METHOD FOR CLEANING BLINDING PARTICLES IN CRUSHERS

Abstract

In a method for cleaning blinding particles in crushers, material to be crushed is fed via a feed stream (1) to a crushing tool, and from there is separated by a screen (7) into a conveyor stream (8) that passes through the screen (7) and a return stream (10) that is held back by the screen (7) and returned to the feed stream (1), thereby forming a conveyance circuit (9). The method makes an efficient, continuous crusher operation possible, wherein idle times for cleaning blinding particles can be avoided. The particle size distribution and/or the material volume are measured at specified intervals in parts of the conveyance circuit (9) and/or conveyor stream (8), and if the particle size distribution and/or material volume deviate above a predefined limit value, the particle size created by the crushing tool is increased for a predefined time period.

Claims

1. A method for cleaning blocking-grains in crushers, said method comprising: feeding material to be crushed to a crushing tool via a feed stream; and separating the material from the feed stream via a screen into a conveyor stream passing through the screen and into a return stream retained by the screen and returned to the feed stream so as to form a conveying circuit; determining a grain size distribution and/or a material volume in predetermined time steps in parts of the conveying circuit and/or in the conveyor stream; and responsive to a determination that the grain size distribution and/or material volume is above a predetermined limit value, increasing a grain size produced by the crushing tool for a predetermined period of time.

2. The method according to claim 1, wherein when the grain size distribution and/or material volume is above the predetermined limit value, a feed speed of the feeding of the material is reduced within the predetermined period of time.

3. The method according to claim 1, wherein when the grain size distribution and/or material volume above the predetermined limit value, a rotor speed of the crusher is reduced within the predetermined period of time.

4. The method according to claim 1, wherein the method further comprises acquiring a two-dimensional depth image of the conveying circuit and/or of the conveyor stream in sections with a depth sensor, transmitting the acquired two-dimensional depth image to a previously trained convolutional neural network that has at least three convolution layers lying one behind the other and, for each class of a grain size distribution, a quantity classifier and/or a volume classifier downstream of the convolutional layers, and transmitting output values of the quantity classifier and/or volume classifier as the grain size distribution and/or as material volume present in a detection area.

5. The method according to claim 4, wherein that the method further comprises removing values of pixels from the depth image where said pixels each have a respective depth that corresponds to or exceeds a previously detected distance between the depth sensor and a background for this pixel.

6. A training method for training a neural network for a method according to claim 4, said training method comprising: acquiring sample depth images of a respective sample grain with a known volume and storing said sample depth images together with the known volume; combining a plurality of sample depth images randomly so as to form a training depth image, said training depth image having assigned thereto a class-wise distribution of material volumes of the combined sample depth images is as grain size distribution and/or a sum of the volumes of the combined sample depth images is assigned as material volume; transmitting the training depth image to the neural network on the input side thereof and the assigned grain size distribution and/or the assigned material volume to the neural network on the output side thereof; and a learning step wherein weights of individual network nodes are adapted.

7. The training method according to claim 6, wherein the sample depth images combined so as to form a training depth image have random alignment.

8. The training method according to claim 6, wherein the sample depth images with partial overlaps are combined to form a training depth image, wherein the depth value of the training depth image in a region of one or more of the overlaps corresponds to the smallest depth of both sample depth images.

9. The training method according to claim 7, wherein the sample depth images with partial overlaps are combined to form a training depth image, wherein the depth value of the training depth image in a region of one or more of the overlaps corresponds to the smallest depth of both sample depth images.

10. The method according to claim 2, wherein when the grain size distribution and/or material volume above the predetermined limit value, a rotor speed of the crusher is reduced within the predetermined period of time.

11. The method according to claim 10, wherein the method further comprises acquiring a two-dimensional depth image of the conveying circuit and/or of the conveyor stream in sections with a depth sensor, transmitting the acquired two-dimensional depth image to a previously trained convolutional neural network that has at least three convolution layers lying one behind the other and, for each class of a grain size distribution, a quantity classifier and/or a volume classifier downstream of the convolutional layers, and transmitting output values of the quantity classifier and/or volume classifier as the grain size distribution and/or as material volume present in a detection area.

12. The method according to claim 2, wherein the method further comprises acquiring a two-dimensional depth image of the conveying circuit and/or of the conveyor stream in sections with a depth sensor, transmitting the acquired two-dimensional depth image to a previously trained convolutional neural network that has at least three convolution layers lying one behind the other and, for each class of a grain size distribution, a quantity classifier and/or a volume classifier downstream of the convolutional layers, and transmitting output values of the quantity classifier and/or volume classifier as the grain size distribution and/or as material volume present in a detection area.

13. The method according to claim 3, wherein the method further comprises acquiring a two-dimensional depth image of the conveying circuit and/or of the conveyor stream in sections with a depth sensor, transmitting the acquired two-dimensional depth image to a previously trained convolutional neural network that has at least three convolution layers lying one behind the other and, for each class of a grain size distribution, a quantity classifier and/or a volume classifier downstream of the convolutional layers, and transmitting output values of the quantity classifier and/or volume classifier as the grain size distribution and/or as material volume present in a detection area.

Description

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0020] A method according to the invention for cleaning blocking-grains in crushers is based, for example, on a preparation process of mineral material to be crushed to fine grain, wherein the material to be crushed, which is not shown in more detail, is fed via a feed stream 1 to a crushing gap 2 of a crushing tool. As this is schematically indicated in the drawing, the crushing gap 2 is formed in an impact chamber 3 between an impact bar 4 and an impact plate 5. The crushing gap is thus to be understood as the minimum distance between an impact bar 4 arranged on a rotor 6 and an impact plate 5. From the crushing gap 2, the feed stream 1 is separated via a screen 7 into a conveyor stream 8 passing through the screen 7 and a return stream 10 retained by the screen 7 and returned to the feed stream 1 to form a conveying circuit 9 indicated by a dashed frame.

[0021] In crusher operation, the material volume and/or the grain size distribution is determined in sections in the conveying circuit 9 and/or in the conveyor stream 8 in predetermined time steps. For example, it has been shown that in the return stream 10 located in the conveying circuit 9, a shift in the grain size distribution towards smaller grain sizes correlates directly with an increase in the screen mesh blockage. As a result, corresponding screen characteristics can be determined and from this a lower grain size limit can be set as a limit value for initiating the cleaning of the blocking-grains.

[0022] For example, when the grain size distribution in the return stream 10 deviates above the predetermined limit value, the crushing gap 2 is enlarged for a predetermined period of time. As a result of the larger crushing gap 2, coarse grain can be produced in a targeted manner within the predetermined period of time, which is continuously fed via the return stream 10 and the feed stream 1 to the impact chamber 3 in the feed circuit 9 in such a way that the screen meshes of the screen 7 are knocked free by the impulse impact of the impinging coarse grain. After the specified time period has elapsed, the crushing gap 2 is set again to the initial or a smaller value so that the previously produced coarse grain can also be crushed again and finally passes through the cleaned screen 7 as fine grain into the conveyor stream 8.

[0023] In order to enable an even more effective cleaning of blocking-grains of the screen 7, the feed speed can be reduced within the specified period of time if the material volume and/or grain size distribution deviates above a specified limit value. Analogously, the speed of the rotor 6 can also be reduced. The lower occurring rotational energy of the rotor 6 arranged together with the impact plates 5 in the impact chamber 3 as a result of this measure leads to the material to be crushed also being acted upon with a lower kinetic energy or lower impulses by the impact bars 4 arranged on the periphery of the rotor 6 and projecting radially from it. This further favors the generation of coarse grain within the specified time period.