A novel methodology to generate an optimal irrigation plan on a copper-oxide ore heap leaching process

Abstract

Heap leaching is a key procedure for treating low-grade ores such as uranium, gold, and copper. It consists of the construction of heap leach piles that are irrigated with an acid solution on the top of the leach pad. This solution flows through the different heap lifts and is collected at the bottom of the leach pad. In the last years, numerous research efforts have been carried out to understand the heap leaching process. However, few efforts aim at optimizing the irrigation plan, i.e., which parts of the heap should be irrigated for a given flow rate and limited availability of acid. In this study, we propose a methodology based on two models. First, a dynamic mass balance model estimates the copper production and acid consumption of a heap leaching operation. Second, an optimization model determines the optimal irrigation plan for maximizing production and/or minimizing acid consumption, applying the methodology to an industrial-scale copper oxide-based synthetic leaching heap. The results of the numerical experiments demonstrate the robustness of the proposed methodology. The optimization models effectively capture the inherent complexity of the heap leaching process by relying on basic estimations of heap behavior. The findings indicate that the methodology can enhance copper recovery either by maximizing production under acid supply constraints or by increasing output when sufficient acid is available. Notably, assuming independent irrigation areas, the copper production estimated by the optimization model accounts for 95% of the copper recovered in a dynamic mass balance simulation. This approach offers a promising framework for the design and operation of multi-lift heap leaching systems, contributing to more efficient and informed production planning.