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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Lili Qian & Xu Zhang & Xianguang Ma & Peng Xue & Xingying Tang & Xiang Li & Shuang Wang, 2024. "A Review on Machine Learning-Aided Hydrothermal Liquefaction Based on Bibliometric Analysis," Energies, MDPI, vol. 17(21), pages 1-12, October.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:21:p:5254-:d:1503961
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    References listed on IDEAS

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    1. Leng, Lijian & Li, Tanghao & Zhan, Hao & Rizwan, Muhammad & Zhang, Weijin & Peng, Haoyi & Yang, Zequn & Li, Hailong, 2023. "Machine learning-aided prediction of nitrogen heterocycles in bio-oil from the pyrolysis of biomass," Energy, Elsevier, vol. 278(PB).
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