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Computational Material Screening Using Artificial Neural Networks for Adsorption Gas Separation
The Journal of Physical Chemistry C ( IF 3.7 ) Pub Date : 2020-09-07 , DOI: 10.1021/acs.jpcc.0c05900
Akhil Arora 1 , Shachit S. Iyer 1 , M. M. Faruque Hasan 1
Affiliation  

We present a computationally efficient methodology for screening microporous materials for adsorption-based gas separation. Specifically, we develop and employ artificial neural network (ANN)-based surrogate models that increase the speed of approximating transient adsorption behavior and breakthrough times by several orders of magnitude without compromising the predictive capability of a high-fidelity process model. We introduce the concept of breakthrough event times and develop ANN-based surrogate models for their accurate prediction. Our results for numerous hypothetical adsorbents indicate that the effects of different materials-centric metrics are well-captured by the column breakthrough times at the process scale, thus providing a scale-bridging measure toward a multiscale framework for materials screening with process insights. Using the framework, we also screen the list of existing pure-silica zeolite frameworks for postcombustion carbon capture and natural gas purification applications. For postcombustion carbon capture, the top materials include WEI, JBW, and GIS, and for natural gas purification, the top materials are GIS, SIV, and DFT. For any binary gas mixture, the developed ANN models can be leveraged for (i) fundamentally studying the materials properties that determine the dynamic breakthrough times and gas concentration profiles and (ii) high-throughput adsorbent screening and identification of novel materials with desired properties.

中文翻译:

利用人工神经网络进行吸附气体分离的计算材料筛选

我们提出了一种计算有效的方法来筛选基于吸附的气体分离的微孔材料。具体来说,我们开发和采用基于人工神经网络(ANN)的替代模型,该模型将逼近瞬态吸附行为和突破时间的速度提高了几个数量级,而又不影响高保真过程模型的预测能力。我们介绍了突破性事件时间的概念,并开发了基于ANN的替代模型以进行准确的预测。我们对许多假设吸附剂的结果表明,以过程规模为目标的色谱柱穿透时间可以很好地捕获以材料为中心的不同度量标准的效果,从而为跨过程的多尺度框架提供了一种规模化的衡量方法,可用于对过程进行洞察的材料筛选。使用该框架,我们还筛选了用于燃烧后碳捕获和天然气净化应用的现有纯硅沸石框架列表。对于燃烧后碳捕获,最主要的材料包括WEI,JBW和GIS,对于天然气净化,最重要的材料是GIS,SIV和DFT。对于任何二元混合气体,可以利用已开发的ANN模型进行以下工作:(i)从根本上研究确定动态穿透时间和气体浓度曲线的材料特性;(ii)高通量吸附剂筛选和鉴定具有所需特性的新型材料。和GIS,以及用于天然气净化的主要材料是GIS,SIV和DFT。对于任何二元混合气体,可以利用已开发的ANN模型进行以下工作:(i)从根本上研究确定动态穿透时间和气体浓度曲线的材料特性;(ii)高通量吸附剂筛选和鉴定具有所需特性的新型材料。和GIS,以及用于天然气净化的主要材料是GIS,SIV和DFT。对于任何二元混合气体,可以利用已开发的ANN模型进行以下工作:(i)从根本上研究确定动态突破时间和气体浓度曲线的材料特性;(ii)高通量吸附剂筛选和鉴定具有所需特性的新型材料。
更新日期:2020-10-02
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