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Review of higher heating value of municipal solid waste based on analysis and smart modelling

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  • Dashti, Amir
  • Noushabadi, Abolfazl Sajadi
  • Asadi, Javad
  • Raji, Mojtaba
  • Chofreh, Abdoulmohammad Gholamzadeh
  • Klemeš, Jiří Jaromír
  • Mohammadi, Amir H.

Abstract

Energy recovery from 252 kinds of solid waste originating from various geographical areas under thermal waste-to-energy operation is investigated. A fast, economical, and comparative methodology is presented for evaluating the heating values resulted from burning municipal solid waste (MSW) based on prior knowledge, specialist experience, and data-mining methods. Development of models for estimating higher heating values (HHVs) of 252 MSW samples based on the ultimate analysis is conducted by simultaneously utilising five nonlinear models including Radial Basis Function (RBF) neural network in conjunction with Genetic Algorithm (GA), namely GA-RBF, genetic programming (GP), multivariate nonlinear regression (MNR), particle swarm optimisation adaptive neuro-fuzzy inference system (PSO-ANFIS) and committee machine intelligent system (CMIS) models to increase the accuracy of each model. Eight different equations based on MNR are developed to estimate energy recovery capacity from different MSW groups (e.g., textiles, plastics, papers, rubbers, mixtures, woods, sewage sludge and other waste). A detailed investigation is conducted to analyse the accuracy of the models. It is indicated that the CMIS model has the best performance comparing the results obtained from different models. The R2 values of the test dataset for GA-RBF are 0.888, for GP 0.979, for MNR 0.978, for PSO-ANFIS 0.965, and for CMIS 0.985. The developed models with an acceptable accuracy would be followed by a better estimation of HHV and providing reliable heating value for an automatic combustion control system. The results obtained from this study are beneficial to design and optimise sustainable thermal waste-to-energy (WTF) processes to accelerate city transition into a circular economy.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:rensus:v:151:y:2021:i:c:s1364032121008686
    DOI: 10.1016/j.rser.2021.111591
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    1. 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.
    2. 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).
    3. Caferra, Rocco & D'Adamo, Idiano & Morone, Piergiuseppe, 2023. "Wasting energy or energizing waste? The public acceptance of waste-to-energy technology," Energy, Elsevier, vol. 263(PE).
    4. Subin Jung & Hyojin Jung & Yuchan Ahn, 2022. "Optimal Economic–Environmental Design of Heat Exchanger Network in Naphtha Cracking Center Considering Fuel Type and CO 2 Emissions," Energies, MDPI, vol. 15(24), pages 1-14, December.
    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. Mollanoori, Mohammad & Dehghan, Ali Akbar, 2024. "Estimating the higher heating value and chemical exergy of solid, liquid, and natural gas fossil fuels," Energy, Elsevier, vol. 302(C).

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