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Special Design of the purging air holes PowerPoint Presentation
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Special Design of the purging air holes

Special Design of the purging air holes

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Special Design of the purging air holes

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  1. Special Design of the purging air holes Endoscope tip (10mm) Multisensor tip (65mm)

  2. Flame analysation using video images Pseudo colour image Video image L1 L0 Burner Camera Polyline L1: Flame diameter Polyline L0: Ignition point

  3. 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)

  4. 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 5-7 Flamen „Centroid“ in X and Y

  5. 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

  6. 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

  7. 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

  8. 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!

  9. 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 !