PREDICTION OF DENSITY OF WASTE COOKING OIL BIODIESEL USING ARTIFICIAL NEURAL NETWORKS

摘要

In this study, biodiesel was produced from waste cooking oil by using sodium hydroxide and methyl alcohol with transesterification method. Three different fuel blends (25, 50 and 75% by volume blending with diesel fuel) were prepared. The densities of fuels were measured at 0.5 °C intervals between 0-93 °C. The densities of each fuel sample decreased linearly with increasing temperature and diesel concentration. Regression analyses were conducted in MATLAB program and R2 (coefficients of determination), correlation constants and root mean squared errors were determined. The experimental results were used to train the artificial neural networks. In the present research, a 3-layer back propagation neural network with 15 neurons in the hidden layer was applied. The best R2 values with mathematical expressions were 0.9996 and 0.9997, respectively. When using artificial neural networks, a R2 value of 0.9999 was obtained. The comparison of artificial neural network model with different density prediction models showed that the use of artificial neural networks in density prediction is successful.