Prediction of the Minimum Spouting Velocity by Genetic Programming Approach

A genetic programming (GP) algorithm is developed to estimate the minimum spouting velocity (Ums) in the spouted beds with a cone base. In order to have a general model, five dimensionless variables including seven critical geometric and operating parameters of spouted beds, namely, column diameter, spout nozzle diameter, base angle, static bed height, particle diameter, particle density, and gas density, have been taken as model inputs. A general correlation including nearly all fundamental and operating variables has been obtained based on the GP approach. The Ums values predicted by the GP are in fair agreement with those obtained by experiments, with a root-mean-square error of 0.1329 m/s. The model results show that GP can be used as an effective tool to provide relatively accurate information on minimum spouting velocity in conical spouted beds.