Special Design of the purging air holes
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Special Design of the purging air holes. Endoscope tip (10mm) Multisensor tip (65mm). Flame analysation using video images. Pseudo colour image. Video image. L1. L0. Burner. Camera. Polyline L1: Flame diameter. Polyline L0: Ignition point.

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Special Design of the purging air holes

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Special design of the purging air holes

Special Design of the purging air holes

Endoscope tip (10mm)

Multisensor tip (65mm)


Special design of the purging air holes

Flame analysation using video images

Pseudo colour image

Video image

L1

L0

Burner

Camera

Polyline L1:

Flame diameter

Polyline L0:

Ignition point


Special design of the purging air holes

Image processing values using 1 camera for a combination

of flames (in this case 5)

View from a side onto a flame

Frontfiring system, view of 1 camera with ROI‘s (Regions of interest)


Special design of the purging air holes

23

21

13

41

42

Polylines and. ROI’s (“Region of Interest”)

L2

L4

L3

R7

R6

L0

R2

R0

R1

L5

L6

L1

R8

R4

R5

Polylines L0-L6 for visualisation only!

R 0-4 Average Temperature of the ROI

R 5-7Flamen „Centroid“ in X and Y


Special design of the purging air holes

Initial Model after 4-6 weeks (ready for prediction)

delay time

Process results

PiT-Image Processing

  • Steam p,v,T

  • CO

  • NOX

  • O2

  • 850°C

  • ...

  • - Online-Characteristics from the camera signals

Process results

  • Steam p,v,T

  • CO

  • NOX

  • O2

  • 850°C

  • CiA

Process and Control Variables

cluster

  • Local secondary air flow

  • Shifter / Feeder / Mill

  • Dumper positions

  • Temperature primary air

  • Temperature secondary air

  • Mass flow prim. airi

  • Mass flow sec. airi

Initial Neural Net


Special design of the purging air holes

Model Predictive Control (multi-dimensional,

non-linear and adaptive)

Process and Control Variables

Process results

  • Local secondary air flow

  • Shifter / Feeder / Mill

  • Dumper positions

  • Temperature primary air

  • Temperature secondary air

  • Mass flow prim. airi

  • Mass flow sec. airi

  • Steam p,v,T

  • CO

  • NOX

  • O2

  • 850°C

  • ...

Adaptive Neural Net


Special design of the purging air holes

PiT Multisensor

Q1 = ...

Hu = ....

Q2 = ....

T1 = ....

Q3 = ....

T2 = ...

Q5 = ....

X1 = ....

A = ...

F = ....

B = ....

G = ....

C = ....

H = ...

D = ...

J = ...

E = ....

K = ....

a = ...

d = ....

b = ....

e = ....

f = ...

g = ....

h = ....

i = ....

k = ....

m = ....

Non-linear ModelPredictive Control

PiT Navigator

Simulated

actuator

vectors

Adaptive Model

Pattern recognition: ‘Feature generation’

Control action generator

Image processing

Process characteristics

Actual process situation

Trainee

Prediction*

OUTPUT from DCS

Data pre- processing

Actuator vector-evaluation

PiT data base

Process data

Set-point corrections

Best allowed and

simulated actuator vector

Closed loop control

INPUT into DCS

Plant limit values & Optimisation targets

* Model prediction as consequence

of simulated control with hypothetical actuator vector

Set point limit values


Special design of the purging air holes

Example 1: Modelling CiA

The second approach finalizes in a correlation coefficient of R = 0,78

The used process data:CO/O2 and all 37 PiT image processing values

Measured CiA (promicon) [%]

Conclusion: The image processing information make a prediction of CiA for online control possible!


Special design of the purging air holes

Example 1: On-line model CiA for combustion control

Comparison Prediction und Measurement CiAduring the test

(Multiple-Correlation-Coefficient R=0,78)

The PiT Navigator with on-line re-training results in a model quality of > 0,9 !


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