Towards a Dynamic Optimisation of Comminution Circuit Under Geological Uncertainties

Geometallurgical programmes are crucial for designing mineral processing plants that maximise comminution throughput. However, the variability of complex ore bodies, such as platinum group element (PGE) deposits, poses challenges in developing these programmes into profitable mine-to-mill production. This paper investigates the geological characteristics of different lithologies hosting the complex PGE orebody located in the Northern Limb of the Bushveld igneous complex in South Africa and assessed their impact on metallurgical efficiency in comminution circuits. Regression machine learning techniques were employed to analyse the ore mineralogical dataset from two lithologies (feldspathic pyroxenite and pegmatoidal feldspathic pyroxenite) and predict the Bond Work Index (BWI), a key comminution parameter for calculating processing plant throughput. The results indicated that BWI is strongly influenced by Chlorite, silicates, iron oxides, and the relative density of the PGE deposit. Using both simulated and laboratory-measured throughput values, a particle swarm optimisation (PSO) algorithm was applied to maximise the plant’s comminution throughput through tactical blending of low-grade and high-grade ore stockpiles. The PSO algorithm was shown to be an effective tool for stockpile management and tactical mine-to-mill operation in response to feed mineralogical variability. This first-time innovative approach addresses complex geological uncertainties and lays the groundwork for future geometallurgical studies. Potential areas for further research include incorporating additional lithologies for tactical ore stockpile blending and optimising parameters critical for ore mineral flotation.