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The mineral industry has been using cyanidation to recover gold from ores for more than a century; however, a systematic study of the best reactant addition strategy in a cascade of agitated leaching tanks is not available in the open literature. A phenomenological mathematical model of the gold cyanidation process, calibrated with a set of industrial data from an Australian plant, together with an economic performance index is used to analyze this problem. The simulated results show that the best compromise between the two antagonistic effects, cyanide consumption and gold recovery, which are both function of cyanide concentrations, leads to a reagent distribution that depends on the leaching and cyanide consumption kinetics, pulp feed characteristics, and economic factors such as the gold market value. For the specific studied plant, in the operating range of low cyanide consumption and fast gold dissolution, all the cyanide must be added in the first tank; however, in the operating conditions of high cyanide consumption, cyanide has to be distributed in the first, second and third tanks.

The objective of process control in the mineral industry is to optimise the recovery of the valuable minerals, while maintaining the quality of the concentrates delivered to the metal extraction plants. The paper presents a survey of the control approaches for ore size reduction and mineral separation processes. The present limitations of the measurement instrumentation are discussed, as well as the methods to upgrade the information delivered by the sensors. In practice, the overall economic optimisation goal must be hierarchically decomposed into simpler control problems. Model-based and AI methods are reviewed, mainly for grinding and flotation processes, and classified as mature, active or emerging.

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