基于先进智能算法模型的CO2 - 盐水界面张力的估计:碳盐水 Aquifer 封存的应用
Meiheriayi Mutailipu1,2, Yande Yang1,2, Kaishuai Zuo1,2
1Engineering Research Center of Northwest Energy Carbon Neutrality, Ministry of Education, Xinjiang University, Urumqi 830017, China.
ACS omega
|September 9, 2024
概括
一个新的贝叶斯优化随机森林模型准确地估计了盐水层中二氧化碳封存的界面张力. 该模型有助于预测存储容量,这对于实现净零排放至关重要.
科学领域:
- 地质科学和环境科学 地球科学和环境科学
- 计算建模和数据科学数据科学
背景情况:
- 碳捕获,利用和储存 (CCUS) 对于减排至关重要.
- 地质碳储存 (GCS),特别是深层盐水层,是实现净零目标的关键策略.
- 二氧化碳和盐水之间的界面张力 (IFT) 极大地影响了二氧化碳储存的安全性和效率.
研究的目的:
- 在盐水层中开发CO2-盐水界面张力 (IFT) 的预测模型.
- 评估贝叶斯优化随机森林 (RF) 模型对IFT估计的准确性和效率.
- 利用预测的IFT值来估计塔林盆地的二氧化碳封存能力.
主要方法:
- 开发一种混合模型,将随机森林 (RF) 和贝叶斯优化 (BO) 结合起来.
- 使用三个启发式算法对实验数据进行RF-BO模型的验证.
- 应用经过验证的模型来预测IFT,并随后估计不同深度的二氧化碳储存能力.
主要成果:
- 贝叶斯优化的射频模型表现出高精度,RMSE=1.7705,MAPE=2.0687%,R2=0.9729. 这两种方法均具有很高的精度.
- 该模型有效地估计了纯和不纯气溶系统的IFT.
- 预测的IFT值显示,埋葬深度对塔林盆地的二氧化碳储存能力的影响有限.
结论:
- 开发的贝叶斯优化射频模型为在地质碳捕获中估计CO2-盐水IFT提供了可靠的工具.
- 准确的IFT预测可以提高盐水层中二氧化碳储存能力的评估.
- 这种方法支持CCUS技术的安全和有效部署,以缓解气候变化.
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