Process plants in the mining sector often contain large populations of duplicate assets – for example, precipitators in alumina production. Scheduling the periodic maintenance of these assets is a major challenge because the assets are inter-connected and there is uncertainty around their condition – often the precise maintenance requirements only become known once the asset is taken out of service.
Currently, such maintenance is manually scheduled using “rules of thumb” driven by legacy practices rather than real data or rigorous science.
The aim of this project is to develop fast optimisation algorithms for scheduling maintenance in populations of inter-related duplicate assets, taking into account the condition estimates, constraints on resource/labour availability, production needs, safety compliance, and asset inter-connections and redundancy. This will lead to automated tools for ensuring schedule compliance, cost control, and reducing unplanned maintenance work. Key challenges in the project will include developing the correct optimisation models that add value to the industry partners, and overcoming the dimensionality challenges that are common in large-scale industrial optimisation problems.