基于泡沫图像特征的浮选槽液位智能优化设定方法

赵洪伟; 谢永芳; 蒋朝辉; 徐德刚; 阳春华; 桂卫华

doi:10.3724/SP.J.1004.2014.01086

基于泡沫图像特征的浮选槽液位智能优化设定方法

doi: 10.3724/SP.J.1004.2014.01086

1.
中南大学信息科学与工程学院长沙 410083

基金项目:

国家自然科学基金重点项目（61134006），国家创新研究群体科学基金（61321003），高等学校博士学科点专项基金博导类资助课题（20120162110076），高等学校博士学科点专项科研基金优先发展领域资助课题（20110162130011），中央科研基本业务费中南大学国家杰青培育专项（2011JQ009）资助

详细信息

作者简介:
赵洪伟中南大学信息科学与工程学院硕士研究生. 主要研究方向为工业过程建模与优化控制研究，智能控制系统.E-mail：zhaohw@csu.edu.cn

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- 被引次数: 0
出版历程
- 收稿日期: 2013-07-04
- 修回日期: 2013-11-14
- 刊出日期: 2014-06-20

An Intelligent Optimal Setting Approach Based on Froth Features for Level of Flotation Cells

1.
School of Information Science and Engineering, Central South University, Changsha 410083

Funds:

Supported by National Natural Science Foundation of China (61134006), the Funds for Creative Research Groups of China (61321003), Doctoral Tutor Classes Funded Project of Specialized Research Fund for the Doctoral Program of Higher Education (20120162110076), Priority Areas of Development Funding Issues of Specialized Research Fund for the Doctoral Program of Higher Education (20110162130011), and the Fundamental Research Funds for the Central Universities (2011JQ009)

摘要

摘要: 浮选生产过程中浮选槽液位通常根据经验人工设定，具有主观随意性﹑液位波动大，使精/尾矿品位不满足要求.为此，提出一种基于浮选泡沫图像多特征的浮选槽液位智能优化设定的方法.在浮选槽工作原理以及液位与泡沫图像特征间关系的分析基础上，将基于案例推理的浮选槽液位预设定﹑基于多泡沫图像特征的改进LS-SVM（Least squares support vector machine）品位预测及基于BP神经网络的自学习模糊推理智能补偿等模型有机集成，提出了充分利用泡沫图像特征的液位智能优化设定方法.将该方法在某铝土矿浮选生产过程进行应用验证，可使粗选槽液位波动减小，提高了粗选精/尾矿品位合格率、总精矿品位合格率及回收率.
- 浮选液位 /
- 泡沫特征 /
- 案例推理 /
- 品位预测 /
- BP神经网络模糊推理 /
- 优化设定
Abstract: In the flotation production process, the liquid level of flotation cells is usually set by on experiences. The liquid level can fluctuate in a large range such that the concentrate grade and tailings grade may not meet the requirement. In this paper, an intelligent optimal setting approach based on forth image features is proposed. On the basis of analysis of flotation cells' working principle and relationship between level and froth image features, the pre-setting model based on CBR, the improved least squares support vector machine (LS-SVM) grade prediction model based on multiple froth features, and the self-learning fuzzy reasoning intelligent compensation model based on BP neural network are integrated together. This method is tested on a bauxite flotation process. The level fluctuation of rougher flotation cells decreases. The pass rates of concentrate and tailings grade in rougher flotation and the pass rate of the concentrate grade and recovery of the overall flotation increase.
- Flotation level /
- froth feature /
- case-based reasoning /
- grade prediction /
- BP neural network fuzzy reasoning /
- optimal setting

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参考文献(26)

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基于泡沫图像特征的浮选槽液位智能优化设定方法

doi: 10.3724/SP.J.1004.2014.01086

作者简介:
赵洪伟中南大学信息科学与工程学院硕士研究生. 主要研究方向为工业过程建模与优化控制研究，智能控制系统.E-mail：zhaohw@csu.edu.cn

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An Intelligent Optimal Setting Approach Based on Froth Features for Level of Flotation Cells

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基于泡沫图像特征的浮选槽液位智能优化设定方法

doi: 10.3724/SP.J.1004.2014.01086

作者简介: 赵洪伟 中南大学信息科学与工程学院硕士研究生. 主要研究方向为工业过程建模与优化控制研究，智能控制系统.E-mail：zhaohw@csu.edu.cn

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出版历程

An Intelligent Optimal Setting Approach Based on Froth Features for Level of Flotation Cells

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出版历程

目录

作者简介:
赵洪伟中南大学信息科学与工程学院硕士研究生. 主要研究方向为工业过程建模与优化控制研究，智能控制系统.E-mail：zhaohw@csu.edu.cn