Prediction of critical properties of biodiesel fuels from FAMEs compositions using intelligent genetic algorithm-based back propagation neural network

Abstract

ABSTRACT Biodiesels are considered as promising fuels to substitute diesel fuel which can fill the gap of energy shortage while maintaining the diesel engine’s efficiency. Variations in the properties of different biodiesel fuels are caused by the varied fatty acid methyl esters (FAMEs) compositions derived from their parent oils. Therefore, correlating key properties such as cetane number (CN), kinematic viscosity (KV), iodine value (IV) and cold filter plugging point (CFPP) with FAMEs compositions of each biodiesel fuel is significant to developing whatever new types of fuels applied on diesel engines. In this study, an intelligent genetic algorithm (GA)-based back propagation neural network (BPNN) model was proposed to predict the properties of biodiesel fuels according to FAMEs compositions. The hybrid BPNN-GA model has five inputs (methyl palmitate, methyl stearate, methyl oleate, methyl linoleate and methyl linolenate) corresponding to the FAMEs compositions and outputs with estimated fuel properties, with the GA assisting on training to find out local minimum deviation and updated weighting configurations. It was found that the intelligent learning-training method proposed hybrid BPNN-GA model enabled to map the non-linear relationships between the FAMEs compositions and key properties of biodiesel fuels with fairly good agreement. The predicted value of fuel properties agrees with measured ones with R-square up to 96%, along with lower value (less than 10%) over Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) than those of other empirical correlations. In addition, a sensitivity analysis was conducted to in-depth investigate the FAMEs compositions on key properties. It was concluded that saturated FAMEs have positive impacts on CN, KV and the CFPP, while IV is typically dependent on unsaturated FAMEs. Therefore, it is attainable to formulate new types of alternative fuels based on the required properties on diesel engine applications.