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Predicting heating value of lignocellulosic biomass based on elemental analysis

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  • Huang, Yu-Fong
  • Lo, Shang-Lien

Abstract

Heating value is one of the most important properties for bioenergy recovery from lignocellulosic biomass. Various correlations have been established to predict the heating value. This study presents a new correlation to predict the higher heating value (HHV) of lignocellulosic biomass based on its elemental composition: HHV = 0.3443C + 1.192H-0.113O–0.024 N + 0.093S. Compared with the correlations reported in the literature, the relative error of the correlation proposed in this study is lowest, and its coefficient of determination (R2) is highest. Therefore, the new correlation should be capable of providing more accurate HHV prediction than other correlations. For most of the lignocellulosic biomass feedstocks, the absolute percentage errors between the measured HHV and those predicted by using the correlation can be less than approximately 3%. The HHV prediction by using the correlation is satisfactory not only for lignocellulosic biomass feedstocks but also for biochar and various kinds of fossil fuels. Besides, the correlation can be also used for predicting the HHV of other organic matters such as municipal solid waste, industrial waste, and sewage sludge, after the modification of the coefficient before oxygen content based on the characteristics of the matter.

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  • Huang, Yu-Fong & Lo, Shang-Lien, 2020. "Predicting heating value of lignocellulosic biomass based on elemental analysis," Energy, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:energy:v:191:y:2020:i:c:s0360544219321966
    DOI: 10.1016/j.energy.2019.116501
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    1. Alex Borges Pereira & Antonio José Vinha Zanuncio & Amélia Guimarães Carvalho & Angélica de Cassia Oliveira Carneiro & Vinícius Resende de Castro & Ana Marcia Macedo Ladeira Carvalho & Olivia Pereira , 2024. "Sustainable Solid Biofuel Production: Transforming Sewage Sludge and Pinus sp. Sawdust into Resources for the Circular Economy," Sustainability, MDPI, vol. 16(11), pages 1-11, May.
    2. Catarina Viegas & Catarina Nobre & Ricardo Correia & Luísa Gouveia & Margarida Gonçalves, 2021. "Optimization of Biochar Production by Co-Torrefaction of Microalgae and Lignocellulosic Biomass Using Response Surface Methodology," Energies, MDPI, vol. 14(21), pages 1-23, November.
    3. Marcin Cichosz & Sławomir Łazarski & Andrzej Butarewicz & Urszula Kiełkowska, 2023. "Biogas Production—The Effect of the Zinc Concentration on the Profile of Volatile Fatty Acids in Fermentation Mixtures," Energies, MDPI, vol. 16(21), pages 1-24, November.
    4. Kartal, Furkan & Özveren, Uğur, 2022. "Prediction of torrefied biomass properties from raw biomass," Renewable Energy, Elsevier, vol. 182(C), pages 578-591.
    5. Hongli Chen & Liqiang Zhang & Zhongliang Huang & Zijian Wu & Mengjiao Tan & Xuan Zhang & Longbo Jiang & Xiaoli Qin & Jing Huang & Hui Li, 2022. "Effect of Anoxic Atmosphere on the Physicochemical and Pelletization Properties of Pinus massoniana Sawdust during Storage," IJERPH, MDPI, vol. 20(1), pages 1-16, December.
    6. Carvalho, Pollyana R. & Medeiros, Samuel L.S. & Paixão, Raul L. & Figueredo, Igor M. & Mattos, Adriano L.A. & Rios, M. Alexsandra S., 2023. "Thermogravimetric pyrolysis of residual biomasses obtained post-extraction of carnauba wax: Determination of kinetic parameters using Friedman's isoconversional method," Renewable Energy, Elsevier, vol. 207(C), pages 703-713.
    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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