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Sustainability-Oriented Furnace Temperature Prediction for Municipal Solid Waste Incineration Using IWOA-SAGRU

Author

Listed:
  • Jinxiang Pian

    (School of Electrical and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China)

  • Mayan Si

    (School of Electrical and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China)

  • Ao Sun

    (Arts and Information Engineering College, Dalian Polytechnic University, Dalian 116400, China)

  • Jian Tang

    (School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China)

Abstract

Municipal solid waste incineration promotes sustainable development by reducing waste, recovering resources, and minimizing environmental impact, with furnace temperature control playing a key role in maximizing efficiency. Accurate real-time temperature prediction is crucial in developing countries to optimize incineration, re-duce emissions, and enhance energy recovery for global sustainability. To address this, we propose a method integrating an improved whale optimization algorithm (IWOA) with a self-attention gated recurrent unit (SAGRU). Using the maximal information coefficient (MIC) to identify key factors, we optimize SAGRU parameters with IWOA, enhancing prediction accuracy by capturing temporal dependencies. Experimental validation from an MSWI plant in China demonstrates that the proposed model significantly enhances prediction accuracy under complex conditions. When compared with the Elman and LSTM models, the error is reduced by 0.7146 and 0.4689, respectively, highlighting its strong potential for practical applications in waste incineration temperature control.

Suggested Citation

  • Jinxiang Pian & Mayan Si & Ao Sun & Jian Tang, 2025. "Sustainability-Oriented Furnace Temperature Prediction for Municipal Solid Waste Incineration Using IWOA-SAGRU," Sustainability, MDPI, vol. 17(20), pages 1-27, October.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:20:p:8987-:d:1768267
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    References listed on IDEAS

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    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. Wang, Yonggang & Zhao, Kaixing & Hao, Yue & Yao, Yilin, 2024. "Short-term wind power prediction using a novel model based on butterfly optimization algorithm-variational mode decomposition-long short-term memory," Applied Energy, Elsevier, vol. 366(C).
    3. Antoine Beylot & Antoine Hochar & Pascale Michel & Marie Descat & Yannick Ménard & Jacques Villeneuve, 2018. "Municipal Solid Waste Incineration in France: An Overview of Air Pollution Control Techniques, Emissions, and Energy Efficiency," Journal of Industrial Ecology, Yale University, vol. 22(5), pages 1016-1026, October.
    4. Tong Li & Tian Xia & Haoming Zhang & Dongyang Liu & Hai Zhao & Zhuolin Liu, 2024. "False Data Injection Attacks Detection Based on Stacking and MIC-DCXGB," Sustainability, MDPI, vol. 16(22), pages 1-13, November.
    5. Fan, Yuchen & Liu, Xin & Zhang, Chaoqun & Li, Chi & Li, Xinying & Wang, Heyang, 2024. "Dynamic prediction of boiler NOx emission with graph convolutional gated recurrent unit model optimized by genetic algorithm," Energy, Elsevier, vol. 294(C).
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