为了估计CO2在纯水和盐水中的可溶性,使用级联前进神经网络和通用回归神经网络:应用于CO2溶解捕获在盐水层中的应用
Xinyuan Zou1,2, Yingting Zhu2,3, Jing Lv2,3
1State Key Laboratory of Enhanced Oil Recovery, Research Institute of Petroleum Exploration and Development, CNPC, Beijing 100083, China.
ACS omega
|February 5, 2024
概括
预测二氧化碳 (CO2) 在盐水中的溶解度对于碳捕获和储存至关重要. 一个带有莱文伯格-马奎特优化 (CFNN-LM) 的级联前进神经网络准确地模拟了二氧化碳溶解度,显示了压力.
科学领域:
- 地质化学和环境科学
- 化学工程是化学工程的重要组成部分.
- 计算科学 计算科学
背景情况:
- 准确预测二氧化碳 (CO2) 在水和盐水中的可溶性,对于碳捕获和储存 (CCS) 计划的成功至关重要.
- 了解盐水层中的二氧化碳溶解捕获是有效的碳捕获策略的关键.
研究的目的:
- 开发和验证一个高度准确的模型,用于预测各种水系统中的二氧化碳溶解度.
- 评估压力,温度和盐度对二氧化碳溶解度的影响.
主要方法:
- 编制了1278个实验CO2-盐水溶解度数据点的综合数据集.
- 使用级联前进神经网络 (CFNN) 和通用回归神经网络 (GRNN) 模型.
- 使用贝叶斯规范化 (BR),布罗登 - 弗莱彻 - 戈德法尔布 - 沙诺 (BFGS) 和莱文伯格 - 马奎特 (LM) 优化算法进行CFNN增强.
主要成果:
- 在CFNN-LM模型中,整个数据集的平均绝对百分比相对误差 (AAPRE) 为5.37%.
- 该CFNN-LM模型表现出卓越的准确性,R2值为0.9949.
- 灵敏度分析显示,压力是唯一对二氧化碳溶解度有积极影响的因素,而温度和所有盐度则对二氧化碳溶解度有负面影响 (脱盐效应).
结论:
- CFNN-LM模型提供了一种可靠且准确的方法,用于预测在不同盐水条件下二氧化碳溶解度.
- 这项研究量化了各种盐 (MgCl2 > CaCl2 > NaCl > KCl > NaHCO3 > Na2SO4) 对二氧化碳溶解度的化效应.
- 开发的模型和数据集支持碳捕获和储存技术的进步.
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