Plant operation monitoring system

Abstract

The objective is to enable easy remote monitoring of the operation and management of biological processing plants. [Solution] The plant operation monitoring system comprises a plant operation monitoring device for monitoring the operation of the plant, sensors installed within the plant, and communication equipment that can be connected to the plant operation monitoring device via a communication network and transmits measurement information measured by the sensors installed within the plant to the plant operation monitoring device. The plant is a biogas plant equipped with a methane fermentation tank 13 for methane fermentation of organic raw materials. The sensors include an ammonia concentration measuring sensor for obtaining ammonia concentration. The ammonia concentration sensor is located downstream of the methane fermentation tank.

Description

[Technical Field]

[0001] The present disclosure relates to a plant operation monitoring system for operation monitoring of various plants such as biological treatment plants. [Background Art]

[0002] As a technique of this kind, for example, Patent Document 1 discloses a configuration of a biogas plant operation monitoring system (hereinafter referred to as "biogas system"), which is an example of a biological treatment plant operation monitoring system (hereinafter referred to as "biological treatment system"), and particularly discloses a configuration of the biogas system aimed at enabling measurement of gas components and the like while suppressing an increase in cost required for the system configuration. [Prior Art Documents] [Patent Documents]

[0003] [Patent Document 1] Japanese Patent No. 7550718 [Summary of the Invention] [Problem to be Solved by the Invention]

[0004] By the way, in the current state (prior art) including the above Patent Document 1, although consideration is given to the system configuration and its operation (such as operation management) at sites such as biogas plants where a biogas system is installed, no consideration is given to remote monitoring.

[0005] At present, main measurements are performed via a Distributed Control System (DCS) of a plant, which has measurement functions of measuring instruments and the like (hereinafter, for convenience of description, appropriately abbreviated as "plant DCS"), and the measurement results are reported. However, for measurement information that needs to be measured but cannot be included in the scope of the plant DCS because there is no communicable measuring instrument or the like available, the measurement is handled on-site.

[0006] Therefore, currently, operators (workers) patrol the plant site, and on-site workers are responsible for managing system operation and recording measurement data, which requires resources. Furthermore, even if operational problems occur on-site, the head office, which is located far away, may not be able to grasp the situation, or the awareness may be delayed, potentially leading to delayed instructions.

[0007] Furthermore, even if measures were taken to address the shortcomings of the aforementioned remote monitoring, simply equipping the system with standard remote monitoring equipment may not function effectively in situations where remote monitoring is absolutely essential, such as when lifelines are paralyzed due to a disaster and workers cannot reach the site. In addition, expanding the system's functionality, such as adding new measurement functions that enable remote monitoring in response to requests for remote monitoring, usually takes considerable time and expense (cost).

[0008] Therefore, this disclosure aims to solve the problems of the prior art described above, and aims to provide a plant operation monitoring system that, for example, enables easy remote monitoring of the operation management of a biological processing plant, can be used for the minimum necessary emergency remote monitoring in the event of a disaster, and can be easily and inexpensively expanded in terms of functionality. [Means for solving the problem]

[0009] To solve the above-mentioned problems, a plant operation monitoring system according to one aspect of the present disclosure comprises a plant operation monitoring device for monitoring the operation of a plant, measuring means installed in the plant, and communication means that can be connected to the plant operation monitoring device via a communication network and transmits measurement information measured by the measuring means installed in the plant to the plant operation monitoring device, wherein the plant is a biogas plant equipped with a methane fermentation tank for methane fermentation of organic raw materials, and the measuring means includes an ammonia concentration measuring means for obtaining ammonia concentration, and the ammonia concentration measuring means is provided downstream of the methane fermentation tank.

[0010] A plant operation monitoring system in one aspect as a reference example of the present disclosure comprises a plant operation monitoring device for monitoring the operation of a plant, and a plurality of communication devices that are connectable to the plant operation monitoring device via a communication network and transmit a plurality of measurement information measured by a plurality of sensors or measuring instruments installed in the plant to the plant operation monitoring device, wherein the plurality of measurement information is transmitted to the plant operation monitoring device in a distributed manner via each of the communication devices, and the plant operation monitoring device monitors the operation of the plant based on the measurement information. [Effects of the Invention]

[0011] According to this disclosure, the operation and management of the processing plant can be easily remotely monitored, used for the minimum necessary emergency remote monitoring in the event of a disaster, and its functionality can be easily and inexpensively expanded. [Brief explanation of the drawing]

[0012] [Figure 1A] Figure 1A is a diagram showing the configuration of a plant operation monitoring system, which is an embodiment of an information processing system according to one embodiment of the present disclosure. [Figure 1B] Figure 1B is a diagram showing the configuration of a plant operation monitoring system, which is another embodiment of an information processing system according to one embodiment of the present disclosure. [Figure 1C] Figure 1C is a diagram showing the configuration of a plant operation monitoring system, which is another embodiment of an information processing system according to one embodiment of the present disclosure. [Figure 2] Figure 2 is an explanatory diagram of the component blocks of a biogas plant function, which is an embodiment of the biological processing plant function. [Figure 3A] Figure 3A is an explanatory diagram showing specific examples of various screen images for reporting (displaying) various data obtained through operation management. [Figure 3B] Figure 3B is an explanatory diagram showing specific examples of various screen images for reporting (displaying) various data obtained through operation management. [Figure 3C]FIG. 3C is an explanatory diagram showing a specific example of various screen images for notification (display) of various data obtained by operation management. [Figure 3D] FIG. 3D is an explanatory diagram showing a specific example of various screen images for notification (display) of various data obtained by operation management. [Figure 4] FIG. 4 is an explanatory diagram showing a specific example of an operation management flowchart by a central control function, which is an embodiment of the operation management function of an information processing system. [Figure 5A] FIG. 5A is an explanatory diagram showing a specific example of an image of a distributed network function in an embodiment of an operation management information network function. [Figure 5B] FIG. 5B is an explanatory diagram showing a specific example of an image of a conventional centralized network function. [Figure 6] FIG. 6 is a diagram showing a schematic configuration of an ammonium ion concentration sensor. [Figure 7] FIG. 7 is a diagram showing an example of calibration characteristics when an ion-responsive membrane electrode is used as an ion electrode. [Figure 8] FIG. 8 is a diagram showing an example of installation locations of ion electrodes in a biogas plant. [Figure 9] FIG. 9 is a diagram showing an example of an installation location of a gas detection probe in a biogas plant. [Figure 10] FIG. 10 is a diagram showing one configuration example in a case where measurement information of a biogas plant is acquired by a camera. [Figure 11] FIG. 11 is a diagram showing an example of a list created by an image processing unit. [Figure 12] FIG. 12 is a diagram showing another configuration example in a case where measurement information of a biogas plant is acquired by a camera. [Figure 13] FIG. 13 is a diagram showing an example of a hardware configuration of a cloud server. MODE FOR CARRYING OUT THE INVENTION

[0013] The concepts of this disclosure are described in more detail below with reference to the accompanying drawings illustrating specific embodiments of the concepts of this disclosure. However, the concepts of this disclosure can be embodied in many different forms and should not be construed as being limited to the embodiments described herein.

[0014] A plant operation monitoring system according to one embodiment of this disclosure comprises a plant operation monitoring device for monitoring plant operation and a plurality of communication devices. The plurality of communication devices are connectable to the plant operation monitoring device via a communication network and transmit a plurality of measurement information measured by a plurality of sensors installed in the plant to the plant operation monitoring device. In this embodiment, the plurality of measurement information is transmitted to the plant operation monitoring device in a distributed manner via each communication device. The plant operation monitoring system monitors the plant's operation based on measurement information. The following example illustrates a case where a plant operation monitoring system is implemented using a cloud service 101 provided by a cloud server, and communication equipment is implemented using an edge device 102B, but this disclosure is not limited to this example.

[0015] Figure 1A is a schematic diagram showing an example of a plant operation monitoring system according to this embodiment. As shown in Figure 1A, the plant operation monitoring system 100A includes a cloud service 101 implemented by a cloud server that provides cloud computing services, and an edge device 102B that reports measurement information via distributed communication from one or more measurement pieces measured using a sensor 102A within the plant (not shown) to the cloud service 101. Based on the measurement information reported to the cloud service 101, the system monitors the operation of the plant. Here, "cloud service 101" is a general term for the various functions provided by the cloud server.

[0016] Figure 13 shows an example of the hardware configuration of a cloud server. As shown in Figure 13, the cloud server is a so-called computer and is equipped with a CPU (Central Processing Unit: processor) 21, main memory 22, secondary storage (memory) 23, and a communication interface 25.

[0017] The CPU 21 may consist of one or more units that cooperate with each other to perform processing.

[0018] The main memory 22 is composed of writable memory such as RAM (Random Access Memory), and is used as a work area for reading the CPU 21's executable program and writing processing data by the executable program. Multiple main memory 22s may be provided.

[0019] The secondary storage device 23 is a non-transitory computer-readable storage medium. The secondary storage device 23 is, for example, a semiconductor memory, such as a flash memory or an SSD (Solid State Drive). Other examples of secondary storage devices 23 include magnetic disks, magneto-optical disks, CD-ROMs, and DVD-ROMs. Multiple secondary storage devices 23 may be provided, and each secondary storage device 23 may store a divided amount of programs and data for implementing the functions described later.

[0020] A series of processes for realizing the cloud service 101 described later (for example, the data acquisition unit 104a, the data storage unit 104b, the alarm issuance unit 104c, the visualization unit 104d, the future prediction function 104e, etc.) are stored in the secondary storage device 23 in the form of a program, for example. The CPU 21 reads this program into the main memory device 22 and performs information processing and calculations to realize various functions. The program may be pre-installed in the secondary storage device 23, provided stored on a computer-readable storage medium, or distributed via wired or wireless communication. Computer-readable storage media include magnetic disks, magneto-optical disks, CD-ROMs, DVD-ROMs, semiconductor memory, etc.

[0021] The communication interface 25 functions, for example, as an interface for connecting to a network, communicating with other devices, and sending and receiving information. For example, the communication interface 25 communicates with other devices via wired or wireless means. Examples of wired communication include serial communication such as RS-232C and RS-485, CAN (Controller Area Network), and Ethernet. Examples of wireless communication include communication via lines such as Bluetooth®, Wi-Fi, mobile communication systems (3G, 4G, 5G, 6G, LTE, etc.), and wireless LAN.

[0022] As shown in Figure 1A, the cloud service 101 includes, but is not limited to, a data acquisition unit 104a, a data storage unit 104b, an alarm issuance unit 104c, a visualization unit 104d, and a future prediction function for measurement data 104e.

[0023] The data acquisition unit 104a includes a function (API (Application Interface) communication) for acquiring and obtaining data within the cloud service 101.

[0024] The data storage unit 104b includes an automatic inspection recording function and a data storage function, providing an environment in which data can be stored and shared, for example, in network-attached storage or cloud storage.

[0025] The alarm generation unit 104c includes judgment functions (threshold judgment, change amount judgment, correlation monitoring, rule-based judgment) and alarm generation functions (alarm display, email transmission, notification to administrator).

[0026] The visualization unit 104d includes a trend plotting function and a correlation analysis function.

[0027] The measurement data future prediction function 104e includes a function to acquire data from a historically accumulated database and process it in the cloud service 101.

[0028] According to this embodiment, for example, data regarding the operating status of a biological processing system (biogas plant) can be acquired from sensors 102A installed in each piece of equipment of the plant via distributed communication, centralized communication, etc., and the information can be aggregated into a cloud service (or on-premise service).

[0029] In other words, Cloud Service 101 performs various processes such as judgment processing (threshold judgment, change amount judgment, correlation judgment, rule-based judgment, etc.), data analysis (correlation analysis, trend monitoring, etc.), data storage and accumulation, and future prediction. Additionally, on-premises systems, shared folders, data centers, file servers, etc., can be used instead of cloud service 101, but this is not limited to these options.

[0030] Multiple sensors 102A are installed within the plant. Each of the multiple sensors 102A includes at least one of the following: a sensor for measuring parameters related to the characteristics of process gases generated within the plant; a sensor for measuring parameters related to the characteristics of liquids used within the plant; a sensor for measuring parameters related to the characteristics of organic raw materials for methane fermentation used within the plant (hereinafter referred to as "raw materials"); and a sensor for measuring parameters related to the characteristics of liquids that fluctuate due to the water treatment process generated within the plant.

[0031] Sensor 102A is installed in plant equipment to measure, for example, pH, temperature, pressure, liquid level, ammonium ions (NH4). + This system acquires various data such as volatile fatty acids (VFAs), flow rate, and methane concentration.

[0032] The sensor 102A and the edge device 102B are connected wirelessly or via a wired connection. Here, the edge device is, for example, a device that measures pH, temperature, pressure, liquid level, ammonium ions (NH4). + It is a device that connects to sensors such as volatile fatty acids (VFAs), flow rate, and methane concentration, processes and analyzes data on-site, and communicates with the cloud or other systems.

[0033] In this embodiment, the configuration consisting of a sensor 102A and an edge device 102B is referred to as "edge sensor 102". This edge device 102B and the cloud service 101 are connected, for example, by distributed communication. Examples of this distributed communication include carrier communication such as SIM (Subscriber Identity Module Card) and e-SIM.

[0034] The cloud service 101 analyzes the data acquired from the edge sensor 102 and notifies each terminal (e.g., personal computer (PC), mobile phone, tablet, display, etc.) 105 of the processing results. This enables display on each terminal and the issuance of alarms (notifications such as alarm displays on the device and email notifications).

[0035] In addition to the data from sensor 102A, other information 106 may also be sent to the cloud service 101. This information does not necessarily have to be sent using distributed communication between the edge device 102B and the cloud service 101. Here, other information 106 includes data from weighing scales (e.g., truck scales for measuring truck load capacity), information on the type of raw materials, measurement information from measuring instruments, alarm information from measuring instruments, data acquisition by reading gauge values ​​such as USB memory, vibration and pressure gauges with a camera. It may also include data read from the plant's control system or from external sources (outside temperature, weather, electricity trading market data, etc.). Details regarding data acquisition using a camera will be described later.

[0036] Furthermore, remote monitoring can be performed from a location far from where the plant operation monitoring system 100A is installed.

[0037] Furthermore, based on the accumulated data, it is possible to predict data trends, enabling plant operation measures (such as feedforward) to maximize gas generation, increase electricity sales revenue, ensure stable plant operation, and prevent malfunctions.

[0038] When an alarm is triggered by this plant operation monitoring system 100A through the process described in the flowchart below, a human (e.g., a field worker) checks the situation on site and makes various adjustments, including the amount of raw materials to be input.

[0039] By making various adjustments such as the amount and type of raw materials input and moisture content, it is possible to prevent, for example, a decrease in the activity or death of methane-producing bacteria in a biological treatment (biogas) plant.

[0040] The platform for this plant operation monitoring system 100A is designed to operate independently of the plant's DCS (Distributed Control System). This ensures that any malfunctions in the platform will not affect the plant itself.

[0041] In this embodiment, an edge sensor 102 consisting of a sensor 102A and an edge device 102B is used to grasp the trend of the measured values. Based on this collected information, for example, in a biological processing (biogas) plant, it is possible to monitor for trends such as a decrease in the activity of methane-producing bacteria in a methane fermentation tank. If a trend of decreasing activity is observed, a human can make a judgment and take feedforward measures such as adjusting the input amount before the methane-producing bacteria die, thereby creating a system that focuses on preventing the decrease in activity and death of methane-producing bacteria in plant equipment.

[0042] This acquired data is saved and stored, and will be processed using AI (Artificial Intelligence) in the future.

[0043] As disclosed herein, by using distributed communication, even if communication between the edge device 102B that acquired sensor 102A information and the cloud service 101 is interrupted, only the sensor information at the point of interruption (e.g., pH value) cannot be acquired; data from other sensors 102A (e.g., pressure, liquid level, temperature, etc.) can continue to be acquired.

[0044] In other words, the advantage of using distributed communication is that even if that communication is interrupted, the only consequence is that data cannot be obtained from the sensor connected to that distributed communication, and as a result, data acquisition from other sensors 102A can continue.

[0045] In other words, as shown in Figure 5A, in this embodiment, the edge device 102B has a distributed network function that has a choice of communication paths that can selectively pass through multiple nodes. Therefore, even if certain communications are interrupted due to disasters, network attacks, or malfunctions, necessary information other than the disconnected communications can still be transmitted, making it easier to maintain stable operation during disasters and other emergencies.

[0046] For comparison, as shown in Figure 5B, in a conventional centralized network system, if a specific communication is interrupted due to some factor (disaster, attack, failure, etc.) (marked with an "x" in the figure), all communications will fail, disrupting remote monitoring operations. As a result, it becomes impossible to acquire data from the entire plant.

[0047] Here, as an example of the judgment process, the criteria for danger are progressively increased from 1) warning (dangerous trends are beginning to be observed) to 2) advisory (it may be getting dangerous soon) to 3) warning (it is dangerous), with the warning receiving more weight as you move from 1) to 3), but this is not the only way to proceed. 1) "Determining the amount of change" means checking the degree of change in the data trend (such as the slope of the graph or the rate of change) and issuing a warning. 2) The "warning threshold" means that a warning will be issued when the predetermined warning threshold is exceeded. 3) "Alarm threshold" means that an alarm will be issued if the predetermined alarm threshold is exceeded.

[0048] Data is sent from the edge sensor (102A+102B) 102 to the cloud service 101 via distributed communication. Within this cloud service 101, various processes are performed, including data acquisition by the data acquisition unit 104a, data storage by the data storage unit 104b, alarm issuance by the alarm issuance unit 104c, data visualization by the visualization unit 104d, and future measurement data prediction function 104e, but the functions are not limited to these.

[0049] The alarm generation unit 104c performs a determination process and sends a sequential message to the terminal 105 according to the degree of determination. An alarm may also be generated at the same time.

[0050] Other information 106: In the case of data other than that from the edge sensor (102A+102B) 102 installed here (for example, vibration data from a vibration meter, temperature data captured by a thermal camera, etc.), that vibration data can also be imported into the cloud service 101.

[0051] The edge sensor (102A+102B) 102 can be configured not as a single unit, but as an edge device 102B for multiple sensors 102A.

[0052] Figure 1B is a schematic diagram of another plant operation monitoring system according to this embodiment. Components identical to those in the plant operation monitoring system 100A in Figure 1A are denoted by the same reference numerals, and redundant explanations are omitted. As shown in Figure 1B, the plant operation monitoring system 100B includes a cloud service 101 which is responsible for the cloud server that provides cloud computing services, and edge devices 102B-1, 102B-2, and 102B-3 which report measurement information via distributed communication from one or more measurement information measured using sensors 102A-1, 102A-2, and 102A-3 within the plant (not shown) to the cloud service 101, and performs plant operation monitoring based on the measurement information reported to the cloud service 101.

[0053] According to this embodiment, for example, regarding the operating status of a biological processing system (biogas plant), data from sensors 102A-1, 102A-2, and 102A-3 installed on each piece of equipment in the plant can be acquired via distributed communication through edge devices 102B-1, 102B-2, and 102B-3, and the information can be aggregated in a cloud service (or on-premise service) 101.

[0054] In this example, multiple edge devices (not shown) are used (three devices: edge devices 102B-1, 102B-2, and 102B-3), but it is also possible to consolidate them using a single edge device.

[0055] Furthermore, distributed communication may be routed through the cloud 102C of the edge device 102B.

[0056] Figure 1C is a schematic diagram of another plant operation monitoring system according to this embodiment. Components identical to those in the plant operation monitoring system 100A in Figure 1A are denoted by the same reference numerals, and redundant explanations are omitted. As shown in Figure 1C, the plant operation monitoring system 100C acquires, in addition to other information 106, plant control system information 107 and external information 108 via the cloud service 101, as shown in Figure 1A.

[0057] When transmitting data via communication, patterns that go through edge devices or carrier communication may be used. Besides the DCS, plant control system information 107 could include other devices such as a Programmable Logic Controller (PLC) or Supervisory Control and Data Acquisition (SCADA). Data held by these control devices may also be acquired. Since these are control devices, it is preferable not to transmit data via a network, as their failure would be problematic. Distributed communication may be used; that is, a secure, closed system is preferable.

[0058] Furthermore, external information 108 may include, for example, climate information (weather forecasts, temperature, humidity, etc.) received via the internet. Alternatively, for the purpose of gaining an advantage in electricity trading using biogas, it may also include, for example, information on wholesale electricity prices traded at the Japan Electric Power Exchange (JPEX).

[0059] An example of a biogas plant facility is shown in Figure 2. Figure 2 is an explanatory diagram of the component blocks of a biogas plant function, which is an embodiment of the biological treatment plant function. For example, a biogas plant facility collects food waste and sewage sludge from various locations in the city, uses these materials as raw materials for methane fermentation treatment, generates biogas, and utilizes the resulting biogas as energy for power generation and other purposes. Figure 2 shows an example of applying the various sensors of the aforementioned plant operation monitoring system 100B to the sensors of each piece of equipment.

[0060] As shown in Figure 2, the biogas plant 10 is equipped with a receiving facility 11, a conditioning tank 12 (which may be a mixing tank, or a mixing tank and an acid fermentation tank; hereinafter referred to as the "conditioning tank"), a methane fermentation tank 13 (also called a digester; hereinafter referred to as the "methane fermentation tank"), and a digestate storage tank 14 (also called a digested sludge storage tank; hereinafter referred to as the "digestate storage tank"). Raw materials 11b, such as materials to be processed (food waste, sludge, etc.), brought in by truck 11a are received at the receiving facility 11 and sent to the conditioning tank 12 via the raw material input line L2. The slurry 12a from the adjustment tank 12 is sent via the slurry transport line L3 to the methane fermentation tank 13 installed outdoors, where it is subjected to methane fermentation. Methane fermentation treatment is carried out in the methane fermentation tank 13, generating biogas G1. The biogas G1 is then discharged via the biogas discharge line L6 to the biogas utilization facility, where it is effectively utilized as an energy source for power generation and other purposes.

[0061] The digestate 13a (also called digested sludge, hereinafter referred to as "digestate") obtained from methane fermentation in the methane fermentation tank 13 is sent to the digestate storage tank 14 via the digestate transport line L4 and stored there. This stored digestate 14a is sent to the water treatment facility 15 via the digestate transport line L5 and treated there.

[0062] The biogas plant 10 is equipped with multiple sensors 102A. These sensors include, for example, sensors that detect specific chemical substances in at least one of the adjustment tank 12, the methane fermentation tank 13, or the digestate storage tank 14.

[0063] For example, the adjustment tank (which may also be equipped with an acid fermentation tank) 12 is equipped with a pH sensor 102A-1 (hereinafter abbreviated as "A-1"), a pH communication function (edge ​​device 102B-1 (hereinafter abbreviated as "B-1")) corresponding to the pH sensor A-1, a temperature sensor A-2, and a temperature communication function (edge ​​device B-2) corresponding to the temperature sensor A-2. Then, the edge devices B-1 and B-2 report to the cloud service 101 of the plant control unit via distributed communication.

[0064] Similarly, the methane fermentation tank 13 is equipped with a fermentation tank liquid level sensor A-3 and a corresponding fermentation tank liquid level communication function (edge ​​device B-3), as well as a fermentation tank pressure sensor A-4 for measuring the internal pressure of the methane fermentation tank 13 and a corresponding fermentation tank pressure communication function (edge ​​device B-4).

[0065] Then, the edge devices B-3 and B-4 report to the cloud service 101 of the plant control unit via distributed communication.

[0066] Similarly, the digestate storage tank 14 is equipped with a pH sensor A-5 for the digestate storage tank, a corresponding pH communication function for the digestate storage tank (edge ​​device B-5), a temperature sensor A-6 for the digestate storage tank, and a corresponding temperature communication function for the digestate storage tank (edge ​​device B-6). Then, the edge devices B-5 and B-6 report to the plant control unit's cloud service 101 via distributed communication.

[0067] Similarly, the biogas plant 10 may be equipped with an ammonia concentration measuring sensor A-7 (hereinafter simply referred to as "sensor A-7") for measuring the ammonia concentration. An example of sensor A-7 is an ammonium ion concentration sensor.

[0068] Sensor A-7 has an ion electrode 50 that is compatible with ammonium ions, as shown in Figure 6, for example. The ion electrode 50 is installed so that ammonium ions can be measured directly, for example, by having a detection part 50a immersed in a liquid tank (e.g., a storage tank) 90. Examples of ion electrode types include ion-responsive membrane electrodes and gas-detection electrodes. Figure 6 shows an ion electrode 50 as an example of an ion-responsive membrane electrode, but is not limited to this.

[0069] Ion electrode 50 is ammonium ion (NH4 + The device outputs an analog signal corresponding to the concentration [mg / L] of the ion electrode. The ion electrode 50 can, for example, be an electrode capable of measuring in the range of 0 to 9,000 [mg / L]. Furthermore, the ion electrode 50 preferably uses a sensor capable of measuring in the concentration range of 0 to 5,000 [mg / L], more preferably 10 to 2,000 [mg / L], and most preferably 50 to 2,000 [mg / L].

[0070] Figure 7 shows an example of calibration characteristics when an ion-responsive membrane electrode is used as the ion electrode 50. In Figure 7, the horizontal axis represents the ammonium ion standard solution concentration [mg / L], and the vertical axis represents the current [mA] (where the horizontal axis is a logarithmic graph). As shown in Figure 7, when an ion-responsive membrane electrode is used, the output signal is a current [mA] corresponding to the ion concentration.

[0071] The output signal from the ion electrode 50 is input to, for example, the converter 51. The converter 51 processes the analog signal (voltage signal) output from the ion electrode 50 and outputs it as a signal within a predetermined voltage range. The output signal from the converter 51 is input to the isolator 52. The isolator 52 converts the voltage signal from the converter 51 into a signal format suitable for the edge device B-7 and outputs it. For example, if the edge device B-7 is specified to receive a current signal within a predetermined range (e.g., 4 to 20 mA), the isolator 52 proportionally converts the voltage signal output from the converter 51 to match the current range of the edge device B-7 and outputs it.

[0072] Here, the configuration between sensor A-7 and edge device B-7 (converter 51 and isolator 52) is not limited to the above example. That is, the configuration between sensor A-7 and edge device B-7 can be appropriately determined according to the type and specifications of sensor A-7 and the specifications of edge device B-7.

[0073] Figure 8 shows an example of the installation location of the ion electrode 50 in the biogas plant 10. Figure 8 also shows the peripheral equipment of the digestate storage tank shown in Figure 2. As shown in Figure 8, the digestate transport line L4 that transports the digestate from the methane fermentation tank 13 to the digestate storage tank 14 is equipped with, for example, a sedimentation tank 61 and a screen box 62. The sedimentation tank 61 separates unsuitable materials for fermentation, such as sand with a high specific gravity, that are transported in the digestate without being digested in the methane fermentation tank 13. The screen box 62 is, for example, located downstream of the sedimentation tank 61 and is a box that houses a bar screen 63 for removing unsuitable materials for fermentation that could not be removed in the sedimentation tank 61. The residue 64a that was not removed in the sedimentation tank 61 is sent to the residue receiving container 64 by the bar screen 63. Furthermore, the digested liquid 13A, from which the residue 64a that has passed through the bar screen 63 has been removed, falls into the digested liquid storage tank 14 through the bottom opening of the screen box 62.

[0074] As shown in Figure 8, the ion electrode 50 is installed in at least one of the following: the sedimentation tank 61, the screen box 62, the drain pan 65 of the sedimentation tank 61, the digested liquid storage tank 14, or the digested liquid transport line L5 that transports the digested liquid from the digested liquid storage tank 14. Alternatively, a measuring container may be provided in the digested liquid transport lines L4 and L5 to extract and store the digested liquid for measurement, and the ion electrode 50 may be installed in this measuring box. Alternatively, a separate measuring tank may be provided on the digested liquid transport lines L4 and L5, and the ion electrode 50 may be installed in this tank. The ion electrode 50 is positioned, for example, so that its detection site 50a is immersed in the digestive fluid.

[0075] Alternatively, as sensor A-7, an ammonia sensor that measures ammonia gas concentration may be used in place of the ammonium ion concentration sensor described above, or in addition to the ammonium ion concentration sensor, in the headspace of a tank where digestate is stored, or in the exhaust gas line or odor gas line leading from the storage tank.

[0076] For example, the ammonia sensor comprises a gas detection probe 70 and a detection unit (not shown). As shown in Figure 9, the gas detection probe 70 is installed in the upper space 14s of the digestate storage tank 14 or in a gas transport line L7 that transports gas from the upper space 14s. The upper space 14s is a semi-sealed space. Alternatively, a projection 70a for collecting gas may be provided at the top of the digestate storage tank 14 in the upper space 14s, and the gas detection probe 70 may be installed on this projection 70a. Since ammonia gas is lighter than air, ammonia gas will accumulate on the projection 70a, making stable measurement possible. Furthermore, this detection method is not limited to the above description and can also be applied to tanks and devices that store digestate.

[0077] The ammonia gas concentration measured by the ammonia sensor is transmitted to the cloud service 101 via the edge device B-7. Furthermore, a converter or other device, according to the specifications of edge device B-7, can be installed between the ammonia sensor and edge device B-7.

[0078] The cloud service 101 has conversion information that shows the correlation between the ammonia gas concentration and the ammonia concentration in the digestate, and this conversion information may be used to obtain the ammonia concentration in the digestate from the ammonia gas concentration measured by the ammonia sensor. Examples of conversion information include a conversion table and a formula for calculating the ammonia concentration that includes the ammonia gas concentration as a parameter.

[0079] The data conversion described above may be performed by edge device B-7 instead of cloud service 101. In this case, the ammonia concentration in the digestate is transmitted from edge device B-7 to cloud service 101.

[0080] The cloud service 101 monitors the operating status of the biogas plant 10 based on the ammonia concentration in the digestate. This allows for the following effects, for example:

[0081] • Prevention of decreased activity and death of methanogenic bacteria. This system allows for real-time monitoring of ammonia concentration, enabling continuous monitoring of the microbial environment within the methane fermentation tank 13. As ammonia concentration increases, its toxicity to methane-producing bacteria increases, leading to decreased activity and death of these bacteria; therefore, early detection of concentration changes is crucial. By utilizing historical data and AI prediction models stored in the cloud service 101, signs of abnormalities can be detected in advance, allowing for control of ammonia concentration and preventative measures such as adjusting raw material input, pH, and moisture content. This enables faster and more accurate responses compared to conventional feedback-type control, contributing to improved biogas production efficiency and prevention of equipment malfunctions.

[0082] • Reducing the burden on on-site workers and speeding up remote response This system eliminates the need for on-site patrols and manual recording by field workers through automatic measurement using sensors and data transmission to cloud service 101. This improves the efficiency of human resources and enables immediate response from remote locations. Data on the cloud can be viewed and analyzed on terminal 105, allowing for quick judgment and instructions in response to signs of abnormalities. The alarm notification function allows for detection of abnormalities even when the site is not present, providing flexibility for responses during nighttime and holidays.

[0083] • Application to feedforward control When abnormal values ​​are detected, preventative feedforward control becomes possible by immediately adjusting operating parameters such as raw material input amount, concentration, type, temperature, and pH. For example, if an increase in ammonia concentration or VFA is detected, the microbial environment can be stabilized by changing the type of raw material or adjusting the input amount. This makes it possible to operate the system in a way that maximizes gas generation while maintaining the activity of methane-producing bacteria. Furthermore, by utilizing data accumulated in cloud service 101 and AI predictive models, signs of abnormalities can be detected in advance, enabling quantitative and reproducible control decisions. This contributes to stable plant operation and improved operating efficiency.

[0084] • Operation support through real-time measurement of ammonium ions By acquiring ammonium ion concentration in real time using the ion electrode 50 and edge device B-7 and transmitting it to the cloud service 101, immediate understanding of on-site conditions becomes possible. Ammonium ion concentration or ammonia concentration is an important indicator that affects the activity of methane-producing bacteria, and monitoring concentration changes allows for early detection of signs of abnormalities. Trend analysis and alarm notifications on the cloud enable quantitative operational support that moves away from subjective judgments.

[0085] • Enhanced data storage, visualization, and alarm notification through Cloud Service 101 Measurement data acquired from various sensors is stored in the cloud service 101, enabling visualization through trend display and correlation analysis. If an abnormal value is detected, an alarm notification is issued based on the set threshold and immediately transmitted to terminal 105. This allows for the detection of abnormalities even when the site is not present, enabling a rapid response. This achieves highly reliable operational management without relying on individual judgment.

[0086] • High-precision concentration estimation In addition to ammonium ion concentration, the system corrects for interference and interference from substances such as alkali metals and alkaline earth metals, including potassium ions, sodium ions, and calcium ions, which interfere with ammonium ion concentration. Furthermore, by aggregating multiple indicators such as electrical conductivity (EC), water temperature, pH, alkalinity, and TDS into Cloud Service 101 and analyzing their complex relationships using AI, more accurate correlation analysis becomes possible. These indicators do not have a simple one-to-one correlation; by using AI, it is possible to derive correlation formulas (for example, ammonium concentration × (item X × coefficient) ÷ item Y + constant) that differ for each plant and series, and by learning and optimizing on the cloud, it becomes possible to build estimation models that are tailored to the field.

[0087] • Concentration display based on automatic equilibrium calculation Originally, ammonium ions (NH4 + While it is desirable to directly measure both the ammonium ion concentration and the ammonia (NH3) concentration, direct measurement of ammonia concentration is often technically and economically difficult. Therefore, in this system, the ammonium ion concentration may be acquired in real time, and the ammonia (NH3) concentration may be estimated by performing chemical equilibrium calculations using the measured values ​​of water temperature and pH.

[0088] Ammonium ions and ammonia are interconverted in water by the equilibrium reaction shown in equation (1) below.

[0089]

number

[0090] The equilibrium of this reaction depends on temperature and pH, and the ammonia concentration can be calculated using the equilibrium constant (Ka) with the following formula.

[0091]

number

[0092] Here, pKa changes with temperature; for example, at 25°C it is approximately 9.25. Cloud service 101 automatically calculates the ammonia concentration using these values ​​and displays and stores it in real time.

[0093] This method allows for rapid and quantitative understanding of ammonia behavior based on ammonium ion concentration measurements, enabling highly accurate assessment of potential inhibiting risks to methane-producing bacteria. Furthermore, prompt alerts can be issued if an anomaly is detected, facilitating early response and optimization of process control, thereby contributing to stable plant operation.

[0094] Figures 3A to 3D are explanatory diagrams showing specific examples of various screen images for reporting (displaying) various data obtained through operation management. Figure 3A shows a threshold line set above the graph, which helps determine whether an alarm has been triggered and indicates the likelihood of a potential danger. Figure 3B is a pie chart that, for example, displays the proportion of input materials (on a truck scale) for incoming raw materials. This allows for visual monitoring of the balance of raw materials and provides an overview of trends. Figure 3C may show raw data or it may generate calculations and issue alarms. Figure 3D shows trend data for each measurement location, with raw data listed in each column. The data can also be displayed in parallel. The measured data is shown as is.

[0095] The parameters that can be measured by the edge sensor 102 include, for example, pH, temperature, pressure, liquid level, ammonia (NH4), volatile fatty acids (VFAs), flow rate, methane concentration, and hydrogen sulfide concentration.

[0096] Furthermore, sensor data acquired and managed by the DCS for plant control may also be separately imported into the cloud service 101. For example, by aggregating pressure information, temperature information, and liquid level information obtained from instruments such as pressure gauges, thermometers, and liquid level gauges installed in each facility into the cloud, it becomes possible to integrate and utilize data from both the DCS and the cloud.

[0097] For example, as shown in Figure 10, the plant operation monitoring system 100 may include a camera 71 and at least one measurement information acquisition device 75 comprising an image processing unit 72 and a communication unit 73. The camera 71 is configured to acquire images of various instruments installed within the plant, process adjustment mechanisms that adjust the parameters of process fluids (for example, pressure adjustment valves and flow adjustment valves), and at least one image of a system screen. The image processing unit 72 obtains measurement information of the biogas plant 10 by processing the image data acquired by the camera 71. The communication unit 73 transmits the measurement information acquired by the image processing unit 72 to the cloud service 101.

[0098] The image processing unit 72 and the communication unit 73 described above may be implemented, for example, by an information processing device equipped with communication functions. The information processing device is, for example, a computer and has a configuration similar to the cloud server described above (see Figure 13). That is, the information processing device includes a processor such as a CPU and a non-temporary computer-readable recording medium such as a secondary storage device. As each component is as described above, a detailed explanation is omitted here. The configuration of the image processing unit 72 and the communication unit 73 is not limited to the above, and known configurations can be adopted. For example, the image processing unit 72 and the communication unit 73 may be implemented by a single computer as described above, or they may be provided separately as distinct components.

[0099] The plant operation monitoring system 100 may include, for example, a camera 71 that captures an image of the instrument display 80. In this case, the image processing unit 72 obtains a measured value (for example, "123.4") from the acquired display image.

[0100] The plant operation monitoring system 100 may include, for example, a camera 71 that images an analog instrument that displays a measured value by the position of a needle on a scale. In this case, the image processing unit 72 analyzes the image data to obtain the measured value from the position of the needle between the scales. This makes it possible to obtain measurement information (e.g., flow rate, temperature, pressure, pH, etc.). In measuring liquid level height, since liquid is stored in a vertical tubular pipe and its liquid level position is displayed in correspondence with the scale, the image processing unit 72 detects the liquid level position and obtains liquid level height information by reading the corresponding scale value.

[0101] Specific examples of the digital and analog instruments mentioned above include the frequency and current values ​​displayed on the inverter, a pH meter that shows the pH of a liquid, a hydrogen sulfide concentration meter that shows the hydrogen sulfide concentration, a vibration meter that measures the vibration state of pumps and motors, a pressure meter that shows the pressure state of a fluid, and an ammonia meter that measures the ammonia concentration. These instruments are important sources of information for understanding the operating conditions within the plant, and by analyzing the image data captured by the camera 71 with the image processing unit 72, various measurement values ​​can be automatically acquired.

[0102] The plant operation monitoring system 100 may include a camera 71 that images a process adjustment mechanism 81 that automatically or manually adjusts the flow rate, pressure, etc. Examples of the process adjustment mechanism 81 include valves or levers. In this case, the image processing unit 72 holds image data corresponding to, for example, a 0% opening state and a 100% opening state as reference images, and identifies the opening degree of the valve, lever, etc. by comparing these reference images with the image data acquired by the camera 71 and performing interpolation processing. Furthermore, measurement information (e.g., valve opening degree, flow rate, pressure, etc.) may be acquired based on the identified opening degree.

[0103] The measurement information acquired by the image processing unit 72 in this manner is transmitted, for example, in real time to the cloud service 101 by the communication unit 73. At this time, the measurement values ​​are accompanied by identification information such as that of an instrument, so that it is possible to identify which part the measurement value corresponds to. Alternatively, instead of measurement information, for example, the opening degree information of the process adjustment mechanism 81 such as the valve or lever described above may be transmitted to the cloud service 101. In this case, the cloud service 101 can obtain the measurement information from the opening degree information.

[0104] In addition, when imaging with camera 71, the illumination may be insufficient depending on the installation location. In this case, the brightness necessary for image processing can be ensured by using additional lighting. Alternatively, since the illumination is easily affected by the influence of natural light, such as morning and evening or weather conditions, the area around the imaging location of camera 71 may be designed to ensure sufficient illumination using only lighting. For example, imaging in a darkroom or imaging with an infrared camera may be used.

[0105] Multiple measurement information acquisition devices 75, as described above, are installed within the plant. By acquiring measurement information from corresponding digital instruments, analog instruments, and process adjustment mechanisms 81, it becomes possible to provide various types of measurement information to the cloud service 101.

[0106] Various measurement data may be transmitted to each terminal in real time via the cloud service 101. This allows workers to understand and judge the equipment status in real time without having to go to the site. Various known methods can be used for notification.

[0107] Furthermore, the plant operation monitoring system 100 may also include, for example, a camera 71 that captures images of a display screen provided by a system that monitors and / or controls the operation of the plant.

[0108] Examples of systems used for monitoring and / or controlling plant operations include DCS, SCADA, and PLC.

[0109] A DCS (Distributed Control System) is a system that manages a wide range of facilities, such as plants and factories, using multiple control devices in a distributed manner. For example, a DCS has a display screen that shows measurement data and process data measured by various sensors installed within the plant.

[0110] SCADA (Supervisory Control and Data Acquisition) is a system for remotely monitoring and controlling a plant, and it is a system that allows for the visualization and operation of the operating status of the monitored system. SCADA has a display screen that shows measurement data and process data measured by various sensors installed in the plant, for example.

[0111] PLCs (Programmable Logic Controllers) and sequencers are control devices used to control factories and equipment. These control devices work in conjunction with a human-machine interface equipped with input and display devices, and measurement data and process data measured by various sensors installed in the plant are displayed on the display screen of the display device.

[0112] Camera 71 may capture the entire display screen, or it may physically mask a portion of the display screen so that only the numerical values ​​of the target are captured. Alternatively, the image data captured by camera 71 may be masked by software processing to extract only the data from a predetermined area of ​​the image data. Additionally, a close-up lens or similar device may be used with the camera 71 to enlarge and clearly photograph a portion of the screen.

[0113] In this way, by acquiring image data using the camera 71, it becomes possible to introduce the plant operation monitoring system 100 without modifying existing equipment. Since it does not involve equipment modification or control system changes, the construction period for introduction can be shortened and initial costs can be reduced. Furthermore, since existing instruments and display devices can be used as they are, flexible adaptation to the site configuration is possible, and it can be easily applied to different plant environments.

[0114] The image processing unit 72 obtains predetermined process data, for example, by performing image analysis on the image data acquired by the camera 71. For example, the image processing unit 72 includes a data storage function for saving image data to a predetermined storage area, a data extraction function for extracting measurement information from the image as text, and a list creation function for inputting the extracted measurement information into a predetermined format.

[0115] The data storage function is implemented, for example, by using RPA (Robotic Process Automation). As a result, image data is saved to a predetermined storage area (for example, in a predetermined folder) of a storage device (not shown) that is accessible to the image processing unit 72.

[0116] The data extraction function is implemented, for example, by OCR and image processing. The data extraction function extracts numerical information from image data as text, then formats the extracted text to extract only the numerical information of the required items, and creates a list in a predetermined format. This list shows the items and the corresponding measurement information.

[0117] Figure 11 shows an example of a list. The list shown in Figure 11 includes items such as GD pressure on the outer cylinder side of the pressure equalization pipe, GD pressure on the inner cylinder side of the pressure equalization pipe, methane fermentation layer outer cylinder level, methane fermentation layer inner cylinder level, digestate temperature, hot water temperature, biogas flow rate, and ammonia ion concentration. Measurement information for each item is also shown.

[0118] In this way, the image processing unit 72 extracts only the predetermined information from the image data acquired by the camera 71. In other words, by constructing the system so that the image processing unit 72 extracts only the data that can be sent to the cloud service 101, it becomes possible to prevent information that the plant wants to keep confidential, which is displayed on the system screen, from being leaked to the outside.

[0119] Furthermore, the image processing unit 72 processes the image data acquired by the camera 71 and automatically extracts measurement information, thus reducing manual recording work. This makes it possible to reduce the burden on on-site personnel.

[0120] By acquiring measurement information using camera 71, it becomes unnecessary to connect control systems such as DCS and SCADA to external devices via a network. This avoids the risk of unauthorized access to the control system from the outside, enabling stable plant operation while maintaining security.

[0121] The list created by the image processing unit 72 may be stored in a predetermined database (storage device). Alternatively, it may be transmitted to the cloud service 101 via the communication unit 73. By storing the list in a designated database, centralized information management can be achieved, facilitating subsequent analysis and visualization. Furthermore, the list information may be notified to each terminal via the cloud service 101. This allows workers to understand and assess the equipment status without having to go to the site. Various publicly known methods can be used for notification. For example, the information could be displayed on a monitor in real time, distributed to each terminal via email, or the list of information could be uploaded to a web page on a web server, allowing each terminal to access this web page and check the information.

[0122] Alternatively, instead of transmitting the measurement information itself as described above, warning judgment information based on various measurement information may be sent to the cloud service 101. In this case, the information acquisition device further includes, for example, an alarm judgment unit 74 that performs an alarm judgment based on the measurement information acquired by the image processing unit 72, as shown in Figure 12.

[0123] The alarm determination unit 74 compares, for example, the measurement information acquired by the image processing unit 72 with a preset threshold or a threshold derived from the trend of past measurement information, and makes a determination regarding the alarm based on the comparison result. The communications unit 73 transmits the alarm determination information from the alarm determination unit 74 to the cloud service 101.

[0124] The alarm determination unit 74 assigns meaning (tags) to the measurement information acquired by the image processing unit 72, providing information that allows for intuitive identification of the abnormality level. Tagging is performed based on the comparison result of the measured value and the set threshold, and labels such as words like "normal," "caution," and "alarm," or color information such as red, yellow, and blue, or keywords such as OK and NG are assigned. Furthermore, by referring to past trends and statistical models, the alarm level can be automatically corrected if a sudden change or abnormal trend is detected. The tagged information is transmitted to the cloud service 101, and the label and numerical value are displayed together on each terminal 105, so the status can be easily understood even without specialized knowledge. This enables quantitative and visual driving support that does not rely on human judgment, achieving both rapid response to abnormalities and assurance of information confidentiality.

[0125] The cloud service 101, for example, notifies each terminal 105 of the received alarm judgment information. As a result, the display screen of each terminal 105 displays the abnormal judgment information for the measurement data of each item.

[0126] By notifying only alarm judgment information tagged with measurement data, detailed operating data is not disclosed externally, ensuring the confidentiality of the plant. Labeled information is displayed in an intuitively understandable format such as "Normal," "Caution," and "Alarm," making it easy for even novice operators without specialized knowledge to make operational decisions. Furthermore, by abstracting numerical information, language dependency is eliminated, enabling visually understandable operational support for hearing-impaired individuals and foreign workers. As a result, information confidentiality and ease of operation are achieved, realizing safe and efficient remote monitoring.

[0127] Automating alarm determination allows for quantitative assessment of the plant's condition without relying on the experience or intuition of workers. This enables even new employees and non-specialists to intuitively understand the plant's status by checking the alarm determination information (normal / caution / alarm).

[0128] The plant operation monitoring system 100 may include a storage device for storing image data (video data) captured by the camera 71. The location of this storage device is not particularly limited. Specifically, it may be located on a network to which the cloud service 101 can be connected or on a cloud server that implements the cloud service 101. In this way, by saving the video data captured by camera 71 and configuring the system so that users with designated access rights can access this video data, it becomes possible to remotely check the management video of the plant. It is also possible to refer back to data from a certain period of time. This makes it easier to track the cause and re-analyze when an anomaly occurs.

[0129] Alternatively, the image data acquired by the camera 71 may be transmitted to the cloud service 101 via the communication unit 73. In this case, the functions of the image processing unit 72 and alarm determination unit 74 described above are installed in the cloud service 101, and the various processes described above are executed in the cloud service 101.

[0130] When retrofitting the Plant Operation Monitoring System 100C to an existing plant, historical data can be acquired. Furthermore, data can be acquired by reading gauges such as vibration and pressure gauges with a camera, as well as data from weighing scales, etc. Various types of data can also be acquired, such as raw material information listed on the receiving manifest, and raw material information entered voluntarily.

[0131] An example of a decision-making flow for issuing an alert is shown. Figure 4 is a flowchart of the decision-making process. As shown in Figure 4, as an example of sensor measurement, the pH of the adjustment tank 12 is measured using pH sensor A-1 (S-1). The data sent to cloud service 101 is analyzed to determine whether the data is appropriate (S-2). The data measurement results are collected, analyzed, and reported.

[0132] Next, the operation continuation management function's continuation necessity determination function performs a measurement result OK judgment (Y=OK / N=NG).

[0133] If the pH measurement result is judged as OK (Y=OK), then the next step is to determine whether operation can be continued (Y=OK / N=NG).

[0134] If the operation continuation OK judgment is Y (=OK), then the process returns to collecting, analyzing, and reporting pH measurement results.

[0135] On the other hand, if the aforementioned measurement result is judged as N (=NG), the alarm activation process (S-3) is executed. Measurements are typically performed once an hour in batch processing, but this can be adjusted as needed depending on the measurement target.

[0136] To summarize the above-mentioned operational management, various sensor data from the biogas plant 10 is measured and sent from the edge sensor (102A+102B) 102 to the cloud service 101 via distributed communication. Within this cloud service 101, various processes are performed, including the data acquisition function of the data acquisition unit 104a, the data storage function of the data storage unit 104b, the alarm issuance function of the alarm issuance unit 104c, the data visualization function of the visualization unit 104d, and the future prediction function of the measurement data 104e, in order to grasp the trends of the measured values.

[0137] Based on this collected information, for example, in a biological processing (biogas) plant, if a tendency for decreased activity of methane-producing bacteria in a methane fermentation tank is observed, a human or AI can make a decision before the methane-producing bacteria die and take feedforward measures such as adjusting the input amount. This allows for the construction of a system focused on preventing decreased activity and death of plant equipment.

[0138] The above explanation uses a biological treatment plant as an example of plant treatment, but the disclosure is not limited to this.

[0139] In this embodiment, the plant is applied to a biogas plant. In the methane fermentation tank 13, in order to prevent a decrease in the activity and death of methane-producing bacteria, the input of heavy raw materials is reduced to stabilize the process. This is achieved by temporarily stopping the input, and by adjusting the raw material concentration and temperature. Furthermore, when an acid fermentation tank (not shown) is installed downstream of the adjustment tank 12, measures are taken to prevent a decrease in the activity or death of acid-producing bacteria. These measures include measuring the pH, reducing the input of raw materials to increase the residence time, and adjusting the raw material concentration and temperature to promote acid production and control the degree of acid fermentation.

[0140] Furthermore, when applying this technology to plants other than biogas plants, such as "photobioreactors," for example, in the treatment tanks (pipes, raceways, bags, etc.) used to fix CO2, measures are taken to prevent algal failure or death by checking the permeability of the pipe surface and the concentration of nutrients (phosphate, iron, etc.) contained in the solvent, thereby maintaining suitability.

[0141] Furthermore, when applying this technology to plants other than biogas plants, such as "Anammox tanks (sewage treatment)," for example, in denitrification treatment using Anammox bacteria, measures are taken to prevent the decrease in activity and death of Anammox bacteria. These measures include measuring the DO (dissolved oxygen) concentration and ORP (oxidation-reduction potential) to control the degree of anaerobic conditions to an appropriate level, and also controlling the nitrite and nitrate concentrations.

[0142] Furthermore, when applying this technology to plants other than biogas plants, such as "activated sludge treatment tanks (sewage treatment)," taking the "activated sludge treatment tank" as an example, measures are taken to prevent decreased activity and death of activated sludge, including optimizing growth conditions, removing harmful substances, improving mixing and aeration, increasing the amount of activated sludge, and improving the process.

[0143] Furthermore, when applying this technology to plants other than biogas plants, such as "organic waste treatment facilities (sewage treatment)," for example, in a "denitrification tank," measures are taken to prevent the decrease in activity and death of denitrifying bacteria by measuring the DO (dissolved oxygen) concentration and ORP (oxidation-reduction potential) and controlling the degree of anaerobic conditions to an appropriate level.

[0144] Furthermore, when applying this technology to plants other than biogas plants, such as "wastewater treatment facilities (sewage treatment)," taking "nitrification tanks" as an example, measures are taken to prevent the decrease in activity and death of nitrifying bacteria by measuring the dissolved oxygen (DO) concentration and controlling the aeration rate so that the degree of aerobicity is appropriate.

[0145] Although one embodiment of the present disclosure has been described above, the present disclosure is not limited to the example described in the above embodiment. [Explanation of Symbols]

[0146] 10 Biogas Plants 71 Camera 72 Image Processing Unit 73 Communications Department 74 Alarm judgment section 100A~100C Plant Operation Monitoring System 101 Cloud Services 102 Edge Sensor 102A Sensor 102B Edge Devices 104a Data acquisition unit 104b Data storage section 104c Alarm alarm unit 104d Visualization section 104e Future prediction function for measurement data 105 devices ((PCs, tablets, mobile phones, etc.)) 106 Other Information 107 Plant Control System Information

Claims

1. A plant operation monitoring system that monitors the operation of the plant, Measuring means installed within the aforementioned plant, A communication means that can be connected to the plant operation monitoring device via a communication network, and transmits measurement information measured by the measurement means installed in the plant to the plant operation monitoring device. It has, The aforementioned plant is a biogas plant equipped with a methane fermentation tank for methane fermentation of organic raw materials, The measurement means includes an ammonia concentration measuring means for obtaining the ammonia concentration, The ammonia concentration measuring means is a plant operation monitoring system located downstream of the methane fermentation tank.

2. The plant is equipped with a digestate storage tank for storing the digestate discharged from the methane fermentation tank, The plant operation monitoring system according to claim 1, wherein the ammonia concentration measuring means is installed in at least one of the following: a transport line that transports digestate from the methane fermentation tank to the digestate storage tank or a device installed on the transport line, the digestate storage tank, and a transport line that transports digestate from the digestate storage tank.

3. The ammonia concentration measuring means includes a sensor, The plant operation monitoring system according to claim 1, wherein the detection portion of the sensor is installed so as to be immersed in the digestive fluid.

4. The plant is equipped with a digestate storage tank for storing the digestate discharged from the methane fermentation tank, The ammonia concentration measuring means includes a sensor for measuring ammonia gas concentration, The plant operation monitoring system according to claim 1, wherein the sensor is installed in at least one of the following: the space above the digestate storage tank or a gas transport line transporting gas from said space; a tank for storing digestate located between the methane fermentation tank and the digestate storage tank; and the apparatus.

5. Equipped with multiple measurement means, The plant operation monitoring system according to claim 1, wherein the plurality of measuring means include at least one of the following: a sensor for measuring parameters relating to the characteristics of process gas generated in the plant; a sensor for measuring parameters relating to the characteristics of liquid used in the plant; a sensor for measuring parameters relating to the characteristics of organic raw materials for methane fermentation used in the plant; and a sensor for measuring parameters relating to the characteristics of liquid that fluctuates due to the water treatment process generated in the plant.

6. The plant operation monitoring system according to claim 5, wherein the measurement information of each of the sensors is transmitted to the plant operation monitoring device by the corresponding communication means.

7. The plant operation monitoring system according to claim 5, wherein the parameters relating to the properties of the liquid used in the plant and / or the properties of the liquid that fluctuate due to the water treatment process occurring in the plant include at least one of pH, temperature, pressure, liquid level, ammonium ion concentration, or flow rate.

8. The plant further comprises a conditioning tank for supplying organic raw materials to the methane fermentation tank, The plant operation monitoring system according to claim 5, wherein the plurality of measurement information includes a sensor that detects specific information in at least one of the adjustment tank, the methane fermentation tank, or the digestate storage tank.

9. The plant operation monitoring system according to claim 8, wherein the plurality of sensors include at least one of a pH sensor for measuring the pH in the adjustment tank, a temperature sensor for measuring the temperature in the adjustment tank, a liquid level sensor for measuring the liquid level in the methane fermentation tank, a pressure sensor for measuring the internal pressure of the methane fermentation tank, a pH sensor for measuring the pH of the digestate storage tank, a temperature sensor for measuring the temperature of the digestate storage tank, or an ammonia concentration measuring sensor for measuring the ammonia concentration in the digestate storage tank.

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