Increasing Underground Mine Value through Stope Optimization

Underground mine are getting deeper while commodity grades are reducing. This dual challenge places immense pressure on the mining industry, pushing companies to seek innovative solutions for maximizing profitability despite these adverse conditions. To solve, mine planners must run recursive and repetitive calculations that usually take significant time to finish. These calculations are essential for evaluating various operational scenarios, and with today’s computers, optimizing mine designs has never been easier. The integration of advanced algorithms and powerful computational tools enables the mining industry to address complex problems with unprecedented efficiency. Mine optimization’s main aim is to achieve optimal profit by maximizing the extracted ore value while minimizing the possible extraction costs.

Initially, mine optimization algorithms were developed for open-pit mines. Lerchs-Grossman (LG), which was developed by Ingo Grossman and Helmut Lerchs in 1964, laid the groundwork for further developments in mine optimization, culminating in software applications that enable more economical and efficient mining operations. The LG algorithm was implemented into an open pit optimization software by Jeff Whittle in 1984 which was later called GEOVIA Whittle.

There are however different complexities and challenges in optimizing underground mines as compared to surfaces, particularly in stope design. While the principles of maximizing profits and minimizing costs remain the same, the complexities of optimizing stope designs arise from the myriad underground mining and stoping methods that require different stope shapes and parameters to consider. Whilst open-pit Planners typically handle a limited number of pit shapes, stope optimization must accommodate a larger number of discrete mineable stope shape possibilities.

Manual methods for stope design involve a labor-intensive process. Engineers design possible stopes one at a time, calculating the volume and grade while factoring in metal selling prices, recovery rates, mining costs, and stope geometry parameters, often using spreadsheets. This approach can be time-consuming and inefficient, especially given the number of scenarios involved.

To address these challenges, many algorithms were proposed to produce optimal stope shapes, one of which is the Stope Shape Optimizer (SSO), which was developed by Alford’s Mining Systems in 2011. This algorithm has been integrated into GEOVIA Surpac mine planning software. The SSO creates economical stope shapes by using grade and tonnage values stored within a block model, alongside deposit dip from optional stope control surfaces. It then considers a range of economic and geotechnical parameters, including metal prices, costs, mining widths, and exclusion zones in its calculation. The current version of the SSO can optimize any shape of orebody, from narrow veins to massive formations with dips ranging from horizontal to steeply dipping and even vertical orebodies. It can accommodate a variety of mining methods, including caving, open stoping, and drifting methods.

By integrating SSO into GEOVIA Surpac, the stope shape algorithm can now be seamlessly added to an existing underground mine planning workflow while still using Surpac’s industry-tested and reliable mine design and geological modelling and estimation functionalities. There is no longer a need to convert files, as the SSO works directly with Surpac’s block model and solids models for optimization runs.

Beyond compatibility, GEOVIA has introduced additional tools such as the Stope Shape Optimizer Multiple (SSOM) and Stope Shape Optimizer Bulk (SSOB). SSOM allows users to define multiple project files and run them in batches, facilitating efficient management of multiple project optimizations. On the other hand, SSOB enables users to conduct multiple optimization runs while incrementally varying the value of a parameter for each run.

The results of SSO include a series of economical stope shapes that are directly compatible with GEOVIA’s mine scheduler, GEOVIA Minesched. This end-to-end solution encompasses modeling, estimation, and design using Surpac’s native functions, optimization with SSO, and then sequencing with Minesched. With the integration of SSO into Surpac, it can also be easily paired with another of Dassault Systèmes’ software solutions, Simulia Isight. This pairing allows for the production of a hill of value study, further extending the value added to the mine planning operation.

In conclusion, mine optimization is an essential strategy for the modern mining industry, particularly in the face of deeper orebodies and declining commodity grades. The integration of advanced computing technology has transformed how engineers and mine planners approach optimization by enabling them to perform complex calculations with unprecedented efficiency. Mine optimization will remain important as the mining industry continues to adapt to changing economic conditions. Through continued innovation and the application of cutting-edge technology, today’s mines can ensure their sustainability and profitability in an increasingly competitive market.

References:

1. Mokos, Peter, Ian Glover, and Shane Clauson (2022). AMS Stope Shape Optimiser Manual.
2. Zia, Shoaib. “A Guide to GEOVIA Stope Optimiser in Surpac“. 3DS Blog, August 5, 2020.
3. https://blog.3ds.com/brands/geovia/a-guide-to-geovia-stope-optimizer/
4. Poblete, Christian. “The Quest for the Best Plan in Strategic Mine Planning”. 3DS Blog, August 18, 2020.
5. https://blog.3ds.com/brands/geovia/the-quest-for-the-best-plan-in-strategic-mine-planning/
6. Alford, Chris and Brian Hall. “Strategy Optimisation for Underground Mines”. Alford Mining Systems, October 2014. https://alfordminingsystems.com/wp-content/uploads/2014/12/Strategy_optimisation_for_underground_mines.pdf