摘要
尾砂絮凝沉降试验是确定尾砂浓密工艺的基础,也是确定浓密机生产参数与设备选型的必要环节。基于图像处理技术,自主研发了一套尾砂絮凝沉降观测与智能分析系统,利用工业摄像头,采集尾砂絮凝沉降图像,利用灰度化处理、高斯滤波、图像分割等方法,通过度量区域面积和坐标属性,智能识别尾砂絮凝沉降过程的固液分离界面位置。以冬瓜山铜尾矿为试验材料,进行尾砂絮凝沉降试验,探究尾砂沉降曲线变化规律,综合考虑分离界面的沉降速度、上清液浊度、静态极限质量分数和固体通量的变化,确定了试验尾砂絮凝沉降的最佳工艺参数为:非离子絮凝剂6003s、絮凝剂单耗30g/t、絮凝剂溶液质量分数0.10%、尾砂浆进料质量分数15%。根据凯奇沉降理论,进一步分析了沉降过程曲线,计算沉降参数,结合矿山实际生产能力需求,推荐该矿山浓密机直径为15m,筒体总高度为19m。
Flocculation and sedimentation experiment of tailings is the basis of tailings thickening process,and it is also a necessary link to determine the production parameters and equipment selection of thickener.Based on image processing technology,a set of flocculation and sedimentation of tailings observation and intelligent analysis system was independently developed.Industrial cameras were used to collect flocculation and sedimentation images of tailings.Gray processing,Gaussian filter,image segmentation and other methods were used to intelligently identify the position of solid-liquid separation interface during flocculation and sedimentation process of tailings by measuring area and coordinate attributes.Taking Dongguashan copper tailings as the experimental material,the flocculation and sedimentation experiment of tailings was carried out to explore the variation law of tailings sedimentation curve.Considering the changes of sedimentation rate,supernatant turbidity,static limit mass fraction and solid flux of separation interface,the optimum process parameters of experimental tailings were determined as follows:nonionic flocculant 6003s,flocculant unit consumption 30g/t,flocculant mass fraction 0.1%,and tailings feed mass fraction 15%.According to the Ketch sedimentation theory,the sedimentation process curve was further analyzed,and the sedimentation parameters were calculated.Combined with the actual production capacity requirements of the mine,it is recommended that the diameter of the thickener in the mine is 15mand the total height of the cylinder is 19m.
作者
杨莹
吴爱祥
王先成
王国立
刘伟涛
YANG Ying;WU Aixiang;WANG Xiancheng;WANG Guoli;LIU Weitao(JCHX Mining Management Co.,Ltd.,Beijing 101500,China;School of Civil and Resource Engineering,University of Science and Technology Beijing,Beijing 100083,China)
出处
《矿业研究与开发》
CAS
北大核心
2023年第6期68-76,共9页
Mining Research and Development
基金
中国博士后科学基金资助项目(2021M701516).
关键词
图像处理
絮凝
尾砂沉降
智能分析
浓密机
Image processing
Tailings
Flocculation and sedimentation
Intelligent analysis
Thickener