Uzoh, Chigozie Francolins
Nnamdi Azikiwe University
Nigeria
Title: Extraction and Analysis of Gmelina Seed Oil Using Different Soft Computing Approaches
Biography
Biography: Uzoh, Chigozie Francolins
Abstract
Artificial Neural Network (ANN)-Genetic Algorithm (GA) interface and Response Surface Methodology (RSM) have been compared as tools for simulation and optimization of gmelina seed oil extraction process. A multi-layer feed-forward Levenberg Marquardt back-propagation algorithm was incorporated for developing a predictive model which was optimized using GA. Equally, Design Expert simulation and optimization tools were also incorporated for a detailed simulation and optimization of the same process using RSM. It was found that oil yield increased with increase in temperature, time and volume of solvent but decreased with increase in seed particle size. Optimal yield of 47.93% and 43.52% were observed for ANN-GA and RSM respectively under the same parameter design space of; 200μm particle size, 40C temperature, 100ml volume of solvent and 40mins extraction period. The performance of the models in predicting the responses was evaluated by mean square error (MSE) and coefficient of determination (R2), and the results show that the models were very efficient. Models validation experiments indicate that the predicted and the actual were in close agreement. Overall, ANN-GA hybrid was found to be more efficient by 10.13%. The extract was analyzed to examine its physico-chemical properties (acid value, iodine value, peroxide value, viscosity, saponification value, moisture and ash content, refractive index, smoke, flash and fire points and specific gravity) and structural elucidation by standard methods and instrumental techniques. Results revealed that the oil is not edible but find potential in biodiesel and alkyd resin production.