Utilizing Big Data Statistical Techniques in Python to Optimize Geometallurgy Workflow for Metallurgical Test Work Sample Selection - SME Annual Meeting 2024
- Organization:
- Society for Mining, Metallurgy & Exploration
- Pages:
- 6
- File Size:
- 322 KB
- Publication Date:
- Feb 1, 2024
Abstract
High-quality sample selection for metallurgical test work
is essential to a geometallurgy study, but the large multidimensional
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 custom-built tools in Python
to standardize the methodology, saving time and costs.
This approach uses the cumulative sum method, principal
component analysis, and k-means clustering method
to elegantly cluster the data and select representative samples.
A case study is used to demonstrate the effectiveness
of the methodology by selecting 40 samples for flotation
test work.
Citation
APA: (2024) Utilizing Big Data Statistical Techniques in Python to Optimize Geometallurgy Workflow for Metallurgical Test Work Sample Selection - SME Annual Meeting 2024
MLA: Utilizing Big Data Statistical Techniques in Python to Optimize Geometallurgy Workflow for Metallurgical Test Work Sample Selection - SME Annual Meeting 2024. Society for Mining, Metallurgy & Exploration, 2024.