Using Machine-Learning-Aided Computational Fluid Dynamics to Facilitate Design of Experiments

IF 3.8 3区 工程技术 Q2 ENGINEERING, CHEMICAL
Ziqing Zhao, Amanda Baumann, Emily M. Ryan
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Abstract

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.

Abstract Image

利用机器学习辅助计算流体力学促进实验设计
新型反应器和化学过程的设计需要了解小空间和时间尺度上的基本化学物理过程,并对这些研究进行系统的放大,以调查该过程在工业规模上的表现。这些研究的资金和时间成本可能会很高。使用基于机器学习的统计方法可以大大降低这些成本。使用实验设计方法有助于设计实验计划,以尽可能少的实验有效探索设计空间。计算流体动力学(CFD)等计算方法是详细研究小尺度物理学的有效工具,也是促进和理解物理实验的重要辅助工具。然而,CFD 方法也很耗时,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,我们研究了如何将机器学习与三维 CFD 模拟相结合,利用轴对称性将三维 CFD 模拟简化为二维,从而促进实验设计。以三维二氧化碳(CO2)捕集反应器为例,我们展示了机器学习和 CFD 如何帮助促进建模和设计优化。二维 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,开发出计算成本较低的模型,以准确预测二氧化碳的吸附。学习到的模型可用于优化反应器的设计。本文展示了通过减少 CFD 维度和机器学习的结合,降低了工业规模化学过程设计的时间成本和财务成本。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机器学习算法的重要性。
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来源期刊
Industrial & Engineering Chemistry Research
Industrial & Engineering Chemistry Research 工程技术-工程:化工
CiteScore
7.40
自引率
7.10%
发文量
1467
审稿时长
2.8 months
期刊介绍: ndustrial & Engineering Chemistry, with variations in title and format, has been published since 1909 by the American Chemical Society. Industrial & Engineering Chemistry Research is a weekly publication that reports industrial and academic research in the broad fields of applied chemistry and chemical engineering with special focus on fundamentals, processes, and products.
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