Toward estimation of biodiesel production from castor oil using ANN

Nowadays, a green replacement for the conventional petrodiesel introduced as biodiesel in which its economical production way is using feedstock. Also, environmentally friendly fuels attracted more attention due to the serious global warming problem. In the present study, two different artificial intelligencebased modeling was utilized to predict the production of biodiesel from castor oil. Also, a comparison between the two methods was carried out, and the more applicable method for the prediction of biodiesel production was introduced. To this end, biodiesel production yield from castor oil assumed to be the target of the model and various parameters such as temperature (T), time (S), methanol to oil molar ratio, and catalyst weight (C) expected as input parameters. ANN modeling shows high accuracy and robustness for the prediction of biodiesel production, and statistical parameters such as coefficient of determination and root-mean-square error are 0.9984 and 1.13, respectively.