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All parameters simultaneously handled,delivering multiple scenarios
All parameters simultaneously handled,delivering multiple scenarios
In open-pit mine planning, blending constraints are essential for optimizing both ore quality and processing efficiency. These constraints involve the strategic mixing of ores from different mine sections to ensure a uniform grade and consistent chemical composition. By implementing effective blending, the value of the extracted ore is maximized, processing costs are reduced, and the overall economic viability of the mining project is improved. This strategy helps address ore variability, ensuring the final product adheres to acceptable specifications and satisfies the requirements of downstream processing facilities.
Periodic average grades of gold and copper, as reported by MiningMath, with a minimum average constraint set at 0.42.
For a mine planner, incorporating blending constraints is essential for enhancing the efficiency, profitability, and sustainability of mining operations. MiningMath integrates blending constraints directly into its objective function rather than applying them after pit optimization.
By including open-pit mine blending constraints in the objective function from the start, MiningMath’s optimization process accounts for all relevant factors at once. This approach results in more accurate and efficient planning, as the system optimizes for maximum ore recovery, profit, and quality control simultaneously. Addressing constraints early in the process helps mine planners design operations that are not only profitable in the short term but also sustainable over the long term, promoting responsible resource use and effective environmental management.
MiningMath allows users to include blending constraints, and consequently, find solutions that are closer to real mining operations.
Blending can be managed using an average constraint, which controls the average value of any quantifiable parameter modeled block by block. In addition to blending, average constraints can also regulate other factors, such as haulage distances based on each block’s destination. This approach helps ensure that operational parameters are optimized to meet specific requirements effectively.
With MiningMath’s single-step, optimization engine, you can uncover opportunities that manual or stepwise planning might miss. Ultimately, this engine is able to optimize resource utilization and can improve project outcomes. Transform your mine planning process by leveraging blending (average) constraints in the optimization process and take your mining projects to new heights of efficiency and success.
Windows 64-Bit (x86_64) - 121 MB
Windows 64-Bit (x86_64) - 121 MB
With constant developments since 2013, MiningMath has reached a mature and robust state. We are the first and only single-step mining optimization engine available in the market!
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