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Markov Chain Simulation of Coal Ash Melting Point and Stochastic Optimization of Operation Temperature for Entrained Flow Coal Gasification

Author

Listed:
  • Jinchun Zhang

    (School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454000, China)

  • Shiheng Guan

    (School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454000, China)

  • Jinxiu Hou

    (State Key Laboratory Cultivation Base for Gas Geology and Gas Control, School of Safety Science and Engineering, Henan Polytechnic University, Jiaozuo 454000, China)

  • Zichuan Zhang

    (School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454000, China)

  • Zhaoqian Li

    (State Key Laboratory Cultivation Base for Gas Geology and Gas Control, School of Safety Science and Engineering, Henan Polytechnic University, Jiaozuo 454000, China)

  • Xiangzhong Meng

    (Henan Pingyuan Optics Electronics Co. Ltd., Jiaozuo 454000, China)

  • Chao Wang

    (Henan Pingyuan Optics Electronics Co. Ltd., Jiaozuo 454000, China)

Abstract

In the entrained flow coal gasification process, the gas production is critically affected by the operating temperature (OT) and coal ash melting point (AMP), and the AMP is one of key factors for the determinations of OT. Considering the fact that coal is a typical nonhomogeneous substance and the coal ash composition varies from batch to batch, this paper proposes the application of the Markov Chain (MC) method in simulation of the random AMP series and the stochastic optimization of OT based on MC simulation for entrained flow coal gasification. The purpose of this paper is to provide a more accurate optimal OT decision method for entrained flow coal gasification practice. In this paper, the AMP was regarded as a random variable, and the random process method, Markov Chain, was used to describe the random AMP series of feed coal. Firstly, the MC simulation model about AMP was founded according to an actual sample data, 200 sets of AMP data from an industrial gasification plant under three simulation schemes (the sample data were individually divided into 16, eight and four state groups,). The comparisons between the simulation results and the actual values show that the founded MC simulation model descries the AMP series very well. Then, a stochastic programming model based on MC simulation for OT optimization was developed. Finally, this stochastic programming optimization model was optimized by genetic algorithm (GA). Comparing with the conventional OT optimization method, the proposed stochastic OT optimization model integrated MC simulation can ascertain a more accurate OT for guiding the coal gasification practice.

Suggested Citation

  • Jinchun Zhang & Shiheng Guan & Jinxiu Hou & Zichuan Zhang & Zhaoqian Li & Xiangzhong Meng & Chao Wang, 2019. "Markov Chain Simulation of Coal Ash Melting Point and Stochastic Optimization of Operation Temperature for Entrained Flow Coal Gasification," Energies, MDPI, vol. 12(22), pages 1-23, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:22:p:4245-:d:284640
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    References listed on IDEAS

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    1. Carapellucci, Roberto & Giordano, Lorena, 2013. "A new approach for synthetically generating wind speeds: A comparison with the Markov chains method," Energy, Elsevier, vol. 49(C), pages 298-305.
    2. Cui, Zhenyu & Lars Kirkby, J. & Nguyen, Duy, 2019. "A general framework for time-changed Markov processes and applications," European Journal of Operational Research, Elsevier, vol. 273(2), pages 785-800.
    3. Bezdek, Roger H. & Wendling, Robert M., 2013. "The return on investment of the clean coal technology program in the USA," Energy Policy, Elsevier, vol. 54(C), pages 104-112.
    4. Tang, Xu & Snowden, Simon & McLellan, Benjamin C. & Höök, Mikael, 2015. "Clean coal use in China: Challenges and policy implications," Energy Policy, Elsevier, vol. 87(C), pages 517-523.
    5. Yu, Xinlian & Gao, Song & Hu, Xianbiao & Park, Hyoshin, 2019. "A Markov decision process approach to vacant taxi routing with e-hailing," Transportation Research Part B: Methodological, Elsevier, vol. 121(C), pages 114-134.
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