THE OBJECTIVE

To validate technical design decisions and assess mine performance at years 1, 7, 15, and 25 of operation.

Tasks included:

• Accounting for equipment downtime caused by weather conditions
• Evaluating the efficiency of a conveyor-truck hybrid haulage system
• Determining the optimal equipment fleet (including tailings transport from the processing plant)

THE CLIENT

A greenfield open-pit iron ore deposit located in a sub-arctic region.

THE SOLUTION

Simulation scenarios were developed for key stages of life of mine.
The model accounted for:

• Seasonality of equipment units’ downtimes
• Transportation of tailings from the processing plant

Performed scenario analysis (CAPEX, OPEX).
Integration with the mine’s geological information system enabled automated scenario setup.

THE RESULTS

• The project layout was updated — the processing plant was relocated closer to the pit.
• Haulage technologies for ore and waste were compared.
• Optimal fleet sizes were determined, and production bottlenecks were identified.

Quantitative Effects:

• Adding one 20 m³ shovel increased production by +1.48 Mt of ore and +2.3 Mt of waste.
• Optimized bulldozer fleet: 8 instead of 9 (saving ≈ USD 300–400K).
• A conveyor system for tailings transportation proved to be over 2 times more efficient than 130-t trucks.

PROJECT CONTEXT

The project simulated the mine’s operation and processing plant for multiple stages (years 1, 7, 15, 25).

Each stage included:

• Comparison of overburden haulage options: trucks, conveyor-based haulage, or a combined system
• Consideration of seasonal effects (low temperatures) causing downtime and reduced productivity
• Validation of engineering design and fleet sizing based on performance and availability criteria
• Scenario-based analysis using discrete-event simulation and interpretation of results

Additionally:

Integration with the geological information system automated block creation for ore and waste, accelerating scenario preparation.

KEY QUESTIONS

Modeling with MineTwin was used to answer:

• Are the design assumptions for the plant and fleet valid?
• What fleet configuration is optimal for achieving ore and waste targets?
• How many bulldozers are required for dumps, cleaning, and ore stockpiles?
• When does the conveyor-based haulage system become economically justified?
• What is more efficient — conveyor or truck transport of tailings?
• How does weather affect mining productivity?

FLEET CALCULATIONS

Bulldozer fleet optimization:

Optimal: 8 instead of 9 units → saving USD 300-400K
Confirmed requirement: 10 units at 260–300 t/h productivity

Excavator fleet (year 7):

Adding one additional excavator (20 m³ shovel) increases production by:

• +1.48 Mt ore,
• +2.3 Mt overburden

PLANT-RELATED FINDINGS

• The conveyor system for tailings transport was more than twice as efficient as 130-t trucks.
• Simulation confirmed the need to relocate the processing plant closer to the pit to reduce haul distance and improve profitability.

WHY MINETWIN

Designed specifically for mining:

• Unlike general-purpose tools, MineTwin accurately reproduces both open-pit and underground operations
• It models detailed equipment interactions, including cyclic-continuous haulage systems, capturing nonlinear constraints and dependencies invisible in Excel or linear programming.

Bridging strategic planning and operations:

• MineTwin validates plan feasibility while considering equipment availability, geological conditions, and operational constraints

Scalable and adaptable:

• It enables creation of an internal competence center capable of building models for multiple mines on a single platform
• MineTwin is flexible enough to adapt to different mine layouts and process configurations
• After implementation, internal teams can independently perform scenario analyses, fleet optimization, and operational assessments — supporting continuous improvement and data-driven investment decisions.