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


ERYILMAZ T. , YEŞİLYURT M. K. , GÖKDOĞAN O.

FRESENIUS ENVIRONMENTAL BULLETIN, vol.24, pp.1862-1870, 2015 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 24
  • Publication Date: 2015
  • Title of Journal : FRESENIUS ENVIRONMENTAL BULLETIN
  • Page Numbers: pp.1862-1870
  • Keywords: Artificial neural networks, biodiesel, density, waste cooking oil, DIESEL, VISCOSITIES, BLENDS

Abstract

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 degrees C intervals between 0-93 degrees C. The densities of each fuel sample decreased linearly with increasing temperature and diesel concentration. Regression analyses were conducted in MATLAB program and R-2 (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 R-2 values with mathematical expressions were 0.9996 and 0.9997, respectively. When using artificial neural networks, a R-2 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.