OPTIMISATION OF WOOD-FIRED BOILERS USING OPTICAL SENSORS

Transcription

1 OPTIMISATION OF WOOD-FIRED BOILERS USING OPTICAL SENSORS Jean-Bernard MICHEL - University of Applied Sciences of Western Switzerland - Geneva O. Sari - University of Applied Sciences of Western Switzerland - Yverdon E. Onillon - Swiss Center for Elecronics and Microtechnology, Neuchâtel, Switzerland D. Koechli - Müller AG Holzfeuerungen, Switzerland CMEFE ::: Chaud 4-Rabat ::: JBM HES-SO / 39 Outline University of Applied Science of Western Switzerland The problems of biomass use for district heating Sensor principle Early results Future perspectives Clean Air 5 ::: JBM June 5 2/ 39

2 University of Applied Science of Western Switzerland Clean Air 5 ::: JBM June 5 3/ 39 Main R&D activities Fluid mechanics and energy conversion systems Lab, Geneva Building design, simulation, HVAC systems Aérodynamics of sports. Renewable energies Thermodynamical cycles Thermal Engineering Institute, Yverdon Phase change materials and ice slurries (IEA working group) Magnetic cooling Combustion Clean Air 5 ::: JBM June 5 4/ 39

3 Participation to Shell Eco-Marathon race 24: km with.1 litre of benzine 25: Use of biobenzine Clean Air 5 ::: JBM June 5 5/ 39 Equipments Clean Air 5 ::: JBM June 5 6/ 39

4 Industrial burner/boiler test rig 27 kw steam boiler modulating Gas/oil burner Clean Air 5 ::: JBM June 5 7/ 39 EU report (23): the share of renewable energy Clean Air 5 ::: JBM June 5 8/ 39

5 EU report: the share of renewable energy in the EU Clean Air 5 ::: JBM June 5 9/ 39 Example: French potential of renewables Electricity generation [TWh/year] Biogas electricity Solid Biomass electricity Biowaste electricity Geothermal electricity Achieved potential / Existing plant Hydro large-scale Hydro small-scale Photovoltaics Solar thermal electricity Tide & Wave Wind onshore Wind offshore Additional potential / New plant 14. Heat generation [ktoe/year] Biomass heat (grid) Geothermal heat (grid) Biomass heat (non-grid) Heat pumps Solar collectors Fuel production [ktoe/year] Biofuels Clean Air 5 ::: JBM June 5 1/ 39

6 Example: German potential of renewables Electricity generation [TWh/year] Achieved potential / Existing plant Additional potential / New plant 12 Heat generation [ktoe/year] Fuel production [ktoe/year] Biogas electricity Solid Biomass electricity Biowaste electricity Geothermal electricity Hydro large-scale Hydro small-scale Photovoltaics Solar thermal electricity Tide & Wave Wind onshore Wind offshore Biomass heat (grid) Geothermal heat (grid) Biomass heat (non-grid) Heat pumps Solar collectors Biofuels Clean Air 5 ::: JBM June 5 11/ 39 Factors limiting the increase of biomass for heating Availability of the biomass Storage Investment costs Variability of the fuel Moisture Size distribution Composition Reliability frequent unwanted stops Economics very much depending on operating variables Need for better monitoring & control of the combustion e.g. excess air, primary/secondary air, CO Need for low cost sensors usable on 3 kwt h 3 MW th boilers Clean Air 5 ::: JBM June 5 12/ 39

7 Flame quality sensor principle (CSEM patent) Result of turbulence Flame sensors Signals (UV, IR) Amplitude Flame signatures (variable with burner parameters) Time Frequency To control unit performance Correlation with performance Optimum Control signal Clean Air 5 ::: JBM June 5 13/ 39 «Neuroflame» sensor 12 mm Price target: 1-5 Euros Clean Air 5 ::: JBM June 5 14/ 39

8 Example of raw signal 3.2 UV sensor Fourier transform UV sensor [V] time [s] -14 f [Hz] 2 1 Clean Air 5 ::: JBM June 5 15/ 39 GaP sensor responses OH CH EPD 365-/2,5 G1962 Clean Air 5 ::: JBM June 5 16/ 39

9 Dyadic time filtering process frequency bands to 3 Hz Gain Frequency (Hz) Clean Air 5 ::: JBM June 5 17/ 39 Preventive maintenance scheme Record «good» flame signature for 1 minutes at constant load 9 wavelet coefficients corresponding to 9 frequency bands Average and Standard deviation Signal/noise ratio Monitor deviation from the norm and provide alerts Distance is correlated to excess air variation Wavelet coefficients (arbitrary units) calibration curve /- 2 std. dev. Instant measurement Frequency bands Clean Air 5 ::: JBM June 5 18/ 39

10 Sensor response to step changes in excess air 8 Forced-draught diffusion burner, 18 kw trained for 15% excess air. O 2 % O2 % alert1 alert2 Alert 1(b) 35 3 distance 2 Alert 2 (r) Dist (g) alerts and distance Samples (1 per 6 s) samples (1 per 6s) Samples (1 per 6 s) Clean Air 5 ::: JBM June 5 19/ 39 Müller AG Boiler schematic Suitable for variety of wood and wood wastes variable moisture content - 8 to 13 kw Turn-down 3% Clean Air 5 ::: JBM June 5 2/ 39

11 Wood chips and bark Clean Air 5 ::: JBM June 5 21/ 39 Sensor placement first trials Clean Air 5 ::: JBM June 5 22/ 39

12 Initial results Signature frequencies much lower than with forced draught burners Reproducible results with UV sensor 3 2 Power Spectrum Magnitude (db) Frequency Clean Air 5 ::: JBM June 5 23/ 39 Furnace from the back Clean Air 5 ::: JBM June 5 24/ 39

13 Conclusions Method already proven and partially implemented in industrial oil burner controllers Low cost and direct monitoring of excess air variation Potentially easy to implement on wood-fired boilers with some improvements needed Single sensor for flame detection and flame quality Mesures through the flame not in the flue Fast response (2 ms.) Potential for air adjustment along the grid Long life time without drift Very suitable for preventive maintenance Clean Air 5 ::: JBM June 5 25/ 39