摘要
在煤泥浮选过程中,存在着人工加药计量不准和隔膜式计量泵误差较大的问题,从而导致精煤灰分不稳定。将神经网络算法应用于加药量预测,通过分析浮选槽泡沫图像特征的方式实时检测产出精煤的灰分,并采用蠕动泵作为加药的执行机构,在提高精煤灰分稳定性基础上,有效地节约了药剂用量。
In the flotation process of coal slime,there are problems such as inaccurate manual dosing measurement and large error of diaphragm metering pump,which lead to the instability of cleaned coal ash.The neural network algorithm was applied to the dosage prediction,the ash content of cleaned coal was detected in real time by analyzing the foam image characteristics of the flotation tank,and the peristaltic pump was used as the actuating mechanism of the dosing,on the basis of improving the stability of cleaned coal ash,the dosage of medicine was effectively saved.
作者
张路
ZHANG Lu(Preparation Plant,Malan Coal Mine,Xishan Coal Electricity Group Co.,Ltd.,Shanxi Coking Coal Group Co.,Ltd.,Gujiao 030205,China)
出处
《机械工程与自动化》
2021年第3期153-155,共3页
Mechanical Engineering & Automation
关键词
煤泥浮选
神经网络
智能加药系统
泡沫图像
coal slime flotation
neural network
intelligent dosing system
foam image
