Freeport-McMoRan. has filed a patent for a system with a secondary irrigation feature that adjusts leaching operations based on tonnage weighted average of metal in different lifts. The method also involves determining acid gap and percentage of compacted material to further optimize leaching operations. GlobalData’s report on Freeport-McMoRan gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Freeport-McMoRan, Extraction by leaching was a key innovation area identified from patents. Freeport-McMoRan's grant share as of January 2024 was 68%. Grant share is based on the ratio of number of grants to total number of patents.
Optimizing leaching operations in mining based on material overlap
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The patent application (Publication Number: US20240037462A1) discloses a method for optimizing metal production in mining operations through a series of innovative steps. The method involves determining the overlap percentage of submodules in different lifts, calculating tonnage and metal content in each submodule, and creating a tonnage weighted average of metal. This average is then used to adjust leaching operations or models to enhance metal production efficiency. Additionally, adjustments based on the total tonnage weighted average of metal can optimize material stacking and ore routing, leading to improved production outcomes.
Furthermore, the method includes utilizing historical data, sensor-acquired mine plan data, and a trained predictive model to forecast mining production targets. By analyzing deviations between forecast and actual data, adjustments are made to leaching operations based on various parameters and the total tonnage weighted average of metal. The patent also covers determining acid gaps, compacted material percentages, and their impact on metal production, providing a comprehensive approach to optimizing leaching processes. Overall, the method outlined in the patent aims to enhance metal production efficiency through real-time adjustments and predictive modeling based on key parameters and historical data.
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