Mining

AMD occurs when sulfide minerals in mine waste react with air and water to produce sulfuric acid, which dissolves heavy metals. Prevention strategies include covering waste with water or soil, adding lime to neutralize acid, and treating contaminated water. We assess AMD risk and design prevention and treatment systems.

We install piezometers and water level sensors around tailings dams to continuously monitor freeboard, saturation levels, and seepage. Real-time data feeds into dam safety systems with automated alerts. This monitoring is critical for dam stability and environmental protection.

A mine water balance tracks all water inputs (rainfall, groundwater inflow, process water) and outputs (evaporation, seepage, discharge, product moisture). We develop water balance models to predict surplus or deficit conditions, plan storage requirements, and optimize water recycling to reduce freshwater intake.

Groundwater models predict dewatering requirements for different pit designs and mining schedules. They identify when and where dewatering wells are needed, estimate pumping costs, and assess impacts on regional water resources. Models are updated as mining progresses and new data becomes available.

Mines must treat discharged water to meet permit limits for pH, suspended solids, metals, and other parameters. Requirements vary by jurisdiction and receiving water sensitivity. We help mines design treatment systems and monitoring programs that ensure consistent compliance.

Pit lakes form when dewatering stops and groundwater refills the pit. Water quality depends on wall rock geochemistry, groundwater inflow quality, and climate. We model pit lake evolution and design closure strategies including covers, treatment wetlands, or active treatment to achieve safe long-term water quality.

Dewatering can lower water levels in nearby wells and reduce streamflow. We assess these impacts through groundwater modeling and monitoring, work with regulators and communities to establish acceptable limits, and design mitigation measures like replacement water supplies when needed.

Mines reduce freshwater use by maximizing recycling of process water, using lower-quality water sources where possible, dry processing techniques, and dust suppression optimization. We conduct water audits to identify reduction opportunities and implement tracking systems to monitor progress.

Comprehensive monitoring includes groundwater levels, surface water flows and quality, pit inflows, discharge volumes and quality, and tailings facility conditions. Monitoring continues for years after closure until conditions stabilize. We design monitoring networks and data management systems for regulatory reporting.

The two answer different questions. MODFLOW predicts inflow to a pit geometry and a dewatering schedule that do not exist yet, which is exactly what a mine plan asks and exactly what a model trained on historical data cannot supply. Machine learning works on the measurement side. Piezometer and flow meter records on an operating site are full of holes left by power failures, blasting and instrument damage, and gap-filling models reconstruct those series so the water balance and the model calibration are not quietly built on straight-line interpolation. Anomaly detection on tailings facility pore pressure and level readings flags an instrument whose behaviour has separated from the rest of its array, which is either a sensor fault or something a geotechnical engineer needs to see that day. Seasonal inflow forecasting from rainfall and pumping history helps size pumping and storage before the wet season rather than during it.