Slide 1 Sneak Peek DeltaV Adaptive Control Coming In DeltaV v8.1 ES Terry Blevins and Darrin Kuchle...

Sneak PeekSneak PeekDeltaV Adaptive ControlDeltaV Adaptive ControlComing In DeltaV v8.1 ESComing In DeltaV v8.1 ES

Sneak PeekSneak PeekDeltaV Adaptive ControlDeltaV Adaptive ControlComing In DeltaV v8.1 ESComing In DeltaV v8.1 ES

Terry Blevins and Darrin Kuchle

DeltaV Advanced Control Team

Austin, Tx.

A Fundamental Control ProblemA Fundamental Control ProblemA Fundamental Control ProblemA Fundamental Control Problem

• To control efficiently we need well tuned controllers• To 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 testing• This entire process must be repeated again and again in

an attempt to maintain acceptable control performance.

Tuning MethodsTuning MethodsTuning MethodsTuning Methods

• First tuning method due to Ziegler & Nichols (1942)– Called Quarter-Amplitude-Damping (QAD)

• Most older tuning methods try for “as fast as possible”• Many people still do not use any method preferring to

“tune-by-feel” – Classical control skills now rare

• “little black books” • Default tuning (gain=1.0, Reset=1 min)

• My Favorites - The Ever Popular SWAG method and the equally effective POMA analysis.

There Must Be A Better WayThere Must Be A Better WayThere Must Be A Better WayThere Must Be A Better Way

Wouldn’t it be nice to have controllers use optimal tuning

parameters all the time (continually) without having to

tune at all, ever?

Permitted Range

Adaptive Control – Continuous AdjustmentAdaptive Control – Continuous AdjustmentAdaptive Control – Continuous AdjustmentAdaptive Control – Continuous Adjustment

Controller Gain

Starting Point

Less Aggressive

More Aggressive

Continuous automatic adjustment of tuning parameters to changing process dynamics

means better control. Easy.

Continuous automatic adjustment of tuning parameters to changing process dynamics

means better control. Easy.

But don’t forget about the time constant and

the dead time.

But don’t forget about the time constant and

the dead time.

DeltaV Adapt Sneak PeakDeltaV Adapt Sneak PeakDeltaV Adapt Sneak PeakDeltaV Adapt Sneak Peak

- Fully Adaptive PID Control Tuning

- Learns Process Dynamics While In Automatic Control

- No Bump Testing Required

- Works On Feedback And Feedforward

- Patents Are Now Awarded!

- See It For The First Time Here

No Tuning Required!

Not an overnight thing…Not an overnight thing…Not an overnight thing…Not an overnight thing…

• EMERSON technology developed in Austin.

• Patents have now been awarded.

• 1997 - Dr. Wojsznis’ concept originated

• 1998 - Started research at Hawk Austin

• 2002 - Started development

• 2003 - Prototypes at Texas Eastman in Longview Texas with good results.

• 2004 - Initial release planned for DeltaV v8.1

Dr. Dale Seborg – UC Santa BarbaraDr. Dale Seborg – UC Santa BarbaraDr. Dale Seborg – UC Santa BarbaraDr. Dale Seborg – UC Santa Barbara

1999 - Dr. Seborg started working on formal proofs of convergence for us along

with his Emerson funded grad student

1999 - Dr. Seborg started working on formal proofs of convergence for us along

with his Emerson funded grad student

Patents Have Now Been Awarded!Patents Have Now Been Awarded!Patents Have Now Been Awarded!Patents Have Now Been Awarded!

Mr. Terry Blevins

Mr. Terry Blevins

Dr. Wilhelm Woszjnis

Dr. Wilhelm Woszjnis

It’s That Easy!It’s That Easy!It’s That Easy!It’s That Easy!

Adaptx

Just Check The BoxJust Check The Box

DeltaV Adaptive Control – Technology DeltaV Adaptive Control – Technology BasisBasisDeltaV Adaptive Control – Technology DeltaV Adaptive Control – Technology BasisBasis

• Process 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 Control – Technology DeltaV Adaptive Control – Technology BasisBasisDeltaV Adaptive Control – Technology DeltaV Adaptive Control – Technology BasisBasis

• Model 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 StructureAdaptive Control – Internal StructureAdaptive Control – Internal StructureAdaptive Control – Internal Structure

Control

Process

Controller Redesign

Supervisor Model Evaluation

Model Interpolation

Set of Models

PID Controller w/Dyn Comp Feedforward

Excitation Generator

Manipulate

Measured Disturbance

Adaptive Control Block

SP

Simple Example – Pure Gain ProcessSimple Example – Pure Gain ProcessSimple Example – Pure Gain ProcessSimple Example – Pure Gain Process

Pure gain Process

K

Estimated Gain

Multiple Model Interpolation with re-centering

Changing process input

2( ) ( ( ) ( ) ) iE t y t Yi t

For each iteration, the squared error is computed for every model I each scan

Where: is the process output at the time t is i-th model output A norm is assigned to each parameter value k = 1,2,….,m in models l = 1,2,…,n.

if parameter value is used in the model, otherwise is 0

For an adaptation cycle of M scans

( ) y t( ) Yi t

1

( ) ( ) N

klkl i

i

Ep t E t

= 1kl klp

1

( )M

kl kl

t

sumEp Ep t

1kl

kk

Ff sumF

1kl klF

sumEp

11( ) ... ...k k kl knk kl knp a p f p f p f

G1-Δ G1 G1+Δ

Initial Model Gain = G1

G2-Δ G2 G2+Δ

G3-Δ G3 G3+Δ

Iteration

1

2

3

Multiple iterations

per adaptation

cycle

Example - First Order Plus Deadtime ProcessExample - First Order Plus Deadtime ProcessExample - First Order Plus Deadtime ProcessExample - First Order Plus Deadtime Process• For a first order plus deadtime

process, twenty seven (27) 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 Deadtime Process

Estimated Gain, time constant, and deadtime

Multiple Model Interpolation with re-centering

Changing process input

Gain

Time Constant

Dead time

sKe DT

1

G1+ Δ G1+ Δ G1+ Δ TC1 -Δ TC1–Δ TC1 -Δ DT1- Δ DT1 DT1+ Δ

G1+ Δ G1+ Δ G1+ Δ TC1 +Δ TC1+Δ TC1 +Δ DT1- Δ DT1 DT1+ Δ

G1+ Δ G1+ Δ G1+ Δ TC1 TC1 TC1 DT1- Δ DT1 DT1+ Δ

G1 G1 G1 TC1 -Δ TC1–Δ TC1 -Δ DT1- Δ DT1 DT1+ Δ

G1 G1 G1 TC1 +Δ TC1+Δ TC1 +Δ DT1- Δ DT1 DT1+ Δ

G1 G1 G1 TC1 TC1 TC1 DT1- Δ DT1 DT1+ Δ

G1-Δ G1- Δ G1- Δ TC1 -Δ TC1–Δ TC1 -Δ DT1- Δ DT1 DT1+ Δ

G1-Δ G1- Δ G1- Δ TC1 +Δ TC1+Δ TC1 +Δ DT1- Δ DT1 DT1+ Δ

G1-Δ G1- Δ G1- Δ TC1 TC1 TC1 DT1- Δ DT1 DT1+ Δ

Operating Condition ImpactOperating Condition ImpactOperating Condition ImpactOperating Condition Impact• Process gain and dynamics may change as a function of

operating condition as indicated by PV, OUT or other measured parameters e.g. plant throughput

Defining Operating RegionsDefining Operating RegionsDefining Operating RegionsDefining Operating Regions• Adaptive 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.

Model Parameters

State Parameter Value

Model Parameters

State Parameter Value

Region 1

Region 2

Region 3

Region 4

Region 5

Region 1

Region 2

Example – Non-Linear Installed Example – Non-Linear Installed CharacteristicsCharacteristicsExample – Non-Linear Installed Example – Non-Linear Installed CharacteristicsCharacteristics

• Process 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.

Flow vs Stem Position

0102030405060708090

100

Stem position %

Flow

FC 3-5

FT 3-5

Bottoms

FC 3-5FC 3-5

FT 3-5

Bottoms

Example – Example – Throughput Dependent ProcessThroughput Dependent Process Example – Example – Throughput Dependent ProcessThroughput Dependent Process

• The process deadtime for superheater outlet temperatue control changes as a function of steam flow rate

• Steam flow rate is used as the state parameter

DT Multiplier

0123456

Throu

ghpu

t 0 10 20 30 40 50 60 70 80 90

DT Multiplier

TT 1 - 1

AC 1 - 1 TC 1 - 1

TT 1 - 2

AC 1 - 1 TC 1 - 2

FT 1 - 3

Process deadtime changes with

Example – Example – Multiple Valves - Split Range Multiple Valves - Split Range Example – Example – Multiple Valves - Split Range Multiple 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.

0 50 100 Controller Output (%)

Cooling Valve

Heating Valve

100

0

FC 1 - 2 TC 1 - 2

TT 1 - 2

Heater Cooler

FY 1 - 2

Example – Example – pH ProcesspH Process Example – Example – pH ProcesspH Process • The process gain

associated with a change in reagent is highly non-linear.

• Extremely high gain around a pH of 7, lower gain above and below this point.

• The control parameter, pH, is used as the state parameter.

Static Mixer

AC 1 - 1 AC 1 - 1

Neutralizer

Feed

AT 1 - 1

FT 1 - 1

FC 1 - 2 FC 1 - 2

FT 1 - 2

Reagent Stage 1

20 pipe

diameters

pH

Reagent Flow Influent Flow

6 8

• The sensitivity of tray temperature to changes in distillate to feed ratio is highly non-linear.

• Tray temperature is used as the

state parameter. Tray 10

Tray 6

Distillate FlowFeed Flow

Temperature

OperatingPoint

Measurement Error

Measurement Error

Tray 10

Tray 6

Distillate FlowFeed Flow

Temperature

OperatingPoint

Measurement Error

Measurement Error

Example: Column Temperature ControlExample: Column Temperature Control Example: Column Temperature ControlExample: Column Temperature Control

TE3-2

TC 3-2

LT 3-2

LC 3-2

Distillate Receiver

Column

Reflux

PT 3-1

Tray 6

Thermocouple

TE3-2

TC 3-2TC 3-2

LT 3-2

LC 3-2LC 3-2

Distillate Receiver

Column

Reflux

PT 3-1

Tray 6

Thermocouple

DeltaV Adaptive Control – Field TrialsDeltaV Adaptive Control – Field TrialsDeltaV Adaptive Control – Field TrialsDeltaV Adaptive Control – Field Trials

• Control automatically adapts based on SP changes in Auto – Caustic loop

DeltaV Adaptive Control – Field TrialsDeltaV Adaptive Control – Field TrialsDeltaV Adaptive Control – Field TrialsDeltaV Adaptive Control – Field Trials

• Model verification showing actual response and response calculated by the identified model.

Configuration of Adaptive ControlConfiguration of Adaptive ControlConfiguration of Adaptive ControlConfiguration of Adaptive Control

• New control block in the advanced control palette.

• Parameters are automatically assigned to the historian.

• No more difficult to use than PID. Initial for model, limits, and time to steady state are automatically defaulted based on block tuning.

Adaptive Control ApplicationAdaptive Control ApplicationAdaptive Control ApplicationAdaptive Control Application

• Used 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.

Initial Overview of Adaptive ControlInitial Overview of Adaptive ControlInitial Overview of Adaptive ControlInitial Overview of Adaptive Control

• Default system overview lists all the adaptive control blocks in service.

• The feedback and feedforward operation and status are summarized

Status indicates if a limit has been reached in adapting the feedback /feedforward model

Adaptive Control Block – Operation ViewAdaptive Control Block – Operation ViewAdaptive Control Block – Operation ViewAdaptive Control Block – Operation View

Feedback ViewFeedback ViewFeedback ViewFeedback View• Adaptive

Operation may be selected:– Observe

– Learn

– Schedule

– Adapt Control

• Tuning rule and speed of response may be changed from defaults.

Model identified

for current region

Calculated tuning for model and selected rule

Calculated tuning for model and selected rule

Operation Selection

Model identified

for current region

Model identified

for current region

Operation Selection

Operation Selection

Expert Selection – Range ViewExpert Selection – Range ViewExpert Selection – Range ViewExpert Selection – Range View• Parameter

high and low limits may be adjusted for each region.

• Current parameter and state are also shown

Expert Selection - SetupExpert Selection - SetupExpert Selection - SetupExpert Selection - Setup

• Advanced features may be enabled e.g. automatic injection of change on periodic basis, integrating process.

The End ResultThe End ResultThe End ResultThe End Result• This capability will allow DeltaV users to assign

“ballpark” tuning parameters and let adaptive PID controllers tighten them up and adapt over time.

• Patented model switching technology means robust control over the long haul without sacrificing performance

• Faster startups, quicker ramp-up of production, less tuning over time, and better control over the life of the system all mean better economics.

Looking For Beta Sites NowLooking For Beta Sites NowLooking For Beta Sites NowLooking For Beta Sites Now

• Email to [email protected]

Slide 1 Sneak Peek DeltaV Adaptive Control Coming In DeltaV v8.1 ES Terry Blevins and Darrin Kuchle...

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