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Prediction of Main Parameters of Steam in Waste Incinerators Based on BAS-SVM

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)

  • Zhuoge Li

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

  • Zheng Zhao

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

  • Ziyu Zhou

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

Abstract

The main steam parameters of a waste-to-energy plant are the key indicator of the safety and stability of its combustion process. Accurate prediction of the main steam parameters can help the control system to reasonably analyze the combustion conditions and, thus, to greatly improve the combustion efficiency. In this paper, we propose an optimized method for predicting the main steam parameters of waste incinerators. Firstly, a grey relational analysis (GRA) is used to obtain the ranking of the correlation degree between 114 characteristic variables in the furnace and the main steam parameters, and 13 characteristic variables are selected as model inputs. A Spearman-based time delay compensation method is proposed to effectively overcome the influence of time asynchrony on the prediction accuracy. At last, the beetle antennae search (BAS) optimized support vector machine (SVM) model is proposed. Taking advantage of the fast iteration of the beetle antennae search algorithm to find the key hyperparameters of the support vector machine, the optimized main steam parameter prediction model is finally obtained. The simulation results show that the prediction accuracy of this model is greatly improved compared with traditional neural network models, such as long short-term memory neural networks (LSTMs) and convolutional neural networks (CNNs), as well as a single SVM.

Suggested Citation

  • Lianhong Chen & Chao Wang & Rigang Zhong & Zhuoge Li & Zheng Zhao & Ziyu Zhou, 2023. "Prediction of Main Parameters of Steam in Waste Incinerators Based on BAS-SVM," Sustainability, MDPI, vol. 15(2), pages 1-16, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1132-:d:1027943
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    References listed on IDEAS

    as
    1. Zixue Luo & Wei Chen & Yue Wang & Qiang Cheng & Xiaohua Yuan & Zhigang Li & Junjie Yang, 2021. "Numerical Simulation of Combustion and Characteristics of Fly Ash and Slag in a “V-type” Waste Incinerator," Energies, MDPI, vol. 14(22), pages 1-12, November.
    2. Zhang, Yagang & Zhang, Jinghui & Yu, Leyi & Pan, Zhiya & Feng, Changyou & Sun, Yiqian & Wang, Fei, 2022. "A short-term wind energy hybrid optimal prediction system with denoising and novel error correction technique," Energy, Elsevier, vol. 254(PC).
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    Citations

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    Cited by:

    1. 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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