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公开(公告)号：US20240026492A1
公开(公告)日：2024-01-25
申请号：US18480354
申请日：2023-10-03
发明人： Casey J. Clayton , Richard Melecio Sanchez , Raquel Crossman , Luciano Kiniti Issoe , Tianfang Ni , Oleksandr Klesov , Luke Gerdes , Muneeb Alam , Joanna M. Robertson , Chase Zenner , John Warren Dean, JR.
CPC分类号： C22B7/007 , C22B3/04 , G05D16/20 , G08B21/182 , C22B15/0065 , Y02P10/20 , G05D16/101
摘要： The present disclosure provides a method comprising determining an ore map for a heap to identify a location of a recoverable metal value in the heap, delivering a leaching solution from a leaching solution source to a leaching solution regulating system, regulating at least one of a pressure, a mass flow rate, or a volumetric flow rate of the leaching solution to achieve a first target operational condition, wherein the first target operational condition is selected to optimize a set of operational parameters to maximize recovery of the recoverable metal value, delivering the leaching solution at the first target operational condition from the leaching solution regulating system to a subsurface leaching distribution system, and delivering the leaching solution at the first target operational condition from the subsurface leaching distribution system to the location of the recoverable metal value under a surface of the heap to leach and recover at least one metal value.
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公开(公告)号：US20240124951A1
公开(公告)日：2024-04-18
申请号：US18398613
申请日：2023-12-28
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
IPC分类号： G06Q10/04 , C22B3/06 , C22B15/00 , G06Q10/0631 , G06Q50/02
CPC分类号： G06Q10/04 , C22B3/06 , C22B15/0067 , C22B15/0095 , G06Q10/0631 , G06Q50/02
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：US20240026493A1
公开(公告)日：2024-01-25
申请号：US18480377
申请日：2023-10-03
发明人： Sarah Lyons , Joanna M. Robertson , Casey J. Clayton , Richard Melecio Sanchez , Raquel Crossman , Luciano Kiniti Issoe , Tianfang Ni , Oleksandr Klesov , Luke Gerdes , Muneeb Alam , Chase Zenner , John Warren Dean, JR.
CPC分类号： C22B7/007 , C22B3/04 , G05D16/20 , G08B21/182 , C22B15/0065 , Y02P10/20 , G05D16/101
摘要： The present disclosure provides a method comprising determining an ore map for a heap to identify a location of a recoverable metal-bearing material in the heap, wherein the metal-bearing material comprises iron and at least one other metal value, delivering a leaching solution from a leaching solution source to a leaching solution regulating system, wherein the leaching solution comprises an effective amount of citric acid and hydrogen peroxide, regulating at least one of a pressure, a mass flow rate, or a volumetric flow rate of the leaching solution to achieve a target operational condition, wherein the target operational condition is selected to optimize a set of operational parameters to maximize recovery of the at least one other metal value, delivering the leaching solution at the target operational condition from the leaching solution regulating system to the subsurface leaching distribution system, and delivering the leaching solution at the target operational condition from the subsurface leaching distribution system to the location of the recoverable metal-bearing material under a surface of the heap to leach and recover the at least one other metal value.
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公开(公告)号：US20230417724A1
公开(公告)日：2023-12-28
申请号：US17850866
申请日：2022-06-27
发明人： Cory A. Demieville , Dana Geislinger , Travis Gaddie , Margaret Alden Tinsley , Muneeb Alam , Steven Chad Richardson , Akaash Sanyal , Raquel Crossman , Tianfang Ni , Luke Gerdes , Robyn Freeman , Oleksandr Klesov , Luciano Kiniti Issoe
CPC分类号： G01N33/24 , G01N15/0227
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：US11823099B1
公开(公告)日：2023-11-21
申请号：US18306749
申请日：2023-04-25
发明人： Luciano Kiniti Issoe , Tianfang Ni , Oleksandr Klesov , Luke Gerdes , Raquel Crossman , Muneeb Alam , Dana Geislinger , Travis Gaddie , Margaret Alden Tinsley , Steven Chad Richardson , Akaash Sanyal , Cory A. Demieville , Robyn Freeman
IPC分类号： G06Q10/04 , G06Q50/02 , C22B15/00 , C22B3/06 , G06Q10/0631
CPC分类号： G06Q10/04 , C22B3/06 , C22B15/0067 , C22B15/0095 , G06Q10/0631 , G06Q50/02
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：US12111303B2
公开(公告)日：2024-10-08
申请号：US17850866
申请日：2022-06-27
发明人： Cory A. Demieville , Dana Geislinger , Travis Gaddie , Margaret Alden Tinsley , Muneeb Alam , Steven Chad Richardson , Akaash Sanyal , Raquel Crossman , Tianfang Ni , Luke Gerdes , Robyn Freeman , Oleksandr Klesov , Luciano Kiniti Issoe
IPC分类号： G01N33/24 , G01N15/0227
CPC分类号： G01N33/24 , G01N15/0227
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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7.发明公开公开(公告)号：US20240242135A1
公开(公告)日：2024-07-18
申请号：US18433776
申请日：2024-02-06
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
IPC分类号： G06Q10/04 , C22B3/06 , C22B15/00 , G06Q10/0631 , G06Q50/02
CPC分类号： G06Q10/04 , C22B3/06 , C22B15/0067 , C22B15/0095 , G06Q10/0631 , G06Q50/02
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：US20230419132A1
公开(公告)日：2023-12-28
申请号：US17850874
申请日：2022-06-27
发明人： Luciano Kiniti Issoe , Tianfang Ni , Oleksandr Klesov , Luke Gerdes , Raquel Crossman , Muneeb Alam , Dana Geislinger , Travis Gaddie , Margaret Alden Tinsley , Steven Chad Richardson , Akaash Sanyal , Cory A. Demieville , Robyn Freeman
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：US20240320571A1
公开(公告)日：2024-09-26
申请号：US18736402
申请日：2024-06-06
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
IPC分类号： G06Q10/04 , C22B3/06 , C22B15/00 , G06Q10/0631 , G06Q50/02
CPC分类号： G06Q10/04 , C22B3/06 , C22B15/0067 , C22B15/0095 , G06Q10/0631 , G06Q50/02
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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10.发明公开公开(公告)号：US20240127135A1
公开(公告)日：2024-04-18
申请号：US18398701
申请日：2023-12-28
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
IPC分类号： G06Q10/04 , C22B3/06 , C22B15/00 , G06Q10/0631 , G06Q50/02
CPC分类号： G06Q10/04 , C22B3/06 , C22B15/0067 , C22B15/0095 , G06Q10/0631 , G06Q50/02
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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