细粒煤分级溢流颗粒粒度在线检测研究  

Research on online detection of particle size in fine-grained coal classification overflow

作　　者：孙豪智 马娇 史长亮 王函露 SUN Haozhi;MA Jiao;SHI Changliang;WANG Hanlu(College of Chemistry and Chemical Engineering,Henan Polytechnic University,Jiaozuo 454003,China;Collaborative Innovation Center of Coal Work Safety and Clean High Efficiency Utilization,Jiaozuo 454003,China)

机构地区：[1]河南理工大学化学化工学院,河南焦作454003 [2]煤炭安全生产与清洁高效利用省部共建协同创新中心,河南焦作454003

出　　处：《工矿自动化》2024年第5期44-51,59,共9页Journal Of Mine Automation

基　　金：河南省科技攻关计划项目(232102231028);河南理工大学博士基金项目(2022-50)。

摘　　要：对细粒煤分选中分级溢流颗粒粒度进行实时在线检测,进而调控分级参数,可减少溢流中粗颗粒含量,提高总精煤回收率。现有研究对溢流颗粒粒度的检测上限普遍在180μm左右,矿浆体积浓度上限为10%,无法满足粒度较粗、粒级较宽且体积浓度较高的细粒煤分级旋流器溢流颗粒粒度检测要求。为提高煤颗粒粒度和矿浆体积浓度检测上限,开发了一套超声波在线颗粒粒度检测系统。基于超声波声衰减模型,构建了适用于煤颗粒粒度为44.5~600μm、矿浆体积浓度为0~40%的细粒煤分级现场工况的煤颗粒粒度检测模型。采用粒子群优化算法优化的BP神经网络建立了煤颗粒粒度分布预测模型,实现对细粒煤分级旋流器溢流矿浆粒度分布预测。基于煤颗粒粒度检测模型的模拟结果表明,超声波衰减值随煤颗粒粒度增大而先减小后增大,随超声波频率和矿浆体积浓度增大而增大。分别使用超声波在线颗粒粒度检测系统和煤颗粒粒度分布预测模型对某矿水力分级旋流器溢流颗粒粒度(实际值为150.0,215.0,315.0μm)分布进行检测,结果表明检测系统测量值相对误差为10.87%,9.81%,8.48%,预测模型的预测值相对误差为9.27%,6.05%,6.92%,均实现了细粒煤分级溢流颗粒粒度的准确检测。Real time online detection of the particle size of the overflow in the selection and classification of fine-grained coal can be carried out,and the classification parameters can be adjusted to reduce the content of coarse particles in the overflow and improve the total clean coal recovery rate.The current research generally limits the detection of overflow particle size to around 180μm,and the upper limit of slurry volume concentration is 10%.It cannot meet the requirements of overflow particle size detection for fine-grained coal classification cyclones with coarse particle size,wide particle size range,and high volume concentration.A set of ultrasonic online particle size detection system has been developed to improve the upper limit of coal particle size and slurry volume concentration detection.Based on the ultrasonic attenuation model,a coal particle size detection model suitable for on-site conditions of fine-grained coal classification with coal particle size of 44.5-600μm and slurry volume concentration of 0-40%is constructed.A coal particle size distribution prediction model is established using a BP neural network optimized by particle swarm optimization algorithm,achieving the prediction of the particle size distribution of the overflow slurry in a fine-grained coal classification cyclone.The simulation results based on the coal particle size detection model show that the ultrasonic attenuation value decreases first and then increases with the increase of coal particle size,and increases with the increase of ultrasonic frequency and slurry volume concentration.The ultrasonic online particle size detection system and coal particle size distribution prediction model are respectively used to detect the distribution of overflow particle size(actual value is 150.0,215.0,315.0μm)in a hydraulic classification cyclone of a certain mine.The results show that the relative errors of the measurement values of the detection system are 10.87%,9.81%,8.48%,and the relative errors of the predicted values of the pred

关 键 词：煤炭洗选 细粒煤分选 水力分级 溢流颗粒粒度检测 煤颗粒粒度分布 超声波衰减 

分 类 号：TD94[矿业工程—选矿]