基于协同计算的矿浆密度自适应智能检测方法  

Adaptive intelligent detection method of pulp density based on collaborative computing

作　　者：王兰豪 卫涛杰 余刚 代伟 Wang Lanhao;Wei Taojie;Yu Gang;Dai Wei(National Engineering Research Center of Coal Preparation and Purification,China University of Mining and Technology,Xuzhou 221116,China;School of Information and Control Engineering,China University of Mining and Technology,Xuzhou 221116,China;State Key Laboratory of Intelligent Optimized Manufacturing in Mining&Metallurgy Process,Beijing 100089,China;Beijing Key Laboratory of Process Automation in Mining&Metallurgy,Beijing 100089,China)

机构地区：[1]中国矿业大学国家煤加工与洁净化工程技术研究中心,徐州221116 [2]中国矿业大学信息与控制工程学院,徐州221116 [3]矿冶过程智能优化制造全国重点实验室,北京100089 [4]矿冶过程自动控制技术北京市重点实验室,北京100089

出　　处：《仪器仪表学报》2023年第10期237-246,共10页Chinese Journal of Scientific Instrument

基　　金：国家重点研发计划项目(2022YFB3304700);国家自然科学基金(52304309,62373361,52261135540);矿冶过程智能优化制造全国重点实验室;矿冶过程自动控制技术北京市重点实验室开放基金项目(BGRIMM-KZSKL-2022-7)资助。

摘　　要：本文针对磨矿分级中传统密度检测方法精度不高且耗时耗力的问题,提出一种矿浆密度智能检测方法。通过对矿浆流体进行机理分析,得到线性已知项和非线性未知项,结合高斯过程回归与正则化随机配置(RSC)算法对矿浆密度进行整体辨识。此外将机理模型估计的方差作为数据驱动模型的训练目标,提高了模型对数据信息的获取程度。同时采用协同计算的方式将自适应智能检测方法应用到工业中,确保矿浆密度检测的实时性和检测模型自适应性。基于工业数据实验分析,本文方法估计密度的平均绝对误差为7.13、均方根误差为9.31、决定系数为99.51%、检测结果相对误差δ<1.0%的样本数量占比83.58%,均优于其他对比算法,极大提高了矿浆密度检测模型的有效性。This article addresses the issues of low accuracy and time-consuming nature associated with traditional density detection methods in ore grinding classification by proposing an intelligent pulp density detection method.Through mechanistic analysis of the pulp fluid,linear known terms and nonlinear unknown terms are identified.A holistic recognition of pulp density is performed by combining Gaussian process regression with a regularized stochastic configuration algorithm.Additionally,the variance estimated by the mechanistic model is set as the training objective for the data-driven model,enhancing the model's capacity to acquire data information.Meanwhile,a collaborative computing method is employed to apply the adaptive intelligent detection method in the industrial domain,ensuring realtime detection and adaptability of the pulp density detection model.Based on industrial data experimental analysis,the proposed method shows an average absolute error of 7.13,a root mean square error of 9.31,a determination coefficient of 99.51%,and a sample quantity proportion of relative errorδ<1.0%at 83.58%.These results are better than those of other comparative algorithms.The effectiveness of the pulp density detection model is enhanced.

关 键 词：矿浆密度 数据驱动 随机配置网络 正则化 协同计算 

分 类 号：TH7[机械工程—仪器科学与技术]