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公开(公告)号：US20230418269A1
公开(公告)日：2023-12-28
申请号：US17850855
申请日：2022-06-27
发明人： Steven Chad Richardson , Robyn Freeman , Luke Gerdes , Margaret Alden Tinsley , Dana Geislinger , Akaash Sanyal , Travis Gaddie , Muneeb Alam , Raquel Crossman , Tianfang Ni , Cory A. Demieville , Oleksandr Klesov , Luciano Kiniti Issoe
IPC分类号： G05B19/418
CPC分类号： G05B19/41865 , G05B19/4183 , G05B19/41885 , C22B3/04
摘要： 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.
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公开(公告)号：US20230417570A1
公开(公告)日：2023-12-28
申请号：US17850880
申请日：2022-06-27
发明人： Dana Geislinger , Margaret Alden Tinsley , Akaash Sanyal , Robyn Freeman , Travis Gaddie , Muneeb Alam , Steven Chad Richardson , Raquel Crossman , Tianfang Ni , Cory A. Demieville , Luke Gerdes , Oleksandr Klesov , Luciano Kiniti Issoe
IPC分类号： G01C21/00
CPC分类号： G01C21/3811 , G01C21/3826
摘要： 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.
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公开(公告)号：US20230417552A1
公开(公告)日：2023-12-28
申请号：US17850884
申请日：2022-06-27
发明人： 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
CPC分类号： G01C21/005 , G06N5/022 , G01C21/3811 , G01C21/3826
摘要： 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.
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公开(公告)号：US11521138B1
公开(公告)日：2022-12-06
申请号：US17850834
申请日：2022-06-27
发明人： 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
摘要： 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.
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