A Review on Machine Learning-Aided Hydrothermal Liquefaction Based on Bibliometric Analysis

Lili Qian (), Xu Zhang, Xianguang Ma, Peng Xue, Xingying Tang, Xiang Li and Shuang Wang
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Lili Qian: School of Energy and Power Engineering, Jiangsu University, Zhenjiang 212013, China
Xu Zhang: School of Energy and Power Engineering, Jiangsu University, Zhenjiang 212013, China
Xianguang Ma: School of Energy and Power Engineering, Jiangsu University, Zhenjiang 212013, China
Peng Xue: School of Energy and Power Engineering, Jiangsu University, Zhenjiang 212013, China
Xingying Tang: Guangxi Key Laboratory on the Study of Coral Reefs in the South China Sea, School of Marine Sciences, Guangxi University, Nanning 530004, China
Xiang Li: Taizhou DongBo New Materials Co., Ltd., Taizhou 225312, China
Shuang Wang: School of Energy and Power Engineering, Jiangsu University, Zhenjiang 212013, China

Energies, 2024, vol. 17, issue 21, 1-12

Abstract: Hydrothermal liquefaction (HTL) is an effective biomass thermochemical conversion technology that can convert organic waste into energy products. However, the HTL process is influenced by various complex factors such as operating conditions, feedstock properties, and reaction pathways. Machine learning (ML) methods can utilize existing HTL data to develop accurate models for predicting product yields and properties, which can be used to optimize HTL operation conditions. This paper presents a bibliometric review on ML applications in HTL from 2020 to 2024. CiteSpace, VOSviewer, and Bibexcel were used to analyze seven key bibliometric attributes: annual publication output, author co-authorship networks, country co-authorship networks, co-citation of references, co-citation of journals, collaborating institutions, and keyword co-occurrence networks, as well as time zone maps and timelines, to identify the development of ML in HTL research. Through the detailed analysis of co-occurring keywords, this study aims to identify frontiers, research gaps, and development trends in the field of ML-aided HTL.

Keywords: hydrothermal liquefaction; machine learning; bibliometric analysis; biomass (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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