Effects of operating conditions and particle properties on mixing performance in an industrial-scale U-shape ribbon mixer

The operating conditions and material properties significantly influence particle mixing performance, yet a quantitative and comprehensive evaluation of these effects is still lacking. In this work, the effects of key operating conditions (e.g., fill level and blade speed) and material properties (e.g., particle density and particle size) on the mixing behaviours in an industrial-scale U-shaped ribbon mixer are studied by use of the discrete element method (DEM). The response surface methodology (RSM) with the central composite design (CCD) method is adopted to quantify the significance of multiple parameters and derive an empirical model of the mixing degree. The total contact number of different particles and final mixing degree increase with the increase in fill level and particle density and with the decrease in blade speed and particle size. The RSM results demonstrate the significant roles of fill level, blade speed, and particle diameter and the insignificant role of particle density in affecting the final mixing degree. A second-order polynomial regression model of mean mixing degree at the dynamic equilibrium state is proposed with the model adequacy and significance confirmed by the analysis of variance (ANOVA), where the predicted R2 of 0.9571 compares well with adjusted R2 of 0.9695, demonstrating that more than 95% of the variability in the data is interpreted. The ideas and methods adopted in this work can be imitated to benefit the optimization and the improvement of mixing efficiency of practical industrial mixers in future work.

Automated summary

This study investigates the effects of operating conditions and material properties on the mixing behavior of an industrial-scale ribbon mixer using the discrete element method. The results show that fill level, blade speed, and particle diameter play significant roles in influencing the final mixing degree, while particle density has an insignificant effect. A regression model is proposed to predict the mixing degree, and the model adequacy and significance are confirmed. The findings and methodology of this study can be applied to optimize and improve the mixing efficiency of practical industrial mixers in future research.