CA3166555C - Method and device for automated operation of a plant for storing bulk material - Google Patents
Method and device for automated operation of a plant for storing bulk material Download PDFInfo
- Publication number
- CA3166555C CA3166555C CA3166555A CA3166555A CA3166555C CA 3166555 C CA3166555 C CA 3166555C CA 3166555 A CA3166555 A CA 3166555A CA 3166555 A CA3166555 A CA 3166555A CA 3166555 C CA3166555 C CA 3166555C
- Authority
- CA
- Canada
- Prior art keywords
- stockpile
- storage
- physical
- data
- digital
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 38
- 239000013590 bulk material Substances 0.000 title claims abstract description 24
- 239000000463 material Substances 0.000 claims abstract description 135
- 239000000126 substance Substances 0.000 claims abstract description 26
- 238000004364 calculation method Methods 0.000 claims description 15
- 238000012545 processing Methods 0.000 claims description 9
- 238000012986 modification Methods 0.000 claims description 8
- 230000004048 modification Effects 0.000 claims description 8
- 238000005516 engineering process Methods 0.000 claims description 4
- 230000001419 dependent effect Effects 0.000 claims description 2
- 238000000605 extraction Methods 0.000 abstract description 11
- BWHMMNNQKKPAPP-UHFFFAOYSA-L potassium carbonate Substances [K+].[K+].[O-]C([O-])=O BWHMMNNQKKPAPP-UHFFFAOYSA-L 0.000 description 18
- 238000005065 mining Methods 0.000 description 8
- 238000012360 testing method Methods 0.000 description 7
- KWYUFKZDYYNOTN-UHFFFAOYSA-M Potassium hydroxide Chemical compound [OH-].[K+] KWYUFKZDYYNOTN-UHFFFAOYSA-M 0.000 description 6
- 229940072033 potash Drugs 0.000 description 6
- 235000015320 potassium carbonate Nutrition 0.000 description 6
- 229910000027 potassium carbonate Inorganic materials 0.000 description 6
- 235000011181 potassium carbonates Nutrition 0.000 description 6
- 239000003245 coal Substances 0.000 description 5
- 238000004590 computer program Methods 0.000 description 5
- 239000002994 raw material Substances 0.000 description 5
- 238000004422 calculation algorithm Methods 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 4
- 238000002156 mixing Methods 0.000 description 4
- 235000019738 Limestone Nutrition 0.000 description 3
- 239000004568 cement Substances 0.000 description 3
- 239000003077 lignite Substances 0.000 description 3
- 239000006028 limestone Substances 0.000 description 3
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 239000004927 clay Substances 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 239000003337 fertilizer Substances 0.000 description 2
- 239000011521 glass Substances 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 239000002245 particle Substances 0.000 description 2
- 238000005192 partition Methods 0.000 description 2
- 150000003839 salts Chemical class 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 239000004575 stone Substances 0.000 description 2
- 235000008733 Citrus aurantifolia Nutrition 0.000 description 1
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 description 1
- 235000011941 Tilia x europaea Nutrition 0.000 description 1
- 208000002697 Tooth Abrasion Diseases 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 229910052570 clay Inorganic materials 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 239000008187 granular material Substances 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 239000010440 gypsum Substances 0.000 description 1
- 229910052602 gypsum Inorganic materials 0.000 description 1
- 229910052742 iron Inorganic materials 0.000 description 1
- 239000004571 lime Substances 0.000 description 1
- 238000005297 material degradation process Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012821 model calculation Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 230000002269 spontaneous effect Effects 0.000 description 1
- 239000011232 storage material Substances 0.000 description 1
- 239000011593 sulfur Substances 0.000 description 1
- 229910052717 sulfur Inorganic materials 0.000 description 1
- 238000001931 thermography Methods 0.000 description 1
- 230000036962 time dependent Effects 0.000 description 1
- 230000005919 time-dependent effect Effects 0.000 description 1
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G1/00—Storing articles, individually or in orderly arrangement, in warehouses or magazines
- B65G1/02—Storage devices
- B65G1/04—Storage devices mechanical
- B65G1/0478—Storage devices mechanical for matrix-arrangements
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G1/00—Storing articles, individually or in orderly arrangement, in warehouses or magazines
- B65G1/02—Storage devices
- B65G1/04—Storage devices mechanical
- B65G1/137—Storage devices mechanical with arrangements or automatic control means for selecting which articles are to be removed
- B65G1/1371—Storage devices mechanical with arrangements or automatic control means for selecting which articles are to be removed with data records
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G2203/00—Indexing code relating to control or detection of the articles or the load carriers during conveying
- B65G2203/02—Control or detection
- B65G2203/0208—Control or detection relating to the transported articles
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G2203/00—Indexing code relating to control or detection of the articles or the load carriers during conveying
- B65G2203/02—Control or detection
- B65G2203/0208—Control or detection relating to the transported articles
- B65G2203/0233—Position of the article
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G2207/00—Indexing codes relating to constructional details, configuration and additional features of a handling device, e.g. Conveyors
- B65G2207/40—Safety features of loads, equipment or persons
Landscapes
- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Warehouses Or Storage Devices (AREA)
- General Factory Administration (AREA)
Abstract
In a method and a device for operating a plant for storing bulk material, having at least one stockpile-like storage region, in particular a plant for material transloading of bulk material or a material extraction plant, in particular, a digital stockpile model is compiled for the at least one storage region and the occurrence of physical and/or chemical states that may arise during the storage of the respectively stored material is predicted on the basis of the compiled stockpile model, and the predicted physical respectively chemical states are used during storage operation in order to ensure material-friendly storage of the stored material.
Description
METHOD AND DEVICE FOR AUTOMATED OPERATION OF A PLANT FOR STORING
BULK MATERIAL
The invention relates to a method for operating a plant for storing bulk material, for example a plant for material transloading of bulk material or a material extraction plant used in strip mining, with storage operation of excavated material respectively bulk material mined in the material extraction plant.
The present invention also relates to a computer program, to a machine-readable data medium for storing the computer program and to a device by means of which the method according to the invention can be carried out.
During stockyard holding in a material transloading site for bulk material, for example a material transloading site of a shipping port or a material mining site, the materials are temporarily stored in so-called stockpiles. These stockpiles consist of mounds of for example ore, lignite, raw material for cement production, or salts, for example potash (potassium carbonate) or the like. During the stockyard holding respectively storage of bulk material, physical influences on the respectively stored material, due to the storage, are often taken into account insufficiently or even not at all.
WO 2009/075945 Al discloses a method for simulating a material reservoir, for example an oil or gas deposit for oil or gas production. A reservoir model is in this case generated, the generated model being partitioned into various domains, each domain of which corresponds to an efficient partition for a particular part of the model.
During the method, the simulation of the reservoir is divided into a plurality of processing elements and a multiplicity of processing elements are processed in parallel on the basis of the partitions.
In the three-dimensional reservoir model, for example, the operation of an oil and/or gas reservoir having one or more vertical boreholes is simulated. The model is divided by a grid network into a plurality of nodes, and the nodes of the model may have different sizes.
From JP 2017 138166 A is a monitoring system for a coal stockpile, various types of coal being stored while grouped together.
Date Recue/Date Received 2024-02-08 CN 104 634 815 B discloses a method for simulating the spontaneous ignition of a coal stockpile.
DE 10 2015 104229 Al discloses a system and a method for operating a stockpile with a three-dimensional stockpile model.
Peuker Urs: "Analyse und Strategien zum Verringern von Anbackungen bei der Lagerung von Mehrstoffsystemen insbesondere Glasgemenge in Rohstoffsilo" [Analysis and strategies for the reduction of caking during the storage of multi-material systems, in particular quantities of glass in a raw materials silo], 12.12.2016, pages 1-116, XP0557793396 discloses a final report of a project for analyzing and developing strategies for reducing caking during the storage of multi-material systems, in particular quantities of glass in raw materials silos.
The invention is based on the concept, in a plant of the type in question here for storing bulk material, for example a plant for material transloading of bulk material (so-called "material transloading site" for transloading of bulk material in a material extraction plant operated in strip mining, to control the storage operation of material stored there on the calculation basis of a computer-assisted stockyard model respectively stockpile model, in a way which is material-friendly and as automated as possible.
It is specifically not material-friendly for the bulk material to have to be broken up again during storage because of hardening or caking before being removed.
It should be emphasized that the invention may also be used in so-called "blending bed"
plants in which different materials and/or different material qualities of a material and/or materials having different material states are (temporarily) stored in a blending bed in order to mix them together. In such an application scenario, in addition to the material state, the digital stockpile model may also describe which material respectively which material quality is present at a particular position in the stockpile. This also makes it possible to predict the precise degree of blending of material to be stored after mixing has been carried out.
The invention is based on the discovery that for the stockyard holding in a material transloading site for bulk material, for example a stockyard for the transloading of
BULK MATERIAL
The invention relates to a method for operating a plant for storing bulk material, for example a plant for material transloading of bulk material or a material extraction plant used in strip mining, with storage operation of excavated material respectively bulk material mined in the material extraction plant.
The present invention also relates to a computer program, to a machine-readable data medium for storing the computer program and to a device by means of which the method according to the invention can be carried out.
During stockyard holding in a material transloading site for bulk material, for example a material transloading site of a shipping port or a material mining site, the materials are temporarily stored in so-called stockpiles. These stockpiles consist of mounds of for example ore, lignite, raw material for cement production, or salts, for example potash (potassium carbonate) or the like. During the stockyard holding respectively storage of bulk material, physical influences on the respectively stored material, due to the storage, are often taken into account insufficiently or even not at all.
WO 2009/075945 Al discloses a method for simulating a material reservoir, for example an oil or gas deposit for oil or gas production. A reservoir model is in this case generated, the generated model being partitioned into various domains, each domain of which corresponds to an efficient partition for a particular part of the model.
During the method, the simulation of the reservoir is divided into a plurality of processing elements and a multiplicity of processing elements are processed in parallel on the basis of the partitions.
In the three-dimensional reservoir model, for example, the operation of an oil and/or gas reservoir having one or more vertical boreholes is simulated. The model is divided by a grid network into a plurality of nodes, and the nodes of the model may have different sizes.
From JP 2017 138166 A is a monitoring system for a coal stockpile, various types of coal being stored while grouped together.
Date Recue/Date Received 2024-02-08 CN 104 634 815 B discloses a method for simulating the spontaneous ignition of a coal stockpile.
DE 10 2015 104229 Al discloses a system and a method for operating a stockpile with a three-dimensional stockpile model.
Peuker Urs: "Analyse und Strategien zum Verringern von Anbackungen bei der Lagerung von Mehrstoffsystemen insbesondere Glasgemenge in Rohstoffsilo" [Analysis and strategies for the reduction of caking during the storage of multi-material systems, in particular quantities of glass in a raw materials silo], 12.12.2016, pages 1-116, XP0557793396 discloses a final report of a project for analyzing and developing strategies for reducing caking during the storage of multi-material systems, in particular quantities of glass in raw materials silos.
The invention is based on the concept, in a plant of the type in question here for storing bulk material, for example a plant for material transloading of bulk material (so-called "material transloading site" for transloading of bulk material in a material extraction plant operated in strip mining, to control the storage operation of material stored there on the calculation basis of a computer-assisted stockyard model respectively stockpile model, in a way which is material-friendly and as automated as possible.
It is specifically not material-friendly for the bulk material to have to be broken up again during storage because of hardening or caking before being removed.
It should be emphasized that the invention may also be used in so-called "blending bed"
plants in which different materials and/or different material qualities of a material and/or materials having different material states are (temporarily) stored in a blending bed in order to mix them together. In such an application scenario, in addition to the material state, the digital stockpile model may also describe which material respectively which material quality is present at a particular position in the stockpile. This also makes it possible to predict the precise degree of blending of material to be stored after mixing has been carried out.
The invention is based on the discovery that for the stockyard holding in a material transloading site for bulk material, for example a stockyard for the transloading of
2 materials arranged in a shipping port or a material mining site, under particular physical influencing factors superficial hardening and/or caking of the stored material may take place to a greater extent. Such influencing factors are, for example, the mechanical pressure on the material due to storage, the material temperature due to external and/or internal influences, the air humidity respectively material moisture content due to external and/or internal influences, and/or the storage time. Said material modifications due to storage may for example occur with materials respectively raw materials, for example iron ore, lignite, raw material for cement production, for example limestone, limestone/marl/clay mixtures, gypsum or clay, and potassium carbonate, granular sulfur, fertilizer, or the like. In addition to the material moisture content, other material-dependent physical respectively chemical influencing factors that are relevant for hardening and/or caking of the material may also be taken into account, for example the lime content in the case of limestone.
According to a first aspect of the method proposed according to the invention, a digital stockpile model is compiled for a respective storage region, respectively a corresponding stockpile. On the basis of this model, the probability of the occurrence of physical states determined empirically in advance, for example by preliminary tests or simulation calculations, that may arise during the storage of the respectively stored material is calculated. These states are then used as process variables in the automation of the storage operation, in order to effectively avoid said undesired physical storage states by suitable storage operation respectively corresponding countermeasures.
It should in this case be noted that said storage region is referred to in the relevant literature as a stockpile, arranged in a stockyard, which represents a storage site for a bulk material in question here. In the case of a material extraction plant in question here, a stockyard is, for example, mainly produced respectively fed by material conveyor apparatuses.
According to the invention, a stockpile of the type in question here is decomposed into a multiplicity of smaller volume elements. These volume elements are preferably elements that are symmetrical in all three spatial directions, for example cubic volume elements, so that the calculation of the physical forces acting on an individual volume element, respectively the calculation of a corresponding physical state of such a volume element, is simplified considerably. During the calculation of a current stockpile model, the
According to a first aspect of the method proposed according to the invention, a digital stockpile model is compiled for a respective storage region, respectively a corresponding stockpile. On the basis of this model, the probability of the occurrence of physical states determined empirically in advance, for example by preliminary tests or simulation calculations, that may arise during the storage of the respectively stored material is calculated. These states are then used as process variables in the automation of the storage operation, in order to effectively avoid said undesired physical storage states by suitable storage operation respectively corresponding countermeasures.
It should in this case be noted that said storage region is referred to in the relevant literature as a stockpile, arranged in a stockyard, which represents a storage site for a bulk material in question here. In the case of a material extraction plant in question here, a stockyard is, for example, mainly produced respectively fed by material conveyor apparatuses.
According to the invention, a stockpile of the type in question here is decomposed into a multiplicity of smaller volume elements. These volume elements are preferably elements that are symmetrical in all three spatial directions, for example cubic volume elements, so that the calculation of the physical forces acting on an individual volume element, respectively the calculation of a corresponding physical state of such a volume element, is simplified considerably. During the calculation of a current stockpile model, the
3 influences of neighboring elements arranged above it and laterally are respectively taken into account for a predetermined volume element. In this case, the number of nearest neighboring elements may optionally be increased in order to improve the accuracy of the model calculation. In test calculations, it has been found that horizontally taking into account only nearest neighbors, and vertically taking into account as many as possible of the volume elements arranged above the volume element considered delivers sufficiently precise results.
According to another aspect of the proposed method, the pressure, preferably isostatic pressure, prevailing locally respectively on average and the temperature existing on average in the respective volume element are taken into account in the calculation of said volume elements, in order to calculate from these factors how probable hardening of the material of the respectively considered volume element is and/or how probable caking of the material of this volume element with the material of one of the neighboring elements is. This is preferably based on data determined empirically in advance for the hardening and/or caking behavior of different materials. The data thus determined in advance comprise, for example, the hardening respectively caking probability (in %) as a function of the pressure prevailing at the interface between two volume elements and the temperature, specifically as a function of the respective material.
According to another aspect of the proposed method, the storage time of the respective material which has already elapsed is additionally taken into account in the calculation of said volume elements of the stockpile model. In this way, time-dependent effects can be taken into account more precisely for the hardening and/or caking behavior of the respective material.
According to another aspect of the proposed method, spatial data, required for the calculation of the stockpile model, of a currently existing material mound are combined respectively compared with topographical surface data recorded by sensors, and adaptation of the stockpile model to the current material mound is optionally carried out by means of the comparison. The determination by sensors of such surface data may in this case be carried out contactlessly by means of conventional camera systems or by laser technology, radar technology, optically, or by means of autonomously acting aerial or ground drones. The physical data likewise required for the calculation of the stockpile model, for example current temperatures on the surfaces respectively in the outer regions
According to another aspect of the proposed method, the pressure, preferably isostatic pressure, prevailing locally respectively on average and the temperature existing on average in the respective volume element are taken into account in the calculation of said volume elements, in order to calculate from these factors how probable hardening of the material of the respectively considered volume element is and/or how probable caking of the material of this volume element with the material of one of the neighboring elements is. This is preferably based on data determined empirically in advance for the hardening and/or caking behavior of different materials. The data thus determined in advance comprise, for example, the hardening respectively caking probability (in %) as a function of the pressure prevailing at the interface between two volume elements and the temperature, specifically as a function of the respective material.
According to another aspect of the proposed method, the storage time of the respective material which has already elapsed is additionally taken into account in the calculation of said volume elements of the stockpile model. In this way, time-dependent effects can be taken into account more precisely for the hardening and/or caking behavior of the respective material.
According to another aspect of the proposed method, spatial data, required for the calculation of the stockpile model, of a currently existing material mound are combined respectively compared with topographical surface data recorded by sensors, and adaptation of the stockpile model to the current material mound is optionally carried out by means of the comparison. The determination by sensors of such surface data may in this case be carried out contactlessly by means of conventional camera systems or by laser technology, radar technology, optically, or by means of autonomously acting aerial or ground drones. The physical data likewise required for the calculation of the stockpile model, for example current temperatures on the surfaces respectively in the outer regions
4 of a material mound, may likewise preferably be recorded contactlessly by sensors, for example by means of infrared thermometers known per se (so-called pyrometers) or by means of thermal imaging cameras known per se.
According to yet another aspect of the proposed method, the results of the calculation of a currently applicable stockpile model, for example in the case of stockyard holding with a machine controller for operating a corresponding transport respectively conveyor technology (for example belt loaders) for bulk material, may be supplied in real time to a programmable logic controller (PLC). In this way, significant automation of the operation of a stockyard of the type in question here may be achieved.
The device likewise proposed according to the invention is configured to operate a stockyard controller of the type in question here or a material extraction plant equipped with stockyard holding, in particular a storage region provided there respectively a corresponding stockpile, by means of the proposed method in a substantially automated but nevertheless material-friendly way.
According to a first aspect, the proposed device comprises sensors for preferably contactless recording of topographical data of the respective stockpile, in particular the current surface data thereof, and/or for recording physical data of the respective stockpile, in particular the temperature data thereof. The device additionally comprises a data processing unit by means of which a current stockpile model is calculated on the basis of the surface data and/or physical data recorded by sensors. The results of the calculations by means of the stockpile model are supplied by the device to a control unit of the respective stockpile plant respectively material extraction plant.
According to another aspect of the proposed device, the data processing unit is configured to calculate current physical and/or chemical state variables of the stockpile by means of the digital stockpile model and the data recorded by sensors, to calculate current physical respectively chemical states of the materials stored in the stockpile by means of the calculated state variables, and to make a prediction of possible material modifications of the stored material by means of the calculated physical respectively chemical states, respectively to provide a corresponding prediction data.
According to yet another aspect of the proposed method, the results of the calculation of a currently applicable stockpile model, for example in the case of stockyard holding with a machine controller for operating a corresponding transport respectively conveyor technology (for example belt loaders) for bulk material, may be supplied in real time to a programmable logic controller (PLC). In this way, significant automation of the operation of a stockyard of the type in question here may be achieved.
The device likewise proposed according to the invention is configured to operate a stockyard controller of the type in question here or a material extraction plant equipped with stockyard holding, in particular a storage region provided there respectively a corresponding stockpile, by means of the proposed method in a substantially automated but nevertheless material-friendly way.
According to a first aspect, the proposed device comprises sensors for preferably contactless recording of topographical data of the respective stockpile, in particular the current surface data thereof, and/or for recording physical data of the respective stockpile, in particular the temperature data thereof. The device additionally comprises a data processing unit by means of which a current stockpile model is calculated on the basis of the surface data and/or physical data recorded by sensors. The results of the calculations by means of the stockpile model are supplied by the device to a control unit of the respective stockpile plant respectively material extraction plant.
According to another aspect of the proposed device, the data processing unit is configured to calculate current physical and/or chemical state variables of the stockpile by means of the digital stockpile model and the data recorded by sensors, to calculate current physical respectively chemical states of the materials stored in the stockpile by means of the calculated state variables, and to make a prediction of possible material modifications of the stored material by means of the calculated physical respectively chemical states, respectively to provide a corresponding prediction data.
5 According to another aspect of the proposed device, the control unit is configured to calculate one or more suitable measures for preventing material modifications respectively material degradation by means of the prediction data delivered by the data processing unit, and to supply the countermeasure data calculated in this way for example to a PLC controller or machine controller for the stocking, by means of which the countermeasures can be carried out during the stocking.
According to yet another aspect of the proposed device, the control unit is configured to carry out at least one suitable measure by means of predetermined possible measures, suitable countermeasures being determined in advance by means of test runs for particular physical respectively chemical states.
The invention may be used particularly in the area of, for example, material transloading sites provided at shipping ports for the transloading of bulk material (for example ore, coal, lignite, fertilizer, or salts such as potassium carbonate) and in the area of corresponding material extraction of such a bulk material, for example in a mine plant operated in strip mining or in a storage device for bulk materials of cement production, with the advantages described herein. The method and the device may, however, also be used correspondingly in stockyards provided for other purposes, for example in stockyards for the storage of stone/natural stone material.
The computer program according to the invention is configured to carry out each step of the method, in particular when it runs on a control apparatus for controlling the stocking of a storage region respectively stockyard of the type in question here. It makes it possible, in particular, to implement the method according to the invention on an electronic control apparatus, for example a PLC control apparatus, without having to carry out structural modifications on the control apparatus. For this purpose, the machine-readable data medium on which the computer program according to the invention is stored is provided.
By installing the computer program according to the invention onto a device, respectively a corresponding electronic control apparatus, the device according to the invention is obtained, which is configured to operate respectively control a stockyard control system of the type in question here respectively a corresponding store respectively temporary store for bulk material respectively excavated material of a material extraction plant by means of the method according to the invention.
According to yet another aspect of the proposed device, the control unit is configured to carry out at least one suitable measure by means of predetermined possible measures, suitable countermeasures being determined in advance by means of test runs for particular physical respectively chemical states.
The invention may be used particularly in the area of, for example, material transloading sites provided at shipping ports for the transloading of bulk material (for example ore, coal, lignite, fertilizer, or salts such as potassium carbonate) and in the area of corresponding material extraction of such a bulk material, for example in a mine plant operated in strip mining or in a storage device for bulk materials of cement production, with the advantages described herein. The method and the device may, however, also be used correspondingly in stockyards provided for other purposes, for example in stockyards for the storage of stone/natural stone material.
The computer program according to the invention is configured to carry out each step of the method, in particular when it runs on a control apparatus for controlling the stocking of a storage region respectively stockyard of the type in question here. It makes it possible, in particular, to implement the method according to the invention on an electronic control apparatus, for example a PLC control apparatus, without having to carry out structural modifications on the control apparatus. For this purpose, the machine-readable data medium on which the computer program according to the invention is stored is provided.
By installing the computer program according to the invention onto a device, respectively a corresponding electronic control apparatus, the device according to the invention is obtained, which is configured to operate respectively control a stockyard control system of the type in question here respectively a corresponding store respectively temporary store for bulk material respectively excavated material of a material extraction plant by means of the method according to the invention.
6 Further advantages and configurations of the invention may be found in the description and the appended drawings. In the drawings, elements respectively features that are identical or functionally equivalent are provided with the same references.
It is to be understood that the features mentioned above and those to be explained below may be used not only in the combination respectively specified but also in other combinations or separately, without departing from the scope of the present invention.
Figure 1 schematically shows a digital stockpile model to illustrate the method according to the invention and the device.
Figure 2 shows a first exemplary embodiment of the method according to the invention for calculating a physical stockpile model by means of a schematic representation of a local arrangement of volume elements.
Figure 3 shows a second exemplary embodiment of the method according to the invention respectively the device by means of a combined flowchart/block diagram.
The stockpile model shown in Figure 1 relates to the temporary storage of potassium carbonate (so-called "potash") in a stockpile. During the storage of potassium carbonate, caking and/or hardening of the material takes place under particular physical influencing factors such as pressure, temperature, air humidity and/or storage time. This or a similar effect may under certain circumstances also be observed with other materials.
Caked material has, for example, the following disadvantageous effects:
- a reduction of the material quality and therefore loss of revenue for the material owner;
- greater wear of reloading machines due to tooth abrasion;
- a reduction of the efficiency of the stockyard device and therefore revenue losses due to delayed removal and through further working steps required, in order to regrind the caked material.
It is to be understood that the features mentioned above and those to be explained below may be used not only in the combination respectively specified but also in other combinations or separately, without departing from the scope of the present invention.
Figure 1 schematically shows a digital stockpile model to illustrate the method according to the invention and the device.
Figure 2 shows a first exemplary embodiment of the method according to the invention for calculating a physical stockpile model by means of a schematic representation of a local arrangement of volume elements.
Figure 3 shows a second exemplary embodiment of the method according to the invention respectively the device by means of a combined flowchart/block diagram.
The stockpile model shown in Figure 1 relates to the temporary storage of potassium carbonate (so-called "potash") in a stockpile. During the storage of potassium carbonate, caking and/or hardening of the material takes place under particular physical influencing factors such as pressure, temperature, air humidity and/or storage time. This or a similar effect may under certain circumstances also be observed with other materials.
Caked material has, for example, the following disadvantageous effects:
- a reduction of the material quality and therefore loss of revenue for the material owner;
- greater wear of reloading machines due to tooth abrasion;
- a reduction of the efficiency of the stockyard device and therefore revenue losses due to delayed removal and through further working steps required, in order to regrind the caked material.
7 According to the stockpile model represented in an isometric top view in Figure 1, the respective stockyard of the type in question is converted into a digital stockyard model by subdividing it in the present exemplary embodiment into cubic digital volume regions respectively volume bodies. The size of these regions determines the resolution of the digital model and may be varied according to requirements.
Each of the volume regions may be assigned different physical respectively chemical state variables (parameters). These factors may be:
- the instantaneous (average) pressure prevailing in the volume region;
- the time when the material in question was stored in the volume region, in order to calculate the storage period which has elapsed since then;
- the external temperature in the vicinity of the stockpile at the time when the material in question was stored;
- the air humidity prevailing in the vicinity of the stockpile at the time when the material in question was stored;
- said pressure in a volume region, specifically multiplied by the storage period which has elapsed since said time of storage, in order to obtain a time-dependent pressure characteristic;
- the nature of the material in question;
- quality information relating to the material in question, for example the chemical composition and purity or the physical fineness of the material particles;
- and other influencing factors respectively influencing quantities for the storage of the material in question, for example its caking probability, specifically as a function of said state variables.
In the stockpile model represented in Figure 1, there are the following values of aforementioned state variables in the volume regions 100 shown here:
Each of the volume regions may be assigned different physical respectively chemical state variables (parameters). These factors may be:
- the instantaneous (average) pressure prevailing in the volume region;
- the time when the material in question was stored in the volume region, in order to calculate the storage period which has elapsed since then;
- the external temperature in the vicinity of the stockpile at the time when the material in question was stored;
- the air humidity prevailing in the vicinity of the stockpile at the time when the material in question was stored;
- said pressure in a volume region, specifically multiplied by the storage period which has elapsed since said time of storage, in order to obtain a time-dependent pressure characteristic;
- the nature of the material in question;
- quality information relating to the material in question, for example the chemical composition and purity or the physical fineness of the material particles;
- and other influencing factors respectively influencing quantities for the storage of the material in question, for example its caking probability, specifically as a function of said state variables.
In the stockpile model represented in Figure 1, there are the following values of aforementioned state variables in the volume regions 100 shown here:
8 - time when the material in question was stored:
2019.04.10 / 12:35:24 - storage period so far: 56:20:54 h - instantaneous average pressure: 10 000 N
- type of material: potash - material quality: xyz - caking probability: 50%
In the present example of potash said values give a (non-negligible) caking risk for the eight volume regions respectively volume elements 105 shaded in light gray, a caking probability of < 30% for the four volume regions 110 shaded in medium gray, and a caking probability of > 90% for the four volume regions 115 shaded in dark gray.
The digital stockpile model therefore existing according to Figure 1 is compiled in the present exemplary embodiment by means of image-recording sensors, for example laser, radar, photogrammetric sensors, or the like, which are preferably arranged on a (belt) loader and/or on a reloading apparatus of the respective stocking device.
When the material is stored, the parameters available at this time, in the present exemplary embodiment the material stored, the storing time, the air humidity prevailing on storage, etc. are assigned to the volume region respectively filled at this time. On the basis of these state variables respectively parameters, a material-specific algorithm calculates the probability of states of the material in the respective, here cubic, volume regions in the present exemplary embodiment according to the following relation:
_ _ Material state(t) = iwi * tvi * Xi * (t ¨ ti)}
i=1 j=0 in which the quantity t indicates the time of the material state, the quantity X, indicates a material-related state variable Xi, the quantity w, indicates a weighting factor for the state variable X1, the quantity v, indicates a weighting factor for time dependency of the state variable X1, and the quantity T1 indicates the number of previous respectively historical time steps which have been considered for the state variable X.
2019.04.10 / 12:35:24 - storage period so far: 56:20:54 h - instantaneous average pressure: 10 000 N
- type of material: potash - material quality: xyz - caking probability: 50%
In the present example of potash said values give a (non-negligible) caking risk for the eight volume regions respectively volume elements 105 shaded in light gray, a caking probability of < 30% for the four volume regions 110 shaded in medium gray, and a caking probability of > 90% for the four volume regions 115 shaded in dark gray.
The digital stockpile model therefore existing according to Figure 1 is compiled in the present exemplary embodiment by means of image-recording sensors, for example laser, radar, photogrammetric sensors, or the like, which are preferably arranged on a (belt) loader and/or on a reloading apparatus of the respective stocking device.
When the material is stored, the parameters available at this time, in the present exemplary embodiment the material stored, the storing time, the air humidity prevailing on storage, etc. are assigned to the volume region respectively filled at this time. On the basis of these state variables respectively parameters, a material-specific algorithm calculates the probability of states of the material in the respective, here cubic, volume regions in the present exemplary embodiment according to the following relation:
_ _ Material state(t) = iwi * tvi * Xi * (t ¨ ti)}
i=1 j=0 in which the quantity t indicates the time of the material state, the quantity X, indicates a material-related state variable Xi, the quantity w, indicates a weighting factor for the state variable X1, the quantity v, indicates a weighting factor for time dependency of the state variable X1, and the quantity T1 indicates the number of previous respectively historical time steps which have been considered for the state variable X.
9 Example of the State Calculation:
The example is based on the material respectively storage material potassium carbonate (potash), which is assumed to cake by 90% after a storage time of x hours at a pressure y. The algorithm above is formed respectively adapted on the basis of corresponding preliminary tests and/or the physical respectively chemical relationships thereby obtained.
The algorithm therefore makes it possible to evaluate probabilities respectively prediction of storage-related states of the type in question here, for example said caking states, which the material takes on in said volume regions.
The plant controller may then be configured respectively programmed on the basis of the probabilities respectively predictions calculated in this way so that the probability that a particular undesired (storage-related) material state will occur is reduced.
Possible measures during the operation of a storage device of the type in question here in order to reduce the occurrence probabilities of particular material states may be:
- prompt removal of material i.e. corresponding reduction of the storage time;
- less stockpiling of material and therefore a lower height of a stockpile at critical points, so that the pressure on respectively in a described volume element is reduced;
- storing new material only at correspondingly noncritical points, likewise in order to reduce said pressure.
Figure 2 shows a schematic representation of a local arrangement of described volume elements, specifically for the sake of simplicity only five contiguous volume elements 200 - 220. By means of this representation, a first exemplary embodiment of the method according to the invention for calculating a physical stockpile model of the type in question here by means of a said algorithm will be described.
The pressure gradient Api,2, shown only for the two volume elements 200, 205, in the vertical direction of the arrangement shown is substantially determined by the pressure exertion due to gravity of the upper volume element 200 on the volume element underneath. The pressure gradient between the two lower volume elements 205, 210 is then given correspondingly, although the total pressure load of the two volume elements 200, 205 on the volume element 210 underneath needs to be taken into account.
The temperature gradient AT1,2, likewise shown only for the two volume elements 200, 205, in the vertical direction of the arrangement shown is substantially determined by external influences such as solar radiation and the ground temperature as well as the temperature difference AT 225 resulting therefrom over the stockpile height (in the y direction 230). The temperature gradient between the two lower volume elements 205, 210 is then given correspondingly.
The average pressures and average temperatures prevailing in the three volume elements 200 - 210 may be derived from said pressure and temperature gradients. From these data, the storage-related physical respectively chemical material modifications may be determined specifically for the material respectively present. In the present exemplary embodiment, this determination is carried out by means of data obtained in test runs for the respective material, which are provided for example in the form of (electronic) tables or databases.
The pressure gradient Api,3, shown only for the two volume elements 205, 215, in the horizontal direction of the arrangement shown is substantially determined by horizontal pressure loading components between horizontally neighboring volume elements, which are caused by the lateral movement of individual material particles that is possible during the pouring of granular material.
The temperature gradient AT1,3, likewise shown only for the two volume elements 205, 215, in the horizontal direction of the arrangement shown is substantially determined by the temperature profile along the stockpile, specifically in the present example along the x direction 235 shown.
A second exemplary embodiment of the method according to the invention, respectively the device, is represented in Figure 3 by means of a combined block diagram/flowchart.
In block respectively step 305, a data processing unit 300 calculates a described digital stockpile model on the basis of current physical and/or chemical data recorded by sensors 310. In the present exemplary embodiment, these data comprise geometrical data respectively corresponding surface data of a stockpiling question, temperature data recorded by sensors in the vicinity of the stockpile in question and/or inside the stockpile, as well as material-specific data relating to the material instantaneously stored in the stockpile. A current respectively instantaneously valid stockpile model is calculated by means of these data 305.
The data processing unit 300 calculates 315 current physical and/or chemical state variables of the stockpile by means of the digital stockpile model 305 and the data 310 recorded by sensors. By means of these calculated state variables, current physical respectively chemical states of the materials stored in the stockpile are calculated 320 and a prediction of possible material modifications of the stored material is made 325 by means of the physical respectively chemical states calculated in this way, respectively corresponding prediction data, are provided 330. For the prediction, the probability of the occurrence of physical states that may arise during the storage of the respectively stored material may in this case be calculated 325, 330.
The prediction data provided in this way 330 are supplied to a control unit 335 of the respective stockyard plant respectively material extraction plant. By means of the present prediction data 330, in the present exemplary embodiment one or more suitable countermeasures for preventing a material modifications are calculated 340 in the control unit 335 and the data thereby obtained are supplied 350 in the present case to a PLC
controller 350, by means of which the calculated 340 countermeasures are carried out during the stockpile operation of the storage device of the type in question here.
It should be noted that the calculation of the suitable countermeasure(s) in the present exemplary embodiment is carried out by means of a predetermined catalog respectively a corresponding selection 345 of possible countermeasures, countermeasures suitable for the particular physical respectively chemical states being determined in advance by means of test measurements respectively test runs.
It should furthermore be noted that a method as described above and a device as described above may, for example, be used in the case of a temporary material store provided in a material extraction plant (ore mining, coal mining, potash mining, etc.) or material transloading site provided in a shipping port for the transloading of corresponding bulk material.
The example is based on the material respectively storage material potassium carbonate (potash), which is assumed to cake by 90% after a storage time of x hours at a pressure y. The algorithm above is formed respectively adapted on the basis of corresponding preliminary tests and/or the physical respectively chemical relationships thereby obtained.
The algorithm therefore makes it possible to evaluate probabilities respectively prediction of storage-related states of the type in question here, for example said caking states, which the material takes on in said volume regions.
The plant controller may then be configured respectively programmed on the basis of the probabilities respectively predictions calculated in this way so that the probability that a particular undesired (storage-related) material state will occur is reduced.
Possible measures during the operation of a storage device of the type in question here in order to reduce the occurrence probabilities of particular material states may be:
- prompt removal of material i.e. corresponding reduction of the storage time;
- less stockpiling of material and therefore a lower height of a stockpile at critical points, so that the pressure on respectively in a described volume element is reduced;
- storing new material only at correspondingly noncritical points, likewise in order to reduce said pressure.
Figure 2 shows a schematic representation of a local arrangement of described volume elements, specifically for the sake of simplicity only five contiguous volume elements 200 - 220. By means of this representation, a first exemplary embodiment of the method according to the invention for calculating a physical stockpile model of the type in question here by means of a said algorithm will be described.
The pressure gradient Api,2, shown only for the two volume elements 200, 205, in the vertical direction of the arrangement shown is substantially determined by the pressure exertion due to gravity of the upper volume element 200 on the volume element underneath. The pressure gradient between the two lower volume elements 205, 210 is then given correspondingly, although the total pressure load of the two volume elements 200, 205 on the volume element 210 underneath needs to be taken into account.
The temperature gradient AT1,2, likewise shown only for the two volume elements 200, 205, in the vertical direction of the arrangement shown is substantially determined by external influences such as solar radiation and the ground temperature as well as the temperature difference AT 225 resulting therefrom over the stockpile height (in the y direction 230). The temperature gradient between the two lower volume elements 205, 210 is then given correspondingly.
The average pressures and average temperatures prevailing in the three volume elements 200 - 210 may be derived from said pressure and temperature gradients. From these data, the storage-related physical respectively chemical material modifications may be determined specifically for the material respectively present. In the present exemplary embodiment, this determination is carried out by means of data obtained in test runs for the respective material, which are provided for example in the form of (electronic) tables or databases.
The pressure gradient Api,3, shown only for the two volume elements 205, 215, in the horizontal direction of the arrangement shown is substantially determined by horizontal pressure loading components between horizontally neighboring volume elements, which are caused by the lateral movement of individual material particles that is possible during the pouring of granular material.
The temperature gradient AT1,3, likewise shown only for the two volume elements 205, 215, in the horizontal direction of the arrangement shown is substantially determined by the temperature profile along the stockpile, specifically in the present example along the x direction 235 shown.
A second exemplary embodiment of the method according to the invention, respectively the device, is represented in Figure 3 by means of a combined block diagram/flowchart.
In block respectively step 305, a data processing unit 300 calculates a described digital stockpile model on the basis of current physical and/or chemical data recorded by sensors 310. In the present exemplary embodiment, these data comprise geometrical data respectively corresponding surface data of a stockpiling question, temperature data recorded by sensors in the vicinity of the stockpile in question and/or inside the stockpile, as well as material-specific data relating to the material instantaneously stored in the stockpile. A current respectively instantaneously valid stockpile model is calculated by means of these data 305.
The data processing unit 300 calculates 315 current physical and/or chemical state variables of the stockpile by means of the digital stockpile model 305 and the data 310 recorded by sensors. By means of these calculated state variables, current physical respectively chemical states of the materials stored in the stockpile are calculated 320 and a prediction of possible material modifications of the stored material is made 325 by means of the physical respectively chemical states calculated in this way, respectively corresponding prediction data, are provided 330. For the prediction, the probability of the occurrence of physical states that may arise during the storage of the respectively stored material may in this case be calculated 325, 330.
The prediction data provided in this way 330 are supplied to a control unit 335 of the respective stockyard plant respectively material extraction plant. By means of the present prediction data 330, in the present exemplary embodiment one or more suitable countermeasures for preventing a material modifications are calculated 340 in the control unit 335 and the data thereby obtained are supplied 350 in the present case to a PLC
controller 350, by means of which the calculated 340 countermeasures are carried out during the stockpile operation of the storage device of the type in question here.
It should be noted that the calculation of the suitable countermeasure(s) in the present exemplary embodiment is carried out by means of a predetermined catalog respectively a corresponding selection 345 of possible countermeasures, countermeasures suitable for the particular physical respectively chemical states being determined in advance by means of test measurements respectively test runs.
It should furthermore be noted that a method as described above and a device as described above may, for example, be used in the case of a temporary material store provided in a material extraction plant (ore mining, coal mining, potash mining, etc.) or material transloading site provided in a shipping port for the transloading of corresponding bulk material.
Claims (13)
1. A method of operating a computer system for operating a plant for storing bulk material, having at least one stockpile storage region, the method comprising:
compiling a digital stockpile model with at least one of the storage regions being decomposed into a multiplicity of smaller volume elements;
calculating each volume element by determining at least one of physical forces and chemical influences acting on the volume element, wherein at least one of a local pressure in the volume element, temperature in the volume element, air humidity in the volume element and material moisture content in the volume element are taken into account when calculating each volume element of the stockpile model;
predicting an occurrence of at least one of physical and chemical states that may arise during the storage of the stored material based on the digital stockpile model and the at least one of physical forces and chemical influences acting on the volume element to determine a probability at least one of hardening of the material of the volume element and caking of the material of the volume element with a material of a neighboring element; and using the predicted occurrence of at least one of physical and chemical states during a storage operation to ensure material-friendly storage of the stored material, wherein the material-friendly storage prevents the bulk material having to be broken up again during storage before being taken out because of hardening or caking.
compiling a digital stockpile model with at least one of the storage regions being decomposed into a multiplicity of smaller volume elements;
calculating each volume element by determining at least one of physical forces and chemical influences acting on the volume element, wherein at least one of a local pressure in the volume element, temperature in the volume element, air humidity in the volume element and material moisture content in the volume element are taken into account when calculating each volume element of the stockpile model;
predicting an occurrence of at least one of physical and chemical states that may arise during the storage of the stored material based on the digital stockpile model and the at least one of physical forces and chemical influences acting on the volume element to determine a probability at least one of hardening of the material of the volume element and caking of the material of the volume element with a material of a neighboring element; and using the predicted occurrence of at least one of physical and chemical states during a storage operation to ensure material-friendly storage of the stored material, wherein the material-friendly storage prevents the bulk material having to be broken up again during storage before being taken out because of hardening or caking.
2. The method as claimed in claim 1, wherein a probability of the occurrence of the physical states that may arise during the storage of the stored material is calculated on the basis of the digital stockpile model.
3. The method as claimed in claim 1 or 2, wherein the material-friendly storage of the stored material is ensured by predetermined measures during the storage operation.
4. The method as claimed in any one of claims 1 to 3, wherein for a predetermined volume element, at least one of physical forces and chemical influences of neighboring elements arranged above and neighboring elements arranged laterally are taken into account.
5. The method as claimed in claim 4, wherein a number of nearest neighboring elements to be taken into account horizontally comprises only nearest neighbors, and Date Recue/Date Received 2024-02-08 vertically comprises as many as possible of the volume elements arranged above the volume element considered.
6. The method as claimed in any one of claims 1 to 5, wherein material-dependent physical and chemical influencing factors that are relevant for at least one of hardening and caking of the material, are taken into account in calculating the volume elements of the digital stockpile model.
7. The method as claimed in any one of claims 1 to 6, wherein data empirically determined in advance for at least one of hardening and caking behavior of different materials are used as a basis, wherein the data determined in advance for a predetermined material comprises a probability of at least one of hardening and caking probability as a function of a pressure at an interface between two volume elements and the temperature.
8. The method as claimed in any one of claims 1 to 7, wherein a storage time of the material which has already elapsed is taken into account in calculating the volume elements of the digital stockpile model.
9. The method as claimed in any one of claims 1 to 8, further comprising comparing spatial data, required for compiling the stockpile model, of a currently existing stockpile material mound with topographical data recorded on the material mound by sensors; and adapting the digital stockpile model to the material mound.
10. The method as claimed in any one of claims 1 to 9, further comprising supplying results of the compiling of the digital stockpile model in real time to a machine controller for operating at least one of a bulk material transport and conveyor technology, provided at the plant.
11. A device configured to control a plant for storing bulk material, having at least one stockpile storage region, according to the method as claimed in any one of claims 1 to 10, wherein the digital stockpile model is compiled by a data processing unit based on at least one of surface data and physical data recorded by sensors, wherein results of the calculations by means of the digital stockpile model are supplied to a control unit of the plant.
Date Recue/Date Received 2024-02-08
Date Recue/Date Received 2024-02-08
12. The device as claimed in claim 11, further comprising the sensors for recording at least one of topographical data of the stored material including the surface data thereof and physical data of the stored material including temperature data thereof.
13. The device as claimed in one of claims 11 to 12, wherein the data processing unit is configured to calculate at least one of current physical and chemical state variables of a stockpile material mound by means of the digital stockpile model and the data recorded by the sensors, to calculate at least one of physical and chemical states of materials in the stockpile material mound by means of the at least one of current physical and chemical state variables, and to make a prediction of material modifications of the materials in the stockpile material mound by means of the calculated at least one of current physical and chemical states to provide a corresponding prediction data.
Date Recue/Date Received 2024-02-08
Date Recue/Date Received 2024-02-08
Applications Claiming Priority (5)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| BEBE2020/5066 | 2020-02-04 | ||
| DE102020201316.0 | 2020-02-04 | ||
| DE102020201316.0A DE102020201316A1 (en) | 2020-02-04 | 2020-02-04 | Method and device for the automated operation of a system for the storage of bulk goods |
| BE20205066A BE1028028B1 (en) | 2020-02-04 | 2020-02-04 | Method and device for the automated operation of a system for the storage of bulk goods |
| PCT/EP2021/051556 WO2021156082A1 (en) | 2020-02-04 | 2021-01-25 | Method and device for automated operation of a plant for storing bulk material |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CA3166555A1 CA3166555A1 (en) | 2021-08-12 |
| CA3166555C true CA3166555C (en) | 2024-05-28 |
Family
ID=74194760
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA3166555A Active CA3166555C (en) | 2020-02-04 | 2021-01-25 | Method and device for automated operation of a plant for storing bulk material |
Country Status (5)
| Country | Link |
|---|---|
| EP (1) | EP4100341B1 (en) |
| AU (1) | AU2021217512B2 (en) |
| BR (1) | BR112022015372A2 (en) |
| CA (1) | CA3166555C (en) |
| WO (1) | WO2021156082A1 (en) |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8437996B2 (en) | 2007-12-13 | 2013-05-07 | Exxonmobil Upstream Research Company | Parallel adaptive data partitioning on a reservoir simulation using an unstructured grid |
| CN104634815B (en) | 2013-11-14 | 2017-03-29 | 辽宁工程技术大学 | A kind of method of simulation self-ignition of coal pile |
| DE102015104229A1 (en) | 2015-03-20 | 2016-09-22 | Hauk & Sasko Ingenieurgesellschaft Mbh | System and method for operating a heap |
| JP6594785B2 (en) | 2016-02-02 | 2019-10-23 | 千代田化工建設株式会社 | Coal storage monitoring system and coal storage monitoring method |
-
2021
- 2021-01-25 EP EP21701135.2A patent/EP4100341B1/en active Active
- 2021-01-25 WO PCT/EP2021/051556 patent/WO2021156082A1/en active Search and Examination
- 2021-01-25 CA CA3166555A patent/CA3166555C/en active Active
- 2021-01-25 AU AU2021217512A patent/AU2021217512B2/en active Active
- 2021-01-25 BR BR112022015372A patent/BR112022015372A2/en unknown
Also Published As
| Publication number | Publication date |
|---|---|
| CA3166555A1 (en) | 2021-08-12 |
| AU2021217512B2 (en) | 2024-07-25 |
| EP4100341C0 (en) | 2024-04-17 |
| WO2021156082A1 (en) | 2021-08-12 |
| BR112022015372A2 (en) | 2022-09-27 |
| AU2021217512A1 (en) | 2022-08-25 |
| EP4100341A1 (en) | 2022-12-14 |
| EP4100341B1 (en) | 2024-04-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102076417B (en) | For the method and apparatus of controlled fragmentation process | |
| AU2018307077B2 (en) | Blend control truck assignment monitoring system and method | |
| CN102782432B (en) | Air leakage detection system for pallet carriage and air leakage detection method for pallet carriage | |
| Agarwal et al. | Analysing blast furnace data using evolutionary neural network and multiobjective genetic algorithms | |
| CA3166555C (en) | Method and device for automated operation of a plant for storing bulk material | |
| WO2016150918A1 (en) | System and method for operating a stockpile | |
| Straka et al. | Simulation of homogeneous production processes | |
| CN103388983A (en) | Automatic proportioning, loading and unloading control system of ferroalloy submerged-arc furnace | |
| US5959870A (en) | Real-time optimization for mix beds | |
| AU2019297315B2 (en) | Method and device for managing units of bulk material, and computer programme | |
| Saderova et al. | Using a model to approach the process of loading and unloading of mining output at a quarry | |
| Yumlu | Backfill practices at Çayeli mine | |
| CN108128639A (en) | A kind of bridge-type feeding device and material stacking and fetching method | |
| BE1028028B1 (en) | Method and device for the automated operation of a system for the storage of bulk goods | |
| CN208439989U (en) | A kind of bridge-type feeding device | |
| Boakye et al. | Improving Kiln Feed Chemistry and Overburden Wasting through Controlled Consistent Quarry Material Mix: A Case Study at Union Bridge Cement Plant | |
| Ilic et al. | Simulation of Transfer Chutes | |
| DE102020201316A1 (en) | Method and device for the automated operation of a system for the storage of bulk goods | |
| JP2567501B2 (en) | Coke production automation system | |
| Yulhendra et al. | Development Mine Scheduling Concept in Making Pit Design | |
| Johansson | Modeling and control of a crushing circuit for platinum concentration | |
| Ritter et al. | Output prediction of semi mobile in pit crushing and conveying systems (SMIPPC) | |
| CA3134733C (en) | Method and apparatus for the automatable operation of a conveyor belt system used in particular in surface mining | |
| Kęsek et al. | A review of computer simulations in underground and open-pit mining | |
| CN108016891A (en) | A kind of windrow method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| EEER | Examination request |
Effective date: 20220729 |
|
| EEER | Examination request |
Effective date: 20220729 |
|
| EEER | Examination request |
Effective date: 20220729 |
|
| EEER | Examination request |
Effective date: 20220729 |
|
| EEER | Examination request |
Effective date: 20220729 |
|
| EEER | Examination request |
Effective date: 20220729 |
|
| EEER | Examination request |
Effective date: 20220729 |
|
| EEER | Examination request |
Effective date: 20220729 |
|
| EEER | Examination request |
Effective date: 20220729 |
|
| EEER | Examination request |
Effective date: 20220729 |
|
| EEER | Examination request |
Effective date: 20220729 |