EP3903231A1 - Procédé de classification approximative de la répartition de tailles de particules d'un produit en vrac - Google Patents

Procédé de classification approximative de la répartition de tailles de particules d'un produit en vrac

Info

Publication number
EP3903231A1
EP3903231A1 EP20712249.0A EP20712249A EP3903231A1 EP 3903231 A1 EP3903231 A1 EP 3903231A1 EP 20712249 A EP20712249 A EP 20712249A EP 3903231 A1 EP3903231 A1 EP 3903231A1
Authority
EP
European Patent Office
Prior art keywords
bulk material
processing
particle size
detection
classifier
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.)
Ceased
Application number
EP20712249.0A
Other languages
German (de)
English (en)
Inventor
Carsten Sachse
Michael Wilczek
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.)
ThyssenKrupp AG
ThyssenKrupp Industrial Solutions AG
Original Assignee
ThyssenKrupp AG
ThyssenKrupp Industrial Solutions AG
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 ThyssenKrupp AG, ThyssenKrupp Industrial Solutions AG filed Critical ThyssenKrupp AG
Publication of EP3903231A1 publication Critical patent/EP3903231A1/fr
Ceased legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
    • G06V10/267—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion by performing operations on regions, e.g. growing, shrinking or watersheds
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/56—Extraction of image or video features relating to colour
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/06—Recognition of objects for industrial automation

Definitions

  • the invention relates to a method for the rough classification of the particle size distribution of a bulk material.
  • a bulk material consists of solid parts that are randomly arranged by a bed.
  • a bed made of a bulk material normally remains unchanged without further action, so that objects that happen to be on the surface can be visually detected by the bed, whereby these can partially cover one another.
  • Particle size is coming. For example, smaller particles can get through gaps more easily, so that their arrangement on a surface is reduced. In the case of very large particles, their distribution is very much dependent on the type of bedding, which is also known as the Brazil nut effect. In the case of bulk goods with different densities or surface properties, on the other hand, the reverse Brazil nut effect can occur, so that a direct connection between the particle size distribution on the surface of a bulk material and the particle size distribution in the volume of the bulk material is not easily possible.
  • a method for adapting operating parameters of a roller mill is known from DE 43 25 187 C2.
  • a particle size determination in liquid is known from US 2017/0045438 A1.
  • Particle size determination in liquid is known from US 2005/0046841 A1.
  • a method for identifying and discriminating different heterogeneous materials is known from WO 2014/009384 A2.
  • a device for determining the size of particles in a particle flow is known from DE 20 2011 109 943 U1.
  • a computer-controlled shredding system is known from DE 16 07 460 B2.
  • a roller mill control method is known from DE 43 25 187 C2.
  • the object of the invention is to provide a simple and fast inline method which gives a rough indication of the particle size distribution of a bed of bulk material.
  • the method according to the invention for the rough classification of the particle size distribution of a bed of bulk material has the following steps:
  • the bulk of a bulk material is characterized in that the particles are arranged touching one another.
  • the bulk material can experience changes over a longer period of time, for example due to shocks or vibrations, but for the detection of the surface by means of a camera, the bulk material can be viewed as stationary.
  • the bulk material can be arranged on a conveyor belt. This can lead to a vibration in the area of the rollers and thus to a change in the packing of the bulk material.
  • a few particles are (partially) visible, which are arranged on the surface of the bed. This distinguishes a bulk material from solutions, suspensions or smoke, in which the particles can regularly be viewed in isolation from one another.
  • Capturing with a camera means that only particles that lie on the surface of the bulk material are observed. This means that the real particle size distribution cannot be determined because the particles below the surface are not taken into account. However, the size of the particles on a surface gives an indication of how the particle size distribution of the bulk material could be as a whole. This very rough classification is sufficient to control and optimize subsequent processes. At the same time, the optical acquisition is comparatively simple, fast and inexpensive and the evaluation of the optical data can be implemented reliably and easily.
  • the classifier By multiplying the average value by the spread, the classifier is spread. Since the very simplified evaluation has shown that the smallest particles are always identified, sometimes also in the form of inclusions within the material, the scatter increases with the particle diameter.
  • a suitable classifier results, even if it cannot represent the real particle size distribution.
  • the determination of a diameter for a contiguous area can be done in different ways. The simplest method takes the size of the area and calculates the diameter of a circle with the same area. Alternatively or additionally, the diameter of the smallest circle can be determined in which the contiguous area just fits. Alternatively or additionally, the diameter of the largest circle that fits into the contiguous area can be determined. A combination of these methods is possible, which in particular reflects a measure of the regularity of the particles.
  • the surface of the bulk material is preferably the surface viewed from above, since a comparatively large surface can often be viewed here. However, the method can also be used laterally, since particles can also be identified on the lateral surfaces. The surface can also be recorded from above and from the side. Either two or more cameras can be used here. Alternatively or additionally, at least one camera and at least one mirror can be used.
  • the detection in step a) takes place from above.
  • the camera is particularly preferably arranged vertically above the bulk material, which is carried out, for example, on a conveyor belt under the camera.
  • the detection in step a) takes place with a shadow-free illumination, in particular with an illumination parallel to the detection direction.
  • a shadow-free illumination in particular with an illumination parallel to the detection direction.
  • lighting from several directions is of course possible for shadow-free lighting.
  • the parallel lighting is preferred because it means the least amount of effort for installation.
  • step d) diameters from 0.1 mm to 250 mm, preferably from 0.5 mm to 100 mm, particularly preferably from 1 mm to 50 mm, are sought.
  • the restriction in the area of the size of the diameter of the particles represents a further simplification, which clearly simplifies the evaluation, the classifier and the resulting possibility for Control of further processes is not negatively influenced, however, since a higher accuracy of the knowledge of the particle size distribution is not required.
  • the method additionally has the following steps:
  • the bulk material is particularly preferably transported between the detection in step a) and the detection in step g), the bulk material being rearranged during transport. This occurs, for example, when the bulk material is transferred from one conveyor belt to another, or when the material is rearranged on the conveyor belt due to an installation. This rearrangement can take place, for example, with a rod, a plow or a guide plate, so that a view into the bed is also possible.
  • the invention relates to a method for controlling a device for processing bulk material, the method having a method according to the invention for coarse classification of the particle size of a bulk material with the method steps already explained.
  • a processing step is carried out at least in step i), the processing step i) being carried out as a function of a first processing parameter.
  • the following step is also carried out:
  • the processing parameter is a pressure, for example a roller pressure or mill pressure, a speed, a volume flow, a mass flow or an amount of water added.
  • the pressure with which the bulk material is further comminuted is reduced if the classifier assumes a small value, which indicates a small particle size.
  • the volume flow is reduced if the classifier assumes a large value, which indicates a large particle size.
  • the feed mass flow is reduced if the classifier assumes a small value that indicates a small particle size.
  • the amount of water added is reduced when the classifier assumes a small value, which indicates a small particle size.
  • the processing parameter is determined in step j) with a variation of the processing parameter.
  • the product quality is determined after processing step i) as a function of the processing parameters and the classifier, the best processing parameter in each case being determined for the respective classifier. This enables the processing parameter to be optimized as a function of the classifier.
  • the processing step i) is a grinding process, a crushing process or a cooling process.
  • the method is carried out in a cement plant.
  • FIG. 1 shows a flow chart of the method according to the invention.
  • step a) the surface of the bulk material is recorded by means of a first camera 40.
  • the bulk material is then transported further into a processing device 30 for processing in step i).
  • the recording of the camera 40 recorded with the camera 40 is evaluated in step b).
  • Related areas are searched for in step c) and a diameter is determined for each connected area found in step d).
  • the mean value is formed from all the diameters found and the scatter is determined in step e).
  • a classifier is determined in step f).
  • a processing parameter is determined as a function of the classifier in step j) and processing step i) is carried out with this processing parameter.
  • Fig. 2 shows a first schematic system.
  • the system has a bulk material source 10.
  • the bulk material source 10 can be, for example, a silo, a crusher, a mill or an oven.
  • the bulk material arrives from the bulk material source 10 on a conveyor belt 20 and is transported from there into a processing device 30.
  • the processing device 30 can be, for example, a mill, a crusher or an oven.
  • a camera 40 is arranged above the conveyor belt 20.
  • a light source 50 is arranged to the right and left of the camera 40 in order to enable shadow-free illumination of the surface.
  • the second system shown in FIG. 3 differs from the first system shown in FIG. 2 in that the viewing direction of the camera 40 is perpendicular to the conveyor belt 20.
  • the third system shown in FIG. 4 differs from the second system shown in FIG. 3 in that the bulk material is transferred from a conveyor belt 20 to a further conveyor belt 20.
  • a camera 40 is arranged above both conveyor belts 20. As a result of the pouring over, different particles lie on the surface, so that the detection accuracy can be increased here.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Disintegrating Or Milling (AREA)
  • Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)

Abstract

La présente invention concerne un procédé pour classer approximativement la répartition de tailles de particules d'un produit en vrac. Le procédé comprend les étapes suivantes : a) la prise d'une photographie de la surface d'un produit en vrac au moyen d'une première caméra (40) à une première position, b) l'évaluation de la photographie prise pour établir des tailles de particules du produit en vrac à la surface du produit en vrac. L'évaluation comprend les étapes suivantes : c) la recherche de surfaces attenantes dans la photographie, d) la détermination d'un diamètre pour chaque surface identifiée, e) la détermination de la valeur moyenne et de la dispersion des diamètres, f) la fixation d'un classificateur par la multiplication de la valeur moyenne par la dispersion.
EP20712249.0A 2019-03-26 2020-03-11 Procédé de classification approximative de la répartition de tailles de particules d'un produit en vrac Ceased EP3903231A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102019204103.5A DE102019204103A1 (de) 2019-03-26 2019-03-26 Verfahren zur Grobklassifizierung der Partikelgrößenverteilung eines Schüttguts
PCT/EP2020/056459 WO2020193137A1 (fr) 2019-03-26 2020-03-11 Procédé de classification approximative de la répartition de tailles de particules d'un produit en vrac

Publications (1)

Publication Number Publication Date
EP3903231A1 true EP3903231A1 (fr) 2021-11-03

Family

ID=69846062

Family Applications (1)

Application Number Title Priority Date Filing Date
EP20712249.0A Ceased EP3903231A1 (fr) 2019-03-26 2020-03-11 Procédé de classification approximative de la répartition de tailles de particules d'un produit en vrac

Country Status (3)

Country Link
EP (1) EP3903231A1 (fr)
DE (1) DE102019204103A1 (fr)
WO (1) WO2020193137A1 (fr)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102021117537B3 (de) 2021-07-07 2022-10-06 Kleemann Gmbh Gesteinsverarbeitungsmaschine mit Bilderfassung und mit Bildverarbeitung durch ein neuronales Netzwerk
DE102022105343A1 (de) * 2022-03-08 2023-09-14 Kleemann Gmbh Verfahren zur Steuerung und/oder Regelung der Zuförderung zu bearbeitenden Materials zu einer Brech- und/oder Siebanlage einer Materialverarbeitungseinrichtung

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3401891A (en) * 1966-10-25 1968-09-17 Gen Electric Control system for a closed circuit grinding system for finish cement
US4115803A (en) * 1975-05-23 1978-09-19 Bausch & Lomb Incorporated Image analysis measurement apparatus and methods
DK176500B1 (da) * 1992-07-28 2008-06-02 Kobe Steel Ltd Fremgangsmåde til styring af en valsemölle
SE523635C2 (sv) * 2001-10-03 2004-05-04 Foss Tecator Ab Sortering av korn under skörd
US7064826B2 (en) * 2003-09-03 2006-06-20 Brightwell Technologies Digital optical measurement of particle populations using reduced magnification
DE202011109943U1 (de) * 2011-10-10 2012-11-15 J. Engelsmann Ag Vorrichtung zur Bestimmung der Größe von Partikeln in Siebgut
DE102012106132A1 (de) * 2012-07-09 2014-05-08 Reinhausen Plasma Gmbh Verfahren und System zur Identifizierung und Diskriminierung von heterogenen Materialien zur Verarbeitung in einer Vorrichtung zur Produktbearbeitung
US20170045438A1 (en) 2014-02-20 2017-02-16 Malvern Instruments Limited High-Throughput Fluid Sample Characterization

Also Published As

Publication number Publication date
DE102019204103A1 (de) 2020-10-01
WO2020193137A1 (fr) 2020-10-01

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