公开(公告)号：US20230417552A1

公开(公告)日：2023-12-28

申请号：US17850884

申请日：2022-06-27

Applicant: FREEPORT MINERALS CORPORATION

Inventor： Luciano Kiniti Issoe ,  Tianfang Ni ,  Luke Gerdes ,  Dana Geislinger ,  Travis Gaddie ,  Margaret Alden Tinsley ,  Muneeb Alam ,  Steven Chad Richardson ,  Akaash Sanyal ,  Raquel Crossman ,  Cory A. Demieville ,  Robyn Freeman ,  Oleksandr Klesov

IPC: G01C21/00 ,  G06N5/02

CPC classification number: G01C21/005 ,  G06N5/022 ,  G01C21/3811 ,  G01C21/3826

Abstract: The method may comprise receiving historical data (e.g., mineralogy data, irrigation data, raffinate data, heat data, lift height data, geographic data on ore placement and/or blower data); training a predictive model using the historical data to create a trained predictive model; adding future assumption data to the trained predictive model; running the forecast engine for a plurality of parameters to obtain forecast data for a mining production target; comparing the forecast data for the mining production target to the actual data for the mining production target; determining deviations between the forecast data and the actual data, based on the comparing; and changing each of the plurality of parameters from the forecast data to the actual data to determine a contribution to the deviations for each of the plurality of parameters.

公开(公告)号：US11521138B1

公开(公告)日：2022-12-06

申请号：US17850834

申请日：2022-06-27

Applicant: FREEPORT MINERALS CORPORATION

Inventor： Dana Geislinger ,  Travis Gaddie ,  Margaret Alden Tinsley ,  Muneeb Alam ,  Steven Chad Richardson ,  Akaash Sanyal ,  Raquel Crossman ,  Tianfang Ni ,  Cory A. Demieville ,  Luke Gerdes ,  Robyn Freeman ,  Oleksandr Klesov ,  Luciano Kiniti Issoe

IPC: G06Q10/04 ,  G06Q50/02 ,  C22B15/00 ,  C22B3/06 ,  G06Q10/06

Abstract: The method may comprise receiving historical data (e.g., mineralogy data, irrigation data, raffinate data, heat data, lift height data, geographic data on ore placement and/or blower data); training a predictive model using the historical data to create a trained predictive model; adding future assumption data to the trained predictive model; running the forecast engine for a plurality of parameters to obtain forecast data for a mining production target; comparing the forecast data for the mining production target to the actual data for the mining production target; determining deviations between the forecast data and the actual data, based on the comparing; and changing each of the plurality of parameters from the forecast data to the actual data to determine a contribution to the deviations for each of the plurality of parameters.