The design of novel reactors and chemical processes requires an understanding of the fundamental chemical-physical processes at small spatial and temporal scales and a systematic scale-up of these studies to investigate how the process will perform at industrial scales. The financial and temporal costs of these studies can be significant. The use of statistical machine-learning-based methods can significantly reduce these costs. The use of the design of experimental methods can help design an experimental plan that efficiently explores the design space using the fewest experiments possible. Computational methods such as computational fluid dynamics (CFD) are effective tools for detailed studies of small-scale physics and are critical aids to facilitate and understand physical experiments. However, CFD methods can also be time-consuming, often requiring hours or days of time on supercomputers. In this research, we investigate the combination of machine learning with reducing 3D CFD simulation to 2D by exploiting axial symmetry to facilitate the design of experiments. Focusing on a 3D carbon dioxide (CO₂) capture reactor as an example, we demonstrate how machine learning and CFD can help facilitate modeling and design optimization. A 2D CFD is used to simulate the chemical–physical processes in the reactor and is then coupled with machine learning to develop a less computationally expensive model to accurately predict CO₂ adsorption. The learned model can be used to optimize the design of the reactor. This paper demonstrates the decrease in temporal and financial costs of designing industrial-scale chemical processes by combining reducing the CFD dimension and machine learning. Equally importantly, this research demonstrates the significance of selecting a proper machine-learning algorithm for different tasks by comparing the performances of different machine-learning algorithms.

中文翻译：

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使用机器学习辅助的计算流体动力学促进实验设计


新型反应器和化学过程的设计需要在小空间和时间尺度上了解基本的化学物理过程，并系统地扩大这些研究的规模，以研究该过程在工业规模上的表现。这些研究的财务和时间成本可能是巨大的。使用基于机器学习的统计方法可以显著降低这些成本。使用实验方法的设计可以帮助设计一个实验计划，使用尽可能少的实验来有效地探索设计空间。计算流体动力学 （CFD） 等计算方法是详细研究小规模物理学的有效工具，是促进和理解物理实验的关键辅助工具。然而，CFD 方法也可能很耗时，通常需要在超级计算机上花费数小时或数天的时间。在这项研究中，我们研究了机器学习与将 3D CFD 仿真简化为 2D 的结合，通过利用轴对称性来促进实验设计。以 3D 二氧化碳 （CO₂） 捕集反应器为例，我们演示了机器学习和 CFD 如何帮助促进建模和设计优化。2D CFD 用于模拟反应器中的化学-物理过程，然后与机器学习相结合，以开发计算成本较低的模型，以准确预测 CO₂ 吸附。学习到的模型可用于优化反应器的设计。本文展示了通过将减小 CFD 维度与机器学习相结合，降低设计工业规模化学工艺的时间和财务成本。 同样重要的是，这项研究通过比较不同机器学习算法的性能，证明了为不同任务选择合适的机器学习算法的重要性。