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有色金属(选矿部分):2021,(3):88-92
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基于NNG-LSSVM的铜精矿品位软测量方法研究
吴浩, 潘冰清, 杨会琳, 孙凯
(齐鲁工业大学)
Research on soft sensor method of copper concentrate grade based on NNG-LSSVM
wuhao, panbingqing, yanghuilin, sunkai
(Qilu University of Technology)
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本文已被:浏览 24次   下载 20
投稿时间:2020-05-22    修订日期:2020-05-27
中文摘要: 摘 要:浮选技术是当今铜矿选矿最主要的方法并得到了广泛地应用。浮选流程中,铜精矿品位决定了最终产品的质量,因此是整个过程的关键变量。然而在实际生产中,该参数的测量耗时较长,难以实时在线测量。论文提出了一种基于非负绞杀(Nonnegative garrote)与最小二乘支持向量机(Least Squares Support Vector Machine)的软测量方法,并利用DCS系统提供的实际生产数据对该变量进行预测建模。仿真结果表明,所研究的软测量方法能够准确预测铜精矿品位的变化,能很好地实现精矿品位的实时预测及估计,并且在模型精度上 明显优于其他软测量方法。
中文关键词: LSSVM  NNG  铜矿浮选  软测量  铜精矿品位
Abstract:Abstract: Flotation technology is the most primary method of beneficiation of copper mines and has been widely used. In the flotation process, the grade of copper concentrate determines the quality of the final product, therefore it’s a key variable in the entire process. Nevertheless, in the actual production, the measurement of this parameter takes a long time, making it difficult to measure in real time online. The paper proposes a soft-sensing method based on non-negative garrote and least squares support vector machine, and uses the actual production data provided by the DCS system to predict and model this variable. The simulation results show that the researched soft-sensing method can accurately predict the change of copper concentrate grade, and can well realize the real-time prediction and estimation of concentrate grade, and the accuracy of model obviously superiors to other soft-sensing methods.
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基金项目:矿冶过程自动控制技术国家(北京市)重点实验室开放研究基金(BGRIMM-KZSKL-2018-01);国家自然科学基金项目(面上项目,重点项目,重大项目)
引用文本:
吴浩,潘冰清,杨会琳,孙凯.基于NNG-LSSVM的铜精矿品位软测量方法研究[J].有色金属(选矿部分),2021(3):88-92.
wuhao,panbingqing,yanghuilin,sunkai.Research on soft sensor method of copper concentrate grade based on NNG-LSSVM[J].Nonferrous Metals(Mineral Processing Section),2021(3):88-92.

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