utilizing deltav adaptive control. presenters peter wojsznis gregory mcmillan willy wojsznis terry...

Utilizing DeltaV Adaptive Control

PresentersPeter Wojsznis

Gregory McMillan

Willy Wojsznis

Terry Blevins

OutlineIntroductionAdaptive Technique BackgroundAdaptive PID Design Application ExamplesField Test Results Adaptive Control DemosActuator DiagnosticsSummary

Control Loop Performance A Never Ending CycleTestCalculateDeployOperateDegradationEvaluateThe more often you Tune, the better the performance.

Operating Condition ImpactProcess gain and dynamics may change as a function of operating condition as indicated by PV, OUT or other measured parameters e.g. plant throughput

There Must Be A Better WayWouldnt it be nice to have controllers use optimal tuning parameters all the time (continually) without having to tune at all, ever?

DeltaV Adapt Fully Adaptive PID Control Tuning Learns Process Dynamics While In Automatic Control No Bump Testing Required Works On Feedback And Feedforward Patents Awarded! See It HereNo Tuning Required!

DeltaV Adapt Applicable to Most Control Loops Today

DeltaV Adapt A Clear Difference

Not an overnight thingEMERSON technology developed in Austin.Patents have been awarded.1997 - Dr. Willy Wojsznis concept originated1998 - Research started at Tech Center - Austin1999 - Dr Seborg started work on formal proofs2002 - Development started2003 - Prototypes at Eastman Chemical, Solutia and UT with good results.

Patents Have Been Awarded!Mr. Terry BlevinsDr. Wilhelm Wojsznis

Solid theoretical background1999 - Dr. Seborg started working on formal proofs of convergence for us along with his Emerson funded grad student

Adaptive PID Techniques

Model Free vs Model Based Adaptation

Model Switching AdaptationUse N-models working in parallel Evaluate model errorSelect model with minimal errorShortcoming: TOO MANY MODELS

Parameter InterpolationEvery parameter value of the model is evaluated independentlyThe weight assigned to the parameter value is inverse of the squared errorAdapted parameter value is weighted average of all evaluated values

Advantages of Parameter InterpolationSequential parameter adaptation less models: Example: Model with 3 parameters (Gain, Lag, Dead Time) and 3 values for every parameter has 3x3x3 model variations for model switching adaptation and 3+3+3 model variations for sequential parameter adaptationBetter convergenceInterpolation gives better model due to continuous adaptation of the model parameter value over the whole assumed range

Parameter Interpolation - CalculationsFor each iteration, the squared error is computed for every model I each scanEi(t) = (y(t) Yi(t))2 Where: y(t) is the process output at the time t Yi(t) is i-th model output A norm is assigned to each parameter value k = 1,2,.,m in models l = 1,2,,n.Epkl(t) = Ni=1 (klEi(t))

=1 if parameter value pkl is used in the model, otherwise is 0

For an adaptation cycle of M scans sumEpkl = Mt=1 (Epkl(t)), Fkl = 1/sumEpkl

pk(a) = pk1fk1++pklfkl++pknfkn

fkl = Fkl / sumFk

Simple Example Pure Gain Process

First Order Plus Dead Time ProcessModel Parameter InterpolationFor a first order plus deadtime process, only nine (9) models are evaluated each sub-iteration, first gain is determined, then deadtime, and last time constant. After each iteration, the bank of models is re-centered using the new gain, time constant, and deadtime

First Order Plus Dead Time Process Model Parameter Interpolation

Model VerificationFinal stage of model adaptation and verification showing actual response and response calculated by the identified models.

DeltaV Adaptive ControlOperational FeaturesProcess models are automatically established for the feedback or feedforward paths. Model adaptation utilizes a data set captured after a setpoint change, or a significant change in the process input or output. Multiple models are evaluated and a new model is determined

DeltaV Adaptive ControlOperational FeaturesModel is internally validated by comparing the calculated and actual process response prior to its application in tuning. The user may select the tuning rule used with the feedback model to set the PID tuning.

Adaptive Control - Internal StructureControl

Defining Operating RegionsAdaptive control allows operating regions to be defined as a function of an input state parameter

Define up to 5 regions

When the state parameter changes from one region to another, the model values (and associated tuning) immediately change to the last model determined for the new region

Limits on model parameter adjustment are defined independently for each region.

Configuration of Adaptive ControlNew control block in the advanced control palette. Parameters are automatically assigned to the historian.No more difficult to use than PID. Initial values for model, limits, and time to steady state are automatically defaulted based on block tuning.

Adaptive Control ApplicationUsed to view the operation of modules that include Adapt blocks.May modify adaptive operation, parameter limits, and default setup parameters from this view. Adapt blocks run independent of the DeltaV Adapt application.

Adaptive Control OperationAdapt Application 1. Select Window

2. Observe loop plots (PV, OUT, SP)

3. Observe Adaptation Status

4. Operate PID loop SP, OUT, Mode

Feedback AdaptationAdapt Application 1. Select Window

2. Observe model parameters trends and controller tuning parameters (Gain, Reset, Rate)

3. Observe current process model and PID tuning parameters

4. Select Adaptive operation mode

5. Select tuning rules

Feedforward Adaptation Adapt Application Select WindowObserve model parameters trends and controller tuning parameters (Gain, Reset, Rate)Observe current process model and PID tuning parametersSelect Feedforward Adaptive operation modeSelect Gain FF Factor

Multi-range adaptation 1. Up to 5 ranges

2. The last adapted process gain, time constant and dead time is displayed for every range

3. State parameter: PV, OUT or feedforward inputRange 1Range 2State parameter

Adaptive Control SetupAdapt Application 1. Trigger to adapt

2. Controller Output pulse injection

3. How fast to adapt

4. Process type: Integrating Non Integrating; Minimum time to steady state

5. Adaptive mode of operation Defaults are set for a typical operation!

OutlineIntroductionAdaptive Technique BackgroundAdaptive PID Design Application ExamplesField Test ResultsAdaptive Control DemosActuator DiagnosticsSummary

Simple Example Non-Linear Installed CharacteristicsProcess gain will change as a function of valve position if the final control element has non-linear installed characteristics.Valve position is used as the state parameter if ranges are applied

Chart1

0

24

42

55

70

76

82

86

89

92

93

Flow

Stem position %

Flow %

Flow vs Stem Position

Sheet1

Stem PositionFlow

00

1024

2042

3055

4070

5076

6082

7086

8089

9092

10093

Sheet1

0

0

0

0

0

0

0

0

0

0

0

Flow

Stem position %

Flow %

Flow vs Stem Position

Sheet2

Sheet3

Example Throughput Dependent Process The process deadtime for superheater outlet temperature control changes as a function of steam flow rateSteam flow rate is used as the state parameter

ExampleMultiple Valves - Split Range The process gain and dynamic response to a change valve position may be different for each valve.Typical example is heating/cooling of batch reactor, extruder, slaker, etc.Valve position is used as the state parameter.

ExampleColumn Temperature Control The sensitivity of tray temperature to changes in distillate to feed ratio is highly non-linear. Tray temperature is used as the state parameter.

Adaptive Field Test

Emerson Process Management - Process Systems and Solutions LabUniversity of Texas reactive distillation columnEastman ChemicalSolutia

DeltaV Adaptive ControlField Trials Eastman ChemicalControl automatically adapts based on SP changes in Auto Caustic loop

DeltaV Adaptive ControlField Trials Solutia, Pensacola, FLCooling Tower WaterCooling Tower WaterHMD(Base)65AC681(pH)65TC68865TC68465TC68565LC682Strike Kettle Process and InstrumentationAcid feed from centrifuge splitter

DeltaV Adaptive ControlField Trials Solutia, Pensacola, FLKettle control regular PID control

DeltaV Adaptive Control Field Trials Solutia, Pensacola, FLKettle control Adapt control

DeltaV Adaptive Control - Field Trials - J.J.Pickle Research Campus, UT, Austin, TXFlow loop

Simple Reactor Demo - Adaptation Demo with model scheduling Adaptation with process state parameter defined for two ranges

Simple Reactor Demo - Feedforward Adaptive Control Feedforward dynamic model is adaptedFeedforward controller is automatically updatedUp to 5 ranges can be defined for feedforward adaptation and model scheduling

Inlet temperature as feedforward input

Adaptive pH Control for Fun and ProfitThe pH electrode offers an extraordinary rangeability and sensitivity. The price is an extreme nonlinearity. This demo shows how the DeltaV adaptive controller can provide a more efficient and faster approach to set points and rejection of disturbances. A high fidelity dynamic simulation and online process performance indices embedded in DeltaV show reagent savings of 40%.

Top Ten Signs of a Rough pH StartupFood is burning in the operators kitchenThe only loop mode configured is manualAn operator puts his fist through the screenYou trip over a pile of used pH electrodes The technicians ask: what is a positioner?The technicians stick electrodes up your noseThe environmental engineer is wearing a maskThe plant manager leaves the countryLawyers pull the plugs on the consolesBob and Bob are on the phone holding for you

Tremendous Rangeability and Sensitivity of pH Creates Exceptional Control OpportunitiespHHydrogen Ion ConcentrationHydroxyl Ion Concentration01.00.0000000000000110.10.000000000000120.010.00000000000130.0010.0000000000140.00010.000000000150.000010.00000000160.0000010.0000000170.00000010.000000180.000000010.00000190.0000000010.00001100.00000000010.0001110.000000000010.001120.0000000000010.01130.00000000000010.1140.000000000000011.0

Severe Strong Acid - Strong Base Nonlinearity (Gain Changes by factor of 10 for each pH unit)Zoom in on 3 to 10 pHEntire Operating RangeThere are no straight lines in pH - graphical deception is common

Weak Acid and Base - Moderated Nonlinearity(Gain changes by factor of 50 from 9 to 7 pH)Optimum set pointFor acidic influent

Model and Tuning Settings are Scheduled Based on What is Learned in Operating RegionsModel and tuning is scheduled based on pH

User Sees Adapted Model Parameters and Chooses Tuning Method

Opportunity in pH is Huge When Moving to a Flatter Portion of Titration CurvepHReagent to Feed Flow Ratio Reagent SavingsOriginal set pointOptimum set point

Adaptive Control Achieves Optimum Set Point more Efficientlyhourly cost of excess reagenthourly cost of excess reagenttotal cost ofexcess reagenttotal cost ofexcess reagentpHpH

Adaptive Control Recovers from Upsets more Effectivelyhourly costof excesshourly cost of excesspHtotal costof excesstotal cost of excesspH

Adaptive Control Returns to Old Set Points with Less OscillationpHpH

Component Balance and Online Process Performance Indicator are Embedded in DeltaV

Charge Balance is Done in Excel Spreadsheet

Advantages of DeltaV Adaptive pH ControlAnticipates nonlinearity by recognizing old territoryModel and tuning settings are scheduled per operating regionRemembers what it has learned for preemptive correctionDemonstrates efficiency improvement during testingSteps can be in direction of optimum set pointExcess reagent useage rate and total cost can be displayed online Achieves optimum set point more efficientlyRapid approach to set point in new operating regionRecovers from upsets more effectivelyFaster correction to prevent violationMore efficient recovery when driven towards constraint Returns to old set points with less oscillation Faster and smoother return with less overshoot

3rd Edition Features Online pH Estimators andAdaptive Control

Components of the self-diagnosed adaptive control loopPerformance -Variablity, Standard DeviationFinal Element/Valve Hysteresis, StickinessLoop Adaptation Model qualityLoop Stability Monitor Oscillations IndexDiagnostic RoutineCorrective Action, Alarm or Message

Loop performanceDeltaV PID loop has two performance indexes as normal loop parameters:Variability IndexStandard deviation

DeltaV Inspect application allows easy setting and review of the loop performance in the systemThe indexes will be used as a part of diagnostic information of the Adaptive loop

Model QualityFinal stage of model adaptation and verification showing actual response and response calculated by the identified models. The model error indicates model quality.Other factors include adaptation history and model convergence

Loop stabilityOscillation index:

Loop oscillation amplitudeOscillation period

The highest priority loop diagnostic parameter

Valve diagnosticsCalculation of the valve parameters:Valve backlashValve stickinessValve hysteresisTwo complementary techniques are used:Loop oscillation analysisUse of valve stem position (BKCAL)

Valve diagnostics based on oscillation analysisPIDAOProcessSPOUTVPPVOUT oscillations caused by valve backlash and stickinessPV oscillations caused by valve stickiness

Valve backlash causes oscillations on the controller outputOUTPV

Valve backlash and stickiness cause oscillations on the controller and process outputOUTPV

Valve diagnostics based on oscillation analysis

Define oscillation amplitude on the controller output -

Define oscillation amplitude on the controller input -

Calculate hysteresis as

Knowing process gain

, calculate valve stickness (resolution) as:

Calculate backlash as

_1153035955.unknown

_1153036582.unknown

_1153038759.unknown

_1153038812.unknown

_1153036162.unknown

_1153035850.unknown

Valve diagnostics based on known valve stem positionPIDAOProcessSPOUTVPPVBKCALParameter representing valve stem position

Hysteresis calculation based on known valve stem position

MACROBUTTON MTPlaceRef \* MERGEFORMAT (1)

Selection of the highest values of

averaged over certain period of time may be considered as actuator backlash and resolution estimate.

maximum values selection


Where

b valve backlash

h - valve hysteresis

- valve resolution

i - back calculation signal delay in scans, accounting for valve speed of response (velocity limit)

_1151836903.unknown

_1151836940.unknown

_1152084739.unknown

_1149407275.unknown

Backlash calculation based on known valve stem position

If


Then


The resolution then can be easy found as:


where



_1153034201.unknown

_1153034329.unknown

_1153036307.unknown

_1153034225.unknown

_1151827797.unknown

Adaptive Loop self-diagnostics overview window

Valve diagnostics summarySelf diagnosed control loop contains four basic components: Performance, Adaptation, Stability and Valve

Model Based Adaptive Control extends diagnostic features and calculation options (valve parameters)

Valve diagnostic is a key component of the loop diagnostic

SummaryAdaptive Control technique with model parameter interpolation is an unique, theoretically sound and practically proven technologyDeltaV Adaptive configuration is compatible with PID controller and can replace in principle PID in every loop Feedforward adaptation and model scheduling enhance adaptive features Easy to use adaptive application makes settings and operation of adaptive loops easy

How to take advantage of Adaptive Control in your plant soonIdentify difficult to tune loopsIdentify loops you want to improve operationContact: [email protected] [email protected] Become an adaptive control Beta installation

DeltaV Product Manager John Caldwell

Title and description should match the submitted abstract information.

The Emerson Exchange logo should not be modified.Insert the presenters and their company logos in the order that they will present.To control efficiently we need well tuned controllersTo tune controllers accurately current process dynamics must be known. This requires process testing.After testing, updated tuning parameters may be calculated and then used for control.Over time process dynamics will change leading to poor control with the current tuning parameters.This means more tuning which requires more testingThis entire process must be repeated again and again in an attempt to maintain acceptable control performance.

Title and description should match the submitted abstract information.

The Emerson Exchange logo should not be modified.To control efficiently we need well tuned controllersTo tune controllers accurately current process dynamics must be known. This requires process testing.After testing, updated tuning parameters may be calculated and then used for control.Over time process dynamics will change leading to poor control with the current tuning parameters.This means more tuning which requires more testingThis entire process must be repeated again and again in an attempt to maintain acceptable control performance.

utilizing deltav adaptive control. presenters peter wojsznis gregory mcmillan willy wojsznis terry...

Documents