A Data-Driven Basis for Sustainable Investment Decisions
Reprocessing or Rehabilitation?A Data-Driven Basis for Sustainable Investment Decisions
A 1.7 million tonne tailings storage facility (TSF) from a Scandinavian gold operation in the 1980s posed a critical question:
invest in costly rehabilitation – or unlock previously untapped economic potential?
We were commissioned to comprehensively assess the TSF and provide a reliable basis for decision-making. To achieve this, we designed and implemented a statistically robust drilling and sampling program across an area of approximately 80,000 m², capturing both lateral and vertical variability.
By integrating geochemical analyses, automated mineralogy, and hyperspectral imaging—in close collaboration with our partners—we developed a high-resolution, fully integrated model of the material composition.
Key Findings:
- Clearly defined mineralogical zoning within the TSF
- Significant gold grades and elevated tellurium concentrations
- Identified resource potential of approximately 40,000–50,000 ounces of gold
Client Benefit:
- Reduced investment risk through statistically validated data
- Reliable resource estimation across the entire facility
- Transparent decision-making basis for comparing rehabilitation and reprocessing scenarios
Data-Driven Process Control at the Kansanshi Mine
From Reactive Troubleshooting to Predictive Flotation PerformanceData-Driven Process Control at the Kansanshi Mine
Achieving predictive control of flotation circuits is particularly challenging in complex ore bodies. At the Kansanshi mine, copper exhibits highly variable behavior across sulfides, oxides, and silicates—making conventional chemical assays insufficient for reliably forecasting plant performance.
To overcome these limitations, we evaluated the implementation of a particle-based separation model (PSM). By analyzing recovery probabilities at the level of individual particles—taking into account particle size, degree of liberation, and mineralogy—we moved beyond bulk chemistry and directly quantified how feed variability impacts metallurgical performance.
The result: a significantly deeper understanding of the process and a foundation for predictive, data-driven plant control.
Key Findings:
- Identification of the primary drivers of copper losses
- Quantification of the impact of unliberated chalcopyrite and gangue minerals
- Quantification of the impact of unliberated chalcopyrite and gangue minerals Development of a robust predictive model capable of forecasting recovery for varying ore types
Client Benefit:
- Predictive performance forecasting instead of reactive troubleshooting
- Improved and more stable plant performance through proactive control
- Reduced metallurgical uncertainty in complex ore blends
- Faster operational decision-making using daily mineralogical data
- Increased copper recovery and enhanced revenue potential