TY - JOUR
T1 - Effects of hydration parameters on chemical properties of biocrudes based on machine learning and experiments
AU - Zhou, Xinxing
AU - Zhao, Jun
AU - Chen, Meizhu
AU - Wu, Shaopeng
AU - Zhao, Guangyuan
AU - Xu, Song
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China (No. 52008235 ). Also, thanks for the support from Hong Kong Environment and Conservation Fund (ECF 2020-46).
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/4
Y1 - 2022/4
N2 - To investigate the effects of temperature and biomass concentration of Hydrothermal liquefaction (HTL) on chemical properties of biocrudes, machine learning (ML) was used to predict the weight of hydration parameters on the properties of biocrudes. The elemental compositions, molecular weights, functional groups, thermal degradation, molecular structure of biocrudes were studied. The optimum yield of biocrudes was 65% and the highest heat value reached up to 34.28 kJ/g, showing comparable fuel properties. It was found that the hydration temperature significantly affects the elemental components, functional groups and molecular weight and structures of biocrudes. In addition, biomass concentration also affect the functional groups and structures of biocrudes. ML results indicated that Support Vector Machine Linear Kernel method is suitable for heat value prediction.
AB - To investigate the effects of temperature and biomass concentration of Hydrothermal liquefaction (HTL) on chemical properties of biocrudes, machine learning (ML) was used to predict the weight of hydration parameters on the properties of biocrudes. The elemental compositions, molecular weights, functional groups, thermal degradation, molecular structure of biocrudes were studied. The optimum yield of biocrudes was 65% and the highest heat value reached up to 34.28 kJ/g, showing comparable fuel properties. It was found that the hydration temperature significantly affects the elemental components, functional groups and molecular weight and structures of biocrudes. In addition, biomass concentration also affect the functional groups and structures of biocrudes. ML results indicated that Support Vector Machine Linear Kernel method is suitable for heat value prediction.
KW - Algorithm optimization
KW - Chemical property
KW - Hydrothermal liquefaction
KW - Machine learning
KW - Woody biocrude
UR - http://www.scopus.com/inward/record.url?scp=85125668994&partnerID=8YFLogxK
U2 - 10.1016/j.biortech.2022.126923
DO - 10.1016/j.biortech.2022.126923
M3 - Journal article
C2 - 35240274
AN - SCOPUS:85125668994
SN - 0960-8524
VL - 350
JO - Bioresource Technology
JF - Bioresource Technology
M1 - 126923
ER -
