Prediction and Interpretative Analysis of Bed Expansion Ratio in Pulsed Fluidized Beds

The bed expansion height serves as a macroscopic representation of the efficiency with which vibrational energy is transmitted within pulsed fluidized beds. Due to its complex nonlinear characteristics, further research is needed to explore the fluidization mechanisms in pulsed fluidized beds and identify effective predictive models for expansion ratios. This work evaluates and analyzes the predictive capabilities of models established based on theoretical learning, as well as three machine learning methods. Additionally, dimensionless numbers are introduced to facilitate dimensionality reduction. Among these methods, the extreme gradient boosting model demonstrated exceptional performance, achieving an R2 value of 0.9907 on the training set and reaching 0.9300 on the testing set. Furthermore, an interpretability analysis of the extreme gradient boosting model was conducted using Shapley additive explanations, revealing that f/fn is the most significant factor influencing the bed expansion ratio, while H0/D has a relatively minor effect. These findings provide a basis for effectively predicting bed expansion ratios and facilitate further scale-up studies in pulsed fluidized beds.