Summary
This case study highlights the monitoring of a tailings storage facility at a copper mine facing risks due to high rainfall and geological activity. Traditional inspection methods failed to detect critical warning signs like rising pore pressure and seepage trends. OctaSense implemented an AI-driven monitoring system with real-time sensors and predictive models, enabling early detection of internal erosion and potential failure risks, allowing timely intervention and ensuring safety and regulatory compliance.
Background & Context
This case study focuses on a tailings storage facility (TSF) at a copper mine located in a high rainfall and tectonically active region. The dam stored approximately 180 million tonnes of tailings and had shown signs of elevated pore pressure in certain sections. Traditional monitoring methods, including periodic inspections and manual readings, failed to identify risk trends, increasing the potential for catastrophic failure. The deployment of continuous AI-powered monitoring enabled early detection of internal risks such as piping and seepage anomalies, significantly improving safety and decision-making.
Sensor Deployment
Key Outcomes & Results
Early detection enabled controlled drawdown of the TSF pond — averting potential failure
11 weeks earlier
Seepage Anomaly Detection
Deployment Snapshot
Location
Latin America, Chile