利用近红外光谱和机器学习模型对沉积岩中的铜矿进行预浓缩

IF 1.5 4区 工程技术 Q3 METALLURGY & METALLURGICAL ENGINEERING Mining, Metallurgy & Exploration Pub Date : 2024-07-12 DOI:10.1007/s42461-024-01013-2
Samira Es-sahly, Abdelaziz Elbasbas, Khalid Naji, Brahim Lakssir, Hakim Faqir, Slimane Dadi, Reda Rabie
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引用次数: 0

摘要

摩洛哥反阿特拉斯地区西部有许多铜矿点,这些铜矿点位于各种沉积岩中,均含有低品位铜矿。本研究旨在评估使用近红外(NIR)分选系统有效处理这些低品位资源的可行性。从本质上讲,这项研究涉及评估短波红外(SWIR)光谱学和机器学习模型的潜力,以便根据 SWIR 光谱特征将矿石碎片分为废料和精矿。为了开展这项研究,我们测量了蒂泽特矿床 475 块岩石样本的 SWIR 反射率。利用 X 射线衍射和扫描电子显微镜进行了矿物学分析,以了解样本的矿物学及其与 SWIR 光谱的关系。还进行了化学分析,根据铜含量对样品进行分类。根据岩性和铜含量特征评估了几种机器学习模型,包括偏最小二乘判别分析(PLS-DA)、随机森林(RF)和支持向量机(SVM)。其中,偏最小二乘法判别分析(PLS-DA)取得了最理想的结果,在岩性分类方面达到了 84% 的准确率,在基于铜含量对样品进行分类方面达到了 90% 的准确率(采用 0.2% 的临界品位)。这项实验室规模的研究验证了 SWIR 光谱法作为沉积铜矿床预富集工具的有效性。它可以生产出铜含量为 1.49% 的精矿和铜含量为 0.12% 的废料,从而使原本铜品位为 1.04% 的进料提升了 43%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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NIR-Spectroscopy and Machine Learning Models to Pre-concentrate Copper Hosted Within Sedimentary Rocks

The western part of the Moroccan Anti-Atlas comprises numerous copper occurrences hosted within various sedimentary rocks, all containing low-grade copper concentrations. This study aims to assess the feasibility of using a near-infrared (NIR) sorting system to efficiently process these low-grade resources. In essence, it involves evaluating the potential of short-wave infrared (SWIR) spectroscopy and machine learning models to classify ore fragments into waste or concentrate based on their SWIR spectral characteristics. In order to conduct this study, the SWIR reflectance of 475 rock samples from the Tizert deposit was measured. Mineralogical analysis was performed, using X-ray diffraction and scanning electron microscopy, to understand the mineralogy of the samples and its relationship to SWIR spectra. Chemical analysis was also performed to categorize samples based on their copper content. Several machine learning models, including partial least squares discriminant analysis (PLS-DA), random forest (RF), and support vector machine (SVM) were evaluated based on both lithology and copper content characteristics. Among these, PLS-DA yielded the most favorable results, achieving an 84% accuracy in lithologies classification and 90% accuracy in classifying samples based on their copper content, utilizing a 0.2% cutoff grade. This laboratory-scale study validates the effectiveness of SWIR spectroscopy as a prominent tool for pre-concentrating sedimentary copper deposits. It enables the production of a concentrate with a copper content of 1.49% and waste with 0.12%, resulting in an upgrading rate of 43% from the feed, which originally has a copper grade of 1.04%.

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来源期刊
Mining, Metallurgy & Exploration
Mining, Metallurgy & Exploration Materials Science-Materials Chemistry
CiteScore
3.50
自引率
10.50%
发文量
177
期刊介绍: The aim of this international peer-reviewed journal of the Society for Mining, Metallurgy & Exploration (SME) is to provide a broad-based forum for the exchange of real-world and theoretical knowledge from academia, government and industry that is pertinent to mining, mineral/metallurgical processing, exploration and other fields served by the Society. The journal publishes high-quality original research publications, in-depth special review articles, reviews of state-of-the-art and innovative technologies and industry methodologies, communications of work of topical and emerging interest, and other works that enhance understanding on both the fundamental and practical levels.
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