Process modeling - Home pages of ESAT

Report
Industrial Process Modelling and
Control
Ton Backx
Emeritaatsviering Joos Vandewalle
Outline
•
•
•
•
•
•
History
Process performance and process control
Model predictive control essentials
Process modeling
Current developments
Future perspective
Emeritaatsviering Joos Vandewalle
4 september 2013
Page 1
Model Predictive Control History
Early developments of Model Predictive Control (MPC)
technology were initiated by two pioneers:
• Dr. Jacques Richalet (Adersa, 1976)
- ‘Model Predictive Heuristic Control’ (MPHC) using IDCOM
as the MPC software for process identification
(IDentification) and for control (COMmand)
- Use of Finite Impulse Response (FIR) models
- Control inputs computed by minimization of a finite horizon
quadratic objective function without consideration of
constraints
- Plant output behavior specified by reference trajectories
Emeritaatsviering Joos Vandewalle
4 september 2013
Page 2
Model Predictive Control History (cont’d)
• Dr. Charles Cutler (Shell Oil, 1979)
- ‘Dynamic Matrix Control’ (DMC)
- Use of Finite Step Response (FSR) model
- Linear objective function subject to linear inequality
constraints using a finite prediction horizon (LP)
- Plant output behavior specified by setpoints
- Optimum inputs calculated by solving a Linear
Programming problem
Emeritaatsviering Joos Vandewalle
4 september 2013
Page 3
Process performance and process control
disturbances
manipulated
variables
Process
controlled
variables
Process performance is governed by:
• Critical process and product variables –”Controlled
Variables”- need to meet specifications
• During startup, shut-down and product changeovers offspec products are produced
- Need for minimization of transition losses
• During production disturbances cause variations in
critical variables
- Need for disturbance rejection
Emeritaatsviering Joos Vandewalle
4 september 2013
Page 4
Process performance and process control
Model predictive control is the supervisory control layer that
enables process optimization by minimization of production
costs ensuring product specifications and production
quantities
Operating
information
– optimum operating conditions are determined by
an optimizer (setpoints, set ranges, priorities and
weights, operating constraints)
– the model predictive control system realizes
targets set by the optimizer
Costs and
Specifications
Optimizer
Operating
information
Targets (setpoints,
setranges, …)
MPC
Process
values
setpoints
PID
Process
Emeritaatsviering Joos Vandewalle
4 september 2013
Page 5
Process performance and process control
Visualization of benefit realization by MPC
Measured process signal
21
probability density function
Cpk
0.96
Cpk == 0.96
Cpk = 4.3
1.6
20.8
20.6
20.4
Standard
Control
Model
Predictive
Predictive
Control
Controlwith
performance
without
optimization
optimization
value
20.2
Economic
benefit
20
19.8
19.6
19.4
000
22
0.5
414
66
1.5
828
10
10
2.5
12
12
3
19.2
19
0
0.2
0.4
0.6
0.8
1.2
1.4
4 september 2013
1.6
1.8
2
4
time
probability density
Emeritaatsviering Joos Vandewalle
1
x 10
Page 6
Model predictive control essentials
MPC strength is based on the explicit use of (a) (set of)
model(s):
•
to predict future process output
behavior
•
to determine the best
future input
manipulations to drive
the process to optimum
conditions
manipulated
variables
Operating
Constraints
measured
disturbances
•
to feedforward
compensate disturbances
•
to respect operating
constraints and to
determine optimum
conditions
•
disturbances
To handle non-linearities
controlled
variables
Unit
Process
f
g
Disturbance
Model
Setpoints
Set ranges
Controller
Optimization
and
constraint handling
Model Predictive Controller
4 september 2013
7
Process
Process
Model
Process
Model
Model
-
+
Model predictive control essentials
Past control manipulations
Future control manipulations
Control horizon
Time (t)
Predicted future process responses
Setpoint value
Past process responses
Prediction horizon
Dead
time
Past
Emeritaatsviering Joos Vandewalle
Output horizon applied for optimization
Present moment
4 september 2013
Time (t)
Future
Page 8
Model predictive control essentials
Linear models are used to calculate the responses to past
and future process input manipulations and similarly to
predict future responses to known disturbances
Past
Future
Y f (t , N f )  Y fp (t , N f , N p )  Y ff (t , N f , N c )
 H ( N f , N p )U p (t , N p )  T ( N f , N c )U f (t , N c )
Past
Future
In this expression:
• Yfp denotes the part of the future outputs stemming from past input
manipulations Cannot be influenced any more
• Yff denotes the part of the future outputs resulting from future input
manipulations Still to be determined by future inputs
Emeritaatsviering Joos Vandewalle
4 september 2013
Page 9
Process modeling
Process application example
Emeritaatsviering Joos Vandewalle
4 september 2013
Page 10
Process modeling
Laboratory
experiment
design and
optimisation
Process
flowsheeting
Detailed design and
optimisation of
process equipment
Model-based automation
applications for decision support
Operator
training
Model Predictive control
DESIGN
Equipment
performance
monitoring
Process
Health
monitoring
CONCEPT DESIGN OPERATION
EVO LVI N G MASTE R M O D EL
New process
design
Detailed design
of complex
units
Pn + M 
Pn+1
….
Emeritaatsviering Joos Vandewalle
Simultaneous
equipment
and control
design and
optimisation
Design of optimal
operating procedures
4 september 2013
Page 11
Troubleshooting
with detailed
predictive
models
Process modeling
System identification is the modeling technique applied in
industry for sufficiently accurate modeling of the relevant
process dynamics for MPC
• Data driven modeling
- Model set: Non-parametric, semi-parametric, parametric
- Model structure
- Parameter estimation criterion: Output error, equation error,
input error
Emeritaatsviering Joos Vandewalle
4 september 2013
Page 12
Process modeling
Required capabilities of models
1. Accuracy
on-line assessment of model validity
2. Adaptability
flexible on-line updating of models (dynamics and
interconnection structure)
3. Active data-driven learning
demands on accuracy, autonomy, robustness
 active probing for information
Emeritaatsviering Joos Vandewalle
4 september 2013
Page 13
Process modeling
Example of current limitations:
• MPC projects in industry highly depend on accurate
plant models and well-tuned controllers
• Controllers and models are verified (identified)
during commissioning
• When during operation process behavior changes:
MPC’s are switched to “manual”
• Loss of performance
• Expensive experimental campaign to re-identify the
models is the only way out
Emeritaatsviering Joos Vandewalle
4 september 2013
Page 14
Process modeling
Back to the core of the problem of data-driven modeling /
identification of Linear Time Invariant (LTI) models
v
v
u
G
+
r
+
- u
G
y
C
Emeritaatsviering Joos Vandewalle
4 september 2013
Page 15
+
y
Process modeling
The classical identification problems:
open loop
closed loop
v
u
G
+
y
r
+
- u
G
v
+
y
C
Identify a plant model
(and possibly r)
on the basis of measured signals u, y
• Several classical methods available (Prediction Error, subspace,
Output Error, non-parametric,..)
• Well known results for identification in known structure
(open loop, closed-loop, possibly known controller)
Emeritaatsviering Joos Vandewalle
4 september 2013
Page 16
Current developments
Next step in the development:
• Bring plant operation / automation to higher level
of autonomy
• Monitor plant performance and detect changes
on-line
• Generate probing signals when necessary and
based on economic considerations
(least costly experiments)
• Re-identify models and retune controllers on-line
• Keep high performance control
• Use economic performance criteria
Autonomous economic model-based operation
of industrial process systems
Emeritaatsviering Joos Vandewalle
4 september 2013
Page 17
Thank you for your attention
Emeritaatsviering Joos Vandewalle
4 september 2013
Page 18

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