THERMAL SCIENCE

International Scientific Journal

Thermal Science - Online First

Authors of this Paper

External Links

Zi-Guo Zhang

,

Liang Pan

,

Hao Wang

online first only

Research on combustion visualization of coal-fired boilers based on thermal imaging technology

ABSTRACT
At present, there's a lack of combustion visualization in the combustion control of heating boilers. To understand the combustion of coal in the furnace, only experienced workers can observe it through visual inspection. Using infrared thermal imaging technology to monitor the combustion can realize combustion visualization. This paper analyzed and solved two problems: the installation position and number of infrared cameras, and the infeasible of using infrared cameras observing the combustion condition in the furnace through heat-resistant glass. Monitored parameters such as oxygen content, furnace temperature and smoke exhaust temperature, and monitored the concentration of PM, NOX, and SO2 in the main atmospheric pollutants in the flue gas. After calculation, the air leakage coefficient when the inspection doors are opened for observation is 0.04. This value still includes the sum of air leakage from coal hopper, furnace door, grate side seal, peep holes and other parts. The monitored average emission concentration of PM decreased by 16.28%, from which we can concluded that the use of thermal imaging technology to monitor the combustion in the furnace is conducive to emission reduction. The application of thermal imaging technology implementation of coal-fired boiler combustion visualization is feasible.
KEYWORDS
Thermal imaging technology, Coal-fired boiler, combustion visualization, oxygen content, furnace temperature, exhaust gas temperature, emission reduction
PAPER SUBMITTED: 2023-06-27
PAPER REVISED: 2023-07-11
PAPER ACCEPTED: 2023-09-18
PUBLISHED ONLINE: 2024-03-10
DOI REFERENCE: https://doi.org/10.2298/TSCI230627044Z
REFERENCES
  1. Han, Z., et al., Prediction of Combustion State Through a Semi-supervised Learning Model and Flame Imaging, Fuel, 289 (2021), pp. 119745
  2. Han, Z., et al., Combustion Stability Monitoring Through Flame Imaging and Stacked Sparse Autoencoder Based Deep Neural Network, Applied Energy, 259 (2020), pp. 114159
  3. Hernández, R., Ballester, J., Flame Imaging as a Diagnostic Tool for Industrial Combustion, Combustion and Flame, 155 (2008), 3, pp. 509-528
  4. Yana, Y., Colechinb, M., A Digital Imaging Based Multi-Functional Flame Monitqring System, IMTC 2003-Instrumentation and Measurement Technology Conference, Vail, CO, USA, 20-22 May 2003, pp. 94-99
  5. Gang Lu, et al., Monitoring of Oscillatory Characteristics of Pulverised Coal Flames Through Image Processing and Spectral Analysis, Proceedings, Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.04CH37510), Como, Italy, 2004, pp. 1801-1805
  6. Abdurakipov, S.S., et al., Combustion Regime Monitoring by Flame Imaging and Machine Learning, Optoelectron. Instrument. Proc., 54 (2018), 5, pp. 513-519
  7. Ballester, J., García-Armingol, T., Diagnostic Techniques for The Monitoring and Control of Practical Flames, Progress in Energy and Combustion Science, 36 (2010), 4, pp. 375-411
  8. Han, Z., et al., A Hybrid Deep Neural Network Based Prediction of 300 MW Coal-fired Boiler Combustion Operation Condition, Sci. China Technol. Sci., 64 (2021), 10, pp. 2300-2311
  9. Lu, G., et al., Vision Based Monitoring and Characterisation of Combustion Flames, J. Phys.: Conf. Ser., 15 (2005), pp. 194-200
  10. González-Cencerrado, A., et al., Characterization of PF Flames Under Different Swirl Conditions Based on Visualization Systems, Fuel, 113 (2013), pp. 798-809
  11. Chin Chongshuang, et al., A New Real-Time Fire Detection Method Based on Infrared Image, Proceedings of IEEE 7th International Conference on Computer Science and Network Technology, CCSNT 2019, pp. 476-479
  12. Ch. Kranz, A New Flame Detection Method for Two Channels Infrared Flame Detectors, Proceedings The Institute of Electrical and Electronics Engineers, 29th Annual 1995 International Carnahan Conference on Security Technology, Sanderstead, UK, 18-20 October 1995, PP. 209-213
  13. Hyeon Bae, et al., Flame Detection for The Steam Boiler Using Neural Networks and Image Information in The Ulsan Steam Power Generation Plant, IEEE Transactions on Industrial Electronics, 53 (1), PP. 338-348
  14. Juan Riaza, et al., Ignition and Combustion of Coal and Biomass, Fuel, 202 (2017), pp. 650-655
  15. Huang Guan, et al., Automatic Fault Diagnosis Algorithm for Hot Water Pipes Based on Infrared Thermal Images, Building and Environment, 218 (2022), pp. 109111
  16. Lu Zhu-Mao, et al., Research on Thermal Fault Detection Technology of Power Equipment Based on Infrared Image Analysis, 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China, 2018, pp. 2567-2571
  17. Ring, E.F.J., Beyond Human Vision: The Development and Applications of Infrared Thermal Imaging, The Imaging Science Journal, 58 (2010), 5, pp. 254-260
  18. Fei Liu, Stefan Seipel, Infrared-visible Image Registration for Augmented Reality-Based Thermographic Building Diagnostics, Visualization in Engineering, (2015) 3:16
  19. Huaiping Mu, et al., Visualization Measurement of The Flame Temperature in a Power Station Using The Colorimetric Method, Energy Procedia, 66(2015), pp. 133-136
  20. Teri Snow Draper, et al., Two-Dimensional Flame Temperature And Emissivity Measurements Of Pulverized Oxy-coal Flames, Applied Energy, 95(2012), pp. 38-44
  21. Zhi-Wei Jiang, et al., A simple Measurement Method of Temperature and Emissivity of Coal-fired Flames from Visible Radiation Image and Its Application in a CFB Boiler Furnace, Fuel 88 (2009) pp. 980-987
  22. Lin Li, et al., Pressurized Oxy-Fule Combustion Characteristics of Single Coal Particle in a Visualized Fluidized Bed Combustor, Combustion and Flame, 211 (2020) 218-228
  23. Chengfeng SUN, et al., A Visualization Method of Quantifying Carbon Combustion Energy in the Sintering Packed Bed, ISIJ International 61 (2021) pp. 1801-1807
  24. Lin, B., Jørgensen, S.B., Soft Sensor Design By Multivariate Fusion Of Image Features and Process Measurements, Journal of Process Control, 21 (2011), 4, pp. 547-553
  25. Chen, H., et al., Burning Condition Recognition Of Rotary Kiln Based On Spatiotemporal Features Of Flame Video, Energy, 211 (2020), pp. 118656
  26. Bai, X., et al., Multi-Mode Combustion Process Monitoring on a Pulverised Fuel Combustion Test Facility Based on Flame Imaging and Random Weight Network Techniques, Fuel, 202 (2017), pp. 656-664
  27. Zhang, Z.-G., Preliminary Study on Application of Infrared Thermal Imaging Technology in Combustion Control of Coal-Fired Boilers, J. Phys.: Conf. Ser., 2009 (2021), 1, pp. 012055
  28. Wang Dunen, et al., Design Calculation Method of Industrial Boiler, Standards Press of China, Beijing, China, 2005
  29. Shi Peifu, et al., Application Technology of Energy Saving and Emission Reduction in Industrial Boilers, 2nd Ed. Chemical Industry Press, Beijing, China, 2016
  30. Wu Weilong, et al., Boiler and Boiler Room Equipment, 5th Ed. China Architecture and Building Press, Beijing, China, 2014
  31. Iman Rahimipetroudi, et al., Development of Environment-Friendly Dual Fuel Pulverized Coal-Natural Gas Combustion Technology for the Co-Firing Power Plant boiler: Experimental and Numerical Analysis, Energy, 228 (2021), pp. 120550
  32. Xiangru JIA, et al., Investigation of the Pollutant Emission Characteristics of Blends of Biomass and Coal Gangue in a Fluidized Bed, Thermal Science 26 (2022), 5B, pp 4333-4343
  33. Chao CHEN, et al., Modelling and Combustion Optimization of Coal-Fired Heating Boiler Based on Thermal Network, Thermal Science 25 (2021), 4B, pp 3133-3140
PDF VERSION [DOWNLOAD]
Research on combustion visualization of coal-fired boilers based on thermal imaging technology