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Date of Award


Document Type

Campus Access Master's Thesis

Degree Name

Master of Science in Mining Engineering (MS)

Administrative Home Department

Department of Geological and Mining Engineering and Sciences

Advisor 1

Snehamoy Chatterjee

Committee Member 1

Thomas Oommen

Committee Member 2

Nathan D. Manser


The optimization of open pit mine design under uncertain factors is one of the most crucial and challenging jobs in the mine planning and design process. Complex geological structures escalate the difficulty of resource estimation and thus the mine planning. The volatility of the market (i.e. price of metal) is another key factor for mine planning and design. In this study, external factor, i.e., price and internal factors, i.e., volume (tonnage) and calorific value (CV) are simulated and integrated in the coal mine planning. The coal price was simulated using Ornstein-Uhlenbeck (OU) mean reversion process combined with Monte Carlo simulation to generate 50 simulations of coal prices for the next 10 years. Volume (tonnage) was simulated using a multiple-point geostatistical method, Single Normal Equation Simulation (SNESIM) to generate 20 equiprobable coal body models for a coal mine in Indonesia. The CV was simulated by generating 50 simulations within each coal body using sequential Gaussian simulation. The results show that the coal model from OU can be applied with confidence interval 5%. The deviation of the simulated coal bodies varies -0.07 to 5.48% compared to training image. The CV simulation generates the average CV for all simulations is 5920.29 kcal/kg, with standard deviation 586.54 kcal/kg, but average CV from different simulations varied from 5305.26 to 6526.55 kcal/kg. All these factors were used to generate ultimate pit limit and production phase-designs with minimum cut maximum flow algorithm. For comparison purposes, the discounted cash flow (NPV) of the stochastic model was compared to the deterministic model. The stochastic model incorporated all the uncertainty factors simulated earlier; whereas, the deterministic model only considers a single orebody model and single price neglecting the uncertainty. The NPV of the stochastic model is 42% higher than the deterministic. The CV profile also shows that 33% of the coal blocks within the deterministic model have CV below 5000 kcal/kg.