Application of an artificial neural networks for predicting the heat transfer in conical spouted bed using the Nusselt module

Artificial neural networks have been used since the last decade as a satisfactory alternative for the prediction of the fluid-dynamic behavior of particles. The aim of this work has been to develop a model based on artificial neural networks (ANN) suitable for quantifying the influence of multiple factors on the heat transfer rate in a conical spouted bed reactor. The Nusselt module has been taken as an exit point and nine input factors have been evaluated, among which are the height of the bed, the diameter of the contactor, the angle of the cone, and the minimum spouting speed, among others. The model has been found to fit appropriately to the equations proposed in the literature and can be used as a suitable model to predict the behavior of heat transfer in conical spouted bed reactors operating with biomass.