Minimum elutriation velocity of the binary solid mixture - Empirical correlation and genetic algorithm (GA) modeling

The solid-water fluidized bed was investigated with a binary mixture of irregularly shaped sand particles. A binary mixture was produced by mixing particles of sand for different weight ratios. The influence of various operating parameters on minimum elutriation velocity was investigated. It was observed that the U-me decreases with the increase of the lighter particles in the binary mixture, and the U-me increases with the increase of column diameter. A simplified empirical correlation has been developed to predict minimum elutriation velocity with acceptable statistical parameters. Application concerning a hybrid of artificial neural network (ANN), and genetic algorithm (GA), is successfully predicted.

Automated summary

The properties of a solid-water fluidized bed were studied using a binary mixture of irregularly shaped sand particles. A binary mixture was created by mixing sand particles in different weight ratios, and the influence of various operating parameters on the minimum elutriation velocity was investigated. It was observed that the minimum elutriation velocity decreases with an increase in the proportion of lighter particles in the binary mixture, and increases with an increase in column diameter. A simplified empirical correlation was developed to predict the minimum elutriation velocity, and its accuracy was validated using artificial neural network (ANN) and genetic algorithm (GA) hybrid approach.