Open Access
| Issue |
E3S Web Conf.
Volume 459, 2023
XXXIX Siberian Thermophysical Seminar (STS-39)
|
|
|---|---|---|
| Article Number | 07012 | |
| Number of page(s) | 4 | |
| Section | Thermophysical Problems of Energetics, Energy Efficiency and Energy Saving | |
| DOI | https://doi.org/10.1051/e3sconf/202345907012 | |
| Published online | 04 December 2023 | |
- WEC. World Energy Scenarios 2019: European Regional Perspectives. Available online: https://www.worldenergy.org/ (accessed on 9 September 2019) [Google Scholar]
- Henderson, C. IEA Clean Coal Center. Increasing the Flexibility of Coal-Fired Power Plants. Available online: www.iea.org (accessed on 12 January 2021) [Google Scholar]
- World Energy Outlook 2017, International Energy Agency. Available online: http://www.eia.gov (accessed on November 2017) [Google Scholar]
- Coal 2019 Analysis and forecast to 2024. International Energy Agency. Available online: http://www.eia.gov (accessed on December 2019) [Google Scholar]
- Qiu, T., Liu, M., Zhou, G., Wang, L., & Gao, K. An unsupervised classification method for flame image of pulverized coal combustion based on convolutional auto-encoder and hidden Markov model. Energies, 12, 13, 2585 (2019) [CrossRef] [Google Scholar]
- Yan, Y., Lu, G., & Colechin, M. Monitoring and characterisation of pulverised coal flames using digital imaging techniques. Fuel, 81, 5, 647 (2002) [CrossRef] [Google Scholar]
- Kumar, R., Nikam, K., Jilte, R. A simulation model to predict coal-fired power plant production rate using artificial neural network tool, in Applied Computer Vision and Image Processing (pp. 150–160). Springer, Singapore (2020) [CrossRef] [Google Scholar]
- Tan, P., Xia, J., Zhang, C., Fang, Q., & Chen, G. Modeling and reduction of NOX emissions for a 700 MW coal-fired boiler with the advanced machine learning method. Energy, 94, 672–679 (2016) [CrossRef] [Google Scholar]
- Adams, Derrick, et al. Prediction of SOx—NOx emission from a coal-fired CFB power plant with machine learning: Plant data learned by deep neural network and least square support vector machine. Journal of Cleaner Production 270, 122310 (2020) [CrossRef] [Google Scholar]
- Yin, X., Niu, Z., He, Z., Li, Z. S., & Lee, D. H. Ensemble deep learning based semi-supervised soft sensor modeling method and its application on quality prediction for coal preparation process. Advanced Engineering Informatics, 46, 101136 (2020) [CrossRef] [Google Scholar]
- Kuznetsov A. et al. Investigation of ignition and thermal decomposition of coal fuel, waste and sawdust of different degrees of metamorphism based on machine learning methods. Interfacial Phenomena and Heat Transfer, 11, 2 (2023) [Google Scholar]
- Butakov E. B. et al. Experimental studies of the combustion of pulverized coal to form the basis for training a neural network. J. Phys.: Conf. Series. 2233, 012016 (2022) [CrossRef] [Google Scholar]
- Abdurakipov S. et al. Application of computer vision and neural network analysis to study the structure and dynamics of turbulent jets. J. Phys.: Conf. Series. 1421, 012018 (2019) [CrossRef] [Google Scholar]
- Abdurakipov S., Butakov E. Application of computer vision and deep learning for flame monitoring and combustion anomaly detection. J. Phys.: Conf. Series. 1421, 012005 (2019) [CrossRef] [Google Scholar]
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