METHOD FOR CONTROLLING A CRUSHER
20230082025 · 2023-03-16
Inventors
Cpc classification
International classification
B02C25/00
PERFORMING OPERATIONS; TRANSPORTING
Abstract
A method for controlling a crusher having a crushing tool and a vibratory conveyor (1) having a drive (5), includes capturing bulk material (2) in a capture region (4) using a sensor (3). So that, in the case of grains with an inhomogeneous grain size distribution, even large grains can be crushed with a constant crushing result without a risk of the crusher being damaged, an effective diameter d.sub.eff, which results from the largest diameter d.sub.max and the direction (9) thereof, transverse to the conveying direction (8) of a grain of the bulk material (2) is determined as the controlled variable in the capture region (4). If the effective diameter d.sub.eff exceeds a predefined power threshold value, the power of the crushing tool is increased and/or, if the effective diameter d.sub.eff exceeds a predefined switch-off limit value, the drive (5) is switched off.
Claims
1. A method for controlling a crusher having a crushing tool and a vibratory conveyor having a drive, said method comprising: capturing bulk material in a capturing region using a sensor; determining an effective diameter d.sub.eff, which is derived from a largest diameter d.sub.max and a direction thereof, that is transverse to a conveying direction of a grain of the bulk material as a controlled variable in the capturing region; and increasing power of the crushing tool when the effective diameter d.sub.eff exceeds a predetermined power threshold value or switching off the drive when the effective diameter d.sub.eff exceeds a predetermined shutdown threshold value.
2. The method according to claim 1, wherein at least two actuators of the drive are controlled so that the effective diameter d.sub.eff is reduced transversely to the conveying direction.
3. The method according to claim 1, wherein, when the effective diameter d.sub.eff transverse to the conveying direction of a grain in the capturing region exceeds a predetermined alignment threshold value, at least two actuators of the drive are controlled so as to reduce the effective diameter d.sub.eff of the grains.
4. The method according to claim 1, wherein the drive is controlled so that a volume, captured at predetermined intervals by a volume sensor, of the bulk material lying in the capturing region corresponds as a controlled variable to a preset value.
5. The method according to claim 4, wherein the sensor comprises a depth sensor that generates a two-dimensional depth image of the bulk material conveyed past the depth sensor, and the two-dimensional depth image is fed to a previously trained convolutional neural network that has at least three successive convolution layers and a downstream classifier, wherein an output value of the neural network is output as a parameter of the bulk material present in the capturing region.
6. A training method for training the neural network for a method according to claim 5, said training method comprising first acquiring example depth images each of a respective example grain with a known individual parameter and storing said example depth images together with the individual parameters thereof; combining a plurality of said example depth images randomly so as to form a training depth image to which a sum of the individual parameters, a maximum value of the individual parameters, or a mean value of the individual parameters of the combined example depth images is assigned as a common parameters; and feeding the training depth image to the neural network on an input side thereof, and feeding the common parameter assigned to said training depth image to the neural network on an output side thereof; and adapting weights of the individual network nodes in a learning step.
7. The method according to claim 2, wherein, when the effective diameter d.sub.eff transverse to the conveying direction of a grain in the capturing region exceeds a predetermined alignment threshold value, the at least two actuators of the drive are controlled so as to reduce the effective diameter d.sub.eff of the grains.
8. The method according to claim 2, wherein the drive is controlled so that a volume, captured at predetermined intervals by a volume sensor, of the bulk material lying in the capturing region corresponds as a controlled variable to a preset value.
9. The method according to claim 3, wherein the drive is controlled so that a volume, captured at predetermined intervals by a volume sensor, of the bulk material lying in the capturing region corresponds as a controlled variable to a preset value.
10. The method according to claim 7, wherein the drive is controlled so that a volume, captured at predetermined intervals by a volume sensor, of the bulk material lying in the capturing region corresponds as a controlled variable to a preset value.
11. The method according to claim 8, wherein the sensor comprises a depth sensor that generates a two-dimensional depth image of the bulk material conveyed past the depth sensor, and the two-dimensional depth image is fed to a previously trained convolutional neural network that has at least three successive convolution layers and a downstream classifier, wherein an output value of the neural network is output as a parameter of the bulk material present in the capturing region.
12. The method according to claim 9, wherein the sensor comprises a depth sensor that generates a two-dimensional depth image of the bulk material conveyed past the depth sensor, and the two-dimensional depth image is fed to a previously trained convolutional neural network that has at least three successive convolution layers and a downstream classifier, wherein an output value of the neural network is output as a parameter of the bulk material present in the capturing region.
13. The method according to claim 10, wherein the sensor comprises a depth sensor that generates a two-dimensional depth image of the bulk material conveyed past the depth sensor, and the two-dimensional depth image is fed to a previously trained convolutional neural network that has at least three successive convolution layers and a downstream classifier, wherein an output value of the neural network is output as a parameter of the bulk material present in the capturing region.
Description
BRIEF DESCRIPTION OF THE INVENTION
[0014] In the drawing, the subject matter of the invention is shown by way of example, wherein:
[0015]
[0016]
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0017] A method according to the invention can be used for the control of a vibratory conveyor 1 shown in
[0018] In addition, the drive 5 can be controlled in such a way that the volume of the bulk material 2 lying in the capturing region 4, which is detected by the sensor 3 at predetermined intervals, corresponds to a preset value as a controlled variable. In this context, the drive 5 is controlled by adjusting the vibration amplitude and/or the vibration frequency in such a way that the controlled variable corresponds to a preset value. Such a preset value can, for example, be a range of a nominal volume input flow to which a crusher to be fed is designed.
[0019] As can be seen from
[0020] To ensure that only bulk material 2 which can actually cause a blockage of the crusher is displaced, the effective diameter d.sub.eff resulting from the largest diameter d.sub.max and direction 9 thereof can be compared with an alignment threshold value. Only if the alignment threshold value is exceeded is a displacement of the bulk material 2 initiated by a corresponding control of the actuators of the unbalance motors 7.
[0021]
[0022] If the effective diameter d.sub.eff is just below the shutdown threshold value, an increase in the crushing tool power can be sufficient. For this purpose, the crushing tool can be controlled by the control device 6 if the effective diameter d.sub.eff exceeds a power threshold value.