Estimating biofuel density via a soft computing approach based on intermolecular interactions

Abstract

In this work, the density of biofuel is estimated using four intelligent models: a Least Square Support Vector Machine (LSSVM), a Radial Basis Function Artificial Neural Network (RBF-ANN), a Multi-layer Perceptron Artificial Neural Network (MLP-ANN), and an Adaptive Network-based Fuzzy Inference System (ANFIS). These models are used to estimate the density of biofuel based on intermolecular interactions and the van der Waals radii of the atoms. Various statistical analyses are performed on the original (experimental) and estimated data. It is found that the LSSVM model can provide more accurate predictions than the other three models. The R-squared value (R2) and the mean absolute relative error (MARE) for the LSSVM, RBF-ANN, MLP-ANN and ANFIS models are 0.847 & 0.056, 52.067 & 0.379, 57.385 & 0.371 and 65.096 & 0.678, respectively. This study shows that the LSSVM model is a promising tool for estimating the density of biofuel, offering an alternative to classic thermodynamic models.