RaPID Model Based Optimizoitan an d Control - CiDiP · RaPID & Model Based Optimizoitan an d ... B-3001 Leuven, Belgium T: ... • Measurements on DCS, PLC, etc… • Transfer to

General Chemical industry Presentation Slide 1

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Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 83, F: +32 16 39 30 80, E:[email protected]

RaPID &

M odel Based Optimization and

Control


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Who are we?

IPCOS Technology


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Boxtel OfficeBosscheweg 145a5282 W V Boxtel Netherlands

Prof.dr.ir. Ton BackxTel: +31 411 613500 Fax: +31 411 616710

e-mail: [email protected]

URL: http//www.ipcos.nl

Staff: 8 people


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Leuven Office

Technologielaan 11/01013001 Heverlee-LeuvenBelgium

ir.Christiaan Moons

Tel: +32 16 39.30.87 Fax: +32 16 39.30.80

e-mail: [email protected]

URL: http//www.ipcos.be

Staff: 12 people


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IPCOS’ Mission for Advanced Process Control

To become one ofthe world leading suppliers for APC

• We offer world class APC products

• We offerallaffiliated services(consulting,feasibility studies,

implementation,training, maintenance) tosatisfy our customers

• We offer complete APC solutions for:

Power Production

Chemical Processing &refining

Glass Manufacturing

OilProduction


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PROJECT INVOLVEM ENT


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Projects Involvement IPCOSChemical Processing Industry

BASF, Fina, Monsanto, Hercules, Bayer, Dow Chemical, Janssen Pharma,Sasol, DSM,…

Oil refining and petro-chemical Industry

Shell, Fina, Statoil, BRC,Repsol, Petroplus,...Glass

GE, Samsung, DEMA, Philips, Schott, Thomson, TNO, ... Electrical Power

Power plants Electrabel, Laborelec, Co-generation, EDF,...

Information Technology

Philips, DuPont, ICI, KNP, ...

Others

Tioxide, Air Liquide, Hoogovens, Kortman Intradal, Unilever, Agfa

Gevaert,Barco, Clarck, LMS, ALZ Steel, Heraeus Electro-Nite, ...


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THE MARKET


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Pressure to improve margins (supply driven)– Global competition

– World-wide saturation of markets

– Tightening legislation on ecosphere load and energy consumption

Tight control of product quality– Meet product specifications with high yields under smooth process operating conditions

Strong need to increase flexibility for enabling demand driven operation of production– Produce primarily those products that have market demand

– Take price advantage of demand niches in a saturated market

Some Observations


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Optimal control generates benefits!


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What are the benefits ?

Improved quality control– reduction in the standard deviation of parameters– stabilized process operations

Increased throughput– reduction in process shutdowns

– longer run times– More instantaneous throughput

Improved process efficiency– less energy usage

Flexible operation

Typical project payback: 3-12 months


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time

Actuator Value

∆ = extratroughput

APC on

How?Higher troughput

APC off

100%

97%


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time

Quality

∆ = Profit

How?Reduced Energy/cost

APC off

APC onAPC off


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time

Process Value

Specification A

Specification B

Ideal Value

Ideal Value

transitionstart

∆ = Profit

controlledtransition

manualtransition

transitionend

transitionend

How?Controlled Transitionsin Automatic Mode


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PRODUCTS&

SERVICES


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Model Based Control & OptimizationBusiness Performance Improvement

Technology ProductDynamic model based trajectory optimisation

PathFinder

High Performance MPCTrajectory tracking MPC

INCA

Inferred Measurements Presto

Optimal Primary PID Controllers

RaPID

ProcessProcess

Process

Model Predictive

Control

Optimal Reference Signals

Model Predictive

Control

Model Predictive

Control

Plant-Wide Model Based

OptimizerOptimal Process

Conditions

DCSDCS

DCS

Primary Control Signals

Control hierarchy


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Process Performance is not as good as you think

• PID controllers at lowest level

• 90 % of the controllers are PID’s

• More than 30 % of PID’s operates in manual

• More than 30 % of loops increase short term variability

• About 25 % of loops use default settings

• About 30 % of loops have equipment problems

• APC not useful when PID’s are badly tuned


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• Model-based optimal PID-tuning•Engineering specifications

RaPIDRobust advanced PID control

HoneywellABBSIEMENSFisher & RosemountYokogawa, … … .


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Tuning methodologies

• Factory settings

• Settings “card”

• Manually by trial and error (ad hoc)

• Look-up tuners

• Auto-tuners & Self-tuners

• PID packages


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Why RaPID?

• Simple, step like experiments

• Graphical inspection models (2 or 3 parameters) are too simple

• Controller design:

• Simple, rule based

• No flexibility (specifications)

• Stability & robustness

Limitations of classical tuners:


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Why RaPID (ctd)

Solution with RaPID:• State of the art identification methods:

• Other inputs (shorter testing)

• Detailed information

• Efficient control design

• Optimisation based

• Engineering specifications

• Robust and stable

• Wide range of applications (slow and fast)


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TheRaPID Cycle

Modeling Linear model

Control Design PID valuesProperties

Open Loop Experiment Real ProcessSimulator

Controller ValidationReal ProcessSimulator


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Step 1: Open Loop Experiment

ProcessInput

ExcitationMeasured

Output


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Com m unication with the process• File Transfer

• Measurements on DCS, PLC, etc… • Transfer to RaPID

• Non-linear simulation model• Simulink (Matlab) model

• Real-Time communication with• I/O boards• Stand alone PID blocks


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Step 2: Process Modelling

ProcessInput

ExcitationMeasured

Output

Model

+

SimulatedOutput

Error

• Linear Models

• Simple first order and more complex models

+

-


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Process Modelling (ctd)• User friendly

• Limited number of choices

• Industrial acceptability

• A-priori knowledge

• Robustness (noise, non-linearities)

• Pre-processing:

• Integrator, filtering, detrending

• Open and closed loop

• Speed


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Model Parameterisation• Classical: polynomials

b z b z bz a z ann

nn

nn

n+ + ++ + +

−−

−−

11

0

11

0

....

Kzz z z zz p z p

delay n

n

− − −− −

( )..( )( )..( )

1

1

• RaPID: poles, zeros, delay

• A-priori knowledge: stability, resonant poles, non-min. phase zeros, DC gain, …


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Modelling Algorithm• Subspace identification

• Fast overview of multiple models

• Closed loop

• Sub-optimal

• Initial model

• Prediction error method

• A-priori knowledge

• Robust for noise and non-linearities

• Optimal


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Modelling (example)


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Step 3: Optimal PID Control

ModelPIDSP

IVPPV

Load Disturbance

• Optimal Model Based PID Parameters

• User specifications

• Robustness


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Optimal PID Control

1. Where will you implement the PID controller?

4 Relevant Questions:

• Often slightly different forms of PID controller

• RaPID Templates


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Optimal PID control (ctd)2. Which type of controller will you implement?

• P, PI, PID, PD etc…?

• Feedforward?


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Optimal PID Control (ctd)3. Which engineering criteria are important?

• Tracking or variance

• Rise time, settling time, overshoot, etc…

• Robustness, noise sensitivity


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Optimal PID Control (ctd)4. Which signals are involved?

• Load

• Set point

• Disturbances


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Engineering criteria: Noise

Noise Sensitivity: High Frequency Gain

ModelPID IVP

Noise

IVP = High Frequency Gain x Noise


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Engineering Criteria: Robustness

Robustness: Combined Gain and phase margin

ModelPIDSP

IVPPV

• Performance if model different from plant?

• High robustness: slow but “stable”

• Low robustness: fast but less “stable”


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PID Control (example)


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Step 4: Controller Validation

ProcessPIDSP

IVPPV

• Track performance on plant or simulator?

• Possibly re-iterate with “closed loop” data


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RaPID • Modules:

• Tuning• Templates• Drivers

• Matlab based• Eight Case studies• Manual, on-line help• RaPID course (twice a year)


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Case study• Reactor

• ReactionA B

Cooling water

Steam


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Controller configuration

SlavePID

MasterPID

SP T reactor

PV T reactor

PV T cool

SP T cool

IVP

• Slave PID optimised using RaPID

• Optimise Master PID


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Simulink Model


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Step 1: Open loop


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Resulting experiment


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Step 2: Modelling


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Resulting signals


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Resulting model


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Step 3: Controller Design


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Specifications and signals


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Final design


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Final controller


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Step 4: Validation


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• Batch Temperature

Normalized time

ProfileReactor

0 100 200 300 400 500 600 700 800 900 10000

0.5

1

Nor

mali

zed

oC

Normalized time

• Remote SetpointSlave Profile

Jacket

Condensor 0 100 200 300 400 500 600 700 800 900 10000

0.5

1

Nor

mali

zed

oC

• CoolwaterFlow

0 100 200 300 400 500 600 700 800 900 10000

0.5

1

Nor

mali

zed

m3 /h

r

Normalized time

Example: Original Controller


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• Batch Temperature

Normalized time

ProfileReactor

0 100 200 300 400 500 600 700 800 900 10000

0.5

1

Nor

mali

zed o

C

Normalized time

• Remote SetpointSlave Profile

Jacket

Condensor0 100 200 300 400 500 600 700 800 900 1000

0

0.5

1

Nor

mali

zed o

C

• CoolwaterFlow

Normalized time0 100 200 300 400 500 600 700 800 900 1000

0

0.5

1

Nor

mali

zed

m3 /hr

Example: OptimizedController


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Technology Product

Dynamic model based trajectory optimisation

PathFinder


INCA



RaPID

ProcessProcess

Process

Model Predictive Control




Plant-Wide Model Based Optimizer

Optimal Process Conditions

DCSDCS

DCS


Control hierarchy


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Softsensors(what?)

Classical

Concentrations,

Density, MI,

pH, NOx, CO2

€€€€€

Confidence level

On-line

Flows, Pressures, Temperatures

Soft Sensor


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Technology Product

Dynamic model based trajectory optimisation

PathFinder


INCA



RaPID

ProcessProcess

Process





Plant-Wide Model Based Optimizer

Optimal Process Conditions

DCSDCS

DCS


Control hierarchy


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INCAIPCOS NOVEL CONTROL

ARCHITECTURE• Fully WindowsNT based

• Multivariable control strategy

• Handles wide dynamic ranges

• Flexible model type to be used

• Hard constraints on Manipulated Variables (MV’s)

• Soft, prioritised constraints on Controlled Variables

• Powerful prioritised optimisation


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INCAPrinciple of MPC

PAST FUTURE

Y(t) Controlhorizon

U(t)^

M O DEL

Y(t)U(t)

U(t)^

Y(t)^

PLANT

Predictionhorizon

Y(t)

Y(t)^


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00.511.522.53

prob

abilit

y de

nsity

func

tion

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

024681012

Cpk

= 0.

96C

pk=

4.3

024681012

Cpk

= 0.

96C

pk=

1.6

Economicbenefit

Standard ControlStandard ControlModel Model Predictive Predictive Control without Control without optimizationoptimization

Model Predictive Model Predictive Control with Control with performance performance optimizationoptimization

INCABusiness Performance Improvement


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s2 s2

INCABusiness Performance Improvement

min{(tol+,tol-) - mean}

3σCpk=

Definition of Cpk: 0 1 2 3 4 5 6 7 8 9 100

0.2

0.4

0.6

0.8

1

1.2

1.4Probability density function

property value

probability

s1


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Example: APC for the EDC/VC cracker

P

Fuel gas

FeedEDC

EDC / VC / HCl

CrackingFurnace

evaporatorsuperheater

waste gas

T

P

L

TF

H

F

condenser

Operation time > 1 year


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Example: APC for the EDC/VC cracker


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DEM O’S


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Conclusions


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Benefits overview

PID Level

MPC

Plant-Wide

Control

Strategy

€€

€€€€€€

€€€€€€€€€€€€

Economical

Benefits

Local Loops

Pressure, level temperature, ...

Destillation, reactor, ...

Complete PE plants, ...

Example


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M odel Based Optimization and

Control

Thank you for your attention!

Documents

RaPID Model Based Optimizoitan an d Control - CiDiP · RaPID & Model Based Optimizoitan an d ... B-3001 Leuven, Belgium T: ... • Measurements on DCS, PLC, etc… • Transfer to