Multiple Modeling Techniques for Assessing Sesame Oil Extraction under Various Operating Conditions and Solvents

This paper compares four different modeling techniques: Response Surface Method (RSM), Linear Radial Basis Functions (LRBF), Quadratic Radial Basis Functions (QRBF), and Artificial Neural Network (ANN). The models were tested by monitoring their performance in predicting the optimum operating conditions for Sesame seed oil extraction yields. Experimental data using three different solvents-hexane, chloroform, and acetone-with varying ratios of solvents to seeds, all under different temperatures, rotational speeds, and mixing times, were modeled by the three proposed techniques. Efficiency for model predictions was examined by monitoring error value performance indicators (R², R²_adj, and RMSE). Results showed that the applied modeling techniques gave good agreements with experimental data regardless of the efficiency of the solvents in oil extraction. On the other hand, the ANN model consistently performed more accurate predictions with all tested solvents under all different operating conditions. This consistency is demonstrated by the higher values of R² and R²_adj ratio equals to one and the very low value of error of RMSE (2.23 × 10^-3 to 3.70 × 10^-7), thus concluding that ANN possesses a universal ability to approximate nonlinear systems in comparison to other models.