摘要
针对浮选过程变量滞后、耦合特征及建模样本数量少所导致精矿品位难以准确预测的问题,提出了一种基于改进麻雀搜索算法(Improved Sparrow Search Algorithm,ISSA)优化混核最小二乘支持向量机(Hybrid Kernel Least Squares Support Vector Machine,HKLSSVM)的浮选过程精矿品位预测方法.首先采集浮选现场载流X荧光品位分析仪数据作为建模变量并进行预处理,建立基于最小二乘支持向量机(Least Squares Support Vector Machine,LSSVM)的预测模型,以此构建新型混合核函数,将输入空间映射至高维特征空间,再引入改进麻雀搜索算法对模型参数进行优化,提出基于ISSA-HKLSSVM方法实现精矿品位预测,最后开发基于LabVIEW的浮选精矿品位预测系统对本文提出方法实际验证.实验结果表明,本文提出方法对于浮选过程小样本建模具有良好拟合能力,相比现有方法提高了预测准确率,可实现精矿品位的准确在线预测,为浮选过程的智能调控提供实时可靠的精矿品位反馈信息.
A flotation process concentrate grade prediction method based on the Improved Sparrow Search Algorithm(ISSA)optimized Hybrid Kernel Least Squares Support Vector Machine(HKLSSVM),a flotation process concentrate grade prediction method is proposed to address the issues of delayed variables,coupling characteristics,and limited modeling sample size in the flotation process,which make it difficult to accurately predict the concentrate grade.Firstly,collect data from the flotation site current carrying X-ray fluorescence grade analyzer as modeling variables and preprocess them to establish a prediction model based on the Least Squares Vector Machine.On this basis,a new mixed kernel function is constructed to map the input space to the high-dimensional feature space.Then,an Improved Sparrow Search Algorithm is introduced to optimize the model parameters,and an ISSA HKLSSVM method is proposed to achieve concentrate grade prediction.Finally,a flotation concentrate grade prediction system based on LabVIEW is developed to verify the proposed method in practice.The experimental results show that the proposed method has a better fitting ability for small sample modeling in the flotation process.It can improve prediction accuracy compared to existing methods,and can achieve accurate online prediction of concentrate grade,providing real-time and reliable concentrate grade feedback information for intelligent control of the flotation process.
作者
高云鹏
罗芸
孟茹
张微
赵海利
GAO Yunpeng;LUO Yun;MENG Ru;ZHANG Wei;ZHAO Haili(College of Electrical and Information Engineering,Hunan University,Changsha 410082,China;State Key Laboratory of Process Automation in Mining&Metallurgy,Beijing 100160,China;Beijing Key Laboratory of Process Automation in Mining&Metallurgy,Beijing 100160,China)
出处
《湖南大学学报(自然科学版)》
EI
CAS
CSCD
北大核心
2024年第2期111-120,共10页
Journal of Hunan University:Natural Sciences
基金
国家重点研发计划资助项目(2021YFF0602402)
矿冶过程自动控制技术国家重点实验室开放基金(BGRIMM-KZSKL-2020-09)。
关键词
浮选
精矿品位
最小二乘支持向量机
改进麻雀搜索算法
预测模型
flotation
concentrate grade
least squares support vector machine
improve sparrow search algorithm
prediction model