Evaluation and improvement of mathematical models for hydrocyclone classifiers part I: Laboratory scale

The separation process occurring in hydrocyclones is complex and influenced by many factors, making engineers rely on empirical correlations to design and operate such classifiers. Drawing on the hindsight of laboratory research and engineering practice, this study introduces the characteristics of some well-known models and assesses their performance based on extensive data. Part I focuses on the laboratory-scale classification results that were carefully collected from a home-made test rig. In particular, the effects of particle size distribution (PSD) on classification performance along its interaction with operating conditions were studied. By evaluating the mathematical correlations in the literature, an improved model combining their merits and PSD effects was established. Reasonable agreements between predicted and measured results in terms of throughput, corrected cut size, water recovery, and cut sharpness confirms the validity of the new model. The results should provide valuable guidance for selection and application of mathematical models in industrial practice.

Automated summary

This study evaluates the performance of well-known models for hydrocyclone separation based on extensive data and introduces a new model that combines the merits of existing models and considers particle size distribution effects. The results confirm the validity of the new model and provide valuable guidance for industrial applications.