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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