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WO2025165946A1 - Estimation de pertes de récupération dues à un matériau grossier à l'aide d'une technologie cyclonetractm pst et d'un apprentissage automatique - Google Patents

Estimation de pertes de récupération dues à un matériau grossier à l'aide d'une technologie cyclonetractm pst et d'un apprentissage automatique

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Publication number
WO2025165946A1
WO2025165946A1 PCT/US2025/013719 US2025013719W WO2025165946A1 WO 2025165946 A1 WO2025165946 A1 WO 2025165946A1 US 2025013719 W US2025013719 W US 2025013719W WO 2025165946 A1 WO2025165946 A1 WO 2025165946A1
Authority
WO
WIPO (PCT)
Prior art keywords
cyclone
signaling
pst
cyclones
particle size
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/US2025/013719
Other languages
English (en)
Inventor
Rodrigo Alfredo BRUNA DURAN
Alejandro Andres RAMOS BARRAZA
Robert John MARON
Alejandro JAQUE ROJAS
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Cidra Corporte Services LLC
Original Assignee
Cidra Corporte Services LLC
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Cidra Corporte Services LLC filed Critical Cidra Corporte Services LLC
Publication of WO2025165946A1 publication Critical patent/WO2025165946A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B04—CENTRIFUGAL APPARATUS OR MACHINES FOR CARRYING-OUT PHYSICAL OR CHEMICAL PROCESSES
    • B04C—APPARATUS USING FREE VORTEX FLOW, e.g. CYCLONES
    • B04C5/00—Apparatus in which the axial direction of the vortex is reversed
    • B04C5/24—Multiple arrangement thereof
    • B04C5/28—Multiple arrangement thereof for parallel flow
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B04—CENTRIFUGAL APPARATUS OR MACHINES FOR CARRYING-OUT PHYSICAL OR CHEMICAL PROCESSES
    • B04C—APPARATUS USING FREE VORTEX FLOW, e.g. CYCLONES
    • B04C11/00—Accessories, e.g. safety or control devices, not otherwise provided for, e.g. regulators, valves in inlet or overflow ducting
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01D—SEPARATION
    • B01D21/00—Separation of suspended solid particles from liquids by sedimentation
    • B01D21/26—Separation of sediment aided by centrifugal force or centripetal force
    • B01D21/267—Separation of sediment aided by centrifugal force or centripetal force by using a cyclone
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/02—Investigating particle size or size distribution
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N2015/1029—Particle size

Definitions

  • This application also relates to the following: US 12,121,906, (WFMB no.712-002.464-1-1//CCS-0207WO), and corresponding PCT/US2019/022943, which claimed benefit to US provisional application no.62/664,672, filed 19 March 2018; US 10,830,623 (WFMB no.712-002.419-1-1//CCS-0135WO), and corresponding PCT/US2016/016721, which claimed benefit to US provisional application no.62/112,433, filed 5 February 2015; and US 10,309,887 (WFMB no.712-002.406-1-1//CCS-0120WO), and corresponding PCT/US2014/012510 , which claimed benefit to US provisional application no.61/755,305, filed 22 January 2013, which are all incorporated by reference in their entirety.
  • Such mineral processing systems may include the use of the Assignee's known CYCLONEtrac TM Particle Size Tracking (PST) technology that provides real-time granulometry measurements of overflow streams of individual cyclones, allowing continuous monitoring of the individual classification performance of each one.
  • PST Particle Size Tracking
  • the present invention provides a new and unique methodology for quantifying the impact of different oversize events on copper recovery, e.g., using historical PST and process data. For example, from historical PST data, an average deviation from a high limit of the control mesh was calculated and then used to train different machine learning models to predict the decrease in the recovery due to oversize events.
  • the present invention may include, or take the form of, apparatus for monitoring overflow streams in a battery of cyclones of a mineral recovery process, comprising: a signal processor or processing module configure to: receive particle size tracking (PSTi) signaling containing information about particle sizes of coarse particles provided by overflows of cyclones in a battery of cyclones, and historic PST i signaling containing information about a consolidated database having time-based entries of plant recovery percentages and deviations from a particle size high limit (HL) in a predetermined time interval (t o , t 1 ) for each cyclone; determine if the particle size tracking (PST i ) signaling contains a particle size of coarse particles provided by an overflow of a cyclone (C i ) in the battery of cyclones that is an outlier in relation to the historic PST i signaling, and if so, then also determine if the cyclone (C i ) has an average
  • the apparatus may include one or more of the following features:
  • the average deviation may be determined by the following equation:
  • the consolidated dataset may include rows of dates and columns for each cyclone Ci, where the columns including a CU_Rec (%), C1_dev, C2_dev, ..., Cn- 1_dev and Cn_dev for each date, where the CU_Rec (%) is a percentage of copper recovery, and where Ci_dev is a deviation of the percentage of copper recovery for a respective cyclone Ci.
  • the CU_Rec (%) may be a percentage of copper recovery determined by the following equation: .
  • the corresponding signaling may include control signaling containing information to change the mineral recovery process, including to change either water addition to, or decreasing tonnage of, crushed ore being processed; or opening another cyclone in the battery of cyclones and then closing the cyclone having oversize coarse particles in an overflow stream.
  • the apparatus may include PST sensors, each PST sensor arranged in relation to a respective overflow of a respective cyclone in the battery of cyclones and configured to sense and track the particle sizes of the coarse particles provided by each cyclone's overflow and provide the PSTi signaling.
  • the apparatus may include a consolidated dataset memory module configured to store the consolidated database of the particle sizes of the coarse particles provided by each cyclone's overflows, including daily plant recovery percentage (%) and deviations from the particle size high limit (HL) for each cyclone.
  • the apparatus may include a machine learning model module configured to receive the PSTi signaling, process the PSTi signaling based a machine learning model, and provide the historical PSTi signaling for storing as the consolidated database, including updating the consolidated database over time.
  • the machine learning model may include one of the following: ⁇ Multiple linear regression, ⁇ Random Forest Regressor, ⁇ Support Vector Machine Regressor, and ⁇ Neural Network.
  • the machine learning model may include using a Singular Value Decomposition (SVD) that decomposes a matrix into component matrices.
  • SSD Singular Value Decomposition
  • the present invention may include, or take the form of, a method for monitoring overflow streams in a battery of cyclones of a mineral recovery process, featuring: configuring a signal processor or processing module to: receive particle size tracking (PSTi) signaling containing information about particle sizes of coarse particles provided by overflows of cyclones in a battery of cyclones, and historic PSTi signaling containing information about a consolidated database having time-based entries of plant recovery percentages and deviations from a particle size high limit (HL) in a predetermined time interval (to, t1) for each cyclone; determine if the particle size tracking (PSTi) signaling contains a particle size of coarse particles provided by an overflow of a cyclone (Ci) in the battery of cyclones that is an outlier in relation to the historic PST i signaling, and if so, then also determine if the cyclone (C
  • PSTi particle size tracking
  • Figure 1 shows a graph of PST signals in relation to time in a normal and desired operation in a mineral processing system.
  • Figure 2 shows a graph of PST signals in relation to time having an oversize event.
  • Figure 3 shows a graph of variations in copper recovery in relation to total PST deviation due to oversized events.
  • Figure 4 shows a signal processor or processing module for implementing the signal processing, according to some embodiments of the present invention.
  • Figure 5 shows other components for implementing a mineral recovery process in conjunction with the signal processor or processing module shown in Figure 4, e.g., including Fig.5A showing a battery of cyclones, Fig.5B showing PST sensors, Fig.5C showing a consolidated dataset memory module, and Fig.5D showing a machine learning model module, all according to some embodiments of the present invention.
  • Figure 6 shows steps for implementing the method, according to some embodiments of the present invention.
  • Figure 7A is a photograph of a CYCLONEtrac TM PST particle sizing sensor mounted on a hydrocyclone overflow pipe, e.g., that is known in the art.
  • Figure 7B is a diagram of a CYCLONEtrac TM PST plant scale installation, e.g., using individual hydrocyclone overflow sensors (AKA PST particle sizing sensor), that is known in the art.
  • the graphs in Figures 1-3 are shown in color.
  • WFMB/CiDRA Docket Nos.712-002.475-1/CCS-0224WO DETAILED DESCRIPTION OF BEST MODE OF THE INVENTION Introduction As set forth above, the introduction of the Assignee's CYCLONEtrac TM PST technology on concentrator plants has enabled monitoring the performance of each cyclone of a battery in real time, the detection of oversize events on a particular cyclone and the ability to take actions to correct it.
  • the average deviation above the high limit of a cyclone in an interval is given by (2): (2)
  • a dataset with eight-month process data in a daily basis was used.
  • the equation (2) was then applied to each cyclone ( batteries, cyclones each) for each day of the process’ dataset to obtain a dataset with a column for the recovery and columns, one for each cyclone with the daily average deviation from the of the control mesh.
  • the dataset contained 180 rows (each one representing a day). Table 1 shows an extract of the consolidated dataset.
  • Truncated Singular Value Decomposition which is a method that decomposes a matrix into three other matrices, reducing the number of dimensions while preserving the most valuable information. It allows to keep components that explain most of the variance of the data.
  • the number of components used in this work was selected for having at least 90% of the variance explained, giving for this dataset, seven components.
  • the four models were trained using the full consolidated dataset and the truncated dataset, having in total eight models.
  • WFMB/CiDRA Docket Nos.712-002.475-1/CCS-0224WO shows the metrics of the eight models, where it can be seen the Random Forest Regressor had the best performance, thus, it was the model selected for the next stage of the study.
  • the x-axis shows the “Total PST deviation” meaning the summation of deviations for all cyclones for a particular date, while the y-axis shows the variation of recovery to the base recovery of the model.
  • FIG. 4 shows apparatus generally indicated as 10 according to some embodiments of the present invention.
  • the apparatus 10 may include a signal processor or processing module 100 that receives particle size tracking (PSTi) signaling containing information about particle sizes of coarse particles provided by overflows of cyclones in a battery of cyclones, and historic PST i signaling containing information about a consolidated database having time-based entries of plant recovery percentages and deviations from a particle size high limit (HL) in a predetermined time interval (t o , t 1 ) for each cyclone; determine if the particle size tracking (PST i ) signaling contains a particle size of coarse particles provided by an overflow of a cyclone (C i ) in the battery of cyclones that is an outlier in relation to the historic PST i signaling, and if so, then also determine if the particle size tracking (PSTi ) signaling contains a particle size of coarse particles provided by an overflow of a cyclone (C i ) in
  • the functionality of the signal processor or processing module 100 may be implemented using hardware, software, firmware, or a combination thereof, although the scope of the invention is not intended to be limited to any particular embodiment thereof.
  • the signal processor would be one or more microprocessor-based architectures having a microprocessor, a random access memory (RAM), a read only memory (ROM), input/output devices and control, data and address buses connecting the same.
  • a person skilled in the art would be able to program such a microprocessor-based implementation to perform the functionality set forth in the signal processing block 100, as well as other functionality described herein without undue experimentation.
  • the scope of the invention is not intended to be limited to any particular implementation using technology now known or later developed in the future.
  • the scope of the invention is intended to include the signal processor being a stand alone module, as shown, or in the combination with other circuitry for implementing another module.
  • the apparatus 10 may include one or more other modules, components, circuits, or circuitry for implementing other functionality associated with the apparatus that does not form part of the underlying invention, and thus is not described in detail herein.
  • the one or more other modules, components, circuits, or circuitry may include random access memory, read only memory, input/output circuitry and data and address buses for use in relation to implementing the signal processing functionality of the signal processor 100, or devices or components related to mixing or pouring concrete in a ready-mix concrete truck or adding chemical additives, etc.
  • the apparatus 10 may include other components and modules, e.g., like elements 20, 30, 40, 50 that are shown in further detail in Figure 5.
  • Figure 5A shows a battery of cyclones 20 configured to process crushed ore, each cyclone having an overflow O (Fig.7A) for providing coarse particles for further processing.
  • FIG. 5B shows PST sensors 30, each PST sensor 30 being arranged in relation to a respective overflow O (Fig.7A) of a respective cyclone in the battery of cyclones and configured to sense and track the particle sizes of the coarse particles provided by each cyclone's overflow O and provide the PSTi signaling.
  • Figure 5C shows a consolidated dataset memory module 40 configured to store the consolidated database of the particle sizes of the coarse particles provided by each cyclone's overflows O (Fig.7A), including daily plant recovery percentage (%) and deviations from the particle size high limit (HL) for each cyclone.
  • Fig.7A daily plant recovery percentage
  • HL particle size high limit
  • Figure 5D shows a machine learning model module 50 configured to receive the PST i signaling, process the PST i signaling based a machine learning model, and provide the historical PST i signaling for storing and/or updating over time as the WFMB/CiDRA Docket Nos.712-002.475-1/CCS-0224WO consolidated database.
  • a machine learning model module 50 configured to receive the PST i signaling, process the PST i signaling based a machine learning model, and provide the historical PST i signaling for storing and/or updating over time as the WFMB/CiDRA Docket Nos.712-002.475-1/CCS-0224WO consolidated database.
  • machine learning models like a Random Forest Regressor, Neural Network, Multiple Linear Regression, and Support Vector Machine Regressor with or with implementing the same using SVC as shown in Table 1 are known in the art; and the scope of the invention is not intended to be limited to any particle type or kind thereof either now known or later developed in the future.
  • the present invention may be implemented using other types or kinds of machine learning models either now known or later developed in the future, e.g., other than a Random Forest Regressor, Neural Network, Multiple linear Regression, and Support Vector Machine Regressor, again without undue experimentation. Detect When a Cyclone is An Outlier.
  • Figure 3 shows variations in recovery due to oversized events, e.g., when one or more outliers are detected.
  • the scope of the invention is not intended to any particular type, kind or way of detecting of one or more outliers, which may includes types, kinds or ways of detecting of one or more outliers either now known or later developed in the future.
  • Figure 6 The Method Figure 6 shows a method generally indicated as 150 for monitoring overflow streams in a battery of cyclones of a mineral recovery process, according to the present invention, having steps 152, 154 and 156. WFMB/CiDRA Docket Nos.712-002.475-1/CCS-0224WO In particular, the method 150 may include configuring a signal processor or processing module like element 100 (Fig.
  • step 162 particle size tracking (PSTi) signaling containing information about particle sizes of coarse particles provided by overflows of cyclones in a battery of cyclones, and historic PSTi signaling containing information about a consolidated database having time-based entries of plant recovery percentages and deviations from a particle size high limit (HL) in a predetermined time interval (to, t1) for each cyclone; determine in step 154 if the particle size tracking (PSTi) signaling contains a particle size of coarse particles provided by an overflow of a cyclone (Ci) in the battery of cyclones that is an outlier in relation to the historic PSTi signaling, and if so, then also determine if the cyclone (Ci) has an average deviation (Dl) of a particle size tracking (PSTi) that is above the particle size high limit (HL) in the predetermined time interval (t o , t 1 ); and provide in step 156 corresponding signaling containing information to control the operation of the cyclone
  • Figures 7A and 7B The PST Particle Sizing Sensor
  • Figure 7A shows a PST particle sizing sensor 30 mounted on a hydrocyclone overflow pipe O
  • Figure 7B shows a PST plant scale installation diagram, e.g., using individual hydrocyclone overflow sensors (aka PST particle sizing sensor).
  • the PST particle sizing sensor includes WFMB/CiDRA Docket Nos.712-002.475-1/CCS-0224WO a mounting bracket MB configured to arrange the PST particle sizing sensor 30 on the hydrocyclone classifier overflow pipe O of at least one hydrocyclone in a hydrocyclone battery.
  • the PST particle sizing sensor 30 is the CYCLONEtrac TM PST particle sizing sensor, which was developed, manufactured and distributed by the Assignee of the present invention.
  • the PST particle sizing sensor 30 is disclosed in the aforementioned US 12,121,906 (WFMB no.712-002.464-1-1//CCS- 0207WO), US 10,830,623 (WFMB no.712-002.419-1-1//CCS-0135WO), and US 10,309,887 (WFMB no.712-002.406-1-1//CCS-0120WO), which are all incorporated by reference in their entirety.
  • the present invention is disclosed using the Assignee's CYCLONEtrac TM PST particle sizing sensor 30, embodiments are envisioned, and the scope of the invention is intended to include, e.g., using other types or kind of particle sizing sensor configured to be arranged on arranged on a hydrocyclone classifier overflow pipe O of at least one hydrocyclone in a hydrocyclone battery that are both now known or later developed in the future within the spirit of the present invention.
  • the present invention may be used in, or form part of, or used in conjunction with, industrial processes like a mineral extraction processing system for extracting or separating minerals in a fluidic medium that are either now known or later developed in the future, including any mineral process, such as those related to processing substances or compounds that result from inorganic processes of nature and/or that are mined from the ground, as well as including either other WFMB/CiDRA Docket Nos.712-002.475-1/CCS-0224WO extraction processing systems or other industrial processes, where the extraction, or separating, or sorting, or classification, of product by size, or density, or some electrical characteristic, is critical to overall industrial process performance.
  • any mineral process such as those related to processing substances or compounds that result from inorganic processes of nature and/or that are mined from the ground, as well as including either other WFMB/CiDRA Docket Nos.712-002.475-1/CCS-0224WO extraction processing systems or other industrial processes, where the extraction, or separating, or sorting, or classification,
  • Nomenclature Particle Size Tracking average deviation of PST signal from the high limit for cyclone . beginning of the analyzed period. end of the analyzed period. PST signal of control mesh for cyclone high limit of control mesh. coefficient of determination. root of the mean squared error. mean absolute error. number of batteries. number of cyclones per battery. References A. S. K. Christoffersen et al. (2021). Benchmarking Machine Learning Algorithms for Greenhouse Gas Flux Estimation from Sparse Data. Environmental Modelling & Software, 143, 105011. https://doi.org/10.1016/j.envsoft.2021.105011 Leys, C., Ley, C., Klein, .

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Abstract

La présente invention concerne une méthodologie nouvelle et unique permettant de quantifier l'impact de différents événements de surdimensionnement sur la récupération de cuivre, par exemple, à l'aide de données PST historiques et de données de traitement. Par exemple, à partir de données PST historiques, un écart moyen par rapport à une limite élevée du maillage de contrôle a été calculé et ensuite utilisé pour entraîner différents modèles d'apprentissage automatique en vue de prédire la baisse de la récupération due à des événements de surtaille. Le meilleur modèle d'apprentissage automatique pour ce procédé est un régresseur Random Forests avec une erreur absolue moyenne de 0,78 point de pourcentage et un coefficient de détermination (r2) égal à 0,901.
PCT/US2025/013719 2024-01-30 2025-01-30 Estimation de pertes de récupération dues à un matériau grossier à l'aide d'une technologie cyclonetractm pst et d'un apprentissage automatique Pending WO2025165946A1 (fr)

Applications Claiming Priority (2)

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US202463626648P 2024-01-30 2024-01-30
US63/626,648 2024-01-30

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6269952B1 (en) * 1996-12-11 2001-08-07 Earth Sciences Limited Methods and apparatus for use in processing and treating particulate material
US20180272362A1 (en) * 2015-01-28 2018-09-27 Cidra Corporate Services Inc. Detection of cyclone wear or damage using individual cyclone overflow measurement
US20210025028A1 (en) * 2016-02-15 2021-01-28 Uranium Beneficiation Pty Ltd Uranium processing using hydrocyclone beneficiation
US20210106930A1 (en) * 2018-03-15 2021-04-15 Vulco S.A. Hydrocyclone Monitoring System And Method

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6269952B1 (en) * 1996-12-11 2001-08-07 Earth Sciences Limited Methods and apparatus for use in processing and treating particulate material
US20180272362A1 (en) * 2015-01-28 2018-09-27 Cidra Corporate Services Inc. Detection of cyclone wear or damage using individual cyclone overflow measurement
US20210025028A1 (en) * 2016-02-15 2021-01-28 Uranium Beneficiation Pty Ltd Uranium processing using hydrocyclone beneficiation
US20210106930A1 (en) * 2018-03-15 2021-04-15 Vulco S.A. Hydrocyclone Monitoring System And Method

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