An appraisal of the competence of mathematical fuzzy logic approach via adaptive neuro-fuzzy inference system (ANFIS) in biodiesel production from algae oil

Abstract The fuel crisis and environmental problems can be resolved using biodiesel from various basic materials. This paper uses the transesterification process and segregation to produce biodiesel from algal oil. According to the four input factors (power, methanol-to-oil percentage, catalyst utilized, and process time), 29 experiments have been conducted to manufacture biodiesel. This work focuses on modeling and estimating the processes involved in producing biodiesel, specifically from algal oil, using the (ANFIS) adaptive neuro-fuzzy inference system methodology. The determination coefficient (R-square) and the mean root-mean-square error (RMSE) were employed to reconcile the ANFIS findings with the true results of the research. During training, the RMSE statistical variables were 1.209 and the R-squared was 0.9742. This instance, involves the ANFIS Framework additionally the Gaussian membership function was used and examined. This modeling approach shows promise for use in the biodiesel manufacturing process, potentially increasing the efficacy and efficiency of the generation of biodiesel from algal oil, given the high estimation accuracy shown by the ANFIS.