碳捕获的人工智能:对基于自然和工程的途径进行多层次的审查
Jianhua Ma1, Yongzhang Zhou1, Luhao He1
1School of Earth Science and Engineering, Sun Yat-Sen University, Zhuhai, 519000, China; Centre for Earth Environment and Resources, Sun Yat-Sen University, Zhuhai, 519000, China; Guangdong Provincial Key Lab of Geological Process and Mineral Resources, Zhuhai, 519000, China.
The Science of the total environment
|November 6, 2025
概括
这篇评论探讨了碳捕获方法,提出了一个AI框架来优化自然和工程解决方案. 它有助于为增强碳捕获和低碳战略选择模型.
科学领域:
- 环境科学 环境科学
- 人工智能的人工智能
- 气候变化缓解缓解 气候变化缓解
背景情况:
- 全球碳中和目标推动了碳捕获技术的发展.
- 自然和人工碳捕集方法在建模,优化和机械理解方面面临挑战.
- 复杂的碳系统表现出非线性和多源异质性.
研究的目的:
- 系统地分类关键的碳捕获途径 (基于自然和工程).
- 为人工智能模型选择和基准测试提出一个结构化的"任务-数据-算法"映射框架.
- 综合AI在建模和优化碳捕获系统中的作用.
主要方法:
- 碳捕集途径的系统审查和分类.
- 对碳系统应用的AI模型 (RF,SVM,CNN,RNN) 的比较分析.
- 开发一个"任务-数据-算法"映射框架.
主要成果:
- 人工智能模型展示了特定的优势:用于遥感碳绘图的CNN,用于土壤有机碳 (SOC) 预测的RF.
- 该框架为各种碳捕获用例的AI模型选择和基准测试提供了便利.
- 人工智能能够在基于自然和工程系统中实现高分辨率的预测,反集成和系统优化.
结论:
- 拟议的框架为改善AI模型概括性和碳封存中的解释性提供了战略参考.
- 这项研究为智能,人工智能支持的碳封存系统奠定了系统的基础.
- 这些发现支持未来的政策和部署有效的低碳战略.
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