Adaptive Control Beta Site Feedback. [File Name or Event] Emerson Confidential 27-Jun-01, Slide 2...

Adaptive Control Beta Site Adaptive Control Beta Site FeedbackFeedback

[File Name or Event]Emerson Confidential27-Jun-01, Slide 2

PresentersPresenters

• Peter Wojsznis

• Terry Blevins

• Bruce Johnson

• Clair Meger

Terry Chmelyk


IntroductionIntroduction

• DeltaV Adaptive Control Overview• Pre-beta and Internal Testing

– Solutia– UT Pickle Research – Fisher Controls

• Customer Beta Test– Husky Energy– Canfor– Eastman Chemical– Lubrizol– Results Achieved

• Summary • References


DeltaV Adaptive ControlDeltaV Adaptive ControlDeltaV Adaptive ControlDeltaV Adaptive Control

• Fully Adaptive Controller Tuning– PID Feedback and Feedforward

• Adapts to Changing Process– Identifies Process Dynamics While

In Automatic Control– Calculates optimal tuning

parameters– No Bump Tests Required

• Remembers Tuning for Different Operating Conditions

• Easy to Use – Out of the Box– Can be applied to all controllers

“Always in Control”


Beta Implementation - Adaptive ControlBeta Implementation - Adaptive ControlBeta Implementation - Adaptive ControlBeta Implementation - 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 values for model, limits, and time to steady state are automatically defaulted based on block tuning.


Adaptive Control - Internal StructureAdaptive Control - Internal StructureAdaptive Control - Internal StructureAdaptive Control - Internal Structure

Control

Process

Controller Redesign

Supervisor Model Evaluation

Model Par. Interpolation

Set of Models

PID Controller w/Dyn Comp Feedforward

Excitation Generator

Manipulate

Measured Disturbance

Adaptive Control Block

SP


DeltaV Adaptive ControlDeltaV Adaptive ControlOperational FeaturesOperational Features

DeltaV Adaptive ControlDeltaV Adaptive ControlOperational FeaturesOperational Features

• Process models are automatically established for the feedback or feedforward paths.

• Model adaptation utilizes a data set captured after a setpoint change or injected pulse (Automatic Mode), or a significant change in the disturbance input change in controller output (manual Mode).

• Multiple models are evaluated and a new model is determined


Adapt Operating RegionsAdapt Operating RegionsAdapt Operating RegionsAdapt 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


Triggering of IdentificationTriggering of IdentificationTriggering of IdentificationTriggering of Identification

• Operator SP change in control system will trigger the Adapt block to collect to create model


Viewing Latest Feedback ModelViewing Latest Feedback ModelViewing Latest Feedback ModelViewing Latest Feedback Model


Feedforward View – Dynamic CompFeedforward View – Dynamic CompFeedforward View – Dynamic CompFeedforward View – Dynamic Comp

Deadtime, Lead and Lag are also shown if Dynamic Compensation is selected in ADAPT_OPTS


Accessing Feedback Models by RegionAccessing Feedback Models by RegionAccessing Feedback Models by RegionAccessing Feedback Models by Region

• To access the last good model identified in each region of operation, then select Region and the Feedback tab.

• Value of state parameter


Control Setup WindowControl Setup WindowControl Setup WindowControl Setup Window

• Trigger to adapt

• Controller Output pulse injection (used in phase 2 of beta)

• How fast to adapt

• Process type: Integrating – Non Integrating; Minimum time to steady state

• Adaptive mode of operation


Common Causes of Uncertain or Bad ModelCommon Causes of Uncertain or Bad ModelCommon Causes of Uncertain or Bad ModelCommon Causes of Uncertain or Bad Model

• Actuator is sticking (coarse resolution) or has deadband.

• The measurement has uncertain or bad status.

• Initial Tuning for the existing loops or (manual entered) Time to Steady state is significantly off from the required value(s).

• Loop feedback and feedforward action (direct or reverse) is set incorrectly

• The identified model is outside the limits on process gain, time constant, and/or deadtime defined in the region view.

• State parameter is constantly switching between regions.


Actuator Deadband and ResolutionActuator Deadband and ResolutionActuator Deadband and ResolutionActuator Deadband and Resolution

• May be evident from trend plot. Also, reference the Health view to quantify deadband and/or resolution.

Deadband

Deadband and ResolutionDeadband and Resolution


Health ViewHealth ViewHealth ViewHealth View• Quantifies

deadband or resolution and Loop Oscillation

• May be configured to create a module alarm


Adaptive Control Test ProgramAdaptive Control Test ProgramAdaptive Control Test ProgramAdaptive Control Test ProgramPre-Beta Sites• Solutia – Pensacola, FL, May, 2004 5 loops• J.J.Pickle Center, UT, Austin, TX Dec, 2004 3 loops

Internal Beta Sites• Fisher Control, Marshalltown, IA Sept, 2004• Rosemount, Eden Prairie and Chanhassen, MN March, 2005• Ovation, Pittsburgh, PENN April, 2005

Customer Beta Sites• Canfor – Prince George, CAN Feb, 2005 47 loops• Husky Energy – Prince George, CAN Feb, 2005 45 loops• Eastman Chemical – Longview, TX, Mar, 2005 147 loops• Lubrizol – Houston, TX 350 loops, April, 2005 350 loops


Pre-Beta – Solutia, Pensacola, FLPre-Beta – Solutia, Pensacola, FLPre-Beta – Solutia, Pensacola, FLPre-Beta – Solutia, Pensacola, FL


Pre-Beta at Solutia, Pensacola, FLPre-Beta at Solutia, Pensacola, FLPre-Beta at Solutia, Pensacola, FLPre-Beta at Solutia, Pensacola, FL

LTTT TT

Cooling Tower Water

Cooling Tower Water

TC

TC

TT

TT

FT

FC

LC

AT

AC

HMD(Base)

65AC681(pH)

65TC68865TC684

65TC685

65LC682

Strike Kettle Process and Instrumentation


Pre-beta – Solutia, Pensacola, FLPre-beta – Solutia, Pensacola, FLPre-beta – Solutia, Pensacola, FLPre-beta – Solutia, Pensacola, FL

Kettle control – regular PID control


Pre-beta – Solutia, Pensacola, FLPre-beta – Solutia, Pensacola, FLPre-beta – Solutia, Pensacola, FLPre-beta – Solutia, Pensacola, FL

Kettle control – Adapt control


Pre-Beta at J.J Pickle Research CampusPre-Beta at J.J Pickle Research CampusPre-Beta at J.J Pickle Research CampusPre-Beta at J.J Pickle Research Campus

• DeltaV is installed at UT’s J.J. Pickle Research Campus, Austin, Texas on a 18”distillation column and 6” reactive distillation columns

• Emerson Process Management is:– Funding a PhD graduate student

under the guidance of Professor Tom Edgar to demonstrate and test DeltaV advanced control on the reactive column.

– Participating in a DOE project to explore a technique for early detection of column flooding – Professor Bruce Eldridge.


Pre-beta - J.J.Pickle Research CampusPre-beta - J.J.Pickle Research CampusPre-beta - J.J.Pickle Research CampusPre-beta - J.J.Pickle Research Campus

Flow loop


Internal Beta – Fisher Controls, Internal Beta – Fisher Controls, Marshalltown, IAMarshalltown, IA

Internal Beta – Fisher Controls, Internal Beta – Fisher Controls, Marshalltown, IAMarshalltown, IA


Internal Beta – Fisher ControlsInternal Beta – Fisher ControlsInternal Beta – Fisher ControlsInternal Beta – Fisher Controls


Valve Installed Characteristics – Gain Valve Installed Characteristics – Gain Change of 7X Over Test RangeChange of 7X Over Test Range

Valve Installed Characteristics – Gain Valve Installed Characteristics – Gain Change of 7X Over Test RangeChange of 7X Over Test Range


Automated TestAutomated TestAutomated TestAutomated Test


Models Automatically Identified by RegionsModels Automatically Identified by RegionsModels Automatically Identified by RegionsModels Automatically Identified by Regions


Customer Beta Test ProgramCustomer Beta Test ProgramCustomer Beta Test ProgramCustomer Beta Test Program

• Phase 1 – first three months– Install beta system and provide initial training for instrumentation

and control

– Monitor the process models and recommended tuning that is automatically identified by DeltaV Adapt. No impact on operations

• Phase 2 – Second three months– Provide more extensive training for operators, instrumentation and

control, and management

– Commission adaptive closed loop control on some of the loops monitored in phase 1. Operators may operate adaptive loops using their normal interface to the control system.


Phase 1 ObjectivesPhase 1 ObjectivesPhase 1 ObjectivesPhase 1 Objectives

• Identify loops that must address changes in process gain and dynamics as a function of operating condition as indicated by PV, OUT or other measured parameters e.g. plant throughput

• Quantify process variability for existing PID control


Beta Implementation– DeltaV to Beta Implementation– DeltaV to DeltaVDeltaV

Beta Implementation– DeltaV to Beta Implementation– DeltaV to DeltaVDeltaV

Parameters Accessed Using OPC and Mirror

Application Station


Beta Module – DeltaV to DeltaVBeta Module – DeltaV to DeltaVBeta Module – DeltaV to DeltaVBeta Module – DeltaV to DeltaV


Pre-Configured Modules for Each SitePre-Configured Modules for Each Site Pre-Configured Modules for Each SitePre-Configured Modules for Each Site


Beta Implementation – DeltaV to ABBBeta Implementation – DeltaV to ABBBeta Implementation – DeltaV to ABBBeta Implementation – DeltaV to ABB

OPC I/F

ABB Controller

Parameters Accessed Using OPC and Mirror

ABB OPC Server

PID LoopPID

LoopPID Loop

Any ABBOperator Console


Beta Module – DeltaV to ABBBeta Module – DeltaV to ABBBeta Module – DeltaV to ABBBeta Module – DeltaV to ABB


Canfor, Northwood Mill, CanadaCanfor, Northwood Mill, CanadaCanfor, Northwood Mill, CanadaCanfor, Northwood Mill, Canada

• Beta addresses a wide variety of loops in the Pulping Group and Power House

• Beta system interfaces to existing Provox control system using OPC


Canfor –Loop Types AddressedCanfor –Loop Types AddressedCanfor –Loop Types AddressedCanfor –Loop Types Addressed

• 8 – Consistency

• 5 – Density

• 15 Level

• 5 pH

• 4 Pressure

• 10 Temperature


What have we learned so far?What have we learned so far?What have we learned so far?What have we learned so far?

• Seeing good model quality on self-regulating and integrating processes

• Very encouraged with the insights we are gaining about modeling dynamics across the operating range of the loops

• Model identification alone is a powerful tool for tuning or control design

• Health view has been enhanced and we are excited about the diagnostic capabilities for troubleshooting


B Digester Temperature Loop B Digester Temperature Loop B Digester Temperature Loop B Digester Temperature Loop

B Digester Temperature Loop

Gain change by factor of 10

TC change by factor of 3

DT change by factor of 2.5


Husky Energy, Prince George, CanadaHusky Energy, Prince George, CanadaHusky Energy, Prince George, CanadaHusky Energy, Prince George, Canada

• Beta addresses all DeltaV loops implemented in the refinery

• Beta system interfaces to the DeltaV control system using OPC


Husky Energy - Initial ResultsHusky Energy - Initial ResultsHusky Energy - Initial ResultsHusky Energy - Initial Results

• Two(2) process control engineers and one(1) Norpac’s field support person attended training on DeltaV Adaptive control

• After the first full day of operation, DeltaV adaptive control automatically identified feedback and feedforward models for 25 of 42 loops based on typical operator changes in setpoint. Preliminary model evaluation indicates good match to the real process. Identification of remaining loops will continue over next 3 months.

• Monitoring will be extended to cover plant expansion for clean fuel production – may be an opportunity to quantify expected time that can be saved on new plant startup.


Husky Energy –Loop Types AddressedHusky Energy –Loop Types AddressedHusky Energy –Loop Types AddressedHusky Energy –Loop Types Addressed

• 12 – Flow

• 14 - Level

• 10 - Pressure

• 6 - Temperature


• 17-TIC-008 HP Receiver Condenser Temperature (A Fin Fan Cooler for the High Pressure Receiver)

• 20-LIC-001 Rich Amine Flash Tank • 23-FIC-023 Stabilizer Bottoms A/C Gasoline (Flow

Through an Air Cooler (A/C) from Stabilizer Tower)• 23-LIC-020 CGHT (Cat Gas Hydrotreater Treater)

Stabilizer Level• 23-PIC-173 HDS Reactor Heater Fuel Gas (Fuel

Pressure Control Loop • 23-PIC-179 Splitter Tower Heater Fuel gas (Fuel

Pressure Control Loop)• 23-TIC-123 Splitter Overhead Air Cooler Out (Fin fan

Control Loop)

Husky Energy – Some interesting loopsHusky Energy – Some interesting loopsHusky Energy – Some interesting loopsHusky Energy – Some interesting loops


Eastman Chemical – Longview, TexasEastman Chemical – Longview, TexasEastman Chemical – Longview, TexasEastman Chemical – Longview, Texas

• Beta addresses a wide variety of loops in the hydrocarbon-cracking units, for the manufacture of solvents, alcohol, ethers, and polyethylene

• Beta system interfaces to existing DeltaV control system using OPC


Eastman –Loop Types AddressedEastman –Loop Types AddressedEastman –Loop Types AddressedEastman –Loop Types Addressed

• 39 Flow

• 50 Level

• 31 Pressure

• 24 Temperature

• 1 Position


Lubrizol – Deer Park, TexasLubrizol – Deer Park, TexasLubrizol – Deer Park, TexasLubrizol – Deer Park, Texas

• Beta addresses a wide variety of loops in continuous and batch processing in the manufacturing of lubricants

• Beta system interfaces to existing DeltaV and ABB control systems using OPC


Lubrizol –Loop AddressedLubrizol –Loop AddressedLubrizol –Loop AddressedLubrizol –Loop Addressed

• 180 Loops – DeltaV

• 170 Loops – ABB


Results – Automatic Model Identification in Results – Automatic Model Identification in First Two Months - Phase 1 of the beta testFirst Two Months - Phase 1 of the beta testResults – Automatic Model Identification in Results – Automatic Model Identification in First Two Months - Phase 1 of the beta testFirst Two Months - Phase 1 of the beta test

51%

3%10%

36% No Models

One Region

Two Regions

Three RegionsLubrizol – DeltaV

Control Network

14%

51%

19%

16%

Lubrizol – ABB Control Network

22%

14%

45%

19%

Canfor

39%

48%

12%1%

Eastman Chemical

12%

65%

21%2%

Husky Energy


SummarySummarySummarySummary

• The DeltaV adaptive control technology is being extensively tested at 4 customer beta sites and three internal sites within Emerson.

• Data on process variation and the identified process models has been collected on over 700 control loops.

• Process models have been automatically identified for a wide variety of loops. Performance improvement will be quantified in Phase 2 of the beta.

• Please direct questions or comments on the DeltaV Adaptive control products to John Caldwell ([email protected]).


Where To Get More InformationWhere To Get More InformationWhere To Get More InformationWhere To Get More Information

• “The Next Generation – Adaptive Control Takes a Leap Forward”. Gregory McMillan, Mark Sowell, Peter Wojsznis, Chemical Processing, September, 2004

• Utilizing DeltaV Adaptive Control (Presentation), Peter Wojsznis, Terry Blevins, Darrin Kuchle, Gregory McMillan, Willy Wojsznis, Emerson Exchange, 2004

• Theoretical Analysis of a Class of Multiple Model Interpolation Controllers (Presentation), Dale Seborg and Joao Hespanha, AIChE Annual Meeting, San Francisco, November 21, 2003

• “Adaptive Feedback/Feedforward PID controller”, Willy Wojsznis, Terry Blevins, and Peter Wojsznis, ISA Conference, 2003

• “User Interface For Adaptive Controller”, Peter Wojsznis, Yan Zhang, and Terrence L. Blevins, ISA Conference, 2003

Adaptive Control Beta Site Feedback. [File Name or Event] Emerson Confidential 27-Jun-01, Slide 2...

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