JP6603822B2 - Waste quality estimation system and crane operation control system using the waste quality estimation system - Google Patents

Description

  The present invention relates to a garbage quality estimation system in a garbage pit. The present invention also relates to a crane operation control system using the waste quality estimation system.

  Conventionally, the waste combustion control has controlled the amount of combustion air and the amount of waste supply based on the situation in the incinerator, but it reacts to changes in the quality of the waste (composition, heat value) input into the incinerator. There were inevitably limits and delays.

Patent Document 1 describes the color, shape, and crushing of individual garbage bags from image information in the vicinity of the furnace inlet for the purpose of enabling stable combustion control even when the composition of combustion is subject to severe composition fluctuations. Disclosed is a method for discriminating waste, sheared waste, and other types of waste, and estimating the weight and calorific value of each piece of waste input based on the waste heat value database.
Patent Document 2 discloses a method for continuously acquiring information related to the amount of heat generated in garbage in real time with high accuracy and performing combustion control of garbage without time delay with respect to the current fuel state. .

  For example, Patent Document 3 discloses a method for detecting the agitation state of dust in order to uniformize the quality of the dust in the garbage pit. Japanese Patent Application Laid-Open No. 2004-228561 gradations imaged dust image data with luminance values to extract edges, calculates a fractal dimension of the extracted edge, and detects the agitation state of the dust based on the calculated fractal dimension. The configuration is disclosed.

Japanese Patent No. 5361595 Patent No. 5996762 Patent No. 6188571

The present invention has a different approach from the above-mentioned patent documents and the like, and more accurately grasps the quality of the garbage in the garbage pit than before, and controls the fuel in the incinerator to be uniform at the time of loading by the crane. The object is to provide a waste quality estimation system capable of suppressing changes in the situation and realizing stable combustion.
Another object of the present invention is to provide a crane operation control system using a waste quality estimation system.

The garbage quality estimation system of the present invention includes:
Input waste that generates input waste quality estimation model that estimates waste quality by intelligent information processing technology using input data of input waste created from image data obtained by picking up the waste pit surface and waste quality teacher data Quality estimation model generator.
The said garbage quality estimation system may have an imaging part which images a garbage pit surface.
The garbage quality estimation system may include an input data creation unit that creates input data of input garbage from image data obtained by imaging a dust pit surface.
The waste quality estimation system may include a storage unit that stores input data of input waste.
The garbage quality estimation system may include a receiving unit that receives input data of input garbage calculated by another system.

In the above invention, the input waste quality estimation model generation unit is based on a time delay amount from when waste is input to the input port of the combustion apparatus until combustion is started, or a record of input waste amount and supplied waste amount, The input garbage quality estimation model may be generated by temporally associating the garbage quality teacher data with the input data.
The amount of time delay may be set in advance, and may be set by an empirical rule or experiment, for example. For example, when the combustion apparatus is a stoker-type incinerator, for example, 30 to 40 minutes may be set as the time delay amount.
The dust quality estimation system may further include a time delay amount calculation unit that calculates a time delay amount from when dust is introduced into the inlet of the combustion apparatus to when combustion is started.

The “garbage quality teacher data” includes, for example, the concentration or amount of waste composition (water, carbon, hydrogen, hydrogen chloride (HCl) and sulfur dioxide (SO ₂ ), etc. in exhaust gas), calorific value, and the like.
The garbage quality estimation system may include a garbage quality teacher data creation unit that creates garbage quality teacher data.
The composition concentration of garbage can be obtained by analyzing exhaust gas with an analyzer corresponding to various components such as an exhaust gas analyzer. Further, other components (for example, carbon dioxide) may be calculated from predetermined components (for example, oxygen and moisture).
The calorific value may be calculated using, for example, the method disclosed in Japanese Patent No. 5996762, or a measured value may be used.

In the above invention, the input data may further include one type or two or more types of information from among the garbage carrying vehicle, date and weather.
The input data creation unit may create input data so as to further include one type or two or more types of information from among the garbage carrying vehicle, date, and weather.
In this case, the garbage quality teacher data may further include one type or two or more types of information from among the garbage carrying vehicle, date and weather.

In the above invention, the imaging unit is, for example, a visible camera, a spectrum camera, an infrared camera, or the like.
When the imaging unit is a spectrum camera, the input data creation unit (or absorbance area ratio calculation system)
A spectral binarized image generating unit that generates a spectral binarized image based on absorbance for each wavelength divided from the spectral image data;
An area ratio calculation unit that calculates an area greater than or equal to a threshold (or less) for each wavelength of each evaluation area and calculates an area ratio for the evaluation area may be included. In this case, the input data is an area ratio.
When the imaging unit is a spectrum camera, the input data creation unit (or absorbance average value calculation system)
You may have the light absorbency average value calculation part which calculates the average value of the light absorbency in each evaluation area for every wavelength divided from the spectrum image data. In this case, the input data is “absorbance average value”.
When the imaging unit is a visible camera, the input data creation unit (or the mixing degree evaluation system)
You may have a mixing degree evaluation part which processes the image data predetermined and evaluates the mixing degree of the garbage of each evaluation area. In this case, the input data is the degree of mixing.
The input data may be one or more of area ratio, absorbance average value, and degree of mixing.
The input data may include a charging time when the garbage is charged into the combustion apparatus (for example, a charging hopper).

In the above invention, the intelligent information processing technique is, for example, machine learning or deep learning.
The configurations of machine learning and deep learning are not particularly limited, and a conventional algorithm configuration may be used.
The input waste quality estimation model is not limited to what is configured as a so-called software program, and may be configured as a dedicated electronic circuit or embedded firmware.
The software program may be stored in memory and the program procedure may be executed by the processor.
The input garbage quality estimation model may be updated using different garbage quality teacher data.

Another crane operation control system of the present invention includes a model storage unit that stores an input waste quality estimation model generated by the waste quality estimation system,
A waste quality estimation unit that inputs input data of the input waste into the input waste quality estimation model and estimates the waste quality;
An automatic operation control unit that automatically controls the crane according to the estimated waste quality output from the waste quality estimation model.
The input data is data including one or more of the input data used in the garbage quality estimation system.
The estimated garbage quality is data including one kind or two or more kinds of the above-mentioned garbage quality teacher data.

The automatic operation control unit may control the crane so that the waste quality is uniform in each evaluation area.
The automatic operation control unit may control the crane so that waste having a high calorific value is thrown when the calorific value of the combustion device is lower than an expected value.
The automatic operation control unit may control the crane so as to throw in garbage having a low calorific value when the calorific value of the combustion device is higher than an expected value.

The following configuration may be used as the mixing degree evaluation system for calculating the mixing degree.
The mixing degree evaluation system
An imaging unit installed so as to image the garbage in the garbage pit from above;
A laser range finder that is movably installed above the garbage so as to detect the garbage distance in the garbage pit, and that detects the distance (A) to the measurement point (P);
Based on the distance (A) and a reference distance (B) from the laser distance meter to the bottom surface of the garbage pit, a measurement point dust height calculation unit that calculates a measurement point dust accumulation height (Z);
A three-dimensional waste height calculation unit for calculating three-dimensional height information of the waste based on the plane coordinates in the waste pit and the information of the measurement point dust accumulation height;
An image conversion unit that converts an image captured by the imaging unit into an aerial viewpoint image based on installation information of the imaging unit;
Based on the three-dimensional height information of the dust, an image correction unit that obtains a corrected image corrected so that all the areas of the sky viewpoint image are on the same height plane;
A binarization processing unit that gradations the corrected image and binarizes with a predetermined threshold to obtain a binarized image;
The binarized image may be divided into two or more evaluation areas having a plurality of divided areas, and a mixing degree evaluation unit that evaluates the mixing degree of dust in each evaluation area may be used.
The mixing degree evaluation unit is
Calculate the extraction area of the bright or dark part of each evaluation area, calculate the variation of the extraction area of the bright or dark part of the divided area for all evaluation areas or for each evaluation area, and evaluate the degree of mixing by the variation It may have a variation evaluation part.
Here, variations in the extraction area include, for example, variance σ ² , standard deviation σ, variation coefficient CV, and the like.
In the evaluation by the variation evaluation unit, the variation, for example, the variance σ ² , the standard deviation σ, the coefficient of variation CV, etc. is larger than a predetermined value, so that various kinds of dust are present in the area, that is, sufficient agitation is performed. It shows that the waste is highly homogenized.
The mixing degree evaluation unit
For example, each particle is extracted by applying discriminant analysis method (binarization of Otsu) to each divided area or binarization by dynamic threshold method and labeling bright part (or dark part). And it may have an area average evaluation part which calculates the particle area average of each evaluation area, and evaluates the degree of mixing by the area average.
In the evaluation of the area average evaluation unit, for example, as the particle area average is smaller than a predetermined value, the rubbing of the garbage progresses, that is, the stirring is sufficiently performed, and the homogenization of the garbage is high. .
The mixing degree evaluation unit
You may have the area ratio evaluation part which calculates the area ratio of the bright part and dark part of each evaluation area, and evaluates a mixing degree by the said area ratio.
Here, the area ratio is, for example, dark part / bright part, bright part / dark part.
In the evaluation by the area ratio evaluation unit, the larger the area ratio (for example, a value less than 1), the larger the predetermined value, the more the garbage is broken, that is, the agitation is sufficiently performed. Since the garbage bags are often brighter than the garbage, it is estimated that the larger the area of the dark part, the more the garbage bags are crushed.

According to the garbage quality estimation system, since the intelligent information processing technology is used, the garbage quality can be suitably estimated.
According to the crane operation control system, it is possible to achieve uniform combustion waste quality, that is, stabilization of combustion.
In addition, by realizing stabilization of combustion, there are advantages such as reduction of auxiliary fuel, reduction of the amount of medicine used by stabilization of exhaust gas properties, and labor saving of operation status monitoring.
In addition, it is possible to optimize the agitation for the purpose of uniforming the quality of dust in the pit, thereby reducing the power consumption of the crane and saving energy.

It is a figure for demonstrating an example of the garbage quality estimation system of Embodiment 1, and a garbage pit side view. It is a figure which shows an example of the luminance image of the whole garbage pit. It is a figure which shows an example of the binarized image which binarized the luminance image of FIG. 2A. It is a figure which shows an example which fractionated the binarized image of FIG. 2B in the evaluation area and the division area. It is a figure which shows the address rule of an evaluation area. It is a figure which shows the address rule of a division area. It is a figure which shows the number of the extraction images of each division area in evaluation area Sa. The flowchart explaining a process sequence. It is a figure for demonstrating the structural example which calculates a mixing degree. It is a figure for demonstrating the example of another structure which calculates a mixing degree. It is a figure for demonstrating the example of another structure which calculates a mixing degree. It is a figure for demonstrating the garbage quality estimation system. It is a figure for demonstrating the structural example which calculates the area ratio of a light absorbency. It is a figure for demonstrating the structural example which calculates a light absorbency average value. It is a figure which shows an example of the binarized image for every divided wavelength. It is a figure which shows an example of the evaluation area of the binarized image for every wavelength division. It is a figure which shows an example of the area ratio (%) for every wavelength which each evaluation area was divided. It is a figure which shows an example of the light absorbency average value for every wavelength divided into each evaluation area. It is a figure which shows an example of the database of input data. It is a figure which shows an example of the input data of the thrown-in waste. It is a figure which shows an example of garbage quality teacher data. It is a figure which shows an example of the accumulation state of the thrown-in waste, and an in-feed state.

(Embodiment 1)
FIG. 1 is an example of a side view of a garbage quality estimation system 500 and a garbage pit 1.

  FIG. 1 shows the inside of the garbage pit 1 in a side view. A garbage truck 9 enters the right side of FIG. 1, and the garbage is sent into the input area 11 from the input door 12. The waste D in the input area is conveyed to the waste agitation area 15 of the waste pit 1 by the crane 22 and the bucket 23. In the stirring area 15, a mixed address that is fractionated to a predetermined size on the floor plane is set. In the first embodiment, the mixed address and the evaluation area are the same. In the mixing area 15, a state where dust is accumulated (D1: accumulated waste) is shown.

(Crane operation control unit)
The crane operation control system 600 moves the crane 22 within the range of any mixed address or across the addresses, grabs and lifts the garbage with the bucket 23, and moves on the spot or to another position within the mixed address. Or move to another mixed address and control to drop the garbage. In addition, the crane operation control system 600 controls the crane 22 and the bucket 23 so as to move garbage at a plurality of mixed addresses. The crane operation control system 600 includes an automatic operation control unit (not shown) and a manual operation control unit 603, and the automatic operation control unit 603 can execute the above control. In addition, the crane operation control system 600 controls the crane 22 and the bucket 23 to control and feed the garbage into the charging hopper 41 of the incinerator 40 by grasping and moving the garbage at an arbitrary mixed address. The method of using the input waste quality estimation model will be described later.

  The waste quality estimation system 500 includes a mixture degree evaluation system 100, an absorbance area ratio calculation system 200, and an absorbance average value calculation system 300, which will be described later. Data is received and stored in the storage unit 501. Details of the garbage quality estimation system 500 will be described later.

(Example 1 of input data: Configuration for obtaining the degree of mixing)
The mixing degree evaluation system 100 for calculating the mixing degree and its processing flow will be described with reference to FIGS. 2 to 4A-4C. The waste mixing degree evaluation system 100 shown in FIG. 4A has the following configuration.

The imaging unit 31 captures an image of the dust in the garbage pit 1 (for example, the stirring area 15) from above. For example, the imaging unit 31 can be fixedly installed on a pillar, a wall, a ceiling, or the like in the garbage pit 1. In the present embodiment, the imaging unit 31 is installed on a wall below the crane gutter 21. The imaging unit 31 may be, for example, a CCD camera, a CMOS camera, or an infrared camera that captures a moving image or a still image. In the case of moving image capturing, a still image may be cut out. The image is stored in a memory (for example, the inside of the imaging unit or the memory of the mixing degree evaluation system 100) with the imaging time associated therewith. The imaging unit 31 may image the garbage regularly or at a predetermined timing, for example, a timing for determining the degree of mixing.
In the first embodiment, the number of the imaging units 31 is one. However, the imaging unit 31 is not limited to this, and a plurality of imaging units may be installed. The agitation area 15 may be divided and imaged by a plurality of imaging units (partially overlapping images may be captured), or each of the plurality of imaging units may image the entire agitation area from different angles. Images captured by a plurality of imaging units may be processed by a synthesis processing unit (not shown) so that the entire stirring area becomes one image, for example. An image captured by the imaging unit 31 of the first embodiment is an RGB image. Note that the image is not limited to an RGB image, and may be a plane image by an infrared camera, for example.

The laser distance meter 32 detects the distance to the accumulated dust D1 in the dust pit 1. The laser rangefinder 32 is installed in the upper part of the garbage pit 1 so as to be fixed or movable. For example, the laser rangefinder 32 may be installed in the crane gutter 21 so as to be movable in the crane traveling direction, and may move together with the crane 22 or move separately. Alternatively, it may be installed near the crane 22. When fixedly installed vertically below, the laser distance meter 32 may be configured to be adjustable in angle so that it can be detected by changing its detection angle.
In the first embodiment, it is fixed to the crane gutter 21, and the distance to the lower garbage (measurement point P) can be detected by changing the detection angle to the left and right in FIG. The crane gutter 21 moves in the direction perpendicular to the paper surface of FIG.
The coordinates (XY plane coordinates) of the measurement point P can be calculated based on the position of the laser distance meter 32 and the detection angle (measurement point angle θ). The position of the laser distance meter 32 can be derived from the position coordinates of the crane garter 21 and the relative position between the crane garter 21 and the laser distance meter 32.
The coordinates of the measurement point P, the detected distance A, the measurement point angle θ, and the detection time are associated with each other and stored in a memory (for example, a laser distance meter or a memory in the mixing degree evaluation system 1).

The imaging time of the imaging unit 31 and the distance detection time of the laser rangefinder 32 do not have to match completely. A configuration may be employed in which an image substantially corresponding to the detection time of the laser distance meter 32 is captured. When at least one or both are processing (distance detection, imaging), it is preferable that stirring by a crane is not performed.
The image may be captured and the distance may be detected at a predetermined timing when the crane is automatically operated for stirring or at a timing when garbage is put into the incinerator 40.

The measurement point dust height calculation unit 101 calculates the measurement point dust accumulation height Z based on the distance A, the measurement point angle θ, and the reference distance B from the laser distance meter 32 to the bottom surface of the dust pit 1. The reference distance B is a distance from the laser rangefinder 32 to the vertically downward floor (here, the floor of the stirring area 15).
Z = B−A × cos θ (1)

The three-dimensional waste height calculation unit 102 calculates the three-dimensional height information W of the waste based on the plane coordinates (XY) in the waste pit 1 and the information of the measurement point dust accumulation height Z.
The three-dimensional height information W may include information on imaging time and detection time.
The three-dimensional garbage height calculation unit 102 calculates the three-dimensional height information W at a predetermined timing.
In the present embodiment, the “predetermined timing” may be a fixed interval, a fixed time, or a timing based on an operator's command operation.

  The image conversion unit 111 converts the image captured by the image capturing unit 31 into the sky viewpoint image based on the installation information of the image capturing unit 31 (for example, the position coordinates in the garbage pit, the image capturing angle, and the angle of view of the lens). Convert to In the first embodiment, the imaging unit 31 captures the dust in the stirring area 15 at a predetermined angle from obliquely above, and thus converts the captured image into an aerial viewpoint image.

Based on the three-dimensional height information W of the garbage, the image correction unit 112 corrects all the areas of the above-described sky viewpoint image so as to be on the same height plane. Let the image obtained here be a correction image.
Thereby, the planar image of the same height is obtained.

The binarization processing unit 113 converts the correction image, which is an RGB image, into an HSI image, and gradations the image with any of hue H, saturation S, and luminance I to obtain a gradation image. Next, the binarization processing unit 113 binarizes the gradation image with a predetermined threshold to obtain a binarized image. Here, the “predetermined threshold value” may be, for example, an average value of elements (hue H, saturation S, luminance I) when gradation is performed in the entire pit. In the first embodiment, gradation is performed according to luminance, and the average of luminance is used as the threshold value.
As another example, “gradation” is not limited to hue, saturation, and luminance, and gradation may be performed in any of RGB. When the imaging unit is an infrared camera, the binarization processing unit 113 may perform gradation using, for example, a wavelength and a frequency after performing the image conversion process and the image correction process.

The degree-of-mixing evaluation unit 130 evaluates the degree of mixing of garbage using the binarized image. In the first embodiment, the mixing degree evaluation unit 130 includes a variation evaluation unit 131.
The variation evaluating unit 131 divides the binarized image into two or more evaluation areas having a plurality of divided areas, and calculates an extraction area of a bright part or a dark part of each divided area.
For example, the bright part is an image with many garbage bags as they are, and the stirring is insufficient, and the dark part is an image in which the garbage bags are crushed, and it is estimated that the stirring is sufficient.
Further, the variation evaluating unit 131 calculates the variance σ ² of the extraction area of the bright part or the dark part of the divided area with respect to all the evaluation areas. In this embodiment, a method of counting the number of pixels is used for area calculation. However, the present invention is not particularly limited to this, and the area of the bright part or the dark part may be calculated by another method.
In addition to or instead of calculating the variance, the variation evaluating unit 131 can calculate the variance σ ² of the extraction area of the bright or dark portion of the divided area for each evaluation area.
Further, the variation evaluation unit 131 can calculate the standard deviation σ and / or the coefficient of variation (CV) together with or instead of the variance σ ² .

The variation evaluation unit 131 evaluates the degree of dust mixing based on the variation. In the present embodiment, the variation evaluating unit 131 determines whether or not the variance σ ² exceeds a predetermined value. The predetermined value is set in advance, and the setting can be changed for each garbage pit (locality, weather). The predetermined value may be set based on an empirical rule by experiment or long-term operation.
The variation evaluation unit 131 indicates that as the variance σ ² exceeds a predetermined value and becomes a larger value, various kinds of dust are present in the area, that is, sufficient stirring is performed.

(Processing flow)
In step S1, the imaging unit 31 captures an image in the garbage pit. The laser distance meter 32 measures the distance A to the garbage (measurement point P) in the garbage pit.
In step S2, the dust accumulation height Z at each measurement point is calculated from the following equation.
Z = B−A × cos θ (1)
Next, the three-dimensional height information W of the dust is calculated based on the plane coordinates (XY) in the dust pit 1 and the information of the dust accumulation height Z.

In step S3, the sky viewpoint image is created, and based on the three-dimensional height information W, a correction image is created so that all the areas of the sky viewpoint image are on the same height plane.
In step S4, the correction image, which is an RGB image, is subjected to HIS conversion of hue H, saturation S, and luminance I to create a luminance image based on the luminance. The luminance image is gradationized with 256 gradations. FIG. 2A shows an example of a gradation image in the garbage pit. Here, the number of pixels corresponds to the actual area (stirring area), and the brightness of each pixel is shown.
As another example, “gradation” is not limited to hue, saturation, and luminance, and gradation may be performed in any of RGB. When the imaging unit is an infrared camera, the binarization processing unit 113 may perform gradation using, for example, a wavelength and a frequency after performing the image conversion process and the image correction process.

In step S5, the average luminance for all the pixels is calculated and set as a threshold value.
Average luminance = (luminance 0 × the number of pixels + luminance 1 × the number of pixels + .. luminance 255 × the number of pixels) / total number of pixels (2)

  In step S6, pixels brighter than the threshold are extracted and binarized. As another example, dark pixels may be extracted and binarized. FIG. 2B shows an example of a binarized image.

In step S7, all pixels are fractionated in the evaluation area. Each evaluation area is further divided into divided areas. The number of extracted pixels (area) is calculated for each of the divided areas. Here, the extracted pixel is a bright portion (white). FIG. 2C shows an example in which all pixels are fractionated in the evaluation area. A thick frame is an evaluation area, and a broken line indicates a minimum unit pixel. The “minimum unit pixel” may be one or plural. FIG. 2D shows an example of the addresses of the evaluation areas Sa to Si. FIG. 2E shows an example of the addresses of the divided areas Sa1 to Sa9 of the evaluation area Sa. Addresses are similarly set for the other evaluation areas Sb to Si. In this embodiment, the evaluation area is the same as the crane address in the stirring area 15.
FIG. 2F shows the number of extracted pixels in each of the divided areas Sa1 to Sa9 of the evaluation area Sa.
Sa1 = 5
Sa2 = 7
Sa3 = 4
Sa4 = 8
Sa5 = 5
Sa6 = 2
Sa7 = 8
Sa8 = 5
Sa9 = 5
Similarly, the number of extracted pixels (corresponding to the extracted area) is calculated for the other evaluation areas Sb to Si.

In step S8-1, the variance σ ^{2 of the} extraction area is calculated for each evaluation area.
Sai is the extracted area of each divided area, and Sa_ave is the average of the extracted areas of each divided area in the evaluation area Sa. n is the number of pixels in the evaluation area, which is 9 × 9.
The same calculation is performed for the other evaluation areas Sb to Si.
As another embodiment, a standard deviation and a variation coefficient may be calculated. The coefficient of variation CV can be obtained by equation (4).
σ is a standard deviation.

In step S8-2, the variance σ ² of the extraction area of each evaluation area is calculated for all evaluation areas.
Sai is the extracted area of each divided area, and So_ave is the average of the extracted areas of each divided area in all evaluation areas (entire pits) Sa to Si. n is the number of pixels in the evaluation area, which is 9 × 9.
The same calculation is performed for the other evaluation areas Sb to Si.
As another embodiment, a standard deviation and a variation coefficient may be calculated instead of the variance.

In step S9, the degree of mixing is evaluated. As the variance σ ² exceeds a predetermined value and becomes larger, it indicates that various kinds of dust are present in the area, that is, stirring is sufficiently performed.
At the time of dispersion for each evaluation area at step S8-1, the degree of mixing in the evaluation area can be evaluated, and at the time of dispersion for all evaluation areas at step S8-2, the degree of mixing of the evaluation area compared to the entire garbage pit is evaluated. it can.

  In step S10, the evaluation result (numerical mixing degree) in step S9 is output. The mixing degree is sent from the mixing degree evaluation system 100 to the storage unit 501 of the waste quality estimation system 500 and stored therein.

(Another configuration example of the first embodiment)
Another configuration of the mixing degree evaluation system 100 according to the first embodiment will be described with reference to FIGS. 4B and 4C. The same reference numerals as those in the first embodiment have the same functions, and thus the description thereof will be omitted, and different features will be described.

4B has the following configuration. The degree-of-mixing evaluation unit 130 includes an area average evaluation unit 132.
In step S5, the area average evaluation unit 132 applies the discriminant analysis method to each divided area for each divided area, or performs binarization by the dynamic threshold method, and labels the bright part (or dark part). Etc. to extract each particle.
The area average evaluation unit 132 calculates the area of each particle and calculates the area average of each particle.
The area average evaluation unit 132 determines whether or not the area average of each particle is smaller than a predetermined value.
In the determination result of the area average evaluation unit 132, the smaller the area average is, the smaller the predetermined value is, the more the rubbing of garbage progresses, that is, the greater the agitation.
Similar to Example 1, the evaluation can be performed in units of evaluation area.

4B may be included in the mixture degree evaluation system 100 together with the first embodiment of FIG. 4A.
The degree of mixing may be evaluated by the respective judgment or comprehensive judgment of the variation evaluation unit 131 and the area average evaluation unit 132.

  Another configuration of FIG. 4C has the following configuration. The mixing degree evaluation unit 130 includes an area ratio evaluation unit 133.

The area ratio evaluation unit 133 calculates the area ratio (= dark part / bright part) between the bright part and the dark part of each evaluation area.
The area ratio evaluation unit 133 determines whether the area ratio is greater than a predetermined value (for example, less than 1).
In the determination result of the area ratio evaluation unit 133, the larger the area ratio is, the greater the predetermined value, the more garbage is being broken, that is, the stirring is sufficiently performed.
Similar to Example 1, evaluation can be performed in units of evaluation areas.

4C may be included in the mixture degree evaluation system 100 together with the above-described first embodiment of FIG. 4A and / or the other configuration of FIG. 4B.
The degree of mixing may be evaluated by the respective judgments or comprehensive judgments of the variation evaluation unit 131, the area average evaluation unit 132, and the area ratio evaluation unit 133.

(Example 2 of input data: Configuration for determining the area ratio of absorbance)
The absorbance area ratio calculation system 200 illustrated in FIG. 6 includes a spectrum binarized image generation unit 201 and an area ratio calculation unit 202.

The imaging unit 31 is a visible camera and a spectrum camera.
The spectrum binarized image generation unit 201 images the entire garbage pit 1 with a spectrum camera and generates a binarized image for each divided wavelength. FIG. 8A shows an example of a binarized image for each wavelength. FIG. 8A only illustrates three types of binarized images, and may be divided into three or more types.

  The area rate calculation unit 202 divides the garbage pits into evaluation areas, calculates an area that is greater than or equal to a threshold value for each wavelength, and calculates a ratio to the evaluation area area. FIG. 8B shows an example of a binarized image for each wavelength section. FIG. 8C shows an example of the area ratio for each wavelength in which each evaluation area is divided. The area ratio may be expressed as a percentage. The area ratio is sent from the area ratio calculation system 200 to the storage unit 501 of the garbage quality estimation system 500 and stored therein.

(Example 3 of input data: Configuration for determining the area ratio of absorbance)
The absorbance average value calculation system 300 illustrated in FIG. 7 includes an absorbance average value calculation unit 301 that calculates an average value of absorbance in each evaluation area for each wavelength divided from spectrum image data. FIG. 8D shows an example of an average absorbance value for each wavelength divided in each evaluation area. This absorbance average value is sent from the absorbance average value calculation system 300 to the storage unit 501 of the waste quality estimation system 500 and stored therein.

(Generation of input waste quality estimation model)
The input data creation unit 502 acquires data on the degree of mixing, the area ratio of absorbance, and the average absorbance value in each evaluation area, and creates a database as original data of the input data.
FIG. 9A shows an example of a database of input data stored in the storage unit 501.
The input data creation unit 502, when the waste in the evaluation area Sa (corresponding to the mixed address) is put into the charging hopper of the incinerator, the mixing degree of the waste, the area ratio of the absorbance, the average value of the absorbance, the weight of the charged waste, the input The time data is stored in the storage unit 501. When garbage from other evaluation areas is thrown in, data is structured in the same way. FIG. 9B shows an example of input data of input waste.

  In the first embodiment, the mixing degree evaluation system 100, the absorbance area ratio calculation system 200, and the absorbance average value calculation system 300 have a partial function of the input data creation unit 502. As another embodiment, these components may be incorporated in the garbage quality estimation system 500 as the input data creation unit 502.

The input waste quality estimation model generation unit 503 generates an input waste quality estimation model for estimating the waste quality by the intelligent information processing technique using the input data and the waste quality teacher data. The garbage quality teacher data may be stored in the storage unit 501 in advance, or may be configured to be acquired from an external device when the input garbage quality estimation model generation unit 503 functions.
In the first embodiment, the waste quality teacher data includes the concentration of the waste composition (water, carbon, hydrogen, hydrogen chloride (HCl) and sulfur dioxide (SO ₂ ), etc. in the exhaust gas), and the calorific value.
As another embodiment, the garbage quality estimation system 500 may include a garbage quality teacher data creation unit that creates garbage quality teacher data.
The garbage composition concentration can be obtained by analyzing the exhaust gas with an analyzer corresponding to various components such as an exhaust gas analyzer. Further, other components (for example, carbon dioxide) may be calculated from predetermined components (for example, oxygen and moisture).
The calorific value may be calculated using the method disclosed in Japanese Patent No. 5996762. The garbage quality teacher data creation unit may include a heat generation amount calculation unit that estimates a heat generation amount.
FIG. 10 shows an example of garbage quality teacher data.

(The calorific value is calculated by the method of Japanese Patent No. 5996762)
The first method is a procedure of R1 to R8.
(R1) The component concentrations of oxygen and moisture in the exhaust gas are measured.
(R2) From the measured oxygen and moisture component concentrations, the carbon dioxide concentration in the exhaust gas is estimated based on the following equation.
[CO ₂ ] = Ro × (100− [H ₂ O]) / 100− [O ₂ ]
Here, the value in [] indicates the percentage display concentration, and Ro indicates a coefficient set by subtracting the amount of oxygen component taken into the ash from the oxygen concentration in the atmosphere.
(R3) The nitrogen concentration in the exhaust gas is calculated using the oxygen concentration, water concentration, and carbon dioxide concentration.
(R4) A conversion factor for the nitrogen concentration in the combustion air is calculated based on the calculated nitrogen concentration, and the converted component concentrations of oxygen, carbon dioxide, and moisture are calculated by multiplying the conversion factor.
(R5) The oxygen consumption per unit supply amount of the combustion air used for the combustion process is calculated from the converted component concentrations of oxygen, carbon dioxide and moisture.
(R6) From the calculated oxygen consumption amount, the calorific value related to carbon dioxide and moisture generated in the combustion process per unit supply amount of combustion air, the generated moisture amount and the waste contained in the waste Calculate the amount of latent heat from the total amount of moisture.
(R7) The amount of waste processed per unit supply amount of combustion air is calculated from the supply amount of waste treated by combustion.
(R8) An estimated calorific value A per treated waste amount is calculated from the calculated calorific value, latent heat amount and waste amount.

The second method is the procedure of S1 to S7.
(S1) The component concentrations of oxygen, carbon dioxide and moisture in the exhaust gas are measured.
(S2) The nitrogen concentration in the exhaust gas is calculated from the measured component concentrations.
(S3) A conversion factor for the nitrogen concentration in the combustion air is calculated based on the calculated nitrogen concentration, and the converted component concentrations of the oxygen, carbon dioxide, and moisture are calculated by multiplying the conversion factor.
(S4) The oxygen consumption per unit supply amount of the combustion air used for the combustion process is calculated from the converted component concentrations of oxygen, carbon dioxide and moisture.
(S5) From the calculated oxygen consumption amount, the calorific value related to carbon dioxide and moisture generated in the combustion process per unit supply amount of combustion air, the generated moisture amount and the waste contained in the waste Calculate the amount of latent heat from the total amount of moisture.
(S6) The amount of waste treated per unit supply amount of combustion air is calculated from the amount of waste treated after combustion.
(S7) From the calculated calorific value, latent heat quantity and waste quantity, an estimated calorific value B per treated waste quantity is calculated.

(Calculate calorific value by other methods)
The third method is the procedure of A1 to A8.
(A1) The exhaust gas flow rate from the combustion furnace is measured.
(A2) The component concentrations of oxygen, carbon dioxide and moisture in the exhaust gas from the combustion furnace are measured.
(A3) The nitrogen concentration [N ₂ ] in the exhaust gas is calculated from the measured component concentrations.
(A4) Based on the calculated nitrogen concentration [N ₂ ], a conversion factor for the nitrogen concentration An in the atmosphere is calculated, and the converted component concentrations Go, Gd, and Gw of oxygen, carbon dioxide, and water multiplied by the conversion factor Is calculated.
(A5) Based on the converted component concentration Gd of carbon dioxide, the amount of carbon in the combusted material per combustible unit supply amount is calculated.
(A6) Subtracting the converted oxygen component concentration Go and carbon dioxide component concentration Gd from the atmospheric oxygen concentration Ao, the amount of hydrogen in the combusted material consumed in the combustion process and the moisture generated by the combustion process Calculate the amount.
(A7) Based on the converted component concentration Gw of water and the calculated amount of water, the amount of water evaporation in the combusted material is calculated.
(A8) Combustion-treated combustible based on the calorific value of reaction heat of carbon and hydrogen in the combustible generated in the combustion treatment and the latent heat of water using the calculated carbon, hydrogen, and moisture The calculated calorific value A per unit supply amount is calculated.

(Calculate calorific value by other methods)
The fourth method is a procedure of B1 to B9.
(B1) The exhaust gas flow rate from the combustion furnace is measured.
(B2) The component concentration of oxygen and moisture in the exhaust gas is measured.
(B3) From the measured oxygen and moisture component concentrations [O ₂ ] and [H ₂ O], the carbon dioxide concentration [CO ₂ ] in the exhaust gas is calculated based on the following formula 1.
[CO ₂ ] = Ro × (100− [H ₂ O]) / 100− [O ₂ ]
Here, the value in [] indicates the percentage display concentration, and Ro indicates a coefficient set by subtracting the amount of oxygen component taken into the ash from the oxygen concentration in the atmosphere.
(B4) The nitrogen concentration [N ₂ ] in the exhaust gas is calculated from the measured oxygen concentration [O ₂ ], moisture concentration [H ₂ O] and the calculated carbon dioxide concentration [CO ₂ ].
(B5) Based on the calculated nitrogen concentration [N ₂ ], a conversion factor for the nitrogen concentration An in the atmosphere is calculated, and the converted component concentrations Go, Gd, and Gw of the oxygen, carbon dioxide, and water multiplied by the conversion factor Is calculated.
(B6) Based on the converted component concentration Gd of carbon dioxide, a carbon amount Ec in the combusted material per combustible unit supply amount is calculated.
(B7) Oxygen concentration Ao in the atmosphere is subtracted from the converted oxygen component concentration Go and carbon dioxide component concentration Gd, and is generated by the amount of hydrogen Eh in the combusted material consumed in the combustion process and the combustion process The amount of water Cw is calculated.
(B8) Based on the converted water component concentration Gw and the calculated water content, the amount of water evaporation in the combusted material is calculated.
(B9) Using the calculated amount of carbon, amount of hydrogen, and amount of water, the combusted material subjected to combustion processing based on the calorific value of the reaction heat of carbon and hydrogen in the combusted material generated by the combustion processing and the amount of latent heat of water The calculated calorific value B per unit supply amount is calculated.
In the above, the amount of mixed air and the amount of oxygen in the combusted material may be calculated.
Based on the following formula, the amount of mixed air other than the combustion air supplied to the combustion furnace is calculated.
(Mixed air amount) = (exhaust gas flow rate × [N ₂ ] −combustion air supply amount × An) / An
Based on the following formula, the amount of oxygen in the combusted material may be calculated.
(Oxygen amount in the combusted material) = (exhaust gas flow rate × [O ₂ ]) − (combustion air supply amount × Aa−exhaust gas flow rate × [CO ₂ ] −moisture amount Cw) − (mixed air amount × An)

  In the first embodiment, the input waste quality estimation model generation unit 503 receives the waste quality teacher data, the input data, and the input data based on the amount of time delay from the introduction of the waste into the input port of the combustion apparatus until the start of combustion. The input waste quality estimation model is generated by temporally correlating the For example, in the case of a stoker-type incinerator, the “time delay amount” is, for example, 30 to 40 minutes.

As another embodiment, “temporal correspondence between garbage quality teacher data and input data” may be performed using the methods (1) to (5).
(1) Store input data and weight data of waste to be charged into the charging hopper in the storage unit.
(2) The accumulation state of the input garbage is fractionated, and the weight of each of the fractionated garbage and the input data are stored in the storage unit. For example, the respective weights of the first fraction accumulated waste G1, the second fraction accumulated waste G2, and the third fraction accumulated waste G3 are stored.
(3) Each time the waste is supplied to the incinerator of the combustion apparatus, the weight of the supplied waste (for example, 50 kg of in-furnace supply) is subtracted from the remaining weight of the first fraction-deposited waste in the lowermost part of the input hopper. The weight of the supplied garbage may be supplied in a fixed amount (for example, 50 kg) or may be measured each time the weight of the supplied garbage is supplied.
(4) Fraction deposits with zero remaining weight are removed from the record. The weight that could not be pulled out is subtracted from the remaining weight of the nth fraction-deposited debris at the bottom.
(5) Correspond the input data of the supplied garbage with the garbage quality teacher data at this time.
FIG. 11 shows the accumulation state and the feeding state of the input waste (trash TAG).

(Another embodiment)
In the first embodiment, three types of input data are used. However, the present invention is not limited to this, and one or more of the area ratio, the average absorbance value, and the degree of mixing may be used.
Moreover, you may further contain 1 type, or 2 or more types of information from a garbage carrying vehicle, a date, and the weather as input data.
In the first embodiment, the waste quality teacher data is the concentration of the waste composition (water, carbon, hydrogen, hydrogen chloride (HCl) and sulfur dioxide (SO ₂ ), etc. in the exhaust gas), the calorific value, etc. However, it may be one or more of the above.
In the first embodiment, input data is created by the mixture degree evaluation system 100, the absorbance area ratio calculation system 200, and the absorbance average value calculation system 300. However, the present invention is not limited to this, and the input data creation unit of the garbage quality estimation system 500 is used. 502 may have these functions.

(Crane operation control system)
The crane operation control system 600 stores a model storage unit 601 that stores the input waste quality estimation model generated by the waste quality estimation system 500, and inputs input data of the input waste into the input waste quality estimation model. There is a waste quality estimation unit 602 that estimates quality, and an automatic operation control unit 603 that automatically controls the crane according to the estimated waste quality output from the waste quality estimation model.
The automatic operation control unit 603 may control the crane so that the waste quality is uniform in each evaluation area.
The automatic operation control unit 603 may control the crane so that waste having a high calorific value is thrown when the calorific value of the combustion device is lower than an expected value.
The automatic operation control unit 603 may control the crane so as to throw in garbage having a low calorific value when the calorific value of the combustion device is higher than an expected value.

  The crane operation control system 600 is configured not only to automatic operation control but also to manual operation control. The operator can operate the crane according to the distribution of estimated waste quality in each evaluation area displayed on the monitor.

  Input data of input waste is obtained by the functions of the mixing degree evaluation system 100, the absorbance area ratio calculation system 200, the absorbance average value calculation system 300, and the dust quality estimation system 500 described above.

  Each component of the mixing degree evaluation system 100, the absorbance area ratio calculation system 200, the absorbance average value calculation system 300, the waste quality estimation system 500, and the crane operation control system 600 is an information processing including a memory, a processor, and a software program. It can be configured by a device (for example, a computer), a dedicated circuit, firmware, or the like.

DESCRIPTION OF SYMBOLS 1 Garbage pit 15 Stirring area 21 Crane garter 22 Crane 23 Bucket 31 Image pick-up part 32 Laser distance meter 100 Mixture evaluation system 101 Measurement point dust height calculation part 102 Three-dimensional waste height calculation part 111 Image conversion part 112 Image correction part 113 Binarization processing unit 130 Mixing degree evaluation unit 131 Variation evaluation unit 132 Area average evaluation unit 133 Area rate evaluation unit 200 Area rate calculation system 201 Spectrum binarized image generation unit 202 Area rate calculation unit 300 Absorbance average value calculation system 301 Absorbance Average value calculation unit 500 Waste quality estimation system 501 Storage unit 502 Input data creation unit 503 Input waste quality estimation model generation unit 600 Crane operation control system 601 Model storage unit 602 Garbage quality estimation unit 603 Automatic operation control unit

Claims (3)

  One or more of the area ratio of absorbance obtained from the spectral image data for each evaluation area, the average absorbance value obtained from the spectral image data for each evaluation area, and the mixing degree of the waste in the evaluation area obtained from the image data Including an input waste quality estimation model generating unit that generates an input waste quality estimation model for estimating waste quality by intelligent information processing technology using input data of input waste and waste quality teacher data. Estimation system. 2. The garbage quality estimation system according to claim 1, wherein the input data further includes one kind or two or more kinds from among a loading time at which garbage is thrown into the combustion apparatus, a garbage carrying vehicle, a date, and weather. A model storage unit for storing an input waste quality estimation model generated by the waste quality estimation system according to claim 1;
A waste quality estimation unit that inputs input data of the input waste into the input waste quality estimation model and estimates the waste quality;
A crane operation control system having an automatic operation control unit that automatically controls a crane according to the estimated waste quality output from the input waste quality estimation model.