Omrani Sina, Ghasemi Mehdi, Singh Mrityunjay, Mahmoodpour Saeed, Zhou Tianhang, Babaei Masoud, Niasar Vahid
Department of Chemical Engineering, The University of Manchester, Manchester M13 9PL, United Kingdom.
Institute of Applied Geosciences, Geothermal Science and Technology, Technische Universität Darmstadt, 64289 Darmstadt, Germany.
Langmuir. 2023 Sep 12;39(36):12680-12691. doi: 10.1021/acs.langmuir.3c01424. Epub 2023 Aug 31.
Hydrogen (H) underground storage has attracted considerable attention as a potentially efficient strategy for the large-scale storage of H. Nevertheless, successful execution and long-term storage and withdrawal of H necessitate a thorough understanding of the physical and chemical properties of H in contact with the resident fluids. As capillary forces control H migration and trapping in a subsurface environment, quantifying the interfacial tension (IFT) between H and the resident fluids in the subsurface is important. In this study, molecular dynamics (MD) simulation was employed to develop a data set for the IFT of H-brine systems under a wide range of thermodynamic conditions (298-373 K temperatures and 1-30 MPa pressures) and NaCl salinities (0-5.02 mol·kg). For the first time to our knowledge, a comprehensive assessment was carried out to introduce the most accurate force field combination for H-brine systems in predicting interfacial properties with an absolute relative deviation (ARD) of less than 3% compared with the experimental data. In addition, the effect of the cation type was investigated for brines containing NaCl, KCl, CaCl, and MgCl. Our results show that H-brine IFT decreases with increasing temperature under any pressure condition, while higher NaCl salinity increases the IFT. A slight decrease in IFT occurs when the pressure increases. Under the impact of cation type, Ca can increase IFT values more than others, i.e., up to 12% with respect to KCl. In the last step, the predicted IFT data set was used to provide a reliable correlation using machine learning (ML). Three white-box ML approaches of the group method of data handling (GMDH), gene expression programming (GEP), and genetic programming (GP) were applied. GP demonstrates the most accurate correlation with a coefficient of determination () and absolute average relative deviation (AARD) of 0.9783 and 0.9767%, respectively.
氢（H）地下储存作为一种潜在的大规模储氢高效策略已引起了相当大的关注。然而，要成功实现氢气的长期储存和提取，需要全面了解氢气与储层流体接触时的物理和化学性质。由于毛细力控制着氢气在地下环境中的迁移和捕集，因此量化地下氢气与储层流体之间的界面张力（IFT）非常重要。在本研究中，采用分子动力学（MD）模拟，在广泛的热力学条件（温度298 - 373 K、压力1 - 30 MPa）和NaCl盐度（0 - 5.02 mol·kg）下，建立了氢 - 盐水体系界面张力的数据集。据我们所知，首次进行了全面评估，以引入用于氢 - 盐水体系预测界面性质的最精确力场组合，与实验数据相比，绝对相对偏差（ARD）小于3%。此外，还研究了阳离子类型对含NaCl、KCl、CaCl和MgCl盐水的影响。我们的结果表明，在任何压力条件下，氢 - 盐水界面张力都随温度升高而降低，而较高的NaCl盐度会增加界面张力。压力增加时，界面张力略有下降。在阳离子类型的影响下，Ca比其他阳离子更能增加界面张力值，相对于KCl而言，最多可增加12%。在最后一步中，利用机器学习（ML）对预测的界面张力数据集进行可靠关联。应用了数据处理分组法（GMDH）、基因表达式编程（GEP）和遗传编程（GP）这三种白盒式机器学习方法。GP表现出最精确的相关性，决定系数（）和绝对平均相对偏差（AARD）分别为0.9783和0.9767%。
