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2025 05 v.15 667-675
基于X射线荧光光谱(XRF)-Pb同位素与深度学习的铜精矿产地溯源
基金项目(Foundation): 海关总署科研项目(2020HK25); 南京海关科技项目(2022KJ08)
邮箱(Email):
DOI: 10.20236/j.CJIAC.2025.05.007
中文作者单位:

南京海关工业产品检测中心;南京大学;

摘要(Abstract):

铜精矿产地溯源研究在保障资源供应链安全、优化冶炼工艺控制、履行ESG监管责任、打击贸易欺诈行为、推动矿业数字化转型等方面具有重要意义。选取了3个铜精矿的典型产地,每个产地在一定的时间跨度内随机选取了8个样品。首先,通过15种元素的X射线荧光光谱(XRF)分析和3种铅稳定同位素分析手段获取关键指标信息。再次,采用Python Scikit-learn对数据集进行分析,建立分类预测模型并评价预测准确率。结果表明,3个产地的铜精矿检测结果存在一定的差异,对原始数据和标准化处理后的数据分别建模评估,数据标准化处理后的模型预测准确率有着显著提高,使用同位素和微量元素标准化处理后的数据集建立的KNN或SVM模型中,测试集样品能够实现100%正确分类。利用分析化学与机器学习的技术交叉优势,其标准化处理-特征选择-模型验证的技术闭环,为矿产资源数字护照体系建设提供了可复用的技术框架。为铜精矿供应链贸易合规监管提供支撑,为冶炼工艺参数优化提供数据支持,为类似矿物分析提供方法参考以及推动传统矿业向数据驱动模式转型。

关键词(KeyWords): 铜精矿;产地溯源;深度学习;X射线荧光光谱;铅稳定同位素
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基本信息:

DOI:10.20236/j.CJIAC.2025.05.007

中图分类号:TD926.3;O657.34

引用信息:

[1]赵伟,杨涛,唐晨等.基于X射线荧光光谱(XRF)-Pb同位素与深度学习的铜精矿产地溯源[J].中国无机分析化学,2025,15(05):667-675.DOI:10.20236/j.CJIAC.2025.05.007.

基金信息:

海关总署科研项目(2020HK25); 南京海关科技项目(2022KJ08)

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