基于机器视觉的矿物浮选过程监控技术研究进展

桂卫华; 阳春华; 徐德刚; 卢明; 谢永芳

doi:10.3724/SP.J.1004.2013.01879

基于机器视觉的矿物浮选过程监控技术研究进展

doi: 10.3724/SP.J.1004.2013.01879

1.
中南大学信息科学与工程学院长沙 410083

基金项目:

国家创新研究群体科学基金项目(61321003),国家自然科学基金(61134006,61025015,61074117),国家科技支撑计划(2012BAK09B00,2012BAF03B05)资助

详细信息

作者简介:
桂卫华中南大学教授.主要研究方向为复杂工业过程建模与优化控制, 分散鲁棒控制及故障诊断.E-mail: gwh@mail.csu.edu.cn

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- 文章访问数: 1904
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- 被引次数: 0
出版历程
- 收稿日期: 2013-07-02
- 修回日期: 2013-08-28
- 刊出日期: 2013-11-20

Machine-vision-based Online Measuring and Controlling Technologies for Mineral Flotation——A Review

1.
College of Information Science & Engineering, Central South University, Changsha 410083

Funds:

Supported by Science Fund for Creative Research Groups of the National Natural Science Foundation of China (61321003), National Natural Science Foundation of China (61134006, 61025015, 61074117), National Science and Technology Support Program (2012BAK09B00, 2012BAF03B05)

摘要

摘要: 矿物浮选流程长、分布范围广、控制变量多、关键工艺参数无法在线检测,导致实时监控困难, 严重制约了浮选生产的优化运行及选矿自动化水平的提升.浮选泡沫表面视觉特征是浮选工况和工艺指标的直接指示器, 为此将机器视觉应用到矿物浮选过程的监控中, 以提高浮选过程的资源回收率. 本文结合矿物浮选泡沫图像特点,从浮选过程的泡沫图像关键特征提取及表征、关键工艺参数检测、工况识别以及基于机器视觉监控系统的实现等方面综述了浮选过程监控技术的研究成果,并指出了基于机器视觉的选矿过程监控技术的发展趋势及面临的挑战.
- 机器视觉 /
- 泡沫浮选 /
- 在线检测 /
- 特征选择 /
- 工况识别
Abstract: The real-time monitoring and control of mineral flotation process are difficult due to several facts/system characteristics: long flotation processes, wide distribution range, multiple-variable control system and undetectability of crucial production parameters. All these facts/system characteristics have greatly restricted the optimal operation and automation level of flotation process. However, the visual features of flotation froth surface play the indication role to illustrate the production states and degree. As a consequence, machine vision technology is employed to facilitate the control strategy design for mineral flotation process and to promote the resource recovery. This paper reviews the key technologies and the corresponding achievements associated to the system design of the flotation process monitoring and control. Concretely, those key technologies involve the skills to extract and characterize the key froth image features, detect and identify the process parameters and production states. Moreover, the realizations of froth image based mineral flotation process monitoring system are discussed. Finally, recommendations for future research encountered in the control strategy design for flotation process based on machine vision are suggested.
- Machine vision /
- froth flotation /
- online measuring /
- feature selection /
- conditions identification

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