State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China.
School of Medicine and Health, Harbin Institute of Technology, Harbin 150040, PR China.
Sci Total Environ. 2024 Jun 1;927:172310. doi: 10.1016/j.scitotenv.2024.172310. Epub 2024 Apr 8.
The growing concerns about environmental sustainability and energy security, such as exhaustion of traditional fossil fuels and global carbon footprint growth have led to an increasing interest in alternative energy sources, especially bioenergy. Recently, numerous scenarios have been proposed regarding the use of bioenergy from different sources in the future energy systems. In this regard, one of the biggest challenges for scientists is managing, modeling, decision-making, and future forecasting of bioenergy systems. The development of machine learning (ML) techniques can provide new opportunities for modeling, optimizing and managing the production, consumption and environmental effects of bioenergy. However, researchers in bioenergy fields have not widely utilized the ML concepts and practices. Therefore, a comparative review of the current ML techniques used for bioenergy productions is presented in this paper. This review summarizes the common issues and difficulties existing in integrating ML with bioenergy studies, and discusses and proposes the possible solutions. Additionally, a detailed discussion of the appropriate ML application scenarios is also conducted in every sector of the entire bioenergy chain. This indicates the modernized conversion processes supported by ML techniques are imperative to accurately capture process-level subtleties, and thus improving techno-economic resilience and socio-ecological integrity of bioenergy production. All the efforts are believed to help in sustainable bioenergy production with ML technologies for the future.
对环境可持续性和能源安全的日益关注，如传统化石燃料的枯竭和全球碳足迹的增长，导致人们对替代能源，特别是生物能源越来越感兴趣。最近，已经提出了许多关于未来能源系统中利用不同来源的生物能源的方案。在这方面，科学家面临的最大挑战之一是管理、建模、决策和对生物能源系统的未来预测。机器学习 (ML) 技术的发展可以为生物能源的生产、消费和环境影响的建模、优化和管理提供新的机会。然而，生物能源领域的研究人员并没有广泛利用 ML 概念和实践。因此，本文对用于生物能源生产的当前 ML 技术进行了比较性回顾。本综述总结了将 ML 与生物能源研究相结合所存在的常见问题和困难，并讨论和提出了可能的解决方案。此外，还对整个生物能源链的各个环节的合适的 ML 应用场景进行了详细讨论。这表明，由 ML 技术支持的现代化转化过程对于准确捕捉过程级别的细微差别至关重要，从而提高生物能源生产的技术经济弹性和社会生态完整性。所有这些努力都有望帮助未来利用 ML 技术实现可持续的生物能源生产。
