燃烧和反应

Online Tracking Simulation System of a 660 MW Ultra-Supercritical Circulating Fluidized Bed Boiler

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  • 1. Key Laboratory of Low-grade Energy Utilization Technologies and Systems, Chongqing University, Chongqing 400044, China
    2. School of Energy and Power Engineering, Chongqing University, Chongqing 400044, China

网络出版日期: 2023-10-23

版权

Science Press, Institute of Engineering Thermophysics, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2023

Online Tracking Simulation System of a 660 MW Ultra-Supercritical Circulating Fluidized Bed Boiler

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  • 1. Key Laboratory of Low-grade Energy Utilization Technologies and Systems, Chongqing University, Chongqing 400044, China
    2. School of Energy and Power Engineering, Chongqing University, Chongqing 400044, China

Online published: 2023-10-23

Copyright

Science Press, Institute of Engineering Thermophysics, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2023

摘要

本文建立了660 MW超超临界循环流化床锅炉在线跟踪仿真系统,并对锅炉冷态启动过程进行了跟踪仿真试验。该系统包括两部分:USCFB锅炉模型和基于滑模控制算法的跟踪机构。USCFB锅炉模型包括水蒸汽系统、空气-烟气系统、材料供应系统和灰循环系统。在线跟踪仿真系统接收与工厂相同的控制信号,并在数字空间中同步运行。跟踪机制更新模型参数以消除模拟值和测量值之间的偏差。基于SMC的多输入多输出算法基于状态空间模型,提供了两个明显的优势。首先,它能够更有效地消除偏差;其次,它对与仿真模型行为相关的不确定性表现出鲁棒性。

本文引用格式

WANG Xiaosheng, YANG Chen, ZHANG Zonglong . Online Tracking Simulation System of a 660 MW Ultra-Supercritical Circulating Fluidized Bed Boiler[J]. 热科学学报, 2023 , 32(5) : 1819 -1831 . DOI: 10.1007/s11630-023-1868-x

Abstract

In this paper, an online tracking simulation system for the 660 MW ultra-supercritical circulating fluidized bed (USCFB) boiler is established, and a tracking simulation test is conducted for the cold start-up process of the boiler. The system comprises two parts: the USCFB boiler model and a tracking mechanism based on sliding mode control algorithm. The USCFB boiler model includes a water-steam system, an air-flue gas system, a material supply system, and an ash circulation system. The online tracking simulation system receives the same control signal as the plant and runs synchronously in digital space. The tracking mechanism updates model parameters to eliminate deviations between simulation values and measured values. The SMC-based multi-input, multi-output algorithm is based on a state-space model, providing two distinct advantages. Firstly, it enables more efficient elimination of deviations; secondly, it exhibits robustness against uncertainties associated with simulation model behavior and measurement noise. Finally, this paper conducts tracking simulation research on the cold start-up process of the boiler.

参考文献

[1] Pantelides C.C., Renfro J.G., The online use of first-principles models in process operations: Review, current status and future needs. Computers & Chemical Engineering, 2013, 51: 136–148.
[2] Oppelt M., Graube M., Barth M., et al., Enabling the integrated use of simulation within the life cycle of a process plant: an initial roadmap Results of an in-depth online study. 13th IEEE International Conference on Industrial Informatics, Cambridge, UK, 2015, 7: 22–24. 
DOI: 10.1109/INDIN.2015.7281709.
[3] Seki T., Fukano G., Kawaguchi K., et al., Innovative plant operations by using tracking simulator. Annual Conference of the SICE, Chofu, Japan, 2008, 8: 20–22. DOI: 10.1109/SICE.2008.4655008.
[4] Wang C.B., Duan Q.Z., Zhang C., et al., Applications of online simulation supporting PWR operations. Nuclear Engineering and Technology, 2021, 53(3): 842–850.
[5] Martinez G.S., Karhela T., Vyatkin V., et al., An OPC UA based architecture for testing tracking simulation methods. 13th IEEE International Symposium on Parallel and Distributed Processing with Applications, Helsinki, Finland, 2015, 8: 275–280.
DOI: 10.1109/Trustcom.2015.644.
[6] Martinez G.S., Karhela T., Niemisto H., et al., A hybrid approach for the initialization of tr-acking simulation systems. 20th IEEE Conference on Emerging Technologies and Factory Automation (ETFA), Luxembourg, Luxembourg, 2015, 9: 8–11.
DOI: 10.1109/ETFA.2015.7301532.
[7] Martinez G.S., Karhela T.A., Ruusu R.J., et al., An integrated implementation methodology of a lifecycle-wide tracking simulation architecture. IEEE Access, 2018, 6: 15391–15407.
[8] Nakaya M., Seki T., Kawaguchi K., et al., Model parameter estimation by tracking simulator for the innovation of plant operation. IFAC Proceedings Volumes, 2008, 41(2): 2168–2173.
[9] Nakaya M., Fukano G., Onoe Y., et al., On-line simulator for plant operation. 6th World Congress on Intelligent Control and Automation, Dalian, China, 2006, 6: 21–23.
DOI: 10.1109/WCICA.2006.1713505.
[10] Martinez G.S., Miettinen T., Aikalaa, et al., Parameters selection in predictive online simulation. 14th IEEE International Conference on Industrial Informatics, Poitiers, France, 2016, 6: 19–21. 
DOI: 10.1109/INDIN.2016.7819254.
[11] Nakaya M., Li X.C., On-line tracking simulator with a hybrid of physical and Just-In-Time models. Journal of Process Control, 2013, 23(2): 171–178.
[12] Kawaguchi K., Onoe Y., Nakaya M., et al., An application of on-line tracking simulator to a PEMFC. SICE-ICASE International Joint Conference, Busan, South Korea, 2006, 10: 18–21. 
DOI: 10.1109/INDIN.2016.7819254.
[13] Ruusu R., Martinez G.S., Karhela T., et al., Sliding mode SISO control of model parameters for implicit dynamic feedback estimation of industrial tracking simulation systems. 43rd Annual Conference of the IEEE-Industrial-Electronics-Society (IECON), Beijing, China, 2017, 10: 6927–6932.
DOI: 10.1109/IECON.2017.8217211.
[14] Shtesssel Y., Edwards C., Fridman L., et al., Sliding mode control and observation. Springer New York, 2014.
[15] Sedaghat M., Taheri B., Farhadi P., Power point tracking in photovoltaic systems by sliding mode control. 10th International Symposium on Advanced Topics in Electrical Engineering, 2017, 3: 23–25.
DOI: 10.1109/ATEE.2017.7905097.
[16] Lu R.N., IOP, Research on the structure of smart power plant based on in-depth learning. 5th International Conference on Environmental Science and Material Application (ESMA), Xi’an, China, 2019, 12: 15–16.
DOI: 10.1088/1755-1315/440/3/032113.
[17] Vagnoni E., Gerini F., Cherkaoui R., et al., Digitalization in hydropower generation: development and numerical validation of a model-based smart power plant supervisor. 30th IAHR Symposium on Hydraulic Machinery and Systems (IAHR), Electr Network, 2021, 3: 21–26. 
DOI: 10.1088/1755-1315/774/1/012107.
[18] Snes A., Willersrud A., Kretz F., et al., Predictive maintenance and life cycle estimation for hydro power plants with real-time analytics. Hydro 2018, Gdansk, Poland, 2018.
[19] Liu Z.F., Zhang Y.Z., Yang C.B., et al., Generalized distributed four-domain digital twin system for intelligent manufacturing. Journal of Central South University, 2022, 29(1): 209–225.
[20] Xu J., Huang E., Hsieh L., et al., Simulation optimization in the era of Industrial 4.0 and the Industrial Internet. Journal of Simulation, 2016, 10(4): 310–320.
[21] Yang C., Zhang Z.L., Wu H.C., et al., Dynamic characteristics analysis of a 660 MW ultra-supercritical circulating fluidized bed boiler. Energies, 2022, 15(11): 20.
[22] Zhang Z.L., Yang C., Wu H.C., et al., Modeling and simulation of the start-up process of a 660 MW ultra-supercritical circulating fluidized bed boiler. Computers & Chemical Engineering, 2023, 169: 108079.
[23] Li S.P., Sheen J., Jang S.J., et al., Modified lumped-parameter method for measurements of dielectric susceptibility in ferroelectrics. Japanese Journal of Applied Physics Part 1-Regular Papers Short Notes & Review Papers, 1994, 33(6A): 3617–3621.
[24] Bermudez A., Pena F., Galerkin lumped parameter methods for transient problems. International Journal for Numerical Methods in Engineering, 2011, 87(10): 943–961.
[25] Su J., Improved lumped models for transient radiative cooling of a spherical body. International Communications in Heat and Mass Transfer, 2004, 31(1): 85–94.
[26] Li N., Yan W.P., Structural analysis of 1000 MW ultra supercritical boiler’s steam-water separator. 2nd International Conference on Advanced Design and Manufacturing Engineering (ADME 2012), Taiyuan, China, 2012, 8: 16–18. 
DOI: 10.4028/www.scientific.net/AMM.220223.867.
[27] Wang H.B., Zhou B., Fang S.S., Sliding mode control of permanent magnet synchronous motor speed control system. Transaction of China Electrotechnical Society, 2009, 24(9): 71–77. (in Chinese)
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