Research on Wear Risk Prediction of Filling Pipeline Based on KPCA-IPSO-LSSVM
Received date: 2020-04-09
Revised date: 2021-01-30
Online published: 2021-05-28
Filling pipeline wear system is a typical high-dimensional,nonlinear,strong coupling and multi-time-varying complex system.It is difficult to accurately predict the wear situation using traditional prediction methods.In order to overcome the poor applicability and insufficient prediction accuracy of traditional prediction models,and the defect of strong randomness in parameter selection and so on,this paper proposed a new method for predicting the wear risk of filling pipeline by combining the kernel principal component analysis (KPCA),improved particle swarm optimization (IPSO) and least squares support vector machine (LSSVM).A comprehensive selection of 12 main influencing factors was used to establish the prediction index of wear risk of the filling pipeline.The KPCA method was used for feature extraction and dimensionality reduction of the influencing factors of the filling pipeline to eliminate redundant information between the data,so as to reduce the correlation between sample data and modeling accuracy impact.Established the corresponding LSSVM prediction model based on the dimensionality-reduced data,and used the IPSO algorithm with strong global search capability to optimize the model parameters to avoid the blindness of artificial parameter selection,thereby improving the model prediction accuracy and establishing KPCA-IPSO-LSSVM combined prediction model.Taking the filling system of Huangling County mining area as an example,combining the 80 sets of sample data measured in the field,MATLAB was used to train and predict the built model,and the prediction results were compared with the prediction results of the IPSO-LSSVM model,LSSVM model,and SVM model.For comparison,multi-error indicators were used to comprehensively analyze the prediction results of the four models.The research results show that the predicted value of the constructed model is basically consistent with the actual value curve.The KPCA method can effectively reduce the redundant information between the data.On the basis of retaining the original sample information to the maximum,five principal components containing 86.97% of the original information are extracted,it simplifies the calculation structure of the model.The prediction accuracy of the adopted IPSO-LSSVM model is 6.79%,the average relative error is 1.95%,and the judgment coefficient is 99.55%.Compared with other prediction models,the prediction model based on KPCA-IPSO-LSSVM has higher prediction accuracy and stronger generalization ability,which provides a more effective prediction method for the prediction of the wear risk of the filling pipeline,and provides a guiding basis for ensuring the smooth progress of the filling operation and the safe production of the mine.
Zhengshan LUO , Renhui HUANG , Guochen SHEN . Research on Wear Risk Prediction of Filling Pipeline Based on KPCA-IPSO-LSSVM[J]. Gold Science and Technology, 2021 , 29(2) : 245 -255 . DOI: 10.11872/j.issn.1005-2518.2021.02.072
新型人工智能矿物填图技术将助力提高矿山运营效率和保障人员安全
澳大利亚昆士兰大学及其研究合作伙伴Plotlogic公司开发了一种新型自动化采矿技术,该技术利用人工智能扫描采矿作业面,以对矿物进行识别和分类,该技术具有提高运营效率并保障作业人员安全的潜力。
昆士兰大学表示,这项新技术的核心是可见光和红外光扫描部件,可对采矿作业面进行实时矿物填图,有助于在采矿作业开始之前确定开采计划。
昆士兰大学机械与采矿工程学院负责人Ross McAree表示,可在采矿过程的各个阶段进行扫描,借助高光谱成像功能,其识别矿石品位的能力也可以帮助其他未来的自主采矿系统。每种矿物对不同波长的光都有自己的特征响应,因此通过该系统扫描采矿作业面几乎可以瞬时填绘岩石中存在的矿物及其含量(矿石品位)。配备这种成像系统的机械设备将能够在开采矿石的同时识别矿石品位,结合人工智能,可以让机械设备在矿山环境中自主运行,将作业人员从采矿过程的危险环节中解脱出来。
昆士兰大学的工作得到了西澳大利亚州矿物研究所(MRIWA)的支持,该研究所表示,这项技术和研究投资将使该国的矿业行业在技术发展上处于领先地位。MRIWA的首席执行官Nicole Roocke表示,“这种扫描成像方法很有前景,采用该方法的矿业企业将快速获取矿石品位,从而在竞争中处于领先地位。”
MRIWA表示,采矿作业面的实时矿石品位分级将大幅提高采矿作业效率,如改善矿山调度、提高资源回收率、最大限度地减少矿物加工中的浪费以及支持自主采矿系统和机械设备。MRIWA为这项研究工作提供了25万澳元的资助,总资助额度超过485万澳元。
http://www.goldsci.ac.cn/article/2021/1005-2518/1005-2518-2021-29-2-245.shtml
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