Biodiesel production from tomato seed and its engine emission test and simulation using Artificial Neural Network

Abstract

In this study, tomato seed oil was used to produce Biodiesel fuel. To reduce the percentage of free fatty acids, the oil was reacted at a temperature of 40, 50, and 60°C with a mixture of sulphuric acid and the industrial methanol with different molar ratios of oil. The best conversion efficiency was achieved at 60°C and a molar ratio of 1:9. In the transesterification step, biodiesel was produced using a mixture of potassium hydroxide reactivity. Then, functional characteristics and pollutant gases of ordinary diesel fuel and mixtures of biodiesel at different speeds and loads were measured and compared. The tests were carried out in a 9-kV direct injection (DI) diesel engine. The results of analysis of variance by SPSS software showed that there was a significant difference in the level of R< 0.01 between the production of pollutants such as NOx, CO, HC, and other fume gases like CO2 and O2 at different speeds and loads. Duncan's multiple range test results also showed that the lowest emissions were generated from the B20 blend. An Artificial Neural Network (ANN) model which was used to predict the emission of the engine showed an excellent conformity with R-values of 0.99 for both the training and test data.