Surface tension prediction of vegetable oils using artificial neural networks and multiple linear regression

The surface tension is one of the main properties for characterization of the quality of the fuel atomization process for its use in a diesel engine. There is a lack of published information about the values of surface tension of vegetable oils. The aim of this research is to obtain a mathematical model based on physical properties that establishes a relationship between the surface tensions of different vegetable oils and their fatty acid composition. For this reason, from literature reports, experimental data of oils related to the surface tensions was collected. Knowing that surface tension as a function of temperature, a total amount of 15 oils from different feedstocks at 20°C was selected. The obtained models were developed based in the use of artificial neural networks and multiple linear regressions fits, based on the experimental data available in the literature. Also, the obtained models present a good correlation between surface tension and the fatty acid composition, with a 95 % of confidence interval and coefficient of correlation higher than 0,95. The coefficient of correlation obtained shown a high correlation between the analyzed variables. According to the obtained results, the proposed models are a useful tool for the surface tension estimation from the oils fatty acid composition.