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深锥形槽体对细粒煤干扰沉降分级实验研究
引用本文:史长亮,王胜楠,马娇,王飞跃,赵继芬.深锥形槽体对细粒煤干扰沉降分级实验研究[J].中国矿业,2018,27(4).
作者姓名:史长亮  王胜楠  马娇  王飞跃  赵继芬
作者单位:河南理工大学化学化工学院;河南省矿产资源绿色高效开采与综合利用重点实验室;昆山浩兴电子科技有限公司;
基金项目:河南省科技攻关项目(152102210107);河南省重点实验室开放基金资助项目(S201615);河南理工大学博士基金资助项目(B2014-010)
摘    要:本文重点研究了自制深锥形干扰沉降分级机在不同影响因素下对细粒煤的分级效率,结果表明:底部和中部同时给水形式的设计有利于细粒煤分级,底部与中部给水量比值对分级效率有一定影响但不明显;给料速度及浓度值越低,细粒煤分级效率越高。该装置的适宜分级条件为流量配比2∶1、给料速度0.50m/s、矿浆浓度40%时,粒级级配(0.25~0.125mm和1.0~0.5mm)分级效率接近65%,较常规分级旋流器分级效率50%~60%提高约5%~15%,实验结果表明深锥形干扰沉降分级机可有效实现细粒煤的分级。

关 键 词:细粒煤  干扰沉降  深锥  分级效率
收稿时间:2017/4/8 0:00:00
修稿时间:2017/8/2 0:00:00

Experiment research on interference sedimentation classification of fine coal with deep cone tub
SHI Changliang,WANG Shengnan,MA Jiao,WANG Feiyue and ZHAO Jifen.Experiment research on interference sedimentation classification of fine coal with deep cone tub[J].China Mining Magazine,2018,27(4).
Authors:SHI Changliang  WANG Shengnan  MA Jiao  WANG Feiyue and ZHAO Jifen
Affiliation:College of Chemistry and Chemical Engineering, Henan Polytechnic University,College of Chemistry and Chemical Engineering, Henan Polytechnic University,College of Chemistry and Chemical Engineering, Henan Polytechnic University,Kunshan HaoXing electronic technology co,LTD,Kunshan Jiangsu,College of Chemistry and Chemical Engineering, Henan Polytechnic University
Abstract:Accurate classification is the basis of fine coal efficient sorting, classifier is becoming a hot spot of research. This paper mainly studied the fine coal classification efficiency with self-developed deep cone interference settling classifier under different influencing factors. The results show that: the water supply using the bottom and the central form at the same time, is conducive to fine coal classification, and the bottom water volume to the central water volume (the ratio of flow) has impact on the classification efficiency, but is not obvious. Also the feeding velocity and density is lower, fine coal classification efficiency is higher. Optimization the suitable conditions as follows: the flow ratio is 2:1, feeding speed is 0.50 m/s, pulp density is 40%, B1 particle size slopes (from 0.25 mm-0.125 mm and 1.0 mm -0.25 mm) classification efficiency is close to 65%. The experimental results show that deep cone interference settling classifier can effectively realize the fine coal classification.
Keywords:fine coal  interference sedimentation  deep cone  classification efficiency
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