DeltaV Adaptive Control Lubrizol Alliance Meeting – Oct. 04 Technology Introduction And Beta...

DeltaV Adaptive ControlDeltaV Adaptive ControlLubrizol Alliance Meeting – Oct. 04 Lubrizol Alliance Meeting – Oct. 04

Technology Introduction And Beta Testing Technology Introduction And Beta Testing Discussion For Deer ParkDiscussion For Deer Park

Terry Blevins and Dewey KuchleTerry Blevins and Dewey Kuchle

©2002 Fisher-Rosemount Systems, Inc. Slide 2

AgendaAgendaAgendaAgenda Introduction To Adaptive Control

Quick Demonstration

Beta Testing

– What Does This Mean For Lubrizol And Emerson


Control Loop Performance - A Never Ending CycleControl Loop Performance - A Never Ending CycleControl Loop Performance - A Never Ending CycleControl Loop Performance - A Never Ending Cycle

Process Testing(bump tests)

Calculate Tuning

Deploy NewController

OperateThe Loop

ControlDegradesOver Time

The more often you Tune, the better the

performance. Period.

The more often you Tune, the better the

performance. Period.

Evaluate PoorPerformance


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 all the time (continually) without having

to tune at all, ever?


Time

O1

O2

I1

I2

Gain = O2 - O1

I2 - I1

T1 T2

Time Constant ( ) = T3 - T2Input

Output

63.2% (O2 - O1)

T3

Dead Time = T2 - T1

Basics: Gain, Dead Time, Time ConstantBasics: Gain, Dead Time, Time ConstantBasics: Gain, Dead Time, Time ConstantBasics: Gain, Dead Time, Time Constant


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 means better control. Easy.

Continuous automatic adjustment of tuning parameters 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 AdaptDeltaV AdaptDeltaV AdaptDeltaV Adapt

- 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!

No Tuning Required!


DeltaV Adapt – Field Trials At EastmanDeltaV Adapt – Field Trials At EastmanDeltaV Adapt – Field Trials At EastmanDeltaV Adapt – Field Trials At Eastman

As Operators Make Set Point Changes Adapt

Gets Smarter!

As Operators Make Set Point Changes Adapt

Gets Smarter!


Slow Response Causes Off- Spec

Better Tuning Dramatically Increases

Performance

Set Point Change Set Point Change


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 product development

2003 - Prototypes at Texas Eastman in Longview Texas with good results. Results presented at ISA2003.

2004 – Pre-beta test at Solutia in Pensacola, Florida. Results are scheduled for publication in Sept issue of Chemical Process magazine – front cover, feature article.

2005 - Initial release planned for DeltaV v8.1


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


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

0102030405060708090100

Stem position %

Flow

FC 3-5

FT 3-5

Bottoms

FC 3-5FC 3-5

FT 3-5

Bottoms


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


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


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.


But What About Previous Attempts?But What About Previous Attempts?But What About Previous Attempts?But What About Previous Attempts? Most Every Other Attempt To Build Adaptive

Controllers Have Been Based On One Of Two Approaches;

– Heuristics or “Rules Based” Adaptation

– Pattern Recognition

Both Methods Have Met With “Limited” Success

Both Methods Have Met With “Limited” Success


So What Makes This Different?So What Makes This Different?So What Makes This Different?So What Makes This Different? Truly Model Based Tuning

Built On Advanced Closed Loop Modeling

Tuning Based On Science And Fact With No Shortcuts

Gain

Time Constant

Dead time

Initial Model Gain = G1

G2-Δ G2 G2+Δ G3-Δ G3 G3+Δ

Multiple iterations

per adaptation

cycle

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


What Does That Mean?What Does That Mean?What Does That Mean?What Does That Mean? In The 90’s The MIT Blackjack

Team Invaded Las Vegas And Broke The House, Taking Casinos For Millions

They Did It Counting Cards Which Is Really A Form Of Modeling


Delivering ResultsDelivering ResultsDelivering ResultsDelivering Results

From This…From This…To This…To This…


Delivering ResultsDelivering ResultsDelivering ResultsDelivering Results

… With NO Hands!

… With NO Hands!

DeltaV AdaptDeltaV AdaptDemonstrationDemonstration

DeltaV Adaptive Control Lubrizol Alliance Meeting – Oct. 04 Technology Introduction And Beta...

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