Application of Adapt Technology for Intelligent Control

©2007 Fisher-Rosemount Systems, Inc.Slide 12 - 1

DeltaV Insight Technology

Application of Adapt Application of Adapt Technology for Technology for Intelligent ControlIntelligent Control



Introducing DeltaV InsightIntroducing DeltaV InsightIntroducing DeltaV InsightIt all comes together with the DeltaV Insight - A revolutionary product.

One suite with seamless transition between loop diagnostics and tuning

Advanced features are possible in all areas based on automatic identification of process models

Process Learning

Duncan just got Smarter!

Gain

Time Constant

Dead time

Gain

Time Constant

Dead time

Gain

Time Constant

Dead time

Initial Model Gain = G1

G2-? G2 G2+?G3-? G3 G3+?

Multiple iterations

per adaptation

cycle

G1+ ? G1+ ? G1+ ?TC1 -? TC1–? TC1 -?DT1- ? DT1 DT1+ ?

G1+ ? G1+ ? G1+ ?TC1 +? TC1+? TC1 +?DT1- ? DT1 DT1+ ?

G1+ ? G1+ ? G1+ ?TC1 TC1 TC1

DT1- ? DT1 DT1+ ?

G1 G1 G1TC1 -? TC1–? TC1 -?DT1- ? DT1 DT1+ ?

G1 G1 G1TC1 +? TC1+? TC1 +?DT1- ? DT1 DT1+ ?

G1 G1 G1TC1 TC1 TC1

DT1- ? DT1 DT1+ ?

G1-? G1- ? G1- ?TC1 -? TC1–? TC1 -?DT1- ? DT1 DT1+ ?

G1-? G1- ? G1- ?TC1 +? TC1+? TC1 +?DT1- ? DT1 DT1+ ?

G1-? G1- ? G1- ?TC1 TC1 TC1

DT1- ? DT1 DT1+ ?



G1+ ? G1+ ? G1+ ?TC1 TC1 TC1

DT1- ? DT1 DT1+ ?



G1+ ? G1+ ? G1+ ?TC1 TC1 TC1

DT1- ? DT1 DT1+ ?



G1 G1 G1TC1 TC1 TC1

DT1- ? DT1 DT1+ ?



G1 G1 G1TC1 TC1 TC1

DT1- ? DT1 DT1+ ?



G1-? G1- ? G1- ?TC1 TC1 TC1

DT1- ? DT1 DT1+ ?



G1-? G1- ? G1- ?TC1 TC1 TC1

DT1- ? DT1 DT1+ ?



Enabling Process Learning – System WideEnabling Process Learning Enabling Process Learning –– System WideSystem WideAs simple as enabling a block on-line in control studio. All controller resident PID blocks may be enabled/disabled on-line from in any Process Area, unit, or cell in DeltaV Insight ApplicationLearning may be enabled when configuring a controller resident PID blockModels are utilized throughout DeltaV Insight



DeltaV Insight – ArchitectureDeltaV Insight DeltaV Insight –– ArchitectureArchitecture

ProPlus

Model ServerDatabase

Operator StationAC Server

Accum, limits

Insight

Insight

PID

Plugins- Adapt - Tune - Alarm

ID EngineQueue

Controller

PID

Model limits Index’s

PID

PID

H1 Card

PID

ProPlus

Model ServerDatabase

Operator StationAC Server

Accum, limits

InsightInsight

Insight

PID

Plugins- Adapt - Tune - Alarm

ID EngineQueue

Controller

PID

Model limits Index’s

PID

PID

H1 Card

PID

Data Analysis and Reporting

at Server

UI from any workstation

Embedded Learning Runs in the Controller • Fast Data Capture • Small Communications Load • Runs in background



Basis of Model IdentificationBasis of Model IdentificationBasis of Model Identification

Gain

Time Constant

Dead time

Initial Model Gain = G1

G2-Δ G2 G2+ΔG3-Δ G3 G3+Δ

Multiple iterations

per adaptation

cycle

G1+ Δ G1+ Δ G1+ ΔTC1 -Δ TC1–Δ TC1 -ΔDT1- Δ DT1 DT1+ Δ

G1+ Δ G1+ Δ G1+ ΔTC1 +Δ TC1+Δ TC1 +ΔDT1- Δ DT1 DT1+ Δ

G1+ Δ G1+ Δ G1+ ΔTC1 TC1 TC1DT1- Δ DT1 DT1+ Δ

G1 G1 G1TC1 -Δ TC1–Δ TC1 -ΔDT1- Δ DT1 DT1+ Δ

G1 G1 G1TC1 +Δ TC1+Δ TC1 +ΔDT1- Δ DT1 DT1+ Δ

G1 G1 G1TC1 TC1 TC1DT1- Δ DT1 DT1+ Δ

G1-Δ G1- Δ G1- ΔTC1 -Δ TC1–Δ TC1 -ΔDT1- Δ DT1 DT1+ Δ

G1-Δ G1- Δ G1- ΔTC1 +Δ TC1+Δ TC1 +ΔDT1- Δ DT1 DT1+ Δ

G1-Δ G1- Δ G1- ΔTC1 TC1 TC1DT1- Δ DT1 DT1+ Δ

Identification algorithm based on Model switching with interpolation and re-centering

Patented technique unique to Emerson Process Management: Patent No. 6,577,908 plus four pending patents

Field proven at 4 beta sites, on over 700 loops.



The Value of Process ModelsThe Value of Process ModelsThe Value of Process ModelsThere is a real value in saving and trending models identified over time

– Peformance based indicies

– Identify sources of variability

Impact of Noise and Unmeasured Disturbances on model identification may be minimized through the application of non-linear filtering.



Non-linear Filtering of ModelsNonNon--linear Filtering of Modelslinear Filtering of ModelsA filter Factor (FF) is calculated for every adapted parameter. Two criteria are used for the FF calculation. 1. Ratio of the maximum residual error to the

minimum residual error (RE). If the ratio is small, it implies high noise level or a significant distance from the true model value.

2. Test if the model with a middle value parameter value has the smallest RE. Satisfaction of these criteria indicates that true model value lies between low and high range of the parameter.



Non-linear Filtering (Cont)NonNon--linear Filtering (Cont)linear Filtering (Cont)



Model QualityModel QualityModel QualityModel quality is calculated based on the following criteria :

– Number of adaptations

– Average filter factor

– Model main parameter (gain) average value and variability of the last n adaptations of that parameter



Tuning IndexTuning IndexTuning Index

Provides a direct indication of the desired control performance vs. current control performance

Identifies loops that would benefit from more aggressive tuning or by less aggressive tuning.

Calculation is based on the identified process model, selected tuning rule and current tuning,



Tuning Index CalculationTuning Index CalculationTuning Index Calculation

Current Tuning

Selected Tuning Rule

Process Model

Tuning Index

Tuning Index

Calculation



DeltaV Insight Tuning IndexDeltaV Insight Tuning IndexDeltaV Insight Tuning IndexTuning index is defined as the ratio of the potential residual PID variability reduction to the actual PID residual variability.

More meaningful measure than the Harris index which is based on minimum variance tuning in statistical sense

11

s

PIDc e λ−

=%

22

s

PIDc e λ−

=%

1λ

2λ

= closed loop time constant for the current PID tuning

= closed loop time constant for recommended tuning

s = sampling period

( )

( )

22 1

22

21 1

21

1max 1 , 1 *100%

1

PID

PID

PID

PID

c ccTI

c cc

%

%

%

%

⎧ ⎫⎛ ⎞−⎪ ⎪⎜ ⎟

−⎪ ⎪⎜ ⎟= − −⎨ ⎬⎜ ⎟−⎪ ⎪⎜ ⎟⎜ ⎟⎪ ⎪−⎝ ⎠⎩ ⎭



DeltaV InSightPerformance MonitoringDeltaV InSightDeltaV InSightPerformance MonitoringPerformance Monitoring

Control Statistics for:– Incorrect mode– Limited control output– Bad/Uncertain input– High variability

Explorer Tree allows easy navigationNew Control Diagnostics– Model based tuning

index pinpoints tuning opportunities

New



Insight Performance Monitoring – Block LevelInsight Performance Monitoring Insight Performance Monitoring –– Block Block LevelLevel

Automatic trending of parameter – utilizing historian if assigned in module.

Information contained on one screen for easy access



DeltaV InSight - Adaptive TuningDeltaV InSight DeltaV InSight -- Adaptive Adaptive TuningTuning

Process model is displayed and saved in the model database

Model Quality and Identification Status

Tuning criteria and desired speed of response

Tuning Recommendation



DeltaV InSight - Model AnalysisDeltaV InSight DeltaV InSight -- Model AnalysisModel AnalysisModels automatically stored in a model database.Various plot options to analyze impact of operating conditions on process modelsAverage of selected models may be utilized to establish the recommended tuning



DeltaV InSight - Control SimulationDeltaV InSight DeltaV InSight -- Control SimulationControl Simulation

Closed loop simulation of setpoint and disturbance changes for recommended tuning

Simulation for both Adaptive and Manual Tuning.



DeltaV Insight NavigationDeltaV Insight NavigationDeltaV Insight NavigationLaunch application from Start Button or in context from DeltaV Explorer or Control Studio

Select Inspect and Tuning View From the same window



SummarySummarySummaryDeltaV Insight is a revolutionary control technology breakthrough in process learning and intelligent control that provide unique capability for loop diagnostics and tuning.

Process models are automatically developed during normal plant operations when Process Learning is enabled.

Models that have been identified are saved in a model database to support analysis of the impact that operating conditions have on process dynamics

Loops that would benefit from application of the recommended tuning are automatically identified and improvement in performance is quantified by a model based Tuning Index.

Application of Adapt Technology for Intelligent Control

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