通过机器学习增强生物质转化为生物能源:收益和问题
Rupeng Wang1, Zixiang He1, Honglin Chen1
1State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China.
The Science of the total environment
|April 10, 2024
概括
机器学习 (ML) 提供了管理生物能源系统的新方法,解决可持续性和能源安全问题. 本综述探讨了生物能源生产中的ML应用,强调了更好的预测和优化的挑战和解决方案.
科学领域:
- 生物能源是生物能源.
- 机器学习 机器学习
- 可持续能源系统 可持续能源系统
背景情况:
- 对化石燃料耗尽和碳足迹的日益担忧推动了对生物能源的兴趣.
- 生物能源系统的有效管理,建模和预测是关键的挑战.
- 机器学习 (ML) 提供了优化生物能源生产和消费的机会.
研究的目的:
- 审查目前用于生物能源生产的ML技术.
- 确定将ML与生物能源研究相结合的挑战.
- 提出解决方案,并讨论整个生物能源价值链的ML应用场景.
主要方法:
- 对生物能源中ML现有文献的比较综述.
- 在ML集成中分析常见问题.
- 在生物能源生产,消费和环境影响中讨论ML应用场景.
主要成果:
- 在生物能源研究中,ML技术未得到充分利用.
- 机器学习的整合面临着与数据,专业知识和方法相关的挑战.
- 机器学习可以提高流程层面的理解,改善技术经济和社会生态方面.
结论:
- 支持ML的现代化生物能源转化过程对于可持续性至关重要.
- 机器学习为提高生物能源生产的弹性和完整性提供了巨大的潜力.
- 进一步研究和采用ML对于推进可持续生物能源解决方案至关重要.
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