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Integrating multi-objective optimization with computational fluid dynamics to optimize boiler combustion process of a coal fired power plant

Author

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  • Liu, Xingrang
  • Bansal, R.C.

Abstract

The dominant role of electricity generation and environment consideration have placed strong requirements on coal fired power plants, requiring them to improve boiler combustion efficiency and decrease carbon emission. Although neural network based optimization strategies are often applied to improve the coal fired power plant boiler efficiency, they are limited by some combustion related problems such as slagging. Slagging can seriously influence heat transfer rate and decrease the boiler efficiency. In addition, it is difficult to measure slag build-up. The lack of measurement for slagging can restrict conventional neural network based coal fired boiler optimization, because no data can be used to train the neural network. This paper proposes a novel method of integrating non-dominated sorting genetic algorithm (NSGA II) based multi-objective optimization with computational fluid dynamics (CFD) to decrease or even avoid slagging inside a coal fired boiler furnace and improve boiler combustion efficiency. Compared with conventional neural network based boiler optimization methods, the method developed in the work can control and optimize the fields of flue gas properties such as temperature field inside a boiler by adjusting the temperature and velocity of primary and secondary air in coal fired power plant boiler control systems. The temperature in the vicinity of water wall tubes of a boiler can be maintained within the ash melting temperature limit. The incoming ash particles cannot melt and bond to surface of heat transfer equipment of a boiler. So the trend of slagging inside furnace is controlled. Furthermore, the optimized boiler combustion can keep higher heat transfer efficiency than that of the non-optimized boiler combustion. The software is developed to realize the proposed method and obtain the encouraging results through combining ANSYS 14.5, ANSYS Fluent 14.5 and CORBA C++.

Suggested Citation

  • Liu, Xingrang & Bansal, R.C., 2014. "Integrating multi-objective optimization with computational fluid dynamics to optimize boiler combustion process of a coal fired power plant," Applied Energy, Elsevier, vol. 130(C), pages 658-669.
    • Handle: RePEc:eee:appene:v:130:y:2014:i:c:p:658-669
    DOI: 10.1016/j.apenergy.2014.02.069
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    1. Álvarez, L. & Gharebaghi, M. & Jones, J.M. & Pourkashanian, M. & Williams, A. & Riaza, J. & Pevida, C. & Pis, J.J. & Rubiera, F., 2013. "CFD modeling of oxy-coal combustion: Prediction of burnout, volatile and NO precursors release," Applied Energy, Elsevier, vol. 104(C), pages 653-665.
    2. Colorado, D. & Hernández, J.A. & Rivera, W. & Martínez, H. & Juárez, D., 2011. "Optimal operation conditions for a single-stage heat transformer by means of an artificial neural network inverse," Applied Energy, Elsevier, vol. 88(4), pages 1281-1290, April.
    3. Palmé, Thomas & Fast, Magnus & Thern, Marcus, 2011. "Gas turbine sensor validation through classification with artificial neural networks," Applied Energy, Elsevier, vol. 88(11), pages 3898-3904.
    4. Galindo, J. & Fajardo, P. & Navarro, R. & García-Cuevas, L.M., 2013. "Characterization of a radial turbocharger turbine in pulsating flow by means of CFD and its application to engine modeling," Applied Energy, Elsevier, vol. 103(C), pages 116-127.
    5. Breault, Ronald W. & Huckaby, E. David, 2013. "Parametric behavior of a CO2 capture process: CFD simulation of solid-sorbent CO2 absorption in a riser reactor," Applied Energy, Elsevier, vol. 112(C), pages 224-234.
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    1. Long, Rui & Li, Baode & Liu, Zhichun & Liu, Wei, 2015. "Multi-objective optimization of a continuous thermally regenerative electrochemical cycle for waste heat recovery," Energy, Elsevier, vol. 93(P1), pages 1022-1029.
    2. Guo, Sisi & Liu, Pei & Li, Zheng, 2016. "Identification and isolability of multiple gross errors in measured data for power plants," Energy, Elsevier, vol. 114(C), pages 177-187.
    3. Chang, Hsuan & Hsu, Jian-An & Chang, Cheng-Liang & Ho, Chii-Dong & Cheng, Tung-Wen, 2017. "Simulation study of transfer characteristics for spacer-filled membrane distillation desalination modules," Applied Energy, Elsevier, vol. 185(P2), pages 2045-2057.
    4. Yin, Chungen, 2015. "On gas and particle radiation in pulverized fuel combustion furnaces," Applied Energy, Elsevier, vol. 157(C), pages 554-561.
    5. Sang-Mok Lee & So-Won Choi & Eul-Bum Lee, 2023. "Prediction Modeling of Flue Gas Control for Combustion Efficiency Optimization for Steel Mill Power Plant Boilers Based on Partial Least Squares Regression (PLSR)," Energies, MDPI, vol. 16(19), pages 1-33, September.
    6. Gavirineni Naveen Kumar & Edison Gundabattini, 2022. "Investigation of Supercritical Power Plant Boiler Combustion Process Optimization through CFD and Genetic Algorithm Methods," Energies, MDPI, vol. 15(23), pages 1-28, November.
    7. Nikula, Riku-Pekka & Ruusunen, Mika & Leiviskä, Kauko, 2016. "Data-driven framework for boiler performance monitoring," Applied Energy, Elsevier, vol. 183(C), pages 1374-1388.
    8. Adeniyi K. Onaolapo & Gulshan Sharma & Pitshou N. Bokoro & Anuoluwapo Aluko & Giovanni Pau, 2023. "A Distributed Control Scheme for Cyber-Physical DC Microgrid Systems," Energies, MDPI, vol. 16(15), pages 1-17, July.
    9. Qianchao Wang & Hongcan Xu & Lei Pan & Li Sun, 2020. "Active Disturbance Rejection Control of Boiler Forced Draft System: A Data-Driven Practice," Sustainability, MDPI, vol. 12(10), pages 1-18, May.
    10. Wu, X.D. & Xia, X.H. & Chen, G.Q. & Wu, X.F. & Chen, B., 2016. "Embodied energy analysis for coal-based power generation system-highlighting the role of indirect energy cost," Applied Energy, Elsevier, vol. 184(C), pages 936-950.
    11. Wang, Yuelan & Ma, Zengyi & Shen, Yueliang & Tang, Yijun & Ni, Mingjiang & Chi, Yong & Yan, Jianhua & Cen, Kefa, 2016. "A power-saving control strategy for reducing the total pressure applied by the primary air fan of a coal-fired power plant," Applied Energy, Elsevier, vol. 175(C), pages 380-388.
    12. Mikulčić, Hrvoje & von Berg, Eberhard & Vujanović, Milan & Wang, Xuebin & Tan, Houzhang & Duić, Neven, 2016. "Numerical evaluation of different pulverized coal and solid recovered fuel co-firing modes inside a large-scale cement calciner," Applied Energy, Elsevier, vol. 184(C), pages 1292-1305.
    13. Tang, Wei & Feng, Huijun & Chen, Lingen & Xie, Zhuojun & Shi, Junchao, 2021. "Constructal design for a boiler economizer," Energy, Elsevier, vol. 223(C).

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