Utilizing DeltaV Adaptive Control
Presenters
• Peter Wojsznis
• Gregory McMillan
• Willy Wojsznis
• Terry Blevins
Outline
• Introduction
• Adaptive Technique Background
• Adaptive PID Design
• Application Examples
• Field Test Results
• Adaptive Control Demos
• Actuator Diagnostics
• Summary
Control Loop Performance
A Never Ending Cycle
Evaluate
Test
Degradation The more often you Tune,
the better the performance.Calculate
Operate
Deploy
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
There 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?
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
DeltaV Adapt Applicable to Most Control
Loops Today
DeltaV Adaptive Control
90%
Difficult Dynamics (MPC)
5%
Multi-variable (MPC)
5%
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
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
Poor
Poor
Poor
Excellent
N
N
N
Y
Technology
Robustness Unmeasured Disturbance
Equipment Diagnostics
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.
Patents Have Been Awarded!
Dr. Wilhelm
Wojsznis
Mr. Terry
Blevins
Solid theoretical background
1999 - Dr. Seborg started working on
formal proofs of convergence for us along
with his Emerson funded grad student
Outline
• Introduction
• Adaptive Technique Background
• Adaptive PID Design
• Application Examples
• Field Test Results
• Adaptive Control Demos
• Actuator Diagnostics
• Summary
Adaptive PID Techniques
Adaptive Tuning
Techniques
Process Variables
Evaluation
PID Terms
Evaluation
Recursive
Recursive
Controller
Switching
Discrete Fourier
Model Switching
Model Free vs Model Based Adaptation
Model Free
No model
- Controller parameters
are adapted
Safety net:
- Acceptable range of
controller parameters
- Oscillation monitor
Model Based
Model (parametric)
- Model parameters are adapted
- Controller is designed from the
adapted model - any controller can
be used
Safety net:
- Model validation based on data
- Acceptable range of model
parameters
- Oscillation monitor
Model Switching Adaptation
•Use N-models working in parallel
•Evaluate model error
•Select model with minimal error
•Shortcoming: TOO MANY MODELS
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
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 3+3+3
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
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) = ∑Ni=1 (γklEi(t))
γ=1 if parameter value pkl is used in
the model, otherwise is 0
For an adaptation cycle of M scans
sumEpkl = ∑Mt=1 (Epkl(t)),
Fkl = 1/sumEpkl
pk(a) = pk1fk1+…+pklfkl+…+pknfkn
fkl = Fkl / sumFk
Simple Example – Pure Gain Process
Multiple Model
Interpolation with
re-centering
Estimated
Gain
K
Changing
process input
Iteration
Pure gain Process
Initial Model
Gain = G1
G1-Δ
G1
G1+Δ
1
G2-Δ
G2
G2+Δ
2
G3-Δ
3
G3 G3+Δ
Multiple
iterations
per
adaptation
cycle
Multiple Model
parameter
Interpolation with
re-centering
Estimated
Gain, time
constant, and
deadtime
Ke TD
1  s
Changing
process input
Time Constant
First Order Plus Dead Time Process
Model Parameter Interpolation
First Order Plus
Deadtime Process
• For a first order plus deadtime process, only
nine (9) models are evaluated each subiteration, first gain is determined, then deadtime,
and last time constant.
• After each iteration, the bank of models is recentered using the new gain, time constant, and
deadtime
1
2
3
First Order Plus Dead Time Process Model
Parameter Interpolation
Time Constant 3rd
Gain 1st
G, DT, TC+∆
G+∆, DT, TC
1
Initial model
G, DT -∆, TC
12
1
Dead time 2nd
G,DT, TC
3
G, DT +∆, TC
Adapted model
True process model
G-∆, DT, TC
G, DT, TC-∆
Model Verification
Final stage of model adaptation and verification showing
actual response and response calculated by the identified
models.
Outline
• Introduction
• Adaptive Technique Background
• Adaptive PID Design
• Application Examples
• Field Test Results
• Adaptive Control Demos
• Actuator Diagnostics
• Summary
DeltaV Adaptive Control
Operational 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
DeltaV Adaptive Control
Operational 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.
Adaptive Control - Internal Structure
Adaptive Control Block
Excitation
Generato
r
Superviso
r
Controller
Redesign
Model Par.
Interpolatio
n
SP
PID Controller
w/Dyn Comp
Feedforward
Model
Evaluatio
n
Set of
Models
Process
Manipulate
Measured
Disturbance
Control
Model Parameters
Defining Operating Regions
Region
2
Region
1
•
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
Region
1
Region
2
Region
3
Region
4
State Parameter Value
Region
5
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.
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.
Adaptive Control Operation
Adapt Application
1. Select Window
2. Observe loop plots
(PV, OUT, SP)
3. Observe Adaptation
Status
4. Operate PID loop –
SP, OUT, Mode
Feedback 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 Adaptive
operation mode
5. Select tuning rules
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
Multi-range adaptation
Range 1
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
State parameter
Range 2
Adaptive Control Setup
Adapt 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!
Outline
• Introduction
• Adaptive Technique Background
• Adaptive PID Design
• Application Examples
• Field Test Results
• Adaptive Control Demos
• Actuator Diagnostics
• Summary
Simple Example
Non-Linear Installed Characteristics
FC
3-5
FT
3-5
Bottoms
• 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
Example
Throughput Dependent Process
6
5
4
3
2
1
0
• 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
80
90
DT Multiplier
60
70
FT
1- 3
40
50
TT
1- 2
20
30
Process
deadtime
changes
with
DT Multiplier
0
10
TT
1- 1
TC
AC
1 -2
1
Th
ro
ug
hp
ut
TC
AC
1 -1
Example
Multiple Valves - Split Range
FC
TC
1 -2
FY
1-2
•
TT
1 -2
Heater
Cooler
•
100
Cooling
Valve
Heating
Valve
0
0
50
Controller Output (%)
100
•
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.
Example
Column Temperature Control
Operating
Point
Distillate
Receiver
Temperature
Measurement Error
Tray 10
Measurement Error
Tray 6
PT
3-1
Reflux
Thermocouple
Tray 6
TE
3-2
TC
3-2
Distillate Flow
Feed Flow
Column
LC
3-2
LT
3-2
• The sensitivity of tray temperature to
changes in distillate to feed ratio is highly
non-linear.
• Tray temperature is used as the state
parameter.
Outline
• Introduction
• Adaptive Technique Background
• Adaptive PID Design
• Application Examples
• Field Test Results
• Adaptive Control Demos
• Actuator Diagnostics
• Summary
Adaptive Field Test
• Emerson Process Management - Process
Systems and Solutions Lab
• University of Texas reactive distillation column
• Eastman Chemical
• Solutia
DeltaV Adaptive Control
Field Trials –Eastman Chemical
Control automatically adapts based on SP
changes in Auto – Caustic loop
DeltaV Adaptive Control
Field Trials – Solutia, Pensacola, FL
HMD
(Base)
65TC685
TT
Acid feed from
centrifuge splitter
65LC682
LC
LT
65TC684 TT
TT
65TC688
TT
Cooling
Tower
65AC681 AC Water
(pH)
AT
FT
FC
Strike Kettle Process and Instrumentation
TC
Cooling
Tower
Water
TC
DeltaV Adaptive Control
Field Trials – Solutia, Pensacola, FL
Kettle control – regular PID control
DeltaV Adaptive Control
Field Trials – Solutia, Pensacola, FL
Kettle control – Adapt control
DeltaV Adaptive Control - Field Trials J.J.Pickle Research Campus, UT, Austin, TX
Flow loop
DeltaV Adaptive Control - Field Trials J.J.Pickle Research Campus, UT, Austin, TX
Flow loop
Outline
• Introduction
• Adaptive Technique Background
• Adaptive PID Design
• Application Examples
• Field Test Results
• Adaptive Control Demos
• Actuator Diagnostics
• Summary
Simple Reactor Demo Adaptation Demo with model scheduling
Adaptation with
process state
parameter
defined for two
ranges
Simple Reactor Demo Feedforward Adaptive Control
1. Feedforward
dynamic
model is
adapted
2. Feedforward
controller is
automatically
updated
3. Up to 5 ranges
can be defined
for
feedforward
adaptation and
model
scheduling
Inlet temperature
as feedforward
input
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%.
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
Tremendous Rangeability and Sensitivity of
pH Creates Exceptional Control Opportunities
pH
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Hydrogen Ion Concentration
1.0
0.1
0.01
0.001
0.0001
0.00001
0.000001
0.0000001
0.00000001
0.000000001
0.0000000001
0.00000000001
0.000000000001
0.0000000000001
0.00000000000001
Hydroxyl Ion Concentration
0.00000000000001
0.0000000000001
0.000000000001
0.00000000001
0.0000000001
0.000000001
0.00000001
0.0000001
0.000001
0.00001
0.0001
0.001
0.01
0.1
1.0
Severe Strong Acid - Strong Base Nonlinearity (Gain
Changes by factor of 10 for each pH unit)
Entire Operating Range
Zoom in on 3 to 10 pH
There are no straight lines in pH - graphical deception is common
Weak Acid and Base - Moderated Nonlinearity
(Gain changes by factor of 50 from 9 to 7 pH)
Optimum set point
For acidic influent
Model and Tuning Settings are Scheduled Based on
What is Learned in Operating Regions
Model and tuning is scheduled based on pH
User Sees Adapted Model Parameters and
Chooses Tuning Method
Opportunity in pH is Huge When Moving to a
Flatter Portion of Titration Curve
pH
Optimum
set point
Reagent
Savings
Reagent to Feed
Flow Ratio
Original
set point
Adaptive Control Achieves Optimum
Set Point more Efficiently
pH
total cost of
excess reagent
hourly cost of
excess reagent
pH
hourly cost of
excess reagent
total cost of
excess reagent
Adaptive Control Recovers from
Upsets more Effectively
hourly cost
pH of excess
pH hourly cost
of excess
total cost
of excess
total cost
of excess
Adaptive Control Returns to Old
Set Points with Less Oscillation
pH
pH
Component Balance and Online Process
Performance Indicator are Embedded in DeltaV
Charge Balance is Done in Excel Spreadsheet
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
3rd Edition Features Online pH Estimators and
Adaptive Control
Outline
• Introduction
• Adaptive Technique Background
• Adaptive PID Design
• Application Examples
• Field Test Results
• Adaptive Control Demos
• Actuator Diagnostics
• Summary
Components of the self-diagnosed adaptive
control loop
Performance Variablity,
Standard Deviation
Final
Element/Valve –
Hysteresis,
Stickiness
Diagnostic
Routine
Loop Adaptation –
Model quality
Corrective Action, Alarm or Message
Loop Stability
Monitor –
Oscillations Index
Loop performance
DeltaV PID loop has two performance indexes as
normal loop parameters:
1. Variability Index
2. 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
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
Loop stability
Oscillation index:
1. Loop oscillation amplitude
2. Oscillation period
The highest priority loop diagnostic parameter
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)
Valve diagnostics based on oscillation
analysis
OUT oscillations caused by valve
backlash and stickiness
SP
VP
OUT
PID
AO
PV
Process
PV oscillations caused by valve
stickiness
Valve backlash causes oscillations on
the controller output
OUT
PV
Valve backlash and stickiness cause oscillations
on the controller and process output
OUT
PV
Valve diagnostics based on oscillation
analysis
1. Define oscillation amplitude on the controller output - Ampl  OUT 
2. Define oscillation amplitude on the controller input - Ampl  PV 
3. Calculate hysteresis as h  2 Ampl  OUT 
4. Knowing process gain K p , calculate valve stickness (resolution) as:
2 Ampl ( PV )
2 Ampl ( PV )  K p

Kp
5. Calculate backlash as b  h  
Valve diagnostics based on known
valve stem position
Parameter representing valve stem position
BKCAL
SP
OUT
PID
PV
VP
AO
Process
Hysteresis calculation based on known
valve stem position
i (t )  abs OUT (t  i )  BKCAL _ IN (t )
(1)
Selection of the highest values of  i (t ) averaged over certain period of time may be considered as
actuator backlash and resolution estimate.
(2)
h  b    avg(max i ) maximum values selection
Where
b – valve backlash
h - valve hysteresis
 - valve resolution
i - back calculation signal delay in scans, accounting for valve speed of response (velocity limit)
Backlash calculation based on known
valve stem position
If OUT (t  i )* BKCAL _ IN (t )  0
(1)
Then b  avg(max i )
(2)
The resolution then can be easy found as:
 h-b
(3)
where
OUT (t  i )  OUT (t  i )  OUT (t  i  1)
(4)
BKCAL _ IN (t )  BKCAL _ IN (t )  BKCAL _ IN (t  1)
(5)
Adaptive Loop self-diagnostics
overview window
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
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
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@EmersonProcess.com
Terry.Blevins@EmersonProcess.com
• Become an adaptive control Beta installation
DeltaV Product Manager
John Caldwell