Interfacial Tension-Temperature-Pressure-Salinity Relationship for the Hydrogen-Brine System under Reservoir Conditions: Integration of Molecular Dynamics and Machine Learning.
Sina OmraniMehdi GhasemiMrityunjay SinghSaeed MahmoodpourTianhang ZhouMasoud BabaeiVahid J NiasarPublished in: Langmuir : the ACS journal of surfaces and colloids (2023)
Hydrogen (H 2 ) underground storage has attracted considerable attention as a potentially efficient strategy for the large-scale storage of H 2 . Nevertheless, successful execution and long-term storage and withdrawal of H 2 necessitate a thorough understanding of the physical and chemical properties of H 2 in contact with the resident fluids. As capillary forces control H 2 migration and trapping in a subsurface environment, quantifying the interfacial tension (IFT) between H 2 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 2 -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 -1 ). For the first time to our knowledge, a comprehensive assessment was carried out to introduce the most accurate force field combination for H 2 -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 2 , and MgCl 2 . Our results show that H 2 -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 2+ 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 ( R 2 ) and absolute average relative deviation (AARD) of 0.9783 and 0.9767%, respectively.
Keyphrases
- molecular dynamics
- ionic liquid
- electronic health record
- density functional theory
- gene expression
- big data
- machine learning
- microbial community
- molecular dynamics simulations
- healthcare
- high resolution
- transcription factor
- quality improvement
- physical activity
- copy number
- mass spectrometry
- magnetic resonance imaging
- deep learning
- binding protein
- atomic force microscopy