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An Efficient Sample Selection Methodology for a Geometallurgy Study Utilizing Statistical Analysis Techniques

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Abstract

A geometallurgy study aims to link metallurgy and geology to reduce technical risk and enhance the economic performance of a mineral-processing plant. It does so by accounting for variability in a deposit to develop cash flow models with variable throughput rates. High-quality sample selection for metallurgical test work that are representative of the deposit is an essential component of a geometallurgy study, but the large multi-dimensional dataset makes sample selection a daunting task, as classifying the dataset while respecting its heterogeneity is difficult. This paper presents a streamlined approach for sample selection, utilizing statistical analysis techniques in Python. It cuts down time to select samples from around 1200 s per drillhole to about 60 s per drillhole for data classification and from 12 h to 8 h for handpicking samples from the classified dataset, translating to cost savings. The cumulative sum method and k-means clustering method are used in the methodology to elegantly classify the data and select representative samples. The effectiveness of the methodology is demonstrated by presenting data from a pre-feasibility study of a copper-iron mine in which 40 samples were selected for flotation test work.

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References

  1. Lishchuk V, Koch PH, Ghorbani Y, Butcher AR (2020) Towards integrated geometallurgical approach: critical review of current practices and future trends. Miner Eng 145:106072. https://doi.org/10.1016/j.mineng.2019.106072

    Article  Google Scholar 

  2. Dominy SC, O’Connor L, Glass HJ, Purevgerel S, **e Y (2018) Towards representative metallurgical sampling and gold recovery testwork programmes. Minerals 8(5):193. https://doi.org/10.3390/min8050193

    Article  Google Scholar 

  3. Michaux S, O’Connor L (2020) How to set up and develop a geometallurgical program. Geol Surv Finland. https://doi.org/10.13140/RG.2.2.16170.24005

    Article  Google Scholar 

  4. CUSUM Control Charts, NIST/SEMATECH e-Handbook of statistical methods. https://www.itl.nist.gov/div898/handbook/pmc/section3/pmc323.htm

  5. Rajabinasab B, Asghari O (2018) Geometallurgical domaining by cluster analysis: iron ore deposit case study. Nat Resour Res 28(3):665–684. https://doi.org/10.1007/s11053-018-9411-6

  6. Jansson NF, Allen RL, Skogsmo G, Tavakoli S (2022) Principal component analysis and K-means clustering as tools during exploration for Zn Skarn deposits and industrial carbonates, Sala area, Sweden. J Geochem Explor 233:106909. https://doi.org/10.1016/j.gexplo.2021.106909

  7. Asuero AG, Sayago A, González AG (2006) The correlation coefficient: an overview. Crit Rev Anal Chem 36(1):41–59. https://doi.org/10.1080/10408340500526766

    Article  Google Scholar 

  8. Cui M (2020) Introduction to the k-means clustering algorithm based on the elbow method. Geoscience Remote Sens 3(1):9–16. https://doi.org/10.23977/geors.2020.030102

    Article  Google Scholar 

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Authors and Affiliations

  1. Ausenco, Vancouver, V6E 3S7, BC, Canada

    Muhammad Usman Siddiqui, Kevin Erwin & Rajiv Chandramohan

  2. Ausenco, Toronto, M5H 4C7, ON, Canada

    Shaihroz Khan

  3. Ausenco, Calgary, T2P 3C5, AB, Canada

    Connor Meinke

Authors
  1. Muhammad Usman Siddiqui
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  2. Kevin Erwin
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  3. Shaihroz Khan
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  4. Rajiv Chandramohan
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  5. Connor Meinke
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Correspondence to Muhammad Usman Siddiqui.

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Competing Interests

The authors of this article are employees of Ausenco, an engineering consultancy that works with the world’s leading mining and mineral processing companies. Ausenco is providing consulting services for a copper mine’s pre-feasibility study. The analysis in this study was done after completing client-funded work, and the consultants got paid by Ausenco’s optimization and debottlenecking practice.

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Siddiqui, M.U., Erwin, K., Khan, S. et al. An Efficient Sample Selection Methodology for a Geometallurgy Study Utilizing Statistical Analysis Techniques. Mining, Metallurgy & Exploration (2024). https://doi.org/10.1007/s42461-024-01011-4

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  • DOI: https://doi.org/10.1007/s42461-024-01011-4

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