Mine design goes user friendly

Mine design has come a long way from the first uses of stratigraphy in Britain’s industrial revolution, with the introduction of Computer-Aided Design (CAD) in the 1970s arguably it’s second great leap.
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The latest mine design software integrates multiple inputs


CAD software has also evolved hugely since MineCAD, which become an essential tool for  surface and underground applications.

But CAD's limitations are being evident in 21st century mining - particularly when it comes to complex 3D modelling and dynamic analysis.

The new school of design products, such as Hexagon's HxGN MinePlan, aim to provide a suite of tools for designing and planning, including 3D modelling, scheduling, drill and blast design, and equipment simulation.

The latest mine design software also aims to offer better data management capabilities, and improved incorporate collaboration features, such as real-time communication and document sharing, to enable teams to work together more effectively.

Mining Magazine discussed these trends in mine design with Jesse Forgues, Head of HxGN MinePlan, Hexagon's Mining division. 

How does mine design software help mining companies to optimise their operations, reduce costs, and improve safety?

Mine design is a critical step in the mine planning workflow. It remains one of the most user driven steps in the workflow and represents a significant and risk to the operations. A well-designed pit can have a greater impact on Net Present Value (NPV) than an optimised schedule, due to the fact that some of the decisions made at this step in the process can live with the mine for its entire lifespan. Mine design software helps the user ensure they are considering these long-term costs, aids in the decision-making process, and enables users to digitise the pit design. Using products that allow the user to be more data driven helps close the gap on the old saying, "give five mining engineers the same pit to design, you will get five different pit designs." Leveraging the data available in a block model, as well as your geological/geotechnical model, means you are making informed decisions based on identified risks. 

What are some of the biggest challenges that mining companies face when implementing mine design software, and how can these challenges be overcome?

The challenges of implementing mine design software within a mining company have reduced significantly over the years. With the strength of computers increasing, the requirement to have a high spec machine has reduced, so mine design software can now be used with more and more devices. Supporting this is the transition of licensing models that further support flexibility of usage, moving away from the old school dongle you would have to carry around towards network/cloud-based licensing and even to subscription and usage-based licensing. As we improve our software and companies transition towards cloud-based models, this means that we can quickly license and deploy software anywhere in the world.

Apart from licensing, there is the learning curve to becoming an effective mine design engineer. Training and education are critical in not only ensuring the user understands the impacts of their decision, but also in ensuring that they are able to utilise the chosen design package confidently and to the fullest of its capabilities.  

How does mine design software integrate with other mining technologies and systems, such as mine planning and scheduling tools?

One of the strengths of HxGN MinePlan is the MinePlan 3D platform that is designed to be the integration point for mine design, mine planning, mine scheduling as well as geology. This means you can visualise your design against the schedule as you make your scheduling decisions. These schedules then seamlessly integrate with our Fleet Management System (FMS), essentially connecting your mine design to your mine schedule. This is ultimately integrated into the hands of your operators, ensuring that the right decisions are made at the right time. 

What role does artificial intelligence and machine learning play in modern mine design software, and how can these technologies be leveraged to improve mining operations?

AI and machine learning are going to become more influential in assisting the decision-making process for mine design in the near future. Mine design already incorporates many aspects of optimisation to achieve a result, and optimisation is based on and impacted by several parameters that are inputs from the users. With most machine learning algorithms, the purpose is to build an optimisation model that learns the impact these parameters have on the objective function, which is typically NPV. Machine learning will aid us due to its ability to manipulate parameters at a far more detailed level and truly push the concept of an optimal design, both with respect to value and safety. 

How does mine design software help mining companies to comply with environmental and regulatory requirements, and to minimise the impact of mining activities on the environment?

Ultimately, mining companies have two main objectives: maximise revenue and minimise costs. Revenue is proportional to extracting every last tonne of resource (without delay), and the lowest cost is achieved by extracting it with the minimum amount of waste. The mine design is the first step in constructing the digital twin of your operation, this allows testing and analysis of "what-if" scenarios that could impact the mining operations. Being able to plan and predict these events allows you to be more robust in your ability to change and minimise the potential for waste. An example is a poorly designed ramp network, which could lead to 10% extra distance required for trucks to drive. Trucks are the largest emitters of CO2, so being able to minimise this in the software saves the expense in the operations.

With respect to regulatory requirements, it's critical to be able to overlay all your spatial information in one central area. Doing so allows users to successfully highlight potential issues when it comes to impacts to the environment, or aspects that impact the traditional owners of the land. 

How can mining companies ensure that their mine design software is accurate, reliable, and up-to-date, and what steps should they take to maintain and update their software?

What a lot of mining companies forget is that the software is built for them, not for the technology companies. These mining companies need to invest in staying connected to the technology companies to ensure they are part of the future, and to stay up to date on what is changing. Most companies have pathways to submit ideas and discuss real problems that they are currently facing. It's these conversations that allow technology companies like us to help identify clients' pain points and solve for them with our solutions. 

What is the future of mine design software, and how do you see this technology evolving in the years to come?

I see a world where machine learning and AI are more involved, and we move to a more parametric design that leverages quicker, more accurate data sets— drastically reducing the turnaround time of decision-making. This will become increasingly more important as the mining methods that have been relatively unchanged for decades— for example, CAT 793 just turned 30 years old— begin to be disrupted by new energy models as well as automation. Being able to model these designs quicker to respond to the fast-changing technology landscape in mining will be critical. We just released a new product, our HxGN MinePlan Block Model Manager, to enable mine planners to quickly populate, manage and share block models from HxGN MinePlan 3D and third-party systems.

The mine design process is going to need to become more reflective of the current digital environment. Digital twin technology will help fuse data sets together to make it possible to have a full digital view of your operation from your computer. An important aspect of this is the actual terrain/surfaces of the mine, and advancements in 3D scanners mean that our design tools will need to be able to handle these big data sets in order to turn this data into usable surfaces in the operations.



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