the tno mission - fhi, federatie van technologiebranches tno egbert jan sol.pdf · 2 7...

1

[email protected]

Egbert-Jan Sol, CTO TNO Industrie & Techniek

Production Process Automation

Paradigm shifts in sensors & control models

Delft, 30 november [email protected]

The TNO missionTo enable scientific knowledge to strengthen the capabilityof businesses and government to innovate.

Strategy: what is behind the horizon - value creation by BV NL in 2010-15

0%

10%20%

30%40%

50%

60%70%

80%90%

100%

1750 1800 1850 1900 1950 2000

China

WestOthers

India

Russia

Brazil


Where do we create value in the Euro State of the Netherlands(Industry: High-Tech Syst., Process, Autom. Supply)

Expo

rt (2

37)30

161

28

18

(c) Berenschot, Min EZ (adapted by E-J Sol)

Dutch (internal) market (324)

100 155

Financial, Media and

ICT services(620.000)

Infra, transport& construction

(917.000)

25

Food & Health Industry

(149.000)

44

Basis, Product, Machine & System Ind

(554.000)Total labor:

(2.240.000)

Process: - Rijnmond- Moerdijk- Terneuzen- Delftzijl- Geleen

Manuf. Industry:High Tech Syst. & Auto. Toelever.

- ZO NL


Anyone can compete on the old factors of economic growthNL should not compete on labor, capital, materials, but on knowledge


How? Not on costs!, so change the rules of the game

CASHCOWmarket leaders(- cost reduction

- life extension)

Price&

size ofmarket

STARfocus on speed

and market share

Old

Current

New

STARfocus on speed& market share


Redox disturbances in container glass production

Disturbance

Product

-4.75

-4.70

-4.65

-4.60

-4.55

-4.50

-4.45

-4.40

-4.35

-4.309:279:5

910:2

311:0

311:3512

:0713:1

114:3

115:1116

:2616

:5518:0

719:1120:3

121

:2722:3

923:430:391:43

2:393:434:395:43

6:397:438:399:43

9:2110:3

911:4312:3

913

:4314:3

915:4316:3

917

:4318:3

919:4

320:3

921

:4322:3

923:4

30:391:3

62:403:364:405:366:40

7:368:409:3610

:4011:3

612:4

013:3

614

:4015:3

616:4

017:3

618

:4019:3620:4

021:3

622:4

023:360:401:362:403:36

4:405:36

6:407:368:409:3610:3

411

:3812:3413:3

814:3

415:3

816:3417:3

818:3

419:3

820

:3421:3

822:3

423:3

80:341:382:383:384:34

5:386:347:388:349:38

10:34

11:38

12:34

13:3814:3

415:3

816:3

417

:3818:3419:3

820:3

421

:3822:3423:3

80:341:382:34

3:384:345:386:347:388:3

49:3810:3411:3812:3

413:3

814

:3415:3816:3

417:3

818:3

419:3820:3

421

:3822:3

423:380:341:3

82:343:384:345:3

86:347:388:349:3

810:3

411:3

812:3413:3

814

:3415:3

816:3417:3

818

:3419:3

820:3421:3822:3

423:3

80:341:382:3

43:384:345:38

6:34

7:38

8:349:3810:3

411

:3412:3

813:3714:3315

:3716:3

317:3

718:3319:3

720:3

321:3

722:3323:3

70:3

31:372:333:3

74:3

35:376:337:378:3

39:3310:3

311:3712

:3313:3

314:3

915:3516:3

917

:3518:3

919:3520:3

921

:3522:3

923:350:391:3

52:393:354:395:3

56:397:358:39

9:38

10:35

11:39

12:3513:3

914

:3515:3

916:3517:3

918

:3519:3

920:3

521:3922

:3523:3

90:351:392:3

53:394:355:396:357:398:359:3610:3

211

:3612:3

213:3

614:3

215

:3616:3

217:3

618:3219

:3620

:3221:3

622:3223:3

60:321:362:323:284:325:366:327:368:32

9:3610:3

211:3

612

:3713:3314:3

715:3316:3

717

:3318:3

719:3320:3

721

:3322:3

723:3

30:371:332:373:334:375:3

36:377:338:379:3310:3711:3

312:3713:3

314

:3815:3

416:3

817:3

418

:3819:3420:3

821:3

422

:3823:340:381:342:383:34

4:385:346:387:348:389:3

410:3

811

:3412:3813:3

414:3

815

:3416:3817:3

418:3

819:3

420:3821:3

422:3

823:3

40:381:342:3

83:344:385:346:3

87:348:389:3410:3

311:3

7

29-jul 30-jul 31-jul 1-aug 2-aug 3-aug 4-aug 5-aug 6-aug 7-aug 8-aug 9-aug 10-aug 11-aug 12-aug

log

(pO

2 (ba

r))

19

20

21

22

23

24

25

Fe2+

/Fe to

t (%

)

log(pO2)Fe 2+/Fetot (Opt.)

29 ju

li

30 ju

li

31 ju

li

1 au

g

2 au

g

3 au

g

4 au

g

5 au

g

6 au

g

7 au

g

8 au

g

9 au

g

10 a

ug

11 a

ug

12 a

ug

Process monitoring (sensor)

Process monitoring (model)

2


Glass Furnessbatch and feeder sensor measure redox state of melt

Change in batch sensor signal leads 10 hours later to change in glass product colour


-5.5

-5.0

-4.5

-4.0

-3.5

-3.0

-2.5

6-8-2003 0:00 8-8-2003 0:00 10-8-2003 0:00 12-8-2003 0:00 14-8-2003 0:00 16-8-2003 0:00

date (dd/mm/yyyy hh:mm)

log(

pO2(

bar))

pO2 batch area (around 1300°C)pO2 feeder (around 1200°C)feeder calculated from batch

Simultaneous measurement of redox near batch and in feeder

soft sensor

measured


GTM-X results:Refractory corrosion

GTM-XProcess

and designanalysis


GTM-X

Main Model•Navier-Stokes

- Finite Volume- Energy (buoyancy)

•Steady-state & transient•State-of-the-art solvers•Multiple Domains•Grid:

- body-fitted- multi-block- multi-level grids- structured- collocated- p-modifiers

•Parallel•Materials Database

GUI•Pre-processor •Post-processor

Glass•Batch models

- 2.5D, 3D, 2-phase•Radiation models:

- Rosseland, (spectral) DOM•Electrical boosting•Bubbling•Foam model•Crown model•Stirring•Energy sources•Particle trace•Redox•Glass colour change•Non-linear mixing•Volatilization•3D & 1D walls•Glass surface height•Thermal homogeneity•Refractory wear•Glass quality indices

Combustion•Radiation models:

- (spectral) DOM•Combustion models:

- soot, NOx, Oil- oxy-fuel, oxy-boosting- f, f-g, dissociation- FLAME

•Turbulence models:- k-ε, RSM, durbin, ellip. Blend.

•Refractory corrosion•3D & 1D walls

Design, Optimization, Trouble-shooting

CVD•Interface with Chemkin•Rarefied flow model (DSMC)•Compressible flow•Surface reactions•Gas Phase reactions•Efficient stiff systems solver•Multi-component diffusion:

- Stefan-Maxwell, Wilke•Plasma…


Model prediction of glass melt in the furnace after disturbances such as:– Furnace load– Fuel consumption and fuel distribution over the different burners– Electric boosting supply– Ambient air temperature– Quality of recycling cullet– Intensity of bubbling and stirring– Changes in raw material composition.


POD - Proper Orthogonal Decomposition:Mathematical technique to make CFD models very fast

+ α2(t) + …

i=1

N

i=1

N

~

POD time functioncoefficients

Basis functions

Reconstructedvariables

10 20 30 40 50 60

0

0

010 20 30 40 50 60

= α1(t)

• Weighted sum of dominant profiles obtained by Singular Value Decomposition (SVD) of a snapshot matrix filled with simulation results over a long period of time with many changes in process conditions:

T(x,y,z,t) = Σ φi (x,y,z) αi (t) , N << original number of unknowns

• Solve original equations for αi(t) after substitution of the weighted sum of (known) dominant profiles.

• Reconstruct solution by substitution of αi(t). This leads to a speed up of computations by a factor of 100 to 10.000!

3


This way you do not become “Weltmeister PPA”

What does this means for you?Delft, 30 november [email protected]

The Paradigm shift from intelligent box to distribution?

70-80

Hardw.(IBM)

Appli. &Services(You ?!)

Death of distance

OpenSource

GiveAway

Hardware

2000-2020

MB/day

Fixed

Mobile

€/ MB/day

1 10

100

3 90 9891800

Legend

MIPS

Costs/MIPS

MainframesPC's

80-90

Softw.(Micro-Soft)

Hardw.

Comm.(KPN)

90-2000

Softw.

Proces-sing isfree.

Intelli.BoxParadig.

GridNetwork

NetworkedServices

Main-Frame 1970 Mini

1979

PC-AT 1984 Pentium

1992Notebk 1997

PDA 2001

S-i-P 2010 Push-Pin 2020

02468

1012

0 5 10 15


Learning curve for smart (punaiske) devices(from mainframe to ambient push-pin computer

Main-Frame 1970 Mini

1979

PC-AT 1984 Pentium

1992Notebk 1997

PDA 2001

S-i-P 2010 Push-Pin 2020

02468

1012

0 5 10 15

Log 10 (Amount of devices) 5 = 100K, 10=10B

Log

10 (V

olum

e (h

xbxl

) mm

3)

500 B 1x1x1 cmdevices by ? 2020

5B 20x20x20 cmdevices by today

(c) TNO Industrial Technologies, Egbert-Jan Sol, [email protected], 2004

Note: SiP = System in a Package

50 B 2x2x2 cmdevices by ? 2010


Agricult.monitoringCrowd

monitoring

Sensor explosion and sensor synthesis/fusion

# of sensor (motes) from a few till thousands and beyond

# of DomainsSynthesisOr Sensor

Fusion

Cameraarray andanteArray

microphone

smartwells

tegelaar


Monitoring: traditional

Sensor Signalprocessing

Featureextraction

Presentation

Control

PROCES Actuator


Model based monitoring

sensor signalprocessing

Featureextraction

Presentation

Control

PROCES Actuator

M

M

M

M

M

4


Let’s get into action


Provide process knowledge to controller: PID >>> MPC >>> RMPC

Z1W3Z1W2

Z1W1

PID

TC2

PID

TC1

PID

TC3

Z3M1

Zone 3 Zone 2 Zone 1

setpoint setpoint setpoint

Z1W3Z1W2

Z1W1

PID

TC2

PID

TC1

PID

TC3

Z3M1

Zone 3 Zone 2 Zone 1

RMPC

RMPCRigorous

Model basedPredictive

Control


RMPC results (improved temperature control glass melt:

1145

1150

1155

1160

1165

1170

1175

1180

1185

1190

0 2000 4000 6000 8000 10000 12000 14000

Time [days]

Tem

pera

ture

[C]

Grid temperature

Temperature set point

MPC controller switched on

Set point changes due to job change

No control actions taken, controller switched off

1186.0

1186.5

1187.0

1187.5

1188.0

13000 13050 13100 13150 13200 13250 13300

1145

1150

1155

1160

1165

1170

1175

1180

1185

1190

0 2000 4000 6000 8000 10000 12000 14000

Time [days]

Tem

pera

ture

[C]

Grid temperature

Temperature set point

MPC controller switched on

Set point changes due to job change

No control actions taken, controller switched off

1186.0

1186.5

1187.0

1187.5

1188.0

13000 13050 13100 13150 13200 13250 133001186.0

1186.5

1187.0

1187.5

1188.0

13000 13050 13100 13150 13200 13250 13300

RMPCRigorous

Model basedPredictive

Control


0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

x 104

1400

1402

1404

1406

1408

1410

1412

1414

1416

1418

1420Measured throat temperature

time

Tem

pera

ture

(0 C)

00.511.522.53

prob

abili

ty d

ensi

ty fu

nctio

nprobability density

Cpk

= 0.

96

024681012

Cpk

= 0.

96C

pk=

4.3

024681012

Cpk

= 0.

96C

pk=

1.6

Economicbenefit

RMPC benefit (stabilize temperature allowing lower average temperature energy savings): RMPC

RigorousModel based

PredictiveControl


Conclusion: new production automation opportunitiesmany low cost sensors combined with advanced process models

80-90 70-80

Comm.

Softw.

Hardw.Hardw.

90-2000

Softw.

Proces-sing isfree.

2000-2020

Paradigm shift:From early adaptorTo visionaries,…To main street

Appli. &Services(You ?!)

Death of distance

OpenSource

GiveAway

HardwareEarly Market

Chasm

Tornado

BowlingAlley


TNO Theme: Maximasing performance in proces industry

• Modeling of physical process (fluidics, heat transfer, acoustics, separation, ..)

• Process (micro)Sensors & Control Models

From a single state variable to FEM models with multiple state

variables at every node combined with many (micro) sensors & sensor synthesis

the tno mission - fhi, federatie van technologiebranches tno egbert jan sol.pdf · 2 7...

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