Optimum cut‐off grade policy for open pit mining operations through net present value algorithm considering metal price and cost escalation

Abstract

Purpose

This paper sets out to propose a new cut‐off optimization algorithm for effective decision making at the open pit mine planning stage.

Design/methodology/approach

The determination of optimum cut‐off grade to maximize the net present value of an open pit mining operation is influenced by the economic parameters including metal price and operating costs, the capacities of mine, mill, and refining stages, and grade distribution of the mineral deposit. The market plays a vital role in changing the economic parameters; therefore, they may escalate during mine life. The effect of these changes could be enormous on optimum cut‐off grade policy. The main motive is to introduce economic parameters escalation into the established theory of optimum cut‐off grades and study the impact of these changes on overall economics of the operation. Therefore, a cut‐off grade optimization algorithm is developed, which considers dynamic metal price and cost escalation during mine life.

Findings

A copper deposit case study shows that, keeping the metal price escalation at a minimum, the impact of mining and milling costs escalation is relatively higher than refining and administrative costs. Hence, a high‐escalation rate in mining and milling costs may change an economic operation into an uneconomic scenario.

Research limitations/implications

Management of stockpiles as a policy may be introduced in the algorithm for improvement in economy through maximum utilization of mineral resources.

Originality/value

The algorithm due to its flexibility allows analysis of various options in the least possible time, which makes it valuable to mine planners in decision making for major mining investments.

Citation

Asad, M.W.A. (2007), "Optimum cut‐off grade policy for open pit mining operations through net present value algorithm considering metal price and cost escalation", Engineering Computations, Vol. 24 No. 7, pp. 723-736. https://doi.org/10.1108/02644400710817961