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Intelligent Control of the Main Steam Flow Rate for the Municipal Solid Waste Incineration Process

Author

Listed:
  • Jinxiang Pian

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

  • Jianyong Liu

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

  • Jian Tang

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

  • Jing Hou

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

Abstract

The stable control of the main steam flow rate (MSFR) can effectively improve the waste combustion efficiency and energy utilization, reduce environmental pollution, and is crucial for promoting the sustainable development of municipal solid waste incineration (MSWI). Developed countries benefit from stable municipal solid waste (MSW) composition, enabling advanced automated combustion control. However, in developing countries, fluctuating waste composition and calorific value cause frequent disturbances, limiting the use of foreign control methods. Therefore, MSFR control technologies suited to developing countries are crucial. This study proposes a two-layer intelligent control method, consisting of an optimization setting layer and a loop control layer. The optimization layer uses a steam flow prediction model (OPTICS and RBF) and an improved antlion optimizer (IALO) for manipulated variable setpoints. The control layer applies reinforcement learning (actor–critic) to fine-tune PI controller parameters. Experimental results show that the proposed method adaptively adjusts manipulated variables, ensuring MSFR control within the target range and maintaining efficient, stable MSWI operation.

Suggested Citation

  • Jinxiang Pian & Jianyong Liu & Jian Tang & Jing Hou, 2025. "Intelligent Control of the Main Steam Flow Rate for the Municipal Solid Waste Incineration Process," Sustainability, MDPI, vol. 17(13), pages 1-30, July.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:13:p:6036-:d:1692446
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    References listed on IDEAS

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    1. 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.
    2. Kumar, Aman & Singh, Ekta & Mishra, Rahul & Lo, Shang Lien & Kumar, Sunil, 2023. "Global trends in municipal solid waste treatment technologies through the lens of sustainable energy development opportunity," Energy, Elsevier, vol. 275(C).
    3. Magnanelli, Elisa & Tranås, Olaf Lehn & Carlsson, Per & Mosby, Jostein & Becidan, Michael, 2020. "Dynamic modeling of municipal solid waste incineration," Energy, Elsevier, vol. 209(C).
    4. 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).
    5. Vesna Antoska Knights & Olivera Petrovska & Jasenka Gajdoš Kljusurić, 2024. "Nonlinear Dynamics and Machine Learning for Robotic Control Systems in IoT Applications," Future Internet, MDPI, vol. 16(12), pages 1-23, November.
    6. Ding, Haixu & Tang, Jian & Qiao, Junfei, 2023. "Dynamic modeling of multi-input and multi-output controlled object for municipal solid waste incineration process," Applied Energy, Elsevier, vol. 339(C).
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