Responsible optimization of long-term production scheduling in open-pit mines

Area of Science:

• Mining Engineering
• Operations Research
• Environmental Management

Background:

• Conventional open-pit mine planning often overlooks environmental costs, royalties, and penalties, leading to unrealistic economic projections.
• There is a need for integrated models that account for these external factors to ensure practical mine plans.

Purpose of the Study:

• To develop and implement a Mixed-Integer Non-Linear Programming (MINLP) model for long-term production scheduling.
• To explicitly integrate environmental costs, government royalties, and operational penalties into mine planning.
• To compare the developed MINLP model with industry-standard software for economic and operational efficiency.

Main Methods:

• Development of an MINLP model incorporating environmental, royalty, and penalty costs.
• Application of the model to the Kal-e Kafi copper deposit using a scaled block model (20,746 selective mining units).
• Utilizing the BARON 23.1.7 solver within a Divide and Conquer framework for optimization.

Main Results:

• The MINLP model achieved a Net Present Value (NPV) of $129.34 million, a stripping ratio of 0.64:1, a 14-year mine life, and 14.24 million tonnes of extractable ore.
• Comparative analysis showed the MINLP model offers better operational efficiency (lower stripping ratio, longer mine life, higher ore recovery) than commercial software by internalizing all costs.
• Sensitivity analysis confirmed model robustness, with environmental costs having near-zero elasticity.

Conclusions:

• Integrating environmental costs, royalties, and penalties leads to more realistic and economically defensible mine plans.
• Responsible mining practices, as demonstrated by the MINLP model, enhance operational efficiency and sustainability.
• The developed model bridges the gap between theoretical optimization and practical, sustainable mining operations.