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Simulation is being increasingly used in mining to help reduce uncertainty in projects. One solution is MineTwin, based on a detailed discrete-event simulation model of a mine, and provides fast and adequate simulation models of underground and open-pit mines, giving mining teams a practical way to test how a mine will operate before committing equipment, capital or production targets.
Developed by Dubai, UAE-based Amalgama Software Design, the mining-specific simulation and decision-support platform represents open-pit and underground operations as connected production systems. The company says its mission is simple: to help mines make better operational and investment decisions by understanding the consequences before implementing change.
IM Editorial Director Paul Moore caught up with Jaco-Ben Vosloo, MineTwin Simulation Consultant, who said the platform is aimed at mine operators, planning teams and mining consultants, with applications from greenfield studies and mine restarts to expansions and ongoing operations. Vosloo: “For managers, the questions are familiar: can the plan be delivered, what is restricting output, and will another machine actually improve performance? MineTwin connects those questions to the way equipment, infrastructure and mining activities interact over time.”
He adds: “Our aim is to give mining teams a defensible basis for their decisions. Before they buy equipment or commit to a production target, they should be able to test whether it is going to be worth it for the mine as a whole, not just a specific part.”
MineTwin uses discrete-event simulation (DES), which follows individual activities and the events that start or stop them. A truck waits for a loader; a working area becomes available after blasting; a production task competes with development for shared equipment. These interactions determine what the mine can achieve, even when each machine has sufficient nominal capacity.
Vosloo says its scope extends across drilling, charging, blasting, loading and haulage, with development, backfill and supporting activities represented where relevant. Mine layouts can include ore passes, intermediate buffers, rail transport, crushers and hoists. Access restrictions, road gradients, equipment availability and shift rules can all affect the result. This breadth helps teams understand why improving one part of a mine may not change total output.
MineTwin combines mining-specific objects with configurable operating logic. Engineers can describe the mine using familiar equipment and infrastructure, then adapt coordination and dispatching rules to the operation being studied. The practical advantage is a mining-focused starting point, while retaining the flexibility needed for different layouts, technologies and working practices.
The platform uses Amalgama’s simulation technology and provides scenario management, mine layout editing and visualisation. Vosloo: “Data can be imported from mine design tools like Deswik and Vulcan, as well as enterprise systems or spreadsheets, with checks to identify inconsistencies. Equipment histories and material flow reports help engineers explain results and trace delay causes. The model therefore supports both technical analysis and discussions with the people responsible for delivering the plan.”
What makes it stand out versus other mine simulation options? Vosloo: “A particularly useful differentiator is Studies, MineTwin’s environment for organising simulation experiments. Users start with a baseline scenario, branch into alternative configurations and compare their results. Automated studies support constraint analysis, fleet sizing and sensitivity testing. Saved studies retain the analytical structure for later use.”
Constraint analysis tests the effect of relaxing individual restrictions, helping engineers identify where further investigation would add the most value. Fleet sizing explores equipment combinations against production and development targets. Sensitivity testing shows how results respond to changing assumptions. Multiple replications allow teams to examine variability as well as average performance.
“The value of Studies is that engineers can build a structured investigation around a decision. They can see what changed, compare the outcomes and return to the analysis when the mine plan or assumptions change,” Vosloo explains.
Studies also includes preconfigured automated reports with interactive charts and other analysis tools to help engineers explore and communicate the results. Reports can be exported as PDFs for sharing or as HTML files that retain their interactive features when opened in a local browser.
For a planning department, that means a reusable approach to recurring questions. A revised production plan or equipment assumption can be assessed within an established modelling framework, with a clear baseline for comparison. The emphasis is on making experimentation part of the planning process.
Testing operational changes and new technology
Vosloo: “Consider a manager deciding whether to add trucks. The first question is whether haulage is actually limiting production. If loading capacity, access to working areas or downstream handling is the restriction, more trucks may simply spend longer waiting. Comparing those alternatives helps turn an equipment request into an assessment of the production system and the investment it actually needs.”
He says the same approach applies to major changes in mining systems. A study of in-pit crushing and conveying can examine how trucks, crushers, buffers and conveyors work together. An open-pit operation considering underground development can investigate future material flows, shared infrastructure and equipment requirements. Autonomous haulage can be assessed through its effect on fleet composition, congestion and achievable production, with operational results informing the financial assessment.
For existing mines, smaller interventions can be equally valuable. Changes to equipment allocation, shift arrangements, traffic priorities or buffer capacity may release production that would otherwise require additional investment. Simulation lets teams compare these options under common assumptions and determine whether the expected benefit survives day-to-day variability.
Evidence from mining applications
Vosloo adds that published real applications illustrate that range. At South Crofty, Tomahee and Amalgama used MineTwin to assess the underground tin mine’s restart plan, identifying equipment allocation and surface handling constraints. In a separate study with Pronto.AI and Whittle Consulting, 924 simulation experiments informed comparisons of autonomous and conventional haulage fleets. A South African coal mine study examined stockpile buffering and fleet requirements together. Each example addresses a specific operating decision rather than treating equipment capacity in isolation.
MineTwin’s value depends on building a credible representation of the operation. That requires suitable data, explicit assumptions and validation with mining specialists before interpreting alternative scenarios. A model can then become a continuing decision support asset as plans evolve.
“What matters is whether a change improves the performance of the mine as a whole. MineTwin helps us test that connection and explain why a proposal works, where it is constrained and what needs to change,” says Vosloo.
MineTwin helps managers check whether production targets are achievable and where changes are likely to improve output. The same model can be updated as the mine develops, giving teams a way to test their next decision.
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