Prediction of the influence of castor oil–ethanol–diesel​ blends on single-cylinder diesel engine characteristics using generalized regression neural networks (GRNNs)

Abstract

With some inferior fuel properties such as cetane number, flash point and calorific value, in this study the ethanol–diesel blend was improved through the use of neat castor oil. According to a preliminary study on blending stability and lubricity, a fuel blend was formulated with 10% ethanol, 10% castor oil and 80% diesel fuel (D80E10C10) which was selected to test in the diesel engine. The generalized regression neural network (GRNNs), which is one of the artificial intelligence algorithms that is suitable for a small data set, was applied to create a model for predicting the influence of castor oil on the characteristics of the diesel engine fueled with an ethanol–diesel blend. The experimental results of fuel properties were firstly analyzed and revealed that the presence of 10% castor oil can improve the defective fuel properties of ethanol–diesel to keep them under the limitations of the diesel fuel specification. A lower thermal efficiency was obtained with the combustion of the castor oil blend. A higher peak of in-cylinder pressure and the earlier start of combustion was achieved with increasing engine operating load and compression ratio. A lower peak of heat release rate was attained with the combustion of the castor oil blend. Hydrocarbons and carbon monoxide were increased with the castor oil blend, while oxides of nitrogen were decreased and no significant change in smoke emissions was found with respect to diesel fuel. Subsequently, the results of the prediction model concluded that the response surface methodology was a good method to optimize the smoothing parameter of the GRNNs model in the case of multi-output factors with different levels of importance. The GRNNs model was achieved with relatively high performance to predict BSFC, thermal efficiency, NOx, HC, CO and smoke emissions, with mean absolute percentage error (MAPE) values in the range of 1.562%–8.181% and coefficient of determination (R2) in the range of 0.708-0.993.