Modeling of vegetable oils cloud point, pour point, cetane number and iodine number from their composition using genetic programming

Vegetable oils (VOs) are composed of 90-98% of triglycerides, i.e. esters composed of three fatty acids and glycerol, and small amounts of mono- and di-glycerides. Due to their physico-chemical properties, VOs have been considered for uses especially in large ships, in stationary engines and low and medium speed diesel engines, in pure form or in blends with fuel oil, diesel, biodiesel and alcohols. There are about 350 VOs with potential as fuel sources, and for most of them, physico-chemical properties values have not yet been measured. In this context, regression models using only VOs fatty acid composition are very useful. In the present paper, regression analysis of VOs cloud point (CP), pour point (PP), cetane number (CN) and iodine number (IN) as a function of saturated and unsaturated fatty acids is conducted. The study is done by using 4 experimental databases including 88 different data of VOs. Concerning the regression technique, genetic programming (GP) has been chosen. The cost function of GP is defined to minimize the Mean Absolute Error (MAE) between experimental and predicted values of each property. The resulting GP models consisting of terms including saturated and unsaturated fatty acids reproduce correctly the dependencies of all four properties on those acids. And they are validated by showing that their results are in good agreement to the experimental databases. In fact, MAE values of the proposed models with respect to the databases for CP, PP, CN and IN are lower than 4.51 degrees C, 4.54 degrees C, 3.64 and 8.01, respectively.

Automated summary

The study suggests that regression analysis using the fatty acid composition of vegetable oils can accurately predict their physico-chemical properties. The models established using genetic programming show low Mean Absolute Error values for cloud point, pour point, cetane number, and iodine number, demonstrating good agreement with experimental data.