Study on the Classification Performance of a Novel Wide-Neck Classifier

A novelty-designed wide-neck classifier (WNC) was proposedto enhancethe passing ability and classification efficiency of fine particles.Using computational fluid dynamics (CFD), we studied the flow fieldand velocity distribution in the newly designed WNC. The velocityof the fluid gradually decreased from the wall to the center and fromthe cylinder to the cone, which facilitates particle classificationand thickening. The Reynolds number (Re) and turbulent intensity (I) inside the WNC were discussed. The turbulent intensityincreased with increasing feed velocity and overflow outlet diameterand decreased with increasing feed concentration and spigot diameter.The classification of coal slurry was performed to analyze the performanceof WNC. The classification efficiency increased with increasing feedvelocity but decreased as the feed concentration, spigot diameter,and overflow outlet diameter increased. The predictive models forclassification efficiency influenced by the operational and structuralparameters were constructed at high correlation coefficients, andthe average error of these models was analyzed at 0.28%. Our resultscan provide valuable insights into the development of mineral classification.