为了预测不纯和纯CO2-盐水系统的界面张力,使用强大的相对方法
Chen Wei-Yu1, Lin Sun1, Jiyong Zhou1
1CNOOC EnerTech-Drilling & Production Co., Ltd., Tianjin 300452, China.
ACS omega
|February 26, 2024
概括
机器学习模型准确地预测了CO2-盐水界面张力,这对于碳捕获和储存 (CCS) 站点选择至关重要. 压力对IFT产生重大影响,杂质和离子类型也在储存安全方面发挥着关键作用.
科学领域:
- 地球和环境科学 地球和环境科学
- 化学工程是化学工程的重要组成部分.
- 数据科学数据科学数据科学
背景情况:
- 全球气候变化需要在可再生能源和碳捕获和储存 (CCS) 方面取得进展.
- 在深层盐水层中安全储存二氧化碳依赖于了解二氧化碳盐水系统的特性,特别是界面张力 (IFT).
- IFT影响用于二氧化碳封存的地质构造的储存能力和安全性.
研究的目的:
- 开发精确的数学相关性,用于模拟纯和不纯CO2-盐水系统的IFT.
- 调查各种参数 (包括压力,温度和杂质) 对二氧化碳盐IFT的影响.
- 评估白盒机器学习方法在CCS应用中预测IFT的性能.
主要方法:
- 编制了 CO2-盐水系统的 2811 个实验数据点的广泛数据集.
- 采用了三个白盒机器学习技术:基因编程 (GP),基因表达编程 (GEP) 和数据处理组方法 (GMDH).
- 采用了两种不同的建模方法:一种是基于杂质组成的,另一种是采用伪临界温度 (Tcm).
主要成果:
- 使用五个输入的GMDH模型显示出极高的准确性 (完整数据集的MAPE为7.38%).
- 六个输入的GEP模型显示出高精度 (完整数据集的MAPE为8.31%).
- 灵敏度分析发现压力是对IFT影响最大的因素,更高的压力降低了IFT. 具有较低临界温度和较高阴离子度 (特别是双价) 的杂质也增加了IFT.
结论:
- 白盒ML模型,特别是GMDH和GEP,为CO2-盐水IFT预测提供了可靠的相关性.
- 了解压力,杂质和盐水组成的影响对于优化CCS地点选择和安全至关重要.
- 开发的模型为CCS操作相关的各种气体混合物提供了多功能预测工具.
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