Application of LS-SVM strategy to estimate H2S loading capacity in different ionic liquids and alkanolamines

作者：Fattahi Maryam; Abedini Hassan; Baghban Alireza^*; Anbaz Mohammad Amin

来源：Petroleum Science and Technology, 2017, 35(11): 1117-1123.

DOI：10.1080/10916466.2017.1310881

• amine
• H2S solubility
• H2S loading
• ionic liquid
• support vector machine

Favorable properties of aqueous solutions are improved with the addition of different materials for separation of hydrogen sulfide (H2S). Also, equilibrium data and available equations for solubility estimation of this gas are only valid for specific solutions and limited ranges of temperature and pressure. In this regard, a machine learning model based on Support vector machine (SVM) algorithm is proposed and developed with mixtures containing different amines and ionic liquids to predict H2S solubility over wide ranges of temperature (298-434.5 K), pressure (13-9319 kPa), overall mass concentration (3.82-100%) and mixture's apparent molecular weight (18.39-556.17 g/mol). The accuracy of the performance of this network was evaluated by regression analysis on calculated and experimental data, which had not been used in network training.

• 出版日期2017
• 单位中国石油大学（北京）