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

Abstract 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.