This work is licensed under the Creative Commons Attribution 4.0 International License.
Lei, Z., Zhou, H., Hu, W., Liu, G. P., Guan, S., & Feng, X. (2022). Toward a web-based digital twin thermal power plant. IEEE Transactions on Industrial Informatics, 18, 1716–1725.LeiZ.ZhouH.HuW.LiuG. P.GuanS.FengX. (2022). Toward a web-based digital twin thermal power plant. IEEE Transactions on Industrial Informatics, 18, 1716–1725.Search in Google Scholar
Song, F., Mehedi, H., Liang, C., Meng, J., Chen, Z., & Shi, F. (2021). Review of transition paths for coal-fired power plants. Global Energy Interconnection, 4, 354–370.SongF.MehediH.LiangC.MengJ.ChenZ.ShiF. (2021). Review of transition paths for coal-fired power plants. Global Energy Interconnection, 4, 354–370.Search in Google Scholar
Isnandar, S., Simorangkir, J. F., Banjar-Nahor, K. M., Paradongan, H. T., & Hariyanto, N. (2024). A multiparadigm approach for generation dispatch optimization in a regulated electricity market towards clean energy transition. Energies, 17.IsnandarS.SimorangkirJ. F.Banjar-NahorK. M.ParadonganH. T.HariyantoN. (2024). A multiparadigm approach for generation dispatch optimization in a regulated electricity market towards clean energy transition. Energies, 17.Search in Google Scholar
Liu, P., Li, D., Liu, Y., Chen, Z., Liao, H., & Feng, Y. (2022). Research progress and prospect of radiative heat transfer calculation method in pulverized coal combustion process on coal-fired power plant. Guangdong Electric Power, 35, 11–20.LiuP.LiD.LiuY.ChenZ.LiaoH.FengY. (2022). Research progress and prospect of radiative heat transfer calculation method in pulverized coal combustion process on coal-fired power plant. Guangdong Electric Power, 35, 11–20.Search in Google Scholar
Huang, S., Liu, C., & Goda, K. (2023). Applicability of smooth particle hydrodynamics method to large sliding deformation of saturated slopes under earthquake action. Chinese Journal of Geotechnical Engineering, 45(2), 336–344.HuangS.LiuC.GodaK. (2023). Applicability of smooth particle hydrodynamics method to large sliding deformation of saturated slopes under earthquake action. Chinese Journal of Geotechnical Engineering, 45(2), 336–344.Search in Google Scholar
Cai, J., Chen, W., Wang, D., Ding, N., Zhang, J., Dong, L., Zhang, X., & Cai, P. (2022). Research and application of coal blockage early warning judgment in coal pulverizing system of thermal power generating units. IEEE Journal of Radio Frequency Identification, 6, 911–916.CaiJ.ChenW.WangD.DingN.ZhangJ.DongL.ZhangX.CaiP. (2022). Research and application of coal blockage early warning judgment in coal pulverizing system of thermal power generating units. IEEE Journal of Radio Frequency Identification, 6, 911–916.Search in Google Scholar
Yao, Z., Romero, C., & Baltrusaitis, J. (2023). Combustion optimization of a coal-fired power plant boiler using artificial intelligence neural networks. Fuel, 344, 128145.YaoZ.RomeroC.BaltrusaitisJ. (2023). Combustion optimization of a coal-fired power plant boiler using artificial intelligence neural networks. Fuel, 344, 128145.Search in Google Scholar
Ghose, P., Sahoo, T. K., & Sahu, A. K. (2023). Pulverized coal combustion computational modeling approach: A review. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 237, 797–818.GhoseP.SahooT. K.SahuA. K. (2023). Pulverized coal combustion computational modeling approach: A review. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 237, 797–818.Search in Google Scholar
Chen, L., Xu, Y., Tian, S., & Lu, H. (2024). Numerical simulation study of combustion under different excess air factors in a flow pulverized coal burner. Processes, 12, 1607.ChenL.XuY.TianS.LuH. (2024). Numerical simulation study of combustion under different excess air factors in a flow pulverized coal burner. Processes, 12, 1607.Search in Google Scholar
Liu, K., Wang, C., Wang, L., Liu, B., Ye, M., Guo, Y., & Che, D. (2023). Dynamic performance analysis and control strategy optimization for supercritical coal-fired boiler: A dynamic simulation. Energy, 282, 128712.LiuK.WangC.WangL.LiuB.YeM.GuoY.CheD. (2023). Dynamic performance analysis and control strategy optimization for supercritical coal-fired boiler: A dynamic simulation. Energy, 282, 128712.Search in Google Scholar
Yan, Z., Lu, Z., Yao, S., Shen, Y., & Mo, J. (2019). Self-ignition characteristic experiment of pulverized coal and discussion on influence factors on outlet temperature of coal mill. Guangdong Electric Power, 32, 15–22.YanZ.LuZ.YaoS.ShenY.MoJ. (2019). Self-ignition characteristic experiment of pulverized coal and discussion on influence factors on outlet temperature of coal mill. Guangdong Electric Power, 32, 15–22.Search in Google Scholar
Gao, Y., Zeng, D., Liu, J., & Jian, Y. (2017). Optimization control of a pulverizing system on the basis of the estimation of the outlet coal powder flow of a coal mill. Control Engineering Practice, 63, 69–80.GaoY.ZengD.LiuJ.JianY. (2017). Optimization control of a pulverizing system on the basis of the estimation of the outlet coal powder flow of a coal mill. Control Engineering Practice, 63, 69–80.Search in Google Scholar
Menhas, M. I., Fei, M., Wang, L., & Qian, L. (2012). Real/binary co-operative and co-evolving swarms based multivariable PID controller design of ball mill pulverizing system. Energy Conversion and Management, 54, 67–80.MenhasM. I.FeiM.WangL.QianL. (2012). Real/binary co-operative and co-evolving swarms based multivariable PID controller design of ball mill pulverizing system. Energy Conversion and Management, 54, 67–80.Search in Google Scholar
Deng, Y., Weng, Z., & Zhang, T. (2023). Metaverse-driven remote management solution for scene-based energy storage power stations. Evolutionary Intelligence, 16, 1521–1532.DengY.WengZ.ZhangT. (2023). Metaverse-driven remote management solution for scene-based energy storage power stations. Evolutionary Intelligence, 16, 1521–1532.Search in Google Scholar
Chen, Z., Yan, Z., Jiang, H., Que, Z., Gao, G., & Xu, Z. (2020). Detecting coal pulverizing system anomaly using a gated recurrent unit and clustering. Sensors, 20, 3271.ChenZ.YanZ.JiangH.QueZ.GaoG.XuZ. (2020). Detecting coal pulverizing system anomaly using a gated recurrent unit and clustering. Sensors, 20, 3271.Search in Google Scholar
Yan, Z., & Xiao, D. (2024). Identification of coal, gangue, and surrounding rock based on LIBS and deep learning. IEEE Transactions on Instrumentation and Measurement, 73, 1–9.YanZ.XiaoD. (2024). Identification of coal, gangue, and surrounding rock based on LIBS and deep learning. IEEE Transactions on Instrumentation and Measurement, 73, 1–9.Search in Google Scholar
Yan, B., & Fang, H. (2020). Neural network model of information fusion for coal storage and kinetic energy of ball mill. Journal of System Simulation, 27, 689-696.YanB.FangH. (2020). Neural network model of information fusion for coal storage and kinetic energy of ball mill. Journal of System Simulation, 27, 689-696.Search in Google Scholar
Hu, G., Zhou, T., & Liu, Q. (2021). Data-driven machine learning for fault detection and diagnosis in nuclear power plants: A review. Frontiers in Energy Research, 9, 663296.HuG.ZhouT.LiuQ. (2021). Data-driven machine learning for fault detection and diagnosis in nuclear power plants: A review. Frontiers in Energy Research, 9, 663296.Search in Google Scholar
Gonzalez-Jimenez, D., Del-Olmo, J., Poza, J., Garramiola, F., & Madina, P. (2021). Data-driven fault diagnosis for electric drives: A review. Sensors, 21, 4024.Gonzalez-JimenezD.Del-OlmoJ.PozaJ.GarramiolaF.MadinaP. (2021). Data-driven fault diagnosis for electric drives: A review. Sensors, 21, 4024.Search in Google Scholar
Danish, M. S. S., Nazari, Z., & Senjyu, T. (2023). AI-coherent data-driven forecasting model for a combined cycle power plant. Energy Conversion and Management, 286, 117063.DanishM. S. S.NazariZ.SenjyuT. (2023). AI-coherent data-driven forecasting model for a combined cycle power plant. Energy Conversion and Management, 286, 117063.Search in Google Scholar
Yang, G., Wang, Y., & Li, X. (2020). Prediction of the NOx emissions from thermal power plant using long-short term memory neural network. Energy, 192, 116597.YangG.WangY.LiX. (2020). Prediction of the NOx emissions from thermal power plant using long-short term memory neural network. Energy, 192, 116597.Search in Google Scholar
Xu, C., Liao, Z., Li, C., Zhou, X., & Xie, R. (2022). Review on interpretable machine learning in smart grid. Energies, 15, 4427.XuC.LiaoZ.LiC.ZhouX.XieR. (2022). Review on interpretable machine learning in smart grid. Energies, 15, 4427.Search in Google Scholar
Zhang, L., Zhang, H., & Cai, G. (2022). The multiclass fault diagnosis of wind turbine bearing based on multisource signal fusion and deep learning generative model. IEEE Transactions on Instrumentation and Measurement, 71, 1–12.ZhangL.ZhangH.CaiG. (2022). The multiclass fault diagnosis of wind turbine bearing based on multisource signal fusion and deep learning generative model. IEEE Transactions on Instrumentation and Measurement, 71, 1–12.Search in Google Scholar
Yadav, S. P., & Yadav, S. (2020). Image fusion using hybrid methods in multimodality medical images. Medical & Biological Engineering & Computing, 58, 669–687.YadavS. P.YadavS. (2020). Image fusion using hybrid methods in multimodality medical images. Medical & Biological Engineering & Computing, 58, 669–687.Search in Google Scholar