Cutoff Grade Optimization on Operative Decision Variables with Geological Uncertainty in an Underground Gold Mine: A Real Case Study

• Published in: Mathematics (Basel) Vol. 12; no. 22; p. 3450
• Main Authors: Toro Morales, Diego Alejandro, Franco Sepúlveda, Giovanni, de la Barra, Erick, Del Río Cuervo, Juan Camilo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2024
• Subjects: Algorithms; Capital budgeting; Capital expenditures; Case studies; Chromosomes; Costs; Cut off grades; Discounted cash flow; Genetic algorithms; Geology; Gold; Gold mines and mining; mine scheduling; Mining; Monte Carlo method; Monte Carlo simulation; Monte Carlo simulations; Mutation; Optimization; Optimization techniques; optimization under uncertainty; Overhead costs; Probabilistic models; Real variables; Uncertainty; Underground mines; Underground mining; Variables
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math12223450

In mine planning, uncertainty assessment is essential to properly value a mining project. Geological, operational, and economical risks have to be considered to find the variable values that maximize the profit of the project. In this research, geological uncertainties are taken into account to assess the economic value of an underground gold mine. The scenarios considered are the tonnage-grade curves which are simulated by the Monte Carlo method. The decision variables are the cutoff grade (CoG), the Life of Mine (LoM), and operational variables, namely the mining and the processing capacity. In order to assess the economic value, we maximize the Net Present Value (NPV), which is carried out by a Genetic Algorithm (GA). This optimization, so-called implicit optimization, generates results of the probabilistic model which are compared with the deterministic one; the results found for a real underground gold mine show that, in the probabilistic case, the Net Present Value is higher and the time horizon is shorter than the results of the deterministic case, and the mining and the processing capacity are higher for the probabilistic case than the deterministic one.