Deploying artificial neural network to predict hybrid biodiesel fuel properties from their fatty acid compositions
Solomon O. Giwa, S. A. Aasa, Raymond Taziwa, Mohsen Sharifpur
Abstract
Measurement-related problems have spurred fuel properties prediction using machine learning techniques. Improved fuel properties offered by hybrid biodiesel (HB) via mixed oils were predicted from their fatty acid compositions (FACs) using artificial neural network (ANN). FACs and fuel properties of HB sourced from the literature were used to develop ANN models. FAC data were used as the input parameters to predict the fuel properties data (kinematic viscosity (KV), density, calorific value (CV), and flash point (FP)) considered as the output parameters of the models. Using the multilayer perception ANN, the models were trained using Levenberg-Marquardt back propagation learning algorithm coupled with different numbers of neurons and activation functions for the prediction of the fuel properties. The models were observed to accurately predict these fuel properties with high prediction accuracy (R2 = 1). The evaluated model performance errors were 0.1014 and 0.0504, 0.2905 and 0.4225, 0.1848, and 0.1038, and 0.4726 and 0.7833 for KV, density, CV, and FP using root mean square error and average absolute deviation respectively. Prediction performance and error estimates were slightly better than those for single feedstock biodiesel. Hence, this study shows the ability of ANN to accurately predict the fuel properties of HB from the FAs.