Multi-scale design of MOF-based membrane separation for CO2/CH4 mixture via integration of molecular simulation, machine learning and process modeling and simulation
Multi-scale design of MOF-based membrane separation for CO2/CH4 mixture via integration of molecular simulation, machine learning and process modeling and simulation
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DOI：
10.1016/j.memsci.2023.121430
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发表时间：
2023-01
影响因子：
9.5
通讯作者：
Xinyi Cheng;Yang Liao;Zhao Lei;J. Li;Xiaolei Fan;Xin Xiao
中科院分区：
文献类型：
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作者：
Xinyi Cheng;Yang Liao;Zhao Lei;J. Li;Xiaolei Fan;Xin Xiao
金属有机框架 (MOF) 膜由于其孔径范围广、表面积大、孔隙率高和开放金属位点，已证明具有较高的 CO2 捕集效率。在这项工作中，我们通过集成分子模拟、机器学习以及过程建模和模拟，提出了一种基于 MOF 的 CO2/CH4 混合物膜分离的多尺度设计框架。 GCMC 和 MD 分子模拟首先用于评估 MOF 基膜（例如 IRMOF-1）在不同操作条件下用于 CO2/CH4 分离的吸附等温线、等量吸附热、自扩散率、活化能、渗透性和选择性。结果发现，298 K 的模拟等温线与实验结果一致。随着操作条件的变化，CO2 渗透率范围为 4.090 × 104 至 3.818 × 105barrer。同样的现象也体现在选择性上。然后，我们使用人工神经网络（ANN）等机器学习方法建立吸附容量和自扩散率的预测模型，用于计算渗透率。均方误差为 0.0086，决定系数为 0.9822。所提出的 ANN 模型与中空纤维膜分离过程的串联罐模型集成，并使用有限体积法进行模拟。三个案例研究说明了所提出的集成框架的可行性和优越性。
Metal-organic framework (MOF) membranes have demonstrated high efficiency for CO2capture due to their wide range of pore sizes, high surface area, high porosity, and open metal sites. In this work, we propose a multi-scale design framework of MOF-based membrane separation for CO2/CH4mixture via integration of molecular simulation, machine learning, and process modelling and simulation. The GCMC and MD molecular simulation is first used to evaluate adsorption isotherms, isosteric adsorption heat, self-diffusivity, activation energy, permeability, and selectivity of a MOF-based membrane (e.g., IRMOF-1) for CO2/CH4separation at different operating conditions. It is found that the simulated isotherms at 298 K are consistent with experimental results. CO2permeability can range from 4.090 × 104to 3.818 × 105barrer with variation in operating conditions. The same phenomenon is also reflected in the selectivity. We then establish prediction models of adsorption capacity and self-diffusivity using machine learning methods such as artificial neural network (ANN), which are used to calculate the permeability. The mean squared error is 0.0086, and the coefficient of determination is 0.9822. The proposed ANN models are integrated with the tanks-in-series model of a hollow fiber membrane separation process, which is simulated using the finite volume method. Three case studies illustrate the feasibility and superiority of the proposed integrated framework.