| Budget Amount *help |
¥4,100,000 (Direct Cost: ¥4,100,000)
Fiscal Year 2003: ¥1,500,000 (Direct Cost: ¥1,500,000)
Fiscal Year 2002: ¥2,600,000 (Direct Cost: ¥2,600,000)
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| Research Abstract |
We have studied a new wet-type centrifugal classification system that employs an almost rigidly rotating flow. This flow consists of geotropic interior regions, ageostrophic Stewartson and Ekman layers, and/or a rigid-rotation region. In this centrifugal system. we can accurately classify very fine feed-particles suspended in water with a low concentration into coarse and fine products by the difference in the centrifugal force and fluid drag exerting particles due to the difference in particle size, because we can increase the centrifugal force arbitrarily without producing any turbulent fluctuation and well manage the centrifugal force and the fluid drag. Hence, this system is adaptable to an accurate submicron classification.
In the present study, we have treated, as the basis of the multi-stage system, a double-stage system that can classify feed particles into coarse, medium and fine products all at once. we have experimentally investigated the classification performance of both batch and semi-continuous types. Consequently, we have drawn the following conclusions, where a centrifugal-effect parameter characterizes the ratio of the centrifugal force acting on a particle to the fluid drag.
(1) the semi-continuous type (both fine and medium products are obtainable continuously) shows a slightly greater coefficient of variation of the medium product than the batch type (only a fine product is obtainable continuously), but shows a smaller mass median diameter.
(2) In both batch and semi-continuous types, with increasing centrifugal-effect parameter, the mass median diameter and coefficient of variation of the medium product become smaller, i.e. the medium product becomes finer and more uniform in particle size.
(3) On the classification performance of each stage as a single-stage classifier, the stage with extraction shows a slightly worse classification accuracy than that without extraction.
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