Application of efficient soft computing approaches for modeling methyl ester yield from Azadirachta Indica (Neem) seed oil: A comparative study of RSM, ANN and ANFIS
Chinedu M. Agu, Kingsely Amechi Ani, Onuabuchi Nnenna Ani, Patrick Chukwudi Nnaji, Chukwuma H. Kadurumba, Chizoo Esonye
Abstract
This work centers on methyl ester yield modeling; by Azadirachta Indica seed oil (AISO) transesterification, using Adaptive Neuro-fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Response Surface Methodology (RSM). AISO was obtained from the seeds of Azadirachta Indica tree. The oils were extracted from the seeds using solvent extraction method. The physicochemical properties of AISO and Azadirachta Indica seed oil methyl ester (MAISOt) were determined using standard methods. Fatty acid composition was determined using, Gas Chromatography (GC). Statistical evaluations of these models show their efficacy in the order RSM < ANN < ANFIS, with ANFIS as the best; as indicated by its very high R2 value of 0.9999 and low RMS error value of 0.0011. The ANFIS predicted minimum and maximum values for % methyl ester yields were 54.66 and 90.25 %, respectively, while the other models predicted similar methyl ester yields. The physicochemical characterization results of AISO and MAISOt samples, show that their respective viscosity, dielectric strength (DS), pour and flash points values were (8.83 and 3.47 mm 2s−1), (33.42 and 48.93 KV), (9 and -6 °C), and (162 and 174 °C). These results indicated the MAISOt sample’s potential use as a transformer fluid. GC result indicated that MAISOt was unsaturated. Finally, on the basis of the gotten model results, ANFIS was adjudged as the best predictive model, followed by ANN and RSM, in that order.