Applications of Advanced Analytics in Minerals Processing Plants--Safer, Smarter, Sustainable Operations

摘要

Mining companies are striving to achieve new levels of operational maturity and drive process improvement across the organisation in the face of unprecedented demand and limited supply of experienced human resources. Advanced analytics techniques can help overcome these fundamental challenges by applying data techniques not previously applied in mining. Today, decisions are usually locally optimised but do not achieve optimum capability for the value chain. Although more information is collected then ever before, it is difficult to link many sources of data together in the same place in real-time, for example plant history, maintenance, mine planning, logistics and engineering data. Consequently complex analysis of data is time consuming and requires specialist skills and knowledge, and is often neglected in the decision-making process. In the future, analytics will close the loop between analysing data and taking action. Analytics will become embedded in the business and routinely applied to improve decision making across the organisation, from senior management to operations and maintenance workers, but will not require specialist skills or be time consuming to analyse data. As analytics becomes embedded in the business process, decisions are supported by data (not on gut feel), and critically, data from many sources are integrated in real-time to improve the quality of decisions. Ultimately, predictive tools will help suggest, or even in some cases automate, courses of action. The main opportunities for analytics exist where the enterprise requires supporting technology to enable it to continue to evolve to higher level of maturity. This will be in the areas of asset management, rapid reconciliation and adaptive planning, and the inbound and outbound supply chains. Advanced analytics are also a key enabler for increasing the effectiveness and realising the true value of Remote Operations Centres to safely and efficiently support operations in remote locations with smaller workforces.