Stochastic analysis of heap leaching process via analytical models

Heap leaching is a technology for which there is great interest in developing mathematical models that allow one to simulate, design and optimise the underlying process. Recently, the authors have presented analytical models for heap leaching that describe time scales for the particle and the heap in a rather simple model but with enough accuracy to be applied in the planning, optimisation, control and design of heap leaching. The simplicity of these models allows us to overcome the mathematical complexities of partial differential equation-based models and the error of empirical models. In this work, we present these analytical models, and then show how uncertainty in the input variables and parameters affects the response of the process by comparing the response curve with fixed parameters to the ones with uncertainty. Here we present simulation experiments of heap leaching processes under uncertainties based upon analytical models and using Monte Carlo techniques. We conclude that, in some cases, there exists an important influence of the input variables’ uncertainty on the response of the heap leaching process.

Mean, lower and upper recovery bands considering flow rate as a random variable.Figure optionsDownload as PowerPoint slideHighlights
► Analytical models for heap leaching are presented.
► Monte Carlo simulation is used to study uncertainty on the recovery curves.
► This stochastic analysis allows to overcome mathematical complexities.
► A strong influence of the irrigation rate on the recovery was found.