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

Abstract

The present study was conducted to present the comparative modeling, predictive and generalization abilities of response surface methodology (RSM) and artificial neural network (ANN) for the thermal structure of stabilized confined jet diffusion flames in the presence of different geometries of bluff-body burners. Two stabilizer disc burners tapered at 30˚ and 60˚ and another frustum cone of 60˚/30˚ inclination angle were employed all having the same diameter of 80 (mm) acting as flame holders. The measured radial mean temperature profiles of the developed stabilized flames at different normalized axial distances () j x d were considered as the model example of the physical process. The RSM and ANN methods analyze the effect of the two operating parameters namely () r , the radial distance from the center line of the flame, and () j x d on the measured temperature of the flames, to find the predicted maximum temperature and the corresponding process variables. A three-layered Feed Forward Neural Network in conjugation with the hyperbolic tangent sigmoid (tansig) as transfer function and the optimized topology of 2:10:1 (input neurons: hidden neurons: output neurons) was developed. Also the ANN method has been employed to illustrate such effects in the three and two dimensions and shows the location of the predicted maximum temperature. The results indicated the superiority of ANN in the prediction capability as the ranges of R 2 and F Ratio are 0.868-0.947 and 231.7-864.1 for RSM method compared to 0.964-0.987 and 2878.8 7580.7 for ANN method beside lower values for error analysis terms.

Comparative Investigation of n-Hexane and Ethanol Solvents Used in Eleais guinesis Kernel Oil Extraction and Optimization via Two Computational Modelling

“…Because of the interest in extracting the optimum oil from the kernel; this work investigated the use of two computational models in optimizing oil extraction. Modeling is a scientific approach that represents ideas about the natural phenomenon under investigation, presenting alternatives to the real phenomenon, by referring to the existing knowledge (Gendy et al, 2020). One of the generally used models is mathematical modeling (Najafi et al, 2019).…”

“…One of the generally used models is mathematical modeling (Najafi et al, 2019). And the two mathematical models used to predict experimental behavior in this research are Response Surface Methodology (RSM) and Artificial Neural Networks (ANNs) which are significant in the field of processes in optimization (Gendy et al, 2020). These methods determine the relationship between the input and output variables through the data derived from the experiment.…”

“…However, ANNs modeling is a statistical technique that solves problems that are not eligible for conventional statistical methods. It handles obscure, complex, incomplete problems; it is a model that produces predictions and generalizations at high speed (Gendy et al, 2020).…”