4.7 Article

Analysis and prediction of characteristics for solid product obtained by hydrothermal carbonization of biomass components

Journal

RENEWABLE ENERGY
Volume 183, Issue -, Pages 575-585

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2021.11.001

Keywords

Hydrothermal carbonization; Biomass; Interaction; Hydrochar; Prediction model

Funding

  1. National Key R&D Program of China [2020YFB0606301]
  2. National Natural Sci-ence Foundation of China [52176105]
  3. Beijing Natural Science Foundation-China [3202030]
  4. Natural Science Foundation of Hebei Province-China [E2020502030]
  5. Fundamental Research Funds for the Central Universities [2020MS109]

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In this study, model biomass samples prepared from five representative components were carbonized hydrothermally under a typical condition of 220 degrees C, and mathematical models were developed to predict the properties of the hydrochar. The results showed synergistic effects between different components and high accuracy of the prediction models.
Hydrothermal carbonization (HTC) is a promising thermochemical treatment technique for converting wet biomass into value-added products, but the hydrochar properties of different biomass differ significantly due to the diversity of biomass feedstock and the complexity of HTC reactions. In this study, model biomass samples prepared from five representative components were carbonized hydrothermally under a typical condition of 220 degrees C, and mathematical models basing on the component recipes were developed to predict the mass yield (MY), higher heating value, energy yield (EY), and equilibrium moisture content of the hydrochar. Synergistic effects occurred between different components in increasing MY and EY, especially when the lipid existed, because the hydrolysed lipid was re-adsorbed on the hydrochar surface or interactively polymerized with other components. All the models showed R-2 values above 89%, moreover, validation experiments using additional model biomass showed that the relative errors were less than 8% between the prediction and experiment values, suggesting the prediction models obtained from this study can be used to assess the hydrochar characteristics and bio-energy generation potential based on biomass components. (C) 2021 Elsevier Ltd. All rights reserved.

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