Industrial Process Modelling and Control Ton Backx Emeritaatsviering Joos Vandewalle.

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 Page 24 september 2013

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


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


Process performance and process control

Process performance is governed by:• Critical process and product variables –”Controlled

Variables”- need to meet specifications• During startup, shut-down and product changeovers off-

spec products are produced Need for minimization of transition losses

• During production disturbances cause variations in critical variables Need for disturbance rejection


manipulatedvariables

disturbances

controlledvariablesProcess



Model predictive control is the supervisory control layer that enables process optimization by minimization of production costs ensuring product specifications and production quantities

– the model predictive control system realizes targets set by the optimizer MPC

Targets (setpoints,setranges, …)

Operatinginformation

Process

setpointsProcessvalues

PID

– optimum operating conditions are determined by an optimizer (setpoints, set ranges, priorities and weights, operating constraints)

Optimizer

Costs andSpecifications

Targets (setpoints,setranges, …)



0

0.51

1.52

2.53

prob

abili

ty d

ensi

ty fu

nctio

n

probability density

Cpk

= 0

.96

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

x 104

19

19.2

19.4

19.6

19.8

20

20.2

20.4

20.6

20.8

21Measured process signal

time

valu

e


Visualization of benefit realization by MPC

0246810

12

Cpk

= 0

.96

Cpk

= 4

.3

0246810

12

Cpk

= 0

.96

Cpk

= 1

.6

Economicbenefit

Standard ControlModel Predictive Control without optimization

Model Predictive Control with performance optimization

4 september 2013Emeritaatsviering Joos Vandewalle Page 7

• to predict future process output behavior

• to determine the best future input manipulations to drive the process to optimum conditions

Model Predictive Controller

SetpointsSet ranges

Controller

DisturbanceModel

measureddisturbances

OperatingConstraints

Optimizationand

constraint handling

Model predictive control essentials

MPC strength is based on the explicit use of (a) (set of) model(s):

• to feedforward compensate disturbances

• to respect operating constraints and to determine optimum conditions

• To handle non-linearities

UnitProcess

manipulatedvariables

disturbances

controlledvariables

ProcessModel

ProcessModel

f g

+-ProcessModel

84 september 2013



Time (t)

Time (t)

Past Future

Deadtime

Prediction horizon

Predicted future process responses

Setpoint valuePast process responses

Past control manipulations Future control manipulations

Control horizon

Output horizon applied for optimization

Present moment


Linear models are used to calculate the responses to past and future process input manipulations and similarly to predict future responses to known disturbances


),(),(),(),(

),,(),,(),(

cfcfpppf

cfffpffpff

NtUNNTNtUNNH

NNtYNNtYNtY

In this expression:• Yfp denotes the part of the future outputs stemming from past input

manipulations• Yff denotes the part of the future outputs resulting from future input

manipulations

Past

Cannot be influenced any more

Past

Future

Still to be determined by future inputs

Future


Process modeling

Process application example

Process modeling


CONCEPT OPERATIONDESIGN

Detailed design and optimisation of process equipment

Model-based automation applications for decision support

Troubleshooting with detailed

predictivemodels

Process flowsheeting

Detailed design of complex units

Design of optimal operating procedures

Simultaneous equipmentand control design and

optimisation

E V O L V I N G M A S T E R M O D E L

Laboratory experiment design and optimisation

Operator training

DESIGN

Model Predictive control

Equipment performance monitoring

Process Health monitoring

New process design

Pn + M Pn+1….

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



Process modeling

Required capabilities of models

1. Accuracyon-line assessment of model validity

2. Adaptabilityflexible on-line updating of models (dynamics and interconnection structure)

3. Active data-driven learningdemands on accuracy, autonomy, robustness

active probing for information


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

Process modeling

G +

v

u y


Back to the core of the problem of data-driven modeling / identification of Linear Time Invariant (LTI) models

+ G +

v

C

- u yr


Process modeling

The classical identification problems:

G +

v

u y

open loop closed loop

Identify a plant model on the basis of measured signals u, y (and possibly r)

• 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)

+ G +

v

C

- u yr


Current developments

Next step in the development:

Autonomous economic model-based operation of industrial process systems

• 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

Thank you for your attention


Industrial Process Modelling and Control Ton Backx Emeritaatsviering Joos Vandewalle.

Documents

process optimization

variables process slide

critical process

supervisory control

future process output

industrial process modelling

performance optimization

optimizer setpoints