Deep-level mining is under severe financial pressure from several unique challenges. One of these is maintaining acceptable underground temperatures for humans to work in while achieving demanding production targets. As mines regularly reach new depths, additional heat is added to the system, contributing to this problem. Accurate mine heat load studies are therefore required to ensure that heat sources are actively evaluated, managed, and mitigated through adequate cooling practices. However, present heat load models are based on design parameters that cater for worst-case scenarios. Most of these models are also based on outdated empirical data taken at a time when mining differed from the present. Industry 4.0 technologies provide potential optimisation benefits when integrated with new heat load models to ensure effective monitoring, and consequently dynamic management, of heat sources. The roll-out strategy presented in this article will serve as a real alternative to earlier and outdated heat load prediction models.

The weather directly impacts ventilation systems, especially large industrial systems found in underground mines. Underground mine ventilation systems have high cost implications that add to the financial strains and uncertainties of future mining operations. In addition, the dynamic nature of underground ventilation systems makes the accurate prediction of underground conditions extremely difficult using traditional steady-state methods. Therefore, improved prediction methods of dynamic underground environmental conditions are needed to ensure cost-effective ventilation systems. This paper investigates simulating the sensitivity that underground ventilation systems have to fluctuating ambient conditions. Simulation software was applied to a case study on a gold mine in South Africa. The results showed that transient software can now be applied to entire mine ventilation systems, and can improve predicting the underground environment because of fluctuating ambient conditions.

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