International Scientific Journal

Authors of this Paper

External Links


Objective: The computer image processing and neural network technology are applied to diagnose the thermal energy of boiler plants, i. e., the flame combustion diagnosis, to verify their effectiveness and superiority. Methods: First, the YD-NQ type endoscopic high temperature video acquisition system is used to collect the images of flame combustion. Second, the images are pre-processed by the gray-scale method and the median filtering method. Then the flame combustion parameter features are extracted. The neural network algorithm is improved, and the boiler combustion model based on the improved neural network algorithm is established. Therefore, the combustion decision base is obtained. Finally, the improved neural network model is compared with the traditional neural network model and the 5-4 model to verify its validity. Results: The experiments have found that the improved neural network model is superior to the traditional neural network model. Meanwhile, its accuracy rate and confidence are relatively higher than those of the traditional model. In addition, a single sample also consumes shorter running time, which is 0.0075 seconds. Comparing with the 5-4 model, the improved neural network model has certain advantages, i. e., its accuracy rate and confidence are relatively higher, which are, respectively 91.28% and 96.69%, however, a single sample consumes longer running time than the 5-4 model. Conclusion: The experimental research has found that the application of computer image processing and neural network technology to the thermal energy diagnosis of boiler plants can effectively determine the stability of flame combustion, timely understand the state of flame combustion, and thus diagnose the thermal energy. Therefore, they have values for applications.
PAPER REVISED: 1970-01-01
PAPER ACCEPTED: 2020-01-18
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2020, VOLUME 24, ISSUE Issue 5, PAGES [3221 - 3228]
  1. Aniruddha Uniyal, P.N. et al., Image processing and GIS techniques applied to high resolution satellite data for lineament mapping of thermal power plant site in Allahabad district, U.P. India. Geocarto International, 31 (2016), 9, pp. 956-965.
  2. Taghadomi-Saberi, S., et al., Determination of Cherry Color Parameters during Ripening by Artificial Neural Network Assisted Image Processing Technique. Journal of Agricultural Science and Technology, 17 (2015), 3, pp. 589-600.
  3. Shaofei Wu. Construction of visual 3-d fabric reinforced composite thermal performance prediction system, Thermal Science, 23(2019), 5, pp.2857-2865
  4. Yong Zhu. Research on the Architecture and Behavior Model of High-Speed Channel for Thermal Image Processing. Wireless Personal Communications, 102 (2018), 4, pp. 3869-3877.
  5. Rajah. Fuzzy-Based Framework for the Selection of Image Processing Software for Diagnosis and Outcome Prediction of Cardiac Diseases. Advanced Science Letters, 24 (2018), 2, pp. 1109-1113.
  6. Lazića, V., et al., Selection and analysis of material for boiler pipes in a steam plant. Procedia Engineering, 149 (2016), pp. 216-223.
  7. Shaofei Wu,A Traffic Motion Object Extraction Algorithm,International Journal of Bifurcation and Chaos, 25(2015),14,Article Number 1540039
  8. Fernández-Alemán, J.L., et al., An Empirical Study of Neural Network-Based Audience Response Technology in a Human Anatomy Course for Pharmacy Students Journal of Medical Systems, 40 (2016), 4, pp. 85.
  9. Soltani, A., et al., A new expert system based on fuzzy logic and image processing algorithms for early glaucoma diagnosis. Biomedical Signal Processing and Control, 40 (2018), pp. 366-377.
  10. Javed, A., et al., Smart Random Neural Network Controller for HVAC Using Cloud Computing Technology. IEEE Transactions on Industrial Informatics, 13 (2016), 1, pp. 1-1.
  11. Liu, W., et al., Optimization Analysis and Energy Saving Strategy for Startup Process of a 660 MW Supercritical Boiler. Dongli Gongcheng Xuebao/Journal of Chinese Society of Power Engineering, 38 (2018), 12, pp. 949-956.

© 2022 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence