A QSPR model for estimating Henry's law constant of H2S in ionic liquids by ELM algorithm
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F21%3A85351" target="_blank" >RIV/60460709:41330/21:85351 - isvavai.cz</a>
Výsledek na webu
<a href="https://www.sciencedirect.com/science/article/pii/S0045653520329416?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0045653520329416?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.chemosphere.2020.128743" target="_blank" >10.1016/j.chemosphere.2020.128743</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
A QSPR model for estimating Henry's law constant of H2S in ionic liquids by ELM algorithm
Popis výsledku v původním jazyce
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 Henrys 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 (S-EP) 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 paramet
Název v anglickém jazyce
A QSPR model for estimating Henry's law constant of H2S in ionic liquids by ELM algorithm
Popis výsledku anglicky
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 Henrys 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 (S-EP) 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 paramet
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
10511 - Environmental sciences (social aspects to be 5.7)
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2021
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Chemosphere
ISSN
0045-6535
e-ISSN
1879-1298
Svazek periodika
269
Číslo periodika v rámci svazku
128743
Stát vydavatele periodika
GB - Spojené království Velké Británie a Severního Irska
Počet stran výsledku
10
Strana od-do
1-10
Kód UT WoS článku
000631725000060
EID výsledku v databázi Scopus
2-s2.0-85094954822