Biodiesel Production with Supercritical Ethanol Estimation using RBF-ANN Approach

Authors: Kuyakhi HR*

DOI: 10.23880/ppej-16000211

Volume 3, Issue 6

Abstract

Biodiesels as a renewable fuel can be an effective alternative for fossil fuels which can obtained from transesterification of triglycerides method. In this study, a radial basis function neural network (RBFNN) was employed to predict the biodiesel yield in supercritical ethanol solvent. The result obtained by RBFNN model was analyzed with the statistical parameters (i.e., MSE, MAAE%, MEAE%, RMSE and R2) and graphical method. The capability of RBFNN model was compared with the previous developed models. According the result obtained the RBFNN has the best performance with R2=0.997, MSE=0.00075 and RMSE=0.0274.

Keywords: Biodiesel; Radial basis function; Fossil fuels; Supercritical ethanol; Statistical parameters

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