Extraction, analysis and desaturation of gmelina seed oil using different soft computing approaches

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. Design Expert simulation and optimization tools were also incorporated for a detailed simulation and optimization of the same process using Response surface methodology (RSM). It was found that oil yield increased with rise in temperature, time and volume of solvent but decreased with increase in seed particle size. The maximum oil yield obtained using the numerical optimization techniques show that 49.2% were predicted by the RSM at the optimum conditions of; 60 °C temperature, extraction time 60 min, 150 μm seed particle size, 150 ml solvent volume and 49.8% by ANN-GA at extraction temperature 40 °C, extraction time 40 min, 200 μm seed particle size, 100 ml solvent volume, respectively. The prediction accuracy of both models were more than 95%. Models validation experiments indicate that the predicted and the actual were in close agreement. 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 non-drying and edible. Desaturation of the oil further reveal its potential in alkyd resin synthesis.

Keywords

• Gmelina seed oil
• Response surface methodology
• Artificial neural network
• Genetic algorithm
• Levenberg–Marquardt algorithm
• Optimization