Prediction device, prediction method, prediction program, facility control device, facility control method, and control program

Abstract

To contribute to improving the operation of an incineration facility.SOLUTION: A prediction device (1) comprises a movement data generation unit (103) that generates movement data showing the moving condition of an incineration object from a plurality of images of hoppers in time series photographed from above, in an incineration facility that feeds an incineration object having been charged into a hopper, to an incineration furnace at a specified speed, and a property prediction unit (105) that predicts the property of the incineration object according to the generated movement data and the speed.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a prediction device for predicting the properties of materials to be incinerated in an incinerator. [Background technology]

[0002] A typical waste incineration facility is equipped with a funnel-shaped device called a hopper. Waste stored in a storage facility called a waste pit is picked up by a crane or the like and dumped into the hopper. The waste in the hopper is then sent to the incinerator by a dust feeder in order of the waste dumped in first, and incinerated there. An example of technology related to hoppers is Patent Document 1 listed below. Patent Document 1 describes the detection of a bridge based on a hopper level that is periodically measured. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 10-238735 Summary of the Invention [Problem to be solved by the invention]

[0004] As mentioned above, the waste fed into the hopper is fed into the incinerator in sequence by the dust feeder and incinerated, but the way the waste burns varies depending on the properties of the waste fed in. For example, if wet waste containing a lot of water is fed in, the temperature inside the incinerator may drop. On the other hand, if dry, easily combustible waste is fed in, the temperature inside the incinerator may rise. This is a common phenomenon when incinerating any object to be incinerated, not just waste.

[0005] Therefore, if the properties of the materials to be incinerated placed in the hopper can be understood before they are incinerated, it is believed that this will contribute to improving the operation of the incineration facility. For example, it will be possible to predict the state inside the incinerator from the properties of the materials to be incinerated, which will make it possible to prevent the condition from deteriorating. In addition, for example, it will be possible to automatically control the incinerator and automatically control the loading of materials to be incinerated into the hopper. However, in the past, there has been no attempt to predict the properties of the materials to be incinerated placed in the hopper. For example, even in the above-mentioned Patent Document 1, there is no suggestion of predicting the properties of the waste loaded into the hopper.

[0006] One aspect of the present invention has been made in consideration of the above problems, and its purpose is to provide a prediction device etc. that contributes to improving the operation of incineration facilities. [Means for solving the problem]

[0007] In order to solve the above problems, a prediction device according to one embodiment of the present invention is provided in an incineration facility where materials to be incinerated are fed into a hopper and sent to an incinerator at a specified speed. The prediction device comprises a movement data generation unit that generates movement data indicating the movement state of the materials to be incinerated from a plurality of time-series images taken from above the hopper, and a prediction unit that predicts the properties of the materials to be incinerated based on the movement data and the speed generated by the movement data generation unit.

[0008] In addition, in order to solve the above problems, a facility control device according to one embodiment of the present invention, in an incineration facility in which materials to be incinerated are fed into a hopper and sent to an incinerator at a specified speed, comprises: a movement data generation unit that generates movement data indicating the movement state of the materials to be incinerated from a plurality of time-series images taken from above the hopper; and an equipment control unit that performs at least one of the following based on the movement data and the speed generated by the movement data generation unit: (1) control of the feeding of the materials to be incinerated into the hopper, (2) control of the speed, (3) control of the transport speed of the materials to be incinerated in the incinerator, (4) control of the combustion of the materials to be incinerated in the incinerator, and (5) control of the stirring of the materials to be incinerated.

[0009] In order to solve the above problems, a prediction method according to one embodiment of the present invention is a method for predicting the properties of an object to be incinerated, executed by one or more information processing devices, and includes the steps of: generating movement data indicating the movement state of the object to be incinerated from a plurality of time-series images taken from above the hopper in an incineration facility where the object to be incinerated is fed into a hopper and sent to an incinerator at a specified speed; and predicting the properties of the object to be incinerated based on the movement data and the speed generated in the steps.

[0010] In order to solve the above problems, a facility control method according to one embodiment of the present invention is a facility control method executed by one or more information processing devices, and includes the steps of: generating movement data indicating the movement state of the objects to be incinerated from a plurality of time-series images taken from above the hopper in an incineration facility in which objects to be incinerated are fed into a hopper and sent to an incinerator at a specified speed; and performing at least one of the following steps based on the movement data and the speed generated in the steps: (1) controlling the feeding of the objects to be incinerated into the hopper; (2) controlling the speed; (3) controlling the transport speed of the objects to be incinerated in the incinerator; (4) controlling the combustion of the objects to be incinerated in the incinerator; and (5) controlling the stirring of the objects to be incinerated. Effect of the Invention

[0011] According to one aspect of the present invention, it is possible to contribute to improving the operation of incineration facilities. [Brief description of the drawings]

[0012] [Figure 1] 1 is a block diagram showing an example of a configuration of a main part of a prediction device according to a first embodiment of the present invention. [Diagram 2] FIG. 2 is a diagram illustrating an example of the configuration of a facility control system including the prediction device. [Diagram 3] 11A and 11B are diagrams illustrating an example of a method for determining the presence or absence of dust. [Figure 4] FIG. 11 is a diagram illustrating an example of a method for generating movement data. [Diagram 5] FIG. 11 is a diagram showing the relationship between dust supply speed and movement amount. [Figure 6] 4 is a flowchart illustrating an example of a process executed by the prediction device. [Figure 7] FIG. 11 is a block diagram showing an example of a configuration of a main part of a prediction device according to a second embodiment of the present invention. [Figure 8] FIG. 1 is a diagram showing the relationship between predicted and actual bulk density values. [Figure 9] FIG. 13 is a diagram illustrating a method for predicting a low-level heat generation amount. [Figure 10] 4 is a flowchart illustrating an example of a process executed by the prediction device. [Figure 11] FIG. 11 is a block diagram showing an example of a configuration of a main part of a facility control device according to a third embodiment of the present invention. [Figure 12] 4 is a flowchart illustrating an example of a process executed by the facility control device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] [Embodiment 1] (System Configuration) The configuration of the facility control system 100 according to this embodiment will be described with reference to Fig. 2. Fig. 2 is a diagram showing an example of the configuration of the facility control system 100. Fig. 2 shows an example in which the facility control system 100 is applied to a waste incineration facility. Note that the facility control system 100 can be applied to any incineration facility that incinerates not only waste but any object to be incinerated.

[0014] The waste incineration facility shown in Figure 2 is equipped with a waste pit B for storing waste G, and an incinerator C for incinerating the waste G. The waste G in the waste pit B is dumped into a hopper A by a crane. The top of the hopper A has an inclined surface A1 that slopes outward, and the waste G slides down this surface A1 and enters the inside of the hopper A, where it is temporarily stored. The waste G in the hopper A is then fed successively from an opening A2 at its bottom into the incinerator C, where it is incinerated.

[0015] The facility control system 100 is a system that predicts the properties of waste to be incinerated in incinerator C, and includes a prediction device 1 and a photographing device 2. The photographing device 2 is disposed above hopper A, and photographs hopper A from above at predetermined time intervals. The prediction device 1 acquires each image taken by the photographing device 2, and uses these images to predict the properties of the waste to be incinerated.

[0016] More specifically, the photographing device 2 photographs a plurality of images in time series of the hopper A. Then, the prediction device 1 predicts the properties of the garbage based on movement data that is generated from the plurality of images in time series photographed by the photographing device 2 and indicates the movement state of the garbage G during the period when the plurality of images were photographed.

[0017] The inventors of the present invention have found, after examining images of the hopper taken from above, that differences in the properties of the materials to be incinerated are reflected in differences in the movement of the materials. For example, even if the speed at which the materials are fed into the incinerator is the same, materials that have a high moisture content and are difficult to burn tend to move at a greater rate per unit time in the hopper than materials that have a low moisture content and are easy to burn.

[0018] The prediction device 1 predicts the waste properties based on this knowledge. That is, differences in the waste properties are reflected as differences in the state of the waste movement in the image, so the above configuration makes it possible to predict the waste properties. Furthermore, understanding the waste properties is useful for the proper operation of incineration facilities, so the prediction device 1 can contribute to improving the operation of incineration facilities.

[0019] Specifically, the prediction device 1 controls the equipment in the waste incineration facility based on the prediction results of the waste properties. This makes it possible to perform appropriate control according to the waste properties and contribute to improving the operation of the incineration facility. Note that the control of the equipment in the waste incineration facility may be performed by the operator of the waste incineration facility, in which case the prediction device 1 only needs to output the prediction results of the waste properties to the operator. In this case, the prediction device 1 does not need to control the equipment.

[0020] The location of the prediction device 1 is not particularly limited, and may be, for example, located at a waste incineration facility, or at a monitoring center that monitors multiple waste incineration facilities. Although Fig. 2 shows an example in which one prediction device 1 predicts the properties of waste at one waste incineration facility, it is also possible to predict the properties of waste at multiple waste incineration facilities using one prediction device 1. In this case, for example, the prediction device 1 may be a cloud server.

[0021] (Configuration of prediction device) The configuration of the prediction device 1 will be described with reference to Fig. 1. Fig. 1 is a block diagram showing an example of the configuration of the main parts of the prediction device 1. As shown in the figure, the prediction device 1 includes a control unit 10 that controls each part of the prediction device 1, and a storage unit 11 that stores various data used by the prediction device 1. The prediction device 1 also includes a communication unit 12 that allows the prediction device 1 to communicate with other devices, an input unit 13 that accepts input of various data to the prediction device 1, and an output unit 14 that allows the prediction device 1 to output various data.

[0022] The control unit 10 includes a data acquisition unit 101, a determination unit 102, a movement data generation unit 103, a standard movement amount calculation unit 104, a property prediction unit (prediction unit) 105, an influence prediction unit 106, and an equipment control unit 107. The storage unit 11 stores an image DB 111 and a standard movement amount calculation formula 112.

[0023] The data acquisition unit 101 acquires a plurality of images in time series taken from above the hopper A. As described above, in the facility control system 100, the photographing device 2 captures a plurality of images in time series taken from above the hopper A, so the data acquisition unit 101 only needs to acquire the images captured by the photographing device 2. The data acquisition unit 101 may acquire images from the photographing device 2 by communication via the communication unit 12, or may acquire images input via the input unit 13. The data acquisition unit 101 records the acquired images in the image DB 111. The photographing device 2 may capture moving images. In this case, the data acquisition unit 101 only needs to acquire a plurality of images in time series (for example, frame images at predetermined intervals) from the moving images captured by the photographing device 2.

[0024] The determination unit 102 determines whether or not dust is present in a target area that is the subject of analysis for predicting the properties of dust in the image acquired by the data acquisition unit 101. The method of determining dust in the target area will be described in detail below with reference to FIG.

[0025] The movement data generation unit 103 generates movement data indicating the movement state of garbage during a period when a plurality of images were taken, from a plurality of images in time series acquired by the data acquisition unit 101 and recorded in the image DB 111. The method of generating the movement data will be described in detail later with reference to FIG.

[0026] The standard movement amount calculation unit 104 specifies the speed at which the waste is fed into the incinerator, i.e., the dust feed speed. Then, the standard movement amount calculation unit 104 calculates the standard movement amount of the waste (hereinafter referred to as the standard movement amount) when the waste is fed into the incinerator at the specified dust feed speed from the specified dust feed speed using a standard movement amount calculation formula 112.

[0027] Standard transfer amount calculation formula 112 is a formula that shows the relationship between the dust feed rate and the standard transfer amount of the waste when the waste is fed into the incinerator at that dust feed rate. Details of standard transfer amount calculation formula 112 and the method of calculating the standard transfer amount using this will be described later with reference to Figure 5.

[0028] The property prediction unit 105 predicts the property of the waste to be incinerated based on the movement data and the dust feeding speed generated by the movement data generation unit 103. Specifically, the property prediction unit 105 calculates the difference between the movement amount indicated by the movement data generated by the movement data generation unit 103 and the standard movement amount calculated by the standard movement amount calculation unit 104 as a prediction result of the property of the waste.

[0029] The impact prediction unit 106 predicts the impact of incinerating the waste on the combustion state of the incinerator based on the waste properties predicted by the property prediction unit 105. The prediction result output by the impact prediction unit 106 may indicate the magnitude of the impact on the combustion state. For example, if the magnitude of the impact on the combustion state is expressed in four stages, large, medium, small, and no impact, the impact prediction unit 106 may output information indicating one of the stages as the prediction result. The impact prediction unit 106 may also output a predicted value of the combustion state (for example, a predicted value of the lower heating value) as the prediction result. The prediction of the impact on the combustion state will be described later with reference to FIG. 5.

[0030] The equipment control unit 107 controls the equipment of the waste incineration facility based on the prediction results of the impact prediction unit 106. For example, the equipment control unit 107 may perform at least one of the following: (1) control of waste input into a hopper, (2) control of dust feeding speed, (3) control of the waste transport speed (grate speed) in the incinerator, and (4) control of waste combustion in the incinerator.

[0031] Regarding (1) above, when it is desired to promote combustion, it is possible to control so that relatively easily combustible waste in the waste pit is put into the hopper, and when it is desired to suppress combustion, it is possible to control so that relatively less combustible waste is put into the hopper. The distribution of the properties of the waste in the waste pit may be managed separately, or may be specified based on the prediction results of the property prediction unit 105 as described below.

[0032] Regarding (2) above, the dust feed rate may be increased if combustion is to be promoted, and may be decreased if combustion is to be suppressed. The same applies to the grate speed in (3) above. Depending on the situation, it may be effective to apply at least one of the following controls to promote combustion: decreasing the dust feed rate and decreasing the grate speed.

[0033] Regarding (4) above, waste combustion control can be achieved, for example, by heating with a burner or by controlling the amount of combustion air supplied. In other words, if you want to promote combustion (increase the amount of heat generated), you can heat with a burner or increase the amount of combustion air supplied, and conversely, if you want to suppress combustion (reduce the amount of heat generated), you can reduce the amount of combustion air supplied. Depending on the situation, reducing the amount of combustion air supplied may be an effective way to control combustion.

[0034] Furthermore, the garbage captured when the hopper is photographed from above is the garbage that was in the garbage pit until just before, and it is possible to identify the position in the garbage pit from which the garbage was acquired. Therefore, it is possible to predict the distribution of garbage properties in the garbage pit from the prediction results of the property prediction unit 105 or the prediction results of the impact prediction unit 106.

[0035] The equipment control unit 107 may control the mixing of the garbage in the garbage pit based on this property distribution. For example, the equipment control unit 107 may control the mixing to be preferentially performed in an area where less burnable garbage is distributed, or may homogenize the garbage in an area where less burnable garbage is distributed by mixing and mixing the garbage in the area where more burnable garbage is distributed. The mixing of garbage is a process for improving the properties of the garbage (especially its flammability), and is performed, for example, by lifting the garbage with a crane and dropping it, or by scattering the garbage that has been picked up.

[0036] As described above, the prediction device 1 is provided with a movement data generation unit 103 that generates movement data indicating the movement state of waste from multiple time-series images taken from above a hopper in an incineration facility where waste put into a hopper is sent to an incinerator at a specified dust feeding speed, and a property prediction unit 105 that predicts the properties of the waste based on the generated movement data and the dust feeding speed.

[0037] As described above, differences in the properties of waste are reflected in the images of the state of waste movement, and therefore the above configuration makes it possible to predict the properties of the waste. Since understanding the properties of the waste is useful for the proper operation of the incineration facility, the above configuration can contribute to improving the operation of the incineration facility.

[0038] In addition, although details will be described later, movement data can be generated with just two images taken at different times. Therefore, there is no need to perform machine learning, and the operation of the prediction device 1 can be started immediately in the waste incineration facility. In addition, even if the installation location or shooting angle of the imaging device 2 is changed slightly, the characteristics of the waste can be predicted without any particular influence. Furthermore, although it is expected that dirt will adhere to the lens of the imaging device 2 installed in the waste incineration facility, the adhered dirt does not move, so the influence of such dirt on the generation of movement data is also limited. In other words, the facility control system 100 including the prediction device 1 has the advantage of being easy to introduce and capable of stable operation.

[0039] (Determining whether or not garbage is present) The determination of the presence or absence of garbage by the determination unit 102 will be described with reference to Fig. 3. Fig. 3 is a diagram showing an example of a method for determining the presence or absence of garbage. Image D shown in Fig. 3 is an image taken from above the hopper. Image D shows garbage sliding down the inclined surface of the hopper, but the garbage does not cover the entire inclined surface. Specifically, garbage is seen in a downstream area d2 of the inclined surface of the hopper, but no garbage is seen in an upstream area d1.

[0040] If the movement data generating unit 103 generates movement data for an area such as area d1 where no dust is captured, the amount of movement indicated by the movement data may be zero or close to zero. When the amount of movement is zero or close to zero, an erroneous prediction result for dust that does not actually exist may be output.

[0041] In order to avoid such erroneous prediction, the prediction device 1 includes a determination unit 102. The determination unit 102 determines whether or not garbage is captured in the target area of ​​the image. Then, the movement data generation unit 103 generates movement data indicating the amount of movement between images in which the determination unit 102 has determined that garbage is captured in the target area. This prevents images in which no object is captured in the target area from being used to predict the nature of the garbage, thereby preventing such erroneous prediction. Although details will be described later, it is preferable to set the target area on the inclined surface of the hopper, more specifically, on the downstream inclined surface as in area d2.

[0042] The presence or absence of garbage may be determined using the captured image, or may be determined after applying a predetermined image processing to the captured image to make it easier to determine the presence or absence of garbage. An example of the image processing is the Canny method. The Canny method is an algorithm for edge detection in an image. Image D1 shown in FIG. 3 is an image generated by detecting edges from image D using the Canny method. In general, the appearance of garbage is much more complicated than the surface shape of a hopper. For this reason, in the example of FIG. 3, many edges are detected in area d2 where garbage is captured, compared to area d1 where garbage is not captured.

[0043] Therefore, the judgment unit 102 may apply the Canny method to detect edges in an image of the hopper, and judge an image in which the number of edges detected in the target area is equal to or greater than a threshold to be an image containing dust, and judge an image in which the number of edges detected in the target area is less than the threshold to be an image not containing dust.

[0044] Of course, an edge detection method other than the Canny method may be applied. Also, the presence or absence of dust can be determined by a method other than edge detection. For example, the determination unit 102 may determine the presence or absence of dust by analyzing at least one of the luminance value and the RGB value of each pixel constituting the image. Also, for example, the determination unit 102 may determine the presence or absence of dust by a trained model (e.g., a neural network model) that has been trained to be able to determine the presence or absence of dust.

[0045] (How movement data is generated) A method for generating movement data by the movement data generating unit 103 will be described with reference to Fig. 4. Fig. 4 is a diagram showing an example of a method for generating movement data. More specifically, Fig. 4 shows an example of generating movement data F, which indicates the movement state of garbage between an image E1 taken on the inclined surface of the hopper at time t and an image E2 taken on the inclined surface of the hopper at time t+Δt, by an image correlation method. In Fig. 4, the y direction is the direction in which garbage descends, that is, the direction from the upstream side to the downstream side of the inclined surface of the hopper (see inclined surface A1 in Fig. 2), and the x direction is the direction perpendicular to the y direction on the inclined surface.

[0046] The image correlation method is a technique in which the amount of movement of each particle in two time-series images is calculated using image processing, and the calculated amount of movement is expressed as a vector. For example, in image E1, dust enclosed in a dashed line frame is captured at position e1, but in image E2, it is captured at position e2, which is lower than position e1. In the movement data F, the movement state of this dust is represented as vector f1, which indicates the displacement from position e1 to position e2. Vectors are also calculated similarly for other positions.

[0047] In this way, the movement data F shows the state of garbage movement at each position in images E1 and E2. Specifically, it can be seen from the movement data F that the garbage at each position in images E1 and E2 is moving in the y direction (the downstream side of the hopper's inclined surface, i.e., the downward direction). Note that in the movement data F, vectors whose y direction component value is equal to or greater than the threshold value are shown with solid arrows, and vectors whose y direction component value is less than the threshold value are shown with dashed arrows. This also shows that there are positions where the garbage is moving fast and slow.

[0048] As described above, the movement data generation unit 103 may generate movement data indicating the amount of movement of an image element appearing at each of a plurality of predetermined positions set on the inclined surface appearing in images E1 and E2 in the downward direction between images E1 and E2.

[0049] On the inclined surface of the hopper, the garbage slides down in roughly the same downward direction, so the inclined surface of the hopper is an ideal position for determining whether the garbage is descending smoothly or is stalled. Thus, with the above-mentioned configuration, movement data can be generated that accurately indicates whether the garbage is descending smoothly or is stalled. Then, by predicting the garbage properties using this movement data, a highly reliable prediction can be made. This is because, as described above, differences in the garbage properties are reflected as differences in the garbage movement state captured in the image.

[0050] The movement data may be generated for image portions other than the inclined surface. In this case, the property prediction unit 105 may predict the property of the garbage using the movement data within a target area set for the portion showing the inclined surface among the movement data generated by the movement data generation unit 103.

[0051] Of course, the target area may be any area where the direction of movement of the waste is generally the same, and may be an area other than the inclined surface. For example, the target area may be an area (usually extending vertically) on the inner surface of the hopper that is connected to the inclined surface downstream of the inclined surface, or an area including both the inclined surface and other parts.

[0052] The movement data is not limited to the above examples as long as it indicates the state of movement of the garbage. For example, the movement data may be the speed at which the garbage moves. Furthermore, the movement data may be a difference image showing the difference between two images taken at different times.

[0053] (Calculation of standard movement amount - Prediction of combustion state) A method for calculating the standard movement amount by the standard movement amount calculation unit 104, a method for predicting the property of waste by the property prediction unit 105, and a method for predicting the combustion state by the influence prediction unit 106 will be described with reference to Fig. 5. Fig. 5 is a diagram showing the relationship between the dust supply speed and the movement amount.

[0054] Graph H in Figure 5 plots the movement amounts of waste with various properties measured under various dust feed rates at a waste incineration facility. The upper side of graph H shows a histogram h1 showing the distribution of dust feed rates, and the right side of graph H shows a histogram h2 showing the distribution of the movement amounts. Note that the movement amount here refers to the amount of downward movement of the waste between two images in a time series in which the waste was photographed.

[0055] As shown in the figure, there is a correlation between the dust feed rate and the amount of garbage moved, such that the higher the dust feed rate, the larger the amount of garbage moved. This correlation can be formulated, for example, by regression analysis. In the example of FIG. 5, h3 denotes a straight line indicating y=F(x), which is a regression equation showing the relationship between the dust feed rate and the amount of garbage moved, obtained by regression analysis. In other words, h3 is a regression line. The prediction device 1 stores such a regression equation in the memory unit 11 as a standard movement amount calculation equation 112.

[0056] The standard movement amount calculation unit 104 calculates the standard movement amount at the current dust supply speed by identifying the current dust supply speed and substituting it into the standard movement amount calculation formula 112. This makes it possible to calculate a standard movement amount of an appropriate value. For example, if the current dust supply speed is x1, the standard movement amount is y=F(x1).

[0057] In this case, the property prediction unit 105 may calculate the difference between the movement amount indicated by the movement data generated by the movement data generation unit 103 and the standard movement amount of the waste as the prediction result. As described above, as a result of the study by the inventors of the present invention, it has been found that waste with a high moisture content and low burnability tends to have a larger movement amount per unit time in the hopper compared to waste with a low moisture content and high burnability. For this reason, it can be said that the difference between the movement amount indicated by the movement data and the standard movement amount indicates the combustibility of the waste. Therefore, according to the above configuration, a value indicating the combustibility of the waste can be calculated as the prediction result.

[0058] For example, as in the example of Fig. 5, when the current dust supply rate is x1, the movement amount indicated by the movement data generated by the movement data generation unit 103 is y1. This combination of dust supply rate and movement amount is represented by point h4 on the graph plane of graph H in Fig. 5. As described above, the standard movement amount when the dust supply rate is x1 is y = F(x1), so in this example, the property prediction unit 105 calculates y' = y1 - F(x1) as the prediction result.

[0059] The larger the value of y' calculated in this way, the more the waste's properties deviate from those of standard waste. More specifically, when y' is a negative value, the larger the absolute value of y' is, the smaller the amount of waste that moves. In general, lighter waste moves slower than standard weight waste, has less moisture and is easier to burn, so it can be said that waste with a negative y' value and a large absolute value of y' is more burnable.

[0060] On the other hand, when y' is a positive value, the larger the absolute value of y', the greater the amount of waste that is moved. Generally, heavy waste moves faster than waste of standard weight, contains more water and is less combustible, so it can be said that waste with a positive y' value and a large absolute value of y' is less combustible.

[0061] For this reason, for example, in Figure 5, if the movement amount and dust feed rate of a certain type of waste are plotted in the region h5 set above the regression line h3, that waste can be said to be difficult to burn. On the other hand, in Figure 5, if the movement amount and dust feed rate of a certain type of waste are plotted in the region h6 set below the regression line h3, that waste can be said to be easy to burn. Point h4 is in region h6 below the regression line h3, so the waste at point h4 can be said to be easy to burn.

[0062] From the above, the influence prediction unit 106 may classify the garbage according to the value of y', and output the classification result as the prediction result. For example, the influence prediction unit 106 may predict that there will be an influence on the combustion state when the value of y' calculated by the property prediction unit 105 is plotted within the region h5 or h6. The influence prediction unit 106 may determine the magnitude of the influence according to the magnitude of the value of y'. Also, the influence prediction unit 106 may predict that there will be no influence on the combustion state when the value is plotted in the region between the regions h5 and h6.

[0063] The method of predicting the combustion state may be any method appropriate for the combustion state to be predicted, and is not limited to the above example. For example, the influence prediction unit 106 may predict the low heat generation amount. In this case, the correlation between the value of y' and the low heat generation amount may be formulated. Then, the influence prediction unit 106 may predict the low heat generation amount using the formula.

[0064] Furthermore, the method of calculating the standard movement amount is not limited to the above example. For example, in a waste incineration facility, the movement amounts of waste with various properties under various dust feeding rates may be measured as in the above example, and the dust feeding rate may be divided into a plurality of numerical ranges to calculate the standard movement amount for each range. The standard movement amount may be, for example, the average or median of the movement amounts for each range. In this case, the standard movement amount calculation unit 104 may specify the standard movement amount depending on which range the current dust feeding rate belongs to.

[0065] Furthermore, instead of calculating the standard movement amount by a linear model such as regression analysis, the standard movement amount may be calculated by a nonlinear model such as a neural network. In this case, a standard movement amount prediction model is prepared in advance by machine learning the relationship between the dust supply speed and the standard movement amount. Then, the standard movement amount calculation unit 104 inputs the dust supply speed into this standard movement amount prediction model to calculate the standard movement amount.

[0066] (Processing flow) The flow of the process (prediction method) executed by the prediction device 1 will be described with reference to Fig. 6. Fig. 6 is a flowchart showing an example of the process executed by the prediction device 1. Note that the process in Fig. 6 is performed, for example, every time the imaging device 2 captures a new image.

[0067] In S1, the data acquisition unit 101 acquires time-series images of the hopper taken from above and records them in the image DB 111. As described above, these images are taken by the photographing device 2 (see FIG. 2), so the data acquisition unit 101 only needs to acquire the images taken by the photographing device 2.

[0068] In S2, the determination unit 102 determines whether or not dust is present in the target area in the image acquired in S1. The method for determining whether or not dust is present in the target area is as described with reference to Fig. 3, and therefore the description will not be repeated here. If the determination in S2 is YES, the process proceeds to S3, and if the determination in S2 is NO, the process in Fig. 6 ends.

[0069] In S3 (step of generating movement data), the movement data generating unit 103 generates movement data indicating the movement state of the waste to be incinerated from the image acquired in S1 and an image taken before that image. For example, when an image taken at time t+Δt is acquired in S1, the movement data generating unit 103 reads out the image taken at time t from the image DB 111. Then, the movement data generating unit 103 generates movement data indicating the movement state of the waste during the period from time t to time t+Δt. The method of generating the movement data is as explained based on FIG. 4, so the explanation will not be repeated here.

[0070] In S4, the standard movement amount calculation unit 104 specifies the dust supply rate, and calculates the standard movement amount from the specified dust supply rate using a standard movement amount calculation formula 112. Note that the value to which the dust supply rate is set may be input by a user of the prediction device 1 via the input unit 13, or may be specified by communicating with a control device of the waste treatment facility via the communication unit 12.

[0071] In S5 (step of predicting properties), the property prediction unit 105 predicts the properties of the waste to be incinerated. More specifically, the property prediction unit 105 calculates, as the prediction result, the difference between the movement amount indicated by the movement data generated by the movement data generation unit 103 in S3 and the standard movement amount calculated by the standard movement amount calculation unit 104 using the dust feed rate in S4. As described above, since the standard movement amount is calculated using the dust feed rate, in S5, it can be said that the property prediction unit 105 predicts the properties of the waste based on the movement data and the dust feed rate.

[0072] In S6, the impact prediction unit 106 predicts the impact on the combustion state of the incinerator of incinerating the waste based on the waste properties predicted in S5 by the property prediction unit 105. Note that prediction of the combustion state is not essential, and it is also possible to perform the processes from S7 onwards based on the prediction results of S5.

[0073] In S7, the equipment control unit 107 judges whether or not to perform control to keep the combustion state in the incinerator good based on the prediction result of S6. If the judgment in S7 is YES, the process proceeds to S8, where the equipment control unit 107 performs control to keep the combustion state in the incinerator good. On the other hand, if the judgment in S7 is NO, the process in S8 is not performed and the process in FIG. 6 ends.

[0074] The judgment criteria in S7 may be determined in advance, and the method for determining the control content in S8 may also be determined in advance. For example, when the magnitude of the influence on the combustion state is predicted in four stages, namely, large, medium, small, and no influence, control may be performed (YES may be determined in S7) if the prediction result is other than no influence. The greater the predicted influence on the combustion state, the greater the amount of control for promoting or suppressing combustion may be, by the device control unit 107.

[0075] Here, the equipment control unit 107 may determine whether to perform control to promote combustion or control to suppress combustion, for example, as follows. First, the equipment control unit 107 determines whether the movement amount indicated by the movement data generated by the movement data generation unit 103 in S3 is greater than the standard movement amount calculated by the standard movement amount calculation unit 104 using the dust feed rate in S4. In this determination, if it is determined that the movement amount indicated by the movement data generated by the movement data generation unit 103 is greater, the waste is considered to have a higher moisture content and is less combustible than the standard waste, so the equipment control unit 107 may determine to perform control to promote combustion (for example, control to heat with a burner or to increase the amount of combustion air). On the other hand, if it is determined that the standard movement amount is greater, the waste is considered to have a lower moisture content and is more combustible than the standard waste, so the equipment control unit 107 may determine to perform control to suppress combustion (for example, control to reduce the amount of combustion air). This allows the combustion state in the incinerator to be maintained in a good state.

[0076] In addition, in S7, control may be performed taking into consideration the time lag between when the waste is put into the hopper and when it is sent to the incinerator. For example, the device control unit 107 may calculate the time between when the waste is put into the hopper and when it is sent to the incinerator using the dust feeding speed, etc., and perform control according to the predicted effect on the combustion state of the waste when that time has elapsed.

[0077] In addition, depending on the content of the control, there may be a time lag before the result of the control is reflected in the combustion state. For this reason, the device control unit 107 may perform control so that the timing at which the result of the control is reflected in the combustion state is the same as or earlier than the timing at which the waste is sent to the incinerator.

[0078] Also, in S7, the device control unit 107 may be configured to determine the control content based on the prediction result of the property prediction unit 105. In this case, the process of S6 is omitted, and the influence prediction unit 106 becomes unnecessary.

[0079] As described above, the prediction method according to this embodiment includes a step (S3) of generating movement data showing the movement state of the waste from a plurality of time-series images of the hopper taken from above in an incineration facility where the waste is fed into the hopper and sent to the incinerator at a specified dust feeding speed, and a step (S5) of predicting the properties of the waste to be incinerated based on the movement data and the dust feeding speed generated in the above step. This prediction method can contribute to improving the operation of the incineration facility.

[0080] As described at the beginning of the first embodiment, the facility control system 100 is not limited to a waste incineration facility, but can be applied to an incineration facility that incinerates any object to be incinerated, and the object to be incinerated stored in the hopper is not limited to waste. In other words, the "waste" described in the above-mentioned embodiment can be read as any "object to be incinerated." This also applies to the second and subsequent embodiments described below.

[0081] [Embodiment 2] Other embodiments of the present invention will be described below. For the sake of convenience, the same reference numerals are used to designate components having the same functions as those described in the above embodiment, and the description thereof will not be repeated. This also applies to the third embodiment described below.

[0082] (Configuration of prediction device) Fig. 7 is a block diagram showing an example of a main configuration of a prediction device 1A according to this embodiment. The prediction device 1A differs from the prediction device 1 shown in Fig. 1 in that it does not include a standard movement amount calculation unit 104, and includes a bulk density prediction unit (prediction unit) 105A and a heat generation amount prediction unit 106A instead of the property prediction unit 105 and the influence prediction unit 106. The prediction device 1A also differs from the prediction device 1 in that the storage unit 11 of the prediction device 1A does not store the standard movement amount calculation formula 112, but instead stores a bulk density prediction formula 112A and a heat generation amount prediction formula 113A.

[0083] The bulk density prediction unit 105A calculates the bulk density using the movement amount and dust feed rate indicated by the movement data generated by the movement data generation unit 103, and a bulk density prediction formula 112A. The bulk density prediction formula 112A is a formula that indicates the relationship between the movement amount and dust feed rate of the waste, and the bulk density of the waste. The details of the method for predicting the bulk density will be described later with reference to FIG. 8.

[0084] The calorific value prediction unit 106A calculates a predicted value of the lower calorific value (calorific value) when the waste is incinerated, using a calorific value prediction formula 113A that indicates the relationship between the bulk density of the waste and the lower calorific value (calorific value) when the waste is incinerated. The method of predicting the lower calorific value (calorific value) will be described in detail later with reference to FIG. 9.

[0085] (Method of predicting bulk density) The inventors of the present invention have studied whether it is possible to express bulk density using the amount of waste moved and the dust feed rate. As a result of this study, they have found that bulk density can be predicted by the following formula (1), which is obtained by multiple regression analysis using the results of measuring the amount of waste moved and the dust feed rate for various bulk densities. y=ax1+bx2+cx1*x2+d (1) In the above formula (1), y is the bulk density [t / m 3 ] (t is the unit of mass: ton), x1 is the dust feed rate [m / h], X2 is the amount of garbage moved (a dimensionless quantity calculated by the image correlation method), and a to d are coefficients obtained by multiple regression analysis.

[0086] FIG. 8 is a graph showing the relationship between the predicted bulk density calculated by the above formula (1) and the actual bulk density, with the vertical axis representing the predicted value and the horizontal axis representing the actual bulk density. Graph J in FIG. 8 shows that the bulk density can be predicted with high accuracy by the above formula (1). In the example shown in graph J, the coefficient of determination was 0.8565.

[0087] For comparison, the inventors of the present invention also attempted to predict the bulk density using the following formula (2). y=ax1+b (2) The bulk density predicted by formula (2) was also consistent with the actual bulk density to a certain extent, but the coefficient of determination was not as good as that obtained by formula (1). This shows that the amount of waste movement contributes to improving the accuracy of the bulk density estimation.

[0088] Of course, it is possible to calculate the predicted bulk density from the dust feed rate and the amount of waste moving by various methods other than using formula (1). In other words, the predicted bulk density can be calculated by using any prediction model that models the relationship between the dust feed rate and the amount of waste moving, and the bulk density.

[0089] As described above, the inventors of the present invention have found that there is a correlation between the amount of waste moving in the hopper, the feed rate, and the bulk density of the waste. Therefore, the prediction device 1A, which includes the bulk density prediction unit 105A that calculates the bulk density using the bulk density prediction formula 112A that shows the relationship between the amount of waste moving, the feed rate, and the bulk density of the waste, can calculate a suitable bulk density value as the prediction result.

[0090] The dust feed rate is a value that changes every second, and is therefore highly real-time. The amount of moving garbage is also highly real-time information that can be obtained, for example, every minute. As will be described below, the amount of moving garbage is also a value that depends on the bulk density.

[0091] First, it is known that there is a correlation between the dust feed rate and the amount of garbage moved. However, even with the same dust feed rate, there is variation in the amount of garbage moved. One of the reasons for this variation is thought to be the difference in the weight of the garbage. In other words, when garbage settles in the hopper, heavier garbage is compressed to a greater extent by its own weight than lighter garbage, and this is thought to result in a larger amount of garbage moved per unit time, i.e., a larger movement speed. In this way, the speed at which garbage settles in the hopper is affected by the dust feed rate and the compression by the garbage's own weight. Therefore, the bulk density, which is the weight per unit volume, changes depending on the dust feed rate and the degree of compression by the garbage's own weight. Therefore, it can be said that the amount of garbage moved depends not only on the dust feed rate but also on the bulk density.

[0092] In this way, with the above configuration, the bulk density of the waste in the hopper can be estimated from information with high real-time accuracy. Specifically, the predicted bulk density can be calculated simply by photographing the hopper from above multiple times and identifying the dust feeding speed at the time of photographing. This makes it possible to estimate the lower heating value of the waste in real time from the bulk density, and the estimated result can be used to control the incinerator.

[0093] The bulk density can also be calculated using the formula (3) described later. However, in this method, in order to obtain an appropriate amount of waste (weight), the above-mentioned predetermined period must be set to a relatively long period, such as one day, and the bulk density cannot be calculated at a timing that can be used to control the incinerator.

[0094] (Method for predicting low-level heating value) The method of predicting the lower heat generation amount by the heat generation amount prediction unit 106A will be described with reference to Fig. 9. Fig. 9 is a diagram for explaining the method of predicting the lower heat generation amount. Fig. 9 also shows a schematic cross-sectional view of the vicinity of the opening A2 at the bottom of the hopper A shown in Fig. 2.

[0095] In waste incineration facilities, waste placed into a hopper remains there for a while before being sent to the incinerator for incineration, making it difficult to measure the low-level heating value of the waste shown in an image taken from above the hopper when it is incinerated.

[0096] Therefore, the inventors of the present invention first determined the relationship between the bulk density and the lower heating value from the average daily heating value in the incinerator and the average bulk density in that day. The heating value prediction unit 106A uses the relationship thus determined to obtain the lower heating value from the bulk density.

[0097] As shown in Figure 9, the waste contained inside hopper A is sent into incinerator C through opening A2. For this reason, assuming that the cross-sectional area of ​​opening A2 is So and the average dust feeding speed is V, the processing capacity of incinerator C when it is operated for one day (24 hours) can be expressed as follows:

[0098] (Daily processing volume) [ton / day] = {V [m / h] × 24 [h / day]} × So [m 2 ]×(bulk density) [ton / m 3 ] Therefore, the bulk density is expressed as follows:

[0099] (Bulk density) = (Daily processing volume) / (V × 24 × So) (3) K in FIG. 9 is a graph showing the relationship between the bulk density obtained as described above and the lower heating value Hu of that day in the incinerator C. As shown in the figure, although there is some variation, it can be seen that there is a correlation between the bulk density and the lower heating value as a whole. More specifically, the bulk density and the lower heating value are in a proportional relationship, and therefore the relationship between the bulk density and the lower heating value can be expressed by a straight line k1.

[0100] Therefore, if the formula for this straight line k1 is stored in the memory unit 11 as the heat generation amount prediction formula 113A, the heat generation amount prediction unit 106A can use this formula to determine the low-level heat generation amount from the bulk density predicted by the bulk density prediction unit 105A.

[0101] As described above, it has been found that the bulk density of garbage correlates with the lower heating value when the garbage is incinerated. Therefore, the prediction device 1A equipped with the heat value prediction unit 106A that predicts the lower heating value using the heat value prediction formula 113A that shows the relationship between the bulk density and the lower heating value can calculate an appropriate lower heating value as a prediction result.

[0102] As described above, since the bulk density can be calculated from the amount of waste moved and the dust feed rate, and the lower heating value can be calculated from the bulk density, it is also possible to calculate the lower heating value from the amount of waste moved and the dust feed rate without calculating the bulk density. In other words, the heat value prediction unit 106A may calculate the lower heating value of the waste using a formula showing the relationship between the amount of movement indicated by the movement data generated by the movement data generation unit 103, the dust feed rate, and the lower heating value when the waste fed into the incinerator at that dust feed rate is incinerated. Even with this configuration, a reasonable value of the lower heating value can be calculated as a prediction result showing the properties of the waste.

[0103] The formula for calculating the lower heating value of the waste is, for example, the regression formula as described above, but is not limited to this example. The heat value prediction unit 106A can calculate the predicted value of the lower heating value using any prediction model that models the relationship between the dust feed rate and the amount of waste movement, and the lower heating value.

[0104] (Processing flow) The flow of the process (prediction method) executed by the prediction device 1A will be described with reference to Fig. 10. Fig. 10 is a flowchart showing an example of the process executed by the prediction device 1A. Note that the description of the process of S11 to S13 in Fig. 10 is omitted because it is the same as S1 to S3 in Fig. 6.

[0105] In S14 (step of predicting properties), the bulk density prediction unit 105A substitutes the movement amount indicated by the movement data generated in S13 and the dust feed speed when the garbage moved that movement amount into the bulk density prediction formula 112A to calculate a predicted value of the bulk density.

[0106] In S15, the heat value prediction unit 106A calculates a predicted value of the lower heating value by substituting the predicted value of the bulk density calculated in S14 into the heat value prediction formula 113A. As mentioned above, it is also possible to calculate the lower heating value from the amount of waste movement and the dust feed rate without calculating the bulk density. In this case, S14 is omitted, and S15 becomes the step of predicting the properties of the waste (specifically, the lower heating value).

[0107] In S16, the equipment control unit 107 judges whether or not to perform control to keep the combustion state in the incinerator good based on the prediction result of S15. If the judgment in S16 is YES, the process proceeds to S17, where the equipment control unit 107 performs control to keep the combustion state in the incinerator good. On the other hand, if the judgment in S16 is NO, the process in S17 is not performed and the process in FIG. 10 ends.

[0108] The judgment criteria of S16 may be determined in advance, and the method of determining the control content of S17 may also be determined in advance. For example, when the predicted value of the lower calorific value calculated in S15 is outside a predetermined normal range, control may be performed (determined as YES in S16). When the predicted value of the amount of generated heat is below the lower limit of the normal range, the equipment control unit 107 may perform control to increase the amount of generated heat (for example, control to increase heating by a burner or the amount of combustion air). On the other hand, when the predicted value of the amount of generated heat exceeds the upper limit of the normal range, the equipment control unit 107 may perform control to reduce the amount of generated heat (for example, control to reduce the amount of combustion air). This allows the combustion state in the incinerator to be maintained in a good state.

[0109] [Embodiment 3] (Configuration of facility control device) 11 is a block diagram showing an example of the main configuration of a facility control device 1B according to this embodiment. The facility control device 1B is a device that controls equipment in a waste incineration facility based on movement data and a dust feeding speed generated from multiple time-series images of a hopper taken from above.

[0110] The facility control device 1B is similar to the prediction device 1 and prediction device 1A described above in that it generates movement data from multiple time-series images of the hopper taken from above, but differs in that it controls equipment without predicting the properties of the waste. Therefore, it is possible to configure a facility control system 100 in which the prediction device 1 in FIG. 2 is replaced with the facility control device 1B.

[0111] 1, the facility control device 1B does not include the standard movement amount calculation unit 104, the property prediction unit 105, and the influence prediction unit 106, but instead includes a control content determination unit 105B. Furthermore, the storage unit 11 of the facility control device 1B does not store the standard movement amount calculation formula 112, but instead stores a control content determination model 112B.

[0112] The control content determination unit 105B determines the control content for the equipment in the waste incineration facility based on the movement data and the dust feeding speed generated by the movement data generation unit 103. For example, the control content determination unit 105B may determine the control content for at least any of (1) control of waste input into the hopper, (2) control of the dust feeding speed, (3) control of the waste transport speed (grate speed) in the incinerator, (4) control of waste combustion in the incinerator, and (5) control of waste stirring in the pit. The control content may include, for example, a control target and a control amount.

[0113] These controls are necessary to maintain a proper combustion state. Therefore, when there is no control to be performed to maintain a proper combustion state, the control content determination unit 105B determines not to perform the control.

[0114] More specifically, the control content determination unit 105B determines the control content using a control content determination model 112B. The control content determination model 112B is a model constructed so that the control content can be determined from the movement data and the dust feed rate. For example, when determining the control amount of the grate speed, the control content determination model 112B may be used in which the movement amount and the dust feed rate are explanatory variables and the optimal grate speed for maintaining an appropriate combustion state is the objective variable. Such a control content determination model 112B may be constructed by multiple regression analysis or by other algorithms such as a neural network.

[0115] Then, the equipment control unit 107 performs the control determined by the control content determination unit 105B on the equipment of the waste incineration facility. As described above, since the control content determination unit 105B determines the control content based on the movement data and the dust supply speed, it can be said that the equipment control unit 107 controls the equipment of the waste incineration facility based on the movement data and the dust supply speed.

[0116] As described above, the facility control device 1B is an incineration facility that sends garbage dumped into a hopper to an incinerator at a specified dust feeding speed. The facility control device 1B is equipped with a movement data generation unit 103 that generates movement data indicating the movement state of the garbage from multiple time-series images taken from above the hopper, and an equipment control unit 107 that performs at least one of the following based on the movement data generated by the movement data generation unit 103 and the dust feeding speed: (1) control of the dumping of garbage into the hopper, (2) control of the dust feeding speed, (3) control of the garbage transport speed in the incinerator, (4) control of the combustion of the garbage in the incinerator, and (5) control of the stirring of the garbage in the pit.

[0117] As described above, differences in the properties of the incineration objects, such as garbage, are reflected in the differences in the movement state of the incineration objects shown in the images. The differences in the properties of the incineration objects affect the content of the control required to maintain the proper combustion state of the objects. Therefore, the control required to maintain the proper combustion state of the garbage can be identified from the movement state of the garbage shown in the images. Therefore, according to the above configuration, appropriate control can be performed according to the properties of the garbage, which can contribute to improving the operation of the incineration facility.

[0118] (Processing flow) The flow of processing (facility control method) executed by facility controller 1B will be described with reference to Fig. 12. Fig. 12 is a flowchart showing an example of processing executed by facility controller 1B. Note that the description of the processing of S21 to S23 in Fig. 12 is omitted because it is the same as S1 to S3 in Fig. 6.

[0119] In S24, the control content determination unit 105B determines the control content using the control content determination model 112B. For example, assume that the movement data generated in S23 is data indicating the movement amount, and the control content determination model 112B is a model in which the movement amount and the dust feed rate are explanatory variables, and the optimal change amount of the dust feed rate for maintaining a proper combustion state is an objective variable. In this case, the control content determination unit 105B determines the optimal change amount of the dust feed rate for maintaining a proper combustion state from the movement amount indicated by the movement data generated in S23 and the value obtained by inputting the dust feed rate into the control content determination model 112B.

[0120] In S25, the equipment control unit 107 judges whether or not to perform control to maintain a good combustion state in the incinerator. If the judgment in S25 is YES, the process proceeds to S26, where the equipment control unit 107 performs the control determined in S24. On the other hand, if the judgment in S25 is NO, the process in S26 is not performed and the process in FIG. 12 ends.

[0121] The judgment criterion for S25 may be determined in advance. For example, it may be determined that control is not performed in S24, or that control is performed (YES in S25) unless it is determined that the control amount is zero. Also, for example, in the case of using a control content decision model 112B that outputs a numerical value indicating the confidence level of the prediction together with the control content predicted as optimal, it may be determined that control is performed (YES in S25) when the confidence level is equal to or greater than a threshold value. Also, for example, the control content determined in S24 may be presented to an operator, and the operator may input whether or not to execute the control.

[0122] As described above, the facility control method according to the present embodiment includes a step (S23) of generating movement data showing the movement state of the waste from a plurality of time-series images of the hopper taken from above in an incineration facility where the waste put into the hopper is sent to the incinerator at a specified dust feed speed, and a step (S26) of performing at least one of (1) controlling the input of the waste into the hopper, (2) controlling the dust feed speed, (3) controlling the transport speed of the waste in the incinerator, (4) controlling the combustion of the waste in the incinerator, and (5) controlling the stirring of the waste in the pit, based on the movement data and the dust feed speed generated in S23. This facility control method can contribute to improving the operation of the incineration facility.

[0123] [About bridge occurrence] In a hopper, a bridge may occur, where the waste inside the hopper becomes clogged like a bridge. When a bridge occurs, the amount of waste moving in the upper part of the hopper (the part where the bridge occurs and above) becomes significantly smaller or zero, regardless of the nature of the waste. Therefore, it is difficult to predict the nature of the waste from an image taken when a bridge occurs.

[0124] For this reason, the prediction devices 1 and 1A described in the above embodiments may be provided with a bridge detection unit that detects the occurrence of a bridge, and may not predict the garbage properties when the bridge detection unit detects the occurrence of a bridge. Also, the prediction devices 1 and 1A may hold the prediction results of the garbage properties while a bridge is occurring, and resume predicting the garbage properties after the bridge is resolved.

[0125] The method for detecting the occurrence and elimination of a bridge is not particularly limited, and for example, the occurrence and elimination of a bridge can be detected based on the amount of movement of waste indicated by the movement data generated by the movement data generation unit 103. Similarly, the facility control device 1B may detect the occurrence of a bridge, hold control while the bridge is occurring, and resume control after the bridge is eliminated.

[0126] Conversely, it is also possible to detect the occurrence of a bridge from the predicted results of the waste properties by utilizing the fact that the occurrence of a bridge affects the predicted results of the waste properties. For example, the prediction device 1A may determine that a bridge has occurred when there is a deviation between the predicted bulk density and the actually measured bulk density. In addition, if a bridge continues for a long time, it will also affect the low heating value, so the prediction device 1A may determine that a bridge has occurred when the predicted low heating value deviates from the straight line k1 shown in FIG. 9.

[0127] [Modifications] The execution subject of each process described in each of the above-mentioned embodiments is arbitrary and is not limited to the above-mentioned examples. In other words, a system having the same functions as the prediction device 1, 1A and the facility control device 1B can be constructed by a plurality of devices capable of communicating with each other. For example, the processes of S1 to S3 in FIG. 6 may be executed by a certain information processing device, the generated movement data may be transmitted to another information processing device, and the other information processing device may execute the processes of S4 to S8. The same applies to each process shown in FIG. 9 and FIG. 12.

[0128] [Software implementation example] The functions of the prediction devices 1, 1A and the facility control device 1B (hereinafter referred to as the "devices") can be realized by a program for causing a computer to function as the device, and a program (prediction program / control program) for causing a computer to function as each control block of the device (particularly each part included in the control unit 10).

[0129] In this case, the device includes a computer having at least one control device (e.g., a processor) and at least one storage device (e.g., a memory) as hardware for executing the program. The control device and storage device execute the program to realize each function described in each of the above embodiments.

[0130] The program may be non-transitory and may be recorded in one or more computer-readable recording media. The recording media may or may not be included in the device. In the latter case, the program may be provided to the device via any wired or wireless transmission medium.

[0131] In addition, some or all of the functions of each of the control blocks can be realized by a logic circuit. For example, an integrated circuit in which a logic circuit that functions as each of the control blocks is formed is also included in the scope of the present invention. In addition, the functions of each of the control blocks can be realized by, for example, a quantum computer.

[0132] The present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of the present invention. [Explanation of symbols]

[0133] 1 Prediction device 103 Movement Data Generation Unit 104 Standard movement amount calculation unit 105 Property Prediction Unit (Prediction Unit) 1A Prediction device 103 Movement Data Generation Unit 105A Bulk density prediction section (prediction section) 1B Facility control device 103 Movement Data Generation Unit 107 Equipment control section

Claims

1. In an incineration facility that sends incineration objects placed in a hopper to an incinerator at a specified speed, a movement data generating unit generates movement data showing the movement state of the incineration objects from a plurality of time-series images taken from above the hopper; A prediction device comprising: a prediction unit that predicts the properties of the incineration target to be incinerated based on the movement data generated by the movement data generation unit and the speed.

2. The movement data is data indicating the amount of movement of the incineration object, The prediction device according to claim 1, wherein the prediction unit calculates the difference between the amount of movement indicated by the movement data generated by the movement data generation unit and the standard amount of movement of the object to be incinerated when the object to be incinerated is sent to the incinerator at the speed as the result of the prediction.

3. The prediction device according to claim 2, further comprising a standard movement amount calculation unit that calculates the standard movement amount using a formula showing the relationship between the speed at which the incineration object is fed into the incinerator and the standard movement amount of the incineration object when the incineration object is fed into the incinerator at that speed.

4. The movement data is data indicating the amount of movement of the incineration object, The prediction device according to claim 1, wherein the prediction unit calculates the bulk density as a result of the prediction using a formula showing the relationship between the amount of movement indicated by the movement data generated by the movement data generation unit, the speed at which the incineration object is fed into the incinerator, and the bulk density of the incineration object.

5. The movement data is data indicating the amount of movement of the incineration object, The prediction device of claim 1, wherein the prediction unit calculates the lower heating value as a result of the prediction using a formula showing the relationship between the amount of movement indicated by the movement data generated by the movement data generation unit, the speed at which the incineration object is fed into the incinerator, and the lower heating value when the incineration object is incinerated.

6. In an incineration facility that sends incineration objects placed in a hopper to an incinerator at a specified speed, a movement data generating unit generates movement data showing the movement state of the incineration objects from a plurality of time-series images taken from above the hopper; A facility control device comprising: an equipment control unit that performs at least one of the following based on the movement data generated by the movement data generation unit and the speed: (1) control of the loading of materials to be incinerated into the hopper; (2) control of the speed; (3) control of the transport speed of the materials to be incinerated in the incinerator; (4) control of the combustion of the materials to be incinerated in the incinerator; and (5) control of the stirring of the materials to be incinerated.

7. A method for predicting properties of an object to be incinerated, executed by one or more information processing devices, comprising: In an incineration facility that sends incineration objects placed in a hopper to an incinerator at a specified speed, a step of generating movement data showing the movement state of the incineration objects from a plurality of time-series images taken from above the hopper; A prediction method comprising: a step of predicting the properties of the material to be incinerated based on the movement data and the speed generated in the step.

8. A facility control method executed by one or more information processing devices, comprising: In an incineration facility that sends incineration objects placed in a hopper to an incinerator at a specified speed, a step of generating movement data showing the movement state of the incineration objects from a plurality of time-series images taken from above the hopper; A facility control method comprising the steps of: (1) controlling the feeding of materials to be incinerated into the hopper based on the movement data and the speed generated in the steps; (2) controlling the speed; (3) controlling the transport speed of the materials to be incinerated in the incinerator; (4) controlling the combustion of the materials to be incinerated in the incinerator; and (5) controlling the stirring of the materials to be incinerated.

9. A prediction program for causing a computer to function as the prediction device according to claim 1, the prediction program causing a computer to function as the movement data generating unit and the prediction unit.

10. 7. A control program for causing a computer to function as the facility control device according to claim 6, the control program causing a computer to function as the movement data generating unit and the equipment control unit.

Images