在不同温度下对CO2 - 盐水界面张力的增强预测,使用基于多分支结构的神经网络方法
Jiarui Fan1, Yimin Jiang2, Zhiqiang Fan3
1Key Laboratory of Ocean Energy Utilization and Energy Conservation of Ministry of Education, School of Energy and Power Engineering, Dalian University of Technology, Dalian 116023, P. R. China.
Langmuir : the ACS journal of surfaces and colloids
|January 14, 2025
概括
这项研究引入了多分支卷积神经网络 (MBCNN) 来预测二氧化碳和盐水之间的界面张力 (IFT). MBCNN准确地模拟受温度和化学成分影响的IFT,改善了复杂地下条件的预测.
科学领域:
- 地质化学和石油工程
- 地球科学中的人工智能
背景情况:
- 二氧化碳和盐水之间的界面张力 (IFT) 对多相系统至关重要.
- IFT受化学成分和温度的影响,这给预测带来了挑战.
- 准确的IFT预测对于碳捕获等地下应用至关重要.
研究的目的:
- 为CO2-盐水界面张力 (IFT) 开发一个预测模型.
- 量化探索化学成分,温度和IFT之间的相关性.
- 在不同的温度和压力条件下实现准确的IFT预测.
主要方法:
- 开发一个多分支卷积神经网络 (MBCNN).
- 来自13个文献来源的1716个测量点的数据集的编制.
- 对广泛的温度 (273.15473.15 K) 和压力 (070 MPa) 范围的分析.
主要成果:
- 在低IFT场景中,MBCNN显示了高预测准确度 (MAE = 0.47,R2 = 0.9921).
- 该模型捕获了影响IFT的温度和化学成分之间的相互关系.
- 为了在三个温度区间 (MAE = 2.3672,R2 = 0.9263) 上进行IFT预测,得出了一个新的零碎函数.
结论:
- 通过考虑温度和化学成分,MBCNN有效地预测了CO2-盐水IFT.
- 由于溶解度的变化,温度显著影响IFT对化学成分的依赖.
- 开发的模型和分片功能为盐水层提供可靠的IFT预测.
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