Utilizing DeltaV Adaptive Control

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Utilizing DeltaV Adaptive Control
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Presenters

• Peter Wojsznis
• Gregory McMillan
• Willy Wojsznis
• Terry Blevins

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Outline

• Introduction
• Adaptive Technique Background
• Adaptive PID Design
• Application Examples
• Field Test Results
• Adaptive Control Demos
• Actuator Diagnostics
• Summary

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Control Loop Performance A Never Ending Cycle
Evaluate
Test
Degradation
The more often you Tune, the better the
performance.
Calculate
Operate
Deploy
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Operating 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

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There Must Be A Better Way
Wouldnt it be nice to have controllers use
optimal tuning parameters all the time
(continually) without having to tune at all, ever?
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DeltaV Adapt
No Tuning Required!

• 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 Here

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DeltaV Adapt Applicable to Most Control Loops
Today
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DeltaV Adapt A Clear Difference
Product Features Product A Product B Product C DeltaV ADAPT
Adapt Feedback Y Y Y Y
Adapt Feedforward Y N N Y
Technology Rule Based/ Pattern Recognition Rule Based/ Pattern Recognition Rule Based/ Pattern Recognition Model Switching With Interpolation and re-centering
Gain /Model Scheduling Y N N Y
Deadtime compensation Y N N N
Process Model Identified / Shown N N N Y
Selectable Tuning rule N N N Y
Model Verification /Adjustment Limits N N N Y
Robustness - Unmeasured Disturbance Poor Poor Poor Excellent
Equipment Diagnostics N N N Y
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Not an overnight thing

• EMERSON technology developed in Austin.
• Patents have been awarded.
• 1997 - Dr. Willy Wojsznis concept originated
• 1998 - Research started at Tech Center - Austin
• 1999 - Dr Seborg started work on formal proofs
• 2002 - Development started
• 2003 - Prototypes at Eastman Chemical, Solutia
  and UT with good results.

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Patents Have Been Awarded!
Dr. Wilhelm Wojsznis
Mr. Terry Blevins
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Solid theoretical background
1999 - Dr. Seborg started working on formal
proofs of convergence for us along with his
Emerson funded grad student
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Outline

• Introduction
• Adaptive Technique Background
• Adaptive PID Design
• Application Examples
• Field Test Results
• Adaptive Control Demos
• Actuator Diagnostics
• Summary

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Adaptive PID Techniques

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Model Free vs Model Based Adaptation

Model Free Model Based
No model - Controller parameters are adapted Model (parametric) - Model parameters are adapted - Controller is designed from the adapted model - any controller can be used
Safety net - Acceptable range of controller parameters - Oscillation monitor Safety net - Model validation based on data - Acceptable range of model parameters - Oscillation monitor

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Model Switching Adaptation

• Use N-models working in parallel
• Evaluate model error
• Select model with minimal error
• Shortcoming TOO MANY MODELS

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Parameter Interpolation

• Every parameter value of the model is evaluated
  independently
• The weight assigned to the parameter value is
  inverse of the squared error
• Adapted parameter value is weighted average of
  all evaluated values

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Advantages of Parameter Interpolation

• Sequential 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 333 model variations for sequential
  parameter adaptation
• Better convergence
• Interpolation gives better model due to
  continuous adaptation of the model parameter
  value over the whole assumed range

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Parameter Interpolation - Calculations
For each iteration, the squared error is computed
for every model I each scan Ei(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) ?Ni1
(?klEi(t)) ?1 if parameter value pkl is used
in the model, otherwise is 0
For an adaptation cycle of M scans sumEpkl
?Mt1 (Epkl(t)), Fkl 1/sumEpkl pk(a)
pk1fk1pklfklpknfkn fkl Fkl / sumFk
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Simple Example Pure Gain Process
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First Order Plus Dead Time ProcessModel
Parameter Interpolation

• For 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

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First Order Plus Dead Time Process Model
Parameter Interpolation
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Model Verification

• Final stage of model adaptation and verification
  showing actual response and response calculated
  by the identified models.

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Outline

• Introduction
• Adaptive Technique Background
• Adaptive PID Design
• Application Examples
• Field Test Results
• Adaptive Control Demos
• Actuator Diagnostics
• Summary

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DeltaV Adaptive ControlOperational Features

• 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

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DeltaV Adaptive ControlOperational Features

• 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.

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Adaptive Control - Internal Structure
Control
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Defining 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.

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Configuration 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 values for model, limits, and time to
  steady state are automatically defaulted based on
  block tuning.

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Adaptive 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.

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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
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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

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Feedforward Adaptation Adapt 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 Feedforward Adaptive operation mode
5. Select Gain FF Factor

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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 input
Range 1
State parameter
Range 2
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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!
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Outline

• Introduction
• Adaptive Technique Background
• Adaptive PID Design
• Application Examples
• Field Test Results
• Adaptive Control Demos
• Actuator Diagnostics
• Summary

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Simple Example Non-Linear Installed
Characteristics

• 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
  if ranges are applied

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Example Throughput Dependent Process

• The process deadtime for superheater outlet
  temperature control changes as a function of
  steam flow rate
• Steam flow rate is used as the state parameter

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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.

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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.

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Outline

• Introduction
• Adaptive Technique Background
• Adaptive PID Design
• Application Examples
• Field Test Results
• Adaptive Control Demos
• Actuator Diagnostics
• Summary

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Adaptive Field Test

• Emerson Process Management - Process Systems and
  Solutions Lab
• University of Texas reactive distillation column
• Eastman Chemical
• Solutia

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DeltaV Adaptive ControlField Trials Eastman
Chemical

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

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DeltaV Adaptive ControlField Trials Solutia,
Pensacola, FL
HMD (Base)
65TC685
Acid feed from centrifuge splitter
65LC682
65TC684
65TC688
Cooling Tower Water
Cooling Tower Water
65AC681 (pH)
Strike Kettle Process and Instrumentation
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DeltaV Adaptive ControlField Trials Solutia,
Pensacola, FL

• Kettle control regular PID control

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DeltaV Adaptive Control Field Trials Solutia,
Pensacola, FL

• Kettle control Adapt control

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

• Flow loop

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

• Flow loop

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Outline

• Introduction
• Adaptive Technique Background
• Adaptive PID Design
• Application Examples
• Field Test Results
• Adaptive Control Demos
• Actuator Diagnostics
• Summary

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Simple Reactor Demo - Adaptation Demo with model
scheduling

Adaptation with process state parameter defined
for two ranges
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Simple Reactor Demo - Feedforward Adaptive
Control

• Feedforward dynamic model is adapted
• Feedforward controller is automatically updated
• Up to 5 ranges can be defined for feedforward
  adaptation and model scheduling

Inlet temperature as feedforward input
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Adaptive pH Control for Fun and Profit

• The 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.

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Top Ten Signs of a Rough pH Startup

• Food is burning in the operators kitchen
• The only loop mode configured is manual
• An operator puts his fist through the screen
• You trip over a pile of used pH electrodes
• The technicians ask what is a positioner?
• The technicians stick electrodes up your nose
• The environmental engineer is wearing a mask
• The plant manager leaves the country
• Lawyers pull the plugs on the consoles
• Bob and Bob are on the phone holding for you

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Tremendous Rangeability and Sensitivity of pH
Creates Exceptional Control Opportunities
pH Hydrogen Ion Concentration Hydroxyl Ion
Concentration 0 1.0 0.00000000000001 1 0.1
0.0000000000001 2 0.01 0.000000000001 3 0.00
1 0.00000000001 4 0.0001 0.0000000001 5 0.
00001 0.000000001 6 0.000001 0.00000001 7
0.0000001 0.0000001 8 0.00000001 0.000001 9
0.000000001 0.00001 10 0.0000000001 0.0001 11
0.00000000001 0.001 12 0.000000000001 0.01
13 0.0000000000001 0.1 14 0.00000000000001 1
.0
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Severe Strong Acid - Strong Base Nonlinearity
(Gain Changes by factor of 10 for each pH unit)
Zoom in on 3 to 10 pH
Entire Operating Range
There are no straight lines in pH - graphical
deception is common
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Weak Acid and Base - Moderated Nonlinearity(Gain
changes by factor of 50 from 9 to 7 pH)
Optimum set point For acidic influent
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Model and Tuning Settings are Scheduled Based on
What is Learned in Operating Regions
Model and tuning is scheduled based on pH
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User Sees Adapted Model Parameters and Chooses
Tuning Method
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Opportunity in pH is Huge When Moving to a
Flatter Portion of Titration Curve
pH
Reagent to Feed Flow Ratio
Reagent Savings
Optimum set point
Original set point
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Adaptive Control Achieves Optimum Set Point more
Efficiently
total cost of excess reagent
pH
hourly cost of excess reagent
total cost of excess reagent
pH
hourly cost of excess reagent
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Adaptive Control Recovers from Upsets more
Effectively
total cost of excess
hourly cost of excess
pH
total cost of excess
hourly cost of excess
pH
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Adaptive Control Returns to Old Set Points with
Less Oscillation
pH
pH
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Component Balance and Online Process Performance
Indicator are Embedded in DeltaV
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Charge Balance is Done in Excel Spreadsheet
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Advantages of DeltaV Adaptive pH Control

• Anticipates nonlinearity by recognizing old
  territory
• Model and tuning settings are scheduled per
  operating region
• Remembers what it has learned for preemptive
  correction
• Demonstrates efficiency improvement during
  testing
• Steps can be in direction of optimum set point
• Excess reagent useage rate and total cost can be
  displayed online
• Achieves optimum set point more efficiently
• Rapid approach to set point in new operating
  region
• Recovers from upsets more effectively
• Faster correction to prevent violation
• More efficient recovery when driven towards
  constraint
• Returns to old set points with less oscillation
• Faster and smoother return with less overshoot

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3rd Edition Features Online pH Estimators
andAdaptive Control
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Outline

• Introduction
• Adaptive Technique Background
• Adaptive PID Design
• Application Examples
• Field Test Results
• Adaptive Control Demos
• Actuator Diagnostics
• Summary

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Components of the self-diagnosed adaptive control
loop

Performance -Variablity, Standard Deviation
Final Element/Valve Hysteresis, Stickiness
Diagnostic Routine
Loop Adaptation Model quality
Loop Stability Monitor Oscillations Index
Corrective Action, Alarm or Message
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Loop performance

• DeltaV PID loop has two performance indexes as
  normal loop parameters
• Variability Index
• Standard deviation
• DeltaV Inspect application allows easy setting
  and review of the loop performance in the system
• The indexes will be used as a part of diagnostic
  information of the Adaptive loop

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Model Quality

• Final 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

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Loop stability

• Oscillation index
• Loop oscillation amplitude
• Oscillation period
• The highest priority loop diagnostic parameter

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Valve diagnostics

• Calculation of the valve parameters
• Valve backlash
• Valve stickiness
• Valve hysteresis
• Two complementary techniques are used
• Loop oscillation analysis
• Use of valve stem position (BKCAL)

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Valve diagnostics based on oscillation analysis
OUT oscillations caused by valve backlash and
stickiness
VP
PV
SP
PID
AO
Process
OUT
PV oscillations caused by valve stickiness
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Valve backlash causes oscillations on the
controller output
OUT
PV
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Valve backlash and stickiness cause oscillations
on the controller and process output
OUT
PV
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Valve diagnostics based on oscillation analysis
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Valve diagnostics based on known valve stem
position
Parameter representing valve stem position
BKCAL
PV
PID
AO
Process
OUT
VP
SP
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Hysteresis calculation based on known valve stem
position
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Backlash calculation based on known valve stem
position
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Adaptive Loop self-diagnostics overview window
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Valve diagnostics summary

• Self 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

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Summary

• Adaptive Control technique with model parameter
  interpolation is an unique, theoretically sound
  and practically proven technology
• DeltaV 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

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How to take advantage of Adaptive Control in
your plant soon

• Identify difficult to tune loops
• Identify loops you want to improve operation
• Contact John.Caldwell_at_EmersonProcess.com
• Terry.Blevins_at_EmersonProcess.com
  
• Become an adaptive control Beta installation

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DeltaV Product Manager

• John Caldwell