Minimum spouting velocity of fine particles in fountain confined conical spouted beds using machine learning and least square fitting approaches

The present study concerns the development of new models to estimate the minimum spouting velocity (Ums) in various configurations of fountain‐confined conical spouted beds (FC‐CSBs) with fine particles. Existing literature correlations were found to be inaccurate for FC‐CSBs. Therefore, smart modelling techniques were employed to design more accurate predictive tools. The radial basis function (RBF) approach provided the best predictions for systems without draft tubes as well as those with open‐sided draft tubes. Additionally, the Gaussian process regression (GPR) approach yielded the best predictions for systems with nonporous draft tubes. The mean absolute percentage error (MAPE) values for the testing phase were 5.80%, 5.67%, and 5.59%, respectively. These models consider how bed shape and particle properties affect Ums. The sensitivity analysis was conducted to determine the factors with more importance in controlling Ums. Finally, simpler correlations were derived for Ums prediction in different FC‐CSB configurations, with accuracy around 12% error.