Bed density prediction of gas-solid separation fluidized bed based on genetic algorithm-back propagation neural network

The fluctuation of bed density in the process of mineral separation in gas-solid fluidized bed is particularly critical, and accurate prediction of bed density will play a crucial role in industrial production. In this paper, the axial and radial distribution of bed density in gas-solid fluidized bed under different ratios of dense medium and gas velocities was investigated. Based on the data of this study and other scholars, the corresponding data set was constructed. A 13-14-1 bed density prediction model was established by using BP neural network algorithm. The overall correlation of the model is 0.9771. The model is more accurate to predict the bed density of single dense medium. The mean absolute percentage error is 4.17 % and the root mean square error is 0.0298. The optimized GA-BP (Genetic Algorithm-Back Propagation) neural network by genetic algorithm results are closer to the actual experimental values. The overall correlation of the model is increased to 0.9793, and the prediction accuracy can reach more than 90 %. The GA-BP neural network prediction model is more accurate in predicting fluidized beds with density fluctuation range of 1.6-2.2 g/cm3, which can meet the needs of industrial applications.