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    Estimation of Speciation Data for Hydrocarbons using Data Science

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    Type
    Conference Paper
    Authors
    Yalamanchi, Kiran
    Chen, Bingjie
    Sarankapani, Rooppesh
    Sarathy, Mani cc
    KAUST Department
    Chemical Engineering Program
    Clean Combustion Research Center
    Combustion and Pyrolysis Chemistry (CPC) Group
    Physical Science and Engineering (PSE) Division
    KAUST Grant Number
    OSR-2019-CRG7-4077
    Date
    2021-09-05
    Permanent link to this record
    http://hdl.handle.net/10754/672098
    
    Metadata
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    Abstract
    Strict regulations on air pollution motivates clean combustion research for fossil fuels. To numerically mimic real gasoline fuel reactivity, surrogates are proposed to facilitate advanced engine design and predict emissions by chemical kinetic modelling. However, chemical kinetic models could not accurately predict non-regular emissions, e.g. aldehydes, ketones and unsaturated hydrocarbons, which are important air pollutants. In this work, we propose to use machine-learning algorithms to achieve better predictions. Combustion chemistry of fuels constituting of 10 neat fuels, 6 primary reference fuels (PRF) and 6 FGX surrogates were tested in a jet stirred reactor. Experimental data were collected in the same setup to maintain data uniformity and consistency under following conditions: residence time at 1.0 second, fuel concentration at 0.25%, equivalence ratio at 1.0, and temperature range from 750 to 1100K. Measured species profiles of methane, ethylene, propylene, hydrogen, carbon monoxide and carbon dioxide are used for machine-learning model development. The model considers both chemical effects and physical conditions. Chemical effects are described as different functional groups, viz. primary, secondary, tertiary, and quaternary carbons in molecular structures, and physical conditions as temperature. Both the Machine-learning models used in this study showed a good prediction accuracy with a test set regression score of 97.75 for support vector regression and 91.07 for random forest regression. This finding shows the great potential of machine learning application on combustion chemistry. By expanding the experimental database, machine-learning models can be further applied to many other hydrocarbons in future work.
    Citation
    Yalamanchi, K., chen, B., Sarankapani, R., & Sarathy, M. (2021). Estimation of Speciation Data for Hydrocarbons using Data Science. SAE Technical Paper Series. doi:10.4271/2021-24-0081
    Sponsors
    This work was supported by King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research under the award number OSR-2019-CRG7-4077, and the KAUST Clean Fuels Consortium (KCFC) and its member companies.
    Publisher
    SAE International
    Conference/Event name
    15th International Conference on Engines & Vehicles
    DOI
    10.4271/2021-24-0081
    Additional Links
    https://www.sae.org/content/2021-24-0081/
    ae974a485f413a2113503eed53cd6c53
    10.4271/2021-24-0081
    Scopus Count
    Collections
    Conference Papers; Physical Science and Engineering (PSE) Division; Chemical Engineering Program; Clean Combustion Research Center

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