Machine learning assisted rediscovery of methane storage and separation in porous carbon from material literature

FUEL

卷:290

Article

Zhang, Chi¹  Li, Dawei¹  Xie, Yunchao¹  Stalla, David²  Hua, Peng³  Nguyen, Duy Tung⁴  Xin, Ming¹  Lin, Jian^1,3,4,5 

[1] Univ Missouri, Dept Mech & Aerosp Engn, Columbia, MO 65211 USA

[2] Univ Missouri, Electron Microscopy Core, Columbia, MO 65211 USA

[3] Univ Missouri, Dept Biomed Biol & Chem Engn, Columbia, MO 65211 USA

[4] Univ Missouri, Dept Phys & Astron, Columbia, MO 65211 USA

[5] Univ Missouri, Dept Elect Engn & Comp Sci, Columbia, MO 65211 USA

关键词: Methane storage;    Gas separation;    Porous carbon;    Machine learning;    Literature mining;   

DOI  :  10.1016/j.fuel.2020.120080

来源: Elsevier

【 摘 要 】

Porous carbon (PC) has been widely regarded as one of the most promising absorbents for methane storage. Studies show that its uptake capacity and selectivity highly depend on textural structures. Although much effort has been made, unveiling their detailed structure-performance relationship remains a challenge. Here, we propose an innovative study where, with the assistance of machine learning, the hidden relationship of the textural structures of PC with the methane uptake and separation can be derived from existing data in material literature. Machine learning models were trained by the data, including specific surface area, micropore volume, mesopore volume, temperature, and pressure as the input variables and methane uptake as the output variable for prediction. Among the tested models, the multilayer perceptron (MLP) shows the highest accuracy in predicting the methane uptake. In addition, the model enables to automatically construct a uptake performance map in terms of micropore volume and mesopore volume. The obtained MLP model was also extended to explore the CO2/CH4 selectivity by retraining it with the data collected from literature of PC for the CO2 uptake. The constructed 2D selectivity map shows that the high selectivity can be achieved in the low CH4 uptake region.

【 授权许可】

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