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A Novel Method for the Estimation of Higher Heating Value of Municipal Solid Wastes

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Listed:
  • Weiguo Dong

    (School of Management, China University of Mining and Technology (Beijing), Beijing 100083, China)

  • Zhiwen Chen

    (Division of Solid Waste Management, School of Environment, Tsinghua University, Beijing 100084, China)

  • Jiacong Chen

    (Division of Solid Waste Management, School of Environment, Tsinghua University, Beijing 100084, China)

  • Zhao Jia Ting

    (Division of Solid Waste Management, School of Environment, Tsinghua University, Beijing 100084, China)

  • Rui Zhang

    (School of Management, China University of Mining and Technology (Beijing), Beijing 100083, China)

  • Guozhao Ji

    (Key Laboratory of Industrial Ecology and Environmental Engineering, School of Environmental Science & Technology, Dalian University of Technology, Dalian 116024, China)

  • Ming Zhao

    (Division of Solid Waste Management, School of Environment, Tsinghua University, Beijing 100084, China)

Abstract

The measurement of the higher heating value (HHV) of municipal solid wastes (MSWs) plays a key role in the disposal process, especially via thermochemical approaches. An optimized multi-variate grey model (OBGM (1, N )) is introduced to forecast the MSWs’ HHV to high accuracy with sparse data. A total of 15 cities and MSW from the respective city were considered to develop and verify the multi-variant models. Results show that the most accurate model was POBGM (1, 5) of which the least error measured 5.41% MAPE (mean absolute percentage error). Ash, being a major component in MSW, is the most important factor affecting HHV, followed by volatiles, fixed carbon and water contents. Most data can be included by using the prediction interval (PI) method with 95% confidence intervals. In addition, the estimations indicated that the MAPE from estimating the HHV for various MSW samples, collected from various cities, were in the range of 3.06–34.50%, depending on the MSW sample.

Suggested Citation

  • Weiguo Dong & Zhiwen Chen & Jiacong Chen & Zhao Jia Ting & Rui Zhang & Guozhao Ji & Ming Zhao, 2022. "A Novel Method for the Estimation of Higher Heating Value of Municipal Solid Wastes," Energies, MDPI, vol. 15(7), pages 1-14, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2593-:d:785705
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    References listed on IDEAS

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    1. Qian, Wuyong & Wang, Jue, 2020. "An improved seasonal GM(1,1) model based on the HP filter for forecasting wind power generation in China," Energy, Elsevier, vol. 209(C).
    2. Dashti, Amir & Noushabadi, Abolfazl Sajadi & Asadi, Javad & Raji, Mojtaba & Chofreh, Abdoulmohammad Gholamzadeh & Klemeš, Jiří Jaromír & Mohammadi, Amir H., 2021. "Review of higher heating value of municipal solid waste based on analysis and smart modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    3. Zhang, Meng & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2022. "A novel flexible grey multivariable model and its application in forecasting energy consumption in China," Energy, Elsevier, vol. 239(PE).
    4. Zhou, Hui & Meng, AiHong & Long, YanQiu & Li, QingHai & Zhang, YanGuo, 2014. "An overview of characteristics of municipal solid waste fuel in China: Physical, chemical composition and heating value," Renewable and Sustainable Energy Reviews, Elsevier, vol. 36(C), pages 107-122.
    5. Chen, Ying-Chu & Jhou, Sih-Yu, 2020. "Integrating spent coffee grounds and silver skin as biofuels using torrefaction," Renewable Energy, Elsevier, vol. 148(C), pages 275-283.
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    Cited by:

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