在储条件下的盐系统的界面张力-温度-压力-盐度关系:分子动力学和机器学习的整合
Sina Omrani1, Mehdi Ghasemi1, Mrityunjay Singh2
1Department of Chemical Engineering, The University of Manchester, Manchester M13 9PL, United Kingdom.
Langmuir : the ACS journal of surfaces and colloids
|August 31, 2023
概括
了解界面张力 (IFT) 是 (H2) 地下储存的关键. 这项研究使用了分子动力学模拟来准确预测H2-盐水IFT在各种条件下,开发出可靠的机器学习相关性.
科学领域:
- 地质科学和储能 储能
- 计算化学和材料科学计算化学和材料科学
背景情况:
- 大规模的地下 (H2) 储存对于可再生能源的整合至关重要.
- 准确预测H2-盐水界面张力 (IFT) 对于管理H2迁移和在地下水库中的捕获至关重要.
- 现有的模型缺乏在各种热力学和盐度条件下对H2液IFT的综合数据.
研究的目的:
- 开发一个精确的力场,用于分子动力学 (MD) 模拟H2-盐水系统.
- 为了生成一个全面的数据集为H2水 IFT在广泛的温度,压力和盐度.
- 调查不同类型对H2-盐水IFT的影响,并开发预测机器学习 (ML) 相关性.
主要方法:
- 采用分子动力学 (MD) 模拟来计算H2-盐水界面张力 (IFT).
- 与实验数据对比验证的模拟准确性,实现绝对相对偏差 (ARD) 低于3%.
- 利用机器学习 (ML) 技术,包括GEP和GP,开发IFT的预测相关性.
主要成果:
- 为H2-盐水IFT预测建立了一个准确的MD模拟方法.
- IFT随着温度和压力的增加而下降,而随着NaCl盐度的增加而增加.
- 阴离子类型显著影响IFT,Ca2+与其他阴离子相比显著增加.
- 开发了一个高度准确的ML相关性 (GP,R2=0.9783,AARD=0.9767%) 来预测H2-盐水IFT.
结论:
- 该研究提供了一种验证的方法来预测H2-盐水IFT,这对于安全有效的地下储存至关重要.
- 开发的ML模型提供了一个可靠的工具,用于在各种地下条件下估计IFT.
- 了解IFT上的阴离子效应可以提高预测储行为的能力.
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