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对燃烧后碳捕获系统的机器学习技术进行比较研究.

Yeping Hu1, Bo Lei1, Yash Girish Shah2,3

  • 1Lawrence Livermore National Laboratory, Livermore, CA, United States.

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概括

机器学习,包括CNN和GNN,可以加速碳捕获系统的设计. 这些方法使用列参数预测二氧化碳捕获效率,减少了昂贵模拟的需要.

关键词:
碳捕获系统 碳捕获系统计算流体动力学的流体动力学.卷积神经网络是一种卷积神经网络.图形神经网络的神经网络机器学习是机器学习.

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科学领域:

  • 化学工程是化学工程的重要组成部分.
  • 计算科学 计算科学
  • 机器学习 机器学习

背景情况:

  • 计算流体动力学 (CFD) 模拟对于分析碳捕获系统 (CCS) 中的包装吸收列至关重要.
  • 差价合约模型捕捉复杂的相互作用和质量转移,但计算密集,阻碍工业规模的设计优化.
  • 加快对各种设计和操作条件的评估对于提高CCS效率至关重要.

研究的目的:

  • 探索机器学习 (ML) 方法的应用,特别是卷积神经网络 (CNN) 和图形神经网络 (GNN),以帮助和加速CCS设计.
  • 训练ML模型使用CFD数据集来预测关键的二氧化碳捕获效率决定因素.
  • 评估不同输入特征对模型准确性和概括性的影响.

主要方法:

  • 在现有的CFD数据集上使用了统计ML方法,CNN和GNN,用于包装吸收列中的反流流.
  • 训练模型以预测二氧化碳捕获效率,使用列几何参数和输入速度条件.
  • 评估各种输入表示对模型性能的影响.

主要成果:

  • 开发了能够估计二氧化碳捕获效率的ML模型,而不需要额外的CFD模拟.
  • 展示了CNN和GNN在基于几何和操作参数准确预测性能方面的潜力.
  • 确定了不同输入类型对ML模型准确性和概括性的影响.

结论:

  • 包括CNN和GNN在内的ML方法为CCS设计和优化提供了传统CFD的计算效率高的替代方案.
  • 这些方法可以显著加快基于溶剂的燃烧后碳捕获技术的扩展过程.
  • 对机器学习应用的进一步研究可以提高二氧化碳捕获属性的预测,并简化更高效的CCS的开发.