数量结构-活动关系
极限学习机
亨利定律
化学
常量(计算机编程)
热力学
甜味剂
适用范围
分子描述符
试验装置
离子液体
算法
数学
人工神经网络
溶解度
计算机科学
人工智能
统计
物理化学
有机化学
立体化学
甜味剂
程序设计语言
催化作用
物理
食品科学
作者
Xuejing Kang,Lv Z,Yongsheng Zhao,Zhongbing Chen
出处
期刊:Chemosphere
[Elsevier]
日期:2021-04-01
卷期号:269: 128743-128743
被引量:6
标识
DOI:10.1016/j.chemosphere.2020.128743
摘要
Ionic liquids (ILs) as green solvents have been studied in the application of gas sweetening. However, it is a huge challenge to obtain all the experimental values because of the high costs and generated chemical wastes. This study pioneered a quantitative structure−property relationship (QSPR) model for estimating Henry’s law constant (HLC) of H2S in ILs. A dataset consisting of the HLC data of H2S for 22 ILs within a wide range of temperature (298.15–363.15 K) were collected from published reports. The electrostatic potential surface area (SEP) and molecular volume of these ILs were calculated and used as input descriptors together with temperature. The extreme learning machine (ELM) algorithm was employed for nonlinear modelling. Results showed that the determination coefficient (R2) of the training set, test set and total set were 0.9996, 0.9989,0.9994, respectively, while the average absolute relative deviation (AARD%) of them were 1.3383, 2,4820 and 1.5820, respectively. The statistical parameters for the measurement of the model exhibited the great reliability, stability, and predictive power of the ELM model. The Applicability Domain (AD) of the ELM model is also investigated. It proves that the majority of ILs in the training and test sets are located in the model’s AD and verifies the reliability of the model. The proposed model is potentially applicable to guide the application of ILs for gas sweetening.
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