	US 12,106,247 B2
	System and method for determining estimated remaining mineral in a stockpile
Oleksandr Klesov, Lexington, MA (US); Luke Gerdes, Framingham, MA (US); Dana Geislinger, Chandler, AZ (US); Margaret Alden Tinsley, Titusville, FL (US); Robyn Freeman, Menlo Park, CA (US); Akaash Sanyal, Boston, MA (US); Muneeb Alam, Alexandria, VA (US); Raquel Crossman, Mesa, AZ (US); Travis Gaddie, Phoenix, AZ (US); Steven Chad Richardson, Thatcher, AZ (US); Tianfang Ni, Boston, MA (US); Cory A. Demieville, Scottsdale, AZ (US); and Luciano Kiniti Issoe, Sao Paulo (BR)
Assigned to FREEPORT MINERALS CORPORATION, Phoenix, AZ (US)
Filed by FREEPORT MINERALS CORPORATION, Phoenix, AZ (US)
Filed on Jun. 27, 2022, as Appl. No. 17/850,864.
Prior Publication US 2023/0419226 A1, Dec. 28, 2023
Int. Cl. G01N 33/24 (2006.01); C22B 15/00 (2006.01); G06Q 10/06 (2023.01); G06Q 10/0637 (2023.01); G06Q 50/02 (2024.01)

CPC G06Q 10/06375 (2013.01) [C22B 15/0065 (2013.01); G01N 33/24 (2013.01); G06Q 50/02 (2013.01)]

20 Claims

1. A method comprising:

obtaining, by one or more processors, days under leach, chemistry data and mineralogy data from a column test of a column of ore from a section of a stockpile;

adjusting, by the one or more processors, process parameters applied to the column of ore to create controlled conditions for simulating leaching in the column of ore;

increasing, by the one or more processors, accuracy of the chemistry data and the mineralogy data based on the controlled conditions;

providing, by the one or more processors, the chemistry data and the mineralogy data to a machine learning model to build a column test predictive model;

determining, by the one or more processors using the machine learning model, estimated remaining mineral in the section of the stockpile based on the column test predictive model;

refining, by the one or more processors, the machine learning model based on the estimated remaining mineral in the section of the stockpile; and

adjusting, by the one or more processors, leaching operations to continue and to optimize mining production based on the estimated remaining mineral in the section of the stockpile.