STUDY OF ECONOMIC FEASIBILITY OF PHOSPHATE ORE BENEFICIATION USING FINANCIAL RISK ANALYSIS, MODELING, AND OPTIMIZATION WITH UNCERTAINTIES

Abstract

Abstract Mine is a risky project with an uncertain nature. The engineering assumptions and the related data from the ore deposit to market through cost and recovery estimations have their variability and uncertainty. Therefore, the business decision to invest capital in developing a mine and constructing a mineral processing plant and services is crucial. Although, the net present values (NPV) can conclude the profitability and feasibility of a mining project, the uncertain nature of the mine and market indicates the investor's need for proper quantitative risk analysis to get an accurate and reliable estimation of the NPV of the mining project, considering their risks and uncertain variables. The conventional (deterministic) NPV excludes variability or uncertainty. However, the financial risk analysis can be used to give a mine realistic NPV evaluation and optimization using stochastic variables. The stochastic estimation of NPV incorporates uncertain parameters and risks to overcome the problem of obtaining a deterministic or single-point NPV. The acute lack of knowledge related to any mine, costs, and market variables proposes the modeling using a uniform or skewed probability distribution. However, the availability of data reduces the considered scenarios and gives investors a clearer vision of the project assessment with a higher level of certainty with the inputs modeled with other distributions of probability. This thesis develops a new dynamic model for financial risk analysis and optimization for Abu-Tartur phosphate ore beneficiation as an example, incorporating uncertainty to estimate the stochastic NPV using the @Risk 8.1 software. The new model uses Monte Carlo (MC) simulation to generate stochastic and simulated data representing the uncertainty assessment for each variable as probability distribution functions, regarding which the data are actual, assumed, or researched. Probability distribution functions evaluate a large number of hypothetical scenarios with related uncertainties by searching in the solution space, giving more accurate results. It allocates a stochastic value to each uncertain variable between its lower and upper boundaries. A determined distribution function follows the frequency of each value. The elimination of the mining project's risk is not the ultimate. Every mining project has the most important stage from the money perspective, which is the optimization of NPV to maximize the expected profit yielded by the mine and minimize the net present value at risk (NPVaR). The optimization process comes down from the simulated NPV. The interpreting sensitivity analysis establishes how to maximize the NPV by adjusting the data about the most sensitive parameters. Finding the optimum NPV, considering the simulated strategies and ultimate conditions, depends on finding the optimal way to meet the other recovery and production parameters adequately; while minimizing the related costs.