Mining Process Optimization Platform

An end-to-end machine learning platform for optimizing mineral processing operations — SAG milling, flotation, and thickening — deployed across multiple mining divisions and processing plants.

The challenge

Large-scale mining operations involve complex, interconnected processes where small improvements in throughput or recovery translate into significant economic impact. SAG mills, flotation banks, and thickeners each have dozens of controllable variables and hundreds of sensor readings, creating a high-dimensional optimization problem that evolves continuously with ore characteristics.

System architecture

The platform follows a modular pipeline architecture built on Kedro for reproducibility and MLOps best practices:

1. Data ingestion: Streaming and batch pipelines pulling from SCADA systems, lab analyses, and operational databases via Azure Data Factory
2. Feature engineering: Domain-informed features including rolling statistics, lag variables, ore property indicators, and operational regime detection
3. Model training: Ensemble of XGBoost, gradient-boosted trees, and neural networks trained on historical operational windows
4. Recommendation engine: Scenario simulation generating actionable setpoint recommendations with confidence intervals
5. Operational dashboard: Real-time monitoring of adherence to recommendations, KPI tracking, and expert feedback loops

Results

• Throughput uplift scenarios exceeding +100 TPH in SAG milling operations
• Measurable improvements in copper recovery across flotation circuits
• Hourly tracking cycles with 4-hourly optimization recommendations in production
• Multi-division deployment with division-specific parameter tuning

Technology stack

• Orchestration: Kedro pipelines with Azure Databricks execution
• Compute: PySpark for distributed processing, Delta Lake for data storage
• ML: XGBoost, scikit-learn, with MLflow experiment tracking
• Infrastructure: Docker containers on Azure Container Registry, CI/CD via Azure Pipelines
• Visualization: Power BI dashboards with Streamlit prototypes for model exploration

Note: This description reflects the general type and architecture of systems I have built. Specific client details, operational data, and proprietary methodologies are omitted.