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Predictions of energy recovery from hydrochar generated from the hydrothermal carbonization of organic wastes

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  • Li, Liang
  • Flora, Joseph R.V.
  • Berge, Nicole D.

Abstract

Hydrothermal carbonization (HTC) is a wet, low temperature thermal conversion process that continues to gain significant attention for the sustainable generation of value-added solid, liquid, and gas products from organic waste streams. Although it is well documented that both waste properties (e.g., elemental composition) and carbonization process conditions influence hydrochar properties, their specific influence on the total energy that can be recovered using HTC remains unclear. Non-linear random forest models were developed based on data collected from HTC-related literature to describe hydrochar yield and energy content, both of which are required to determine the total energy recovered in the hydrochar. Results indicate that total recoverable energy from organic wastes using HTC is correlated with feedstock carbon content; overall, the total energy content for feedstocks with carbon contents ranging from approximately 40 - 48% are similar. In addition, the total energy that can be recovered from the feedstock remains fairly constant when the initial solids concentrations are greater than 20%. Reaction time appears to have little influence on total recoverable energy from each feedstock at reaction times greater than approximately 150 min, while increases in reaction temperature result in a slight decline in total recoverable energy because of decreases in hydrochar yields at higher temperatures.

Suggested Citation

  • Li, Liang & Flora, Joseph R.V. & Berge, Nicole D., 2020. "Predictions of energy recovery from hydrochar generated from the hydrothermal carbonization of organic wastes," Renewable Energy, Elsevier, vol. 145(C), pages 1883-1889.
    • Handle: RePEc:eee:renene:v:145:y:2020:i:c:p:1883-1889
    DOI: 10.1016/j.renene.2019.07.103
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    Cited by:

    1. Wilk, Małgorzata & Śliz, Maciej & Lubieniecki, Bogusław, 2021. "Hydrothermal co-carbonization of sewage sludge and fuel additives: Combustion performance of hydrochar," Renewable Energy, Elsevier, vol. 178(C), pages 1046-1056.
    2. Li, Jie & Suvarna, Manu & Pan, Lanjia & Zhao, Yingru & Wang, Xiaonan, 2021. "A hybrid data-driven and mechanistic modelling approach for hydrothermal gasification," Applied Energy, Elsevier, vol. 304(C).
    3. Śliz, Maciej & Wilk, Małgorzata, 2020. "A comprehensive investigation of hydrothermal carbonization: Energy potential of hydrochar derived from Virginia mallow," Renewable Energy, Elsevier, vol. 156(C), pages 942-950.
    4. Aragon-Briceño, Christian & Pożarlik, Artur & Bramer, Eddy & Brem, Gerrit & Wang, Shule & Wen, Yuming & Yang, Weihong & Pawlak-Kruczek, Halina & Niedźwiecki, Łukasz & Urbanowska, Agnieszka & Mościcki,, 2022. "Integration of hydrothermal carbonization treatment for water and energy recovery from organic fraction of municipal solid waste digestate," Renewable Energy, Elsevier, vol. 184(C), pages 577-591.
    5. Djandja, Oraléou Sangué & Salami, Adekunlé Akim & Wang, Zhi-Cong & Duo, Jia & Yin, Lin-Xin & Duan, Pei-Gao, 2022. "Random forest-based modeling for insights on phosphorus content in hydrochar produced from hydrothermal carbonization of sewage sludge," Energy, Elsevier, vol. 245(C).
    6. Aragón-Briceño, C.I. & Ross, A.B. & Camargo-Valero, M.A., 2021. "Mass and energy integration study of hydrothermal carbonization with anaerobic digestion of sewage sludge," Renewable Energy, Elsevier, vol. 167(C), pages 473-483.
    7. Wang, Ruikun & Liu, Senyang & Xue, Qiao & Lin, Kai & Yin, Qianqian & Zhao, Zhenghui, 2022. "Analysis and prediction of characteristics for solid product obtained by hydrothermal carbonization of biomass components," Renewable Energy, Elsevier, vol. 183(C), pages 575-585.
    8. González-Arias, Judith & González-Castaño, Miriam & Sánchez, Marta Elena & Cara-Jiménez, Jorge & Arellano-García, Harvey, 2022. "Valorization of biomass-derived CO2 residues with Cu-MnOx catalysts for RWGS reaction," Renewable Energy, Elsevier, vol. 182(C), pages 443-451.
    9. Li, Jie & Pan, Lanjia & Suvarna, Manu & Tong, Yen Wah & Wang, Xiaonan, 2020. "Fuel properties of hydrochar and pyrochar: Prediction and exploration with machine learning," Applied Energy, Elsevier, vol. 269(C).

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