Response Surface Methodology and Artificial Neural Network Methods Comparative Assessment for Fuel Rich and Fuel Lean Catalytic Combustion

摘要

Modeling, predictive and generalization capabilities of response surface methodology (RSM) and artificial neural network (ANN) have been performed to assess the thermal structure of the experimentally studied catalytic combustion of stabilized confined turbulent gaseous diffusion flames. The Pt/γAl₂O₃ and Pd/γAl₂O₃ disc burners were located in the combustion domain and the experiments were accomplished under both fuel-rich and fuel-lean conditions at a modified equivalence (fuel/air) ratio ( style="white-space:nowrap;"> style="font-family:Verdana, Helvetica, Arial;white-space:normal;background-color:#FFFFFF;">ø) of 0.75 and 0.25, respectively. The thermal structure of these catalytic flames developed over the Pt and Pd disc burners w style="white-space:normal;font-family:;" "="">as style="white-space:normal;font-family:;" "=""> scrutinized via measuring the mean temperature profiles in the radial direction at different discrete axial locations along with the flames. The RSM and ANN methods investigated the effect of the two operating parameters namely (r), the radial distance from the center line of the flame, and (x), axial distance along with the flame over the disc, on the measured temperature of the flames and predicted the corresponding temperatures beside predicting the maximum temperature and the corresponding input process variables. A three style="white-space:normal;font-family:;" "="">- style="white-space:normal;font-family:;" "="">layered Feed Forward Neural Network was developed in conjugation with the hyperbolic tangent sigmoid (tansig) transfer function and an optimized topology of 2:10:1 (input neurons:hidden neurons:output neurons). Also the ANN method has been exploited to illustrate style="white-space:normal;font-family:;" "="">the style="white-space:normal;font-family:;" "="">effects of coded R and X input variables on the response in the three and two dimensions and to locate the predicted maximum temperature. The results indicated the superiority of ANN in the prediction capability as the ranges of  & F_Ratio are 0.9181 style="white-space:normal;font-family:;" "=""> style="white-space:normal;font-family:;" "="">- 0.9809 & 634.5 style="white-space:normal;font-family:;" "=""> style="white-space:normal;font-family:;" "="">- 3528.8 for RSM method compared to 0.9857 style="white-space:normal;font-family:;" "=""> style="white-space:normal;font-family:;" "="">- 0.9951 & 7636.4 style="white-space:normal;font-family:;" "=""> style="white-space:normal;font-family:;" "="">- 24 style="white-space:normal;font-family:;" "="">, style="white-space:normal;font-family:;" "="">028.4 for ANN method beside lower values style="white-space:normal;font-family:;" "="">for error analysis terms.