Cleaner Engineering and Technology (Feb 2022)
Modeling of methyl ester yield from Terminalia catappa L. kernel oil by artificial neural network and response surface methodology for possible industrial application
Abstract
This study is on the modeling of methyl ester yield; obtained by the transesterification of Terminalia catappa L. kernel oil (TCKO), using Artificial Neural Network (ANN) and Response Surface Methodology (RSM). TCKO was obtained from the seeds/kernels of Terminalia catappa L tree, which is a member of the combretaceae family. The oils were extracted from the kernels using solvent extraction method. The physicochemical properties of TCKO and Terminalia catappa L. kernel oil methyl ester (MTCKOt) were determined using standard methods. Fatty acid composition andprevalent methyl esters in MTCKOt sample were determined using, Gas Chromatography (GC) and Gas Chromatography-Mass Spectrometry (GC-MS), respectively. At optimum conditions of temperature (65 °C), mole ratio (7:1), catalyst concentration (2.5 wt%), stirring speed (600 revolutions per minute) and time (150 min), the RSM predicted and validated methyl ester yields were 90.94%, and 90.84%, respectively; while ANN predicted and validated yields were 90.41% and 90.50%, respectively. The physicochemical characterization results of TCKO and MTCKOt samples, show that their respective viscosity, dielectric strength (DS), pour and flash points values were (20.29 and 10.29 mm2s-1), (30.61 and48.55 KV), (3 and −5 °C), and (260 and 275 °C). These results indicated the MTCKOt sample's potential use as a transformer fluid. GC result indicated that MTCKOt was unsaturated; while that of GC-MS showed that Hexane,2,2-dimethyl, Pentane,2,2,3-trimethyl, and Hexane,2,2,5-trimethyl were the predominate methyl esters. Finally, on the basis of the gotten model results, ANN was adjudged as better predictive model when compared to RSM.
