Estimation of surface tension of methyl esters biodiesels using computational intelligence technique

Due to environmental benefits, methyl esters biodiesel got a considerable attention as a viable substitute to petroleum-based diesel. Surface tension plays significant role in atomization of this biodiesel since it controls the combustion process inside the engine through fuel-air mixing. Experimental determination of the surface tension of biodiesel is expensive and time consuming which limits its application as substitute for petroleum-based diesel. This is because proper choice of any methyl esters for diesel engine applications depend on the value of surface tension as high value of surface tension brings about difficulty in droplet formation. This work employs computational intelligence technique on the platform of sensitivity based linear learning method (SBLLM) to develop methyl esters surface tension estimator (MESTE) which estimates surface tension of methyl esters biodiesel with high degree of accuracy. Surface tensions of eight different classes of methyl esters were estimated at different temperatures by training and testing of neural network using SBLLM. The estimated surface tensions were compared with experimental results as well as surface tension obtained from Parachor model and Goldhammer model. The outstanding performance of the developed MESTE suggests its potential in estimating surface tension of methyl esters biodiesel for enhancing the atomization in biodiesels engine applications.