Smart computing approach for design and scale-up of conical spouted beds with open-sided draft tubes

Open-sided draft tubes provide an optimal gas distribution through a cross flow pattern between the spout and the annulus in conical spouted beds.The design,optimization,control,and scale-up of the spouted beds require precise information on operating and peak pressure drops.In this study,a multi-layer perceptron(MLP)neural network was employed for accurate prediction of these hydrodynamic characteristics.A relatively huge number of experiments were accomplished and the most influential dimensionless groups were extracted using the Buckingham-pi theorem.Then,the dimensionless groups were used for developing the MLP model for simultaneous estimation of operating and peak pressure drops.The iterative constructive technique confirmed that 4-14-2 is the best structure for the MLP model in terms of absolute average relative deviation(AARD%),mean square error(MSE),and regression coefficient(R²).The developed MLP approach has an excellent capacity to predict the transformed operating(MSE=0.00039,AARD%=1.30,and R²=0.76099)and peak(MSE=0.22933,AARD%=11.88,and R2=0.89867)pressure drops.