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Solubility of hydrocarbon and non-hydrocarbon gases in aqueous electrolyte solutions: A reliable computational strategy

机译:碳氢化合物和非碳氢化合物气体在电解质水溶液中的溶解度:可靠的计算策略

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摘要

Determining solubility of hydrocarbon and non-hydrocarbon components of natural gas is crucial for theoretical studies and engineering design. In this study, new solubility prediction models were developed for both hydrocarbon gases (methane, ethane, propane, and butane) and non-hydrocarbon gases (CO2 and N-2) in aqueous solutions of strong electrolytes using a hybrid modeling strategy, which links the Coupled Simulated Annealing (CSA) to the Least-Squares Support Vector Machine (LSSVM) technique. Comparing the models' predictions with experimentally determined solubility values, a very good agreement was noticed, leading to the overall correlation coefficients of 0.9880 and 0.9907 for the hydrocarbon and non-hydrocarbon gases, respectively. These models were also found to succeed in capturing the physical trends among experimental datasets through performing sensitivity analysis between the dependent and independent parameters. Developed models can be utilized to predict the solubility of pure and/or a mixture of hydrocarbon and non-hydrocarbon gases in aqueous electrolyte solutions, covering wide ranges of ionic strength, pressures, and temperatures up to supercritical conditions. Such a reliable predictive tool helps researchers and engineers to successfully obtain the key thermodynamic properties (e.g., solubility, vapor pressure, and compressibility factor), which are central to properly design and operate the corresponding units in a variety of chemical plants such as petrochemical plants, natural gas processing plants, and refineries.
机译:确定天然气中烃和非烃组分的溶解度对于理论研究和工程设计至关重要。在这项研究中,使用混合建模策略,针对强电解质水溶液中的烃类气体(甲烷,乙烷,丙烷和丁烷)和非烃类气体(CO2和N-2)开发了新的溶解度预测模型将模拟退火(CSA)耦合到最小二乘支持向量机(LSSVM)技术。将模型的预测值与实验确定的溶解度值进行比较,可以发现非常好的一致性,导致烃类气体和非烃类气体的总相关系数分别为0.9880和0.9907。通过在相关参数和独立参数之间进行敏感性分析,还发现这些模型成功捕获了实验数据集中的物理趋势。已开发的模型可用于预测纯水和/或碳氢化合物和非碳氢化合物气体混合物在电解质水溶液中的溶解度,涵盖离子强度,压力和温度的各种范围,直至超临界条件。这种可靠的预测工具可帮助研究人员和工程师成功获得关键的热力学性质(例如,溶解度,蒸气压和可压缩系数),这对于正确设计和操作各种化工厂(如石化厂)中的相应单元至关重要,天然气加工厂和精炼厂。

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