Prediction of biodiesel physico-chemical properties from its fatty acid composition using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@Article{ALVISO:2020:Fuel,

• author = "Dario Alviso and Guillermo Artana and Thomas Duriez",
• title = "Prediction of biodiesel physico-chemical properties from its fatty acid composition using genetic programming",
• journal = "Fuel",
• volume = "264",
• pages = "116844",
• year = "2020",
• ISSN = "0016-2361",
• DOI = "doi:10.1016/j.fuel.2019.116844",
• URL = "http://www.sciencedirect.com/science/article/pii/S0016236119321982",
• keywords = "genetic algorithms, genetic programming, Biodiesel, Fatty acid, Properties, Regression analysis",
• abstract = "This paper presents regression analysis of biodiesel physico-chemical properties as a function of fatty acid composition using an experimental database. The study is done by using 48 edible and non-edible oils-based biodiesel available data. Regression equations are presented as a function of fatty acid composition (saturated and unsaturated methyl esters). The physico-chemical properties studied are kinematic viscosity, flash point, cloud point, pour point (PP), cold filter plugging point, cetane (CN) and iodine numbers. The regression technique chosen to carry out this work is genetic programming (GP). Unlike multiple linear regression (MLR) strategies available in literature, GP provides generic, non-parametric regression among variables. For all properties analyzed, the performance of the regression is systematically better for GP than MLR. Indeed, the RSME related to the experimental database is lower for GP models, from approx3percent for CN to approx55percent for PP, in comparison to the best MLR model for each property. Particularly, most GP regression models reproduce correctly the dependence of properties on the saturated and unsaturated methyl esters",

}

Genetic Programming entries for Dario Alviso Guillermo Artana Thomas Duriez

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