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Intelligent Modeling of the Incineration Process in Waste Incineration Power Plant Based on Deep Learning

Author

Listed:
  • Lianhong Chen

    (Shenzhen Energy Environment Engineering Co., Ltd., Shenzhen 518048, China)

  • Chao Wang

    (Shenzhen Energy Environment Engineering Co., Ltd., Shenzhen 518048, China)

  • Rigang Zhong

    (Shenzhen Energy Environment Engineering Co., Ltd., Shenzhen 518048, China)

  • Jin Wang

    (School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China)

  • Zheng Zhao

    (School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China)

Abstract

The incineration process in waste-to-energy plants is characterized by high levels of inertia, large delays, strong coupling, and nonlinearity, which makes accurate modeling difficult. Therefore, an intelligent modeling method for the incineration process in waste-to-energy plants based on deep learning is proposed. First, the output variables were selected from the three aspects of safety, stability and economy. The initial variables related to the output variables were determined by mechanism analysis and the input variables were finally determined by removing invalid and redundant variables through the Lasso algorithm. Secondly, each delay time was calculated, and a multi-input and multi-output model was established on the basis of deep learning. Finally, the deep learning model was compared and verified with traditional models, including LSSVM, CNN, and LSTM. The simulation results show that the intelligent model of the incineration process in the waste-to-energy plant based on deep learning is more accurate and effective than the traditional LSSVM, CNN and LSTM models.

Suggested Citation

  • Lianhong Chen & Chao Wang & Rigang Zhong & Jin Wang & Zheng Zhao, 2022. "Intelligent Modeling of the Incineration Process in Waste Incineration Power Plant Based on Deep Learning," Energies, MDPI, vol. 15(12), pages 1-12, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4285-:d:836385
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    References listed on IDEAS

    as
    1. Magnanelli, Elisa & Tranås, Olaf Lehn & Carlsson, Per & Mosby, Jostein & Becidan, Michael, 2020. "Dynamic modeling of municipal solid waste incineration," Energy, Elsevier, vol. 209(C).
    2. Zhang, Yagang & Zhao, Yunpeng & Shen, Xiaoyu & Zhang, Jinghui, 2022. "A comprehensive wind speed prediction system based on Monte Carlo and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 305(C).
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    Cited by:

    1. Guang Wang & Jiale Xie & Shunli Wang, 2023. "Application of Artificial Intelligence in Power System Monitoring and Fault Diagnosis," Energies, MDPI, vol. 16(14), pages 1-3, July.
    2. Johan De Greef & Quynh N. Hoang & Raf Vandevelde & Wouter Meynendonckx & Zouhir Bouchaar & Giuseppe Granata & Mathias Verbeke & Mariya Ishteva & Tine Seljak & Jo Van Caneghem & Maarten Vanierschot, 2023. "Towards Waste-to-Energy-and-Materials Processes with Advanced Thermochemical Combustion Intelligence in the Circular Economy," Energies, MDPI, vol. 16(4), pages 1-19, February.

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