases 500 and 600 series.

AxM X-side releases 200 and higher

Release Information

This is document version 1.11 for all releases of ProfitController (RMPCT) software.

Who Should Use This Book

Engineers This book is intended for process and control engineers responsible forinstalling and tuning Profit Controller. The education and practical experienceappropriate for these tasks is assumed.

This book provides a detailed description of RMPCT functionality without going into amathematical description of the algorithms. See RMPCT Course Information forinformation about the courses Honeywell offers that explain the mathematicalunderpinnings of RMPCT.

Operators Operators can probably find useful information here and there, but this book

is not composed with Operator duties in mind.


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About This Publicati onWhat This Book Tells you about RMPCT

iv Advanced Process Control Profit Controller Concepts Reference Guide R32011/08

What This Book Tells you about RMPCT

Honeywells Robust Multivariable Predictive Control Technology (RMPCT) is a multi-input multi-output control application that controls and optimizes highly interactiveindustrial processes.

ThisRMPCT Concepts Referenceexamines the design concepts and mathematicalprinciples behind the application, and gives advice about configuration and tuningappropriate to any implementation.

RMPCT Course Information

Honeywell offers several classes that explain the math behind RMPCT and how toimplement an RMPCT application.

Engineers who would like a more technical introduction to RMPCT should contact

Honeywell Automation College

2500 W. Union Hills DrivePhoenix, AZ 85027

How This Book Is OrganizedThe following table summarizes what each section in this book tells you about thispublication and about Profit Controller (RMPCT):

In This Section You Can Find This Information

About This Publication(You are here)

How to make the best use of this book,and how the information is ordered.

What information you can find in thedifferent sections.

What writing conventions have been usedthroughout this and other publications in

the Profit library.


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About This Publicat ionHow This Book Is Organized

R320 Advanced Process Control Profit Controller Concepts Reference Guide v11/08

In This Section You Can Find This Information

Section 1, Quick Tour The different RMPC and Profit Controller(RMPCT) versions that are supported.

What Profit Controller (RMPCT) utilitiesare available to help you collect data,identify a process, and build and install acontroller.

How to implement a controller on anautomated control system.

Section 2, Profit Controller (RMPCT)Variables

How Profit Controller (RMPCT) usescontrolled variables (CVs), manipulatedvariables (MVs), and disturbance variables(DVs) to control a process.

Section 3, Control Interval How settling time, time constants, CVconstraints, independent variables, andblocking help you choose the best controlinterval.

Section 4, Economic Optimization How Profit Controller (RMPCT) uses anobjective function for optimizing control,and how the optimization horizon and theCV and MV soft limits influenceoptimization calculations.

Section 5, Robust Control How Profit Controller (RMPCT) controlsolutions get their robustness throughrange control, limit funnels, and singular-value thresholding.

Section 6, Quick Reference to the ProfitController (RMPCT) Displays

How to quickly locate on the ProfitController (RMPCT) displays theconfiguration, tuning, and optimizationparameters described in this book.


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About This Publicati onWriting Conventions Used in This Book

Writing Conventions Used in This Book

The following writing conventions have been used throughout this book and other booksin the Profit Suite library.

vi Advanced Process Control Profit Controller Concepts Reference Guide R32011/08

Words in double quotation marks " " name sections or subsections in thispublication.

Words in italicsname book titles, add grammatical emphasis, introduce words thatare being referenced or defined, or represent mathematical variables. The context

makes the meaning and use clear.

Words in bold typeindicate paragraph topics or bring important phrases to yourattention.

Shading brings paragraphs and table entries to your attention.

Windows pull down menus and their options are separated by an angle bracket >.For example, Under Settings> Communications, set the baud rate.

Messages and information that you type appear in Cour i er font.

Acronyms, Scan parameters, point names, file names, and paths appear inUPPERCASE. The context makes the meaning and use clear.

Command keys appear in UPPERCASE within angle brackets. For example, press.

TPS user station touch-screen targets appear in rounded boxes. For example, touch

MODIFY NODE.

Graphic buttons appear in UPPERCASE within brackets [ ]. For example, touch[TAG].

Point-dot-parameter means a point name and one of its parameters. For example,point-dot-SP means the SP parameter for the point.

Zero as a value and when there is a chance for confusion with the letter O is given as. In all other cases, zero as a numerical place holder is given as 0. For example, 1.0,

10, 101, CV1, parameter P.

The terms screenand displayare used inter changeably in discussing the graphicalinterfaces. The verbs displaya screen and calla screen are also used interchangeably.


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About This Publicat ionSelected Bibliography

R320 Advanced Process Control Profit Controller Concepts Reference Guide vii11/08

Selected Bibliography

The following technical publications can be helpful. More recent editions might beavailable since compiling this list. Contact your company or university librarian, or localbookstore for complete bibliographic and ordering information for these and other booksabout model identification and multivariable control.

Author Tit le City/Pub lisher/Year

strm andWittenmark Computer-Controlled Systems Englewood Cliffs: Prentice, 1987

Choi ARMA Model Identification NY: Springer-Verlog, 1992

Ljung System Identification Theory forthe User

Englewood Cliffs: Prentice, 1987

Golub and Loan Matrix Computations Baltimore: John Hopkins UP, 1989

Morari and Zafiriou Robust Process Control Englewood Cliffs: Prentice, 1989

Pankratz Forecasting with DynamicRegression Models

NY: Wiley, 1991

Seborg, et al Process Dynamics and Control NY: Wiley, 1989

Stephanopoulos Chemical Process Control Englewood Cliffs: Prentice, 1990

Zhu and Backx Identification of MultivariableIndustrial Processes forSimulation, Diagnosis, andControl

London: Springer-Verlog, 1993


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R320 Advanced Process Control Profit Controller Concepts Reference Guide ix11/08

Contents

1. QUICK TOUR...............................................................................13

1.1 Overview ........................................................................................................ 13In This Section .....................................................................................................................13

1.2 Prof it Cont roller (RMPCT) Applications ...................................................... 14Typical Applications .............................................................................................................14SISO vs Multivariable Control ..............................................................................................14

1.3 Implementation ..............................................................................................15The Variables.......................................................................................................................15The Controller Model............................................................................................................15Predict-Back and Estimated Disturbance Models ................................................................16Implementing a Controller ....................................................................................................16

2. PROFIT CONTROLLER (RMPCT) VARIABLES.........................17

2.1 Overview ........................................................................................................ 17In This Section .....................................................................................................................17CVs, MVs, and DVs Defined ................................................................................................17

2.2 Control led Variables .....................................................................................18Characteristics .....................................................................................................................18

Feedback Performance Ratio...............................................................................................18Correction Horizon ...............................................................................................................18Tuning for Response............................................................................................................19Speed vs AccuracyThe Tradeoffs...................................................................................20Finding the Best Performance Ratio ....................................................................................20Degrees of Freedom ............................................................................................................20Minimizing the Error .............................................................................................................21Setting the Engineering Unit (EU) give-up factors................................................................21EU give-ups vs Controller Speed .........................................................................................22CV Tracking .........................................................................................................................22Setpoint vs Range................................................................................................................22Limit Ramping ......................................................................................................................23Periodic Sampling ................................................................................................................23Bad Value TreatmentCritical CVs ...................................................................................24Bad Value TreatmentNon Critical CVs............................................................................24

Predicted Values ..................................................................................................................24State Estimation...................................................................................................................25State Estimation for Integrating Processes ..........................................................................25Ramp Correction Settings ....................................................................................................25A Caution about Ramp Correction .......................................................................................26


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Ramp Correction Trade-Offs ............................................................................................... 26State Estimation Settings Summary.................................................................................... 27

2.3 Manipulated Variables...................................................................................28Characteristics..................................................................................................................... 28Rate-of-Change (Max Move) Limits..................................................................................... 28Limit Ramping ..................................................................................................................... 28Movement Weights.............................................................................................................. 29Degrees of Freedom ........................................................................................................... 29Setting MV Priorities............................................................................................................ 30Use Lower Numbers............................................................................................................ 30MV Tracking ........................................................................................................................ 30Bad Value Treatment .......................................................................................................... 31Anti-Windup......................................................................................................................... 31Predict-Back and Estimated Disturbance Compensation.................................................... 31

Predict-BackImplementation Suggestions ..................................................................... 32Predict-BackAn Illustration............................................................................................. 32Predict-Back IllustrationThe Disturbances ..................................................................... 33Predict-Back IllustrationThe Problem............................................................................. 34Predict-Back IllustrationThe Solution............................................................................. 34Predict-Back IllustrationThe Benefits............................................................................. 35On/Off ControlConfiguration .......................................................................................... 35On/Off ControlAn Example ............................................................................................ 36Automatic Mode Switching .................................................................................................. 36Configuring Automatic Mode Switching ............................................................................... 37Taking MVs Off Profit Controller (RMPCT) Control ............................................................. 37MV Move Accumulation....................................................................................................... 37

2.4 Disturbance Variables ...................................................................................40

Characteristics..................................................................................................................... 40DV Influence on CV Tuning................................................................................................. 40Feedforward Performance Ratio ......................................................................................... 41

3. CONTROL INTERVAL................................................................. 43

3.1 Overview .........................................................................................................43Read This............................................................................................................................ 43In This Section..................................................................................................................... 43

3.2 Control Interval and Settling Time ...............................................................44The Ideal Interval-to-Settling Time ...................................................................................... 44Shorter Intervals.................................................................................................................. 44Longer Intervals................................................................................................................... 44

When Time Constants Vary ................................................................................................ 44Recommended Intervals ..................................................................................................... 44Intervals > Settling Time...................................................................................................... 45

3.3 Blocking ..........................................................................................................46


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Contents

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Defined.................................................................................................................................46Control Quality .....................................................................................................................46CV Constraints .....................................................................................................................46MV Moves ............................................................................................................................46Independent Variables .........................................................................................................47Reducing the Calculation Time ............................................................................................47

4. ECONOMIC OPTIMIZATION .......................................................49

4.1 Overview ........................................................................................................ 49In This Section .....................................................................................................................49Optimization Concepts .........................................................................................................49

4.2 Object ive Funct ion ........................................................................................ 50Degrees of Freedom ............................................................................................................50General Form.......................................................................................................................50Weighting Coefficients .........................................................................................................51Maximizing Profit vs Minimizing Cost ...................................................................................51Setting Linear Objective Coefficients ...................................................................................52When Limits Do Better Than SetPoints................................................................................52Range Limits and Product Quality........................................................................................52When Setpoints Do Better Than Limits ................................................................................53

4.3 Optimization Horizon and Optimization Speed Factor .............................. 54Optimization Horizon............................................................................................................54Optimization Speed Factor...................................................................................................54

4.4 Soft Limits ......................................................................................................55How Soft Limits Work...........................................................................................................55

CV Soft Limits ......................................................................................................................55MV Soft Limits ......................................................................................................................56

5. ROBUST CONTROL ....................................................................57

5.1 Overview ........................................................................................................ 57In This Section .....................................................................................................................57Revisiting the Process Model ...............................................................................................57Model Error is Unavoidable..................................................................................................57Coping with Model Error.......................................................................................................57

5.2 Range Control ................................................................................................ 59Defined.................................................................................................................................59Setpoint ControlAn Example...........................................................................................59

The Problem.........................................................................................................................59Range ControlAn Example .............................................................................................60The Control Matrix................................................................................................................60Ranges and Optimization.....................................................................................................60


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5.3 Limit Funnels .................................................................................................61Defined................................................................................................................................ 61Three Examples .................................................................................................................. 61How the Funnels Work ........................................................................................................ 63Funnel Type and Decouple Ratio........................................................................................ 63When to Use Funnel Type 1 and 2 ...................................................................................... 64

5.4 Singular-Value Thresholding........................................................................66Defined................................................................................................................................ 66Matrix Condition .................................................................................................................. 66Determining a Threshold ..................................................................................................... 66Maximizing the Matrix Condition.......................................................................................... 66

5.5 Min-Max Design .............................................................................................68Your Input to the Offline Design .......................................................................................... 68Overview of the Design Procedure...................................................................................... 68Benefits ............................................................................................................................... 69

5.6 Optimal Scaling..............................................................................................70Purpose of Optimal Scaling................................................................................................. 70How Values Display on Profit Controller (RMPCT) Screens ............................................... 70

6. QUICK REFERENCE TO THE PROFIT CONTROLLER DISPLAYS 71

6.1 Overview .........................................................................................................71In This Section..................................................................................................................... 71Target Access ..................................................................................................................... 71

6.2 Finding Targets on the Profit Controller (RMPCT) Displays .....................72Tuning and Configuration Parameters................................................................................. 72Optimization Parameters..................................................................................................... 73Control (Operating) Parameters .......................................................................................... 74


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R320 Advanced Process Control Profit Controller Concepts Reference Guide 1311/08

1. Quick Tour

1.1 Overview

In This Section

This section introduces you to Profit Controller (RMPCT). Read this section to learnabout:

What you can expect from an Profit Controller (RMPCT) controller, and

What it takes to implement a controller on an automated control system.


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1. Quick Tour1.2. Profit Controller (RMPCT) Applications

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1.2 Profit Control ler (RMPCT) Applications

Typical Applications

Here are typical applications for Profit Controller (RMPCT).

An entire process unit, such as a distilling unit or a catalytic cracking unit.

A unit operation, such as a reactor and associated equipment.

Complex equipment, such as a paper machine.

Any system that encompasses variables that are related or interact with each other.

SISO vs Multivariable Control

The variables in the process that must be maintained at some value or within some rangeare referred to as controlled variables (the CVs). In order to keep the CV values wherethey should be, the controller adjusts the values of the manipulated variables (the MVs).

In a single-input single-output controller, there is one CV (the controller input or processvalue) and there is one MV (the controller output). With RMPCT, there are multiple CVsand multiple MVs. The controller views all the variables taken together as one systemand simultaneously considers the effects of all MVs on the CVs. Quite often the numberof MVs and CVs are not equal; there can be more or fewer MVs than CVs.


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1. Quick Tour1.3. Implementation


1.3 Implementation

The Variables

There are three types of process variables that RMPCT uses as control input and output:

Controlled Variables (CVs)variables that the controller attempts to keep atsetpoint or within a range that the Operator specifies. The first priority of thecontroller is to keep the CVs within their constraints.

Manipulated Variables (MVs)variables that the controller adjusts in order to keepthe CVs within constraints and to optimize the process, while not moving any of the

MVs outside of their constraints.

Disturbance Variables (DVs)measured variables not under control of the controller(they may come from an upstream process, for example) but which affect the valuesof the CVs. By predicting the future effects of the DVs on the CVs, the controllercan take action to prevent CV excursions outside constraints before they develop.DVs provide feedforward information to the controller.

The Controller Model

Sub-Process ModelsRMPCT uses a model to predict process behavior. The overallprocess model is composed of a matrix of dynamic sub-process models, each ofwhich describes the effect of one of the independent variables (MVs and DVs) onone of the CVs. A sub-process model describes how the effect of an independent

variable on a CV evolves over time.Sub-process models are null when a particular independent variable has no effect ona particular CV.

Dynamic Response of Sub-Processes RMPCT uses a generic form of sub-processmodel that provides a reasonably good description of the dynamic behavior of thevast majority of processes that are encountered in the processing industries. Thisgeneric model contains a number of coefficients whose values determine thedynamic response of a sub-process.

Identifying the Model To make the generic models into specific models, you haveto determine coefficient values where the predicted process responses agree with theactual process responses. This procedure is known as identifying the model, orfitting the model to the process. This model identification is typically done oncewhen the controller is installed.

To identify the model you obtain data from the process while any existing controlloops between the MVs and CVs are open. During this open-loop testing, the MVs


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1. Quick Tour1.3. Implementation


are moved independently. The MV input values and the CV response values arerecorded.

Model CoefficientsThis test data is used to determine the model coefficients. Withthe appropriate coefficients, the model can predict CV responses quite accurately.

Predict-Back and Estimated Disturbance Models

With its predict-back and estimated disturbance models, RMPCT can predict the effectunmeasured disturbances have on CVs:

Predict-back models back-outthe known effects of MVs and DVs from a signal thatcontains disturbance information, but that is also correlated with one or more of the

MVs or DVs. This result is an estimate of the disturbance.

Estimated disturbance models describe the effect of the estimated disturbance on theCVs.

RMPCT incorporates predict-back and estimated disturbance models beginning withsoftware release 130.0.

Implementing a Controller

Here is a summary of how you implement a controller:

1. Decide what the CVs, MVs, and DVs are.

2. Open existing loops between CVs and MVs. Apply test input signals to the MVs

(the simplest test signal is a series of steps made by the Operator). Apply test signalsto the DVs, if possible; otherwise, a period of time must be found when the DVvalue is undergoing significant change.

3. Record the CV, MV, and DV signals during the test. The variables values aresampled at the interval (or a sub interval) at which the controller is expected toexecute. The sampled values are collected into one or more files.

4. Identify the process model, using the collected data from the open-loop testing.

5. Build the controller from the model. Using the Honeywell Controller Builder, theresult is two files that define this particular controller application. These are read bythe controller to define its operation when it is first activated.

6. Run the controller in simulation to verify that the controller works as expected.

7. Install the controller. Use WARM mode to test control before turning control ON.

The remaining sections in this book provide more detailed information about RMPCTvariables and about the control functions that RMPCT performs.


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2. Profit Controller (RMPCT) Variables

2.1 Overview

In This Section

This section explains how Profit Controller (RMPCT) uses controlled variables (CVs),manipulated variables (MVs), and disturbance variables (DVs) to control a process.

CVs, MVs, and DVs Defined

This is what these variables mean to the Profit Controller (RMPCT):

CVs Controlled variables are the process conditions to be controlled (that is whythey are also called PVs, process variables). The temperature of an outflow streamcan be a CV.

MVsManipulated variables are the control handles on the process. These are thevariables whose conditions are manipulated (changed) to control the CVs. The gasflow to a furnace can be an MV.

DVs Disturbance variables are measured disturbances (changes) in the process thatinfluence the CVs, but that are not under RMPCT control. The temperature of aninflow stream can be a DV.


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2. Profit Control ler (RMPCT) Variables2.2. Controlled Variables


2.2 Control led Variables

Characteristics

In a typical Profit Controller process, some or all of the CVs interact with each other.This means that any action taken to change the value of one CV (to bring it back withinlimits, for example) can change the value of other CVs, perhaps in unwanted directions.

The controller must coordinate changes to a number of MVs to move a particular CV adesired amount in a desired direction without causing undesired changes to other CVs.This is why a collection of single-loop controllers fares badly in attempting to controlinteracting variablesnone of the controllers know what the other controllers are doing.

A CV can have either a setpoint that defines the desired value for the CV, or a high andlow limit that define a range of allowable values. The Operator can switch betweencontrolling by setpoint or by range at any time.

A setpoint is treated as a range with the high and low limits set to the same value.

Feedback Performance Ratio

DefinedThe feedback performance ratio is the ratio of the closed-loop to open-loopsettling times for a CV.

The nominal open-loop settling time for a CV is the gain-weighted average of thesettling times for the sub-process models for the CV.

The nominal dead time for a CV is the gain-weighted average of the dead times forthe CV.

Controller Response Each CV has a performance ratio. The performance ratiospecifies how fast the controller returns the CV to setpoint or within limits when acontrol change or a disturbance causes the CV to stray.

The performance ratios are the only knobs that you need to tune the controllerresponse. You can change the performance ratios while on control.

Correction Horizon

The controller uses the performance ratio to determine the correction horizon. Thecorrection horizon is the time within which the controller must bring the CV to zeroerror.

To determine the correction horizon, the controller multiplies the performance ratio timesthe nominal open-loop settling time of the CV, then adds the nominal dead time.


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So performance ratio of:


1.0 means the controller returns the CV to zero error within the nominal open-loopsettling time.

0.5 means the controller returns the CV to zero error in half the nominal open-loopsettling time.

2.0 means the controller returns the CV to zero error in twice the nominal open-loopsettling time (with the nominal dead time added to all these).

Tuning for Response

The following figure illustrates how controller aggressiveness changes when thefeedback performance ratio is set to less than and greater than 1.0.

Figure 2-1 Tuning for Response - Speed vs Accuracy

The performance ratio determines the trade-offs that inherently exist between speed ofresponse, model accuracy, and MV movement.

A smaller performance ratio gives faster response, results in larger MV movement,and requires a more accurate model for stable control.

A larger performance ratio gives slower response, results in smaller MV movement,and works well with a less accurate model.


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Speed vs Accuracy The Tradeoffs

Here are the important trade-offs that you tend to encounter when increasing anddecreasing the feedback performance ratio:

Figure 2-2 Speed vs Accuracy - The Tradeoffs

Finding the Best Performance Ratio

A 1.0 ratio (the default) works well for most processes. If you find that the CV responseis too slow, decrease its performance ratio. In testing ratios with the controller on-line, donot stray too far in a single change, perhaps .1 at a time.

If control performance becomes oscillatory or unstable, the remedy generally is a largerperformance ratio. If this longer response time is not acceptable, the alternative is a moreaccurate model.

Degrees of Freedom

Defined The controller keeps all CVs at setpoint or within range if there are sufficientdegrees of freedom to do so.

Basically, the number of degrees of freedom is the number of MVs not at a limit minusthe number of CVs that either have setpoints or are at or outside a limit. The controllerchooses MV values so as to minimize the number of CVs that are away from setpoint oroutside limits.

As long as the degrees of freedom are zero or positive, all CV constraints can besatisfied. If the degrees of freedom become negative, it is physically impossible to keepall CVs at setpoint or within range.

ExampleTake, for example, a fractionation column with two CVs (top composition andbottom composition), and two MVs (reflux rate and reboil rate). Both CVs havesetpoints. As long as the reflux and reboil flow control valves are not fully open, thedegrees of freedom are zero and both CVs can be kept at their setpoints.



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However, if feed to the column continues to increase (and nothing bad happens, such asflooding), there can come a point when the reflux valve, for example, becomes wideopen. Now the degrees of freedom are negative, and it is impossible to maintain bothCVs at setpoint. This limitation is imposed by the physical process, not by any limitationin the controller.

Minimizing the Error

When there are negative degrees of freedom, the controller maintains the best possiblecompromise. The best compromise is defined as minimizing the weighted sum of thesquared CV errors, where a CV error is the amount that the CV is away from its setpointor outside a limit:

where iis the CV index.

In this formula, the error is the scaled CV error. An advantage in using the scaled error isthat the scaling automatically results in equal increments of different CVs having equalimportance on the process. You can influence error trade-off by specifying engineeringunit give-ups for each of the CVs. Weights are inversely related to scaling factors and EUgive-ups by:

For more information, see Optimal Scaling on page 70.

Setting the Engineering Unit (EU) give-up factors.

You set the value of a CVs EU give-up based on the importance of keeping the CVwithin constraints. The smaller the EU give-up, the more the controller attempts tominimize the error for that CV. EU give-ups are relative to each other. The controlleruses an average value of the low EU give-up and high EU give-up.

All CVs are assigned an initial EU give-up based on CV scaling factors that give an errorweight of 1.0, which is neutral weighting.

Continuing the fractionation column example from the previous page, the errors in CV1

and CV2 can be traded off equally. If you set the EU give-up of CV1 to 3.0 and the EUgive-up of CV2 to 1.0, CV1 will approximately have 3 units of error for every 1 unit oferror in CV2 (when there are not sufficient degrees of freedom to keep errors on bothCVs zero). When the optimization problem is infeasible the controller calculates a



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minimum effort solution using error weights determined by EU give-ups. CV error offsetratios are not guaranteed to be equal to those specified by EU give-up factors wheninfeasibility occurs. EU give-up factors only specify a relative preference for CV errors.

EU give-ups are analogous to concern factors used in some other controllers.

EU give-ups vs Controller Speed

It is important to know that EU give-ups have no effect when there are sufficient degreesof freedom to bring all CV errors to zero, which usually is true most of the time. Theonly purpose of EU give-ups is to influence steady-state error trade-offs when it isphysically impossible to bring all errors to zero.

ATTENTION

CV EU give-ups do not affect the speed at which the controller corrects CVerrors, and should not be used in an attempt to tune the controller response.Use performance ratios for this.

EU give-ups can be changed while on control. You do not have to take RMPCT off-process.

CV Tracking

ConfigurationYou can configure a CV to tracks its limits or its setpoint. Trackingmeans that the controller adjusts the Operator-set CV limit or setpoint so that there is noCV error on initialization.

Objective of CV Tracking CV tracking and limit ramping have the same objective: toprevent an initial jolt to the process that can occur if CVs are far outside limits or areaway from setpoint when control is initiated. The difference is this:

CV tracking moves both the external (Operator-set) and internal (controller-honored)violated limit or setpoint to the current CV value. It is up to the Operator, then, toreturn the limit or setpoint gradually to the desired or appropriate value.

Limit ramping moves only the internal, violated limit to the current CV value. Thecontroller then automatically returns the internal limit gradually to the external limitor setpoint.

Setpoint vs Range

The limits that are adjusted depend on whether the CV has a setpoint (high limit equal tolow limit) or a range:



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Setpoint: Both high and low limits are set equal to the CV value (which thereforebecomes the setpoint value) when the CV is placed on control.

Range: If the CV is within limits when the CV is placed on control there is noadjustment. If the CV is outside of a limit, the value of the violated limit is set equalto the CV value when the CV is initialized, and the other limit is not modified.

A CV is placed on control when the Operator turns RMPCT control ON, or when theOperator changes the state of an individual CV from dropped to ON.

Limit Ramping

The controller can make excessively large MV movements to bring a CV back within

limits or to setpoint within the error correction horizon when:

The Operator makes a large change to a CV limit or setpoint, or

A CV is far outside a limit or away from setpoint when control is turned on.

Limit ramping minimizes the disruption, establishing the rate at which the old limitramps to the new limit.

You configure limit ramping by specifying the amount that the controller must move theold limit towards the new limit at each control interval. Different ramp settings can beentered for the high and low limits.

Limit ramping applies (1) When a CV is placed on control, and (2) Anytime anOperator change violates an internal (active) CV limit or setpoint.

CV tracking applies only when a CV is placed on control.

Periodic Sampling

ConfigurationYou can configure periodic (asynchronous) sampling for any CV toindicate that its source value is updated at irregular intervals or at an interval longerthan the control interval.

Example What if you have a control interval of one minute, but one of the CVsreceives its input from an analyzer that has a sampling cycle of 10 minutes? In thiscase the value read by the controller changes only every 10 minutes and is constantin between.

If the controller uses the constant value between updates, there is the potential for a

noticeable jolt every 10 minutes if the value changes in one direction at a fast rate.The jolt happens because the controller sees a large change every 10 minutes insteadof a one-tenth change every one minute. This problem is avoided by configuring theCV for periodic sampling.


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Bad Value Treatment Critical CVs

By configuring a CV critical or non critical, you can specify the action the controllertakes when the source value for a CV goes bad.

For critical CVs, the controller sheds all control if the CV goes bad and remains bad forlonger than the number of controller executions that you specify.

While the CV is bad but before control is shed, the controller uses the predicted value ofthe CV. You can set the number of bad reads to zero to make the controller shed controlimmediately when a critical CV goes bad.

Bad Value Treatment Non Critical CVs

If the CV is non critical, then overall control continues when the CV source value goesbad.

An additional configuration option lets you set whether or not a non critical CV with abad source value continues to be controlled using its predicted value with no feedback, orwhether it is dropped from the controller.

A non critical CV is dropped if its value remains bad longer than the number of bad readsallowed. You can keep the controller from dropping a non critical CV with persistent badvalues by setting the number of bad reads allowed to -1.

Predicted Values

A non critical CV continues to be controlled if its value goes bad, using its predicted

value rather than measured feedback.

The predicted value of a non critical CV that remains on control will begin to deviatefrom the true (but unknown, because it is bad) process value. The prediction generallycontinues to worsen as time passes.

ATTENTION

The Operator screens indicate when a CV is being controlled withoutfeedback and when a CV has been dropped from the controller. It isimportant that Operators understand the consequences of this control.

In such cases, Operators need to find other measurements to ensure that

operating problems do not develop.



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State Estimation

State Estimation A Quick Definition State estimation in RMPCT is offset andramp rate correction.

See Table 2-1 for a discussion of state estimation settings and their usage.

Prediction A Quick ReviewAs explained when we talked about Bad ValueTreatment, RMPCT adjusts its predicted values into the future based on acomparison of the predicted and actual values at the present and past intervals. Thisconstitutes the controller's feedback information.

Controller OutputThe controller can correct either (1) The offset (the prediction

error only), or (2) Both the offset and the rate-of-change of the error (the ramp).

Default SettingsRMPCTs default state estimation settings are determined by thepresence or absence of integrators across a CV (in model terms these are the CV-MV[i j] pairs, the sub-processes):

When there are no integrating processes across a CV, ramp correction is OFF.

When at least one sub-process across a CV is an integrating process, ramp correctionis ON. Integrators require both offset correction and ramp correction to avoidsustained offsets.

State Estimation for Integrating Processes

For a CV that has one or more integrating sub-processes, the controller corrects for both

offset and rate-of-change (this is the default setting).

Correcting for both offset and rate-of-change eliminates long-term CV error in thepresence of model errors and unmeasured disturbances. If you turn rate-of-changecorrection off for a CV that has integrators, the CV develops an error offset that thecontroller cannot account for.

Ramp Correction Settings

Correcting for Offset (Ramp Correction OFF)For a CV that has sub-processmodels that are all stable, the default is to correct for the offset only. The assumptionis that the unmeasured disturbance is a step change that occurred at the currentinterval.

Correcting for Offset and Rate of Change (Ramp Correction ON)If the

disturbance is, in fact, a long-duration ramp, then the controller lags in its correctionbecause it does not know that the error caused by the disturbance will steadilyincrease.


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In this case, you might improve performance by having the controller correct forboth the offset and its rate-of-change.

A Caut ion about Ramp Correct ion

Long-duration cyclic disturbances can be approximated by a series of ramps (a saw-toothsignal).

If a CV experiences this type of disturbance, the rate-of-change correction might providesignificant improvement during the times when the ramp rate is approximately constant.This correction, then, can more than compensate for the poorer performance when theramp rate is changing.

STATE EST = ON means both offset correction and ramp correction are ON:


Always use STATE EST = ON for CVs with integrators.

Use with caution STATE EST = ON for CVs with sub-process models that are allstable. Use state estimation to better reject the slow drift disturbance. Do not usestate estimation when fast disturbance is more significant than the slow driftdisturbance.

Ramp Correction Trade-Offs

For a stable process, here is what happens to prediction on the transition slope of a longduration ramp. See how the prediction is good, until ramping stops or changes direction?

Figure 2-3 Ramp Correction


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However, when the ramp stops or changes sign, which it eventually must, prediction isworse during transition when both offset and ramp correction are used than predictionotherwise would be using offset correction only.

State Estimation Settings Summary

The following table summarizes what state estimation settings work best for integratingand non integrating processes:

Table 2-1 State Estimation Settings and Usage

Setting Integrator?DisplayShows

CorrectsOffset

CorrectsRamp Usage

No D-OFF Yes No Use for nonintegratingCVs

RMPCT-Set Default

(AlwaysWorks) Yes D-ON Yes Yes Use for

integratingCVs

No OFF Yes No Use for nonintegratingCVs

User-SetOff

Yes OFF Yes No Do not usefor

integratingCVs

No ON Yes Yes Use for nonintegratingCVs withslow driftdisturbances

User-SetOn

Yes ON Yes Yes Use forintegratingCVs


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2. Profit Control ler (RMPCT) Variables2.3. Manipulated Variables

2.3 Manipulated Variables

Characteristics

RMPCT adjusts MV values to achieve control and optimization objectives. Each MV hasa high and low limit. The controller never moves an MV outside its limits, and returns anMV within limits when:

The controller is started up with the MV outside its limits (unless tracking is on).

The Operator changes an MV limit such that the MV value is outside the limit.

An MV can be either a direct output to some actuator, or the setpoint of a secondarycontroller. The usual case is that an MV is the setpoint of a PID controller, which canoutput directly to an actuator or can be cascaded to other, downstream PID control loops.

Rate-of-Change (Max Move) Limits

In addition to positional limits, rate-of-change limits can be set on each MV.

These limits are the maximum move that the controller can impose on an MV in a singlecontrol interval. Separate rate-of-change limits can be set for positive and negativechanges.

Rate-of-change limits prevent the controller from making excessively large changeswhen an abnormal event occurs. This gives the Operator a chance to intervene.

If a rate-of-change limit is set so small that the controller is hitting it much of the time,this takes away some freedom for the controller to determine the most stable and robusttrajectory to use in correcting CV errors.

ATTENTION

If MV movement is excessive, it is generally better to increase some of theCV performance ratios to reduce MV movement instead of reducing rate-of-change limits.

Limit Ramping

Although the controller never moves an MV outside a limit, an MV can be outside a limit

when the MV initializes if MV tracking is not configured. Also, the Operator can changea limit to a value such that the MV is outside the new limit.



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Rather than force the controller to move an MV to a violated limit in one interval, youcan specify the minimum amount that the controller must move the MV toward theviolated limit at each control interval. This in effect determines the minimum rate atwhich the MV ramps to its limit. The controller, however, is free to move the MV alarger amount, subject to the rate-of-change limits.

If the MV is not violating the old limit but is violating the new limit, the limit ramps tothe new limit at ten times the configured ramp rate until the limit reaches the MV value.The limit continues to ramp at the configured ramp rate until the new limit is reached. Toavoid unnecessary interference with possible future MV values that the controller may beplanning, the limit is not immediately moved to the MV value.

Different limits for high and low ramping can be set.

Movement Weights

Defined Conceptually, MV weighting is analogous to CV weighting in that weights areapplied to encourage, or discourage, controller action on particular variables.

CV engineering give-upsencourage the resolution of particular CV errors at theexpense of other CV errors.

MVmovementweights discourage the movement of particular MVs in resolving CVerror, which results in larger movement of other MVs.

When you want an MV to move less than others, or not to move at all unless necessary,you apply a movement weight. The movement weight penalizes movement of the MV,

and influences the controllers choice of alternate MV moves.

All MVs are assigned an initial movement weight of 1.0, which is a neutral weighting.

ExampleIf you set the movement weight of MV1 to twice that of MV2, the controllermoves MV1 half as much as MV2, all other conditions being equal.

Degrees of Freedom

The controller minimizes MV movement whenever possible while still meeting both theoperating and the economic objectives.

When there are more MVs than are required to meet the objectives, the controller spreadsthe total MV movement across the MVs to minimize the sum of the squared changes.The controller minimizes the sum of the squared changes of the MVs, with each change

multiplied by the movement weight for the MV.


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Setting MV Priorities

You can force the controller to move particular MVs by assigning much larger weights tothe MVs you prefer to move very little or not at all.

If you give MV1 a weight of 5, for example, and MV2 a weight of 1, the controller tendsto leave MV1 alone, moving MV2 instead so long as MV2 is not constrained and canaffect the CVs that need to be changed. If MV2 hits a constraint, only then does thecontroller move MV1, and only so much as to achieve the control and economicobjectives.

Use Lower Numbers

Use lower numbers to establish ranges (ratios). The controller is happier when theaverage MV weighting is around one.

This does not mean, however, that you have to strive to average the weighting acrossMVs. It means that in setting weights, it is better to establish a range with the low end ofthe range nearer to one.

For example, a range between 10 and 1.0 is a better range than a range between 100 and10. The ratio is 10-1 for both ranges. However, when the lower end of the range is closerto one the average weight is closer to one, and the controller is happier.

ATTENTION

An important distinction between RMPCT and some other controllers is that

in RMPCT movement weights do not affect the speed of response or thestability of the controller. The feedback performance ratio is used to tune thedynamic response.

Movement weights are used only to set priorities, which MVs you prefer tomove when more than one MV can do the job. If there are redundancies inthe MVs, the MV movement weights have no affect.

MV Tracking

MV tracking is analogous to CV tracking. When you configure an MV for tracking, thecontroller adjusts the appropriate MV limit so that the MV is not violating a limit whenthe MV initializes. An MV is initialized when RMPCT control is turned on or when anindividual MV is returned to RMPCT control.

If the MV is within limits when it initializes, there is no adjustment. If the MV is outsidea limit, the value of the violated limit is set equal to the current MV value; the other limitis left alone.



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Bad Value Treatment

The action the controller takes when an MV goes bad depends on whether you configurethe MV as critical or non critical:

If a critical MV goes bad, control sheds.

If a non critical MV goes bad, control continues with the MV value frozen at its lastgood value.

Anti-Windup

The controller checks the windup status of all control loops cascaded from the MV to theultimate output.

If MV movement up or down worsens a windup condition anywhere in the cascade, thecontroller does not move the MV in that direction.

Predict-Back and Estimated Disturbance Compensation

Capturing Information from DisturbancesUsually, the requirement that MVs andDVs be independent of each other does not restrict the controller design. Sometimes,however, important disturbance information can be contained in a variable that isaffected by one or more MVs or DVs, but the variable cannot be used directly as aDV because the variable is not independent.

Valuable disturbance information in such processes, then, is normally lost. But, withthe predict-back and estimated disturbance compensation features of RMPCT, this

information can be gleaned, giving the controller an intelligence about disturbancesit otherwise could not have.

Tactics Predict-back control is an open loop, feedforward control that uses twomodels:

1. A predict-back model estimates the unrejected disturbance (the leak-through).

2. An estimated disturbance model then rejects the leak-through effects of thedisturbance.

Finding the ModelsPredict-back requires models of the effects of the MVs andDVs on the variable that contains the disturbance information, which in thefollowing illustration is the process units inlet temperature.

Estimated disturbance compensation requires models of the effects of the estimateddisturbance on the CVs. Control quality is equally influenced by the estimateddisturbance models as by the predict-back models.


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Tuning The predict-back ratio tuning parameter filters the portion of the estimateddisturbance that is used for feedforward compensation. Its value is zero to 1.0. Thedefault is 1.0, which uses the full value of the estimated disturbance forcompensation.

Predict-Back Implementation Suggestions

The decision to use predict-back is typically made at the design phase. Predict-back isusually used when:

1. The MV is a setpoint of a downstream controller, and

2. The disturbance rejectionis slower than the effects of the unrejected (leak-through)

disturbances on the RMPCT controlled variables.

Predict-back on MVs that have relatively fast PID control yields negligible results.

Before building predict-back models, try first to improve PID performance.

Predict-Back An Illustration

To see how predict-back and estimated disturbance compensation can be used toadvantage, examine the variables and the controls on this process:


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RM09-400

Figure 2-4 A Predict-Back Problem

Predict-Back Illustration The Disturbances

In this illustration, one of the MVs is the setpoint to the temperature controller of the feedheater. The measured temperature of the feed into the process unit is controlled by thetemperature controller, but it is also affected by disturbances such as changes in the fuelcomposition, the feed flow rate, or the feed heater inlet temperature.



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Predict-Back Illustration The Problem

True, the temperature controller eventually compensates for these disturbances. But, ifthe dynamics of the temperature controller and the furnace are slow, relative to thedynamics of the process unit, some of the CVs of the Profit Controller are disturbed.These CVs stay disturbed until the temperature controller can return the temperature tosetpoint.

The Profit Controller takes action independently of the temperature controller to correctthe CV errors that develop before the effects of the temperature controller's actions arefelt. The Profit Controller then has to undo these actions as the temperature controller'sactions take effect.

Predict-Back Illustration The Solution

It is better to use the inlet temperature as a DV so that RMPCT can use the disturbanceinformation to do a better job. However, this is not possible because the inlet temperatureis dependent on the temperature controller's setpoint, which is an MV. Predict-back andestimated disturbance compensation get around this, though.

Heres how:

Predict-back uses a model of the effects of the MVs and DVs on the variable thatcontains the disturbance information. In the preceding illustration, the predict-backmodel includes the dynamic effects of the:

Temperature controller,

Fuel-flow controller,

Furnace on the inlet temperature to the process unit, and

Feed flow rate, if the feed flow rate is included as a regular DV.

The effects predicted by the predict-back model are backed outof the variable thatcontains the disturbance information, in this case the inlet temperature to the processunit. This gives an estimate of the disturbance.

The estimated disturbance compensation models are then used to obtain the effectsof the estimated disturbance on the CVs.

In this way, the Profit Controller obtains the disturbance information and can compensatefor the disturbance.


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Predict-Back Illustration The Benefits

The predict-back model includes the temperature controller's behavior of eventuallyreturning the feed inlet temperature to its setpoint. This way the Profit Controller seesthat the CV errors due to a disturbance are eventually corrected even if Profit Controllerdoes nothing.

Profit Controller may decide to do something to help out in the interim, but its actions aresmoothly coordinated with the temperature controller. The over-compensation thatoccurs when Profit Controller operates independently of the temperature controller iseliminated.

On/Off Control Configuration

Toggling Control The Operator can switch non critical MVs on or off ProfitController control at any time. If a non critical MV is off control, Profit Controllerdoes not move that MV but continues controlling the process using the MVs thatremain on control.

Critical MVs cannot be taken off control. Profit Controller sheds control when acritical MV mode is changed.

Configuring Feedforward A feedforward option determines how the controllertreats a non critical MV when the MV is off Profit Controller control.

An MV that is off Profit Controller control but that has the feedforward option set isstill monitored so that its effects on the CVs are taken into account in case the MV is

moved by something else (typically the Operator).In other words, when a non critical MV with the feedforward option is taken offcontrol, the effect is to convert it to a feedforward variable.

Dropped MVs When a non critical MV without the feedforward option is taken offProfit Controller control, the controller stops using the MV value to update the CVpredictions. In effect, the MV is entirely dropped from the controller.


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On/Off Contro l An Example

Consider this example:

RMPCT

PID

TC

PID

FC

SP

MV

SPOperator

RM09-400

Figure 2-5 Dropping Non Critical MVs

When the MV is taken off Profit Controller control, the backup regulatory schemecontrols the valve. The setpoint to the flow controller no longer moves the process.Instead, the Operator-controlled setpoint to the temperature controller moves the process.

When non critical MVs are dropped and backup control assumes the process, such as itdoes in the illustration, the MV value cannot represent the results on the CVs that arepresumed in the model. To avoid this, configure the MVs for DROP instead ofFEEDFORWARD. This prevents Profit Controller from assuming that the MV is movingthe process congruently with the models, which it is not, when the backup PIDs havecontrol.

Automatic Mode Switching

In many applications there is a conventional control scheme in place prior to ProfitController. The existing control scheme may not be completely compatible with thechoice of variables that you make for the Profit Controller.

The Profit Controller may output to the setpoint of a flow controller that is the secondaryof a temperature controller in the conventional control scheme. When Profit Controllercontrol is turned on, the temperature controller cascade must open so that RMPCT hascontrol of the flow setpoint.

If the Operator turns Profit Controller (RMPCT) control off or if Profit Controller(RMPCT) control is automatically shed, perhaps due to a bad value of a critical variable,

control should shed to the conventional control scheme. This means that the temperaturecascade must close. There could be a number of control mode changes of this type thathave to be made in order to switch between conventional control and Profit Controller



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(RMPCT) control. These changes are difficult for an Operator to make, so a mechanismis provided to automate the mode switching.

Configuring Automatic Mode Switching

To configure automatic mode switching, you enter the number of loops in the cascadefrom each MV to the ultimate output, and fill in an array that specifies the mode of eachloop that should be in effect for conventional control when the MV is not on ProfitController (RMPCT) control.

If one or more MVs are taken off Profit Controller (RMPCT) control, the controller setsup the modes that you configure so that a backup conventional control scheme takesover.

Taking MVs Off Profit Cont roll er (RMPCT) Contro l

MVs can be taken off Profit Controller (RMPCT) control in the following ways:

Operator turns off Profit Controller (RMPCT) control. Here, all MVs are shed tobackup control.

The value of a critical CV or critical MV goes bad and stays bad longer than theallowed number of bad reads. Here, all MVs are shed to backup control.

The Operator opens the cascade from Profit Controller (RMPCT) to an MVsultimate output, and the MV is critical. Here, all MVs are shed to backup control.

The Operator opens the cascade from Profit Controller (RMPCT) to an MVs

ultimate output, and the MV is non critical. Here, the MV is shed, subject to thesecontingencies:

If the cascade is opened between Profit Controller (RMPCT) and the first loop,the MV switches to feedforward while off control if it is so configured.

Otherwise, the MV value is not used.

The converse of these conditions puts Profit Controller (RMPCT) back on control. Whencontrol starts or resumes, the controller automatically establishes the cascades for eachaffected MV based on the configured loops.

MV Move Accumulation

MV moves are calculated as single-precision floating-point values in IEEE format. When

the output to a DCS such as the LCN accepts values in this same format, there is no lossof information. However, some DCSs have a coarser resolution. For example, a DCSmight be able to store only integer values from 0 to 1023 unscaled. If the scaled rangewas 1000 to 3000, an MV output value of 1000 would be represented exactly on the DCS


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(as unscaled 0) but the next greatest value that could be represented on the DCS would be1001.953 (as unscaled 1). MV output values between 1000 and 1001.953 would have tobe rounded to one value or the other.

Coarse DCS resolution causes a problem when small MV moves are being calculated,because the change at any one interval may be too small to kick the DCS value to itsnext increment. The controller reads back the value from the DCS at the next interval toestablish where the MV actually is, and in this case will read the same value as at theprevious interval, and again add a move that will not be big enough to actually changethe DCS value.

MV move accumulation solves the problem of ignoring MV moves that are smaller thanDCS resolution. To implement MV move accumulation you either enter the DCS

resolution if it is known or tell the controller to estimate the resolution. The controllerrounds the MV output value to the nearest value that the DCS can actually represent andoutputs this value. The controller remembers the difference between the desired fullresolution output and the rounded value that was actually output, and algebraically addsthis to the move at the next interval. In this way, small moves accumulate until theaccumulation is eventually large enough to change the DCS value to its next increment.

The following parameters (per MV) are used with move resolution:

Resolution This should be set as follows:

-1 Move accumulation calculations are not performed. The MV output value is sentdirectly to the external system. Use this value for output to a DCS such as the LCN thatsupports IEEE single-precision resolution.

0 Automatic resolution calculation is performed. The controller attempts todetermine the resolution of the DCS by accumulating the maximum difference betweenthe read back of the MV output and the value that was output at the previous interval.Use this value if the DCS has coarse resolution and you do not know what the resolutionis.

>0 Resolution calculation is performed using Resolution as the DCS resolution.The value of Resolution should be the smallest increment to the MV output that can berepresented at the DCS, expressed in the units of MV output. Use this setting if the DCShas coarse resolution and you know what the resolution is. This may be somewhat morereliable than the automatic resolution calculation.


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Calculated Resolution If Resolution = -1 or Resolution = 0, Calculated Resolutionis the estimated resolution of the DCS value (i.e., the smallest increment of MVoutput that can be represented at the DCS). This should be zero if the DCS supportsIEEE single-precision. If Resolution > 0, Calculated Resolution is the maximummismatch between the MV value output at one interval and read back at the next,which should be zero or very small. If Resolution > 0 and Calculated Resolution issignificant, the value entered for Resolution is probably incorrect.

Resolution Mismatch The difference between the value read back from the DCSand the value output to the DCS at the previous interval.

Resolution Residual The amount of unrealized MV output (i.e., the difference

between the full-resolution value and the nearest value that can be represented on theDCS).


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2. Profit Control ler (RMPCT) Variables2.4. Disturbance Variables


2.4 Disturbance Variables

Characteristics

DVs affect the CVs in the same way that MVs affect the CVs. The DVs, however, arenot under Profit Controller (RMPCT) control, typically because DV behavior isdetermined outside the scope of the process unit being controlled.

But, by including the DVs in the controller model, their future effects on the CVs can beanticipated by the controller. Profit Controller (RMPCT), then, can take corrective actionto prevent disturbances from driving CVs outside their limits or away from setpoint.

The usefulness of a DV depends on the dynamics between the DVs measured value andits effect on the CVs. If a DV instantaneously affects a CV, the DV is less useful becausea CV error already exists by the time the controller can do anything about it.

To be useful, DVs need time constants or a dead time comparable to, or larger than, atleast one of the MVs.

If a DVs dead time is not larger than at least one of the MVs, it is impossible for thecontroller to cancel all of the effects of the DV.

If dead times are equal, it is theoretically possible for the controller to cancel theeffects of the DV. However, large MV changes are required if the DVs timeconstants are small relative to the MVs.

DV Influence on CV Tuning

You can configure the desired feedforward response time of a CV to a DV differentlythan the feedback response time of a CV to the effects of unmeasured disturbances andmodel error.

Feedback tuning is set by the CV Feedback Performance Ratio. Inevitable errors in themodel limit how fast you can set the feedback response. At some point, attempts toachieve faster response result in oscillations and eventually unstable control. Theseinstabilities are caused by feedback, which is necessary to correct unmeasureddisturbances and model error.

If feedforward information is treated outside the feedback loop, as it is with RMPCT,then feedforward does not contribute to unstable control and the feedforward responsecan be tuned much faster.


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2. Profit Control ler (RMPCT) Variables2.4. Disturbance Variables


Feedforward Performance Ratio

Configuration You configure the feedforward response time by setting the feedforward-to-feedback performance ratio.

This ratio multiplies the feedback performance ratio, which in turn multiplies the nominalopen-loop settling time to determine the response time.

Example The feedback performance ratio is set to 0.8 and the feedforward-to-feedbackperformance ratio is set to 0.5 for some CV.

Here, the controller corrects:

Feedback errors (the unmeasured disturbances and the contribution of model error)in 0.8 of the nominal open-loop settling time.

Feedforward errors (caused by DVs) in 0.4 of the nominal open-loop settling time(0.5 x 0.8 = 0.4).

Plus the nominal dead time for both.


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3. Contro l Interval

3.1 Overview

Read This

The control interval (also called the execution interval or execution period) is establishedduring the controller design and build process. This interval cannot be changed on-line.Once a controller is installed on an automated control system, the only way to change thecontrol interval is to rebuild and reinstall the controller.

In This Section

This section explains the relationship between the control interval and the settling time,time constants, CV constraints, independent variables, and blocking. Understanding theserelationships and their interplay can help you pick the best control interval for yourapplication.


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3. Control Interval3.2. Control Interval and Settling Time


3.2 Control Interval and Settling Time

The Ideal Interval-to-Settling Time

If all the sub-process models had approximately the same time constants, you wouldtypically choose the control interval to be approximately 1/40 of the settling time. Thesettling time is approximately four times the major time constant, so the control intervalwould be about 1/10 of the major time constant.

Shorter Intervals

A smaller interval than 1/10 of the major time constant does not noticeably improve

control because the process does not appreciably respond in less than this interval.

Smaller control intervals entail more processing overhead and more memory to hold thestep response coefficients, so the idealinterval size is a reasonable compromise betweencomputational efficiency and control performance.

Longer Intervals

The only deleterious effect of a long control interval is that it delays recognition ofdisturbances and subsequent corrective action.

When Time Constants Vary

Many processes exhibit a wide range of time constants in the various sub-processes. Thiscomplicates the choice of a control interval.

It can be necessary to choose an interval that is longer than ideal for the sub-processeswith short time constants in order to keep the computational and memory overheadreasonable for the sub-processes with long time constants.

Recommended Intervals

There are no hard rules, but it is generally desirable to have the longest settling times lessthan 200 - 300 intervals, subject to having the shortest settling times for important CVsmore than 10 intervals.

If the time required to detect an error is not a problem, the control interval can be set aslarge as the settling time of a CV, or even larger.


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3. Control Interval3.2. Control Interval and Settling Time


Intervals > Settling Time

If the performance ratio for a CV is set to 1.0 and the control interval is set equal to theopen-loop settling time, the controller corrects limit violations within the open-loopsettling time, which in this case is one control interval.

This is no more difficult for the controller than if the settling time consisted of 40 controlintervals.

However, if a large disturbance occurs just after a control calculation, no correctiveaction is taken until one settling time elapses. Then it takes an additional settling time toachieve the correction. For many CVs, this is not a problem.


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3. Control Interval3.3. Blocking


3.3 Blocking

Defined

In principle, the controller should consider a constraint on each CV at each controlinterval in the future out to the correction horizon (where steady state is achieved). Inpractice, however, this can result in too large a problem to be solved efficiently. So, thecontroller places constraints only at selected intervals. This is blocking.

Control Quality

Blocking doesnt appreciably reduce the quality of control because there is little

incentive for the controller to generate moves that cause CVs to wiggle outside ofconstraints in the intervals between the blocking intervals when constraints are notactually imposed. Such behavior requires high frequency changes to the MVs, which thecontroller tries to avoid because MV movement results in less robust control.

CV Constraints

The default number of constraints for a CV is 10 (10 is also the maximum number ofconstraints). You can specify a smaller value for each CV.

The specified number of constraints are distributed approximately uniformly over thetime range from the current interval out to the correction horizon. The last CV blockingwill always be placed around the longest setting time interval of all the associatedsubmodels of a particular CV. The controller can deviate from a uniform distribution toplace constraints where they do the most good, as determined from the distribution ofdead times and correction horizons.

MV Moves

Ideally, if the computing power were available, the controller would consider each MVan independent variable at each interval from the present out to the longest correctionhorizon of any CV affected by the MV (minus the dead time).

Unfortunately, just as with CVs, this can result in too large a problem to be solvedefficiently. Therefore, the controller places independent variables only at selectedintervals. When the performance ratio of a CV is tuned greater than 1, the associated MVcontrol horizon will also be extended.


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3. Control Interval3.3. Blocking


Independent Variables

The default number of independent variables for an MV is 10 (10 is also the maximumnumber of independent variables). You can specify a smaller value for each MV.

The specified number of independent variables are distributed from the present timeinterval out to the longest correction horizon of any CV affected by the MV, minus thedead time. The controller places the independent variables closer together at first andthen farther apart the further they are into the future.

Reducing the Calculation Time

If the controller takes too long to execute its algorithms (more than half the length of the

profit controller concepts reference guide

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