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    Enhancement of light naphtha quality and environment using new synthetic nano-catalyst for oxidative desulfurization: Experiments and process modeling

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    Accepted manuscript (677.1Kb)
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    Publication date
    2020-09
    Author
    Jarullah, A.T.
    Ahmed, G.S.
    Al-Tabbakh, B.A.
    Mujtaba, Iqbal M.
    Keyword
    Oxidative desulfurization
    Environment friendly fuel
    Manganese dioxide
    Batch reactor
    Kinetic parameters estimation
    Rights
    © 2020 Elsevier. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license.
    Peer-Reviewed
    Yes
    Open Access status
    openAccess
    
    Metadata
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    Abstract
    Batch oxidative desulfurization (ODS) process is investigated here for the removal sulfur compound from light naphtha using homemade new nano-catalyst. The catalyst is made of manganese dioxide supported on zeolite nanoparticles which shows an excellent catalytic performance with good impregnation, high activity, good pore size distribution and larger surface area. Different reaction temperature, time and initial sulfur concentration are used to have a deeper insight of the process. The experimental results reveal that the conversion of sulfur compound is increased by increasing the initial sulfur concentration, the reaction temperature and batch time. A mathematical model of the process is developed and validated using the experimental data within gPROMS software with high accuracy. The validated model (errors less than 5% between experimental and predicted results) is then utilized to obtain the optimal operation conditions of the process giving maximum conversion of sulfur (higher than 99%) resulting in an environmentally friendly fuel.
    URI
    http://hdl.handle.net/10454/18969
    Version
    Accepted manuscript
    Citation
    Jarullah AT, Ahmed GS, Al-Tabbakh BA et al (2020) Enhancement of light naphtha quality and environment using new synthetic nano-catalyst for oxidative desulfurization: Experiments and process modeling. Computers and Chemical Engineering. 140: 106869.
    Link to publisher’s version
    https://doi.org/10.1016/j.compchemeng.2020.106869
    Type
    Article
    Collections
    Engineering and Informatics Publications

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