Prediction of ammonia absorption in ionic liquids based on extreme learning machine modelling and a novel molecular descriptor S-EP
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F20%3A82220" target="_blank" >RIV/60460709:41330/20:82220 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/abs/pii/S001393512030846X" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S001393512030846X</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.envres.2020.109951" target="_blank" >10.1016/j.envres.2020.109951</a>
Alternative languages
Result language
angličtina
Original language name
Prediction of ammonia absorption in ionic liquids based on extreme learning machine modelling and a novel molecular descriptor S-EP
Original language description
The large amounts of ammonia emissions generated from industrial production have caused serious environmental pollution problems, such as soil acidification, eutrophication, the formation of fine particles and changes in the global greenhouse balance, and also greatly endanger human health. At present, effectively reducing ammonia emissions or recovering ammonia is still a huge challenge. Ionic liquids (ILs) as a new class of green solvent have been introduced for ammonia absorption with great potential, but a huge number on combination systems of ILs lead to the difficulty of measuring the ammonia solubility in all ILs by experiments (e.g., danger and cost). Hereby, this study proposed a novel approach for estimating the ammonia solubility in different ILs. A predictive model was developed based on the novel Algorithm - extreme learning machine (ELM) and the molecular descriptors of electrostatic potential surface areas (SEP) as input parameters. Besides, 502 data points of ammonia solubility in 17
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10511 - Environmental sciences (social aspects to be 5.7)
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Environmental Research
ISSN
0013-9351
e-ISSN
1096-0953
Volume of the periodical
189
Issue of the periodical within the volume
109951
Country of publishing house
US - UNITED STATES
Number of pages
9
Pages from-to
1-9
UT code for WoS article
000576641600010
EID of the result in the Scopus database
2-s2.0-85089025080