IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i5p2042-d1349223.html
   My bibliography  Save this article

An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process

Author

Listed:
  • Jian Tang

    (Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
    Beijing Laboratory of Smart Environmental Protection, Beijing 100124, China)

  • Tianzheng Wang

    (Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
    Beijing Laboratory of Smart Environmental Protection, Beijing 100124, China)

  • Heng Xia

    (Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
    Beijing Laboratory of Smart Environmental Protection, Beijing 100124, China)

  • Canlin Cui

    (Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
    Beijing Laboratory of Smart Environmental Protection, Beijing 100124, China)

Abstract

Artificial intelligence (AI) has found widespread application across diverse domains, including residential life and product manufacturing. Municipal solid waste incineration (MSWI) represents a significant avenue for realizing waste-to-energy (WTE) objectives, emphasizing resource reuse and sustainability. Theoretically, AI holds the potential to facilitate optimal control of the MSWI process in terms of achieving minimal pollution emissions and maximal energy efficiency. However, a noticeable shortage exists in the current research of the review literature concerning AI in the field of WTE, particularly MSWI, hindering a focused understanding of future development directions. Consequently, this study conducts an exhaustive survey of AI applications for optimal control, categorizing them into four fundamental aspects: modeling, control, optimization, and maintenance. Timeline diagrams depicting the evolution of AI technologies in the MSWI process are presented to offer an intuitive visual representation. Each category undergoes meticulous classification and description, elucidating the shortcomings and challenges inherent in current research. Furthermore, the study articulates the future development trajectory of AI applications within the four fundamental categories, underscoring the contribution it makes to the field of MSWI and WTE.

Suggested Citation

  • Jian Tang & Tianzheng Wang & Heng Xia & Canlin Cui, 2024. "An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process," Sustainability, MDPI, vol. 16(5), pages 1-41, February.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:5:p:2042-:d:1349223
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/5/2042/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/5/2042/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Adriana Gómez-Sanabria & Gregor Kiesewetter & Zbigniew Klimont & Wolfgang Schoepp & Helmut Haberl, 2022. "Potential for future reductions of global GHG and air pollutants from circular waste management systems," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    2. Liu, Gengfeng & Zhang, Xiangwen & Liu, Zhiming, 2022. "State of health estimation of power batteries based on multi-feature fusion models using stacking algorithm," Energy, Elsevier, vol. 259(C).
    3. Vilardi, Giorgio & Verdone, Nicola, 2022. "Exergy analysis of municipal solid waste incineration processes: The use of O2-enriched air and the oxy-combustion process," Energy, Elsevier, vol. 239(PB).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Kai & Liu, Xing & Guo, Xin & Wang, Jianhang & Wang, Zhuang & Huang, Lianzhong, 2024. "A novel high-precision and self-adaptive prediction method for ship energy consumption based on the multi-model fusion approach," Energy, Elsevier, vol. 310(C).
    2. Kılkış, Şiir & Ulpiani, Giulia & Vetters, Nadja, 2024. "Visions for climate neutrality and opportunities for co-learning in European cities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 195(C).
    3. Malak Anshassi & Timothy G. Townsend, 2023. "The hidden economic and environmental costs of eliminating kerb-side recycling," Nature Sustainability, Nature, vol. 6(8), pages 919-928, August.
    4. Wen, Shuang & Lin, Ni & Huang, Shengxu & Wang, Zhenpo & Zhang, Zhaosheng, 2023. "Lithium battery health state assessment based on vehicle-to-grid (V2G) real-world data and natural gradient boosting model," Energy, Elsevier, vol. 284(C).
    5. Camilo Andrés Guerrero-Martin & Juan Sebastián Fernández-Ramírez & Jaime Eduardo Arturo-Calvache & Harvey Andrés Milquez-Sanabria & Fernando Antonio da Silva Fernandes & Vando José Costa Gomes & Wanes, 2023. "Exergy Load Distribution Analysis Applied to the Dehydration of Ethanol by Extractive Distillation," Energies, MDPI, vol. 16(8), pages 1-14, April.
    6. Xi Sun & Sophie M. Behr & Merve Kücük, 2024. "Enabling Circular Economy Dynamics in the Plastics and Steel Industries: Perspectives from Multiple Stakeholders," Discussion Papers of DIW Berlin 2093, DIW Berlin, German Institute for Economic Research.
    7. Chen, Heng & Li, Jiarui & Li, Tongyu & Xu, Gang & Jin, Xi & Wang, Min & Liu, Tong, 2022. "Performance assessment of a novel medical-waste-to-energy design based on plasma gasification and integrated with a municipal solid waste incineration plant," Energy, Elsevier, vol. 245(C).
    8. Sun, Jing & Fan, Chaoqun & Yan, Huiyi, 2024. "SOH estimation of lithium-ion batteries based on multi-feature deep fusion and XGBoost," Energy, Elsevier, vol. 306(C).
    9. Natasya Nabilla Hairon Azhar & Desmond Teck-Chye Ang & Rosazlin Abdullah & Jennifer Ann Harikrishna & Acga Cheng, 2022. "Bio-Based Materials Riding the Wave of Sustainability: Common Misconceptions, Opportunities, Challenges and the Way Forward," Sustainability, MDPI, vol. 14(9), pages 1-15, April.
    10. Alessandra Zanoletti & Luca Ciacci, 2022. "The Reuse of Municipal Solid Waste Fly Ash as Flame Retardant Filler: A Preliminary Study," Sustainability, MDPI, vol. 14(4), pages 1-11, February.
    11. Zhang, Junting & Qin, Quande & Li, Guangming & Tseng, Chao-Heng & Fang, Guohao, 2023. "Assessing the impact of waste separation on system transition and environmental performance through a city-scale life cycle assessment," Ecological Economics, Elsevier, vol. 211(C).
    12. Adriana Gómez-Sanabria & Florian Lindl, 2024. "The crucial role of circular waste management systems in cutting waste leakage into aquatic environments," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    13. Chen, Si-Zhe & Liu, Jing & Yuan, Haoliang & Tao, Yibin & Xu, Fangyuan & Yang, Ling, 2025. "AM-MFF: A multi-feature fusion framework based on attention mechanism for robust and interpretable lithium-ion battery state of health estimation," Applied Energy, Elsevier, vol. 381(C).
    14. Yang, Qingchun & Fan, Yingjie & Liu, Chenglin & Zhou, Jianlong & Zhao, Lei & Zhou, Huairong, 2023. "A promising alternative potential solution for sustainable and economical development of coal to ethylene glycol industry: Dimethyl oxalate to methyl glycolate process," Energy, Elsevier, vol. 277(C).
    15. Fu, Hongming & Xue, Kaili & Li, Zhaohao & Zhang, Heng & Gao, Dan & Chen, Haiping, 2023. "Study on the performance of CO2 capture from flue gas with ceramic and PTFE membrane contactors," Energy, Elsevier, vol. 263(PA).
    16. Chen, Si-Zhe & Liang, Zikang & Yuan, Haoliang & Yang, Ling & Xu, Fangyuan & Fan, Yuanliang, 2023. "A novel state of health estimation method for lithium-ion batteries based on constant-voltage charging partial data and convolutional neural network," Energy, Elsevier, vol. 283(C).
    17. Wienchol, Paulina & Korus, Agnieszka & Szlęk, Andrzej & Ditaranto, Mario, 2022. "Thermogravimetric and kinetic study of thermal degradation of various types of municipal solid waste (MSW) under N2, CO2 and oxy-fuel conditions," Energy, Elsevier, vol. 248(C).
    18. Xiong, Ran & Wang, Shunli & Huang, Qi & Yu, Chunmei & Fernandez, Carlos & Xiao, Wei & Jia, Jun & Guerrero, Josep M., 2024. "Improved cooperative competitive particle swarm optimization and nonlinear coefficient temperature decreasing simulated annealing-back propagation methods for state of health estimation of energy stor," Energy, Elsevier, vol. 292(C).
    19. Mahmud, Sadab & Ponkiya, Binaka & Katikaneni, Sravya & Pandey, Srijana & Mattimadugu, Kranthikiran & Yi, Zonggen & Walker, Victor & Wang, Congjian & Westover, Tyler & Javaid, Ahmad Y. & Heben, Michael, 2024. "Design and optimization of a modular hydrogen-based integrated energy system to maximize revenue via nuclear-renewable sources," Energy, Elsevier, vol. 313(C).
    20. Wang, Cong & Chen, Yunxia & Zhang, Qingyuan & Zhu, Jiaxiao, 2023. "Dynamic early recognition of abnormal lithium-ion batteries before capacity drops using self-adaptive quantum clustering," Applied Energy, Elsevier, vol. 336(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:5:p:2042-:d:1349223. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.