Waste treatment system, waste treatment method, waste treatment program, and waste treatment plant

Abstract

To provide a garbage disposal system, a garbage disposal method, a garbage disposal program, a garbage disposal plant and the like capable of stably generating biogas from collected garbage.SOLUTION: A waste treatment system includes acquisition means for acquiring a captured image including an image of waste in a waste receiving and storing facility captured by an imaging device, determination means for classifying the captured image into a plurality of areas and determining a ratio of a waste composition corresponding to each area, and estimation means for estimating an amount of generated biogas based on the ratio of each waste composition and an amount of generated biogas derived from each waste composition.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present disclosure relates to a waste treatment system, a waste treatment method, a waste treatment program, and a waste treatment plant. [Background technology]

[0002] Conventionally, there has been a need in waste treatment plants to properly treat the waste after understanding the condition of the collected waste. It is known that such waste treatment plants use technology to understand the condition of the waste before and / or during the waste treatment.

[0003] For example, Patent Document 1 discloses technology for a waste treatment plant having a function for determining the combustion state during the incineration process in waste treatment. In the waste treatment plant disclosed in Patent Document 1, an imaging device such as an infrared imaging device or a visible light imaging device is installed inside the incinerator, and the combustion state of the waste is evaluated based on the imaging data from the imaging device. Then, measures are taken to stabilize the combustion of the waste based on the evaluation of the combustion state of the waste. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 7390581 Summary of the Invention [Problem to be solved by the invention]

[0005] The waste treatment plant disclosed in Patent Document 1 employs technology specialized in evaluating the state of combustion of waste. As a result, this waste treatment plant does not determine the composition of collected waste or control the amount of gas (biogas, etc.) generated from the waste.

[0006] The present disclosure aims to provide a waste treatment system, a waste treatment method, a waste treatment program, and a waste treatment plant that enable stable generation of gas from collected waste. [Means for solving the problem]

[0007] The disclosed waste treatment system includes an acquisition means for acquiring an image including an image of waste in a waste receiving and storage facility captured by an imaging device, a determination means for classifying the image into a plurality of regions and determining the proportion of the waste composition corresponding to each region, and an estimation means for estimating the amount of biogas generated based on the proportion of each waste composition and the amount of biogas generated from each waste composition.

[0008] In addition, in the disclosed waste treatment system, it is preferable that the estimation means further includes a determination means for estimating the component ratios of carbon and nitrogen contained in the waste based on the proportions of each waste composition, and determining measures for the amount of biogas generated in accordance with the component ratios estimated by the estimation means.

[0009] In addition, in the disclosed waste treatment system, it is preferable that the estimation means estimates the carbon and nitrogen content contained in each waste composition based on the proportion of each waste composition and the total weight of the waste, and estimates the amount of biogas generated based on the carbon and nitrogen content contained in each waste composition.

[0010] Furthermore, in the disclosed waste disposal system, in classifying the areas, it is preferable that the determination means inputs the captured image into a trained model, thereby classifying the image of waste contained in the captured image into multiple areas, and that the trained model is a model that has been trained using teacher captured images, including images of other waste, previously captured by the imaging device as input data, and label information for each area associated with a specified area contained in the teacher captured image as output data.

[0011] In the disclosed waste treatment system, the determining means preferably determines the proportion of each waste composition corresponding to each region based on the area of ​​each region.

[0012] Furthermore, in the disclosed waste treatment system, it is preferable that the acquisition means acquires information indicating the absorption spectrum of the waste in the waste receiving and storage facility measured by the spectroscopic analysis means, the estimation means estimates the component ratio of carbon and nitrogen contained in the waste based on the information indicating the acquired absorption spectrum, and the system further includes a determination means for determining measures to be taken regarding the amount of biogas generated in accordance with the component ratio estimated by the estimation means.

[0013] In addition, in the disclosed waste treatment system, it is preferable that the spectroscopic analysis means be placed at a position where it can measure the vicinity of the outlet of the crushing and sorting device that crushes the waste in the receiving and storage facility and turns it into slurry, or at a position where it can measure the waste dropped into the receiving and storage facility.

[0014] In addition, in the disclosed waste treatment system, it is preferable that the measures are a first measure related to work to reduce the amount of biogas generated from the waste, or a second measure related to work to increase the amount of biogas generated from the waste.

[0015] The disclosed waste treatment system includes an acquisition means for acquiring information indicating the absorption spectrum of waste in a waste receiving and storage facility measured by a spectroscopic analysis means, a determination means for determining the proportion of waste compositions in the receiving and storage facility based on the information indicating the absorption spectrum, and an estimation means for estimating the amount of biogas generated based on the proportion of each waste composition and the amount of biogas generated from each waste composition.

[0016] The disclosed waste disposal method includes an acquisition step in which an information processing device acquires an image including an image of waste inside a waste receiving and storage facility captured by an imaging device; a determination step in which the image is classified into multiple areas and the proportion of the waste composition corresponding to each area is determined; and an estimation step in which the amount of biogas generated is estimated based on the proportion of each waste composition and the amount of biogas generated from each waste composition.

[0017] The disclosed waste disposal program causes an information processing device to execute an acquisition step of acquiring an image including an image of waste inside a waste receiving and storage facility captured by an imaging device, a determination step of classifying the image into multiple areas and determining the proportion of waste composition corresponding to each area, and an estimation step of estimating the amount of biogas generated based on the proportion of each waste composition and the amount of biogas generated from each waste composition.

[0018] The disclosed waste treatment plant is a waste treatment plant equipped with a waste receiving and storage facility, and is equipped with: an acquisition means for acquiring an image including an image of waste inside the receiving and storage facility captured by an imaging device; a determination means for classifying the image into a plurality of areas and determining the proportion of the waste composition corresponding to each area; and an estimation means for estimating the amount of biogas generated based on the proportion of each waste composition and the amount of biogas generated from each waste composition. [Effects of the Invention]

[0019] The waste treatment system, waste treatment method, waste treatment program, and waste treatment plant disclosed herein make it possible to stably generate gas from collected waste. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a schematic diagram showing an example of the general configuration of a waste treatment plant. [Figure 2] FIG. 1(a) is a schematic diagram for explaining the arrangement of the imaging device, and FIG. 1(b) is a diagram showing an example of an image captured of waste in a receiving and storage facility. [Figure 3] FIG. 1 is a diagram illustrating an example of a schematic configuration of an information processing device. [Figure 4] FIG. 1A is a schematic diagram for explaining a determination process using a trained model, and FIG. 1B is a diagram showing an example of a determination result obtained by the determination process. [Figure 5] FIG. 4 is a diagram illustrating an example of a data structure of a component table. [Figure 6] FIG. 2 is a schematic diagram for explaining the arrangement of a spectroscopic analysis means. [Figure 7] FIG. 10 is a diagram showing an example of a measurement result obtained by a spectroscopic analysis means. [Figure 8] FIG. 10 is a diagram illustrating an example of an operational flow of a first estimation process. [Figure 9] FIG. 10 is a diagram illustrating an example of an operation flow of a second estimation process. DETAILED DESCRIPTION OF THE INVENTION

[0021] Various embodiments of the present invention will be described below with reference to the drawings. However, it should be noted that the technical scope of the present invention is not limited to these embodiments, but extends to the inventions set forth in the claims and their equivalents.

[0022] (Outline of Waste Treatment Plant 1) FIG. 1 is a schematic diagram showing an example of the general configuration of a waste treatment plant 1. The waste treatment plant 1 is a plant that has the function of treating waste. For example, the waste treatment plant 1 is a biogas plant. The waste treatment plant 1 may also be a municipal waste incineration plant or an industrial waste treatment plant.

[0023] When the waste treatment plant 1 shown in FIG. 1 is a biogas plant, the waste treatment plant 1 is equipped with, for example, at least a food waste receiving hopper 2, a crushing and sorting device 3, a mixing tank 4, an acid fermentation tank 5, and a methane fermentation tank 6. Here, although the acid fermentation tank 5 is provided in this embodiment, it may not be provided. In addition to the facilities and devices shown in FIG. 1, the waste treatment plant 1 may also be equipped with a sewage sludge adjustment tank, a digester liquid storage tank, etc. (These are not shown in the drawings.) As will be described later, food waste 11 input into the food waste receiving hopper 2 is digested in the methane fermentation tank 6 to become digester liquid 11E, which generates biogas 11D. The digester liquid storage tank is a facility installed downstream of the methane fermentation tank 6 for the purpose of storing the digester liquid 11E after generating biogas 11D.

[0024] The food waste receiving hopper 2 is a receiving and storage facility that receives and stores food waste 11 and other waste collected by the garbage compactor G. Hereinafter, the food waste 11 and other waste dropped into the food waste receiving hopper 2 may be simply referred to as "garbage." The garbage compactor G is also called a compactor truck or garbage collection truck, and is a vehicle that collects and transports various types of garbage generated by homes, businesses, etc.

[0025] The food waste receiving hopper 2 temporarily stores the waste 11 dropped from the garbage compactor G and then sends it to the next process. In the example shown in Figure 1, the waste 11 temporarily stored in the food waste receiving hopper 2 is sent to the crushing and sorting device 3. If the next process is an incineration process using an incinerator, the temporarily stored waste 11 is continuously sent into the incinerator.

[0026] The crushing and sorting device 3 crushes the garbage 11 sent from the food waste receiving hopper 2, removes inappropriate materials, and then turns the garbage into a slurry, automatically sorting the slurried garbage (hereinafter, sometimes referred to as "food waste slurry 11A"). The food waste slurry 11A is transferred to the mixing tank 4 by a food waste slurry transfer pump (not shown). The mixing tank 4 is a facility that mixes (agitates) the food waste slurry 11A with sludge 11B from a sewage sludge adjustment tank (not shown) and sends the resulting mixture to the acid fermentation tank 5. The sewage sludge adjustment tank is a facility that dilutes or concentrates at least one type of sludge from collected paper, sewage sludge, and waste cooking oil to adjust the sludge concentration. Hereinafter, the mixture of food waste slurry 11A and sludge 11B mixed in the mixing tank 4 will sometimes be referred to as "organic waste 11C." The organic waste 11C is transferred from the mixing tank 4 to the acid fermentation tank 5, and may be transferred using a transfer pump (not shown).

[0027] The acid fermentation tank 5 is a fermentation apparatus that stores the organic waste 11C while maintaining a temperature of, for example, 35 to 38°C or 50 to 56°C and a pH value of 5 to 7. The storage time of the organic waste 11C in the acid fermentation tank 5 is, for example, about 1 to 2 days. The organic waste 11C is transferred from the acid fermentation tank 5 to the methane fermentation tank 6, and may be transferred using a transfer pump (not shown).

[0028] The methane fermentation tank 6 is a fermentation apparatus that retains the acid-fermented organic waste 11C at a temperature of, for example, 35 to 38°C or 50 to 56°C and a pH value of 6.5 to 8.5. The organic waste 11C is digested in the methane fermentation tank 6 to produce digested liquid 11E. The retention time of the digested liquid 11E in the methane fermentation tank 6 is, for example, about 5 to 30 days, but is not limited thereto. The acid-fermented organic waste 11C undergoes methane fermentation in the methane fermentation tank 6 to produce digested liquid 11E, and biogas 11D is generated from the digested liquid 11E. The biogas 11D mainly contains methane gas (CH4) and carbon dioxide (CO2) and is sometimes referred to as digestion gas. The biogas 11D is, for example, recovered by a gas recovery unit (not shown) and supplied to a gas holder (not shown) or the like.

[0029] It is also possible to provide an anaerobic digestion tank instead of the acid fermentation tank 5 and the methane fermentation tank 6, so that acid fermentation and methane fermentation can be carried out simultaneously in a single anaerobic digestion tank.

[0030] Generally, waste collected from households and other sources varies widely in content, making it difficult to predict in advance the amount of biogas 11D that will be generated from the waste. Depending on the composition of the waste dropped into the food waste receiving hopper 2 installed in the waste treatment plant 1 (biogas plant) shown in Figure 1, the amount of biogas 11D generated may not be appropriate. For this reason, the waste dropped into the food waste receiving hopper 2 must be monitored at the waste treatment plant 1, and measures must be taken based on the monitoring results to ensure an appropriate amount of biogas 11D is generated.

[0031] (Imaging device 7) 2(a) is a schematic diagram illustrating the placement of an imaging device 7 that captures images of waste 11 in a food waste receiving hopper 2 (receiving and storing facility). The image of the waste 11 captured by the imaging device 7 is used to determine the composition of the waste in the food waste receiving hopper 2.

[0032] The imaging device 7 is a camera having an imaging optical system, an imaging element, an image processing unit, etc. The imaging optical system is, for example, an optical lens, and focuses a light beam from a subject on the imaging surface of the imaging element. The imaging element is, for example, a charge coupled device (CCD) or a complementary metal oxide semiconductor (CMOS), and outputs an image of the subject imaged on the imaging surface. The image processing unit creates moving image data in a predetermined file format from images continuously generated by the imaging element at predetermined intervals and outputs the moving image data as imaging data. Alternatively, the image processing unit creates still image data in a predetermined file format from images generated by the imaging element and outputs the moving image data as imaging data.

[0033] The imaging device 7 is placed in a position where it can capture an image of the waste in the food waste receiving hopper 2. In the example shown in FIG. 2(a), the imaging device 7 is placed in a predetermined position above the inlet 21 of the food waste receiving hopper 2. FIG. 2(b) is a diagram showing an example of a captured image including an image of waste 11 in the receiving and storage facility, captured by the imaging device 7. Information on the captured image (CI) captured by the imaging device 7 is transferred to the information processing device 8 by wireless or wired communication, or via a storage medium.

[0034] (Information processing device 8) 3 is a diagram showing an example of the schematic configuration of the information processing device 8. The information processing device 8 has a function of determining the waste composition based on the image captured by the imaging device 7 and executing a process of estimating the amount of biogas 11D generated. To realize such a function, the information processing device 8 includes a communication unit 81, a storage unit 82, a display unit 83, an input unit 84, and a processing unit 85.

[0035] The communication unit 81 is hardware, communication software such as a TCP / IP (Transmission Control Protocol / Internet Protocol) driver, or a combination of these. The information processing device 8 can receive data from the imaging device 7 via the communication unit 81. The communication unit 81 may include a network controller for interfacing with a communication network. The network controller may also be compliant with wireless communication standards such as Bluetooth (registered trademark), near field communication (NFC), or infrared. A storage medium receiving unit may also be provided instead of or in addition to the communication unit 81.

[0036] The storage unit 82 is, for example, a semiconductor memory device such as a ROM (Read Only Memory) or a RAM (Random Access Memory). The storage unit 82 stores an operating system program, a driver program, a control program, data, and the like used for processing in the processing unit 85. The driver programs stored in the storage unit 82 include an output device driver program that controls the display unit 83 and an input device driver program that controls the input unit 84. The control program stored in the storage unit 82 is, for example, an application program for determining the waste composition and estimating the amount of biogas 11D generated. The data stored in the storage unit 82 may also include a trained model M, which will be described later.

[0037] The display unit 83 is a liquid crystal display. However, the display unit 83 may be an organic EL (Electro-Luminescence) display or the like. The display unit 83 displays, on a display screen, moving images corresponding to the moving image data and / or still images corresponding to the still image data supplied from the processing unit 85.

[0038] The input unit 84 is an input key or the like. A user can use the input unit 84 to input letters, numbers, and symbols, or a position on the display screen of the display unit 83, etc. The input unit 84 may also be a pointing device such as a touch panel. When operated by a user, the input unit 84 generates a signal corresponding to the operation. The input unit 84 then supplies the generated signal to the processing unit 85 as an instruction from the user.

[0039] The processing unit 85 is a processor that loads the operating system program, driver program, and control program stored in the storage unit 82 into memory and executes instructions contained in the loaded programs. The processing unit 85 is, for example, an electronic circuit such as a CPU (Central Processing Unit). Although the processing unit 85 is illustrated as a single component in FIG. 2, the processing unit 85 may be a collection of multiple physically separate processors. For example, multiple processors that operate cooperatively in parallel to execute instructions may be implemented.

[0040] The processing unit 85 executes instructions included in the control program to function as an acquisition unit 851, a determination unit 852, an estimation unit 853, and a decision unit 854. The functions of the acquisition unit 851, the determination unit 852, the estimation unit 853, and the decision unit 854 will be described in detail later.

[0041] The acquisition unit 851 acquires the captured image (FIG. 2(b)) captured by the imaging device 7, and stores the acquired captured image in the storage unit .

[0042] The determination unit 852 has a function of classifying the captured image acquired by the acquisition unit 851 into a plurality of regions and determining the proportion of the dust composition corresponding to each region. The process of classifying the captured image into a plurality of regions is a process in which the captured image (D1) is input to the trained model M, and the image of the dust contained in the captured image is classified (D2) into a plurality of regions.

[0043] Fig. 4(a) is a schematic diagram for explaining an example of a determination process using the trained model M. For example, the trained model M is a trained model for semantic segmentation that identifies an object for each pixel that constitutes image information, and is a deep-trained trained model based on a fully convolutional network (FCN) or the like.

[0044] In semantic segmentation, when the captured image D1 stored in the storage unit 82 is input to the trained model M, feature information is output on a pixel-by-pixel basis. The output feature information is an example of segment information. Note that the segment information may be label information based on the output feature information.

[0045] The label information is numerical information corresponding to each waste composition. For example, if the waste composition category is "meat," information indicating "1" is assigned to the pixel corresponding to "meat," and if the waste composition category is "seafood," information indicating "2" is assigned to the pixel corresponding to "seafood." If the waste composition category is "unknown," information indicating "0" is assigned to the pixel corresponding to "unknown." Note that waste composition categories may also include "staple foods" (rice, rice crackers, etc.), "prepared dishes," "vegetables," or "fruit."

[0046] Furthermore, sub-categories of the above-mentioned categories may be used as categories of waste composition corresponding to the label information. For example, categories such as "udon," "bread," "instant noodles," "Chinese noodles (fresh)," "rice (rice balls)," "rice (mochi)," and "rice crackers" may be used as sub-categories of "staple food."

[0047] Other categories that may be used as sub-categories of "meat" include "beef shoulder loin (raw)" and "pork shoulder loin (raw)." Furthermore, categories that may be used as sub-categories of "seafood" include "boiled clams," "scallops (raw)," "Atka mackerel (raw)," "sockeye salmon (raw)," "octopus (raw)," "boiled wakame seaweed," and "boiled kelp."

[0048] Returning to the explanation of the trained model M, in semantic segmentation, other methods such as U-Net (Convolutional Networks for Biomedical Image Segmentation) may be used instead of FCN. Note that the trained model M is not limited to a training model related to semantic segmentation, and may be, for example, a discrimination model for object recognition based on CNN, such as YOLO (You Only Look Once) and SSD (Single Shot MultiBox Detector).

[0049] The trained model M based on the FCN is generated or updated by performing well-known deep learning using a computing device or the like having a learning function. Note that, if the information processing device 8 has a learning function, the information processing device 8 may perform well-known deep learning to generate or update the trained model M based on the FCN.

[0050] The learning function realizes the learning process of the trained model M using training data including training images that include images of other garbage previously captured by the imaging device 7, and label information for each area associated with a specific area included in the training images.

[0051] In this learning process, the teacher captured image is used as input data, and the label information of each region is used as output data to perform well-known deep learning (for example, Fujiyoshi Hironobu, "Part 1: Changes in Image Recognition Technology with the Advancement of Machine Learning," Measurement and Control, Vol. 58, No. 4, April 2019, pp. 291-297, etc.), thereby generating or updating a learned model M.

[0052] Fig. 4(b) is a diagram showing an example of the determination result obtained by the determination process. In the example shown in Fig. 4(b), the image of the garbage 11 included in the captured image is classified into four types of regions. The four types of regions correspond to the garbage composition categories of "meat 11a," "seafood 11b," "vegetables 11c," and "unknown 11d."

[0053] The determination unit 852 classifies the captured image and determines the proportion of the garbage composition corresponding to each region based on the area of ​​each region. For example, in the example shown in Figure 4(b), the garbage composition of "meat 11a" is 35%, the garbage composition of "seafood 11b" is 30%, the garbage composition of "vegetables 11c" is 10%, and the garbage composition of "unknown 11d" is 25%.

[0054] In addition, garbage is often disposed of in bags, and when the bag is dropped into the food waste receiving hopper 2, the garbage inside the bag may not be visible to the imaging device 7. In addition to the garbage composition, the determination unit 852 may also determine the proportion of "bags" that do not show the contents of the garbage. In this case, the label information of the training data for training the trained model M includes information indicating "bags." The determination unit 852 determines the garbage composition from images of garbage excluding "bags."

[0055] Furthermore, if the captured image contains an area corresponding to a "bag" in the image of the garbage, the proportion of each garbage composition in the "bag" measured in advance by the operator of the garbage treatment plant 1 may be used to apportion the proportion of the "bag" and add it to each garbage composition. For example, let us consider a case where the proportion of each category corresponding to each area is 30 wt% (hereinafter simply referred to as "%)" for "bag," 20% for "meat," 10% for "seafood," 10% for "vegetables," and 30% for "unknown." In this case, the breakdown of the garbage composition in the "bag" is 30% for "meat," 30% for "seafood," 20% for "vegetables," and 20% for "unknown."

[0056] Under the above conditions, the breakdown of the 30% of the "bag" proportion is 9% "meat," 9% "seafood," 6% "vegetables," and 6% "unknown." Therefore, the waste composition in food waste receiving hopper 2 is estimated to be 29% "meat" (20% + 9%), 19% "seafood" (10% + 9%), 16% "vegetables" (10% + 6%), and 36% "unknown" (30% + 6%). The breakdown of the waste composition in the "bag" can be the survey value for the same month last year, or the survey value for the most recent month. The breakdown of the waste composition in the "bag" can also be the average survey value for the past year, or the actual value from a nearby waste treatment plant.

[0057] Based on the proportion of each waste composition and the amount of biogas 11D (gas containing methane gas and carbon dioxide) generated from each waste composition, the estimation unit 853 estimates the amount of biogas 11D generated from all of the waste in the food waste receiving hopper 2. Then, based on the amount of nitrogen and the amount of carbon contained in each piece of waste, the estimation unit 853 calculates (estimates) the component ratio of carbon to nitrogen (the so-called C / N ratio) in the food waste receiving hopper 2.

[0058] For example, the protein content of 100g of meat, fish, and vegetables is 12g, 18g, and 1.6g, respectively. And since 16% of the mass of protein is nitrogen, the nitrogen content of 100g of meat, fish, and vegetables is 1.92g, 2.88g, and 0.256g, respectively. Thus, 1.92% of fish, 2.88% of meat, and 0.25% of vegetables are nitrogen.

[0059] In the above example (29% "meat," 19% "seafood," and 16% "vegetables"), the nitrogen content of the waste in the food waste hopper 2 is 1.14% (29 x 0.0192 + 19 x 0.0288 + 16 x 0.00256). Therefore, if the total weight of waste in the food waste hopper 2 is 800 kg, the nitrogen content is estimated to be 9.16 kg (800 x 0.0114). Note that the total weight of waste is the weight measured at the weigh station before it is dumped into the food waste hopper 2. As with nitrogen, the weight per 100 g of meat, fish, and vegetables is known. Therefore, the amounts of methane gas (CH4) and carbon dioxide (CO2) generated from the waste in the food waste hopper 2 can be calculated based on the total weight of the waste, the waste composition, and the amount of carbon per 100 g.

[0060] In this way, the estimation unit 853 has the function of referencing the component table shown in Figure 5 and estimating the carbon and nitrogen component ratio (C / N ratio) in the food waste receiving hopper 2 and the carbon and nitrogen content of the food waste in the food waste receiving hopper 2 based on the proportion of each waste composition determined by the determination unit 852 and the total weight of the food waste in the food waste receiving hopper 2. The estimation unit 853 has the function of estimating the amount of biogas to be generated, consisting of methane gas (CH4), carbon dioxide (CO2), etc., based on the carbon and nitrogen content. Note that Figure 5 is a diagram showing an example of the data structure of the component table. The component table is data stored in the memory unit 82. The amount of biogas to be generated may also be estimated based on the calories of each category, such as "meat" and "seafood."

[0061] The determination unit 854 can stabilize the amount of biogas generated from the waste by determining measures according to the component ratio (C / N ratio) estimated by the estimation means. The measures are a first measure related to work to reduce the amount of methane gas generated from the waste, and a second measure related to work to increase the amount of methane gas generated from the waste.

[0062] For example, when the C / N ratio estimated by the estimation unit 853 is within a target range, the determination unit 854 determines that the amount of gas generated is appropriate and operates to maintain the amount within this target range. The target range for the C / N ratio is, for example, "10 to 45", and preferably "20 to 40".

[0063] If the C / N ratio estimated by the estimation unit 853 is higher than the target range, it is assumed that the digested liquid 11E in the methane fermentation tank 6 contains a high amount of carbon and a low amount of nitrogen, and therefore the determination unit 854 determines that the estimated amount of biogas generated is excessive.

[0064] On the other hand, if the C / N ratio estimated by the estimation unit 853 is lower than the target range, it is assumed that the digested liquid 11E in the methane fermentation tank 6 has a low carbon content and a high nitrogen content. In this case, not only will the amounts of methane gas (CH4) and carbon dioxide (CO2) generated in the biogas be low, but the large amount of nitrogen will also produce ammonia nitrogen (NH4-N) in the digested liquid 11E, inhibiting the production of methane gas (CH4), and therefore the determination unit 854 determines that the estimated value of the amount of biogas generated is too low.

[0065] The first measure is to take steps to reduce the amount of biogas generated when it is determined that the estimated amount of biogas generated is excessive, so that the C / N ratio falls within a range of, for example, 10 to 45, preferably 20 to 40. Examples of measures include reducing the amount of waste input, reducing raw materials with high C / N ratios, inputting waste in a dispersed manner rather than all at once, and suppressing the agitation of the digested liquid 11E in the methane fermentation tank 6. These measures may be implemented automatically in response to a decision made by the decision unit 854.

[0066] The second countermeasure is to take steps to increase the amount of biogas generated when the estimated amount of biogas generated is determined to be too low, so that the C / N ratio falls within a range of, for example, 10 to 45, preferably 20 to 40. Examples of the countermeasure include increasing the amount of waste input, increasing raw materials with a high C / N ratio, adding separately stored raw materials with a high carbon content (e.g., cornstarch, breadcrumbs, etc.) to adjust the C / N ratio to, for example, 10 to 45, preferably 20 to 40, increasing the frequency of stirring the digested liquid 11E in the methane fermentation tank 6, or issuing an instruction to increase the stirring rotation speed. These countermeasures may be implemented automatically in response to a decision made by the decision unit 854.

[0067] (Spectroscopic analysis means 9) To monitor the waste dropped into the food waste receiving hopper 2, a spectroscopic analysis means 9 may be used in place of or in addition to the imaging device 7 shown in FIG. 2(a). The spectroscopic analysis means 9 is a measuring instrument that measures information indicating the absorption spectrum of the sample and analyzes its structure at the molecular level using the principle of near-infrared spectroscopy, which measures the light absorbed by the sample when light of a certain wavelength is irradiated onto the sample. The spectroscopic analysis means 9 may also be a measuring instrument that uses the principle of Raman or infrared spectroscopy.

[0068] Fig. 6(a) is a schematic diagram illustrating a first arrangement of the spectroscopic analysis means 9 for monitoring waste dropped into the food waste receiving hopper 2. As shown in Fig. 6(a), the spectroscopic analysis means 9 is placed in a position where it can measure the waste dropped into the food waste receiving hopper 2. For example, the spectroscopic analysis means 9 is placed in a predetermined position above the inlet 21 of the food waste receiving hopper 2.

[0069] FIG. 6(b) is a schematic diagram illustrating a second arrangement of the spectroscopic analysis means 9. As shown in FIG. 6(b), the spectroscopic analysis means 9 may be arranged at a position where it can measure the vicinity of the discharge outlet 31 of the crushing and sorting device 3, which crushes and slurries the waste dropped into the food waste receiving hopper 2, and measure the slurried waste (11A). Furthermore, when the food waste slurry 11A discharged from the discharge outlet 31 is temporarily stored in a pit (not shown), the spectroscopic analysis means 9 may be arranged on the side or top of the pit, and the slurried waste (11A) may be measured through a window installed on the side of the pit or a window or manhole installed on the top of the pit. Note that two spectroscopic analysis means 9 may be arranged at the first arrangement position and the second arrangement position, respectively.

[0070] The information indicating the absorption spectrum measured by the spectroscopic analysis means 9 is transferred to the information processing device 8 by wireless or wired communication, or via a storage medium. Then, the acquisition unit 851 of the information processing device 8 acquires the information indicating the absorption spectrum measured by the spectroscopic analysis means 9.

[0071] Fig. 7 is a diagram showing an example of the measurement results obtained by the spectroscopic analysis means 9. In Fig. 7, the first to seventh peaks are shown from the left. The first peak corresponds to OH, the second peak to CH, the third peak to OH, the fourth peak to CH, the fifth peak to NH, the sixth peak to OH, and the seventh peak to CH+NH.

[0072] Next, the estimation unit 853 of the information processing device 8 estimates the component ratio (C / N ratio) of carbon and nitrogen contained in the waste based on information indicating the acquired absorption spectrum. For example, the estimation unit 853 uses a calibration curve prepared in advance using substances whose carbon and nitrogen concentrations are known. The estimation unit 853 substitutes the peak top intensity or peak area in FIG. 7 into the calibration curve prepared in advance to estimate the respective carbon and nitrogen contents. Note that the peak top intensity or peak area is that of a peak arbitrarily selected from at least one of the first to sixth peaks in FIG. 7. The estimation unit 853 then estimates the component ratio (C / N ratio) of carbon and nitrogen based on the estimated respective carbon and nitrogen contents.

[0073] Then, the determination unit 854 determines measures to be taken against the dust in accordance with the component ratio (C / N ratio) estimated by the estimation means.

[0074] FIG. 8 is a diagram illustrating an example of the operation flow of the first estimation process.

[0075] First, the garbage truck G collects garbage (S101). Next, the total weight of the collected garbage is measured at a weigh station (S102). Next, the garbage is dumped into the receiving and storage facility (food waste receiving hopper 2) (S103).

[0076] Next, the imaging device 7 captures an image including an image of the garbage in the food waste receiving hopper 2 (receiving and storing facility) (S104). Next, the acquisition unit 851 of the information processing device 8 acquires the image (FIG. 2(b)) captured by the imaging device 7 and stores the acquired image in the storage unit 82.

[0077] Next, the determination unit 852 of the information processing device 8 classifies the captured image acquired by the acquisition unit 851 into a plurality of regions and determines the category (meat, seafood, etc.) corresponding to each region (S105). Next, the estimation unit 853 of the information processing device 8 compares the determined category with the ingredient table shown in Fig. 5 (S106).

[0078] Next, the estimation unit 853 calculates the component ratio (nitrogen content ratio, etc.) of the determined category (S107). Next, the estimation unit 853 calculates the C / N ratio (S108). Next, the determination unit 854 of the information processing device 8 determines whether the amount of gas generated is appropriate based on the component ratio estimated by the estimation means (S109).

[0079] If it is determined that the amount of gas generated is appropriate (S109-Yes), the determining unit 854 outputs appropriateness information indicating that the amount is appropriate (S110), and the first estimation process ends.

[0080] If it is determined that the amount of gas generated is not appropriate (S109-No), the determining unit 854 determines whether the amount of gas generated is large or not (S111).

[0081] If it is determined that the amount of gas generated is large (S111-Yes), information indicating a first countermeasure is output (S112), and the first estimation process ends. If it is determined that the amount of gas generated is small (S111-No), information indicating a second countermeasure is output (S113), and the first estimation process ends.

[0082] FIG. 9 is a diagram illustrating an example of the operation flow of the second estimation process.

[0083] First, the garbage truck G collects garbage (S201). Next, the total weight of the collected garbage is measured at a weigh station (S202). Next, the garbage is dumped into the receiving and storage facility (food waste receiving hopper 2) (S203).

[0084] Next, the crushing and sorting device 3 crushes the waste sent out from the food waste receiving hopper 2 (S204), removes inappropriate materials (S205), and converts the waste into a slurry (S206). The spectroscopic analysis means 9 measures the waste dropped into the food waste receiving hopper 2 (S207) and detects its absorption spectrum (S208).

[0085] Next, the estimation unit 853 calculates the component ratio (nitrogen content ratio, etc.) of the waste dropped into the food waste receiving hopper 2 based on the absorption spectrum (S209). Next, the estimation unit 853 calculates the C / N ratio (S210). Next, the determination unit 854 of the information processing device 8 determines whether the amount of gas generated is appropriate based on the component ratio estimated by the estimation means (S211).

[0086] If it is determined that the amount of gas generated is appropriate (S211-Yes), the determining unit 854 outputs appropriateness information indicating that the amount is appropriate (S212), and the second estimation process ends.

[0087] If it is determined that the amount of gas generated is not appropriate (S211-No), the determining unit 854 determines whether the amount of gas generated is large or not (S213).

[0088] If it is determined that the amount of gas generated is large (S213-Yes), information indicating a first countermeasure is output (S214), and the second estimation process ends. If it is determined that the amount of gas generated is small (S213-No), information indicating a second countermeasure is output (S215), and the second estimation process ends.

[0089] 8 and 9, it is possible to omit some of the steps constituting the procedure, to add steps not explicitly stated as constituting the procedure, and / or to change the order of the steps. Such omissions, additions, and changes in the order of the steps in the procedure shown in FIGS. 8 and 9 are also included within the scope of the present invention, provided they do not deviate from the spirit of the present invention. In addition to the above-described procedure, it is possible to omit some of the steps in each step of the present disclosure, to add steps not explicitly stated in the present disclosure, and / or to change the order of the steps. Such omissions, additions, and changes in the order of the steps are also included within the scope of the present invention, provided they do not deviate from the spirit of the present invention.

[0090] As described above in detail, the information processing device 8 and waste treatment plant 1 of this embodiment measure the component ratio of collected waste and enable stable generation of gas from the waste.

[0091] (Variation 1) It should be noted that the present invention is not limited to this embodiment. For example, the first estimation process and the second estimation process may be executed in parallel or sequentially. In this case, if it is determined that the amount of gas generated is appropriate in both the first estimation process and the second estimation process, appropriateness information may be output. Furthermore, if it is determined that the amount of gas generated is large in both the first estimation process and the second estimation process, information indicating a first measure may be output. Furthermore, if it is determined that the amount of gas generated is small in both the first estimation process and the second estimation process, information indicating a second measure may be output.

[0092] In the above description of the embodiments, a "first" estimation process and a "second" estimation process are described with respect to the "estimation process." However, these terms "first" and "second" do not indicate an order. The "first" estimation process described in the description of the embodiments may be referred to by other names in the claims (e.g., "second" estimation process, "second process," etc.). Similarly, the "second" estimation process described in the description of the embodiments may be referred to by other names in the claims (e.g., "first" estimation process, "first process," etc.). Thus, the "first" estimation process, etc. described in the claims should not be interpreted as being limited to the "first" estimation process in the description of the embodiments, but should be understood by reasonably interpreting the claims, the specification, and the drawings. Similarly, the "second" estimation process, etc. described in the claims should not be interpreted as being limited to the "second" estimation process in the description of the embodiments, but should be understood by reasonably interpreting the claims, the specification, and the drawings.

[0093] Similarly, the terms "first," "second," "third," etc. described elsewhere in the claims do not indicate an order. The term "first" described in the embodiments may be referred to by other names (e.g., "second," "third," etc.) in the claims. The term "second" described in the embodiments may be referred to by other names (e.g., "first," "third," etc.) in the claims. The terms "third," "fourth," etc. described in the embodiments may also be referred to by other names in the claims.

[0094] Although the present embodiment and its modifications have been described in detail above, the present invention is not limited to the specific embodiment, and various changes, substitutions, and modifications can be made to the present invention without departing from the scope of the present invention. [Explanation of symbols]

[0095] 1. Waste treatment plant 2 Food waste receiving hopper 3 Crushing and sorting equipment 4 Mixing tank 5 Acid Fermentation Tank 6. Methane fermentation tank 7. Imaging device 8. Information processing equipment 81 Communications Department 82 Memory section 83 Display section 84 Input section 85 Processing section 851 Acquisition Department 852 Judgment section 853 Guessing Department 854 Decision Section 9 Spectroscopic analyzer 11. Garbage 11A Food waste slurry 11B Sludge 11C Organic waste 11D Biogas 11E Digestive juice

Claims

1. an acquisition means for acquiring an image including an image of waste in a waste reception and storage facility, the image being captured by an imaging device; A determination means for classifying the captured image into a plurality of regions and determining the proportion of the waste composition corresponding to each of the regions; an estimation means for estimating the amount of biogas generated based on the ratio of each waste composition and the amount of biogas generated from each waste composition; A garbage disposal system comprising:

2. The estimation means estimates the component ratios of carbon and nitrogen contained in the waste based on the ratios of each of the waste compositions, The waste treatment system according to claim 1 , further comprising a determination means for determining a measure to be taken for the amount of biogas generated in accordance with the component ratio estimated by the estimation means.

3. The estimation means Estimating the carbon and nitrogen contents of each of the waste components based on the proportion of each of the waste components and the total weight of the waste; The waste treatment system according to claim 1 or 2, wherein the amount of biogas generated is estimated based on the carbon and nitrogen contents contained in each of the waste compositions.

4. In classifying the regions, the determination means inputs the captured image into a trained model to classify the image of the dust included in the captured image into a plurality of regions; The waste disposal system of claim 1 or 2, wherein the trained model is a model trained using teacher images, including images of other garbage, previously captured by the imaging device as input data, and label information for each of the regions associated with a specific region included in the teacher images as output data.

5. 3. The waste treatment system according to claim 1, wherein the determining means determines the proportion of each of the waste compositions corresponding to each of the regions based on the area of ​​each of the regions.

6. The acquisition means acquires information indicating an absorption spectrum of the waste in the waste reception and storage facility measured by the spectroscopic analysis means, The estimation means estimates the component ratio of carbon and nitrogen contained in the waste based on the information indicating the acquired absorption spectrum, The waste treatment system according to claim 1 , further comprising a determination means for determining a measure to be taken for the amount of biogas generated in accordance with the component ratio estimated by the estimation means.

7. The waste treatment system of claim 6, wherein the spectroscopic analysis means is positioned at a location where it can measure the vicinity of the outlet of a crushing and sorting device that crushes the waste in the receiving and storage facility and turns it into slurry, or at a location where it can measure the waste dropped into the receiving and storage facility.

8. A waste treatment system as described in claim 2 or 6, wherein the measure is a first measure related to work to reduce the amount of biogas generated from the waste, or a second measure related to work to increase the amount of biogas generated from the waste.

9. an acquisition means for acquiring information indicating the absorption spectrum of the waste in the waste reception and storage facility measured by the spectroscopic analysis means; a determining means for determining the proportion of the waste composition in the receiving and storing facility based on the information indicating the absorption spectrum; an estimation means for estimating the amount of biogas generated from the waste in the receiving and storage facility based on the proportion of each waste composition and the amount of biogas generated from each waste composition; A garbage disposal system comprising:

10. The information processing device an acquiring step of acquiring an image including an image of waste in a waste reception and storage facility, the image being captured by an imaging device; a determination step of classifying the captured image into a plurality of regions and determining the proportion of the waste composition corresponding to each of the regions; an estimation step of estimating the amount of biogas generated based on the proportion of each waste composition and the amount of biogas generated from each waste composition; A garbage disposal method characterized by carrying out the following.

11. In the information processing device, an acquiring step of acquiring an image including an image of waste in a waste reception and storage facility, the image being captured by an imaging device; a determination step of classifying the captured image into a plurality of regions and determining the proportion of the waste composition corresponding to each of the regions; an estimation step of estimating the amount of biogas generated based on the proportion of each waste composition and the amount of biogas generated from each waste composition; A garbage disposal program to run.

12. A waste treatment plant having a waste reception and storage facility, an acquisition means for acquiring an image including an image of the waste in the receiving and storage facility, the image being captured by an imaging device; A determination means for classifying the captured image into a plurality of regions and determining the proportion of the waste composition corresponding to each of the regions; an estimation means for estimating the amount of biogas generated based on the ratio of each waste composition and the amount of biogas generated from each waste composition; A waste treatment plant comprising:

Images