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research-article

Prediction of X-ray fluorescence copper grade using regularized stochastic configuration networks

Authors:

Dianhui WangAuthors Info & Claims

Volume 659, Issue C

https://doi.org/10.1016/j.ins.2024.120098

Published: 12 April 2024 Publication History

Abstract

In copper ore flotation processes, accurate measurement and estimation of the copper grade of the final products are crucial for field operations and control. Due to the presence of multicollinearity and outliers in the measured data, the existing on-stream X-ray fluorescence grade analyzer is inaccurate. To address these issues, this study proposes a robust data modeling algorithm to improve the performance of copper grade prediction. Specifically, a regularized stochastic configuration network and a generalized M−estimation method are used to overcome the uncertainties in the processing data. The experimental results clearly demonstrate that the proposed model outperforms other modeling algorithms in terms of prediction accuracy and robustness.

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Cited By

Meng LDing JLi XCao GLi YZhang D(2024)Novel shape control system of hot-rolled strip based on machine learning fused mechanism modelExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124789255:PDOnline publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124789

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Published In

cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 659, Issue C

Feb 2024

640 pages

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Elsevier Inc.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 12 April 2024

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Cited By

Meng LDing JLi XCao GLi YZhang D(2024)Novel shape control system of hot-rolled strip based on machine learning fused mechanism modelExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124789255:PDOnline publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124789

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