Evaluation of mining projects subjected to economic uncertainties using the Monte Carlo simulation and the binomial tree method: Case study in a phosphate mine in Egypt

Abstract

Mining project evaluation is a very complex process and time-consuming task. Any mining project passes through several study stages, like conceptual, preliminary feasibility, and feasibility studies, using a systematic and standardized approach. These studies allow investors to evaluate the exploitations alternatives according to economic and technical criteria to make a better decision. This research aims to study a model for investment risk analysis developed for the case study of “Abu Tartur mining project, Egypt.” Abu Tartur plateau is rich in phosphate rock, turned into phosphoric acid and fertilizers. This study used site-specific data that has been taken from the literature to build evaluation models using the Monte Carlo Simulation (MCS) and the Binomial Decision Tree (BDT) methods. The Discounted Cash Flow (DCF) is considered a benchmark method for all other evaluation methods. The MCS method uses a set of parameters that, in some cases, will give a higher NPV of the project and some others with lower values, but this depends on the probability of the input parameters. The developed evaluation models allowed a specific range of values, with confidence intervals as the MCS model. The advantage of using a probabilistic approach helps in the decisional phase, allowing for a more precise overview of the variability of the final economic value of the mine. Deterministic methods, like the DCF method, offer a solution that is limited to uncertainty analysis.