在水溶液中估计的溶解度,使用机器学习技术,用于深层盐水层中储存
Mohammad Rasool Dehghani1, Hamed Nikravesh1, Maryam Aghel2
1Department of Petroleum Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf University, Bushehr, Iran.
Scientific reports
|October 29, 2024
概括
研究人员开发了九种机器学习模型,以估计地下储存水中的溶性. LSBoost 模型实现了最高的精度,为优化储存系统提供了一个有前途的工具.
科学领域:
- 储能 储能 储能 储能 储能 储能
- 计算化学计算化学
- 环境科学 环境科学
背景情况:
- 地下多孔结构由于其高容量,对气储存有希望.
- 准确估计水溶液中的水溶性对于储存效率至关重要.
- 现有的可溶性预测方法需要用于实际应用的增强.
研究的目的:
- 开发和比较机器学习模型,用于预测水中的溶性.
- 确定用于估计溶性的最准确模型.
- 分析环境因素对溶性的影响.
主要方法:
- 收集并预处理了有关溶性的实验数据,删除了异常值.
- 开发了九种不同的机器学习模型,包括LSBoost,人工神经网络和支持矢量机器.
- 使用贝叶斯优化来进行超参数调整,并使用皮尔森相关性来进行因子分析.
主要成果:
- 该LSBoost模型表现出卓越的性能,其中R2=0.9997和RMSE=4.18E-03.
- 其他模型显示了不同程度的准确性,线性回归表现最不有效.
- 压力显著影响溶性 (0.8188),而盐度则有相反的关系 (-0.5506).
结论:
- 该LSBoost模型非常准确地预测水溶液中的溶性.
- 机器学习为存应用提供了传统状态方程的强有力的替代方案.
- 这些发现支持通过准确的溶解率预测优化地下储存系统.
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