Alviso, Dario published the artcilePrediction of biodiesel physico-chemical properties from its fatty acid composition using genetic programming, Recommanded Product: Methyl docosanoate, the main research area is biodiesel physicochem property fatty acid composition genetic programming.
This paper presents regression anal. of biodiesel physico-chem. properties as a function of fatty acid composition using an exptl. 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 Me esters). The physico-chem. 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 exptl. database is lower for GP models, from ≈ 3% for CN to ≈ 55% 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 Me esters.
Fuel published new progress about Biodiesel fuel. 929-77-1 belongs to class esters-buliding-blocks, name is Methyl docosanoate, and the molecular formula is C23H46O2, Recommanded Product: Methyl docosanoate.
Referemce:
Ester – Wikipedia,
Ester – an overview | ScienceDirect Topics