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Generalized models to predict the lower heating value (LHV) of municipal solid waste (MSW)

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  • Wang, Dan
  • Tang, Yu-Ting
  • He, Jun
  • Yang, Fei
  • Robinson, Darren

Abstract

Accurately and efficiently predicting the LHV of MSW is vital for designing and operating a waste-to-energy plant. However, previous prediction models possess limited geographical applicability. In this paper, we employ multiple linear regression and artificial neural network (ANN) techniques to predict LHV. These data-driven models utilize 151 globally distributed datasets identified during a systematic literature review, describing the wet physical composition of MSW and measured LHV. The results show that models built via both methods exhibited acceptable and compatible levels of performance in predicting LHV, based on the multiple statistical indicators. However, the ANN model proved to be more robust in handling of datasets of diverse quality. Models developed from both methods demonstrate clearly that the wet proportion of food waste has a negative impact on LHV. Supported by the strong and significant correlation between food waste and moisture content, we concluded that the negative impact of high moisture content in food waste on LHV outweighed its calorific value. Separating food waste or any other waste with high moisture content from the MSW for incineration can significantly improve energy recovery efficiency. Contrary to expectation, the models also reveal a higher contribution of paper waste to the LHV of MSW than plastic waste.

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  • Wang, Dan & Tang, Yu-Ting & He, Jun & Yang, Fei & Robinson, Darren, 2021. "Generalized models to predict the lower heating value (LHV) of municipal solid waste (MSW)," Energy, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:energy:v:216:y:2021:i:c:s0360544220323860
    DOI: 10.1016/j.energy.2020.119279
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    1. Thakur, Disha & Kumar, Sanjay & Kumar, Vineet & Kaur, Tarlochan, 2024. "Estimation of calorific value using an artificial neural network based on stochastic ultimate analysis," Renewable Energy, Elsevier, vol. 228(C).
    2. Zhao, Shuchun & Guo, Junheng & Dang, Xiuhu & Ai, Bingyan & Zhang, Minqing & Li, Wei & Zhang, Jinli, 2022. "Energy consumption, flow characteristics and energy-efficient design of cup-shape blade stirred tank reactors: Computational fluid dynamics and artificial neural network investigation," Energy, Elsevier, vol. 240(C).
    3. Tuo He & Dongjie Niu & Gan Chen & Fan Wu & Yu Chen, 2022. "Exploring Key Components of Municipal Solid Waste in Prediction of Moisture Content in Different Functional Areas Using Artificial Neural Network," Sustainability, MDPI, vol. 14(23), pages 1-14, November.
    4. Kumar, Atul & Samadder, Sukha Ranjan, 2023. "Development of lower heating value prediction models and estimation of energy recovery potential of municipal solid waste and RDF incineration," Energy, Elsevier, vol. 274(C).
    5. 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).
    6. Vlasopoulos, Antonis & Malinauskaite, Jurgita & Żabnieńska-Góra, Alina & Jouhara, Hussam, 2023. "Life cycle assessment of plastic waste and energy recovery," Energy, Elsevier, vol. 277(C).
    7. Chen, Xiaoling & Zhang, Yongxing & Xu, Baoshen & Li, Yifan, 2022. "A simple model for estimation of higher heating value of oily sludge," Energy, Elsevier, vol. 239(PA).

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