为了稳定铜粗选选矿指标,提高矿产资源的利用水平,根据铜粗选过程中泡沫尺寸分布随药剂量改变而动态变化的特点,提出一种基于泡沫尺寸随机分布的铜粗选过程药剂量控制方法.首先,针对泡沫尺寸分布具有非高斯统计特性,基于方差和均值的统...为了稳定铜粗选选矿指标,提高矿产资源的利用水平,根据铜粗选过程中泡沫尺寸分布随药剂量改变而动态变化的特点,提出一种基于泡沫尺寸随机分布的铜粗选过程药剂量控制方法.首先,针对泡沫尺寸分布具有非高斯统计特性,基于方差和均值的统计参量难以表征该分布形态变化的问题,提出了B样条估计方法以描述泡沫尺寸的概率密度函数(Probability density function,PDF);然后,针对B样条权值相互关联的特点,建立多输出最小二乘支持向量机模型(Multi-output least square support vector machine,MLS-SVM)以表征权值和药剂量的动态关系;最后,为减少系统的随机性,采用基于熵的优化算法以确定药剂量,实现对给定泡沫尺寸分布的跟踪控制.工业数据仿真验证了所提方法的有效性,能有效稳定铜粗浮选的生产指标.展开更多
Conventional feature description methods have large errors in froth features due to the fact that the image during the zinc flotation process of froth flotation is dynamic,and the existing image features rarely have t...Conventional feature description methods have large errors in froth features due to the fact that the image during the zinc flotation process of froth flotation is dynamic,and the existing image features rarely have time series information.Based on the conventional froth size distribution characteristics,this paper proposes a size trend core feature(STCF)considering the froth size distribution,i.e.,a feature centered on the time series of the froth size distribution.The core features of the trend are extracted,the inter-frame change factor and the inter-frame stability factor are given and two calculation methods of the feature factors are proposed.Meanwhile,the STCF feature algorithm was established based on the core features by adding the inter-frame change factor and the inter-frame stability factor.Finally,a flotation condition recognition model based on BP neural network was established.The experiments show that the recognition model has achieved excellent results,proving that the method proposed effectively overcomes the limitation of the lack of dynamic information in the existing traditional size distribution features and the introduction of the two factors can improve the classification accuracy to varying degrees.展开更多
文摘为了稳定铜粗选选矿指标,提高矿产资源的利用水平,根据铜粗选过程中泡沫尺寸分布随药剂量改变而动态变化的特点,提出一种基于泡沫尺寸随机分布的铜粗选过程药剂量控制方法.首先,针对泡沫尺寸分布具有非高斯统计特性,基于方差和均值的统计参量难以表征该分布形态变化的问题,提出了B样条估计方法以描述泡沫尺寸的概率密度函数(Probability density function,PDF);然后,针对B样条权值相互关联的特点,建立多输出最小二乘支持向量机模型(Multi-output least square support vector machine,MLS-SVM)以表征权值和药剂量的动态关系;最后,为减少系统的随机性,采用基于熵的优化算法以确定药剂量,实现对给定泡沫尺寸分布的跟踪控制.工业数据仿真验证了所提方法的有效性,能有效稳定铜粗浮选的生产指标.
基金Project(U1701261)supported by the National Science Foundation of China,Guangdong Joint Fund of Key ProjectsProject(61771492)supported by the National Natural Science Foundation of ChinaProject(2018GK4016)supported by Hunan Province Strategic Emerging Industry Science and Technology Research and Major Science and Technology Achievement Transformation Project,China。
文摘Conventional feature description methods have large errors in froth features due to the fact that the image during the zinc flotation process of froth flotation is dynamic,and the existing image features rarely have time series information.Based on the conventional froth size distribution characteristics,this paper proposes a size trend core feature(STCF)considering the froth size distribution,i.e.,a feature centered on the time series of the froth size distribution.The core features of the trend are extracted,the inter-frame change factor and the inter-frame stability factor are given and two calculation methods of the feature factors are proposed.Meanwhile,the STCF feature algorithm was established based on the core features by adding the inter-frame change factor and the inter-frame stability factor.Finally,a flotation condition recognition model based on BP neural network was established.The experiments show that the recognition model has achieved excellent results,proving that the method proposed effectively overcomes the limitation of the lack of dynamic information in the existing traditional size distribution features and the introduction of the two factors can improve the classification accuracy to varying degrees.
