Prediction of biodiesel density for extended ranges of temperature and pressure using adaptive neuro-fuzzy inference system (ANFIS) and radial basis function (RBF)

Density is a very important fuel property because of its direct relation with the fuel injection process for engines. This paper presents models based on Adaptive Neural Fuzzy interface System (ANFIS) and Radial Basis Function (RBF) to predict the density of biodiesel in a wide temperature and pressure range. Furthermore, this study is also aimed to evaluate and compare the predicted density of biodiesel by using ANFIS and RBF. The models ANFIS and RBF developed in this study were trained and tested with the experimental data obtained from literature. The temperature and pressure were the input variables in the models. The results indicated that there is an excellent agreement between the predicted and experimental data, with high R2. Consequently, The R-squared was 0.95 in ANFIS model and 0.93 in RBF model.