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(mechanical engineering)
Any apparatus for separating mixtures of materials into their constituents according to size and density.



(in mineral concentration), an apparatus for separating mixtures of mineral particles into fractions according to size, shape, or density. Depending upon the medium in which separation of the materials occurs, classifiers are divided into hydraulic and pneumatic. Depending upon the force used, there are gravity classifiers, centrifugal classifiers, and high-tension separators.

Wet mechanical classifiers used for preparing ores for flotation are the most common. Drag tanks and spiral classifiers (in which the settled material is removed by rakes or spirals similar to Archimedes’ screw) are usually used for this purpose. The drag tank is an inclined trough with the suspension (pulp) being delivered into the lower third of the trough. The settled “sands” are moved up along the bottom of the trough by the reciprocating motion of the rakes along an ellipsoid path; in addition, the “sands” are freed of water. The fine particles which do not have time to settle are removed with the “overflow.” The fineness of the overflow depends on the rocking rate of the rakes and the incline of the trough. The pulp density (the mass content of the solid phase in the pulp) is the most important method for the operational control of the classifier. With an increase in the density, the size of the particles passing into the overflow increases (since the speed at which they fall decreases in a viscous, dense medium). But with a very great dilution of the pulp the size of the particles in the overflow also increases, since there is an increase in the rate of flow of the upward water stream which removes all the particles, including the large ones. The principle of operation of spiral classifiers is the same; they merely have a simpler and more dependable design.

A mechanical classifier is most often used together with ball and rod mills in crushing ore for the continuous extraction of rather fine particles from the material being crushed. An important advantage in the design of such classifiers is the raising of the sands above the feed entry point, as this makes it possible to carry out closed-circuit crushing easily. The productivity of the mechanical classifiers depends upon the width of the overflow. For this reason simplex, duplex, and quadruplex classifiers are used (with an overflow width of from 200 to 3,000 mm), as well as a single-, double-, and four-spiral classifiers (with a width from 300 to 2,400 mm). Conical or pyramidal hydraulic classifiers without moving parts were also widely used until the beginning of the 1950’s. They are suitable primarily for the precision classification of small quantities of the finest products. Hydrocyclones and centrifuges are types of centrifugal classifiers.

Pneumatic classifiers are subdivided into chamber and centrifugal classifiers, with the latter being used more often. They consist of a blower, a distributing plate, and a centrifugal bladed wheel which spins the large particles toward the walls of the inner cone. These particles are carried by the airstream and settle in the inner cone. The fine fraction settles in the outer cone where the velocity of the airstream is low. There are many modifications of pneumatic classifiers.


See references under CLASSIFICATION.




(also called numerative), an auxiliary lexeme or noun that has lost its basic meaning to a greater or lesser degree and is used to designate countable objects. Classifiers are used in an attributive word group that contains a numeral and a noun; an example in Russian is piat’ shtuk karandashei (“five pencils”; literally, “five pieces of pencils”). Classifiers exist in many languages of Eastern and Southeastern Asia, such as Chinese, Vietnamese, and Indonesian, and in the Indo-Iranian, Turkic, and Dravidian languages.

Classifiers generally indicate the semantic class to which a noun belongs: compare Tadzhik du nafar korgar (“two workers”), du sar gusfand (“two sheep”), and du dona seb (“two apples”). Suffixal elements may fulfill the same function: compare Tadzhik duta odam (“two people”) and duta kitob (“two books”).

In some languages, classifiers also function as particles indicating singleness and indefiniteness: compare Oriya mote khaṇḍe pustaka die (“give me some [any] book”) and cāri khaṇḍe āmba (“four mangoes”; literally, “four pieces of mangoes”). The words khaṇḍe and khaṇḍa are particles and classifiers for a class of nouns designating round, flat, and oblong objects.


References in periodicals archive ?
In the series combination of the two classifiers, GMM is the base model while SVM is applied as a post-processing for curtailing the classification error.
Thus, we conducted a total of 255 experiments for each of the four classifiers (see section 2.
The same algorithm is then reapplied several times and weighted voting is used to combine the predictions of the resulting series of classifiers (Pal, 2007).
The output of the filter is given to four classifiers Multinomial Naive Bayes (MNB) [15], Discriminative Multinomial Naive Bayes (DMNB) [16], Support Vector Machine (SVM) [17] with normalized poly kernel and Random Forest [18] with 100 random trees.
Boolean algebra (Sharma & Jain, 2011) comprises the binary operations AND and OR, which were used to estimate the possible logical relations in the identified classifiers to predict IDH occurrence.
The BRAF V600E and parathyroid classifiers were designed to further reduce unnecessary surgeries in thyroid cancer diagnosis and guide appropriate treatment for patients whose thyroid nodules are likely cancerous.
The proposed DNN is compared with the state-art-methods including the SVM, NB and DT classifiers over OPD and PSD datasets.
In that proposed method, the features were extracted using Grey Level Co-occurrence Matrix (GLCM) techniques and linear SVM classifier was used as a classifier in order to improve the classification accuracy.
FLSmidth reported that in the pilot trials conducted at Rana Gruber's facility, the Reflux classifier technology demonstrated consistent separation and high separation efficiency in the 0-150 micrometer (pm) size fraction from material currently discarded to tailings.
Selected features were then used to classify the data using different classifiers.
Kwon analyzed dataset by using Decision Tree and NB classifiers to factors which are affecting more in a road accident.