Optimization of biodiesel production from waste cooking sunflower oil by taguchi and ann techniques

Abstract

Sunflower oil is commonly used for cooking purposes. After their repetitive usage, these oils are treated as waste and being dumped. Due to the huge population, the utilization of oil for daily requirements is also high. Every day tonnes of waste cooking oil (WCO) are being discarded which eventually increases environmental pollution. Therefore, WCO was proposed to use for biodiesel production. To achieve maximum biodiesel yield with limited experiments, optimization techniques are popular. In this endeavor, nine experiments were conducted based on Taguchi orthogonal array and Artificial Neural Network (ANN) based feedforward backpropagation was used for validation of the transesterification process. A maximum yield of 92.17% was achieved at a molar ratio (MR) of 12:1, catalyst concentration (CC) of 15, reaction temperature at 55°C and reaction time for one hour. CC was observed as the highest influence factor on biodiesel yield. The accuracy of the chosen optimization models was determined by the coefficient of determination which is almost the same for Taguchi (0.9959) and ANN (0.9955). This shows that these models are highly accurate in prediction. As a conclusion, by utilizing WCO for biodiesel production can decrease the overall production cost and the obtained biodiesel properties meet the international standards.