Machine learning assisted rediscovery of methane storage and separation in porous carbon from material literature
Machine learning assisted rediscovery of methane storage and separation in porous carbon from material literature
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DOI：
10.1016/j.fuel.2020.120080
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发表时间：
2021-04
期刊：
影响因子：
7.4
通讯作者：
Chi Zhang;Dawei Li;Yunchao Xie;D. Stalla;Peng Hua;Tung D. Nguyen;Ming Xin;Jian Lin
中科院分区：
文献类型：
--
作者：
Chi Zhang;Dawei Li;Yunchao Xie;D. Stalla;Peng Hua;Tung D. Nguyen;Ming Xin;Jian Lin
多孔碳（PC）被广泛认为是最有前途的甲烷储存吸收剂之一。研究表明，其吸收能力和选择性高度依赖于结构结构。尽管已经付出了很多努力，但揭示其详细的结构与性能关系仍然是一个挑战。在这里，我们提出了一项创新研究，在机器学习的帮助下，PC的纹理结构与甲烷吸收和分离之间的隐藏关系可以从材料文献中的现有数据中得出。机器学习模型由数据训练，包括比表面积、微孔体积、中孔体积、温度和压力作为输入变量，甲烷吸收量作为输出变量进行预测。在测试的模型中，多层感知器（MLP）在预测甲烷吸收方面表现出最高的准确性。此外，该模型能够自动构建微孔体积和中孔体积的吸收性能图。所获得的 MLP 模型还可以通过使用从 PC 文献中收集的 CO2 吸收数据进行重新训练来探索 CO2/CH4 选择性。构建的二维选择性图表明，在低 CH4 吸收区域可以实现高选择性。
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/CH4selectivity by retraining it with the data collected from literature of PC for the CO2uptake. The constructed 2D selectivity map shows that the high selectivity can be achieved in the low CH4uptake region.