SmartRView forecasts empower mine planning

Forecast operating costs

SmartRView captures data to characterize and evaluate mining activity. Each observation can be used to build and refine predictive linear models. Once the models have been established, mine planners can run the models by setting variables related to haul distance, vertical travel and tonnage as prescribed by the planned mine evolution.

Plan to efficiency

SmartRView can be used to determine the optimal ramp angle based on targeted payload and equipment in use. Optimize the plan by striking the appropriate balance between productivity and efficiency.

Optimize haul route maintenance

SmartRView controllers are equipped with vibration sensors and GPS. Understanding the relationship between road surface conditions and efficiency allows for for targeted mine road maintenance.


Challenge: Predict operating costs and productivity associated with mine evolution


SmartRView captures data from mining trucks which is analyzed to produce haul cycle aligned observations. Each cycle is characterized in-terms of fuel use, payload, (un)loaded distance over ground, (un)loaded vertical travel, cycle time, average ramp grade and more. These characterisations are used to train linear regression models which predict cycle properties as a function of others.

Mine planners then run the models based on the planned evolution of the mine. The most common examples include

predicting fuel use by running the models with cycle distance and pit depth informed by the mine plan

Predicting cycle length, and therefore production by running the models with cycle distance


With model-based predictions for fuel use, and production, mine planners can improve forecasting and budgeting associated with mine expansion.