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About MiningMath

MiningMath has been formerly conceived as a consulting firm from the union of multidisciplinary professionals from engineering, geology, computational mathematics, and computer science to provide services and solve complex challenges within the mining industry. Holding a solid expertise in mining, applied mathematics, data science, and optimization, MiningMath started a new stage focused on its own technological development to help mining companies to create sustainable value.

After 6 years of Research & Development, MiningMath consolidated a pioneer tool for Strategy Optimization, formerly known as SimSched and now re branded to MiningMath.

Mining specialists, leading-edge consultants, and innovative mining companies are now able to incorporate more of the real-life complexities into their decision-making analysis. Among these achievements, this approach has been awarded in Russia, while being applied to a multi-mine project in Chile, and it has been well-accept academically for addressing socio-environmental aspects in an unique manner.

Read more about academic research.

About MiningMath's Technology

Increasing Revenue and Mitigating Risks

Mining companies usually focus their innovation initiatives on reducing costs, promoting billionaire investments in logistics, infrastructure, and productivity systems in general. However, the other part of this formula also has great opportunities, and it is exactly where we can help.

Our mathematical intelligence allows mining professionals to generate hundreds of strategic scenarios for each project and base high-level decisions prior to detailed mine scheduling. MiningMath’s technology integrates well with most of the mining packages for scheduling optimization.

MiningMath Uniqueness

MiningMath's engine integrates the business’ areas and allows managers to structure, develop, and efficiently analyze different decision-trees and their multiple scenarios. This is possible through a global and simultaneous optimization of the entire mining system, comprising constraints from different areas of the company.

The one-step optimization is crucial to take the most out of the solution space for a mining project and takes into account the interdependence of constraints altered across multiple teams. This flexibility means each project assumption changed can be immediately combined with the consequent changes in project assumptions from other areas of the company.

Additionally, the optimization is not constrained by arbitrary decisions for cut-off grades or pushbacks. Such decisions are usually guided by previous knowledge or automated trial-and-error. Thus, each set of constraints has potential to deliver an entirely new project development, including economic, technical, and socio-environmental indicators along with a mine schedule while aiming to maximize the NPV of the project.

License to operate is the main risk for the mining industry in 2019-20, according to EY. Any mining company willing to be a market leader should be committed to incorporating socio-environmental aspects into strategic evaluation, quantifying possibilities and impacts to discuss with society. This is only possible by bringing mathematical optimization into the decision-making process.

Mathematical Intelligence: Behind the Interface

MiningMath's software is a pioneer tool based on the Direct Block Scheduling approach, including Mixed Integer Linear Programming and proprietary heuristics. MiningMath uses only LP models and the intelligence to convert continuous into integer and non-linear solutions is made by MiningMath's proprietary "non-linear branch and cut algorithm".

MiningMath aims to maximize the Net Present Value (NPV) of a project deciding, based on an imported block model, which blocks will be mined, when, and to which destination they must be sent. The ideal destination considers, during a single optimization, the inherent opportunity costs of the time value of money, plant and mine capacities, physical limits, geometries, and variables controlled by summed or average values. Cut-off grades and mining phases are no longer arbitrary inputs, becoming an optimized consequence of each set of constraints.

The following video shows an introduction to MiningMath, approaching technical concepts from Engineering.


MiningMath: Products & Services

The following table summarizes MiningMath's optimization modules and services for custom assistance.