The accurate prediction and analysis of bed expansion characteristics in liquid–solid fluidized bed based on machine learning methods.

[Display omitted] • Machine learning methods were employed to predict bed expansion ratio. • XGBoost model has good regression and prediction ability with R2 higher than 0.97. • The relative importance of operating variables on bed expansion ratio was obtained. • Particle size has the strongest effect on bed expansion ratio. Liquid–solid fluidizations are mainly affected by particle density, particle size, particle shape, superficial velocity, liquid viscosity, etc. Owing to these complex high-dimensional impact processes, rapid and accurate prediction of fluidization characteristics is necessary but, still challenging. In this study, machine learning models were developed and employed to predict bed expansion ratio. Machine learning models, namely, linear, random forest, and XGBoost regression models, were trained using a measured 183- bed expansion ratio dataset under different operating conditions. By comparison, the XGBoost model had strongest regression and prediction ability with R2 higher than 0.97 and the predictions were very time-saving with calculation time less than 1 s. Finally, the bed expansion characteristics were analyzed and the operating conditions were evaluated by the developed model. The relative importance of operating conditions on bed expansion ratios was particle size > particle density > superficial velocity. [ABSTRACT FROM AUTHOR]