Classification of concentrated suspensions using inclined settlers

Sedimentation vessels with parallel inclined walls are able to clarify or classify particle suspensions more rapidly than conventional settlers due to their increased surface area and decreased settling distances. The use of continuous inclined settlers to classify or separate particles by size and/...

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Veröffentlicht in:International journal of multiphase flow 1996, Vol.22 (3), p.563-574
Hauptverfasser: Davis, R.H., Gecol, H.
Format: Artikel
Sprache:eng
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Zusammenfassung:Sedimentation vessels with parallel inclined walls are able to clarify or classify particle suspensions more rapidly than conventional settlers due to their increased surface area and decreased settling distances. The use of continuous inclined settlers to classify or separate particles by size and/or density from concentrated suspensions is considered. Mass balances are applied to each particle species in order to predict its concentration in the settler overflow (fine fraction) and settler underflow (coarse fraction), given the settler geometry, operating conditions, fluid and particle properties, and the particle c concentration and size distribution in the feed. A hindered settling function with no adjustable parameters is employed for bidisperse and polydisperse suspensions. Unlike many processes, it is shown that the efficiency of the classification process increases with increasing particle concentration in the feed, as measured by an increasing fraction of smaller or slower-settling particles partitioning to the fine fraction rather than contaminating the coarse fraction. This is a direct result of hindered settling, which reduces the settling rate and increases the relative concentration of slower-settling particles in the upper portion of the vessel. Experimental data are presented for bidisperse suspensions of glass and acrylic beads and of coal particles and yeast cells, and for polydisperse suspensions of polystyrene latex beads. The results are in good agreement with the model predictions.
ISSN:0301-9322
1879-3533
DOI:10.1016/0301-9322(95)00077-1