Metso gave its take on the evolution of mining towards a more autonomous mining future from the perspective of a mineral processing technology major in a strategy implementation discussion contained in its FY2019 results released today. “We expect mining and minerals processing to be highly autonomous and optimised by 2050. Going forward, minerals processing assets are not only being connected to the cloud, but they are also becoming increasingly autonomous. Introducing autonomy to the mining process provides many advantages. Increased safety is an important benefit, as people working underground and in other hazardous places can instead run the daily operations from clean and safe control rooms. Productivity is another benefit: when more trivial tasks are automated, people have more time to do other tasks, like higher level planning and optimisation work.”

During 2019, Metso says it has been developing its digital offering for the mine of the future. “Already today, Metso has a wide portfolio of technologies that can be used to collect, analyse, and act on data from minerals processing plants, and now the whole offering has been combined under one digital portfolio, Metso Foresight, consisting of a cloud-based IoT solution that collects and analyses machine data, as well as a comprehensive array of services for plant-level performance optimisation.” The service will be supported by Metso Foresight Performance Centers in Chile and China. These remote monitoring support service centers are hubs for Metso’s digital offerings, and are staffed with multi-disciplinary teams of equipment, process and technology experts.

Finally, an interesting update on the depth of uptake of Metso’s core remote conditioning monitoring solution, Metso Metrics, for the mining, aggregates and recycling industries that was launched in 2018. It is today connected to eight fixed customer plants and it has over 800 fixed assets and mobile crushers connected to it. Metso Metrics provides users with analytics about their machines and processes, enabling condition monitoring and predictive maintenance and process engineering.