Processor, plant facility including processor, and processing method

Abstract

To provide a processor allowing realization of feedback of a plant operation, a plant facility including the processor, and a processing method.SOLUTION: A processor has: a mixing tank 21 of a first tank for mixing a processing object; a circulation line for extracting and circulating the processing object in the mixing tank 21 via a pump; a solid-liquid separator provided on the circulation line for separating a solid matter in the processing object to be returned into the tank; a detection part for detecting a blockage state in at least one or more sites of the circulation line and the pump; a determination part for determining whether or not it is in the blockage state based on detection data from the detection part; and an output part for outputting a control signal when the blockage state is determined by the determination part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a treatment device, a treatment method, and a plant facility equipped with the treatment device. [Background technology]

[0002] Biogas plant facilities (hereinafter also referred to as "plant facilities") include facilities that collect food waste and sewage sludge from various locations within the city, process the food waste and sewage sludge as raw materials through methane fermentation, and generate biogas, which is then used to generate electricity and other energy sources. The collected food waste and other materials to be treated are crushed and put into a tank as food waste slurry. The slurry is stirred by circulating it with a pump. When solid-liquid separation is performed using a cyclone, foreign matter and oil contained in the slurry that may become sediment other than food waste adhere to the pipes, causing clogging inside the pipes and resulting in blockages in the transfer pipes.

[0003] Conventionally, a monitoring device for observing a supply processing object has a technique of directly capturing an image from the outside using a video camera or the like (see Patent Document 1).

[0004] However, the technique disclosed in Patent Document 1 involves directly observing an image of the processing object using a monitoring device, which poses a problem in that a monitoring window must be provided.

[0005] For example, there have been proposals for technology to inspect the health of facilities and equipment by reading values ​​such as temperature, pressure, and vibration of transfer piping. However, these methods cannot inspect the load on the pump or the accumulation of foreign matter in piping installed at high places or inside walls, and therefore have the problem of not being able to detect abnormalities such as pump load or accumulation of foreign matter in piping in advance.

[0006] Furthermore, a technique for determining the deposition state of deposits has also been proposed (see Patent Document 2).

[0007] However, the proposal in Patent Document 2 is limited to estimating the state of deposits, and has the problem that it cannot be used as feedback for plant operation.

[0008] Therefore, there is a need for technology that can grasp the condition of areas that cannot be visually confirmed, such as inside transfer pipes and walls, and that can provide feedback on plant operation. [Prior art documents] [Patent documents]

[0009] [Patent Document 1] Japanese Patent Application Laid-Open No. 2002-317913 [Patent Document 2] Patent Publication No. 2021-112687 Summary of the Invention [Problem to be solved by the invention]

[0010] The present invention has been made to solve these problems, and its object is to provide a treatment device, plant equipment equipped with the treatment device, and treatment method that can grasp the condition of areas that cannot be visually confirmed, such as inside transfer pipes or pipes laid inside walls, and can realize feedback on plant operation. [Means for solving the problem]

[0011] A treatment device according to a first aspect of the present invention includes a first tank for mixing treatment objects; a circulation line that extracts the material to be treated from the first tank via a pump and circulates it; a solid-liquid separator provided in the circulation line and configured to separate solids from the material to be treated that is returned to the tank; a detection unit that detects a blockage in at least one location of the circulation line and the pump; a determination unit that determines whether or not a blocked state is present based on detection data from the detection unit; an output unit that outputs a control signal when the determination unit determines that the state is blocked; The present invention is characterized by having the following.

[0012] A plant facility according to a second aspect of the present invention is characterized by including the treatment device according to the first aspect.

[0013] A processing method according to a third aspect of the present invention uses the processing apparatus according to the first aspect, a detection step of detecting a blockage in at least one location of the circulation line and the pump; a determination step of determining whether or not a blocked state is present based on the detection data obtained in the detection step; and an output step of outputting a control signal when the determining step determines that the valve is blocked. [Effects of the Invention]

[0014] According to the present invention, it is possible to indirectly grasp the condition of areas that cannot be visually confirmed, such as inside transfer pipes or pipes laid inside walls, thereby maintaining the soundness of plant facilities and improving workability. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a schematic diagram of a plant facility equipped with a treatment device according to an embodiment of the present invention. [Figure 2] 1 is a schematic diagram showing a monitoring situation of an imaging device in a processing device of the present embodiment. FIG. [Figure 3] FIG. 1 is a schematic diagram showing a monitoring situation of a vibrometer in the processing apparatus of the present embodiment. [Figure 4] FIG. 4 is a schematic diagram showing a monitoring state of a pressure gauge in the processing apparatus of the present embodiment. [Figure 5] FIG. 10 is a schematic diagram showing a monitoring situation of a flow meter in the processing apparatus of the present embodiment. [Figure 6] FIG. 2 is a schematic diagram showing a monitoring situation on the primary side in the processing apparatus of the present embodiment. [Figure 7]FIG. 10 is a schematic diagram showing a monitoring situation on the secondary side in the processing device of the present embodiment. [Figure 8] FIG. 2 is a schematic diagram of a circulation line in the processing apparatus of the present embodiment. [Figure 9] FIG. 2 is a schematic diagram showing division of a circulation line in the processing apparatus of the present embodiment. [Figure 10] FIG. 2 is a schematic diagram showing division of a circulation line in the processing apparatus of the present embodiment. [Figure 11] FIG. 2 is a schematic diagram showing division of a circulation line in the processing apparatus of the present embodiment. [Figure 12] FIG. 2 is a schematic diagram showing division of a circulation line in the processing apparatus of the present embodiment. [Figure 13] FIG. 10 is a flow diagram of the imaging device in the processing steps. [Figure 14] FIG. 2 is a functional block diagram of the processing device. [Figure 15] FIG. 2 is a block diagram of a model generating device that performs processing to generate a determination model used in a temperature state determining unit. [Figure 16] FIG. 2 is a schematic diagram of a neural network according to the present embodiment. [Figure 17] FIG. 1 is a diagram illustrating the configuration of a dataset. [Figure 18] 10 is a flowchart illustrating a process for generating a determination model in the model generating device. DETAILED DESCRIPTION OF THE INVENTION

[0016] An embodiment of the present invention will be described in detail below with reference to the drawings. Note that the present invention is not limited to the following detailed description of the invention (hereinafter referred to as the embodiment). Furthermore, the components in the following embodiment include those that can be easily imagined by a person skilled in the art, those that are substantially the same, and those that are within the so-called equivalent range. Furthermore, the components disclosed in the following embodiment can be combined as appropriate. In the embodiments of this specification, the same components are denoted by the same reference numerals throughout. Note that this embodiment is merely an example that embodies the configuration of the present invention, and various design changes can be made without departing from the scope of the claims.

[0017] FIG. 1 is a schematic diagram of a biogas plant facility (hereinafter also referred to as "plant facility") according to this embodiment of the present invention. FIG. 2 is a schematic diagram showing the monitoring status of an imaging device in a treatment device of this embodiment. FIG. 3 is a schematic diagram showing the monitoring status of a vibrometer in a treatment device of this embodiment. FIG. 4 is a schematic diagram showing the monitoring status of a pressure gauge in a treatment device of this embodiment. FIG. 5 is a schematic diagram showing the monitoring status of a flow meter in a treatment device of this embodiment. FIG. 6 is a schematic diagram showing the monitoring status on the primary side in a treatment device of this embodiment. FIG. 7 is a schematic diagram showing the monitoring status on the secondary side in a treatment device of this embodiment. FIG. 8 is a schematic diagram of a circulation line in a treatment device of this embodiment. FIG. 9 is a schematic diagram of the division of the circulation line in a treatment device of this embodiment. FIG. 10 is a schematic diagram of the division of the circulation line in a treatment device of this embodiment. FIG. 11 is a schematic diagram of the division of the circulation line in a treatment device of this embodiment. FIG. 12 is a schematic diagram of the division of the circulation line in a treatment device of this embodiment. FIG. 13 is a flow diagram of an imaging device in a treatment process. FIG. 14 is a functional block diagram of the treatment device. Fig. 15 is a block diagram of a model generation device that performs processing to generate a judgment model used in a temperature state judgment unit. Fig. 16 is a schematic diagram of a neural network of this embodiment. Fig. 17 is a configuration diagram of a data set. Fig. 18 is a flowchart related to processing to generate a judgment model in the model generation device.

[0018] For example, the biogas plant facility collects food waste and sewage sludge from various parts of the city, processes them as raw materials through methane fermentation, and generates biogas, which is then used to generate electricity and other energy sources. As shown in FIG. 1, in a biogas plant facility 100 according to this embodiment, materials to be treated 11 (11A to 11D) are collected from within the city by material to be treated collection vehicles 12 (12A to 12D). For example, food waste and marine waste 11A are transported to the treatment facility in a garbage truck 12A, sewage sludge 11B in a sewage sludge transport truck 12B, paper 11C in a paper transport truck 12C, and waste cooking oil 11D in a garbage truck 12D. Note that these are just examples, and the types of materials to be treated are not limited to these.

[0019] In the biogas plant facility 100, of these transported materials to be treated 11, food waste and marine waste 11A are fed into a food waste receiving hopper 13 via a first line L1, where they are crushed and sorted in a crushing and sorting device 14 installed below the food waste receiving hopper 13, and sent as food waste slurry 11a to a mixing tank 21 via a food waste slurry transfer pump 15. The crushing and sorting device 14 also separates bags containing food waste and solids larger than a predetermined size as materials unsuitable for treatment, and the sorted materials unsuitable for treatment 14a are discharged outside the system via a sixth line L6.

[0020] The sewage sludge 11B is sent via a sewage sludge receiving hopper 16 to a sewage sludge adjustment tank 18 through a second line L2. The paper 11C passes through a shredder 17 and is sent to a sewage sludge adjustment tank 18 via a third line L3. The waste edible oil 11D is sent directly to the sewage sludge adjustment tank 18 via a fourth line L4.

[0021] In the sewage sludge adjustment tank 18, sewage sludge adjustment tank slurry (also called sewage sludge adjustment tank overflow water) 19 is sent to a mixing tank 21 via a fifth line L5.

[0022] The mixing tank 21 is connected to a circulation line (first circulation line L) that extracts and circulates the input material 11 in the tank via a pump P. 11 , Second circulation line L 12 , mixing tank circulation line L 13 ) is provided. The sewage sludge adjustment tank slurry 19 from the sewage sludge adjustment tank 18 and the food waste slurry 11a are mixed into the mixing tank 21, and the resulting mixture is passed through a first circulation line L connected to the mixing tank 21. 11 and the second circulation line L 12 and mixing tank circulation line L 13 The mixture is gradually stirred and mixed by forcibly generating a circulating flow using a pump P that connects these components. This circulating mixed flow promotes stirring and mixing inside the mixing tank 21.

[0023] Here, the circulation time is preferably about 3 hours to thoroughly agitate and mix the contents in the tank, but this may vary depending on the season, the type of material to be treated, etc., and is not limited thereto.

[0024] In the circulation in this mixing tank 21, solid-liquid separation is performed using a pump P that generates a circulating flow and a cyclone (solid-liquid separator) 30 that is a solid-liquid separation means, and unsuitable materials 30a such as sand and shells are discharged outside the system via a seventh line L7.

[0025] The pump P in this embodiment is a pump capable of transporting slurry.

[0026] Then, the mixing tank slurry 20 circulated in the mixing tank 21 for, for example, about 3 hours becomes the mixing tank slurry 20a after passing through the cyclone, which is the object of fermentation treatment. The mixing tank slurry 20a after passing through the cyclone is sent to the acid fermentation tank 22, which is the second tank in the pretreatment stage of methane fermentation, through the 14th line L by closing the switching valve V2 of the mixing tank circulation line and opening the switching valve V1. 14 is transported via

[0027] The acid fermentation tank 22 adjacent to the mixing tank 21 is used for pre-fermentation of methane fermentation. After pre-fermentation, the slurry 22a in the acid fermentation tank is transferred to the 15th line L 15The wastewater is sent to a methane fermentation tank 24 installed outdoors, where it is subjected to methane fermentation. The methane fermentation process produces biogas, which is then used effectively as energy for power generation, etc.

[0028] When the mixing tank slurry 20 is stirred and mixed in the mixing tank 21, it is circulated and transferred by the pump P, and when the solid-liquid separation is performed by the cyclone, for example, the second circulation line L 12 In the narrow part (not shown) where the ejector effect is generated, clogging of the piping is likely to occur due to clogging, etc., and the second circulation line on the secondary side of the pump (between the pump P and the cyclone (solid-liquid separator) 30) 12 There is a problem that blockage occurs in the

[0029] To address this problem, this embodiment installs a detection unit 40 (imaging device 40A, vibration meter 40B, pressure meter 40C, flow meter 40D) described below to detect abnormal conditions that could cause blockage in advance. In this case, as will be described later, in data processing, detection accuracy may be improved by machine learning (artificial intelligence (AI)).

[0030] <Imaging device 40A> 2 is a schematic diagram showing a monitoring situation using the imaging device 40A in the plant facility 100 of this embodiment. In FIG. 2, the symbol "▲" schematically indicates the imaging situation. The imaging device 40 monitors a portion where blockage is likely to occur at a fixed point. The imaging may be performed by scanning rather than at a fixed point. For example, "scanning" means capturing an image of a specific location for a predetermined period of time using imaging device 40A.

[0031] In this embodiment, the imaging device (hereinafter simply referred to as "imaging device") 40A, which is the detection unit that monitors the temperature state, is preferably a thermographic camera. This thermographic camera is a device that captures the surface temperature of the desired measurement location from the outside without contact. The imaging device 40 can be positioned to measure each location, such as by taking an overall image, individual images, or continuous images using a drone or the like.

[0032] This thermography camera 40A is installed on the first circulation line L on the primary side of the pump P. 11 , the main body of the pump P, the second circulation line L on the secondary side of the pump P 12 Temperature data is collected from the piping line. It is preferable to collect the temperature data by measuring an area (range) rather than by spot measurement. In spot measurement, which captures a point, the measurement range is narrow, and it is possible that only the spot measurement point will have an abnormal temperature. In so-called spots, there can be large errors in temperature differences. When capturing a wide area, with the idea that there are many spots forming an area, measurements are taken over a wide range, improving accuracy.

[0033] Here, as an example, the second circulation line L on the secondary side of the pump P 12 If a blockage occurs in a narrow section or on the inner surface of the piping, the fluid near the pump P will be affected by the high-temperature fluid that has been overheated inside the pump. As a result, the high-temperature state of the piping near the pump P will be captured by the imaging device 40A.

[0034] As another example, the first circulation line L on the primary side of the pump P 11 When clogging occurs, the fluid (slurry) temperature of mixing tank slurry 20 from mixing tank 21 becomes stagnant in the piping and becomes equilibrated with the room temperature. As a result, imaging device 40A captures an image of the fluid temperature that has become equilibrated between the fluid (slurry) temperature of mixing tank slurry 20 and room temperature. Thereafter, due to the influence of overheating of pump P, imaging device 40A captures an image of a high temperature state. By flushing and backwashing the inside of the pipe, the obstructions are discharged out of the pipe. For example, if the secondary side is blocked, high-pressure fluid cleaning water (water used for flushing and backwashing) is discharged from near the cyclone 30 to the secondary side pipe L 12 For example, if the primary side is blocked, the high-pressure fluid cleaning water will be discharged from the primary side piping L 11 For example, if the secondary side pipe or the primary side pipe is clogged, the high-pressure fluid cleaning water is discharged from the vicinity of the cyclone 30 through the secondary side pipe L 12 through the bypass piping that bypasses the pump P, and then through the primary piping L 11 The pump operation is stopped and backwashing is performed. If the blockage is not resolved by backwashing, the pump primary side piping L 11 In the case of pump blockage, flushing is also performed by sending high-pressure fluid cleaning water from the pump to the cyclone 30 and discharging the blockage from the cyclone. 11 High pressure fluid passes through pump P and into secondary piping L 12 When flushing is performed, the cyclone 30 is also cleaned by passing high pressure fluid through it.

[0035] In addition, in the imaging device 40A, one camera captures the first circulation line L 11 and the secondary side second circulation line L 12 Furthermore, one camera may be used to monitor both the first circulation line L on the primary side. 11 and the secondary side second circulation line L 12 An overall monitoring camera (not shown) for monitoring the entire system relating to the pump P and the mixing tank slurry 20 may be separately installed.

[0036] Alternatively, a drone (not shown) equipped with the image capturing device 40A may be used to perform multi-faceted measurements, or a wearable camera may be used to capture images.

[0037] By using the imaging device 40A, signs of a clogged state can be monitored from outside the piping, which has the advantage that the imaging device 40A can be retrofitted to existing equipment.

[0038] As an example of the fluid temperature of the mixing tank slurry 20 under normal conditions using the imaging device 40A, a situation will be described in which the temperature of the mixing tank slurry 20 in the mixing tank 21 is approximately 35°C in summer, approximately 25°C in winter, and approximately 30°C in spring and autumn.

[0039] For example, when a blockage occurs in the narrow section just before the cyclone (solid-liquid separator) 30, the fluid temperature (approximately 35°C in summer, approximately 25°C in winter) is compared with the room temperature (approximately 20°C in summer, approximately 10°C in winter), and when the pipe is clogged, the fluid temperature at the blockage point will approach the low temperature of room temperature of approximately 10°C over time in winter.

[0040] For example, if the pump P continues to overheat due to complete blockage, the fluid inside the pump may reach 60°C or higher. For example, in winter, fixed-point observations are made using a thermographic camera (normal winter conditions: fluid temperature approximately 25°C). In this embodiment, the normal state is 20°C to 30°C, which is within a range of ±5°C in winter. If the detected temperature is below 20°C or above 30°C, a "warning" is issued.

[0041] Therefore, detection data indicating that the detected temperature in winter is below 20°C or above 30°C, as captured by the thermographic camera, is sent to the determination unit 210, and when the determination unit 210 determines that a blockage has occurred, a predetermined control signal is sent from the output unit 220. The control signal is appropriately selected to be optimal depending on the situation. For example, the control signal may illuminate a rotating warning light (not shown) as an alarm unit 230 provided in the treatment device, or sound an alarm. The control signal may also automatically open and close a valve (not shown), and issue instructions to transition to a flushing operation or a backwashing operation. The reason why a thermographic camera is used here is that it can reveal the temperature distribution. For example, it is possible to determine which parts are hot or cold, making it easier to investigate blockages.

[0042] An example of the detection unit 40 other than the imaging device 40A will be described below. <Vibration meter 40B> 3 is a schematic diagram showing a monitoring situation using a vibration meter 40B in the plant facility 100 of this embodiment. As shown in FIG. 3, in this embodiment, the vibration meter 40B is installed outside the main body of the pump P to monitor the vibration of the pump P, the first line L of the primary piping, and the like. 11 , the second line L of the secondary piping 12 Monitor the vibration status of the

[0043] As shown in FIG. 3, the vibration meter 40B is connected to the first circulation line L 11 The sensor 40B-1 is connected to the pump P body, the sensor 40B-2 is connected to the pump P main body, and the second circulation line L of the secondary piping 12 The sensor 40B-3 is installed at the upstream side of the pipe, the pump, and the downstream side of the pipe to collect vibration data when the pipe is blocked.

[0044] Regarding countermeasures, the degree of the problem is judged, and depending on the degree, cautions, warnings, automatic cleaning (flushing and backwashing), emergency stop, etc. are implemented.

[0045] Regarding the degree of blockage, threshold registration based on past knowledge or threshold proposals using machine learning is used.

[0046] The vibration meter 40B can be attached to the outside of a pipe for external monitoring, and vibration instrumentation equipment can be retrofitted.

[0047] Continuous data acquisition allows for the detection of occlusion events and allows data to be collected when an occlusion occurs.

[0048] As shown in FIG. 3, the sensors 40B-1 to 40B-3 of the vibration meter 40B are installed in the first circulation line L 11, pump P body, secondary piping second circulation line L 12 However, additional locations may be added as needed. This allows the degree of blockage to be confirmed based on the degree of vibration of the pump P, the primary piping, and the secondary piping.

[0049] <Pressure gauge 40C> FIG. 4 is a schematic diagram showing a monitoring situation using a pressure gauge 40C in the plant facility 100 of this embodiment. Pressure gauge 40C is connected to the second circulation line L on the secondary side of the pump. 12 It measures the pressure of the moving fluid in the

[0050] An example of monitoring using pressure gauge 40C will be explained. This can be determined using a pressure gauge installed on the secondary side near pump P. Pressure abnormalities tend to differ depending on whether the pressure is lower than normal (normal) or higher than normal (normal). It can be determined that a pressure lower than normal (normal) indicates a blockage on the primary side, and a pressure higher than normal (normal) indicates a high possibility of blockage on the secondary side.

[0051] If the pressure is higher than normal, 12 If the pressure is lower than normal, it is judged to be a blockage in the primary piping L. 11 It is determined that there is a blockage. For example, if the normal pressure is 0.13 to 0.17 MPa, a secondary blockage can be determined if the pressure is 0.18 MPa or higher. On the other hand, a primary blockage can be determined if the pressure is 0.12 MPa or lower.

[0052] <Flowmeter 40D> FIG. 5 is a schematic diagram showing a monitoring situation using a flow meter 40D in the plant facility 100 of this embodiment.

[0053] The flow meter 40D is connected to the second circulation line L on the secondary side of the pump. 12It measures the flow rate at the point where the valve is opened. The difference between the set flow rate and the flow rate value determines the degree of blockage, such as a tendency to blockage or complete blockage. As a result of this determination, it issues a warning, issue an automatic cleaning (flushing / backwashing), or perform an emergency stop.

[0054] The mixing tank slurry 20 flowing into the pump is fed through the primary side piping L 11 , pump P body, secondary side piping L 12 Blockages within the pipe also reduce the pump's performance and decrease the flow rate. For example, if sediments accumulate in the pipe, they impede the flow, preventing the pump from suctioning and reducing the flow rate. As a result, the pump is unable to maintain the appropriate pressure, preventing the fluid from moving properly and resulting in a decrease in flow rate. In this way, the accuracy of the judgment can be improved by combining the pump's capacity with not only pressure but also flow rate. Furthermore, if the flow rate becomes zero, it is judged to be a complete blockage. If the actual measured flow rate is less than the set flow rate, it can be judged to be on the verge of blockage.

[0055] Sudden changes in pump pressure affect the movement of fluid inside the pump, causing vibration. This vibration also has a negative effect on pump performance, making it unstable. It becomes difficult for the pump to deliver a constant flow rate, and unstable operation causes increased vibration, gradually affecting the durability and efficiency of the machine.

[0056] In addition to pump vibration, pump diagnosis based on the pump current value may also be included. For example, when a centrifugal pump is used, a force is applied to the fluid by an impeller inside the pump casing, and the fluid is pumped by centrifugal force, so it may be possible to determine whether a blockage has occurred by detecting an abnormal current value. For example, if the current value is less than half of the rated value, it is determined to be "idle operation," and if the current value is about half of the rated value, it is determined to be "blocked operation."

[0057] Although it is possible to determine whether a blockage exists based on each of these detected data individually, the accuracy of the determination can be improved by, for example, combining the data from the imaging device 40A and the vibration meter 40B to determine the presence or absence of a blockage and the location of the blockage. Furthermore, the accuracy of the degree of blockage can be further improved by combining data from the pressure meter 40C and / or the flow meter 40D. Combining multiple means leads to improved measurement accuracy. In other words, by complementing other measurement data rather than using only one piece of data, it is possible to obtain data with higher overall reliability.

[0058] The information detected by these detection units 40 may be subjected to machine (AI) learning based on the overall judgment results, using an algorithm described below, to create a learned model, and based on this AI judgment, the information may be fed back to the operation of flushing or backwashing (internal pipe cleaning control).

[0059] By installing the detection unit 40 according to this embodiment, abnormalities such as blockages in the pipes that cannot be found by visual inspection can be detected early, and measures can be taken in advance.

[0060] In this embodiment, even if visual confirmation is not possible, it is possible to indirectly check the blockage state. Therefore, when it is desired to remove deposits and sediments inside the pipe before the pipe is completely blocked, the efficiency of the work can be improved because in the past, flushing and backwashing work had to be carried out blindly based on the prediction that the pipe might be prone to blockage.

[0061] By installing this detection unit 40, work can be improved through automation. Furthermore, the pipe cleaning effect reduces the load on the pump, reduces the degree of wear and tear on pump parts, and extends the life of the pump, thereby maintaining the health of plant equipment over the long term. Therefore, according to the present invention, it is possible to indirectly grasp the condition of areas that cannot be visually confirmed, such as inside transfer pipes or walls, thereby maintaining the integrity of plant equipment and improving workability.

[0062] 6 and 7, an example will be shown in which the control unit 200 judges various data detected by the detection unit 40, performs machine learning, and issues control instructions for flushing, backwashing, etc. However, the present invention is not limited to these processes.

[0063] FIG. 6 is a schematic diagram showing the monitoring situation on the primary side of the biogas plant facility 100 in this embodiment. As shown in FIG. 6, various measurement data (imaging data from the imaging device 40A, vibrometer 40B, pressure meter 40C, and flow meter 40D) are sent to a control unit 200 using various detection units 40 (imaging data from the imaging device 40A, vibrometer data from the vibrometer 40B, pressure meter data from the pressure meter 40C, and flow rate data from the flow meter 40D). The control unit 200 executes a control process described later, and, for example, after machine learning and performing AI judgment, determines whether the first circulation line L on the primary side is in a high-pressure state. 11 If the control unit 200 determines that the blocked portion is a clogged portion, it issues an instruction to control cleaning of the inside of the piping, for example. Specifically, the control unit 200 issues an instruction to a cleaning operation unit (not shown) to automatically open and close a valve (not shown) and to transition to a flushing operation or a backwashing operation. Then, based on instructions from the control unit 200, the cleaning operation unit performs a flushing operation and a backwashing operation toward the blocked area. The control unit 200 may also have the function of a cleaning operation unit, and may be configured to perform flushing and backwashing operations toward the blocked area. Furthermore, after receiving instructions, the pump P is automatically stopped and a valve (not shown) is automatically opened and closed to perform flushing and backwashing operations, and the imaging unit 40A determines whether any obstructions or deposits on the pipes have been removed, and if the result is an improvement (normal), the valve (not shown) is automatically opened and closed to return to its original state, the pump P starts operating automatically, and an instruction is given to move from the flushing and backwashing process to the normal process. If it is determined that no improvement has been made, the pump P may continue to be stopped and an abnormality may be issued.

[0064] FIG. 7 is a schematic diagram showing the monitoring situation on the secondary side in the biogas plant facility 100 in this embodiment. As shown in FIG. 7, various measurement data (imaging data from the imaging device 40A, vibrometer 40B, pressure meter 40C, and flow meter 40D) are sent to a control unit 200 using various detection units 40 (imaging data from the imaging device 40A, vibrometer data from the vibrometer 40B, pressure meter data from the pressure meter 40C, and flow rate data from the flow meter 40D). The control unit 200 executes a control process described later, and, for example, after machine learning and performing AI judgment, determines whether the secondary circulation line L 12 If the control unit 200 determines that the blocked portion is a clogged portion, it issues an instruction to control cleaning of the inside of the piping. Specifically, the control unit 200 issues an instruction to a backwashing operation unit (not shown) to automatically open and close a valve (not shown) and to transition to a flushing operation and a backwashing operation. Furthermore, after receiving instructions, the pump P is automatically stopped and a valve (not shown) is automatically opened and closed to perform flushing and backwashing operations, and the imaging unit 40A determines whether any obstructions or deposits on the pipes have been removed, and if the result is an improvement (normal), the valve (not shown) is automatically opened and closed to return to its original state, the pump P starts operating automatically, and an instruction is given to move from the flushing and backwashing process to the normal process. If it is determined that no improvement has been made, the pump P may continue to be stopped and an abnormality may be issued.

[0065] The algorithm according to this embodiment will now be described. Fig. 8 is a schematic diagram of a circulation line in the processing apparatus of this embodiment. Figs. 9 to 12 are schematic diagrams of division of the circulation line in the processing apparatus of this embodiment. Fig. 13 is a flow diagram of the imaging device in the processing step.

[0066] As shown in FIG. 8, image data of a piping line including the primary side, pump, and secondary side acquired by an imaging device (thermography camera 40A) is divided into predetermined ranges (for example, blocks of an appropriate size). As a method for dividing into this predetermined range, for example, as shown in FIG. 9, the first circulation line L 11 The pump P is also divided into five blocks (B6). Second circulation line L on the secondary side between pump P and cyclone (solid-liquid separation device) 30 12 Divide into 11 blocks (B7 to B17). The block size is variable depending on the pipe diameter of the line and the characteristics of the fluid of the mixture 20 to be treated. For example, if the material being transferred contains a lot of fine particles, a smaller block may be used, and if the material being transferred contains a lot of larger solids, a larger block may be used.

[0067] In this embodiment, the colors of the image data of each of the blocks B1 to B17 are converted into numerical values. When converting the color to a numerical value, the color within the block may be averaged to obtain a measurement value, or the color at the center of the block may be converted to a numerical value. This sets the representative temperature for each block.

[0068] Next, the representative temperatures for each block are compared. For comparison, if there is no deposit inside the pipe (no blockage), the temperature will be the same. As a result, the temperatures of blocks B1 to B17 are approximately the same (note that, since there is measurement error, a certain range is set to be within the allowable range).

[0069] These rules ensure that the numbers for each block from block B1 to block B17 are almost the same. If the values ​​for each block are almost the same, it can be determined that there is no blockage.

[0070] On the other hand, if there is adhesion inside the piping (blockage or blockage initiation), the heat from the pump will be transferred, and the temperature will be measured as high, especially in the blocks closest to the pump. For example, the first circulation line L on the primary side 11 If there is a blockage at block B3, the temperature of blocks B4 to B7 will be higher than that of blocks B1 to B2.

[0071] Also, for example, the second circulation line L 12 If there is a blockage at block B8, the temperature of blocks B5 to B8 will be higher than that of blocks B9 to B10.

[0072] The above comparison is carried out at regular intervals, and the comparison frequency is set according to the characteristics of the fluid.

[0073] If the number of times and the number of blocks are not the same, a countermeasure is taken. The number of times is set according to the characteristics of the fluid.

[0074] Regarding countermeasures, the location and degree of blockage are determined based on the number of times, boundary of change, and amount of change, and a caution, warning, automatic cleaning (flushing, backwashing), emergency stop, etc. are performed. When cleaning the pump P, maintenance of the pump P can be performed by switching to a bypass line (not shown). Furthermore, if multiple pumps are prepared, the blocked pump can be replaced with a new pump. The blocked area is the first circulation line L 11 If it is determined that 11W Washing water is introduced through the first circulation line L 11 Washing water may be supplied to the In addition, the blocked area is the second circulation line L 12 If it is determined that 12W Washing water is introduced through the second circulation line L 12 Washing water may be supplied to the In addition, by switching the cleaning line with a valve, the first circulation line L 11 or secondary circulation line L 12 Washing water may be supplied to the The clogging matter removed by the washing water is discharged from a discharge port near a pump (not shown) or discharged into the mixing tank 21 through a discharge line (not shown).

[0075] For the location and degree of blockage, thresholds are registered based on past knowledge or thresholds proposed using machine learning.

[0076] The algorithm will be explained using the vibrometer 40B. Vibrations of the primary piping, pump body, and secondary piping are measured in batches at timings set according to the characteristics of the fluid.

[0077] The influence of floor vibrations may be eliminated by subtracting the readings from a vibration meter separately installed on the floor. At each location, if the value is different from the previous vibration value, it is determined that an abnormality such as a blockage has occurred. However, since there is also measurement error, multiple measurement values ​​are compared and action is taken if there is a consecutive difference. The number of comparisons is set according to the characteristics of the fluid.

[0078] And the first circulation line L on the primary side 11 When the vibration occurs, the second circulation line L 12 When the pump P is vibrating, the location of the blockage is determined by a comprehensive judgment based on the degree of vibration at the three locations.

[0079] When dealing with this issue, the location and degree of blockage are determined, and a warning, automatic cleaning (flushing and backwashing) or emergency stop is performed. For the location and degree of blockage, thresholds are registered based on past knowledge or thresholds proposed using machine learning.

[0080] For machine learning, the first circulation line L 11 The degree of vibration of the first circulation line L 11 It may also be determined that the second circulation line L is blocked. 12 It may be determined that the secondary side is blocked based on the degree of vibration of the pump body. It may also be determined that the pump body P is blocked or that the pump P is abnormal based on the degree of vibration of the pump body. It may also be possible to determine the location of the blockage by comprehensively determining the degree of vibration of the primary side, secondary side, pump body, and the pump body.

[0081] The above has described the detection unit 40 as determining whether a blockage occurs using the thermographic camera of the imaging device 40A and the vibration meter 40B, but the same applies to determining whether a blockage occurs using the pressure meter 40C or the flow meter 40D.

[0082] FIG. 14 is a functional block diagram of the biogas plant facility 100. The detector 40 sends the captured image to the controller 200, which converts the image into image data. The control unit 200 is also connected to a determination unit 210 and transmits the converted image data to the determination unit 210 .

[0083] The determination unit 210 is configured by a calculation device such as a central processing unit (CPU), a graphics processing unit (GPU), or a tensor processing unit (TPU). The determination unit 210 determines the first circulation line L based on the image data acquired by the control unit 200. 11 , Second circulation line L 12 The temperature condition of the piping of the pump P, etc. is determined.

[0084] The determination unit 210 is connected to the first circulation line L 11 , Second circulation line L 12 The temperature determining unit 210A determines whether the temperature of the pump P and the like is normal.

[0085] The embodiment further includes a vibration determination unit 210B that determines the vibration state, a pressure determination unit 210C that determines the pressure state, and a flow rate determination unit 210D that determines the flow rate state. By determining whether the temperature state is normal, it is possible to indirectly determine whether there is an abnormality such as a blockage in the pipes.

[0086] The division process of the image blocks B1 to B17 will now be described in detail with reference to FIGS. The image data acquired by the control unit 200 is divided into grid-like image blocks. The determination unit 210 extracts feature amounts contained in the image data acquired by the control unit 200 and can determine the temperature state of the pipe. Note that the image blocks B1 to B17 are square, but this is not limited to this.

[0087] The temperature determination unit 210A may include a fluid state determination unit that determines whether the flow of fluid inside the piping is normal using a machine learning determination model based on features of the temperature transition trends analyzed by analyzing the image data.

[0088] If the temperature is within a predetermined cleaning temperature range, it is considered to be in a "normal state," and if it is in any other state, it is considered to be in an "abnormal state."

[0089] 14 also includes a storage unit DB, and stores in the storage unit DB a determination model used in the determination process of image data by the determination unit 210. The storage unit DB also stores a control signal table used to determine the control signal to be output by the output unit 220. Furthermore, the recording unit DB stores various data such as image data and a program including various commands to be executed by the determination unit 210. This program may be stored and installed on a non-transitory computer-readable recording medium such as a CD-ROM, flash memory, or SSD memory.

[0090] The output unit 220 is connected to the determination unit 210 via the control unit 200, and outputs a control signal based on the determination result of the determination unit 210. The output unit 220 may be connected to a control panel in a control room, receive operation input via the control panel, and output a control signal in accordance with the operation input. The output unit 220 may also perform display processing of the temperature state of the pipes on the control panel (not shown).

[0091] 15 is a block diagram of a model generation device 70 that performs processing to generate a determination model used in the determination units (temperature state determination unit 210A, vibration determination unit 210B, pressure determination unit 210C, and flow rate determination unit 210D). The model generation device 70 includes a dataset acquisition unit 71 that acquires a dataset, a model generation unit 72, a dataset storage unit 73, and a trained model storage unit 74. The data set acquisition unit 71 is preferably configured to acquire temperature state feature amounts or color distribution data as a data set based on image data acquired by the control unit 200. The data set may also be its change feature amounts.

[0092] A general-purpose computer can be used as the model generation device 70. The model generation device 70 includes, as hardware components, an arithmetic unit such as a CPU, a main storage unit such as a RAM, an auxiliary storage unit, a communication unit, an input / output unit, and the like.

[0093] The determination unit 210 and the model generation device 70 are configured to be able to communicate data via wire or wirelessly. The determination unit 210 may also be configured to include the functional components of the model generation device 70 (dataset acquisition unit 71, model generation unit 72, dataset storage unit 73, and trained model storage unit 74).

[0094] FIG. 16 is a schematic diagram of the neural network of this embodiment. In this embodiment, the machine learning algorithm may employ a neural network N. As shown in FIG. 16, the neural network N has an input layer N1, a hidden layer N2, and an output layer N3. Each layer is composed of a plurality of neurons having an activation function. The input layer N1 receives input of input data from a dataset. The input layer N1 is composed of a plurality of neurons according to the input data, and outputs a calculation result for the input data to the hidden layer N2. The hidden layer N2 is composed of one or more layers, each having a plurality of neurons. The hidden layer N2 receives input of the calculation result from the input layer N1, and further outputs the calculation result for the input to an adjacent layer in the hidden layer N2 or to the output layer N3. The output layer N3 outputs an estimated value according to the input from the hidden layer N2. The accuracy of determining the output data relative to the input data can be improved by adjusting the coefficients of each neuron so as to reduce the error between the estimated value of the output layer N3 and the output data of the dataset.

[0095] The machine learning algorithm is not limited to a neural network, and a regression analysis model, a support vector machine, a k-nearest neighbor method, a decision tree model, etc. may also be adopted.

[0096] FIG. 17 shows an example of the configuration of a dataset used for machine learning of a decision model. The data sets are configured as shown in Figures 17(a) and 17(b). Figure 17(a) is a data set for image data, color distribution data, and status, and Figure 17(b) is a data set for image data and control signals.

[0097] In the temperature determination unit 210A, color distribution data generated based on image data or a change feature amount of color distribution data is used as input data, and the piping state is used as output data.

[0098] The data set may have image data as input data and a control signal as output data, as shown in FIG. 17(b).

[0099] The output data may also be a numerical value based on how much the temperature differs from the normal pipe temperature state. In this case, a threshold value is pre-recorded in the storage unit DB, and when the determination result exceeds the threshold value, the output unit 220 outputs a control signal corresponding to the threshold value. Alternatively, the determination unit 210 may transmit the determination result according to the threshold value recorded in the storage unit DB to the output unit 220, and the output unit 220 may output a control signal.

[0100] FIG. 18 is a flowchart showing the process of generating a determination model in the model generating device 70.

[0101] Step S11: The dataset acquisition unit 71 acquires a dataset and stores it in the dataset storage unit 73. The dataset acquisition unit 71 preferably sets the image data acquired by the control unit 200 as input data for the dataset. The dataset storage unit 73 may be configured to store in advance list data of temperature states and control signals as output data for the dataset, and the dataset acquisition unit 71 may be configured to accept selection of output data corresponding to the image data from the list data and determine the dataset.

[0102] Step S12: The model generating device 72 executes machine learning processing of the model using the datasets stored in the dataset storage unit 73. The number of datasets used in the machine learning processing is not particularly limited.

[0103] Step S13: The model generation device 72 completes the machine learning process in step S12 and generates a trained model, thereby generating a judgment model, storing the model in the trained model storage unit 74, and then ends the process.

[0104] The determination model stored in the learned model storage unit 74 is stored in the storage unit DB, and the determination unit 210 can use the determination model to determine the state of the determination unit 210 based on the image data.

[0105] The determination models may be generated in accordance with the temperature determination section 210A provided in the determination section 210 and the other determination sections 210B to 210D.

[0106] The output unit 220 outputs a control signal based on the determination result obtained by the determination unit 210. The control unit 200 is configured so that the threshold settings and whether or not to send a control signal to each output destination can be changed using a control panel.

[0107] The output section 220 includes a data set summarizing the appropriate control signals that the output section 220 outputs based on the temperature condition data that is output.

[0108] <Imaging step (S01)> As shown in FIG. 13, in step S01, the imaging device 40A acquires an image by thermography of the surface of the piping and pump in the imaging area, converts the acquired image into image data, and causes the determination unit 210 to acquire the image data. Here, the imaging method used by imaging device 40A may be continuous imaging, a combination of large images, or a full image taken from a distance, but it is advisable to select a method that provides high accuracy.

[0109] <Blocking process> In step S02, as shown in FIG. 9, the piping route is divided into a plurality of blocks, and the temperature distribution obtained by the thermography camera is divided into a plurality of blocks (B1 to B17). It is preferable to set the block size appropriately depending on the characteristics of the moving fluid. In order to determine this appropriate size, data analysis may be performed using the initial learning period. FIG. 10 shows a state in which blocks B1 to B17 are arranged from the entrance side to the exit side.

[0110] <Block-by-block averaging process> In step S03, the colors in each of the blocks B1 to B17 are averaged and stored in the database DB. In FIG. 11, the colors in each of the blocks B1 to B17 are averaged, or the median color of each block is used as the representative color. If there is no temperature difference with the adjacent block after averaging, it is recorded and learned as a normal value (no blockage). Note that the degree of change that is considered to be outside the normal range (an abnormal value) is learned during the initial learning period based on data already learned by humans or other facilities.

[0111] For example, in cold regions or hot regions where there are large annual temperature and environmental changes, an initial learning period of a certain length of time (for example, about one year) may be required.

[0112] <Comparative review process> In step S04, the color of the result of the previous measurement and the result of the current measurement are compared to determine whether there is a temperature difference. It should be noted that the comparison may be made not only with the result of the previous measurement but also with the result of the measurement before the previous one.

[0113] <Judgment process 1> In step S05, if it is determined that there is no temperature difference, the process waits for a predetermined time, then returns to step S01, and the image measurement operation is performed again. As shown in Figure 12, if a temperature difference occurs between adjacent blocks, it is stored in a buffer as a possible anomaly and recorded and learned. Figure 13 is a schematic diagram of an example of secondary-side blockage. Comparing block B12 and block B13 indicates that B13 has a higher or lower temperature, and comparing block B13 and block B14 indicates that B14 has a higher or lower temperature. Comparing block B14 and block B15 indicates that B15 has a higher or lower temperature, comparing block B15 and block B16 indicates that B16 has a higher or lower temperature, and comparing block B16 and block B17 indicates that B17 has a higher or lower temperature. The piping is divided into blocks, and if a temperature difference occurs between adjacent blocks, it is determined to be an anomaly within the piping, such as a blockage or deposits. By making the above determination, the previous color information and the current color information are compared.

[0114] <Judgment process 2> In step S06, it is determined whether the number of times that it has been determined that there is a temperature difference as a result of comparing the previous and current colors in step S04 has exceeded a predetermined number. If it is determined that the predetermined number of times has not been exceeded, the process proceeds to step S05, and after waiting for a predetermined time, the process returns to step S01 and performs the image measurement operation again. After waiting for a predetermined time, if it is determined that the number of times that a temperature difference has been determined exceeds a predetermined number, processing becomes necessary, and the process proceeds to step S07, where a threshold determination is performed.

[0115] <Judgment process 3> If it is determined in step S06 that the predetermined number of times has been exceeded, the process proceeds to step S07, where a threshold determination is performed. The threshold determination determines the location and degree of blockage based on various parameters (number of times there is a temperature difference, temperature change boundary, amount of temperature change, etc.). This criterion is set during initial learning.

[0116] <Threshold judgment 1> If it is determined that there is no problem as a result of the threshold judgment, no further processing is required, and the process proceeds to step S05, where after waiting for a predetermined time, the process returns to step S01 and performs the image measurement operation again. <Threshold judgment 2> If it is determined that there is a problem as a result of the threshold judgment, processing is required.

[0117] <Treatment implementation process> If it is determined after waiting for a predetermined time that there is a problem with the threshold judgment, processing is required, and the process proceeds to step S08, where measures are taken based on the judgment result.

[0118] Regarding countermeasures, the location and degree of blockage are determined and feedback control is performed. Specifically, the determination result made by the determination unit 210 (temperature determination unit 210A) is fed back to the control unit 200, and the control unit 200 performs control such as "notification (caution / warning)", "automatic cleaning by automatic valve opening / closing (flushing / backwashing)", "stop (pump emergency stop)" according to the determination result. The countermeasure level is set in the initial learning.

[0119] For the location and degree of blockage, thresholds are registered based on past knowledge or thresholds proposed using machine learning.

[0120] Based on such an algorithm, machine learning is performed based on the overall judgment results, a trained model is created, and based on the machine learning judgment, the location and degree of blockage are fed back to control the operation of flushing and backwashing (pipe cleaning control), etc. This allows the plant equipment of this embodiment to operate soundly.

[0121] Here, we will provide additional information about taking photos with a thermographic camera. The outer surface of each piece of equipment to be measured is positioned so that it is within the field of view of the thermographic camera while the plant equipment is in operation or idle, and the target area is photographed with the thermographic camera.

[0122] The results of photographs taken with a thermographic camera will have different color tones depending on the temperature difference between the area being observed and other areas being observed.

[0123] This difference in color tone qualitatively indicates the difference in temperature between the observation area and other observation areas, and also provides information that makes it possible to discover differences in physical conditions (whether or not it is operating, whether or not it is clogged, whether or not it is generating heat, etc.).

[0124] Regarding the observation area, areas that are clearly different in color tone from other observation areas are detected through image diagnosis, and the presence or absence of abnormalities in the observation area is estimated.

[0125] The results of the photography will be reflected in daily inspections, and additional information will be provided on data accumulation and machine learning.

[0126] Workers can view the detected thermographic camera images and check for abnormal areas in real time. Inspections can be carried out using devices such as tablets, mobile phone communication devices, and smart glasses.

[0127] Images that workers recognize as abnormal (or possibly abnormal) are automatically saved, and this is also automatically recorded in the daily inspection book.

[0128] The recorded data may be stored in an on-site system or in the cloud, or may be accumulated as data on plant equipment, etc.

[0129] If the same type of abnormality (or the possibility of an abnormality) is recognized from the next time onwards, the data will be automatically accumulated and machine learning will be used to determine which part is abnormal and to what extent. The normal / abnormal threshold is determined based on the learned data.

[0130] This section provides additional information on determining whether something is different from normal. The threshold determined by machine learning is compared with the data measured by the thermographic camera, and if the value exceeds (or falls below) the threshold, a control signal is output from the output unit, and an abnormality is reported on the screen of a control PC installed in the plant equipment control room or on a device such as a mobile phone or tablet.

[0131] Here, a supplementary explanation will be given regarding emergency stops. If the data measured by the thermography camera far exceeds the threshold, an emergency shutdown can be performed to ensure the safety of the plant equipment.

[0132] As described above, the present invention provides the following functions and effects. - It is possible to indirectly grasp the condition of areas that cannot be visually confirmed, such as inside transfer pipes or pipes inside walls. -It is possible to grasp the blockage status even in pipes located at high altitudes. · Temperature abnormalities and fluid flow conditions of the pump P can be detected, allowing safety to be confirmed. -Maintains the integrity of equipment and improves workability. -Inspection time can be shortened. -The accuracy of inspection results can be improved. Accumulating data on normal and abnormal conditions can improve the accuracy of judgment.

[0133] In particular, whereas in the past, a clog event was only detected when a complete clog led to a pump shutdown or breakdown, the application of the present invention makes it possible to detect a clog at the stage when the pump is about to become clogged. This prevents the maintenance time, labor costs, and replacement part costs that would be incurred if a pump were to fail, as well as damage caused by a processing line being stopped due to a pump failure.

[0134] In this embodiment, a cyclone (solid-liquid separator) 30 is used as the solid-liquid separation means because continuous processing is possible, but the present invention is not limited to this, and known solid-liquid separation means such as a centrifugal separator typified by a decanter, a filter, or a screen can be used, and the present invention can be applied to prevent clogging in such a case. Clogging can also be caused by factors such as rust, scale, or sludge adhesion inside the piping, and the present invention can be applied in such cases as well.

[0135] In addition, in this embodiment, both a "mixing tank" and an "acid fermentation tank" are installed for treatment, but the present invention is not limited to this, and a tank that serves both the roles of the first tank, the mixing tank 21, and the second tank, the acid fermentation tank 22, may be used.

[0136] In this embodiment, the solution to the clogging phenomenon at the narrow part of the cyclone inlet, which is a location where clogging is likely to occur, has been described in detail, but the application of the present invention is not limited to this. Also, in this embodiment, the present invention has been described in relation to a biogas plant facility that treats food waste, sewage sludge, etc., but the present invention can also be applied to plant facilities equipped with treatment equipment other than biogas plants.

[0137] Since the present invention can be applied to any plant facility equipped with a treatment device, it can also be applied to blockages in pipes caused by the accumulation of sediments or aging in water treatment facilities, the adhesion of reaction products in chemical treatment, blockages caused by the aggregation of sulfur compounds or metal fine particles contained in engine fuel oil, food residues or oils and fats in food plants, blockages caused by fibers or particles generated during the papermaking process in paper mills, blockages caused by ore or dust in mining plants, etc. In these facilities, a cyclone may be used, or other solid-liquid separation means such as a centrifuge, a filter, or a screen, typified by a decanter, may be used. [Industrial Applicability]

[0138] The present invention can be used in a treatment device that can monitor the blockage state of a pipe from the outside and can take action before a malfunction occurs, and in plant facilities in general that are equipped with a treatment device. [Explanation of symbols]

[0139] 100 Biogas plant equipment 11. Materials to be processed 11a Food waste slurry 11A Food waste and marine waste 11B Sewage sludge 11C Paper 11D Waste cooking oil 12 Collection vehicle for waste to be disposed of 12A Garbage truck 12B Sewage sludge transport truck 12C Paper Transport Truck 12D garbage truck 13 Food waste receiving hopper 14 Crushing and sorting equipment 14a Materials unsuitable for disposal 30a Sand, shells, etc. 15 Food waste slurry transfer pump 16 Sewage sludge receiving hopper 17. Shredder 18 Sewage sludge adjustment tank 19 Sewage sludge adjustment tank slurry (sewage sludge adjustment tank overflow water) 20 Mixing tank slurry 20a Slurry in the mixing tank after passing through the cyclone 21 Mixing tank 22 Acid Fermenter 22a Acid Fermenter Slurry 24 Methane fermentation tank 30 Cyclone (solid-liquid separator) L1~L7 1st line~7th line L 11 First circulation line (primary side of pump) L 12 Second circulation line (secondary side of pump) L 13 Mixing tank circulation line L 14 Acid fermentation tank transfer line L 15 Methane fermentation tank supply line P pump V1 Switching valve (switching valve) V2 Switching valve (switching valve) 40 Detector 40A Imaging device 40B Vibration meter 40C pressure gauge 40D flow meter 70 Model Generation Device 71 Dataset Acquisition Section 72 Model Generation Unit 73 Dataset storage unit 74 Trained model memory 200 control section 210 Judgment section 210A Temperature judgment section 210B Vibration judgment section 210C Pressure Judgment Unit 210D Flow rate determination section 220 Output section DB storage unit N neural networks N1 input layer N2 middle tier N3 output layer

Claims

1. a first tank for mixing the materials to be treated; a circulation line for extracting the material to be treated from the first tank via a pump and circulating the material; a solid-liquid separator provided in the circulation line and configured to separate solids from the material to be treated that is returned to the tank; a detection unit that detects a blockage in at least one location of the circulation line and the pump; a determination unit that determines whether or not a blocked state is present based on detection data from the detection unit; an output unit that outputs a control signal when the determination unit determines that the state is blocked; A processing device comprising:

2. 2. The treatment apparatus according to claim 1, further comprising a second tank for transferring the entire amount of the material to be treated from the solid-liquid separator.

3. 2. The processing device according to claim 1, further comprising a notification unit that performs notification processing based on the control signal from the output unit.

4. 2. The processing apparatus according to claim 1, wherein the operation of the pump is controlled based on a control signal from the output section.

5. 2. The treatment apparatus according to claim 1, wherein the solid-liquid separator, the circulation line, or the pump is cleaned based on a control signal from the output unit.

6. the determination unit extracts detection information related to the detection data of the detection unit; 2. The processing apparatus according to claim 1, wherein the transfer state of the input material is determined based on the detection information.

7. The processing device according to claim 1, characterized in that the judgment unit uses the detection information of the detection unit as input data and judges the transport state of the object to be processed using a judgment model that has been machine-learned as change data of the detection information.

8. 8. The processing apparatus according to claim 1, wherein the detection unit is an imaging unit that acquires temperature images of the surface temperatures of at least one or more locations of the circulation line and the pump.

9. 8. The processing apparatus according to claim 1, wherein the detection unit is a pressure gauge that measures a pressure caused when the object to be processed is transferred.

10. 8. The processing apparatus according to claim 1, wherein the detection unit is a vibrometer that measures vibrations caused when the object to be processed is transferred.

11. 8. The processing apparatus according to claim 1, wherein the detection unit is a flow meter that measures a flow rate when the object to be processed is transferred.

12. A plant facility comprising the treatment device according to claim 1.

13. Using the processing device of claim 1, a detection step of detecting a blockage in at least one location of the circulation line and the pump; a determination step of determining whether or not a blocked state is present based on the detection data obtained in the detection step; an output step of outputting a control signal when the determining step determines that the state is blocked.

Images