Modelling and optimization of an inclined plane classifier using CFD-DPM and the Taguchi method
•The classification mechanism was modelled using CFD-DPM.•The influence of operational and design variables was studied.•The influence of the inlet particle location has never been studied before.•Two new indexes were defined for the evaluation of the classifier performance.•The classifier was succe...
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Veröffentlicht in: | Applied Mathematical Modelling 2020-01, Vol.77, p.617-634 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | •The classification mechanism was modelled using CFD-DPM.•The influence of operational and design variables was studied.•The influence of the inlet particle location has never been studied before.•Two new indexes were defined for the evaluation of the classifier performance.•The classifier was successfully optimized for manufactured sand processing.
The inclined plane air classifier is a device designed for the classification of limestone particles. It presents a simple classification mechanism and low energy requirements. Despite its advantages, the industrial application of the classifier has not been reported. This study was carried out to model the classification mechanism inside the inclined plane classifier and to propose its use in the aggregate industry where the adjustment of the particle size distribution of manufactured sands is required. The velocity and pressure fields inside the equipment were modelled using computational fluid dynamics and the particle trajectories were computed using a Lagrangian discrete phase modelling. The collisions between particles were modelled by using the discrete element method. The Taguchi method was used to study the influence of the operational and design variables on the classification performance and to optimize the equipment. The inclined plane classifier was successfully modelled and optimized for the improvement of the particle size distribution of manufactured sands showing excellent results. |
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ISSN: | 0307-904X 1088-8691 1872-8480 0307-904X |
DOI: | 10.1016/j.apm.2019.07.059 |