Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
Key Laboratory of Continental Shale Hydrocarbon Accumulation and Efficient Development (Northeast Petroleum University), Ministry of Education, Northeast Petroleum University, Daqing, 163318, Heilongjiang, China.
Sci Rep. 2022 Sep 2;12(1):14943. doi: 10.1038/s41598-022-18983-2.
Knowledge of the solubilities of hydrocarbon components of natural gas in pure water and aqueous electrolyte solutions is important in terms of engineering designs and environmental aspects. In the current work, six machine-learning algorithms, namely Random Forest, Extra Tree, adaptive boosting support vector regression (AdaBoost-SVR), Decision Tree, group method of data handling (GMDH), and genetic programming (GP) were proposed for estimating the solubility of pure and mixture of methane, ethane, propane, and n-butane gases in pure water and aqueous electrolyte systems. To this end, a huge database of hydrocarbon gases solubility (1836 experimental data points) was prepared over extensive ranges of operating temperature (273-637 K) and pressure (0.051-113.27 MPa). Two different approaches including eight and five inputs were adopted for modeling. Moreover, three famous equations of state (EOSs), namely Peng-Robinson (PR), Valderrama modification of the Patel-Teja (VPT), and Soave-Redlich-Kwong (SRK) were used in comparison with machine-learning models. The AdaBoost-SVR models developed with eight and five inputs outperform the other models proposed in this study, EOSs, and available intelligence models in predicting the solubility of mixtures or/and pure hydrocarbon gases in pure water and aqueous electrolyte systems up to high-pressure and high-temperature conditions having average absolute relative error values of 10.65% and 12.02%, respectively, along with determination coefficient of 0.9999. Among the EOSs, VPT, SRK, and PR were ranked in terms of good predictions, respectively. Also, the two mathematical correlations developed with GP and GMDH had satisfactory results and can provide accurate and quick estimates. According to sensitivity analysis, the temperature and pressure had the greatest effect on hydrocarbon gases' solubility. Additionally, increasing the ionic strength of the solution and the pseudo-critical temperature of the gas mixture decreases the solubilities of hydrocarbon gases in aqueous electrolyte systems. Eventually, the Leverage approach has revealed the validity of the hydrocarbon solubility databank and the high credit of the AdaBoost-SVR models in estimating the solubilities of hydrocarbon gases in aqueous solutions.
了解天然气中烃类组分在纯水中和电解质水溶液中的溶解度对于工程设计和环境方面非常重要。在当前的工作中，提出了六种机器学习算法，即随机森林、Extra Tree、自适应提升支持向量回归（AdaBoost-SVR）、决策树、数据处理群方法（GMDH）和遗传编程（GP），用于估算甲烷、乙烷、丙烷和正丁烷纯气体和混合物在纯水中和电解质体系中的溶解度。为此，在广泛的操作温度（273-637 K）和压力（0.051-113.27 MPa）范围内准备了一个巨大的烃类气体溶解度数据库（1836 个实验数据点）。采用了两种不同的方法，包括八个和五个输入，用于建模。此外，还使用了三个著名的状态方程（EOSs），即 Peng-Robinson（PR）、Valderrama 对 Patel-Teja（VPT）的修正和 Soave-Redlich-Kwong（SRK），与机器学习模型进行比较。采用八个和五个输入开发的 AdaBoost-SVR 模型在预测混合物或/和纯烃类气体在纯水中和电解质体系中的溶解度方面优于本研究中提出的其他模型、EOSs 和可用的智能模型，在高达高压和高温条件下，平均绝对相对误差值分别为 10.65%和 12.02%，相关系数分别为 0.9999。在 EOSs 中，VPT、SRK 和 PR 分别按良好预测进行排序。此外，使用 GP 和 GMDH 开发的两个数学相关性具有令人满意的结果，可以提供准确和快速的估计。根据敏感性分析，温度和压力对烃类气体溶解度的影响最大。此外，增加溶液的离子强度和气体混合物的拟临界温度会降低烃类气体在电解质水溶液中的溶解度。最终，杠杆作用方法验证了烃类溶解度数据库的有效性以及 AdaBoost-SVR 模型在估算烃类气体在水溶液中的溶解度方面的高可信度。
