IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i7p2593-d785705.html
   My bibliography  Save this article

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

Author

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/7/2593/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/7/2593/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen, Zhiwen & Zhao, Ming & Lv, Yi & Wang, Iwei & Tariq, Ghulam & Zhao, Sheng & Ahmed, Shakil & Dong, Weiguo & Ji, Guozhao, 2024. "Higher heating value prediction of high ash gasification-residues: Comparison of white, grey, and black box models," Energy, Elsevier, vol. 288(C).
    2. Leng, Lijian & Li, Tanghao & Zhan, Hao & Rizwan, Muhammad & Zhang, Weijin & Peng, Haoyi & Yang, Zequn & Li, Hailong, 2023. "Machine learning-aided prediction of nitrogen heterocycles in bio-oil from the pyrolysis of biomass," Energy, Elsevier, vol. 278(PB).
    3. Leng, Lijian & Zhou, Junhui & Zhang, Weijin & Chen, Jiefeng & Wu, Zhibin & Xu, Donghai & Zhan, Hao & Yuan, Xingzhong & Xu, Zhengyong & Peng, Haoyi & Yang, Zequn & Li, Hailong, 2024. "Machine-learning-aided hydrochar production through hydrothermal carbonization of biomass by engineering operating parameters and/or biomass mixture recipes," Energy, Elsevier, vol. 288(C).

    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. Chen, Zhiwen & Zhao, Ming & Lv, Yi & Wang, Iwei & Tariq, Ghulam & Zhao, Sheng & Ahmed, Shakil & Dong, Weiguo & Ji, Guozhao, 2024. "Higher heating value prediction of high ash gasification-residues: Comparison of white, grey, and black box models," Energy, Elsevier, vol. 288(C).
    2. Hu, Mian & Guo, Dabin & Ma, Caifeng & Hu, Zhiquan & Zhang, Beiping & Xiao, Bo & Luo, Siyi & Wang, Jingbo, 2015. "Hydrogen-rich gas production by the gasification of wet MSW (municipal solid waste) coupled with carbon dioxide capture," Energy, Elsevier, vol. 90(P1), pages 857-863.
    3. Wang, Yadong & Wang, Delu & Shi, Xunpeng, 2023. "Sustainable development pathways of China's wind power industry under uncertainties: Perspective from economic benefits and technical potential," Energy Policy, Elsevier, vol. 182(C).
    4. Patrik Šuhaj & Jakub Husár & Juma Haydary, 2020. "Gasification of RDF and Its Components with Tire Pyrolysis Char as Tar-Cracking Catalyst," Sustainability, MDPI, vol. 12(16), pages 1-14, August.
    5. Ding, Song & Tao, Zui & Zhang, Huahan & Li, Yao, 2022. "Forecasting nuclear energy consumption in China and America: An optimized structure-adaptative grey model," Energy, Elsevier, vol. 239(PA).
    6. Liu, Xiangfei & Ren, Mifeng & Yang, Zhile & Yan, Gaowei & Guo, Yuanjun & Cheng, Lan & Wu, Chengke, 2022. "A multi-step predictive deep reinforcement learning algorithm for HVAC control systems in smart buildings," Energy, Elsevier, vol. 259(C).
    7. Nketiah, Emmanuel & Song, Huaming & Obuobi, Bright & Adu-Gyamfi, Gibbson & Adjei, Mavis & Cudjoe, Dan, 2022. "Citizens' willingness to pay for local anaerobic digestion energy: The influence of altruistic value and knowledge," Energy, Elsevier, vol. 260(C).
    8. Soltanian, Salman & Kalogirou, Soteris A. & Ranjbari, Meisam & Amiri, Hamid & Mahian, Omid & Khoshnevisan, Benyamin & Jafary, Tahereh & Nizami, Abdul-Sattar & Gupta, Vijai Kumar & Aghaei, Siavash & Pe, 2022. "Exergetic sustainability analysis of municipal solid waste treatment systems: A systematic critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    9. Yang, Hufang & Jiang, Ping & Wang, Ying & Li, Hongmin, 2022. "A fuzzy intelligent forecasting system based on combined fuzzification strategy and improved optimization algorithm for renewable energy power generation," Applied Energy, Elsevier, vol. 325(C).
    10. Lifang Zhang & Jianzhou Wang & Zhenkun Liu, 2023. "Power grid operation optimization and forecasting using a combined forecasting system," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 124-153, January.
    11. Xing, Zhou & Ping, Zhou & Xiqiang, Zhao & Zhanlong, Song & Wenlong, Wang & Jing, Sun & Yanpeng, Mao, 2021. "Applicability of municipal solid waste incineration (MSWI) system integrated with pre-drying or torrefaction for flue gas waste heat recovery," Energy, Elsevier, vol. 224(C).
    12. Roozbeh Feiz & Jonas Ammenberg & Annika Björn & Yufang Guo & Magnus Karlsson & Yonghui Liu & Yuxian Liu & Laura Shizue Moriga Masuda & Alex Enrich-Prast & Harald Rohracher & Kristina Trygg & Sepehr Sh, 2019. "Biogas Potential for Improved Sustainability in Guangzhou, China—A Study Focusing on Food Waste on Xiaoguwei Island," Sustainability, MDPI, vol. 11(6), pages 1-25, March.
    13. Jiang, Xuguang & Chen, Dandan & Ma, Zengyi & Yan, Jianhua, 2017. "Models for the combustion of single solid fuel particles in fluidized beds: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 410-431.
    14. Siwal, Samarjeet Singh & Zhang, Qibo & Devi, Nishu & Saini, Adesh Kumar & Saini, Vipin & Pareek, Bhawna & Gaidukovs, Sergejs & Thakur, Vijay Kumar, 2021. "Recovery processes of sustainable energy using different biomass and wastes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    15. Zhang, Haipeng & Wang, Jianzhou & Qian, Yuansheng & Li, Qiwei, 2024. "Point and interval wind speed forecasting of multivariate time series based on dual-layer LSTM," Energy, Elsevier, vol. 294(C).
    16. Bilgili, Mehmet & Pinar, Engin, 2023. "Gross electricity consumption forecasting using LSTM and SARIMA approaches: A case study of Türkiye," Energy, Elsevier, vol. 284(C).
    17. Junhan Huang & Rui Zhao & Tao Huang & Xiaoqian Wang & Ming-Lang Tseng, 2018. "Sustainable Municipal Solid Waste Disposal in the Belt and Road Initiative: A Preliminary Proposal for Chengdu City," Sustainability, MDPI, vol. 10(4), pages 1-15, April.
    18. Gomez, William & Wang, Fu-Kwun & Lo, Shih-Che, 2024. "A hybrid approach based machine learning models in electricity markets," Energy, Elsevier, vol. 289(C).
    19. Song, Xiang & Wang, Dingyu & Zhang, Xuantao & He, Yuan & Wang, Yong, 2022. "A comparison of the operation of China's carbon trading market and energy market and their spillover effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    20. Duan, Tianyao & Guo, Huan & Qi, Xiao & Sun, Ming & Forrest, Jeffrey, 2024. "A novel information enhanced Grey Lotka–Volterra model driven by system mechanism and data for energy forecasting of WEET project in China," Energy, Elsevier, vol. 304(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:jeners:v:15:y:2022:i:7:p:2593-:d:785705. 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.