Stochastic modeling approaches based on neural network and linear-nonlinear regression techniques for the determination of single droplet collection efficiency of countercurrent spray towers

Abstract

This paper presents a new mathematical model and a two-layer neural network approach to predict the single droplet collection efficiency (SDCE), eta(d), of countercurrent spray towers. SDCE values were calculated using MATLAB(R) algorithm for 205 different artificial scenarios given in a large range of operating conditions. Theoretical results were compared with outputs obtained from a two-layer neural network and DataFit(R) scientific software. The predicted model developed from linear-nonlinear regression analysis and network outputs agreed with the theoretical data, and all predictions proved to be satisfactory with a correlation coefficient of about 0.921 and 0.99, respectively. By using the proposed model, iterations between Reynolds number (Re), drag coefficient (C-D) and terminal velocity values (v(T)) were neglected for a large range of operating conditions. SDCE values were also obtained speedily and practically for five main operating inputs used in the model.