A Review on Boiler Modeling and Simulation Research for Supercritical Power Generation Units

Jing Chen; Changlong Ma; Geng Liang

doi:10.62051/

Authors

Jing Chen
Changlong Ma
Geng Liang

DOI:

https://doi.org/10.62051/

Keywords:

Supercritical Power Generation Unit with Direct Current Boiler, Mechanism Modeling, Data-driven Modeling, Hybrid Modeling

Abstract

With the global energy structure undergoing a transition towards low-carbonization, supercritical coal-fired power generation technology, due to its high efficiency and low emission characteristics, has become one of the core paths for the clean-up transformation in the power generation sector. This paper mainly discusses and summarizes three types of methods for the mechanism modeling, data-driven modeling, and hybrid modeling of the DC boilers in supercritical power generation units. The hybrid modeling integrates the mechanism and data-driven methods, possessing both physical interpretability and data adaptability, and holds significant theoretical and application value.

References

[1] Vigorously promote the revolution of energy production and consumption in our country [J]. Contemporary Guizhou, 2017, (13): 61.

[2] Zhang Ning. Research on Dynamic Characteristics of Boiler Based on Mechanism and Data Integration Modeling [D]. North China Electric Power University (Hebei), 2010.

[3] Suzuki Y, Sik P, Uchida Y. Simulation of a supercritical once-through boiler[J]. Simulation, 1979, 33(6): 181-193.

[4] K.J. Astrom, R.D. Bell, Drum-boiler dynamics [J], Automatica, 36 (2000): 363-378. [5]De-Mello F.P.,Fellow. Boiler models for system dynamic performance studies [J].IEEE Transaction on Power System, 1991, 6(1):66-73.

[5] Liu Jizhen, Tian Liang, Zeng Deliang, et al. Analysis of Load-Pressure Nonlinear Characteristics of 660MW Unit [J]. Journal of Power Engineering, 2005, (04): 533-536 + 540.

[6] Flynn M E, O'Malley M J. A drum boiler model for long term power system dynamic simulation[J]. IEEE Transactions on Power Systems, 1999, 14(1): 209-217.

[7] Fonseca Jr J G S, Schneider P S. Simulation of a thermal power plant with district heating: Comparative results of 5 different codes[J]. Energy, 2006, 31(12): 1955-1968.

[8] Niu Z, Wong K F V. Adaptive simulation of boiler unit performance[J]. Energy Conversion and Management, 1998, 39(13): 1383-1394.

[9] Liu Shuqing, Yu Shengfang, Zhou Long. Mathematical Model and Simulation Research on 900MW Supercritical Direct Current Boiler [J]. Computer Simulation, 2005, (10): 222-224+252.

[10] Yang Chen, Zhang Lei. Simulation Research on Startup System of 600MW Supercritical Direct Current Boiler [J]. Computer Simulation, 2014, 31(2): 174-178+192.

[11] Wang Chao, Wang Yankai, Chen Xiangrui, et al. Simulation Research on Dynamic Characteristics of Water-cooled Wall of 660MW Supercritical Direct Current Boiler [J/OL]. Journal of Power Engineering, 2024, 44(7): 1001-1009. DOI: 10.19805/j.cnki.jcspe.2024.230297.

[12] Kalogirou S A. Applications of artificial neural-networks for energy systems[J]. Applied energy, 2000, 67(1-2): 17-35.

[13] Dong Yan, Niu Xin, Zhu Yongsheng, et al. Modeling of Decomposition Furnace Temperature Control System Based on Wavelet Extreme Learning Machine [J]. Journal of Zhongyuan Institute of Technology, 2013, 24(2): 1-4.

[14] Wei Liang. Research on Soft Measurement Modeling Method Based on Artificial Intelligence and Its Application in Thermal Process [D]. North China Electric Power University, 2014.

[15] Ogilvie T, Swidenbank E, Hogg B W. Use of data mining techniques in the performance monitoring and optimisation of a thermal power plant[C]//IEE Two-day Colloquium on Knowledge Discovery and Data Mining. London UK: IEE, 1998: 7/1-7/4.

[16] Zhang Xiaotao, Ni Weidou, Li Zheng, et al. Identifiability of Thermal Process Object Based on Field Data Modeling [J]. Journal of Tsinghua University (Science and Technology), 2004, (11): 1544-1547. DOI: 10.16511/j. cnki. qhdxxb. 2004.11.028.

[17] Xu Hongbing, Yuan Shitong, Ding Man. Modeling of Combustion System for 1000MW Ultra-supercritical Boiler [J]. Electric Power, 2011, 44 (12): 42-46.

[18] Fu Zhongguang, Jin Tao, Zhou Lijun, et al. Research on Reverse Modeling Method and Application of Partial Least Squares Modeling in Complex Systems [J]. Proceedings of the CSEE, 2009, 29 (02): 25-29.

[19] Rusinowski H, Stanek W. Hybrid model of steam boiler[J]. Energy, 2010, 35(2): 1107-1113.

[20] Gao Y, Zeng D, Liu J, et al. Optimization control of a pulverizing system on the basis of the estimation of the outlet coal powder flow of a coal mill[J]. Control Engineering Practice, 2017, 63: 69-80.

[21] Cao Xia. Research on Hybrid Model of Double-Input Double-Output Coal Mill Based on Neural Network [D]. Shenyang University of Technology, 2011.

[22] Li Bingnan, Zhu Feng, Yan Zhiwei, et al. Research on Modeling of Coordination System for Ultra-supercritical Thermal Power Plant and Model Predictive Control Algorithm [J]. Thermal Energy Power Engineering, 2020, 35 (02): 117-125. DOI: 10.16146/j.cnki.rndlgc.2020.02.016.

[23] Jia Zoushi, Sun Haoshun, Li Wenhui, et al. Modeling Method of Thermal Power Plant Based on Mechanism and Data Fusion [J]. Energy Conservation Technology, 2022, 40 (03): 267-271.

[24] Li Maoyuan. Research on Dynamic Characteristics of Boiler Based on Mechanism and Data Integration Modeling [J]. Heilongjiang Science and Technology Information, 2012, (05): 108.

[25] Zhang Ning. Research on Dynamic Characteristics of Boiler Based on Mechanism and Data Integration Modeling [D]. Hebei University of Technology (Hebei), 2010.

[26] Qin Zhiming. Research on Dynamic Model and Control of Ultra-supercritical Unit [D]. North China Electric Power University, 2014.