Over the last 25 years industries, such as manufacturing and business services, have seen a steady improvement in productivity and efficiency. However, the mining industry did not experience this same development, and productivity remained stagnant. In a recent study, McKinsey tracked companies in the manufacturing and business services industries, and the ten largest companies saw their productivity index grow between 15% to 25% over the past 25 years, while the ten largest companies in the mining industry only saw growth of ~1% during the same period.
While major solutions focus in the mining industry on ensuring human safety and reducing the number of accidents, industry leaders still have to look at optimising the use of energy and resources. Moreover, regulations for the use of natural water resources, such as groundwater, become more strict. Still, many mining companies start their operations without complying with regulations that are already in place. Another challenge is that now with ore deposits being in more remote locations or with reduced grade (the degree of concentration of metals or minerals), availability issues and eliminating maintenance stoppages are key. To ensure 100% of production operations is a tough goal... For this reason, issues such as diagnostics and predictive maintenance take on a relevant value today. A lot of attention is paid also to the interconnection between IT / OT, due to the data collection issues, etc. In general, mining companies work a lot on islands, and the data integration of these islands is becoming an essential task in mining operations.
To improve productivity is crucial to have an overview of the equipment (such as silos, locks, crushers, grinders, ovens, stackers and reclaimers, etc.) and formalise the processes and management rules. Digital twinning technologies and simulation modeling can help to visualise mining operations and identify bottlenecks to propose solutions to these issues.
For example, simulation is widely applied to right-size supply chain in pit-to-port operations: the concentration, rails/sidings, stockpiles, conveyors, berths and shipping accounting for uncertainties in operations, to optimise mill relining processes during grinder maintenance in terms of duration and performance or to evaluate alternative scenarios to enable higher mill availability, through-put and mine site profitability.
One of the recent simulation studies deployed by Royal HaskoningDHV was done for the design of the port facility to export magnetite concentrate in South Australia. Undertaken feasibility study helped to validate the model that represented key elements of iron project configuration and operations: by simulating the movement and interaction between different elements of the operations from the open pit to the vessel. Witness simulation model by Twinn helped the mining facility to test multiple scenarios, and dynamic simulation allows to measure key operation data in blocks of the mining process model and address the following key queries:
Many experts state that operational excellence which stands on people, processes, assets and cash, can close the gap in productivity. Based on a continuous improvement mindset, this framework strives for a consistent and efficient execution of business strategy, that considers all aspects of production, safety and cost optimisation to maximize productivity and profitability.
By employing methodologies such as Lean, Six Sigma and engineering principles to enhance efficiency, eliminate waste and streamline workflows industry leaders get improved productivity and quality as a result. Maximising the value and performance of physical assets through systematic optimisation, asset management, reliable maintenance practices and efficient warehouse management ensures optimal utilization and longevity of equipment and infrastructure.
This approach of the continuous improvement and optimisation of operational processes, combined together with efforts in decarbonisation - looking for opportunities to run engines on the green energy or use electric motors and digitalisation to keep footprint and CapEx low gives good result and opens up opportunities for the industry transformation and more sustainable mining operations. Just like other industries the mining industry is having to adapt to changes of becoming a more sustainable industry, not just environmentally, but also in the use of resources and operational footprint.
Given that digitalisation is key to boosting productivity at scale, mining companies need efficient and cost-effective ways to drive deployments. Predictive simulation helps you identify and unlock the right opportunities.
In short, predictive simulation helps you understand all equipment and processes holistically, identify and address bottlenecks, and make productivity enhancements based on evidence rather than instinct.
There are many mining use cases for predictive simulation, from scoping out digital investments to enabling predictive maintenance. Common examples include
One recent project involved a feasibility study for a South Australian port exporting magnetite concentrate. Using our Twinn predictive simulation software, we modelled asset configurations and operations from pit to vessel, so as to answer key questions across the lifecycle:
• What’s the ideal size for crushed ore stockpiles?
• What volume is required for tailings?
• What water volume is needed to feed the comminution circuit and concentrate batch tank?
Pipeline from the pit to the port
• What volume is required for the scuttle pond?
• Are there bottlenecks at the concentrate batch tank and/or port tank?
• What volume is required for the filtrate pond, emergency stockpiles and product stockpiles?
• What impact do an additional standby berth and second ship loader have on stockpile size?
• What impact do a mix of export vessels and berth commitment levels have on product stockpile size?
• What’s the capacity of material handling equipment in various scenarios?
Generally, these are the questions that are too complex for standard spreadsheets or ERP systems – the ones that involve myriad variables and dynamic processes and systems. Predictive simulation can give you that ‘Eureka’ moment where you finally have an answer, so you can home in on and eliminate bottlenecks.
Let’s chat about those questions and how predictive simulation can help boost productivity.