Characterization of physico-chemical properties of biodiesel components using smart data mining approaches

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@Article{ABOOALI:2020:Fuel,

• author = "Danial Abooali and Reza Soleimani and Saeed Gholamreza-Ravi",
• title = "Characterization of physico-chemical properties of biodiesel components using smart data mining approaches",
• journal = "Fuel",
• volume = "266",
• pages = "117075",
• year = "2020",
• ISSN = "0016-2361",
• DOI = "doi:10.1016/j.fuel.2020.117075",
• URL = "http://www.sciencedirect.com/science/article/pii/S0016236120300703",
• keywords = "genetic algorithms, genetic programming, Fatty acid ester, Density, Speed of sound, Isentropic and isothermal compressibility, Stochastic gradient boosting",
• abstract = "Biodiesels are the most probable future alternatives for petroleum fuels due to their easy accessibility and extraction, comfortable transportation and storage and lower environmental pollutions. Biodiesels have wide range of molecular structures including various long chain fatty acid methyl esters (FAMEs) and fatty acid ethyl esters (FAEEs) with different thermos-physical properties. Therefore, reliable methods estimating the ester properties seems necessary to choose the appropriate one for a special diesel engine. In the present study, the effort was developing a set of novel and robust methods for estimation of four important properties of common long chain fatty acid methyl and ethyl esters including density, speed of sound, isentropic and isothermal compressibility, directly from a number of basic effective variables (i.e. temperature, pressure, molecular weight and normal melting point). Stochastic gradient boosting (SGB) and genetic programming (GP) as innovative and powerful mathematical approaches in this area were applied and implemented on large datasets including 2117, 1048, 483 and 310 samples for density, speed of sound, isentropic and isothermal compressibility, respectively. Statistical assessments revealed high applicability and accuracy of the new developed models (R2 > 0.99 and AARD < 1.7percent) and the SGB models yield more accurate and confident predictions",

}

Genetic Programming entries for Danial Abooali Reza Soleimani Saeed Gholamreza-Ravi

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