• Flat Bottom Classifiers/Lites-Out™ McLanahan

    Lites-Out™ Classifiers, aka Flat-Bottom Classifiers, are typically used to remove lightweight, deleterious materials, such as lignite and organic matter, from a <4 mesh (5mm) sand product stream. Flat-Bottom Classifiers are part of the family of hindered settling classification equipment that includes McLanahan Hydrosizers™.

  • Gold Classifiers Gold Prospecting Mining Equipment

    Gold classifiers, also called sieves or screens, go hand in hand with a gold pan. Designed to fit on the top of 5 gallon plastic buckets used by most prospectors, and over most gold pans, the classifier's job is to screen out larger rocks and debris before you pan the material.

  • Classification of Materials and Types of Classifiers

    Oct 31, 2015· Classification of Materials and Types of Classifiers. Size control of particles finer than 1 mm, are out of the practical range of conventional screens. Separation of such particles is carried out by classification. Classification implies the sorting of particulate material into different size ranges. It is a method of separation of fines from

  • AIR CLASSIFIER TURBO CLASSIFIER aaamachine

    classifier smaller without impairing the ac-curacy and functionality of Turbo Classifier. Since its height was made shorter, a free space was made above the powder inlet, which enables the unit to be installed at a place with limited space and smooth feeding to the unit. Classifier-specific control system, providing full automatic operation

  • Spiral Classifiers 911Metallurgist

    The Spiral Classifier is availe with spiral diameters up to 120". These classifiers are built in three models with 100%, 125% and 150% spiral submergence with straight side tanks or modified flared or full flared tanks. All sizes and models are availe with single-, double- or triple-pitch spirals. The Spiral Classifier offers the greatest sand raking capacity of any classifier availe

  • All the Steps to Build your first Image Classifier (with

    Mar 01, 2019· For example, for my piece of 2D chess classifier, I had 160 images for each possible piece (and the empty case), so about 2,000 images in total (which is not that much) but the size of the dataset depends on the projects (my 2D pieces always have the same aspects, while cats have a lot of breeds, different sizes, different postures, ).