Hydrocyclone cut-size estimation using artificial neural networks

Abstract

The hydrocyclone is widely used throughout the mineral processing industry when working with slurries. It is either used for classifying, desliming or dewatering. Hydrocyclones are inexpensive, application-efficient and relatively small to employ. In order to quantify its separation efficiency, models are utilised to estimate the cut-size and sharpness of classification coefficient, usually in the form of a partition curve. Most models are based on experimentally obtained data and are therefore not always universally applicable. Over the last decade researchers have started employing Artificial Neural Networks (ANNs) in order to obtain a dynamic model. This study endeavoured to use experimentally acquired data to develop models that predict the cut-size. The models are discussed and evaluated in detail and the best predicting model was compared to a conventional model from literature