A personal view on MPC

Prof. Cesar de Prada

University of Valladolid

Spain

From Minimum variance to

MBPC

)dt()q(A

)q(C)t(u

)q(A

)q(B)dt(y

)t()q(A

)q(C)t(u

)q(A

)q(Bq)t(y

1

1

1

1

1

1

1

1d

1-n degree qf...qff)q(F

1-d degree qd...qd1)q(D

F and Din equation diofantine )q(A

)q(Fq)q(D

)q(A

)q(C

1n

1n

1

10

1

1d

1d

1

1

1

1

1d1

1

1

2

1

1

1

112 )t(w)t(y

)q(C

)q(F)t(u

)q(C

)q(B)q(D)w)t|dt(y(E

delay d

Predictions were computed

at t+d

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

)t(u)q(A

)q(B)t(z

1

1

)t()q(D

)q(C)t(n

1

1

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

1,0

0),,u,x(g

0),,u,x,x(F

dt),,u,x(L))T(x(JminT

0,u

Thank you for your

attention