| 磨矿分级智能控制研究进展 |
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| 引用本文： | 吴沅峻,张覃,黄宋魏,卯松.磨矿分级智能控制研究进展[J].有色金属(选矿部分),2021(1). |
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| 作者姓名： | 吴沅峻 张覃 黄宋魏 卯松 |
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| 作者单位： | 贵州大学矿业学院;喀斯特地区优势矿产资源高效利用国家地方联合工程实验室;贵州省非金属矿产资源综合利用重点实验室;贵州科学院;昆明理工大学国土资源工程学院 |
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| 基金项目： | 黔科合平台人才资助项目([2020]4105)。 |
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| 摘 要： | 磨矿分级受影响因素多、机理复杂,其生产效率和产品质量直接关系到选矿厂的技术经济指标,在选矿工艺过程中起着承上启下的作用。传统控制主要以安全稳定控制为目标,难以实现优化控制。生产实践表明,智能控制可以显著提高磨矿分级的效率和产品质量,起到增产、降耗、提质、增效的作用。模糊控制、专家控制和神经网络控制是磨矿分级最为常用和高效的智能控制技术,得到了大量的应用,取得了显著的经济效益和社会效益。本文主要对磨矿分级智能控制的发展过程、研究现状以及实际应用等方面进行综述。
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| 关 键 词： | 磨矿分级 模糊控制 神经网络控制 专家控制 |
Research Progress on Intelligent Control of Grinding Classification |
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| Authors: | WU Yuanjun ZHANG Qin HUANG Songwei MAO Song |
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| Affiliation: | (School of Mining,Guizhou University,Guiyang 550025,China;National and Local Joint Engineering Laboratory for Efficient Utilization of Superior Mineral Resources in Karst Area,Guiyang 550025,China;Guizhou Key Laboratory for Comprehensive Utilization of Nonmetal Mineral Resources,Guiyang 550025,China;Guizhou Academy of Sciences,Guiyang 550001,China;College of Land Resources Engineering,Kunming University of Science and Technology,Kunming 650093,China) |
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| Abstract: | Grinding classification is affected by many factors and its mechanism is complex.Its production efficiency and product quality are directly related to the technical and economic indicators of the concentrator and play a connecting role in the beneficiation process.The traditional control mainly aims at safety and stability control,which is difficult to achieve optimal control.The production practice shows that,the intelligent control can significantly improve the efficiency of grinding-classification and product quality which plays a role in increasing production,reducing consumption,improving quality and increasing efficiency.Fuzzy control,expert control and neural network control are the most common and efficient intelligent control technology of grinding-classification,which have been widely used and achieved significant economic and social benefits.In this paper,the development process,research status and practical application of intelligent control of grinding-classification are reviewed. |
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| Keywords: | grinding classification fuzzy control neural network control expert control |
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