(270a) Scaling up Segregation Learnings in Industrial Bin Flow | AIChE

(270a) Scaling up Segregation Learnings in Industrial Bin Flow


Stoltz, C. G., Procter & Gamble Company
Hecht, J. P., Procter & Gamble
Recent advancements in continuum and numerical modeling of granular flows provide hope of quantitatively predicting mixing and segregation in powder flows at large scale. We are interested in applying these models to plant-scale bins, and we have studied the scale up of size-driven segregation learnings through bench and pilot scale experiments, alongside continuum and discrete element method (DEM) models. We found that the filling mode of 3D bins is in practice often by intermittent avalanches and that the segregation during bin filling continues to increase inversely with the filling rate well into the intermittent-avalanching regime, down to the lowest flow rates tested. As a result of the intermittent filling mode, the intensity of size segregation did not scale as expected (with the rise velocity of the heap surface) with bin size and fill rate in our experiments. We also used the multiple scale bins in experiment and DEM to design and test bin inserts for altering the discharge flow patterns in the geometrically similar bins, incorporating known design principles from the literature. Open challenges to implementing continuum segregation models in industrial bin flows include linking predictions for the size and frequency of intermittent avalanches during 3D hopper filling to the continuum model for segregation.