Adjustment of the Minimum Spouting Velocity in a Conical Spouted Bed from Artificial Neural Networks

The minimum spouting velocity is one of the fundamental parameters in the application of the conical spouted bed pyrolysis, gasification and combustion, therefore it is necessary the adequate calculation and the use of a correlation with good adjustment, for this an artificial neural network has been applied, in order to improve the correlations of the literature. Six variables that involve different geometrical parameters and operation of the bed have been used. With the purpose to compare the results of the model with the experimental data and those predicted by the empirical equations, the quadratic error has been used. Although there is a complex relationship between the input variables and the output variables, and despite numerous of available data, the training and test steps of the network show a good adjustment with respect to the experimental values. This shows that an artificial neural network is an agile method to predict the minimum velocity of the bed at the pump, especially when the relationship between geometric parameters, operating parameters and minimum speed is complex and difficult to define.