Geometallurgy Team - Complex copper uranium deposit
WGA increased the productivity of the Geometallurgy team at a complex copper uranium deposit by automation of powerful visualisations for ore characterisation data. Working alongside the Owner’s team, WGA’s data analysts and metallurgists developed the team’s proficiency in data consolidation, visualisation and model generation using advanced tools.
WGA delivered a three-part workshop in automation of data consolidation, reporting, and powerful visualisations to increase productivity of the Geometallurgy team at a complex copper uranium deposit. The material was tailored toward current geometallurgy projects, and focused on comparison of operational data, drill hole data and metallurgical response tests. WGA provided ongoing support to the team over a six-month period to progress project deliverables and further develop capabilities in data visualisation and modelling for metallurgical data sets. WGA delivered automation of:
- Statistical analysis and supporting visualisations of mineral and elemental content, flotation, reagent consumption, grade, recovery, and other fundamental ore characteristics
- Multivariable metallurgical models and supporting dashboard for rapid on-site visualisation of furnace fluxing targets
- Metal recovery model visualisations
- Stope coverage visualisations
The three workshops were delivered over an eight-week period, by WGA’s Lead Process Engineer and Manager of Data Analytics, Jess Page, and Data Analyst and Mathematician, Samuel Mercer. The key components of the workshops were:
- Data Import and Manipulation: An introduction to programming language and proficiency in data consolidation, manipulation, sanitisation, and advanced skills in statistical analysis using python.
- Data Visualisation: Generation of powerful visualisations for effective communication, on the go statistical and quality check plots, and quantitative comparison plots. Advanced concepts in visualisation, including loops for rapid plot generation, interpolation for heatmaps, and numerical analysis were covered in course.
- Data Modelling: Introduction to machine learning through the generation of a predictive model.
“It was great having it tailored to us, and having Jess and Sam so familiar with both (the Client’s) IT and some of the types of data we have”