[1] Huang Y.S., Shi M.S., What are the environmental advantages of circulating fluidized bed technology? ——A case study in China. Energy, 2021, 220: 119711. https://doi.org/10.1016/j.energy.2020.119711
[2] Yue G., Cai R., Lu J., et al., From a CFB reactor to a CFB boiler – The review of R&D progress of CFB coal combustion technology in China. Powder Technology, 2017, 316: 18–28. https://doi.org/10.1016/j.powtec.2016.10.062
[3] Zhang H., Lyu J., Yue G., A review on research and development of CFB combustion technology in China. Powder Technology, 2023, 414: 118090. https://doi.org/10.1016/j.powtec.2022.118090
[4] Hong F., Yan G., Gao M., The operation control and application of CFB boiler unit with high blending ratio of coal slurry. Control Engineering Practice, 2019, 85: 80–89. https://doi.org/10.1016/j.conengprac.2018.12.001
[5] Gao M., Hong F., Yan G., et al., Mechanism modelling on the coordinated control system of a coal-fired subcritical circulating fluidized bed unit. Applied Thermal Engineering, 2019, 146: 548–555. https://doi.org/10.1016/j.applthermaleng.2018.09.119
[6] Lee B.-H., Bae Y.-H., Kim K.-M., et al., Application of the CPFD method to analyze the effects of bed material density on gas-particle hydrodynamics and wall erosion in a CFB boiler. Fuel, 2023, 342: 127878. https://doi.org/10.1016/j.fuel.2023.127878
[7] Chen Y., Lu X., Zhang W., et al., An experimental study on the hydrodynamic performance of the water-wall system of a 600 MW supercritical CFB boiler. Applied Thermal Engineering, 2018, 141: 280–287.
https://doi.org/10.1016/j.applthermaleng.2018.01.022
[8] Madejski P., Taler D., Taler J., Thermal and flow calculations of platen superheater in large scale CFB boiler. Energy, 2022, 258: 124841. https://doi.org/10.1016/j.energy.2022.124841
[9] Li D., Kim K., Kim M., et al., Effects of particle size on bed-to-surface heat transfer in bubbling fluidized bed heat exchangers of 550 MWe ultra-supercritical CFB boiler. Energy, 2021, 222: 119889. https://doi.org/10.1016/j.energy.2021.119889
[10] Yan J., Zheng X., Lu X., et al., Enhanced combustion behavior and NOx reduction performance in a CFB combustor by combining flue gas recirculation with air-staging: Effect of injection position. Journal of the Energy Institute, 2021, 96: 294–309. https://doi.org/10.1016/j.joei.2021.03.012
[11] Ke X., Cai R., Zhang M., et al., Application of ultra-low NOx emission control for CFB boilers based on theoretical analysis and industrial practices. Fuel Processing Technology. 2018, 181: 252–258. https://doi.org/10.1016/j.fuproc.2018.10.001
[12] Ke X., Engblom M., Yang H., et al., Prediction and minimization of NOx emission in a circulating fluidized bed combustor: A comprehensive mathematical model for CFB combustion. Fuel, 2022, 309: 122133. https://doi.org/10.1016/j.fuel.2021.122133
[13] Ke X., Li D., Li Y., et al., 1-Dimensional modelling of in-situ desulphurization performance of a 550 MWe ultra-supercritical CFB boiler. Fuel, 2021, 290: 120088. https://doi.org/10.1016/j.fuel.2020.120088
[14] Liang T., Chai C., Sun H., et al., Wind speed prediction based on multi-variable Capsnet-BILSTM-MOHHO for WPCCC. Energy, 2022, 250: 123761. https://doi.org/10.1016/j.energy.2022.123761
[15] Wang X., Liu W., Wang Y., et al., A hybrid NOx emission prediction model based on CEEMDAN and AM-LSTM. Fuel, 2022, 310: 122486. https://doi.org/10.1016/j.fuel.2021.122486
[16] Nakhle F., Harfouche A.L., Ready, Steady, Go AI: A practical tutorial on fundamentals of artificial intelligence and its applications in phenomics image analysis. Patterns, 2021, 2(9): 100323. https://doi.org/10.1016/j.patter.2021.100323
[17] Zhou H., Cen K., Fan J., Multi-objective optimization of the coal combustion performance with artificial neural networks and genetic algorithms. International Journal of Energy Research, 2005, 29(6): 499–510. https://doi.org/10.1002/er.1070
[18] Kang L., Yuan X., Sun K., et al., Feed-forward active operation optimization for CCHP system considering thermal load forecasting. Energy, 2022, 254: 124234. https://doi.org/10.1016/j.energy.2022.124234
[19] Grochowalski J., Jachymek P., Andrzejczyk M., et al., Towards application of machine learning algorithms for prediction temperature distribution within CFB boiler based on specified operating conditions. Energy, 2021, 237: 121538. https://doi.org/10.1016/j.energy.2021.121538
[20] Lv Y., Hong F., Yang T., et al., A dynamic model for the bed temperature prediction of circulating fluidized bed boilers based on least squares support vector machine with real operational data. Energy, 2017, 124: 284–294. https://doi.org/10.1016/j.energy.2017.02.031
[21] Hong F., Long D., Chen J., et al., Modeling for the bed temperature 2D-interval prediction of CFB boilers based on long-short term memory network. Energy, 2020, 194: 116733. https://doi.org/10.1016/j.energy.2019.116733
[22] Sleiti A.K., Kapat J.S., Vesely L., Digital twin in energy industry: Proposed robust digital twin for power plant and other complex capital-intensive large engineering systems. Energy Reports, 2022, 8: 3704–3726. https://doi.org/10.1016/j.egyr.2022.02.305
[23] Söderäng E., Hautala S., Mikulski M., et al., Development of a digital twin for real-time simulation of a combustion engine-based power plant with battery storage and grid coupling. Energy Conversion and Management, 2022, 266: 115793. https://doi.org/10.1016/j.enconman.2022.115793
[24] Deon B., Cotta K.P., Silva R.F.V., et al., Digital twin and machine learning for decision support in thermal power plant with combustion engines. Knowledge-Based Systems, 2022, 253: 109578. https://doi.org/10.1016/j.knosys.2022.109578
[25] Zhao G., Cui Z., Xu J., et al., Hybrid modeling-based digital twin for performance optimization with flexible operation in the direct air-cooling power unit. Energy, 2022, 254: 124492. https://doi.org/10.1016/j.energy.2022.124492
[26] Hou L., Zhang J., Wu O., et al., Method and dataset entity mining in scientific literature: A CNN + BiLSTM model with self-attention. Knowledge-Based Systems, 2022, 235: 107621. https://doi.org/10.1016/j.knosys.2021.107621
[27] Ewees A.A., Al-qaness M.A.A., Abualigah L., et al., HBO-LSTM: Optimized long short term memory with heap-based optimizer for wind power forecasting. Energy Conversion and Management, 2022, 268: 116022. https://doi.org/10.1016/j.enconman.2022.116022
[28] KasapbaŞI A., Elbushra A.E.A., Al-Hardanee O., et al., DeepASLR: A CNN based human computer interface for American Sign Language recognition for hearing-impaired individuals. Computer Methods and Programs in Biomedicine Update, 2022, 2: 100048.
https://doi.org/10.1016/j.cmpbup.2021.100048
[29] Ni P., Li G., Hung P.C.K., et al., StaResGRU-CNN with CMedLMs: A stacked residual GRU-CNN with pre-trained biomedical language models for predictive intelligence. Applied Soft Computing, 2021, 113: 107975. https://doi.org/10.1016/j.asoc.2021.107975
[30] Lv H., Chen J., Pan T., et al., Attention mechanism in intelligent fault diagnosis of machinery: A review of technique and application. Measurement, 2022, 199: 111594. https://doi.org/10.1016/j.measurement.2022.111594
[31] Zhang H., Gao M., Yu H., et al., A dynamic nonlinear model used for controller design of a 600 MW supercritical circulating fluidized bed boiler-turbine unit. Applied Thermal Engineering, 2022, 212: 118547. https://doi.org/10.1016/j.applthermaleng.2022.118547
[32] Zhang H., Gao M., Fan H., et al., A dynamic model for supercritical once-through circulating fluidized bed boiler-turbine units. Energy, 2022, 241: 122914. https://doi.org/10.1016/j.energy.2021.122914
[33] Liu J.-Z., Yan S., Zeng D.-L., et al., A dynamic model used for controller design of a coal fired once-through boiler-turbine unit. Energy, 2015, 93: 2069–2078. https://doi.org/10.1016/j.energy.2015.10.077
[34] Niu Y., Du M., Ge W., et al., A dynamic nonlinear model for a once-through boiler-turbine unit in low load. Applied Thermal Engineering, 2019, 161: 113880. https://doi.org/10.1016/j.applthermaleng.2019.113880
[35] Fan H., Zhang Y.-F., Su Z.-G., et al., A dynamic mathematical model of an ultra-supercritical coal fired once-through boiler-turbine unit. Applied Energy, 2017, 189: 654–666. https://doi.org/10.1016/j.apenergy.2016.11.074
[36] Hu Y., Zeng D.L., Liu J.Z., et al., Dynamic model for controller design of condensate throttling systems. ISA Transactions, 2015, 58: 622–628. https://doi.org/10.1016/j.isatra.2015.07.002
[37] Yu H., Gao M., Zhang H., et al., Dynamic modeling for SO2-NOx emission concentration of circulating fluidized bed units based on quantum genetic algorithm - Extreme learning machine. Journal of Cleaner Production, 2021, 324: 129170. https://doi.org/10.1016/j.jclepro.2021.129170
[38] Konar D., Bhattacharyya S., Sharma K., et al., An improved Hybrid Quantum-Inspired Genetic Algorithm (HQIGA) for scheduling of real-time task in multiprocessor system. Applied Soft Computing, 2017, 53: 296–307. https://doi.org/10.1016/j.asoc.2016.12.051
