A personal view on MPC
Prof. Cesar de Prada
University of Valladolid
Spain
From Minimum variance to
MBPC
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Predictions were computed
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How to go further away?
Model Based Predictive Control
MBPC
Robin de
Keyser,
EPSAC
David Clarke
GPC
Richalet
Prett, Gillete,
Cutler,
Ramaker
ESPRIT EU Program CIDIC
(Computer Integrated Design of Industrial
Control), a Tematic Interest group of CIM-
Europe CIDIC
CIM-Europe, ESPRIT WG 9815
1990/97
CIDIC course in Madrid 1992
H. Eder, Exxon Chemical,(B)
N. Jensen, T. University of Denmark (DE)
J. Richalet, Adersa, (F)
S. Abu el Ata, Adersa, (F)
R. de Keyser, University of Ghent, (B)
D. Clarke, University of Oxford (UK)
E. Mosca, University of Florence (I)
R. de Vries, University of Delft (NL)
J. S. Anderson, ICI, (UK)
10.45 Practical exercises on real processes
In parallel sessions:
* Servo motor.
R. de Keyser, University of Ghent, (B)
CIDIC Ghent 1991
ADAPTIVE PREDICTIVE CONTROL OF A PULP DRYER
C. Prada, P. Vega
Department of System Engineering and Automatic Control.
Faculty of Sciences, University of Valladolid, Spain.
INTRODUCTION
In this paper we describe the implementation of an adaptive
predictive controller in an industrial process: a pulp dryer
of a beet sugar factory in Valladolid, Spain.
EPSAC, NL-EPSAC
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Network
Institution Country Name University of Ghent Automatic Control Lab.
Ghent Belgium
R. De Keyser
ADERSA Paris France
J. Richalet
University of Oxford, Engineering Science Dpt.
Oxford United Kingdom
D. Clark
Open University (UNED) Computing and Control Dpt.
Madrid Spain
S. Dormido
Aachen University of Technology, Automatic Control Institute
Aachen Germany
H. Rake
University of Valladolid, Systems Engineering and Automatic Control Dpt.
Valladolid Spain
C. de Prada
MBPC proposed research topics
a deeper understanding of the fundamental theory behind the algorithms is
needed. In particular we shall concentrate on the following aspects:
robustness with respect to modeling errors or disturbances
handling of control and state constrains
global stability analysis
design methods and tuning rules
non linear and multivariable processes
on-off control
Thematic Networks ALFA - UE
“Industrial Computing, RAPII", 1996
"Master en Industrial Control Systems“ 1998-
99
MECDAL “Mechatronics for the Latin
American development”, 2001
"Process and Systems Engineering“ 2004-06
Ghent 2005
Ice Exhibition
Erasmus interchange from 2000
"Identification and Control of
Complex Systems", ICCoS
Robin in Valladolid
IMC Performance, stability, Robustness, Implementation , Examples
Two worlds
Control applications (intelligent systems) are present in many products and places of daily life. (cars, buildings, domestic appliances, offices, ….)
These type of industries are looking for: – Easy to use and configure control systems
– Reliable systems easy to maintain
– Model based, but simple control solutions cheap to implement
– Low energy consumption
– Best performance is not the first requirement, but to improve the one of current systems
Design for millions of similar embedded device
Multiparametric MPC, PFC,..
Process Industry
More technology More complex
processes
More norms
and regulations
Reduced
technical staff Higher market
pressures
More data than ever
Applications in the
process industry
are often unique
and the
development costs
cannot be spread
among many
applications.
What is the situation of process
control?
Mature technology
Basic architectures and
functions have not
changed very much
The challenge is to be
able to deal with the
new problems at plant-
wide scale
Economic Planning
Production supervision and
optimization /RTO
Advanced Control /MPC
Basic Control and Instrumentation
ERP
MES / MOM
LP/ MPC
Instrumentation, SCADA / DCS / PLC
Manufacturing
Execution Systems
Enterprise
Resource
Planing
Software / Hardware
Enterprise view
Manufacturing
Operation
Management
Process plant
Control pyramid Academic view
Optimal plant-wide operation
Many opportunities:
Shared resources, utilities
Bottlenecks avoidance
Optimal energy use
Smooth transitions against
production changes
Scheduling
Model based operation optimization
Modelling and Simulation MPC and RTO
23
Steady state
detector
Data treatment
Gross errors
Modelling
Re-Design
Data
reconciliation
Basic control
MPC
Performance
KPI
• Many different tasks
and tools
• Lack of Integrated
design environments
• Maintenance:
Integration with
MES functions
• Education and
Training
RTO
Supervision, FDD
MPC MPC
Prices, aims
ERP/RTO/MPC Integration
Differences between the steady
state models in the RTO layer and
the ones in the MPC controllers
creates inconsistencies that lead to
suboptimal performance.
RTO/MPC take very limited
advantage of the time varying
environment (day/night, electricity
prices,…)
Diversity in the processes, aims,..
make difficult to formulate and
solve adequately the global RTO
problem 24
Planning
Little or no
feedback at all
Economic NMPC
25
Process
Basic Control
MPC with
economic target
Non- centralized
approaches
Economic NMPC offers a good
alternative for process units, but
centralized E-NMPC formulations
presents also important
challenges in large scale systems
regarding problem size, model
maintenance, human interface etc.
Focus on specific kind of
processes, incorporating the
knowledge available on them
Implementable architectures for
plant –wide optimal operation are
needed
Hierarchical/ distributed architecture
26
Continuous plant and batch units are the natural single pieces to build an
optimization strategy: this fits into the control room needs and allows for
distributed calculus
But the interactions among them, the global behaviour and the scheduling of
the batch units must be taken into account explicitly. This requires a
coordination layer that takes care of the overall behaviour
Scheduling, targets and constraints generation
EMPC EMPC EMPC
Continuous plant Batch
plant …
Targets,
constraints
rich syrup
sugar B
syrup
steam
sugar A
Tank B
molasses
Melter
massecuite
Malaxador
massecuite
Vacuum pans A Tacha B
Centrifugal separators
rich syrup
steam
A
poor syrup
Continuous – Batch
On/off decisions
Plant start/stop
Scheduling + continuous
operation
Tank park
Hybrid MPC
Incorporate uncertainty
Multi-stage stochastic
optimization
Control and Optimization
Measurements and
process excitation
are required
Model
Estimate the errors in the constraints
and in the gradients of the cost
and constraints
Incorporate the errors in the modified optimization problem
Modifier adaptation
Computational complexity
Distributed-Coordinated decision
making and optimization
CDU1
CDU2
HVU1BBUHVU1BBU
HVU2
BDU
HCU
HMU
HDS2
SplitHDT
PlatDePr
HDS1
Fuel
Gas
Fuel
Gas
GasOil
Blend
GasOil
Blend
Mogas
Blend
Mogas
Blend
FuelOil
Blend
FuelOil
Blend
RGRG
RGRGH2H2H2RGRG
H2H2H2
RGRG
H2H2H2
RGRG
H2H2H2
RGRG
Natural Gas
Syngas and Reformates
Condensates and Low
Sulphur Crudes
Condensates and Low
Sulphur Crudes
High Sulphur Crudes
Imported Residues
High Sulphur Crudes
Low Sulphur Crudes
Imported Residues
Naphtha
Kero
LGO
HGO
Long Residue
Naphtha
Kero
LGO
HGO
LGO
HGO
Long Residue
Sulphur
Refinery Gas
Regular Mogas
Premium Mogas
Kero / Avtur
CO2CO2
Automotive Gasoils
and Marine
Automotive Gasoils
and Marine
Light Fuel Oil
Bunkers
Heavy Fuel Oil
Refinery Fuel
Bitumen 80/100
Bitumen 180/200
Bitumen 45/55
Hierarchical
Distributed
Process 1 Process 2 Process n
Process 1 Process 2 Process n Process 1 Process 2 Process n
Controllers / Optimizers
Market
Prices
Price
coordination
Operation efficiency and
dynamic plant behaviour
depend on plant design
Choice of best process
estructure
Dimensioning of the
equipment
Flexibility
Integrated Design and Operation
Joint process + control design is very important
to facilitate the operation of integrated and
complex processes
Main problems:
Suitable dynamic operability indicators
Large scale and difficult mix-integer
optimization problems
Lack of systems engineering vision
ABCD
A|B
CD
AB
|CD
AB
C/D
B|C
DB
C|D
C|D
A|B
CA
B|C
B|C
A|B
Superestructures+
dynamics
Plant optimization
Integrated design
Scheduling
Simulation
…
Tools currently used for
designing and
implementation
New integrated, open and flexible
Optimization/Simulation tools
Management and
Engineering of CPSoS in the
process industry
Software tools
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Thank you for your
attention