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Predictions of the Key Operating Parameters in Waste Incineration Using Big Data and a Multiverse Optimizer Deep Learning Model

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  • 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)

  • Ye Lu

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

  • Zhuoge Li

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

  • Qiang Wei

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

  • Hongbin Xu

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

Abstract

In order to accurately predict the key operating parameters of waste incinerators, this paper proposes a prediction method based on big data and a Multi-Verse Optimizer deep learning model, thus providing a powerful reference for controlling the optimization of the incinerator combustion process. The key operating parameters that were predicted, according to the control objectives, were determined to be the steam flow, gas oxygen, and flue temperature. Firstly, a large amount of measurement data were collected, and 27 relevant control system parameters with a high correlation with the predicted variables were obtained via a mechanism analysis. The input variables of the prediction model were further determined using the improved WesselN symbolic transfer entropy algorithm. The delay time between the variables was found using a gray correlation coefficient, the prediction time was determined to be 6 min according to the delay time distribution of the flame feature, and the time delay compensation was applied to each parameter. Finally, the support vector machine was optimized using a Multi-Verse Optimization algorithm to complete the prediction of the key operating parameters. Experiments showed that the root mean square error of the proposed model for the three output variables—the steam flow, gas oxygen, and flue temperature—were 0.3035, 0.2477, and 1.6773, respectively, which provides a high accuracy compared to other models.

Suggested Citation

  • Zheng Zhao & Ziyu Zhou & Ye Lu & Zhuoge Li & Qiang Wei & Hongbin Xu, 2023. "Predictions of the Key Operating Parameters in Waste Incineration Using Big Data and a Multiverse Optimizer Deep Learning Model," Sustainability, MDPI, vol. 15(19), pages 1-22, October.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:19:p:14530-:d:1254577
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    References listed on IDEAS

    as
    1. En-Chih Chang & Chun-An Cheng & Lung-Sheng Yang, 2019. "Nonsingular Terminal Sliding Mode Control Based on Binary Particle Swarm Optimization for DC–AC Converters," Energies, MDPI, vol. 12(11), pages 1-14, June.
    2. Mekalathur B Hemanth Kumar & Saravanan Balasubramaniyan & Sanjeevikumar Padmanaban & Jens Bo Holm-Nielsen, 2019. "Wind Energy Potential Assessment by Weibull Parameter Estimation Using Multiverse Optimization Method: A Case Study of Tirumala Region in India," Energies, MDPI, vol. 12(11), pages 1-21, June.
    3. Agnieszka Starzyk & Kinga Rybak-Niedziółka & Przemysław Łacek & Łukasz Mazur & Anna Stefańska & Małgorzata Kurcjusz & Aleksandra Nowysz, 2023. "Environmental and Architectural Solutions in the Problem of Waste Incineration Plants in Poland: A Comparative Analysis," Sustainability, MDPI, vol. 15(3), pages 1-20, February.
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