peter van overschee & christiaan moons slide 1 creators in control technologielaan 11/0101,...

Peter Van Overschee & Christiaan Moons

Slide 1

Creators in control

Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]

Industrial Challenges for

the Identification and Control

Society


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Creators in control


Background ISMC (Spin Off KU Leuven)

+IPCOS Technology (TU Eindhoven / TU Delft)

• Advanced Process Control (APC) products

• All affiliated services: consulting, feasibility studies, implementation, training, maintenance

Power Production

Chemical Processing &

refining

Glass Manufacturing

OilProduction


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Academic versus Industrial• Academic research is mathemically / problem driven

– How challenging is the APC problem ?– How do I make an as good as possible model ?

• Industrial Advanced Process Control (APC) applications are economically driven – How can I make money by solving the APC problem ? – How do I make a good enough model as cheap as possible ?

Typical Payback of a good APC project must lie within 3-12 months

Industrial Challenges are always economically driven


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Presentation Structure

•Plant Operation Layers•Typical Advanced Process Control applications

Low Level Control Tuning Soft Sensors Multivariable/Predictive Control Plant-Wide Dynamic Optimization

Presentation Goal•Understand Principles of each layer •Understand Economics of each layer •Discuss Academic Challenges


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Plant

Operation Layers


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Plant Operation Layers

Process Plant Operation is layered

1. Low Level Control (PID)

2. Supervisory Control (Softsensors & MPC)

3. Plantwide Optimization (Optimisation)

At each layer other technologies & timescales apply and different benefits result


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Model Based Control & Optimization

Technology Layer

Dynamic model based trajectory optimisation

Plant-Wide Optimisation

Optimal Primary PID Controllers

Low Level Control

ProcessProcess

Process

Model Predictive

Control

Optimal Reference Signals

Model Predictive

Control

Model Predictive

Control

Plant-Wide Model Based

Optimizer

Optimal Process Conditions

DCSDCS

DCS

Primary Control Signals

Control hierarchy

High Performance MPCTrajectory tracking MPC

Supervisory Control

Soft Sensors


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1. Low Level Control Layer

Control: PID, On-Off… Platform: DCS, PLC…Timescale: secondsBenefits: stability

FIC

FIC

FICCW

SplitFIC

FIC

Ratio Station

TIC T-Setpoint

PICp-Setpoint

LIC

L-Setpoint


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2. Supervisory Control Layer

Steam Flow Setpoint for low level control

Multivariable Controller

Concentration SetpointTopConcentration SetpointBottom

Ratio Station

FIC

Ratio Setpoint for low level control

Control: MPC,… Platform: PC, DCSTimescale: seconds - minutesBenefits: operate closer to

constraints


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Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]. Plantwide Optimization

Layer

E

D

A

BC

E

D

A

BC

Plantwide Optimization : 2 types

Static Optimizer : Detects steady stateFind optimal steady stateHands optimal setpoints down to Supervisory control layer

Dynamic Optimizer: Find Optimal Dynamic Trajectories

Platform: PCTimescale: hours-daysBenefits: economical optimization

(plant constraints)


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Principles

Economics &

Challenges


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Creators in control

Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected] Based Control &

OptimizationPID Controllers

Technology Product


PathFinder


INCA

Soft Sensors Presto


RaPID

ProcessProcess

Process

Model Predictive

Control


Model Predictive

Control

Model Predictive

Control


Optimizer

Optimal Process Conditions

DCSDCS

DCS


Control hierarchy


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Creators in control


PID Controller Principles

R(s) Y(s)U(s)E(s) System P(s)

Controller

Proportional Part

Integral Part

Derivative Part

dt

tdedTdtte

TteKtu d

t

ip

)()(

1)()(

0


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PID Controller EconomicsProcess Performance is not as good as you think

• PID controllers at lowest level

• PID controllers are the “workhorse” of Process Industry

• 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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Creators in control

Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected] Controller

Economics Chemical example

A B

Cooling water

Steam

• Where: Antwerp• Company: Confidential • Product: Confidential • Solution: optimal PID control for batch • Benefit: 1.000.000 €/year/reactor• Payback: 3 weeks• How was the benefit generated:

• Batch time reduction through increasedthroughput in a non saturated market


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PID Controller Economics Refining Example

• Where: Antwerp (Belgium)• Company: BRC• Product: refining • Solution: Optimization of primary loops (ES)• How was the benefit generated:

• More stable operation (operator load)• Less blending • Less system load (lifetime)• First step towards APC


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PID Controller Challenges (1) Industrial Requirements

Y(s)U(s)E(s)R(s) Controller C(s)

System P(s)

LoadL(s)

Disturbance D(s)

Industrial Requirements• Good Load Rejection• No nervous control signal• No overshoot• Fast Tracking (for reference or master/slave controllers)• Robust


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PID Controller challenges (2)

• Often academically “miss-treated” because of apparent simplicity

• Main Challenge lies in the trade off that must be made between performance and robustness with a limited PID control structure

• Operators have to tune & maintain PID Controllers: Automate tuning as much as possible

• Use operational (closed loop) data

• Simple operational requirements

• Simple trade off tracking and load rejection requirements

• Auto detection of need for re-tuning

• Fast i.e. within ¼ day


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PID Controller Challenges (3)

Identification Society

• Fully automatic identification of SISO dynamic systems including estimation of delay, #poles, #zeros,

integrator…

• From badly excited, closed-loop data with low frequent disturbances

• Leading to physically acceptable models


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Creators in control


PID Controller Challenges (4)

Control Society

• Simple, engineering based statements of the PID control problem with operational constraints (MV saturation)

• Good and fast optimisation strategies

• Keeping the industrial form of the PID controllers in mind

• Pairing of MV to CV

• PID structures & paradigms (cascade, split range)

• Automatic detection of troublemakers within x00 PID’s


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OptimizationSoft Sensors

Technology Product


PathFinder


INCA

Soft Sensors Presto


RaPID

ProcessProcess

Process

Model Predictive

Control


Model Predictive

Control

Model Predictive

Control


OptimizerOptimal Process Conditions

DCSDCS

DCS


Control hierarchy


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Soft Sensor Principles

Classical

Concentrations,

Density, MI,

pH, NOx, CO2

€€€€€

Confidence level

On-line

Flows, Pressures, Temperatures

Soft Sensor


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Soft Sensor Economics

• Avoid using expensive measurement equipment

• Less use of laboratory

• Closed Loop (High bandwidth)

• Possible to put derived process variables (e.g. Efficiency, Emissions) in control loops

More competitive operation

Better environmental protection

• Predict possible future problems (pumps, fans, valves)

Less emergency stops

Maintenance can be planned better; predictive maintenance


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Soft Sensor Economics Oil industry example

• Where: Asia• Company: Confidential • Product: Oil • Solution: On-line estimation of multi-phase flows • Benefit: x.000.000 €/year/platform• Payback: weeks• How was the benefit generated:

• Continuous soft-measurement allows for on-line monitoring and optimization


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Soft Sensor Challenges (1)

Identification Society

1) Input Selection: How to select a subset of inputs (10) for the model from the huge set of available inputs (100)

• Need heuristics to avoid computation for years by exhaustive search

• PCA, PLS, CCA only determine a linear combination

• Avoid over-fitting in the input space

• Huge data sets (1 Mio samples x 300 variables)


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2) Modelling: How to make accurate models from the data

• Structure: Static and dynamic, linear and non-linear

• Huge amount of data (1 Mio x 300): computationally efficient

• Good initial guesses for optimisation

• Highly correlated historical (closed loop) data

• Automatic trade-off between accuracy and generality (overfitting)

0 200 400 600 800 1000 1200 14000.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

2.4

2.6

Training Steps

Err

or

Validation ErrorTraining Error


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3) Make accuracy of models depending on local input densities

4) Allow “reasonable” extrapolation of Soft Sensors through a-priori knowledge.

Controller

*

Much Data Few Data Much Data

*** *

**

**

**

** *

***


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Creators in control



5) On-line updating based on new information

• Bias correction, Kalman Filter or Receding Horizon Estimator

• As long as it is robust and easy to use

• And cheap

6) On-line: Track accuracy of model and flag when model leaves training region and extrapolates excessively


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Creators in control


Model Based Control & Optimization

Model Based Predictive ControlTechnology Product


PathFinder


INCA

Soft Sensors Presto


RaPID

ProcessProcess

Process

Model Predictive

Control


Model Predictive

Control

Model Predictive

Control



DCSDCS

DCS


Control hierarchy


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Creators in control


Principles

Y1(t)

U1(t)

Y1(t)U1(t)PLANT

Y2(t)U2(t)

^

^

Y1(t)U1(t)Model

Y2(t)U2(t)

^

^

U2(t)

Safety

Disch. Pres. Air compr

Feed CH4

T exit Prim Reformer

Gas composition change

Gas composition Change (DISTURBANCE)

Y2(t)%CH4 (sec ref.)

Quality

Throughput


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Creators in control


MPC Economics: Chemical Example

•Where: Burgkirchen-Gendorf (Germany)• Company: Vinnolit • Product: Vinylchlorid• Solution: APC (Products and ES)• Benefit: confidential• How the benefit was generated generated:

• Energy• Throughput• Reduced maintenance cost

P

Fuel gas

FeedEDC

EDC / VC / HCl

CrackingFurnace

evaporatorsuperheater

waste gas

T

P

L

TF

H

F

condenser


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Creators in control


time

100%

97%

Actuator Value

= extra throughput

APC on

How?Higher throughput

APC off

MPC Economics Variance Reduction


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Creators in control


time

Quality

= Profit

APC off

APC onAPC off

How?Reduced Energy/cost


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Creators in control


time

Process Value

Specification A

Specification B

Ideal Value

Ideal Value

transitionstart

= Profit

controlledtransition

manualtransition

transitionend

transitionend

How?Controlled Transitions in Automatic

Mode


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Creators in control


MPC Economics on an EDC/VC cracker


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Creators in control


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 ControlStandard ControlModel Model Predictive Predictive Control Control without without optimizationoptimization

Model Model Predictive Predictive Control with Control with performance performance optimizationoptimization

MPC Economics Variance Reduction


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MPC Challenges (1) Identification

• Need accurate multivariable dynamic models

• With a minimal test time – Multivariable Models are Expensive

• Up to 40 % of an MPC project costs

• Excite multiple input variables at the same time

• Avoid waiting for the process to settle (settling times of 24 hours and more)

• Insensitive for low frequent disturbances

• Insensitive for (de-tuned) controller in the loop

• Carefully designed experiments (cfr. stiff systems)


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MPC Challenges: Testing

Model

€€ €€ €€ €€

MODEL

€€


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Creators in control


MPC Challenges: Use of Step Response Model

NNNN u

u

u

u

sss

sss

ss

s

Y

y

y

y

y

2

1

0

01

012

01

0

02

1

0

0

0

00

Linear Relationship: Y = F + G U• Holds also for Multiple Input Multiple Output system case• Easy model building• Low performance (high frequency content)• Long testing time

U Ys0

s1

s2


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Creators in control

Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected] Challenges:

Use of State Space Models xk+1 = A xk + B uk

yk = C xk + D uk

N

NNNN u

u

u

u

DBCABCA

DCBCAB

DCB

D

x

CA

CA

CA

C

y

y

y

y

2

1

0

21

02

2

1

0

0

0

00

y0 = Cx0 + Du0

y1 = CAx0 + CB u0

y2 = CA2x0 + CABu0 + CBu1 + Du2 …

Linear Relationship: Y = F + G U

• Holds also for Multiple Input Multiple Output system case• Easy adaptation for Linear Time Variant model (Ak,Bk,Ck,Dk)• Easy Identification from test data or from rigorous process model• Stiff systems


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MPC Challenges (2)

y(t)+

+

first principlessimulation

model

process

Low pass filter H1(s)

High pass filter H2(s)

u(t)

First principle model use for identification

Re-use as much a-prior knowledge as possible by using First Principle Models - Multivariable Models are Expensive

• Use of model reduction techniques on extremely badly conditioned models (2500 states to 10)

• Use of data driven (hybrid) models


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MPC Challenges (3) Linear MPC

• Origin of “Linear” MPC lies in plants running in one operating point (refineries, large crackers)

• Final challenge is the solution of large scale constrained QP problems

• 30 MVs, 30 CVs

• Parameterisation of freedom per MV

• Use of structure in QP problems

• Needs to be solved in limited and predictable time


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Creators in control

Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected] Challenges (3)

Non-Linear MPC• New application areas of MPC are:

• Transition Control (broad operating regions)

• Batch Control

• Need MPC valid over a non-linear region of the plant:

• Multiple linear models (more tests)

• Non linear explicit models with fast integration time

• Bounded time for non-linear optimisation part of the MPC

• Convergence and stability ?

• Simple hybrid models ?


Slide 44

Creators in control


OptimizationDynamic Optimization

Technology Product


PathFinder


INCA

Soft Sensors Presto


RaPID

ProcessProcess

Process

Model Predictive

Control


Model Predictive

Control

Model Predictive

Control



DCSDCS

DCS


Control hierarchy


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Creators in control


Plant-Wide Dynamic Optimisation

Planning/Scheduling

INCAModeler

Process Identification

INCAEngine

LinearModelsLinearModels

Model Predictive Control

Primary Process Control and Instrumentation Systems (DCS’s, PLC’s, etc)

(Simulated) Process (gPROMS, ...)

Process Simulator gPROMS, SpeedUp, ...Rigorous Dynamic

Process Model

RaPID

Soft sensor

Presto

Dynamic OptimizationTrajectory Generation

PathFinder


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Creators in control


Plant-Wide Economic Dynamic Optimization Principles

‘Find dynamic MV’s such that objective is optimized

subject to process operation constraints’

Trajectory Optimizer

gPROMS, SpeedUp, ACM,…

Process Model

MV CV

ObjectiveCalculation


Slide 47

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Dynamic Optimization Economics

Gasphase Polyethylene reactor

Density--

MI++

Production++


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Creators in control


0 5 10 15 20 25 30 35 40 45 50900

950D

ensi

ty

0 5 10 15 20 25 30 35 40 45 503

4

5

LNM

I

0 5 10 15 20 25 30 35 40 45 502k

4k

6k

Pro

duct

ion

0 5 10 15 20 25 30 35 40 45 500

5

10x 10

4

Coo

ling

wat

er12 hours

25 hours

15.000 €/gradechange

No optimization

PathFinder

Dynamic Optimization Economics


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Creators in control

Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected] Optimization

Economics Benefits for polymers

• Extension of the production capacity by exploiting the capabilities of the process and pushing towards bottle-neck

constraintsRange: up to 2.5%

• Minimizing operating costs by exploiting the operation freedom

Range: up to 2.5% • Reduce production losses related to grade

transitions • Faster transition policy• Faster settling in the new grade specifications

Range: up to 20.000 Euro/gradechange

• Minimize off-spec production during normal operation

Range: up to 500.000 Euro/year


Slide 50

Creators in control

Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected] Optimization Challenges (1)

Economically Optimal Dynamic Transitions

Smoothly Non Linear Long Calculation Time

Highly Non Linear Short Calculation Time

gPROMS, ACM,

SpeedUp,…

Economic Objective

Mass flow

QualityMV’s ObjectiveConstraints

Economic ObjectiveProcess Model

PricesSpecifications

• Need fast way to do optimise this specific mixed problem with a minimum number of iterations over the Process Model

• Optimal parameterisation of the MV space


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Creators in control

Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected] Optim Challenges (2) State of

the Art

y+-

+

+

MPC INCA®

u

Optimal Trajectory Recipe

PathFinder

yu Latest Process Model

Off-Line

On-Line

yopt

uProcess

uopt

y

Extended Kalman Filter

On line model


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Creators in control


Dynamic Optimization Challenges (2)

Optimization Society

• On-line Dynamic Optimization: Why not use the whole first principle model to optimize and control the plant

• Need fast and reliably convergent algorithms to solve the on-line optimisation problem

• Need reliable on-line observer algorithms that allow tracking of the model when the plant drifts

• Need fast computers… future


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Conclusions


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Conclusions • Industrial APC projects are economically driven

• Industrially relevant challenges for identification and control:

• Need algorithms that minimise engineering time

• Need algorithms that allow for simple interaction: high level of automation, easy to use, easy to configure, minimal knowledge required to operate, robust, fast

• Models are expensive ! Need algorithms that reduce testing time and increasing model accuracy

• Need algorithms that allow formulation of the identification and control problems as close as possible to the operational and economic reality

• Need algorithms that can make use of all a-priori knowledge available (physical models and engineering insight)

• Need engineers with Process Knowledge full algorithmic abstraction is a

myth !


Slide 55

Creators in control


Industrial Challenges for

the Identification and Control

SocietyThank you for your attention!

peter van overschee & christiaan moons slide 1 creators in control technologielaan 11/0101,...

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