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Gold Science and Technology >

2024 , Vol. 32 >Issue 4: 620 - 630

DOI: https://doi.org/10.11872/j.issn.1005-2518.2024.04.022

Mining Technology and Mine Management

Research on PPV Prediction Model of Open-pit Mine Based on PSO-XGBoost

  • Zhenyang LI , 1 ,
  • Baogang ZHANG 1 ,
  • Xin XIONG 2 ,
  • Chengye YANG 2 ,
  • Yuqi BAI 1
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  • 1. Beijing Aoxin Chemical Technology Co. , Ltd. , Beijing 100040, China
  • 2. School of Resources and Safety Engineering, Central South University, Changsha 410083, Hunan, China

Received date: 2024-01-11

  Revised date: 2024-06-04

  Online published: 2024-08-27

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Abstract

The peak particle velocity（PPV） resulting from blasting vibration serves as a crucial metric in assessing the efficacy of blasting and mining design parameters.To enhance the accuracy of PPV predictions，a novel parameter self-optimizing PSO-XGBoost prediction model is introduced，leveraging the Particle Swarm Optimization （PSO） algorithm in conjunction with optimized Extreme Gradient Boosting （XGBoost）.The research focuses on the LK open-pit copper-cobalt mine and examines five influencing factors，namely the maximum single explosive charge，total explosive charge，measured distance to blast center，average depth of blastholes，and elevation difference，as study parameters.A total of 187 sets of measured data from on-site blasting operations are gathered for further investigation into predicting PPV using the PSO-XGBoost model.The findings indicate that the PSO-XGBoost prediction model produces superior prediction evaluation metrics （R 2=0.921，RMSE=0.0752，MAE=0.0717，MBE=0.0683）， compared to alternative models，including the traditional XGBoost model，the SSA-XGBoost hybrid optimization model，and the Sariakaliski empirical formula.The sensitivity analysis reveals that the total explosive charge，significantly influences PPV prediction outcomes，underscoring the importance of using an appropriate amount of explosives to avoid energy inefficiency in blasting activities. Furthermore，the research demonstrates that the PSO-XGBoost prediction model，is capable of effectively addressing the nonlinear attributes of various factors，as well as integrating nonlinear and discrete data to develop a dependable，straightforward，and efficient PPV prediction model.This study offers valuable insights for promptly predicting blasting vibration in open-pit mining and assessing the impacts of blasting activities.

Key words： open-pit mine; hybrid optimizing algorithm; machine learning; blasting vibration; regression analysis

Cite this article

Zhenyang LI , Baogang ZHANG , Xin XIONG , Chengye YANG , Yuqi BAI . Research on PPV Prediction Model of Open-pit Mine Based on PSO-XGBoost[J]. Gold Science and Technology, 2024 , 32(4) : 620 -630 . DOI: 10.11872/j.issn.1005-2518.2024.04.022

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紫金矿业新一轮找矿突破战略行动取得重大成果

紫金矿业旗下黑龙江多宝山铜业据黑龙江省自然资源厅出具的“铜山铜矿Ⅲ、Ⅴ号矿体勘探报告”矿产资源储量评审意见书，2个矿体合计新增铜金属资源量365万t。其中，Ⅴ号矿体铜金属资源量达到281万t，系中国东北地区近40年来唯一探明的超大型单体铜矿，标志着紫金矿业全面落实国家新一轮找矿突破战略行动取得重大成果。

铜山深部矿体勘查工作于2018年启动，2020年取得重大找矿突破，累计投入钻探工作量为7.3×104 m，新发现铜山断层以下Ⅴ号矿体、Ⅲ号矿体规模扩大，除新增铜资源量外，另新增伴生资源钼金属量13万t、金金属量55 t、银金属量1 104 t。

铜山深部矿体的发现，丰富和完善了古生代斑岩型铜矿成矿理论，为多宝山铜山铜矿田周边资源拓展提供了重要参考和借鉴，具有重要的经济和科研价值。多宝山铜业将持续创新科研手段，围绕多宝山矿集区开展成矿规律研究与深边部找矿预测工作，深耕本区域关键矿带和靶区，持续开展风险地质勘查工作，力争实现新突破。

多宝山铜（钼）矿和铜山矿为超大型低品位斑岩型铜矿。本次新增后，矿山铜金属资源量将超过560万t。多宝山铜业已成为我国重要的铜矿生产基地之一，2023年生产铜11万t、金2.6 t。

中关村绿色矿山产业联盟）

http://www.goldsci.ac.cn/article/2024/1005-2518/1005-2518-2024-32-4-620.shtml

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