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Modeling of the solubility of H_2S in [bmim] [PF_6] by molecular dynamics simulation, GA-ANFIS and empirical approaches

机译:通过分子动力学模拟,GA-ANFIS和经验方法对H_2S在[bmim] [PF_6]中的溶解度建模

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摘要

Predicting the solubility of acid gases in ionic liquids (ILs), has lately appeared as advantageous for natural gas purifying, which is equipped by powerful models considering technical and economic aspects. Important issue in the assessment of ILs for potential utilization in gas sweetening process is estimating the H2S solubility at various temperatures and pressures Experimental measurements are costly and take considerable time and effort. As a result, proposing methods for predicting the behavior of this system over a wide range of conditions is vital. In this regard, molecular dynamics simulation (MD) technique as well as artificial intelligence knowledge of hybrid genetic algorithm-adaptive neuro fuzzy inference system (GA-ANFIS) and an empirical polynomial regression (PR) model were employed to estimate the solubility of H2S in [bmim][PF6] IL. Pressure and temperature are considered as the independent input variables and H2S solubility as the dependent output variable. The results of this study reveal that the simple fourth-order PR model and GA-ANFIS have the highest accuracy. As a result of the simplicity and accuracy of PR model, it can be used without any prior knowledge about MD and artificial intelligence (AI). According to the accuracy and precision of model proved by the obtained result, the solubility of H2S in ILs has been estimated. The results show that the PR method is more trustworthy than other models.
机译:预测酸性气体在离子液体(ILs)中的溶解度最近对于天然气净化似乎是有利的,它具有考虑技术和经济方面的强大模型。在气体甜味工艺中潜在利用IL的评估中,重要的问题是估算在各种温度和压力下的H2S溶解度。实验测量成本高昂,并且花费大量时间和精力。结果,提出用于在广泛条件下预测该系统行为的方法至关重要。在这方面,采用分子动力学模拟(MD)技术以及支持混合遗传算法的神经模糊推理系统(GA-ANFIS)的人工智能知识和经验多项式回归(PR)模型来估算H2S在水中的溶解度。 [bmim] [PF6] IL。压力和温度被视为独立输入变量,H2S溶解度被视为因变量。这项研究的结果表明,简单的四阶PR模型和GA-ANFIS具有最高的准确性。由于PR模型的简单性和准确性,因此无需任何有关MD和人工智能(AI)的先验知识即可使用它。根据所得结果证明的模型的准确性和准确性,估计了H2S在ILs中的溶解度。结果表明,PR方法比其他模型更值得信赖。

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