根据机器学习算法快速计算NaCl溶液的活性系数
Bowen Qin1,2, Yizhong Zhang1,2, Long Yang3
1School of Petroleum Engineering, Yangtze University, Wuhan City 430100, China.
ACS omega
|November 25, 2024
概括
机器学习快速计算二氧化碳储存模拟活动系数. 这种方法将计算速度提高48%,并使用温度和压力来确定地质化学建模的Debye-Hückel参数.
科学领域:
- 地质化学 地质化学
- 计算化学的计算化学
- 机器学习应用 机器学习应用
背景情况:
- 活动系数量化了与理想溶液的偏差,影响了二氧化碳在盐水中的溶解度和扩散,这对地质储存至关重要.
- 当前的数值模拟通常使用Helgeson-Kirkham-Flowers (HKF) 方程,需要通过缓慢的插值算法计算Debye-Hückel (DH) 参数.
- 有效计算活动系数对于优化二氧化碳地质储存模拟至关重要.
研究的目的:
- 开发一种更快,更准确的方法来计算地化学反应建模中的活性系数.
- 使用基于温度和压力的机器学习来建立Debye-Hückel (DH) 参数的经验公式.
- 将这些经验公式集成到Helgeson-Kirkham-Flowers (HKF) 方程中,以便快速计算活动系数.
主要方法:
- 机器学习算法在Debye-Hückel (DH) 参数上进行训练,这些参数来自IAPWS-95方法.
- 建立了经验公式来表达DH参数作为温度和压力的函数.
- 这些公式被替换到HKF方程中来计算活动系数.
主要成果:
- 机器学习方法产生了活动系数,与实验值相对偏差很小 (平均R2为0.9463,平均相对误差为2.28%).
- 活动系数计算的计算速度提高了48%.
- 该方法允许仅根据温度和压力在广泛范围内 (0300°C,0200MPa) 计算DH参数.
结论:
- 这项研究提出了一种高精度和高效的方法来计算活动系数,大大减少了地质化学模拟中的计算时间.
- 开发的经验公式使得DH参数的快速确定成为可能,从而促进了精确的二氧化碳储存建模.
- 这种方法对于在深海盐水中推进地化学反应的数值模拟至关重要.
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