Analysis of noise pollution emitted by stationary MF285 tractor using different mixtures of biodiesel, bioethanol, and diesel through artificial intelligence.

In the present study, the noise pollution from different compositions of biodiesel, bioethanol, and diesel fuels in a four-cylinder and four-stroke engine of MF285 tractor was studied. Further, the noise pollution was measured from two positions, the driver and bystander, at 1000, 1600, and 2000 revolutions, and ten different fuel levels resulting from different compositions of biodiesel, bioethanol, and diesel fuels. For data analysis, adaptive network-based fuzzy inference system (ANFIS), artificial neural network (ANN), and response surface methodology (RSM) were applied. Comparing the means of noise pollution at different levels demonstrated that the B25E6D69 fuel, made up of 25% biodiesel and 6% bioethanol, had the lowest noise pollution. The lowest noise pollution was at 1000 rpm, and with the increase of engine speed, the noise pollution intensified. The models laid by the RSM were better than other.