Particle Classification Optimization of a Circulating Air Classifier

Abstract

Undoubtedly, wet processing of ores requires huge quantity of water. This provides enough incentive for dry beneficiation of ores which has great promise predominantly from an environmental standpoint and water scarcity in the mining and processing industries. Therefore, the present investigation made an attempt to effectively address the issues related to dry classification of minerals. In this study, a three-factor-three-level Box-Behnken factorial design combined with response surface methodology (RSM) was employed for modeling and optimization of operational parameters of a circulating air classifier. The three main operating parameters studied were air flow rate, feed rate, and guide vane angle. The primary and interaction effects of operating variables were evaluated using RSM while generating the second-order response functions for both the responses, cut size and size selectivity increment. The values of cut size and size selectivity increment obtained using predictive models were in excellent agreement with the observed values. The optimization of these predictive response models resulted in the optimal values of air flow rate, feed rate, and guide vane angle for achieving better classification efficiency. This study establishes that the Box-Behnken factorial design combined with RSM effectively model the performance of circulating air classifier.