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

Abstract

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, R2adj, 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 R2adj 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.

Comparative Appraisal of Response Surface Methodology and Artificial Neural Network Method for Stabilized Turbulent Confined Jet Diffusion Flames Using Bluff-Body Burners

“…The need for a model that can accurately predict experimental behavior has been the utmost challenge for researchers over the years; such models can dramatically reduce the time and operational cost in many engineering aspects. From here emerged the need to model processes [11].…”

“…Despite its simplicity and efficiency, RSM provides efficient and accurate solutions. Therefore, it has successfully been applied in many engineering problems [11] [13].…”

“…RSM and ANN were studied and compared for modeling highly nonlinear responses found in impact-related problems. Despite the computation cost of ANN, these studies concluded the supremacy of ANN over RSM in such optimization problems [11]. The present study focuses on the evaluation of the predictive capabilities of the RSM and ANN two methodologies for the previously reported experimental data of thermal structure of the stabilized flames in the presence of different geometries of bluff-body burners [17].…”

“…They are widely used by researchers to solve a variety of problems in science and engineering, forecasting, multivariate data analysis using experimental data, field observations or even incomplete or fuzzy data sets particularly for some areas where the conventional modeling methods fail such as prediction of internal combustion engine performance characteristics [35]. The key privilege of ANN-model is that it is not necessary to specify a preceding proper fitting function; so, it has a complete calculation capability to estimate practically all types of nonlinear functions which helps us to develop the most accurate prediction model [11] [36]. The prediction by a well-trained ANN is normally much faster than the conventional simulation programs or mathematical models as no lengthy iterative calculations are needed to solve differential equations using numerical methods but the selection of an appropriate neural network topology is important in terms of model accuracy and model simplicity [35].…”

“…In order to evaluate the goodness of the model fitting and prediction accuracy of the constructed models, R 2 , F_ratio and error analyses were performed between the experimental and predicted data in the RSM, and ANN models. Many approaches for error analyses are stated in the literature, with some listed in a previous study [11] [45]. The formulas employed in this study for performance evaluation and error analyses are listed in Tables 1(a)-(c).…”