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 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 24
  • Publication Date: 2015
  • Journal Name: FRESENIUS ENVIRONMENTAL BULLETIN
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1862-1870
  • Keywords: Artificial neural networks, biodiesel, density, waste cooking oil, DIESEL, VISCOSITIES, BLENDS
  • Yozgat Bozok University Affiliated: Yes

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.