JP2935171B2 - Extrusion granulation method and apparatus - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an extrusion granulation method and an apparatus generally used in industrial fields such as organic rubber chemicals, pharmaceuticals, fertilizer and agricultural chemicals.

[0002]

2. Description of the Related Art Extruded granules are extruded from a perforated screen (lattice-shaped hole screen) in a so-called extrusion granulation plant, and are spontaneously broken or cut into pelletized granules. Chemical fertilizers, various chemicals, additives,
In the field of manufacturing plastic raw materials and the like, such extrusion granulation is employed. In the granulation by the extrusion molding, the properties of the finished product differ depending on the raw materials and additives to be blended and the environment (temperature, humidity, etc.) to be produced.

In order to produce a stable product, it is necessary to adjust the compounding state of additives and the like while checking the properties of the product. Conventionally, when extruding this type of granulation, an additive is added, and the wet raw material is kneaded with a kneader or the like, followed by kneading. At this time, if the wet powder material has a certain range of plasticity, a good granulated product can be obtained. However, if the balance between the amount of the additive and the kneading state is lost, the amount of the additive must be changed in order to return to a good granulation state.

Usually, these operations are performed by an operator while checking the state of the granulated material directly or visually through a monitor or the like.

[0005]

Some proposals have been made for performing such human operations automatically, and some proposals have been made. However, it is difficult to formulate visual psychological judgment criteria. It is not sufficient for an unmanned driving system, and it does not reach a method comparable to human sensation, especially visual judgment and adjustment, and is non-contact, non-destructive and high-speed processing using image processing In addition, the plant could not be operated with ambiguous control based on visual information.

A first object of the present invention is to solve the above-mentioned problems of the prior art, and to provide a non-contact and non-destructive method using image processing.
Another object of the present invention is to provide an extrusion granulation method and apparatus capable of processing at high speed and operating a plant under fuzzy control based on visual information. Without significant remodeling
High-speed processing with non-contact, non-destructive and vague (fuzzy) control using the above image processing, automatically adjusting the amount of important additives and easily obtaining good granulated products Is to provide.

[0007] It is a second object of the present invention to provide an extrusion granulator for performing the above method.

[0008]

In extrusion granulation, the quality of a granulated product (product) mainly appears as the state of the surface of the granulated product. Therefore, a good granulated product can be obtained by analyzing the granulated product at the outlet of the extruding granulator and adding a device for performing ambiguous control in a state closer to human judgment and adjustment.

The configuration of the present invention will be described below with reference to FIG. FIG. 1 is a schematic diagram illustrating the basic structure of an extrusion granulation method and apparatus according to the present invention, wherein 1 is a hopper for supplying raw materials, 6 and 7 are flow rate control valves for additives, and 8 is extrusion. Granulator, 9 is its drive motor, 10
Is a temperature sensor for detecting the temperature of the granulated material, 11 is an automatic sampling device, 13 is a video camera, 15 is a data processing device, and 16 and 17 are flow meters.

Reference numeral 20 denotes an existing extrusion granulator, 30
Indicates a control device. The present invention relates to an existing extrusion granulator 2
0 is connected to the control device 30 having a new configuration.
That is, in order to achieve the first object, the first aspect
According to a first aspect of the present invention, a kneaded product of the powder material and the additive is generated by kneading an additive to a powder material, and the kneaded material is extruded from a plurality of holes formed in a lattice shape. In the extrusion granulation method for producing a granulated product, the state of the surface of the granulated product is image-analyzed, and the result of the image analysis is evaluated using a neural network capable of learning a visual evaluation standard of an inspector. , and controlling the supply amount of the additive on the basis of the evaluation results.

According to a second aspect of the present invention, a plurality of holes formed in a lattice are formed by kneading an additive into a powder material to form a kneaded product of the powder material and the additive. Extruder 8 for producing a granulated product by extruding the kneaded material from the above, a fixed amount feeder 2 for supplying the powdered material to the extruder 8 in a fixed amount , and a flow control device for supplying the additive Flowmeter 1 for detecting 6, 7 and its supply amount
In extrusion granulation apparatus 20 that includes a 6, 17,
A video camera 13 that captures the surface state of the granulated product extruded from the extruder 8 and generates image data
And a temperature sensor 10 for detecting the temperature of the extruded portion of the kneaded material of the extrusion granulator 8, and processing image data from the video camera 13, and learning a visual evaluation standard of an inspector based on the processing result. Neural networks that can be
Is evaluated using the temperature sensor 10 and the evaluation result.
Based on the detected temperature data and the flow rate detection data of the flow meters 16 and 17, the quality of the granulated product is evaluated, and the flow control devices 6, 7 and the extrusion granulator are evaluated according to the evaluation result. 8 and a control device 15 for controlling the kneading speed.

Further, a third aspect of the present invention provides a third aspect of the present invention,
In the second invention, the flow control device is one of a flow control valve and a metering pump.

[0013]

DESCRIPTION OF THE PREFERRED EMBODIMENTS In the configuration of the first invention,
The surface state of the extruded and granulated material is imaged by a video camera 13 equipped with an image sensor suitable for a CCD. The captured image data of the granulated material is subjected to separation of noise such as a background in the processing device 15 and a luminance distribution is generated.

The visual evaluation criteria of the inspector are evaluated by a learned neural network, and based on the evaluation result, the supply amount of the additive is controlled by a fuzzy operation which models the empirical rule of the inspector. Further, in the configuration of the second invention, the sampling device 11 automatically and quickly collects a part of the granulated product and transports the granulated product to the imaging unit. The imaging unit magnifies the surface state of the granulated product with the video camera 13. Although it is preferable to use a zoom-up lens mechanism for the enlarged imaging, a method of imaging the video camera by bringing the video camera itself close to the granulated material may be adopted.

Although the above description assumes that the granulated material collected by the sampling device 11 is imaged by the video camera 13, the present invention is not limited to this. It is good also as composition which images by. Then, an image signal from the video camera is input, and a luminance distribution is obtained from an image in which a specific frequency band having surface roughness is emphasized or extracted by image processing. Next, using this result as an input, the smoothness of the surface of the granulated material is emphasized and extracted by image processing using a quality evaluation method visually performed by the inspector as an example.

With this result as input, the quality is evaluated by a neural network that has learned the visual evaluation criteria of the inspector. This learning is performed when the apparatus is installed and adjusted, by presenting the image of the granulated material to the inspector and giving the evaluation result given by the inspector to the neural network as a teacher signal.

Next, a fuzzy operation modeling the empirical rule of the inspector from the quality evaluation results evaluated by the neural network and the data from the sensors such as the temperature of each part of the granulation system and the load current value of the granulator. Automatically controls the additive feed device. The rule of thumb of the inspector is constructed in advance as a fuzzy model from the correlation between the degree of granulation of the granulated product and the amount of operation of the granulation system performed at that time.
By repeating the above process and performing feedback control, a high-quality granulated product can be stably obtained.

In the second aspect of the present invention, the flow rate adjusting device of the second aspect uses either a flow rate adjusting valve or a metering pump, so that the flow rate of the additive is accurately controlled.

[0019]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the present invention will be described below in detail with reference to the drawings. FIG. 2 is a system configuration diagram for explaining a first embodiment of an extrusion granulation apparatus according to the present invention, wherein 1 is a hopper for supplying raw materials, 2 is a fixed-quantity feeder, 3 is a continuous kneader, and 4 and 5 are additive tanks. , 6, 7 are additive flow control valves, 8 is a basket type extrusion granulator, 9 is its drive motor, 10 is a temperature sensor, 11 is an automatic sampling device, 12 is a fluid dryer, 13 is a video camera, 14 is a basket, 15 is a data processing device,
Reference numeral 17 denotes an additive flow meter.

In this embodiment, an organic vulcanization accelerator for rubber 2-
Granulation of a water-containing powder of mercaptobenzothiazole (hereinafter referred to as MBT) will be described as an example. In the figure, first, a water-containing powder of MBT is charged into the hopper 1 as a raw material. This is quantitatively supplied to the continuous kneader 3 using the quantitative feeder 2. At this time, an aqueous solution of a granulating activator polyoxyethylene octyl ether (trade name: Pionin, manufactured by Takemoto Yushi Co., Ltd.) is added from the additive tank 4 while adjusting the flow rate with the flow rate control valve 6. Similarly, an aqueous solution of a granulating thickener methylcellulose (trade name: Metrolose SM, manufactured by Shin-Etsu Chemical Co., Ltd.) is supplied from the additive tank 5 to the flow control valve 7.
Add while adjusting the flow rate.

These raw materials and additives are kneaded by a biaxial continuous kneader 3 and supplied to a basket 14 of an extrusion granulator 8. The extrusion granulator 8 extrudes the kneaded material by pressing it into a plurality of lattice holes to form a noodle, which is naturally or forcibly formed into a pellet to form a granulated material. The extruded granules are conveyed to the fluidized dryer 12 and dried,
Product MBT.

A part of the granulated material extruded from the extruding granulator 8 is sampled by a sampling device 11. The sampled granules are conveyed to an image pickup unit, picked up by a video camera 13 having a CCD image pickup device, and taken into a data processing device 15 as image data. The data processing device 15 performs image processing on the image data from the video camera 13 and determines the quality of the granulated material based on the processing result.

The temperature of the granulated product at the outlet of the granulated product of the extruding granulator 8 is detected by a temperature sensor 10 suitable for measuring temperature and low antibody, and the load of the extruding granulator 8 is controlled by a driving motor 9. Fuzzy calculation by a neural network that models the empirical rules of the inspector by detecting the supply amount of each additive into the processing device 15 using flow meters 16 and 17 that are suitable for an oval flow meter. To generate control data.

By adjusting the flow control valves 6 and 7 based on the control data, it is possible to always obtain a good granulated product. At this time, the number of rotations of the drive motor 9 of the extrusion granulator 8 (that is, the granulation load) may be changed. Further, a metering pump can be used in place of the flow control valves 6 and 7. FIG. 3 is a block diagram illustrating an example of an image processing method employed in the present invention. Reference numeral 13 denotes a video camera that captures an image of a granulated substance and converts the surface state into two-dimensional image data; A layering circuit for creating a hierarchical structure for each spatial frequency band having a different spatial frequency by a two-dimensional discrete wavelet transform of the image signal of the camera 13 ; 31b separates the background of the granulated material using the hierarchical structure; A background separation / feature extraction circuit for extracting image data of the granulated material; and a contour extraction circuit for extracting a contour of the granulated material by performing an inverse wavelet transform based on an upper layer of the hierarchical structure. , 3
1d is an image resynthesis circuit for performing inverse wavelet transform on the pixels in the outline extracted by the outline extraction circuit up to the resolution of the lowermost layer of the hierarchical structure to obtain reproduced image data from which each of the granules is separated, 31e Is a quality estimating circuit having a multilayer perceptron type neural network for evaluating the surface state of the granulated material with respect to the image reconstructed by the image resynthesizing means, and 32 is an extruding granulator controller. In addition, 30 is a control device in FIG. 1, and 31 is an image processing device.

In this embodiment, the quality evaluation based on the surface condition of the granulated material is performed by the image processing apparatus having the structure shown in FIG. It is configured to be provided to the extrusion granulator 8.
Specifically, when the granulated material collected by the sampling device 11 comes to the surface, the luminance distribution processed by the data processing device 15 becomes a defective determination area, and the flow control valves 6 and 7 are determined based on the result of the ambiguous processing. Operates in the opening direction. As a result, the viscosity at the time of kneading increases, and the granulated product has the original good surface condition.

Table 1 shows the physical properties of the water-containing MBT powder and the product MBT obtained every 30 minutes in this example.

[0027]

[Table 1]

As can be seen from Table 1, when the water content of the hydrated MBT is large, the amount of the additive is small, and when the water content of the hydrated MBT is large, the amount is large, and the surface state (appearance) of the produced granules is always good. It becomes something. FIG. 4 is a system configuration diagram for explaining a second embodiment of the extrusion granulation apparatus according to the present invention.
a is a metering pump, and the same symbols as those in FIG. 2 correspond to the same parts.

The embodiment shown in FIG. 2 has the same configuration except that the sampling device 11 in FIG. 2 is not used.
In this embodiment, granulation is performed in the same manner as in the first embodiment, using a water-containing powder of an organic vulcanization accelerator for rubber, tetramethylthiuram monosulfide (TMTM), as a raw material. In addition,
For flow rate adjustment, a metering pump 7a is provided instead of the flow rate control valve.

The video camera 13 is installed so as to face the extruded hole of the extruder 8,
The extruded granules are directly imaged. In the figure, first, a water-containing powder of TMTM is charged into the hopper 1 as a raw material. This is quantitatively supplied to the continuous kneader 3 using the quantitative feeder 2. At this time, an aqueous solution of a polyoxyethylene octyl ether for granulation is added from the additive tank 4 while adjusting the flow rate with the flow control valve 6. Similarly, an aqueous solution of a thickening agent for granulation, methylcellulose, is added from the additive tank 5 while adjusting the addition amount by the metering pump 7a.

These raw materials and additives are kneaded in a biaxial continuous kneader 3 and supplied to a basket 14 of an extrusion granulator 8. The extrusion granulator 8 extrudes the kneaded material by pressing it into a plurality of lattice holes to form a noodle, which is naturally or forcibly formed into a pellet to form a granulated material. The extruded granules are conveyed to the fluidized dryer 12 and dried,
Product TMTM.

The granulated material extruded from the extruding granulator 8 is picked up by a video camera 13 equipped with a CCD image pickup device, and taken into a data processor 15 as image data. The data processing device 15 performs image processing on the image data from the video camera 13 and determines the quality of the granulated material based on the processing result.

The temperature of the granulated product at the outlet of the granulated product of the extruding granulator 8 is detected by a temperature sensor 10 suitable for measuring low temperature antibody, and the load of the extruding granulator 8 is controlled by a driving motor 9. Fuzzy calculation by a neural network that models the empirical rules of the inspector by detecting the supply amount of each additive into the processing device 15 using flow meters 16 and 17 that are suitable for an oval flow meter. To generate control data.

By adjusting the flow control valve 6 and the metering pump 7a according to the control data, a good granulated product can always be obtained. At this time, the number of rotations of the drive motor 9 of the extrusion granulator 8 (that is, the granulation load) may be changed. In this embodiment, the supply amount of the additive from the additive tank 5 is adjusted by the metering pump 7a. However, the flow rate control valve 7 equivalent to that of the first embodiment can be used. The supply amount of the additive from (5) and (5) may be adjusted by a metering pump similar to the metering pump 7a.

More specifically, when the granulated material extruded from the hole of the granulator comes to the surface, the luminance distribution processed by the data processing device 15 becomes a defective determination area, and the flow rate is adjusted based on the result of the fuzzy processing. The valves 6, 7 operate in the opening direction.
As a result, the viscosity at the time of kneading increases, and the granulated product has the original good surface condition. In this embodiment, 3
Hydrous TMTM powder and product TMT obtained every 0 minutes
Table 2 shows each physical property of M.

[0036]

[Table 2]

As can be seen from Table 2, the water-containing TMTM
When the amount of water is large, the amount of the additive is small, and when the amount is opposite, the amount is large, and the surface state (appearance) of the produced granulated material is always good. FIG. 5 is a system configuration diagram illustrating a third embodiment of the extrusion granulation apparatus according to the present invention,
8a is a screw press-type granulator, and the same reference numerals as those in FIG. 2 correspond to the same parts.

The embodiment shown in the figure differs from the first embodiment in that a screw pre-press type granulator is used as a granulator. In the present example, an organic vulcanization accelerator for rubber, zinc 2-butyldithiocarbamate (ZnBDC), was used as a raw material.
Is granulated in the same manner as in the first embodiment. In the figure, first, a water-containing powder of ZnBDC is charged into the hopper 1 as a raw material. This is quantitatively supplied to the continuous kneader 3 using the quantitative feeder 2.

At this time, an aqueous solution of polyoxyethylene octyl ether for granulation is added from the additive tank 4 while adjusting the flow rate with the flow rate control valve 6. Similarly, a granulating thickener aqueous solution of methylcellulose is added from the additive tank 5 while adjusting the flow rate with the flow rate adjusting valve 7. These raw materials and additives are kneaded in a biaxial continuous kneader 3 and supplied to a screw pre-press granulator 8a. The screw pre-press type granulator 8a feeds and kneads the kneaded material into a plurality of extrusion holes and extrudes it, and naturally or forcibly shapes it into a noodle shape into a pellet shape to obtain a granulated product.

The extruded granules are conveyed to the fluidized dryer 12 and dried to obtain a product ZnBDC. Part of the granulated product extruded from the screw pre-press granulator 8a is sampled by the sampling device 11.
The sampled granules are transported to the imaging unit and
An image is picked up by a video camera 13 having an image pickup device, and is taken into the data processing device 15 as image data.

The data processing device 15 performs image processing on the image data from the video camera 13 and determines the quality of the granulated material based on the processing result. Further, the temperature of the granulated article at the outlet of the granulated material of the screw pre-press type granulator 8a is detected by the temperature sensor 10 which is suitable for a low temperature measuring antibody, and the load of the screw pre-press type granulator 8a is driven. A neural network that detects the current value of the motor 9 for use, and further takes in the supply amount of each additive into the processing device 15 using flow meters 16 and 17 that are suitable for an oval flow meter, and models the empirical rule of the inspector. Is performed to generate control data.

By adjusting the flow control valves 6 and 7 in accordance with the control data, a good granulated product can be always obtained. At this time, the screw press-type granulator 8a
Of the drive motor 9 (that is, the granulation load) can be changed. In addition, the flow control valve 6,
A metering pump can be used in place of one or both of 7.

In the present embodiment, the granulated material extruded from the screw pre-press granulator 8a is collected by the sampling device 11 and is imaged by a video camera. Similarly to the embodiment described above, a configuration may be adopted in which a granulated material coming out of an extrusion hole of the screw pre-press type granulator 8a is directly imaged by a video camera without using a sampling device.

More specifically, when the granulated material extruded from the hole of the granulator comes to the surface, the luminance distribution processed by the data processing device 15 becomes a defective determination area, and the flow rate is adjusted based on the result of the vague processing. The valves 6, 7 operate in the opening direction.
As a result, the viscosity at the time of kneading increases, and the granulated product has the original good surface condition. In this embodiment, 3
Hydrous ZnBDC powder and product Zn obtained every 0 minutes
Table 3 shows the physical properties of BDC.

[0045]

[Table 3]

As can be seen from Table 3, water-containing ZnBD
When the amount of water of C is large, the amount of the additive is small, and when the amount of water is reversed, the amount is large, and the surface state (appearance) of the produced granules is always good. The present invention is not limited to the embodiments described above, and it is also possible to replace the elements constituting each embodiment with means for achieving the same function.

[0047]

As described above, according to the present invention,
For the method that the inspector used to perform visually, the visual evaluation criteria were learned using a neural network, and the experience was modeled and fuzzy computation was performed. Is unnecessary, and the dispersion of the raw materials and additives of the kneaded material is automatically adjusted, and a good granulated product can be obtained.

Further, since non-contact processing is performed by image processing, it is not necessary to significantly modify an existing extruder, and quality control can be automated simply by adding a control unit according to the present invention. It is possible to easily configure the extrusion granulation apparatus.

[Brief description of the drawings]

FIG. 1 is a schematic configuration diagram illustrating a basic configuration of an extrusion granulation method and an apparatus therefor according to the present invention.

FIG. 2 is a system configuration diagram illustrating an embodiment of an extrusion granulation apparatus according to the present invention.

FIG. 3 is a block diagram illustrating an example of an image processing method employed in the present invention.

FIG. 4 is a system configuration diagram illustrating a second embodiment of the extrusion granulation apparatus according to the present invention.

FIG. 5 is a system configuration diagram illustrating a third embodiment of the extrusion granulation apparatus according to the present invention.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 Hopper which supplies raw material 2 Constant-quantity feeder 3 Continuous kneader 4,5 Additive tank 6,7 Additive flow control valve 7a Constant-quantity pump 8 Basket type extrusion granulator 8a Screw pre-press type granulator 9 Drive motor 10 Temperature Sensor 11 Automatic sampling device 12 Fluid dryer 13 Video camera 14 Basket 15 Data processing device 16, 17 Flow meter for additives.

────────────────────────────────────────────────── ─── Continued on the front page (72) Inventor Kyoji Tanaka 11-46 Shibanaka-cho, Ube City, Yamaguchi Prefecture Surpass Shiba Naka II-703 (72) Inventor Sadagami Shirakami 3012 Sasami, Asahimura, Abu-gun, Abu-gun, Yamaguchi 2-2 72) Inventor Makoto Nakamura 3-8-25 Ookoji, Ube City, Yamaguchi Prefecture Nakamura 1167-80 (72) Inventor Norihisa Taniguchi 1099-1 Kami-Ube Yamamon, Ube City, Yamaguchi Prefecture (56) References JP-A-5-262590 (JP, A) JP-A-4-222628 (JP, A) (58) ) Field surveyed (Int. Cl. ⁶ , DB name) B01J 2/00

Claims (3)

(57) [Claims] A kneaded product of the powder material and the additive is produced by kneading an additive into a powder material, and the granulated material is extruded by extruding the kneaded material from a plurality of holes formed in a grid. In the extrusion granulation method to be manufactured, the state of the surface of the granulated material is image-analyzed, and the result of the image analysis is evaluated using a neural network capable of learning a visual evaluation standard of an inspector. extrusion granulation method, and characterized by controlling the supply amount of the additive on the basis of the evaluation results. 2. A kneaded product of the powder material and the additive is produced by kneading an additive into a powder material, and the granulated material is extruded by extruding the kneaded product from a plurality of holes formed in a grid. Extrusion comprising an extrusion granulator to be manufactured, a quantitative supply device for quantitatively supplying the powder material to the extrusion granulator, and a flow control device for supplying the additive and a flow meter for detecting the supply amount. in granulator, a video camera for generating image data by imaging the surface condition of the granulated material extruded from the extrusion granulator, to detect the temperature of extrusion of the kneaded material of the extrusion granulator A temperature sensor and image data from the video camera are processed, and the processing result is evaluated using a neural network capable of learning an inspector's visual evaluation standard. Control for evaluating the quality of the granulated material based on the output temperature data and the flow rate detection data of the flow meter, and controlling the kneading speed of the flow rate adjusting device and the extrusion granulator according to the evaluation result. An extrusion granulation apparatus characterized by comprising an apparatus. 3. The method according to claim 2, wherein the flow rate adjusting device comprises:
An extrusion granulation device, which is either a flow control valve or a metering pump.