EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 412 (2025) MATEC Web Conf., 412 (2025) 01006 References
Open Access
Issue
MATEC Web Conf.
Volume 412, 2025
42nd. Annual Conference “Meeting of the Departments of Fluid Mechanics and Thermomechanics” in the connection with XXIV. International Scientific Conference “The Application of Experimental and Numerical Methods in Fluid Mechanics and Energy” (42nd. MDFMT & XXIV. AENMMTE-2025)
Article Number 01006
Number of page(s) 12
Section Measurement and Calculation of State Variables in the Fluid Flow
DOI https://doi.org/10.1051/matecconf/202541201006
Published online 05 September 2025
  1. M. Szubel, K. Papis-Frączek, S. Podlasek, Impact of the air supply system configuration on the straw combustion in small scale batch-boiler – experimental and numerical studies. Renew. Energy 220, 119540 (2024). https://doi.org/10.1016/J.RENENE.2023.119540 [Google Scholar]
  2. N. Čajová Kantová, M. Holubčík, J. Trnka, A. Čaja, Analysis of Ash Melting Temperatures of Agricultural Pellets Detected during Different Conditions. Fire 6, 88 (2023). https://doi.org/10.3390/fire6030088 [Google Scholar]
  3. A. Backa, N. Čajová Kantová, R. Nosek, M. Patsch, Evaluating the combustion of various biomass pellets in a small heat source with underfeed pellet burner: Heat output, gas emission and ash melting behavior. J. Energy Inst. 118, 101936 (2025). https://doi.org/10.1016/J.JOEI.2024.101936 [Google Scholar]
  4. N. Čajová Kantová, R. Cibula, A. Szlek, A. Čaja, R. Nosek, P. Belany, The energy assessment of COVID-19 medical waste as a potential fuel. Energy Rep. 9, 4995–5003 (2023). https://doi.org/10.1016/J.EGYR.2023.04.018 [Google Scholar]
  5. N. Čajová Kantová, M. Holubčík, A. Čaja, J. Trnka, J. Jandačka, Analyses of pellets produced from spruce sawdust, spruce bark, and pine cones in different proportions. Energies 15, 2725 (2022). https://doi.org/10.3390/en15082725 [Google Scholar]
  6. T. Ye, M. Dong, J. Long, Y. Zheng, Y. Liang, J. Lu, Multi-objective modeling of boiler combustion based on feature fusion and Bayesian optimization. Comput. Chem. Eng. 165, 107913 (2022). https://doi.org/10.1016/J.COMPCHEMENG.2022.107913 [Google Scholar]
  7. W. Wang et al., Dynamic adaptive control of boiler combustion based on improved GNG algorithm. Meas. Sensors 31, 101004 (2024). https://doi.org/10.1016/J.MEASEN.2023.101004 [Google Scholar]
  8. Y. Zhu, C. Yu, W. Jin, L. Shi, B. Chen, P. Xu, Mechanism-enhanced data-driven method for the joint optimization of boiler combustion and selective catalytic reduction systems considering gas temperature deviations. Energy 291, 130432 (2024). https://doi.org/10.1016/J.ENERGY.2024.130432 [Google Scholar]
  9. L. Jing, N. Jingxia, L. Jianhua, L. Shaofeng, L. Zhi, Intelligent control system of stack-boiler. Phys. Procedia 25, 1205–1211 (2012). https://doi.org/10.1016/J.PHPRO.2012.03.221 [Google Scholar]
  10. M. Holubčík, J. Trnka, N. Čajová Kantová, Using heat exchanger for construction of electrostatic precipitator in a small heat source. J. Electrostat. 128, 103884 (2024). https://doi.org/10.1016/J.ELSTAT.2023.103884 [Google Scholar]
  11. Y. Ma, H. Wang, X. Zhang, L. Hou, J. Song, Three-objective optimization of boiler combustion process based on multi-objective teaching–learning based optimization algorithm and ameliorated extreme learning machine. Mach. Learn. Appl. 5, 100082 (2021). https://doi.org/10.1016/J.MLWA.2021.100082 [Google Scholar]
  12. W. Xu, et al., A new online optimization method for boiler combustion system based on the data-driven technique and the case-based reasoning principle. Energy 263, 125508 (2023). https://doi.org/10.1016/J.ENERGY.2022.125508 [Google Scholar]
  13. Z. Wang, et al., BoilerNet: Deep reinforcement learning-based combustion optimization network for pulverized coal boiler. Energy 318, 134804 (2025). https://doi.org/10.1016/J.ENERGY.2025.134804 [Google Scholar]
  14. Y. Xu, et al., Unit load prediction method based on weighted just-in-time learning with spatio-temporal characteristics for gas boiler power generation process. Control Eng. Pract. 160, 106325 (2025). https://doi.org/10.1016/J.CONENGPRAC.2025.106325 [Google Scholar]
  15. T. Wang, J. Tang, H. Xia, C. Yang, W. Yu, J. Qiao, Data-driven multi-objective intelligent optimal control of municipal solid waste incineration process. Eng. Appl. Artif. Intell. 137, 109157 (2024). https://doi.org/10.1016/J.ENGAPPAI.2024.109157 [Google Scholar]
  16. X. Wu, H. Zhang, H. Chen, S. Wang, L. Gong, Combustion optimization study of pulverized coal boiler based on proximal policy optimization algorithm. Appl. Therm. Eng. 254, 123857 (2024). https://doi.org/10.1016/J.APPLTHERMALENG.2024.123857 [Google Scholar]
  17. M. A. Nemitallah et al., Artificial intelligence for control and optimization of boilers’ performance and emissions: A review. J. Clean. Prod. 417, 138109 (2023). https://doi.org/10.1016/J.JCLEPRO.2023.138109 [Google Scholar]
  18. R. Suntivarakorn, W. Treedet, Improvement of boiler’s efficiency using heat recovery and automatic combustion control system. Energy Procedia 100, 193–197 (2016). https://doi.org/10.1016/J.EGYPRO.2016.10.164 [Google Scholar]
  19. A.H. Gomaa Haroun, Y. Ya Li, A novel optimized hybrid fuzzy logic intelligent PID controller for an interconnected multi-area power system with physical constraints and boiler dynamics. ISA Trans. 71, 364–379 (2017). https://doi.org/10.1016/J.ISATRA.2017.09.003 [Google Scholar]
  20. Q. Wang et al., Detailed gas/particle flow characteristics of an improved down- fired boiler with respect to a critical factor affecting coal burnout: Vent-air inclination angle. Energy 182, 570–584 (2019). https://doi.org/10.1016/J.ENERGY.2019.06.057 [Google Scholar]
  21. DFROBOT, DFROBOT. https://wiki.dfrobot.com/ (accessed June 1, 2025). [Google Scholar]
  22. DFROBOT, DFRobot O2 Sensor. https://www.dfrobot.com/product-2052.html (accessed June 1, 2025). [Google Scholar]
  23. DFROBOT, DFRobot PM2.5 sensor. https://www.dfrobot.com/product-2439.html (accessed June 1, 2025). [Google Scholar]
  24. T.R. Lambert, Introduction to Microcontrollers and Embedded Systems [Online]. 2017. https://www.researchgate.net/publication/340062601_Introduction_to_Microcontrollers_and_Embedded_Systems (accessed June 1, 2025). [Google Scholar]
  25. J. Wu, A Basic Guide to I2C: Texas Instruments [Online]. 2017. https://www.ti.com/lit/an/sbaa565/sbaa565.pdf?ts=1748722493977&ref_url=https%253A%252F%252Fwww.google.com%252F (accessed June 1, 2025). [Google Scholar]
  26. F. F. S. N. Tomas Persson, Methodology for identifying parameters for the TRNSYS model Type 210 – wood pellet stoves and boilers [Online]. 2006. https://www.diva-portal.org/smash/get/diva2:522996/FULLTEXT01.pdf (accessed May 30, 2025). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.