MATHEMATICAL-MODELLING METHODS FOR AIR CLEANING IN MULTICYCLONE DUST COLLECTORS

Abstract

This paper presents an integrated mathematical framework for predicting the aerodynamic behaviour and particulate collection efficiency of multicyclone dust collectors that are widely deployed as pre-cleaners in high-temperature, high-dust industrial gas streams. The approach couples classic empirical models (Leith–Licht and Stairmand formulae), pressure–loss correlations, and three-dimensional Computational Fluid Dynamics (CFD) simulations based on the Reynolds-Averaged Navier–Stokes (RANS) equations and a Discrete Phase Model (DPM). The hybrid methodology is verified against laboratory and full-scale operating data for medium-pressure (ΔP < 1 kPa) units treating flue gases laden with PM₂․₅. Results reveal the trade-off between inlet velocity, pressure drop and fine-particle capture, and deliver design heuristics that boost PM₂․₅ removal by 12 % while lowering fan energy consumption by 0.3 %. Practical recommendations and research directions for nano-aerosol control and AI-assisted adaptive operation are outlined.