摘要
浮选过程是一种复杂的物理化学过程,对于矿产资源的提取具有重要意义。然而,传统的人工观察方法主观性高、效率低,易出现浮选性能波动、资源损失等问题。使用基于图像处理的浮选过程监测可提高生产效率和自动化水平,目前国内涉及对泡沫的图像分析较少,本文旨在解决浮选泡沫的特征提取和品位分析难题,研究适应性强,计算复杂度较低,有助于提高选矿生产效率和自动化水平。
The flotation process is a complex physical and chemical process that plays an important role in the extraction of mineral resources.However,traditional manual observation methods are subjective,inefficient,and often result in fluctuations in flotation performance and loss of resources.The use of image-based flotation process monitoring can improve production efficiency and automation levels.Currently,there are few studies in China on image analysis of froth.This paper aims to solve the problem of feature extraction and grade analysis of froth in flotation,and research on adaptive,low-complexity computation methods that can help improve the efficiency and automation level of mineral processing production.
作者
侯卫钢
张晓淼
朱琳
毛瑞
赵浩博
张雪峰
HOU Weigang;ZHANG Xiaomiao;ZHU Li;MAO Rui;ZHAO Haobo;ZHANG Xuefeng(Ansteel Mining Engineering Corporation,Anshan 114014,China;Liaoning Zhongxin Automation Control Group co.,LTD,Anshan 114001,China)
出处
《矿业工程》
CAS
2024年第4期84-87,92,共5页
Mining Engineering
关键词
浮选
特征提取
机器视觉
神经网络
flotation
feature extraction
machine vision
neural network