Systems and methods for assisting in wastewater treatment

Abstract

To provide a technique capable of automatically determining a suitable control target value for a blower.SOLUTION: A sewage treatment support system comprises a clustering unit, a transition condition learning unit, and a control target value determining unit. The clustering unit carries out clustering of state data in every specified period showing a state of activated sludge present in an aeration tank disposed in a sewage treatment plant carrying out sewage treatment according to a standard activated sludge method. The transition condition learning unit learns a condition of transition between clusters in a plurality of clusters generated by the clustering. The control target value determining unit determines a control target value for a blower delivering air for aerating the aeration tank on the basis of the learned transition condition and the time series of new state data showing the state of the activated sludge present in the aeration tank. The state data includes water quality data showing the water quality of secondary treatment water in the sewage treatment plant and operation data showing an operation state of the sewage treatment plant.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to computer technology for supporting sewage treatment. [Background technology]

[0002] In sewage treatment plants that treat sewage using the standard activated sludge process, ammonia (NH3) in wastewater is nitrified using the metabolic activity of various nitrifying bacteria that live in the activated sludge in the aeration tank. Examples of nitrifying bacteria used in this process include Ammonia Oxidizing Bacteria (AOB) and Ammonia Oxidizing Archaea (AOA), which oxidize ammonia to produce nitrite (HNO2), and Nitrite Oxidizing Bacteria (NOB), which oxidize nitrite to produce nitrate (HNO3).

[0003] The strength of the nitrification ability of activated sludge depends on the viable cell count and cell concentration of various nitrifying bacteria in the activated sludge. Furthermore, the viable cell count and cell concentration of various nitrifying bacteria in the activated sludge generally correlate positively with the dissolved oxygen concentration (DO) in the activated sludge. Therefore, sewage treatment plants that treat sewage based on the standard activated sludge process often include a means for aerating the aeration tank in order to maintain a constant dissolved oxygen concentration in the activated sludge in the aeration tank (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 4552393 Summary of the Invention [Problem to be solved by the invention]

[0005] Aeration in an aeration tank is typically performed using a blower attached to the aeration tank. To ensure that the sewage treatment plant where the blower is installed meets legal water quality standards, the blower is typically controlled to maintain a constant dissolved oxygen concentration in the activated sludge in the aeration tank and a constant inflow ratio, which represents the ratio of the amount of air discharged by the blower (hereinafter referred to as the "air discharge rate") to the amount of sewage inflow to the sewage treatment plant (hereinafter referred to as the "sewage inflow rate"). However, indicators such as the dissolved oxygen concentration in the activated sludge and the inflow ratio are not definitive and are difficult to uniquely determine because they change with fluctuations in the amount of sewage inflow to the sewage treatment plant and changes in the condition of the activated sludge in the aeration tank. Therefore, when attempting to set these indicators as control target values ​​for the blower and maintain them at constant levels, skilled operators must adjust the blower's operation in a timely manner, which places a burden on the individual operator and the sewage treatment plant management.

[0006] Furthermore, since reducing power consumption is an urgent issue in the operation of sewage treatment plants these days, it is preferable that the blower control target value be one that allows the sewage treatment plant to meet the effluent quality standards while also achieving energy conservation.

[0007] In view of the above-mentioned problems, an object of the present invention is to provide a technique capable of automatically determining an appropriate control target value for a blower. [Means for solving the problem]

[0008] The sewage treatment support system according to the present invention comprises a clustering unit, a transition condition learning unit, and a control target value determination unit. The clustering unit performs clustering of status data for each predetermined period that represents the status of activated sludge in an aeration tank of a sewage treatment plant that performs sewage treatment based on a standard activated sludge process. The transition condition learning unit learns the conditions for transitions between clusters in a plurality of clusters generated by the clustering. The control target value determination unit determines the control target value of a blower that discharges air to aerate the aeration tank, based on the learned transition conditions and a time series of new status data that represents the status of the activated sludge in the aeration tank. The status data includes water quality data that represents the quality of secondary effluent from the sewage treatment plant and operating data that represents the operating status of the sewage treatment plant.

[0009] Other problems and solutions disclosed in this application will be made clear by the description of the preferred embodiment and drawings. [Effects of the Invention]

[0010] According to the present invention, an appropriate blower control target value can be automatically determined. [Brief explanation of the drawings]

[0011] [Figure 1] 1 shows an example of the configuration of a sewage treatment support system according to an embodiment. [Figure 2] 1 shows an example of the overall flow of processing performed in an embodiment. [Figure 3] 10 shows an example of the flow of a transition condition learning process. [Figure 4] An example of the water quality table configuration is shown below. [Figure 5] 10 shows an example of the configuration of an operation table. [Figure 6] 10 shows an example of the configuration of a state table. [Figure 7] 10 shows an example of the configuration of a clustering state table. [Figure 8] 1 shows an example of transition between clusters. [Figure 9] An example of a transition condition learning matrix is ​​shown below. [Figure 10] 10 shows an example of the flow of a control target value determination process. [Figure 11] 10 shows an example of the flow of a transition destination estimation process. [Figure 12] An example of a transition destination estimation matrix is ​​shown below. [Figure 13A] 1 shows an example of a display screen. [Figure 13B] 1 shows an example of a display screen. [Figure 14] 10 shows an example of the flow of a transition condition learning process when learning transitions over multiple periods. [Figure 15] 10 shows an example of a mean matrix for transition condition learning. DETAILED DESCRIPTION OF THE INVENTION

[0012] In the following description, an "interface apparatus" may refer to one or more interface devices, which may be at least one of the following: One or more I / O (Input / Output) interface devices. The I / O (Input / Output) interface devices are interface devices for at least one of the I / O device and a remote display computer. The I / O interface device for the display computer may be a communications interface device. The at least one I / O device may be a user interface device, for example, either an input device such as a keyboard and a pointing device, or an output device such as a display device. One or more communication interface devices. The one or more communication interface devices may be one or more homogeneous communication interface devices (e.g., one or more NICs (Network Interface Cards)) or two or more heterogeneous communication interface devices (e.g., an NIC and an HBA (Host Bus Adapter)).

[0013] In the following description, "memory" refers to one or more memory devices, which are an example of one or more storage devices, and may typically be a primary storage device. At least one memory device in the memory may be a volatile memory device or a non-volatile memory device.

[0014] In the following description, a "persistent storage device" may refer to one or more persistent storage devices, which are an example of one or more storage devices. A persistent storage device may typically be a non-volatile storage device (e.g., an auxiliary storage device), and more specifically, may be, for example, a hard disk drive (HDD), a solid state drive (SSD), a non-volatile memory express (NVME) drive, or a storage class memory (SCM).

[0015] In the following description, the term "storage device" may refer to at least one of memory and persistent storage device.

[0016] Furthermore, in the following description, a "processor" may refer to one or more processor devices. The at least one processor device may typically be a microprocessor device such as a CPU (Central Processing Unit), but may also be another type of processor device such as a GPU (Graphics Processing Unit). The at least one processor device may be single-core or multi-core. The at least one processor device may also be a processor core. The at least one processor device may also be a processor device in a broader sense, such as a circuit that is a collection of gate arrays written in a hardware description language that performs some or all of the processing (for example, an FPGA (Field-Programmable Gate Array), a CPLD (Complex Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit)).

[0017] In the following description, functions may be described using the expression "yyy unit." However, the functions may be realized by one or more computer programs executed by a processor, by one or more hardware circuits (e.g., FPGAs or ASICs), or by a combination thereof. When a function is realized by a program executed by a processor, the specified processing is performed using a storage device and / or an interface device, as appropriate, and therefore the function may be considered to be at least a part of the processor. Processing described using a function as the subject may be processing performed by a processor or a device having the processor. A program may be installed from a program source. The program source may be, for example, a computer from which the program is distributed or a computer-readable recording medium (e.g., a non-transitory recording medium). The description of each function is an example, and multiple functions may be combined into one function or one function may be divided into multiple functions.

[0018] In the following description, a process may be described using a "program" as the subject, but the process described using a program as the subject may also be a process performed by a processor or a device having that processor. Two or more programs may be realized as one program, or one program may be realized as two or more programs.

[0019] In the following description, information that provides an output for an input may be described using expressions such as "xxx table," but this information may be a table of any structure, or may be a neural network that generates an output for an input, or a learning model such as a genetic algorithm or random forest. Therefore, the "xxx table" may be referred to as "xxx information." In the following description, the structure of each table is an example, and one table may be divided into two or more tables, or all or part of two or more tables may be one table.

[0020] In the following description, "UI" is an abbreviation for User Interface, which typically refers to GUI (Graphical User Interface).

[0021] In the following description, a system that supports sewage treatment will be referred to as a "sewage treatment support system." The sewage treatment support system may be one or more physical computers, a software-defined system implemented by at least one physical computer executing predetermined software, or a system implemented on a cloud platform (typically multiple types of computing resources including processors and storage devices). For example, if a computer has a display device and displays information on its own display device, the computer may be a sewage treatment support system. Also, for example, if a first computer (e.g., a server) transmits output information to a remote second computer (a display computer (e.g., a user terminal described below)) and the display computer displays the information (the first computer displays information on the second computer), at least the first computer of the first and second computers may be a sewage treatment support system. In other words, when the sewage treatment support system "displays output information," it may mean that the output information is displayed on a display device possessed by the computer, or that the computer sends the output information to a display computer (in the latter case, the output information is displayed by the display computer).

[0022] This embodiment will be described in detail below.

[0023] The following description of the embodiment will be given using as an example a sewage treatment support system that determines a control target value for a blower that is installed in an aeration tank of a sewage treatment plant that performs sewage treatment based on the standard activated sludge process and that discharges air to aerate the aeration tank. The blower is controlled by constant dissolved oxygen concentration control, which aims to maintain a constant dissolved oxygen concentration in the activated sludge in the aeration tank (hereinafter simply referred to as "dissolved oxygen concentration"). Therefore, the control target value for the blower determined by the sewage treatment support system of this embodiment is the dissolved oxygen concentration in the activated sludge in the aeration tank.

[0024] The blower can also be controlled by constant inflow ratio control, which aims to maintain a constant inflow ratio, which represents the ratio of the blower's discharge air volume to the amount of sewage inflow into the sewage treatment plant. In this case, the sewage treatment support system determines the blower control target value as the inflow ratio.

[0025] The sewage treatment support system according to this embodiment determines the blower control target value based on weekly data (hereinafter referred to as "status data") representing the state of activated sludge in the aeration tank. This status data includes data (hereinafter referred to as "water quality data") representing the weekly water quality of water after secondary treatment at a sewage treatment plant (hereinafter referred to as "secondary treated water") and data (hereinafter referred to as "operational data") representing the hourly operating status of various equipment in the sewage treatment plant, including the blower. Here, the quality of the secondary treated water reflects the state of the activated sludge in the aeration tank, for example, the level of nitrification activity. As described above, the level of nitrification activity of activated sludge is influenced by indicators such as the viable cell count and bacterial cell concentration, which represent the growth status of various nitrifying bacteria in the activated sludge. Therefore, water quality testing to obtain new water quality data is typically performed weekly, taking into account the growth rate of microorganisms in the activated sludge, including nitrifying bacteria. The water quality data in this embodiment also represents the results of weekly water quality testing.

[0026] When setting the blower's control target value, it is necessary to set the control target value for the following week's operation, taking into account a time lag of about one week after obtaining the results of the weekly water quality test. However, because the water quality of the secondary effluent changes during that time, it is difficult to set an appropriate control target value for the activated sludge state during operation if the control target value is derived directly from water quality data or status data including water quality data. Furthermore, each measurement value included in the water quality data and operation data changes with each measurement, and the exact same value is never measured. Therefore, it is difficult to extract general trends from these measurements and build a learning model. Therefore, the sewage treatment support system of this embodiment clusters the weekly status data to generate clusters containing multiple status data. Here, multiple status data included in the same cluster each represent the activated sludge state for the week in which the blower was operated under the same or similar conditions. In other words, each cluster represents the activated sludge state for a period in which the same or similar control target value was set. The sewage treatment support system of this embodiment focuses on the transitions between clusters that occur as the operating conditions of the blower change for the multiple clusters generated by clustering, and determines the control target values ​​of the blower appropriate for the state of the activated sludge during operation by learning the conditions for the transition between clusters (hereinafter referred to as "transition conditions") when a desirable transition occurs. This makes it possible to set the control target values ​​appropriate for the state of the activated sludge during blower operation based on the results of a water quality test conducted about one week before the start of blower operation.

[0027] FIG. 1 shows an example of the configuration of a sewage treatment support system according to this embodiment.

[0028] Each component of the sewage treatment support system 100 shown in Figure 1 is realized by hardware including processor devices such as a CPU (Central Processing Unit) and various co-processors (hereinafter simply referred to as "processors"), storage devices such as memory and storage, wired or wireless communication lines and interface devices that connect them, and software that is stored in the storage devices and supplies processing instructions to the arithmetic unit.

[0029] The storage device stores at least a sewage treatment support program. The sewage treatment support program is a computer program for supporting sewage treatment. When the sewage treatment support program is executed by the processor, processes such as a transition condition learning process, a control target value determination process, and a transition destination estimation process are performed. Details of these processes will be described later.

[0030] The sewage treatment support program may be composed of a device driver, an operating system, various application programs located at higher layers, and a library that provides common functions to these programs. Each block described below represents a functional block, not a hardware configuration.

[0031] The sewage treatment support system 100 has the following functional blocks: a data processing unit 10, a data storage unit 30, a user interface unit (not shown), and a communication unit (not shown).

[0032] The data processing unit 10 performs various data processing operations based on the user's operation input detected by the user interface unit, the data acquired by the communication unit, and the programs and data stored in the data storage unit 30. The data processing unit 10 also functions as an interface between the user interface unit, the communication unit, and the data storage unit 30.

[0033] The data processing unit 10 has the functional blocks of a condition learning unit 120 and an operation planning unit 140. The condition learning unit 120 executes various processes for constructing a machine learning model. The operation planning unit 140 executes various processes for controlling the blower.

[0034] The condition learning unit 120 includes a clustering unit 220 and a transition condition learning unit 240. The operation planning unit 140 includes a control target value determination unit 420 and a power consumption estimation unit 440.

[0035] The clustering unit 220 performs clustering processing on state data for each predetermined period that represents the state of activated sludge in an aeration tank of a sewage treatment plant that performs sewage treatment based on the standard activated sludge method.

[0036] The transition condition learning unit 240 executes a process for learning transition conditions between multiple clusters generated by clustering. This process executed by the transition condition learning unit 240 is referred to as a transition condition learning process. In this transition condition learning process, the transition condition learning unit 240 outputs a model that has learned the transition of clusters from one week to the next week when one or more values ​​including a control target value among multiple values ​​are input. This model is a model that uses a pair of cluster numbers that transitioned between one week and the next week as a response variable and one or more values ​​as an explanatory variable. Details of the transition condition learning process will be described later with reference to FIGS. 2 and 3.

[0037] The control target value determination unit 420 executes a process to determine a control target value for a blower that discharges air for aerating the inside of the aeration tank, based on the learned transition conditions and a time series of new status data that represents the status of activated sludge in the aeration tank. This process executed by the control target value determination unit 420 is referred to as a control target value determination process. Details of the control target value determination process will be described later with reference to FIG. 10.

[0038] The above-described control target value determination process includes a transition destination estimation process, the details of which will be described later with reference to FIG.

[0039] The power consumption estimation unit 440 executes a process to estimate the power consumption of the blower based on the amount of air discharged from the blower. This allows the reduction of power consumption to be taken into consideration when determining the blower's control target value, without having to separately measure the amount of power consumed by the blower. Note that there is a roughly positive correlation between the amount of air discharged from the blower and the amount of power consumed by the blower.

[0040] The data processing unit 10 can realize these functional blocks by executing a predetermined program.

[0041] The data storage unit 30 is configured using a storage device such as RAM or flash memory, and stores in the storage device a program that supplies various processing commands to the data processing unit 10, and data representing various information used in the processing executed by the data processing unit 10. For example, water quality data stored in the water quality data storage unit 320, operation data stored in the operation data storage unit 340, etc. are stored in the storage device by the data storage unit 30. By reading and writing this information to the data storage unit 30, the data processing unit 10 can realize the functional blocks of the clustering unit 220, transition condition learning unit 240, control target value determination unit 420, and power consumption estimation unit 440 described above.

[0042] The data storage unit 30 has the functional blocks of a water quality data storage unit 320 and an operation data storage unit 340 .

[0043] The water quality data storage unit 320 mainly stores water quality data represented by a water quality table shown in Figure 4. The water quality table is a table for managing water quality data. Details of the water quality data and the water quality table will be described later with reference to Figures 3 and 4.

[0044] The driving data storage unit 340 mainly stores driving data represented by a driving table exemplified in Fig. 5. The driving table is a table for managing driving data. Details of the driving data and the driving table will be described later with reference to Figs. 3 and 5.

[0045] The user interface unit accepts input operations from the user and is responsible for user interface-related processing such as image display and audio output. The user interface unit has functional blocks for an input unit and an output unit. The input unit detects various operations from the user. The input unit is configured using, for example, a keyboard, pointing device, touch panel, etc. The output unit displays the determined blower control target value on the display device 50 and also displays various screens and outputs audio on the display device 50. The display device 50 is configured using, for example, an LCD display, a touch screen, etc.

[0046] The communication unit is responsible for processing communications with user terminals owned by each user of the sewage treatment support system 100 and other devices such as server devices constituting the sewage treatment plant monitoring and control system 70 via the Internet (an example of a communication network). The monitoring and control system 70 is a system that monitors and controls the operating status of various pieces of equipment in the sewage treatment plant, including blowers. The communication unit is configured using, for example, a NIC (Network Interface Card) or an HBA (Host Bus Adapter).

[0047] In this embodiment, the functions of the sewage treatment support system 100 have been described as being implemented integrally by a single computer. However, these functions may also be implemented by multiple interconnected computers or server devices. Furthermore, the sewage treatment support system 100 may be configured to include a general-purpose computer such as a laptop PC and a web browser installed thereon, or may be configured to include a web server and various portable devices.

[0048] Next, the flow of each process executed by the sewage treatment support system 100 will be described.

[0049] FIG. 2 is a flowchart showing an example of the overall flow of processing performed in this embodiment.

[0050] In step S210, the data processing unit 10 executes a process in which the transition condition learning unit 240 determines whether or not re-learning of the transition conditions between clusters is necessary. The sewage treatment support system 100 of this embodiment executes each process from step S220 onward when a water quality test of secondary treated water is conducted and new water quality data representing the test results is stored in the water quality data storage unit 320—in other words, when a new record representing the test results is recorded in the water quality table described below. Therefore, the determination of whether or not re-learning is necessary is made, for example, by determining whether or not the water quality data storage unit 320 has stored new water quality data. If it is determined that re-learning is necessary (step S210: Y), the process proceeds to step S220 to re-learn the transition conditions between clusters. If it is determined that re-learning is not necessary (step S210: N), the process proceeds directly to step S240.

[0051] In step S220, the data processing unit 10 executes a transition condition learning process using the transition condition learning unit 240. This allows the control target value determination process in step S230 to be executed with high accuracy. When the transition condition learning process is completed, the data processing unit 10 proceeds to step S230.

[0052] In step S230, the data processing unit 10 executes a control target value determination process using the control target value determination unit 420. This control target value determination process is performed based on the result of the transition condition learning process in step S220. As a result, an appropriate blower control target value is automatically determined. When the control target value determination process is completed, the data processing unit 10 proceeds to step S240.

[0053] In step S240, if the data processing unit 10 determines in step S210 that re-learning of the transition conditions is necessary (step S210: Y), the data processing unit 10 causes the display device 50 to display a screen showing the results of the control target value determination process currently executed in step S230. On the other hand, if the data processing unit 10 determines in step S210 that re-learning of the transition conditions is unnecessary (step S210: N), the data processing unit 10 causes the display device 50 to display a screen showing the results of the control target value determination process previously executed in step S230. The output unit causes the display device 50 to display the determined control target value of the blower, which is the result of the control target value determination process, in the manner of the display screens exemplified in FIGS. 13A and 13B. When the data processing unit 10 completes the process in step S240, it ends the process shown in the flowchart of FIG. 2.

[0054] In this manner, the sewage treatment support system 100 of this embodiment executes the processes of steps S210 to S240 in Fig. 2 to determine favorable blower control target values ​​and displays them on the display device 50. Of these, the processes for favorably determining the blower control target values ​​can be broadly divided into a transition condition learning process executed by the transition condition learning unit 240 in step S220, and a control target value determination process executed by the control target value determination unit 420 in step S230. Therefore, the transition condition learning process executed in step S220 will first be described in detail with reference to the flowchart in Fig. 3. Furthermore, the control target value determination process executed in step S230 will be described in detail later with reference to the flowchart in Fig. 10.

[0055] FIG. 3 shows an example of the flow of the transition condition learning process.

[0056] In step S301, the data processing unit 10 acquires one of the most recent pieces of water quality data stored in the water quality data storage unit 320 in the water quality table. An example of the configuration of this water quality table is shown in FIG. 4. As mentioned above, water quality tests to acquire water quality data are conducted weekly. Therefore, the water quality table 400 has a record for each week in which a water quality test was conducted. The record includes as items at least the date the water quality test was conducted (hereinafter referred to as the "measurement date") and the biochemical oxygen demand (BOD) of the secondary treated water on that measurement date. The records may also include measurements of water quality test items, such as the chemical oxygen demand (COD), total phosphorus (TP), and total nitrogen (TN) of the secondary treated water on the measurement date, that are stipulated in the effluent quality standards, which set standards for the water quality of effluent from sewage treatment plants (hereinafter simply referred to as "effluent quality"). That is, the water quality table 400 displays the results of weekly water quality tests by linking the measurement date with each measurement value representing the water quality of the secondary treated water or the effluent water on that measurement date for each record. In the example shown in FIG. 4, the water quality table 400 records that the biochemical oxygen demand of the secondary treated water was 4.2 mg / L and the chemical oxygen demand was 7.2 mg / L in a water quality test conducted on April 8, 2020. When the process in step S301 is completed, the data processing unit 10 proceeds to step S302.

[0057] In step S302, the data processing unit 10 acquires the operation data stored by the operation data storage unit 340 in the operation table. An example of the configuration of this operation table is shown in FIG. 5. As described above, the operation data is the hourly operating results of various pieces of equipment in the sewage treatment plant, including the blower, recorded by the monitoring and control system 70. Therefore, the operation table 500 has a record for each hour. The record includes, as items, at least the date and time when the monitoring and control system 70 recorded the operation data, the amount of air discharged from the blower from the date and time when the operation data was last recorded until the current date and time, the dissolved oxygen concentration in the activated sludge in the aeration tank at the current date and time, the amount of sludge returned to the aeration tank at the current date and time (hereinafter referred to as the "returned sludge amount"), and the amount of sewage inflow into the sewage treatment plant at the current date and time. The records may also include values ​​such as the concentration of activated sludge in the aeration tank (mixed liquor suspended solids; MLSS), measurements of water quality test items such as total phosphorus and total nitrogen, the amount of polyaluminum chloride (PAC) injected, and the amount of nitrification liquid inflow. That is, the operation table 500 records, for each record, a date and time in association with the value of each index representing the operational performance of the sewage treatment plant at that date and time, thereby representing the hourly operational status of various pieces of equipment in the sewage treatment plant, including the blower. According to the example shown in FIG. 5, the operation table 500 records the data that shows the blower discharge air volume at "00:00:00 on April 1, 2020" as "20,000 m 3 " and it is recorded that the dissolved oxygen concentration in the activated sludge in the aeration tank was "1.3 mg / L." In order to match the time unit of the water quality data in the processing of step S303 described below, the data processing unit 10 acquires the operating data for the most recent week and converts it into a weekly average in the processing of step S302. When the processing of step S302 is completed, the data processing unit 10 proceeds to step S303.

[0058] In step S303, the data processing unit 10 generates status data based on the latest water quality data acquired in step S301 and the operating data representing the weekly average for the most recent week acquired in step S302. The generated status data is stored in the data storage unit 30 in a status table. An example of the configuration of this status table is shown in FIG. 6. As described above, the status data includes water quality data representing the results of weekly water quality tests. Therefore, the status table 600 has a record for each period equivalent to one week. The record represents the period equivalent to one week, the average daily value of the blower discharge air volume for the week represented by the period (hereinafter referred to as the "week"), the average hourly value of the dissolved oxygen concentration in the activated sludge for the week, the average daily value of the amount of sludge returned to the aeration tank for the week, the average daily value of the amount of sewage inflow to the sewage treatment plant for the week, and the average daily value of the biochemical oxygen demand of the secondary effluent for the week. Of these, the daily average value of the biochemical oxygen demand of the secondary treated water is derived from the water quality data, and the other values ​​are derived from the operational data. That is, the status table 600 represents the weekly state of the activated sludge in the aeration tank by linking and recording, for each record, a period equivalent to one week with the water quality data and operational data values ​​for that week. For example, the data processing unit 10 generates new status data by storing water quality data representing the results of a water quality test conducted on April 8 in a record that stores operational data representing the weekly average for the week from April 1 to April 7. According to the example shown in FIG. 6, the status table 600 records, for the "week from April 1 to April 7, 2020," a daily average value of the blower's discharge air volume of "520,000 m" 3 ", and it is recorded that the hourly average value of the dissolved oxygen concentration in the activated sludge was "1.5 mg / L." When the processing in step S303 is completed, the data processing unit 10 proceeds to step S304.

[0059] In step S304, the data processing unit 10 causes the clustering unit 220 to perform clustering processing on the status data generated in step S303. The clustering processing is performed by generating a matrix with the values ​​of each row of the status data as elements and applying various clustering methods to this matrix as input. This results in clustering of the weekly status data. Status data classified into one of the multiple clusters generated by clustering is referred to as post-clustered status data. In this embodiment, clustering is performed using the k-means method. The status data is classified into one of eight clusters. For convenience, eight clusters are used in this embodiment, but the user can specify any number of clusters in advance. The data storage unit 30 stores the post-clustered status data in a post-clustered status table. An example of the configuration of this post-clustered status table is shown in FIG. 7. Like the status table 600 shown in FIG. 6, the post-clustered status table 700 shown in FIG. 7 also has a record for each period equivalent to one week. The record indicates a period equivalent to one week, the average daily value of the blower discharge air volume for that week, the average hourly value of the dissolved oxygen concentration in the activated sludge for that week, the average daily value of the amount of sludge returned to the aeration tank for that week, the average daily value of the amount of sewage inflow into the sewage treatment plant for that week, the average daily value of the biochemical oxygen demand of the secondary effluent for that week, and a cluster number for uniquely identifying the cluster into which the status data has been classified. That is, the clustered status table 700 is a table in which a cluster number is added to each record of the status table 600 illustrated in FIG. 6. According to the example illustrated in FIG. 7, the clustered status table 700 indicates that the average daily value of the blower discharge air volume is "520,000 m 3", and it is recorded that the status data for "the week from April 1 to April 7, 2020," which indicates that the hourly average value of the dissolved oxygen concentration in the activated sludge was "1.5 mg / L," is classified into a cluster assigned cluster number "1." Note that the clusters may be identified using other identification codes, such as letters or symbols, instead of the above cluster numbers. When the data processing unit 10 completes the processing in step S304, it proceeds to step S305.

[0060] In step S305, the data processing unit 10 generates a transition condition learning matrix (hereinafter simply referred to as the "learning matrix") as shown in FIG. 9 based on the clustered state data using the transition condition learning unit 240. This learning matrix represents explanatory variables and target variables in the transition condition learning process as a matrix. Among these, the matrix representing explanatory variables represents the cluster number of the cluster to which the state data belongs, the average daily blower discharge air volume for the previous week, the average hourly dissolved oxygen concentration in the activated sludge for the previous week, the average daily returned sludge volume to the aeration tank for the previous week, the average daily sewage inflow volume to the sewage treatment plant for the previous week, the average daily biochemical oxygen demand of the secondary effluent for the previous week, and the average hourly dissolved oxygen concentration in the activated sludge for the week following the current week (DO at the next state). The matrix representing the dependent variable is constructed by pairs of cluster numbers (equivalent to the combined values ​​of the respective numerical values ​​treated as character strings) that correspond to the cluster numbers belonging to a certain week and the cluster numbers belonging to the following week. For example, if data belonged to cluster i in the previous week and cluster j in the following week, the value constituting the dependent variable matrix is ​​ij. The learning matrix illustrated in FIG. 9 represents the actual cluster transitions that occurred when the measured values ​​obtained during a certain week of operation were recorded. In this way, the transition condition learning unit 240 constructs explanatory variables as a learning matrix including the amount of air discharged by the blower in the previous week, the dissolved oxygen concentration in the activated sludge in the aeration tank in the previous week, the amount of returned sludge to the aeration tank in the previous week, the amount of sewage inflow to the sewage treatment plant in the previous week, the biochemical oxygen demand of the secondary effluent from the sewage treatment plant, and the predicted dissolved oxygen concentration in the activated sludge in the aeration tank in the following week. When the process in step S305 is completed, the data processing unit 10 proceeds to step S306.

[0061] In step S306, the data processing unit 10 executes a process of learning transition conditions between clusters using the transition condition learning unit 240. The transition condition learning is performed by calculating the angle of the centroid vector representing the transition direction and the time series before and after for two clusters with adjacent time series, and learning the condition when the centroid vector has an angle equal to or less than a predetermined threshold. If no learning data is selected, the angle threshold is set to any value. The centroid of each cluster may be calculated, for example, when applying the k-means method, and stored in memory for use. In this embodiment, this process is performed using a decision tree. However, the transition condition learning process in step S306 may also be performed using other machine learning methods capable of classification, such as a support vector machine (SVM). After completing the process in step S306, the data processing unit 10 proceeds to step S307.

[0062] In step S307, the data processing unit 10 outputs the learning model using the transition condition learning unit 240. The learned model (projection of the input matrix x onto y) is saved in memory. When the processing in step S307 is completed, the data processing unit 10 ends the transition condition learning process shown in the flowchart of FIG. 3.

[0063] In the transition condition learning process, the transition condition learning unit 240 may calculate the angle between the center of gravity vector of the transitioned path and a vector of a good transition derived in advance, and learn only transitions represented by an angle equal to or less than a predetermined threshold. In this case, the transition condition learning unit 240 may also exclude data showing bad transitions. This allows for the selection of states taking into consideration medium-term transitions, and makes it possible to avoid learning transitions that are good in the short term but turn bad in the medium term. Details of the transition condition learning process for such cases will be described later with reference to the flowchart of FIG. 14.

[0064] The sewage treatment support system 100 of this embodiment inputs newly generated status data at the time of water quality testing. It then calculates the cluster with the smallest distance between the center of gravity and the status data, representing the current treatment performance of the sewage treatment plant, to identify the current cluster. It then searches for a dissolved oxygen concentration value that allows the plant to either move to a better cluster or remain in the current cluster, determines it as the blower control target value, and presents it to the user. In this process, explanatory variables for the transition conditions are constructed from the newly generated status data and the provisionally set dissolved oxygen concentration during the search process, and the system estimates which cluster the plant will transition to based on the provisionally set dissolved oxygen concentration (i.e., the next week's control target value). The system then determines the optimal control target value for the next week by verifying whether the destination cluster satisfies at least one of minimizing the blower air volume or improving water quality. This makes it possible to estimate the sewage treatment performance of a sewage treatment plant and predict whether the next water quality improvement will be necessary, based on a variety of easily available general measurement results contained in water quality data and operational data, without using activated sludge models (ASMs), which are vulnerable to disturbances, or without arranging a large number of expensive measuring instruments.In other words, each cluster represents the sewage treatment performance of the sewage treatment plant.

[0065] Figure 8 shows an example of transition between clusters. Figure 8 shows the average daily volume of blower discharge air (m 3The post-clustering status data for each week is displayed along with the cluster number on a scatter plot with the horizontal axis representing the average daily value (mg / L) of the biochemical oxygen demand (BOD) of the secondary effluent for that week (day / week) and the vertical axis representing the average daily value (mg / L) of the biochemical oxygen demand (BOD) of the secondary effluent for that week. The arrows indicate movement from the measurement point for one week to the measurement point for the next week. The points representing the post-clustering status data on the scatter plot transition depending on the blower discharge air volume. Basically, when the blower discharge air volume is increased, the points representing the post-clustering status data transition to a point where the value of the biochemical oxygen demand of the secondary effluent becomes lower. On the other hand, when the blower discharge air volume is decreased, the points representing the post-clustering status data transition to a point where the value of the biochemical oxygen demand of the secondary effluent becomes higher. Therefore, the sewage treatment support system 100 assumes that the weekly status data represents the state of the activated sludge in the aeration tank for that week and generates a transition destination estimator by performing a transition condition learning process to learn an operating method that will improve the state or maintain the current state. Then, in the control target value determination process, the generated transition destination estimator is used to estimate the dissolved oxygen concentration value that can move in the direction of a good cluster, and this is determined as the blower control target value and presented to the user of the sewage treatment support system 100 (e.g., the blower operator).

[0066] FIG. 10 shows an example of the flow of the control target value determination process.

[0067] In step S1001, the data processing unit 10 generates post-clustering state data using the control target value determination unit 420. In this process in step S1001, the control target value determination unit 420 performs the same processes as those in S1101 to S1104, which will be described later, to identify the cluster of the latest value. When the process in step S1001 is completed, the data processing unit 10 proceeds to step S1002.

[0068] In step S1002, the data processing unit 10 constructs an optimization model for determining an optimal control target value using the control target value determination unit 420. This optimization model uses the dissolved oxygen concentration value, which is the blower control target value for the following week, as a decision variable. To search for this decision variable, an optimization algorithm that can be applied without using a derivative, such as the Nelder-Mead method, is used. The objective variable of this optimization model is expressed by the following (Equation 1).

[0069] minimize w1·a1+w2(max(0,b1-b0))···(Formula 1) w1, w2: weights (any real number) a1: Air volume value of the cluster center of gravity after transition b1: Biochemical oxygen demand value of the cluster centroid after transition b0: Latest value of biochemical oxygen demand

[0070] In the above (Equation 1), if b1 is greater than b0, it is considered that the water quality has deteriorated, and therefore b0 is a penalty term. After completing the process in step S1002, the data processing unit 10 proceeds to step S1003.

[0071] In step S1003, the data processing unit 10 sets the value of the dissolved oxygen concentration to be the control target value for the blower for the next week as the decision variable using the control target value determination unit 420. When the processing in step S1003 is completed, the data processing unit 10 proceeds to step S1004.

[0072] In step S1004, the data processing unit 10 executes a transition destination estimation process, which will be described later with reference to FIG. 11, using the control target value determination unit 420. The results of the transition destination estimation process are stored in memory in the form of a table, for example, as shown in FIG. 10. In the table illustrated in FIG. 10, x is an explanatory variable in the transition destination estimation process and represents multiple values ​​included in the clustered state data for each predetermined period, which is represented as a transition destination estimation matrix (hereinafter referred to as the "transition destination estimation matrix") illustrated in FIG. 12. These multiple values ​​include the latest value of the dissolved oxygen concentration, which is the control target value immediately before the predetermined period, and the value of the dissolved oxygen concentration to be predicted, which is the control target value for the predetermined period. Furthermore, y represents the estimation result as a two-digit number consisting of the cluster number of the transition source cluster and the cluster number of the transition destination cluster. In addition, the table also records the loop count, which indicates the number of times the optimization process has been repeated. Among the components of x, the value of the dissolved oxygen concentration for the next week is a decision variable, and therefore changes each time loop processing is performed. The amount of change in the dissolved oxygen concentration value at this time depends on the applied optimization algorithm. Note that the effluent water quality does not necessarily need to be improved or maintained. As mentioned above, the blower's discharge air volume is generally positively correlated with power consumption. Therefore, for example, if the purpose is to reduce power consumption, the blower's discharge air volume may be reduced even if it worsens the effluent water quality. The effluent water quality needs to be within the range that complies with the effluent water quality standards; specifically, the biochemical oxygen demand value does not need to exceed the standard value. The priority between effluent water quality and power consumption reduction is determined by how the values ​​of w1 and w2 are set in the above (Equation 1). After completing the transition destination estimation process in step S1004, the data processing unit 10 proceeds to step S1005.

[0073] In step S1005, the data processing unit 10 calculates an objective function using the control target value determination unit 420. At this time, the data processing unit 10 may estimate the power consumption of the blower from the air discharge volume of the blower based on the correlation between the air discharge volume of the blower and the power consumption using the power consumption estimation unit 440. This allows the reduction of power consumption to be taken into consideration when determining the control target value of the blower without separately measuring the power consumption of the blower. After completing the processing in step S1005, the data processing unit 10 proceeds to step S1006.

[0074] In step S1006, the data processing unit 10 determines whether or not the optimization has converged using the control target value determination unit 420. If it is determined that the optimization has converged (step S1006: Y), the process proceeds directly to step S1007, and if it is determined that the optimization has not converged (step S1006: N), the process returns to step S1003 again to reset the decision variables.

[0075] In step S1007, the data processing unit 10 causes the control target value determination unit 420 to output the optimized decision variables as the dissolved oxygen concentration value that will be the blower's control target value for the following week. At this time, the control target value determination unit 420 also outputs the improved values ​​of the blower discharge air volume, which represents the center of gravity of the cluster after the transition, the blower's power consumption, and the biochemical oxygen supply volume, which is the distance between the centers of gravity of the clusters, as shown in FIG. 10. The output unit displays these values ​​output by the control target value determination unit 420 on the display device 50, for example, in the manner of the display screen shown in FIG. 13B. Upon completing the processing in step S1007, the data processing unit 10 terminates the control target value determination process shown in the flowchart of FIG. 10.

[0076] FIG. 11 shows an example of the flow of the transition destination estimation process.

[0077] In step S1101, the data processing unit 10 acquires water quality data stored in the water quality data storage unit 320 using the water quality table 400. Note that this processing in step S1101 is the same as the processing in step S301 in Figure 3. When the processing in step S1101 is completed, the data processing unit 10 proceeds to step S1102.

[0078] In step S1102, the data processing unit 10 acquires the operating data stored in the operating data storage unit 340 by the operating table 500. Note that this processing in step S1102 is the same as the processing in step S302 in Fig. 3. When the processing in step S1102 is completed, the data processing unit 10 proceeds to step S1103.

[0079] In step S1103, the data processing unit 10 generates status data as a one-row matrix consisting of the latest values ​​based on the water quality data acquired in step S1101 and the operation data acquired in step S1102. The generated status data is stored by the data storage unit 30 as a new record in the status table 600. Note that this processing in step S1103 is the same as the processing in step S303 in FIG. 3. However, when step S1004 is called, the control target value for the next week, which is one of the explanatory variables used to estimate the transition destination, is substituted with a value provisionally set in the optimization process. When the processing in step S1103 is completed, the data processing unit 10 proceeds to step S1104.

[0080] In step S1104, the data processing unit 10 specifies a post-transition cluster using the control target value determination unit 420. This process is performed by estimating the cluster whose center of gravity, determined in the process of step S306 in FIG. 3, is closest to the latest value vector as the post-transition cluster. The control target value determination unit 420 assigns a cluster number representing the cluster specified as the transition destination, and generates post-clustering state data as a one-row matrix consisting of the latest value and the cluster number. Upon completing the process of step S1104, the data processing unit 10 proceeds to step S1105.

[0081] In step S1105, the data processing unit 10 causes the control target value determination unit 420 to generate a transition destination estimation matrix, as shown in FIG. 12, based on the post-clustering state data generated in step S1104. This matrix represents the explanatory variable X in the transition destination estimation process. The arrangement of the elements of the transition destination estimation matrix is ​​the same as the arrangement of the elements of the learning matrix generated in step S305 of FIG. 3. When the processing in step S1105 is completed, the data processing unit 10 proceeds to step S1106.

[0082] In step S1106, the data processing unit 10 executes a process of estimating a post-transition cluster using the control target value determination unit 420. This process is executed by providing the transition destination estimation matrix generated in step S1105 as an input to the learning model output in step S307 of Fig. 3. When the process in step S1106 is completed, the data processing unit 10 proceeds to step S1107.

[0083] In step S1107, the data processing unit 10 outputs the estimated post-transition cluster number to the memory by the control target value determination unit 420. When the processing in step S1107 is completed, the data processing unit 10 ends the transition destination estimation processing shown in the flowchart of FIG.

[0084] As described above, the output unit displays the determined control target value of the blower, which is the result of the control target value determination process, on the display device 50 in the manner of the display screens exemplified in Figures 13A and 13B. In this case, the output unit may display, for example, a past history including the latest measurement results, a cluster transition history, and a determination process for the dissolved oxygen concentration that is set as the control target value, on the display device 50, as shown in Figures 13A and 13B. The output unit may also illustrate the final set value of the dissolved oxygen concentration and the cluster to which it transitions as a result, as shown in Figure 13B, for example.

[0085] Furthermore, the transition condition learning unit 240 may generate the learning matrix as an average vector of the past period T. This allows the sewage treatment support system 100 to learn transitions over multiple periods.

[0086] FIG. 14 shows an example of the flow of the transition condition learning process executed by the transition condition learning unit 240 in such a case.

[0087] In step S1401, the data processing unit 10 generates status data based on the water quality data acquired from the water quality data storage unit 320 and the operating data acquired from the operating data storage unit 340. The generated status data is stored in the status table 600 by the data storage unit 30. Note that this processing in step S1401 is the same as the processing in steps S301 to S303 in Fig. 3 and the processing in steps S1101 to S1103 in Fig. 11. When the processing in step S1401 is completed, the data processing unit 10 proceeds to step S1402.

[0088] In step S1402, the data processing unit 10 sets a period T for determining a medium-term directionality using the transition condition learning unit 240. If the period T is, for example, one month, it is set to T=4. The transition condition learning unit 240 may accept an input operation for the period T from the user via the communication unit or the input unit. When the processing in step S1402 is completed, the data processing unit 10 proceeds to step S1403.

[0089] In step S1403, the data processing unit 10 generates a mean matrix X for transition condition learning (hereinafter referred to as the "learning mean matrix") in the manner illustrated in FIG. 15 using the transition condition learning unit 240. This learning mean matrix X is obtained by calculating the mean vector of a past period T based on a certain date and using these values ​​as components. The learning mean matrix X illustrated in FIG. 15 is for T=4, which indicates that the period T is one month. Upon completing the processing in step S1403, the data processing unit 10 proceeds to step S1404.

[0090] In step S1404, the data processing unit 10 performs clustering using the transition condition learning unit 240 to generate post-clustering state data. The content of the processing performed here is the same as the processing performed in step S304 in Fig. 3. When the processing in step S1404 is completed, the data processing unit 10 proceeds to step S1405.

[0091] In step S1405, the data processing unit 10 generates a matrix for medium-term learning using the transition condition learning unit 240. The elements of this matrix are the same as the elements of the learning matrix illustrated in FIG. 9. The values ​​of this matrix are made up of the mean vector calculated in the processing of step S1403. When the processing of step S1405 is completed, the data processing unit 10 proceeds to step S1406.

[0092] In step S1406, the data processing unit 10 executes learning processing using the transition condition learning unit 240. The content of the processing executed here is the same as the processing executed in step S306 of Fig. 3. That is, this learns the transition destination for the next period T from the operating state of the blower in the past period T. When the processing in step S1406 is completed, the data processing unit 10 proceeds to step S1407.

[0093] In step S1407, the data processing unit 10 outputs a model using the transition condition learning unit 240. The content of the processing executed here is the same as the processing executed in step S307 in Fig. 3. When the processing in step S1407 is completed, the data processing unit 10 ends the transition condition learning processing shown in the flowchart in Fig. 14.

[0094] In the above embodiment, the blower control method is constant dissolved oxygen concentration control, and the blower control target value determined by the sewage treatment support system 100 is the dissolved oxygen concentration value in the activated sludge in the aeration tank. However, as described above, the blower can also be controlled by constant inflow rate ratio control. When constant inflow rate ratio control is used as the blower control method, the blower control target value determined by the sewage treatment support system 100 is the inflow rate ratio. In this case, the data processing unit 10 of the sewage treatment support system 100 appropriately calculates the inflow rate ratio based on, for example, the value of the sewage inflow rate to the sewage treatment plant and the value of the blower discharge air volume, which are included in the operation data.

[0095] According to the embodiment of the present invention described above, the following advantageous effects are achieved.

[0096] (1) The sewage treatment support system 100 includes a clustering unit 220, a transition condition learning unit 240, and a control target value determination unit 420. The clustering unit 220 performs clustering of status data for each predetermined period, which represents the status of activated sludge in an aeration tank of a sewage treatment plant that performs sewage treatment based on a standard activated sludge process. The transition condition learning unit 240 learns the conditions for transitions between clusters among multiple clusters generated by clustering. The control target value determination unit 420 determines a control target value for a blower that discharges air to aerate the aeration tank, based on the learned transition conditions and a time series of new status data representing the status of activated sludge in the aeration tank. The status data includes water quality data representing the quality of secondary effluent from the sewage treatment plant and operating data representing the operating status of the sewage treatment plant. This allows the sewage treatment support system 100 to automatically determine an optimal control target value for the blower.

[0097] (2) The control target value is at least one of the following: - Dissolved oxygen concentration in activated sludge in the aeration tank, Inflow ratio: This indicates the ratio of the blower's discharge air volume to the amount of sewage inflow into the sewage treatment plant. This allows the blower to perform constant dissolved oxygen concentration control or constant inflow rate control.

[0098] (3) The water quality data includes the biochemical oxygen demand of the secondary effluent of the sewage treatment plant as an index. This allows the sewage treatment support system 100 to properly execute the transition condition learning process and the control target value determination process.

[0099] (4) The operating data includes, as indicators, the amount of sewage inflow into the sewage treatment plant, the dissolved oxygen concentration in the activated sludge in the aeration tank, the amount of sludge returned to the aeration tank, and the amount of air discharged by the blower. This allows the sewage treatment support system 100 to properly execute the transition condition learning process and the control target value determination process.

[0100] (5) The state data for each predetermined period includes a plurality of values, and the plurality of values ​​in the state data for each predetermined period include the control target value immediately before the predetermined period and the control target value for the predetermined period, and the transition condition learning unit 240 outputs a combination of cluster transitions from one week to the next week and learns a model using one or more values ​​including the control target value among the plurality of values ​​as input. In this way, the sewage treatment support system 100 can estimate the transition destination when a certain control target value is assumed based on the execution result of the transition condition learning process.

[0101] (6) The model is a model in which the combination of cluster transitions from one week to the next week is used as the objective variable and one or more values ​​are used as the explanatory variables. As a result, the sewage treatment support system 100 can estimate possible cluster transitions based on the results of the transition condition learning process.

[0102] (7) The transition condition learning unit 240 calculates the angle of the centroid vector representing the transition direction and the time series before and after for two clusters with adjacent time series, and learns the transition condition when the angle of the centroid vector is equal to or less than a predetermined threshold. In this way, the sewage treatment support system 100 can estimate the strength of the nitrification ability of the aeration tank, i.e., the sewage treatment performance of the sewage treatment plant, based on fragmentary information.

[0103] (8) The predetermined period is one week. As a result, the sewage treatment support system 100 can effectively support sewage treatment by taking into account the growth rate of microorganisms in the aeration tank.

[0104] (9) The transition condition learning unit 240 constructs explanatory variables as a learning matrix including the amount of air discharged by the blower in the previous week, the dissolved oxygen concentration in the activated sludge in the aeration tank in the previous week, the amount of sludge returned to the aeration tank in the previous week, the amount of sewage inflow to the sewage treatment plant in the previous week, the biochemical oxygen demand of the secondary effluent from the sewage treatment plant, and the predicted value of the dissolved oxygen concentration in the activated sludge in the aeration tank in the next week. In this way, the sewage treatment support system 100 can successfully execute the transition condition learning process and determine an appropriate blower control target value based on the execution results.

[0105] (10) The transition condition learning unit 240 generates the learning matrix as an average vector of the past period T. In this case, the sewage treatment support system 100 can learn transitions over multiple periods.

[0106] (11) An output unit that displays the determined blower control target value on the display device 50 is further provided. As a result, the sewage treatment support system 100 can present the determined blower control target value to the user as visual information.

[0107] (12) The sewage treatment support system 100 further includes a power consumption estimation unit 440 that estimates the power consumption of the blower based on the amount of air discharged by the blower. As a result, when determining the control target value of the blower, the sewage treatment support system 100 can take into consideration the reduction of power consumption without separately measuring the amount of power consumption of the blower.

[0108] (13) The control target value determination unit identifies a latest location cluster, which is a cluster in which the latest state data is classified, and estimates a cluster to which the latest location cluster will transition based on the latest location cluster, the latest state data, and the control target value for the next specified period. By repeating the process including the following, the control target value is searched for such that the cluster to which the latest location cluster will transition will be a cluster that reduces the amount of air discharged by the blower or improves the quality of secondary treated water from the sewage treatment plant; - Change the latest control target value for the next predetermined period based on the learned transition conditions. -estimating a cluster to which the latest position cluster will transition based on the latest changed control target value for the next predetermined period; The found control target value is output. Note that this found control target value is the control target value at the time of optimization convergence. What is achieved up to the transition condition learning stage is that when the current cluster, operating data, and next week's control target value are input, a model is obtained that outputs (can estimate) the destination cluster. In addition, by further incorporating this configuration, the sewage treatment support system 100 can specifically determine the blower control target value for next week that will allow transition to a good cluster.

[0109] The present invention is not limited to the above-described embodiment, and can be implemented using any components without departing from the spirit of the present invention.

[0110] The above-described embodiments and modifications are merely examples, and the present invention is not limited to these details as long as the features of the invention are not impaired. Furthermore, although various embodiments and modifications have been described above, the present invention is not limited to these details. Other aspects that can be considered within the scope of the technical idea of ​​the present invention are also included within the scope of the present invention. [Explanation of symbols]

[0111] 100: Sewage treatment support system

Claims

1. a clustering unit that performs clustering of status data for each predetermined period that represents the status of activated sludge in an aeration tank of a sewage treatment plant that performs sewage treatment based on a standard activated sludge method; a transition condition learning unit that learns conditions for transitions between clusters among the plurality of clusters generated by the clustering; a control target value determination unit that determines a control target value of a blower that discharges air for aerating the inside of the aeration tank based on the learned transition condition and a time series of new state data that represents the state of the activated sludge in the aeration tank; Equipped with The status data includes water quality data representing the quality of secondary treated water from the sewage treatment plant and operation data representing the operating status of the sewage treatment plant, The operating data includes, as indicators, an amount of sewage inflow into the sewage treatment plant, a dissolved oxygen concentration in the activated sludge in the aeration tank, an amount of sludge returned to the aeration tank, and an amount of air discharged by the blower. Sewage treatment support system.

2. The control target value is at least one of the following: - Dissolved oxygen concentration (DO) in the activated sludge in the aeration tank, - an inflow ratio representing the ratio of the discharge air volume of the blower to the sewage inflow volume into the sewage treatment plant; The sewage treatment support system according to claim 1 .

3. 2. The sewage treatment support system according to claim 1, wherein the water quality data includes a biochemical oxygen demand (BOD) of secondary effluent from the sewage treatment plant as an index.

4. The status data for each predetermined period includes a plurality of values, In the state data for each predetermined period, the plurality of values ​​include a control target value immediately before the predetermined period and a control target value for the predetermined period, the transition condition learning unit learns a model in which a state transition from a certain record to a next record of the state data is an output, and one or more values ​​including a control target value among the plurality of values ​​is an input. The sewage treatment support system according to claim 1 .

5. The sewage treatment support system according to claim 4 , wherein the model is a model in which the state transition is a response variable and the one or more values ​​are explanatory variables.

6. The sewage treatment support system of claim 1, wherein the transition condition learning unit calculates the angle of the center of gravity vector representing the transition direction and the before and after of the time series for two clusters with adjacent time series, and learns the transition condition when the angle of the center of gravity vector is less than a predetermined threshold.

7. The sewage treatment support system according to claim 1 , wherein the predetermined period is a week.

8. The sewage treatment support system of claim 7, wherein the transition condition learning unit constructs explanatory variables as a learning matrix including the amount of air discharged by the blower in the previous week, the dissolved oxygen concentration in the activated sludge in the aeration tank in the previous week, the amount of sludge returned to the aeration tank in the previous week, the amount of sewage inflow into the sewage treatment plant in the previous week, the biochemical oxygen demand of the secondary treatment water of the sewage treatment plant, and a predicted value of the dissolved oxygen concentration in the activated sludge in the aeration tank in the next week.

9. The sewage treatment support system according to claim 8 , wherein the transition condition learning unit generates the learning matrix as an average vector of a past period T.

10. The sewage treatment support system according to claim 1 , further comprising an output unit that displays the determined control target value for the blower on a display device.

11. The sewage treatment support system according to claim 1 , further comprising a power consumption estimation unit that estimates the power consumption of the blower based on the amount of air discharged by the blower.

12. The control target value determination unit Identifying a latest location cluster, which is a cluster in which the latest state data is classified; estimating a cluster to which the latest position cluster will transition based on the latest position cluster, the latest state data, and a control target value for a next predetermined period; By repeating the process including the following, a control target value is searched for such that the cluster to which the latest position cluster is to be transitioned will be a cluster in which the discharge air volume of the blower is reduced or the quality of secondary effluent from the sewage treatment plant is improved; - changing the latest control target value for the next predetermined period based on the learned transition condition; - estimating a cluster to which the latest position cluster will transition based on the latest changed control target value for the next predetermined period; Output the control target value found. The sewage treatment support system according to claim 1 .

13. The computer performs clustering of status data for each predetermined period that represents the status of activated sludge in an aeration tank of a sewage treatment plant that performs sewage treatment based on the standard activated sludge method, a computer learning conditions for transitions between the clusters generated by the clustering; a computer determines a control target value for a blower that discharges air for aerating the aeration tank based on the learned transition condition and a time series of new status data that represents the status of the activated sludge in the aeration tank; The status data includes water quality data representing the quality of secondary treated water from the sewage treatment plant and operation data representing the operating status of the sewage treatment plant, The operating data includes, as indicators, an amount of sewage inflow into the sewage treatment plant, a dissolved oxygen concentration in the activated sludge in the aeration tank, an amount of sludge returned to the aeration tank, and an amount of air discharged by the blower. Sewage treatment support methods.

14. Clustering is performed on status data for each predetermined period that represents the status of activated sludge in an aeration tank of a sewage treatment plant that performs sewage treatment based on the standard activated sludge method; learning conditions for transitions between clusters among the plurality of clusters generated by the clustering; A control target value for a blower that discharges air for aerating the inside of the aeration tank is determined based on the learned transition condition and a time series of new state data that represents the state of the activated sludge in the aeration tank. Let the computer do that, The status data includes water quality data representing the quality of secondary treated water from the sewage treatment plant and operation data representing the operating status of the sewage treatment plant, The operating data includes, as indicators, an amount of sewage inflow into the sewage treatment plant, a dissolved oxygen concentration in the activated sludge in the aeration tank, an amount of sludge returned to the aeration tank, and an amount of air discharged by the blower. Wastewater Treatment Assistance Program.

Images