On the evaluation of surface tension of biodiesel

Abstract

Over time, with the increase in population and the subsequent increase in energy consumption and also due to the non-renewability of fossil fuels, the study of alternative fuels has increased. One of these fuels is biodiesel, which is a suitable alternative to fossil fuels such as diesel and received much attention from researchers today. For this reason, measuring the physical properties of biodiesel is of great importance. Due to the high cost and time-consuming nature of laboratory methods, numerical methods are used to estimate material properties. The novelty of this research was the use of two white box models, including Group method of data handling (GMDH) and Gene expression programming (GEP), which work on the basis of artificial intelligence. By using these models, two simple mathematical equations with high accuracy were presented to predict the surface tension of biodiesel. These models can be used at different temperatures and molecular weights. To do modeling, 78 laboratory data available in the literature were gathered and the data were randomly divided into two groups, train and test, in a ratio of 80 and 20. The input parameters include mass fraction of fatty acid ethyl esters and temperature (T), and esters are divided into three groups according to their molecular weight: less than 200 (Mw 1 ), between 200 and 300 (Mw 2 ), and greater than 300 (Mw 3 ). The statistical error parameters were calculated for the two models developed in this research and after comparing the results, it was found that the GMDH model estimates the surface tension of biodiesel with a higher accuracy. The average absolute relative error for GMDH and GEP models was reported as 0.97 and 1.89, respectively. Also, other statistical error parameters of GMDH such as RMSE, SD, and R 2 for the GMDH model were obtained as 0.444, 0.000233, and 0.9233, respectively. Moreover, sensitivity analysis showed that temperature has the highest impact on the surface tension of biodiesel, which is also an inverse effect. Finally, suspicious laboratory and outlier data points were identified using the Leverage technique. According to this analysis, only five data points were identified as outliers and suspicious laboratory data.