Part 4: Using generative design and MODSIM to define and analyse all your options
I started the Re-Imagining the Way We Work series by saying that we need to take what works from product lifecycle management (PLM) — especially viewing what we do as creating a product, from ideation to end-of-life — but then customise that approach to suit the complexities of our industry and the lengthy process of moving from mineral discovery to mine decommissioning.
For me, the best way to customise that approach is to combine PLM with virtual twin technology because it allow us to visualise the mining value chain as a complex series of interconnected systems. For the optimal mining value chain, we need to:
- identify our systems
- understand the links and relationships between each system
- understand the boundaries of, and remove any barriers between, the systems, and
- encourage interaction between the systems to take advantage of synergies and ideas.
In the articles that followed, I discussed:
- how you can use virtual twinning to visualise your systems as a collection of tiered virtual twins, each with its own data sources, processes, and areas of knowledge
- how properly consolidating, controlling, and managing your data will help you get ready to take full advantage of that data within a virtual twin experience, and
- how parametric modeling allows you to use your cleaned and consolidated data to run comprehensive virtual scenarios — not just within each tier but between all your tiered virtual twins — to stress-test designs and engineering principles.
For this final article, I focus on the next leap in creating the best mine designs possible: the combination of generative design and MODSIM.
Generative design
Mines today face more and more constraints when designing a new mine, from geotechnical to environmental, financial to regulatory and everything in between. To ensure the best possible mine design, designers need to be able to evaluate thousands of configurations against possibly hundreds of constraints in order to find the one design that offers the best trade-off.
Generative design, a form of artificial intelligence, is an iterative design process where designers tell the software what the limits and the possibilities are for the constraints they have identified. Many of today’s foremost manufacturers use generative design to improve product performance, reduce costs, and improve sustainability.
For example, say a car manufacturer wants to test three new materials that may help make its car doors lighter, which will in turn help reduce fuel consumption. The manufacturer will use generative design to explore — in a matter hours instead of days — multiple designs based on each material to determine such things as how many kilograms of weight could be reduced on each door and how many kilograms of fuel could be saved by that weight reduction, against how much the material costs to buy and how difficult (more time consuming) it might be to work with.
But generative design, while it will help designers eliminate a whole range of possible designs, will still only go so far in helping the car manufacturer (or mine) be absolutely confident it is making the right design choice. It needs to test its final choices in the most rigorous way possible, through modeling and real-life scenarios and simulations. This is where MODSIM comes in.
Generative design + MODSIM
The traditional approach is for mine design/modeling and simulation to be done by different teams with different schedules and priorities, which often results in simulation and simulation analysis starting only toward the very end of the design process.
Integrated modeling and simulation — or MODSIM — on the other hand, allows geoscientists, engineers, and analysts to work together from the very beginning of a mine project, opening up opportunities for teams to explore many more design alternatives in less time and with less friction (no having to translate data from system to system and avoiding the risk of file sharing errors). It also ensures faster review cycles and, ultimately, a better, more reliable mine design.
In other words, the ability to combine design, planning, and simulation is no longer science fiction. It is already within reach today, and can be a game changer in capital project evaluations.
Use case
Here's how it might work:
A mine company in Australia is doing a feasibility study for a new mining operation. Like all mines today, the company is interested in maximising revenue early to reduce the impact of capital investment. It also wants to design an efficient mine that is capable of offering a steady rate of extraction over the long term.
The planning and design team know their design must consider the size of the mine, the number of phases, phase starting points, and direction of phases. They also understand that variables they cannot control — such as price and cost fluctuations, the likelihood of unpredicted downtime, and inconsistencies in ore grade — will influence each decision, along with variables they can control, like designing the safest mine possible, fleet dimensioning, sequencing alternatives, and scales of construction.
Generative design and MODSIM together allow the design team to evaluate various planning and design implications through a one-stop planning and design process.
Using Dassault Systèmes suite of software solutions, the team first generates 23 pit options analysed from eight different directions, then generates 1,600 alternatives for the 23 pits based on the Net Present Value (NPV) obtained for each possible pushback direction plus the target KPIs that most affect their decision making — in a matter of hours, not days or weeks.
After whittling down the options to the one with the best pushback dimensions for extracting maximum ore, the team moves on to developing a production plan that considers such factors as mining rate, CAPEX impacts, location of waste facilities and haulage logistics. Once again using MODSIM, the team generates 9,680 possible sequences (and the hill of value visual, below) with different cut-off grades, prices, production capabilities, and corresponding CAPEX. All in less than 24 hours.
From there, each of the possible plans automatically generates its own NPV, with its own hill of value visual, making it easy for the team to see which plan creates the optimal production range. This is good, but the team wants even more precision, so they now use parametric design to identify optimal and practical trade-offs that will ensure their final design plan meets all critical geometrical and safety rules.
PLM + virtual twins + data consolidation + parametric modeling + generative design + MODSIM
Mining is a complex and risky endeavour, but there are ways to tame the complexity and reduce the risk by using your data to generate new insights at every stage of the mining process.
Because data consolidation normalises and connects data sources, it allows mines to first combine their real world data with virtual world data and then create customisable, drill-down views into that that data that will provide multiple new perspectives into what has happened, is happening, or will happen, at a mine site.
And with more perspectives — including from real-time data and feedback that will help mines automatically modify their original models parametrically and simulate thousands of scenarios in a matter of hours (something no human alone can do) — comes a greater chance of making the best possible choices that will lead to the best possible results.
It’s a different way to work. It takes a little imagination and no small leap of faith. But the technology is here, it works, and it’s time to for the world of mining to catch up with what product manufacturers have been doing for years.
About the author
@AJ - GEOVIA R&D Portfolio Management Director
Anthony is an experienced Product Management Director with a demonstrated history of working in the natural resources software industry. He has worked as a consultant using and teaching the use of software applications as well as providing guidance on the development of software applications for the last 10 years. Skills developed during this time are largely focused towards geological modelling, resource evaluation, mine planning and product management.
