IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i22p4245-d284640.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/22/4245/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/22/4245/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    4. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Meizhen Zhang & Tao Lv & Xu Deng & Yuanxu Dai & Muhammad Sajid, 2019. "Diffusion of China’s coal-fired power generation technologies: historical evolution and development trends," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 95(1), pages 7-23, January.
    2. Nannan Wang & Xiaoyan Chen & Guobin Wu, 2019. "Public Private Partnerships, a Value for Money Solution for Clean Coal District Heating Operations," Sustainability, MDPI, vol. 11(8), pages 1-18, April.
    3. Zhao, Changhong & Zhang, Weirong & Wang, Yang & Liu, Qilin & Guo, Jingsheng & Xiong, Minpeng & Yuan, Jiahai, 2017. "The economics of coal power generation in China," Energy Policy, Elsevier, vol. 105(C), pages 1-9.
    4. Kirkby, J. Lars & Nguyen, Duy, 2021. "Equity-linked Guaranteed Minimum Death Benefits with dollar cost averaging," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 408-428.
    5. Cui, Zhenyu & Kirkby, J. Lars & Nguyen, Duy, 2021. "Efficient simulation of generalized SABR and stochastic local volatility models based on Markov chain approximations," European Journal of Operational Research, Elsevier, vol. 290(3), pages 1046-1062.
    6. Ma, Jinrui & Fouladirad, Mitra & Grall, Antoine, 2018. "Flexible wind speed generation model: Markov chain with an embedded diffusion process," Energy, Elsevier, vol. 164(C), pages 316-328.
    7. Sang-Hwan Lee & Hyun Park & Jeong-Gyu Kim, 2023. "Current Status of and Challenges for Phytoremediation as a Sustainable Environmental Management Plan for Abandoned Mine Areas in Korea," Sustainability, MDPI, vol. 15(3), pages 1-13, February.
    8. J. Lars Kirkby & Shi-Jie Deng, 2019. "Swing Option Pricing By Dynamic Programming With B-Spline Density Projection," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(08), pages 1-53, December.
    9. Wang, Jianbiao & Miwa, Tomio & Morikawa, Takayuki, 2023. "Recursive decomposition probability model for demand estimation of street-hailing taxis utilizing GPS trajectory data," Transportation Research Part B: Methodological, Elsevier, vol. 167(C), pages 171-195.
    10. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
    11. Chen, Jiandong & Cheng, Shulei & Song, Malin & Wang, Jia, 2016. "Interregional differences of coal carbon dioxide emissions in China," Energy Policy, Elsevier, vol. 96(C), pages 1-13.
    12. Zaman, Rafia & Brudermann, Thomas & Kumar, S. & Islam, Nazrul, 2018. "A multi-criteria analysis of coal-based power generation in Bangladesh," Energy Policy, Elsevier, vol. 116(C), pages 182-192.
    13. Bowen Da & Chuanzhe Liu & Nana Liu & Sidun Fan, 2021. "Strategies of Two-Level Green Technology Investments for Coal Supply Chain under Different Dominant Modes," Sustainability, MDPI, vol. 13(7), pages 1-24, March.
    14. Ma, Yong & Pan, Dongtao & Shrestha, Keshab & Xu, Weidong, 2020. "Pricing and hedging foreign equity options under Hawkes jump–diffusion processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    15. Zhang, Gongqiu & Li, Lingfei, 2023. "A general method for analysis and valuation of drawdown risk," Journal of Economic Dynamics and Control, Elsevier, vol. 152(C).
    16. Xingyuan Wang & Fan Jia & Yutao Wang, 2015. "Evaluation of Clean Coal Technologies in China: Based on Rough Set Theory," Energy & Environment, , vol. 26(6-7), pages 985-995, November.
    17. Zhang, Xiang & Li, Lingfei & Zhang, Gongqiu, 2021. "Pricing American drawdown options under Markov models," European Journal of Operational Research, Elsevier, vol. 293(3), pages 1188-1205.
    18. Hashemi-Petroodi, S. Ehsan & Thevenin, Simon & Kovalev, Sergey & Dolgui, Alexandre, 2023. "Markov decision process for multi-manned mixed-model assembly lines with walking workers," International Journal of Production Economics, Elsevier, vol. 255(C).
    19. Liu, Chang & Zhu, Bei & Ni, Jinlan & Wei, Chu, 2021. "Residential coal-switch policy in China: Development, achievement, and challenge," Energy Policy, Elsevier, vol. 151(C).
    20. Teng, Ye & Zhang, Zhimin, 2023. "On a time-changed Lévy risk model with capital injections and periodic observation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 214(C), pages 290-314.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:22:p:4245-:d:284640. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.