Mathematical Optimisation Methods For Improving The Performance Of Multi-Cyclone Dust Collectors
Ключевые слова:
Multi-cyclone, multi-objective optimisation, genetic algorithmАннотация
This study presents an integrated multi-objective optimisation (MOO) framework that simultaneously minimises pressure drop (ΔP) and maximises collection efficiency (η) of industrial multi-cyclone (MSC) dust collectors. A Response-Surface-CFD metamodel is coupled with a genetic algorithm (GA), differential evolution (DE) and a final NSGA-II Pareto filter to search a four-dimensional design space comprising the barrel diameter D, inlet width B, cone length L_c and vortex-finder diameter D_v. The hybrid GA → DE strategy converged from an initial objective value J = 1.0 to J = 0.17, cutting ΔP by 32 % while raising η by 9 %. Figure 1 traces objective-function convergence; Figure 2 displays the ΔP–η Pareto front. The proposed approach reduces fan-power demand by ≈ 14 MWh yr⁻¹ (≈ 11 t CO₂) for a mediumscale plant
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