机器学习应用在液化热水过程中的现有情景通过文献分析分析
Tossapon Katongtung1,2, Somboon Sukpancharoen3, Sakprayut Sinthupinyo4
1Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand.
F1000Research
|June 30, 2025
概括
机器学习 (ML) 越来越多地应用于可再生能源的水热液化 (HTL). 研究显示,全球兴趣日益增长,中国在生物质转化工程出版物方面处于领先地位.
科学领域:
- 可再生能源可再生能源是可再生能源.
- 生物质转换生物质转换
- 化学工程是化学工程的重要组成部分.
背景情况:
- 全球能源短缺和气候变暖需要可持续的,环保的能源.
- 生物质能源通过水热液化 (HTL) 转化为生物原油,是关键的可再生能源选择.
- 水热液化 (HTL) 是一个复杂的过程,引发了人们对预测模型的兴趣.
研究的目的:
- 分析机器学习 (ML) 应用在热水液化 (HTL) 的新兴领域.
- 在ML和HTL研究中绘制关键词关联和共同引用.
- 了解当前的格局和研究趋势在ML中用于HTL过程.
主要方法:
- 从Scopus数据库中对出版物的图书法分析.
- 专注于与机器学习 (ML) 和热水液化 (HTL) 相关的关键词.
- 审查出版趋势,国家贡献和关键词的同时出现.
主要成果:
- 在结合ML和HTL工艺的研究中出现了显著的上升趋势.
- 增加国际参与这一研究领域.
- 中国是主要的贡献者,大多数研究都在工程领域.
结论:
- 机器学习 (ML) 在热水液化 (HTL) 中的整合是一个快速扩展的研究领域.
- "液化"是最常见的关键词,突出了核心过程.
- 预计生物质转化技术的ML驱动研究将继续增长.
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