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Articles

Improving Coal Flotation by Classified Conditioning

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Pages 361-373 | Received 12 Nov 2015, Accepted 29 Nov 2016, Published online: 13 Jan 2017
 

ABSTRACT

How to improve coal flotation performance has been examined with a focus on the effect of particle size on flotation conditioning. A coking coal sample collected from the flotation feed of a coal preparation plant located in Lu’an, Shanxi province of China was investigated. The characters including mineral composition, size fraction, density fraction, and contact angle were analyzed. Classified and unclassified conditioning tests were carried out using different size fractions. The relationship between flotation indices (combustible recovery and flotation efficiency) and conditions (particle size, collector dosage, and conditioning stirring speed) was investigated. The results showed that the optimum conditions for different size fractions were different. The collector dosage and the conditioning stirring speed increased with the decrease of particle size. Meanwhile, the collision frequency between fine coal and diesel particles was much higher than the coarse particles, resulting most of the flotation agents adsorbed by fine particles, though the kinetic energy of coarse particles was higher. So a classified conditioning process was proposed. By conditioning optimizing, the flotation performance of classified conditioning was significantly improved compared with unclassified conditioning, especially for the +0.125 mm fractions.

Funding

The authors thank the financial support from the National Natural Science Foundation of China (grant numbers 51504262, 51604280), the Open Fund of State Key Laboratory of Mineral Processing (grant number BGRIMM-KJSKL-2015-03), and the National Key Basic Research Program of China (grant number 2012CB214905).

Additional information

Funding

The authors thank the financial support from the National Natural Science Foundation of China (grant numbers 51504262, 51604280), the Open Fund of State Key Laboratory of Mineral Processing (grant number BGRIMM-KJSKL-2015-03), and the National Key Basic Research Program of China (grant number 2012CB214905).

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