Minimum Fluidization Velocities of Binary Solid Mixtures: Empirical Correlation and Genetic Algorithm-Artificial Neural Network Modeling

Experimental investigation of the fluidization behavior in single and binary solid-liquid fluidized beds of nonspherical particles as solid phase and water as liquid phase was performed in a Perspex column. Different particle sizes were used to prepare single and binary mixtures with different weight ratios for fluidization. Minimum fluidization velocity increased with increasing average particle size and decreasing sphericity for the binary mixture. An empirical correlation was developed to predict the minimum fluidization velocity. Genetic algorithm-artificial neural network (GA-ANN) modeling was applied to predict the minimum fluidization velocity for single and binary solid-liquid fluidized beds. The application of GA-ANN analysis leads to designing binary solid-liquid fluidization systems without experimentation.