At present, the more common classification method is based on gravity field and centripetal field stop classification.
The grading principle of gravity field is an ancient and classical theory, which is based on Stokes law in laminar flow. In the process of classification, it is assumed that the flow field stops in the laminar state, and that the ultrafine solid particles are spherical and settle freely in the medium. These are quite different from the actual situation. In centripetal field, particles can achieve much greater centrifugal acceleration than gravitational acceleration, so the settling speed of the same particles in centrifugal field is much faster than that in gravitational field, in other words, even smaller particles can achieve greater settling speed.
In addition, the classification of ultrafine powders can be divided into dry classification and wet classification according to the medium used. Dry classification is characterized by the use of air as a fluid, low cost and easy operation, but it has two shortcomings: one is easy to form air pollution, the other is low classification accuracy. The liquid is used as classification medium in wet classification. There are many post-treatment problems, such as dehydration, dryness and disposal of dispersed wastewater after classification, but it has the characteristics of high classification accuracy and no explosive dust.
Ultra-fine classification equipment
Up to now, super-fine classification equipment can be described as a hundred flowers blooming. According to the different fluid medium, it can be divided into dry classification and wet classification. In dry classification, according to the different classification principles, it can be divided into gravity, centrifugal, inertia and other varieties.
Gravity Superfine Classifier
The gravity Superfine Classifier stops classification by using different particle sizes with different settling velocities in gravity field. There are two types of gravity classifier, i. e. degree flow type and vertical flow type.
Particles have a certain kinetic energy when they move. When they move at the same speed, the kinetic energy of particles with larger mass is larger, that is, the inertia of motion is larger. When they are subjected to the action of changing their direction of motion, the different inertia will form different trajectories, thus completing the classification of particles. At present, the grading particle size of the classifier has reached 1 micron. If the particle gathering and the eddy current in the classifier room can be effectively prevented, the grading particle size is expected to reach the sub-micron level, and the grading accuracy and efficiency will also be significantly improved.
Centrifugal classifier is a kind of super-fine classifier developed so far because it is easy to produce centripetal force field far stronger than gravity field. According to the difference of flow pattern in centripetal field, it can be divided into free vortex and forced vortex. Common free-vortex (or quasi-free-vortex) classifiers include DS classifier, SLT classifier, etc. Forced-vortex classifier includes MC classifier, MS classifier, MSS classifier, ATP classifier, MP classifier, turbine classifier, etc.
Jet classifier is a classifier which combines the principles of inertia classification, rapid classification and Coanda effect of fine particles to stop superfine classification. Simply speaking, Coanda effect is the characteristic of fine particles moving along the curved wall with airflow. Compared with other classifiers, jet classifier has the following characteristics:
(1) There are no moving parts in the grading part, so the maintenance workload is small and the work is reliable.
(2) The powder can be well pre-dispersed by radiation jet.
(3) Once the particles are dispersed, they enter the classifier immediately to stop rapid classification, which prevents the secondary agglomeration of the particles to a limit.
(4) Multi-level products can be obtained, and the granularity of products at all levels can be adjusted sensitively by grading blade angle and outlet pressure.
(5) High classification efficiency and granularity.