矿业科学技术学报(英文版)2024，Vol.34Issue(4) ：545-556.DOI:10.1016/j.ijmst.2024.04.002

Enhancing XRF sensor-based sorting of porphyritic copper ore using particle swarm optimization-support vector machine(PSO-SVM)algorithm

Zhengyu Liu Jue Kou Zengxin Yan Peilong Wang Chang Liu Chunbao Sun Anlin Shao Bern Klein
矿业科学技术学报(英文版)2024，Vol.34Issue(4) ：545-556.DOI:10.1016/j.ijmst.2024.04.002
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  • 维普
  • 万方数据

Enhancing XRF sensor-based sorting of porphyritic copper ore using particle swarm optimization-support vector machine(PSO-SVM)algorithm

Zhengyu Liu 1Jue Kou 2Zengxin Yan 2Peilong Wang 3Chang Liu 4Chunbao Sun 2Anlin Shao 5Bern Klein6
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作者信息

  • 1. School of Civil and Resources Engineering,University of Science and Technology,Beijing 100083,China;Institute of Minerals Research,University of Science and Technology,Beijing 100083,China
  • 2. School of Civil and Resources Engineering,University of Science and Technology,Beijing 100083,China
  • 3. School of Civil and Resources Engineering,University of Science and Technology,Beijing 100083,China;State Key Laboratory of Mineral Processing,Beijing 102628,China
  • 4. School of Civil and Resources Engineering,University of Science and Technology,Beijing 100083,China;Ansteel Group Mining Co.Ltd.,Anshan 114000,China
  • 5. Institute of Minerals Research,University of Science and Technology,Beijing 100083,China
  • 6. Norman B.Keevil Institute of Mining Engineering,University of British Columbia,Vancouver,BC V6T 1Z4,Canada
  • 折叠

Abstract

X-ray fluorescence(XRF)sensor-based ore sorting enables efficient beneficiation of heterogeneous ores,while intraparticle heterogeneity can cause significant grade detection errors,leading to misclassifica-tions and hindering widespread technology adoption.Accurate classification models are crucial to deter-mine if actual grade exceeds the sorting threshold using localized XRF signals.Previous studies mainly used linear regression(LR)algorithms including simple linear regression(SLR),multivariable linear regression(MLR),and multivariable linear regression with interaction(MLRI)but often fell short attain-ing satisfactory results.This study employed the particle swarm optimization support vector machine(PSO-SVM)algorithm for sorting porphyritic copper ore pebble.Lab-scale results showed PSO-SVM out-performed LR and raw data(RD)models and the significant interaction effects among input features was observed.Despite poor input data quality,PSO-SVM demonstrated exceptional capabilities.Lab-scale sorting achieved 93.0％accuracy,0.24％grade increase,84.94％recovery rate,57.02％discard rate,and a remarkable 39.62 yuan/t net smelter return(NSR)increase compared to no sorting.These improvements were achieved by the PSO-SVM model with optimized input combinations and highest data quality(T=10,T is XRF testing times).The unsuitability of LR methods for XRF sensor-based sorting of investigated sam-ple is illustrated.Input element selection and mineral association analysis elucidate element importance and influence mechanisms.

Key words

XRF sensor-based sorting/PSO-SVM algorithm/Copper ore pebble/Receiver operating curve(ROC)/Net smelter return(NSR)

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基金项目

State Key Laboratory of Mineral Processing(BGRIMM-KJSKL-2022-16)

中国博士后科学基金(2021M700387)

国家自然科学基金(G2021105015L)

科技部项目(2022YFC2904502)

出版年

2024
矿业科学技术学报(英文版)
中国矿业大学

矿业科学技术学报(英文版)

CSTPCDEI
影响因子：1.222
ISSN：2095-2686
参考文献量42
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