Modelling of biodiesel production from transesterification process of sandbox (Hura crepitans L.) seed oil: performance comparison of artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS)

Abstract Biodiesel has been seen as an alternative to diesel (fossil) fuel as a result of its favourable properties, energy security reasons and environmental benefits. In this research, transesterification of sandbox seed oil with ethanol to form biodiesel has been modelled using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) techniques. Temperature (oC), time (min.), catalyst concentration (w/w) and catalyst type (g) were used as input variables while biodiesel yield was used as output variable for modelling the efficiency of biodiesel production from sandbox seed oil. The results showed that ANN model gave R2 of 0.925, RSME of 2.99255, MAE of 0.62196, SEP of 0.03689 and AD of 0.03194 while ANFIS model gave R2 of 0.961, RSME of 1.97379, MAE of 0.0001, SEP of 0.02433 and AD of 0.000005136. The results prove that ANFIS model is more reliable in predicting biodiesel yield from sandbox seed oil than ANN model.