Information processing device, moisture content prediction method, and moisture content prediction program

Abstract

To accurately predict a water content of a dehydrated cake without increasing a load of an operator by manual work analysis of the water content.SOLUTION: In an information processor (1), a prediction part (102) predicts a water content of a dehydrated cake discharged from a dehydrator at a passage time point when residence time at which a liquid resides in a dehydrator passes from a measurement time point of measurement data acquired by an acquisition part (101), using a prediction model (111), from the measurement data. The prediction model (111) is learned with the measurement data as an explanatory variable, and with an estimation value by an estimation model (112) for the water content of the dehydrated cake at the passage time as an objective variable. The estimation model (112) is learned with the measurement data relating to operation of the dehydrator as an explanatory variable, and with the water content of the dehydrated cake at the measurement time point as an objective variable.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a technique for predicting the moisture content of a dehydrated cake obtained by dehydrating a liquid containing suspended solids in a dehydrator. [Background technology]

[0002] Sludge treatment carried out in wastewater treatment facilities such as sewage treatment plants includes a step of dewatering the sludge using a dehydrator. For efficient sludge treatment, it is important to maintain the moisture content of the dehydrated cake obtained by dehydration within a predetermined range. However, when dehydration treatment is performed under constant operating conditions of the dehydrator, the moisture content of the dehydrated cake fluctuates due to factors such as inconsistent properties of the supplied sludge, making it difficult to maintain the moisture content of the dehydrated cake within the predetermined range.

[0003] For this reason, development of technology for predicting the moisture content of dehydrated cake has been underway. If the moisture content can be predicted, it will be possible to maintain the moisture content within a predetermined range through feedforward control. For example, Patent Document 1 listed below discloses a technology for estimating the moisture content by generating a moisture content estimation model using multiple parameters, such as the amount of sludge supplied to a centrifugal dehydrator and values ​​related to the centrifugal effect of the dehydrator. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 2020-114569 Summary of the Invention [Problem to be solved by the invention]

[0005] Regarding the training data for generating the moisture content estimation model described above, explanatory variables are various parameters related to the sludge supplied to the dehydrator, the operating conditions of various devices, etc. Furthermore, the objective variable is the moisture content of the dehydrated cake discharged from the dehydrator at the time when the retention time of the sludge in the dehydrator has elapsed from the time when the parameters are measured.

[0006] However, when the moisture content is measured automatically, the measurement can be performed frequently, but the measurement error is large. On the other hand, when the moisture content is measured by manual analysis, the measurement error is small, but performing the measurement frequently (for example, 10 times or more per day) increases the burden on the worker. If the number of measurements is small, the amount of training data also becomes small, and as a result, the accuracy of the moisture content estimation model trained using the training data decreases.

[0007] An object of one aspect of the present invention is to accurately predict the moisture content without increasing the burden on an operator in manually analyzing the moisture content. [Means for solving the problem]

[0008] In order to solve the above-described problems, an information processing device according to one aspect of the present invention includes an acquisition unit that acquires at least one of measurement data related to a liquid supplied to a coagulation tank to which a chemical agent for coagulating suspended solids is added to the liquid containing the suspended solids, measurement data related to the chemical agent supplied to the coagulation tank, measurement data related to the liquid in the coagulation tank, measurement data related to the operation of the coagulation tank, and measurement data related to the operation of a dehydrator that dehydrates the liquid while transporting it discharged from the coagulation tank; and a prediction model that uses the measurement data acquired by the acquisition unit to predict the progress of the liquid in the dehydrator from the time of measurement of the measurement data. and a prediction unit that predicts the moisture content of the dehydrated cake discharged from the dehydrator at the time when the retention time during which the measurement data is retained has elapsed, wherein the prediction model is a prediction model trained using the measurement data acquired by the acquisition unit as an explanatory variable and an estimated value by an estimation model for the moisture content of the dehydrated cake discharged from the dehydrator at the time when the retention time has elapsed since the time when the measurement data was measured as a dependent variable, and the estimation model is an estimation model trained using measurement data related to the operation of the dehydrator as an explanatory variable and the moisture content of the dehydrated cake discharged from the dehydrator at the time when the measurement data was measured as a dependent variable.

[0009] A moisture content prediction method according to one aspect of the present invention is a moisture content prediction method executed by one or more information processing devices, comprising: an acquisition step of acquiring at least one of measurement data on a liquid supplied to a coagulation tank to which an agent for coagulating suspended solids is added to the liquid containing the suspended solids, measurement data on the agent supplied to the coagulation tank, measurement data on the liquid in the coagulation tank, measurement data on the operation of the coagulation tank, and measurement data on the operation of a dehydrator that dehydrates the liquid while transporting it, from the measurement data acquired in the acquisition step using a prediction model; and and a prediction step of predicting the moisture content of the dehydrated cake discharged from the dehydrator at the time when the retention time for the liquid in the dehydrator has elapsed, wherein the prediction model is a prediction model trained using the measurement data acquired in the acquisition step as an explanatory variable and an estimated value by an estimation model for the moisture content of the dehydrated cake discharged from the dehydrator at the time when the retention time has elapsed from the time when the measurement data was measured as a dependent variable, and the estimation model is an estimation model trained using measurement data related to the operation of the dehydrator as an explanatory variable and the moisture content of the dehydrated cake discharged from the dehydrator at the time when the measurement data was measured as a dependent variable. [Effects of the Invention]

[0010] According to one aspect of the present invention, the moisture content can be predicted with high accuracy without increasing the burden on the worker in manually analyzing the moisture content. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a block diagram showing an example of a main configuration of an information processing device according to an embodiment of the present invention; [Figure 2] FIG. 10 is a diagram illustrating an example of the configuration of a control system including the information processing device. [Figure 3] 10 is a graph showing the variation in moisture content estimated values ​​by the estimation model in the information processing device relative to actual moisture content measurements by an operator. [Figure 4] FIG. 2 is a model diagram illustrating a concept of creating training data for a prediction model in the information processing device. [Figure 5] 10 is a graph showing the variation in moisture content predicted values ​​by the prediction model trained using moisture content estimated values ​​by the estimation model, relative to actual moisture content measured by operators. [Figure 6] 10 is a graph showing a comparative example, illustrating the variation of the moisture content predicted by the prediction model trained using the moisture content measured by an operator, relative to the moisture content measured by the operator. [Figure 7] 10 is a flowchart showing an example of a process for predicting the moisture content of a dehydrated cake in the information processing device. [Figure 8] 10 is a flowchart illustrating an example of an update process of the prediction model and the estimation model in the information processing device. DETAILED DESCRIPTION OF THE INVENTION

[0012] [System Configuration] The configuration of a control system according to one embodiment of the present invention will be described with reference to FIG. 2. FIG. 2 is a diagram showing an example of the configuration of a control system 100. The control system 100 is a system used in a plant that adds an agent that coagulates suspended solids to a liquid to be treated in a coagulation tank to form flocs, and then performs solid-liquid separation of the liquid to be treated in which the flocs have formed. In the following, an example will be described in which the liquid to be treated is sludge, but the control system 100 can also be applied to plants that treat liquids other than sludge. Note that sludge is a liquid containing fine solids that is generated during wastewater treatment, etc., and can also be called slurry.

[0013] As will be explained in detail below, the control system 100 performs each process of the sludge treatment process, from the process of converting the sludge to be treated into flocculated sludge by flocculating solid suspended matter in the sludge to be treated to form flocs, to the process of dewatering the flocculated sludge to obtain dehydrated sludge (also called dehydrated cake) and dehydrated filtrate. As shown in Fig. 2, the control system 100 includes an information processing device 1, a control device 3, a flocculator 5, and a dehydrator 9.

[0014] The flocculator 5 is a device that adds a chemical agent that coagulates suspended solids to the liquid being treated in a coagulation tank and moderately stirs the liquid to form flocs. Specifically, the flocculator 5 uses sludge as the liquid being treated, coagulates suspended solids in the sludge to form flocs, and produces coagulated sludge. The flocculator 5 in FIG. 2 includes a coagulation tank 51, a stirring blade 52, a motor 53, and an inspection window 54. The flocculator 5 also includes a sludge inlet 55, a chemical agent inlet 56, and a discharge outlet 57.

[0015] Furthermore, a photographing device 72 and a lighting device 71 for photography are attached to the inspection window 54. The photographing device 72 may be any device capable of taking at least still images. It is preferable that the coagulation tank 51 is opaque so that the way light hits the flocs does not change while the control system 100 is in operation. Furthermore, it is preferable that the photographing device 72 and the lighting device 71 are housed in a light-blocking dark box with an opening on the inspection window 54 side, as in the illustrated example.

[0016] The dehydrator 9 is a device that performs solid-liquid separation of the liquid to be treated in which flocs have formed. Specifically, the dehydrator 9 is disposed downstream of the flocculator 5 and performs solid-liquid separation by dehydrating the coagulated sludge (liquid) discharged from the flocculator 5. The dehydrator 9 in FIG. 2 is a screw press type dehydrator equipped with an outer screen 91 and a screw 92. The dehydrator 9 is also provided with a sludge inlet 93, a filtrate outlet 94, and a dehydrated cake outlet 95. Although not shown, the dehydrator 9 also includes a motor for rotating the screw 92. Of course, the dehydrator 9 is not limited to the screw press type as long as it can dehydrate the coagulated sludge. For example, a centrifugal dehydrator, a filter press dehydrator, or a belt press dehydrator may also be used.

[0017] In the control system 100, the sludge to be treated is continuously or intermittently supplied from a sludge inlet 55 into the coagulation tank 51 of the flocculator 5 by a supply device (not shown). The supply rate of the sludge may be automatically controlled by the supply device or its control device 3 according to the sludge treatment rate by the flocculator 5 and the dehydrator 9.

[0018] Then, chemicals (including at least a flocculant) for flocculating the sludge are fed into the sludge in the coagulation tank 51 through chemical inlet 56. In this state, motor 53 is driven to rotate agitator blade 52, which mixes the sludge and chemicals and forms flocs. The flocculated sludge, which is a mixture of the formed flocs and the water contained in the sludge, is discharged from outlet 57.

[0019] Subsequently, this flocculated sludge is supplied into the outer body screen 91 from the sludge inlet 93 of the dehydrator 9. In the dehydrator 9, the flocculated sludge is dehydrated under pressure by the screw 92, and the filtrate is discharged from the filtrate outlet 94, while the dehydrated cake, which is a mass of dehydrated flocculated sludge, is discharged from the dehydrated cake outlet 95.

[0020] The sludge is supplied into the coagulation tank 51 from the sludge inlet 55, whereby the coagulated sludge is pushed out and discharged from the outlet 57 of the coagulation tank 51, and the discharged coagulated sludge is supplied to the dehydrator 9. Therefore, the flow rate of the sludge supplied to the coagulation tank 51 and the flow rate of the sludge supplied to the dehydrator 9 coincide with each other at the same time.

[0021] As will be described in detail below, the information processing device 1 acquires at least one of measurement data related to the liquid supplied to the coagulation tank 51, measurement data related to the chemicals supplied to the coagulation tank 51, measurement data related to the liquid in the coagulation tank 51, measurement data related to the operation of the coagulation tank 51, and measurement data related to the operation of the dehydrator 9. Then, the information processing device 1 predicts the moisture content of the dehydrated cake based on the acquired measurement data.

[0022] The information processing device 1 can also control the operation of various devices (e.g., the flocculator 5, the dehydrator 9, and a sludge and chemical supply device (not shown)) that are components of the control system 100 via the control device 3. The control device 3 controls the operation of various devices that are components of the control system 100. The control device 3 may be, for example, a PLC (Programmable Logic Controller).

[0023] [Device configuration] The configuration of the information processing device 1 will be described with reference to Fig. 1. Fig. 1 is a block diagram showing an example of the configuration of the main parts of the information processing device 1. As shown in the figure, the information processing device 1 includes a control unit 10 that controls each unit of the information processing device 1 in an integrated manner, and a storage unit 11 that stores various data used by the information processing device 1. The information processing device 1 also includes a communication unit 12 that enables the information processing device 1 to communicate with other devices, an input unit 13 that accepts input of various data to the information processing device 1, and an output unit 14 that enables the information processing device 1 to output various data.

[0024] The control unit 10 also includes an acquisition unit 101, a prediction unit 102, and an update unit 103. The update unit 103 will be described later in the section "Regarding the update unit 103."

[0025] The storage unit 11 includes a prediction model 111 and an estimation model 112. Details of these will be explained later in the sections "Regarding the prediction model" and "Regarding the estimation model," respectively.

[0026] The acquiring unit 101 acquires at least one of measurement data related to the liquid supplied to the coagulation tank 51, measurement data related to the chemicals supplied to the coagulation tank 51, measurement data related to the liquid in the coagulation tank 51, measurement data related to the operation of the coagulation tank 51, and measurement data related to the operation of the dehydrator 9. Details of each measurement data will be explained later in the section "Regarding the prediction model."

[0027] The prediction unit 102 predicts the moisture content of the dehydrated cake from the measurement data acquired by the acquisition unit 101 using a prediction model 111 stored in the memory unit 11. This dehydrated cake is the dehydrated cake that is discharged from the dehydrator 9 at a point in time (hereinafter referred to as the "elapsed time") when the retention time of the flocculated sludge in the dehydrator 9 has elapsed from the time of measurement of the measurement data. The prediction unit 102 performs a prediction using the prediction model 111 every time the acquisition unit 101 acquires new measurement data. This allows the prediction unit 102 to predict the moisture content of the dehydrated cake in real time, that is, continuously at short time intervals (for example, every minute). A specific method for calculating the moisture content of the dehydrated cake will be described later in the section "Method for predicting moisture content using a prediction model."

[0028] [About the prediction model] The prediction model 111 is a prediction model trained using the measurement data at the time of measurement acquired by the acquisition unit 101 as an explanatory variable and the moisture content of the dehydrated cake discharged from the dehydrator 9 at an elapsed time point (hereinafter referred to as the "moisture content at the elapsed time point") as a dependent variable. In this embodiment, the moisture content, which is the dependent variable, is an estimated value by the estimation model 112. The estimation model 112 will be described later in the section "Estimation Model". The measurement data acquired by the acquisition unit 101 and used for prediction by the prediction model 111 includes the following:

[0029] (1) Measurement data regarding the sludge supplied to the coagulation tank 51. The measurement data is, for example, at least one of the supply flow rate of the sludge per unit time and the concentration of the sludge, and is measured before the sludge inlet 55.

[0030] (2) Measurement data relating to the chemical agent supplied to the coagulation tank 51. The measurement data is, for example, the supply flow rate of the chemical agent per unit time, and is measured before the chemical agent inlet 56.

[0031] (3) Measurement data on the sludge in the coagulation tank 51. The measurement data is at least one of the average gray value of the flocs and the average unit area of ​​the gaps between the flocs, and is obtained by image processing of the still images taken by the image capture device 72. The average gray value serves as an index of the color tone (lightness) of the sludge. The average unit area serves as an index of the floc diameter.

[0032] (4) Measurement data related to the operation of the coagulation tank 51. The measurement data is the rotation speed of the stirring blades in the coagulation tank 51, and is acquired from the control device 3.

[0033] (5) Measurement data related to the operation of the dehydrator 9. The measurement data is at least one of the operation time of the dehydrator 9, the rotation speed of the screw of the dehydrator 9, the flow rate of the coagulated sludge supplied to the dehydrator 9 per unit time, and the feeding pressure of the coagulated sludge fed to the dehydrator 9, and is acquired from the control device 3.

[0034] In this embodiment, various measurement data can be employed as shown in (1) to (5) above. Therefore, the moisture content of the dehydrated cake can be accurately predicted using multiple explanatory variables. Among the measurement data shown in (1) to (5) above, the measurement data that contribute significantly to the prediction accuracy of the prediction model 111 include the operation time, the screw rotation speed, and the supply flow rate shown in (5) above, and the average floc density value shown in (3) above.

[0035] [About the estimation model] The estimation model 112 is an estimation model trained using the measurement data acquired by the acquisition unit 101, which is related to the operation of the dehydrator 9, as an explanatory variable, and the moisture content of the dehydrated cake discharged from the dehydrated cake discharge outlet 95 of the dehydrator 9 at the time of measurement of the measurement data (hereinafter referred to as the "moisture content at the time of measurement") as a dependent variable.

[0036] The measurement data includes at least one of the following: the operating time of the dehydrator 9, the rotation speed of the screw of the dehydrator 9, the drive current or torque of the screw, the back pressure of a back pressure plate provided at the dehydrated cake discharge port 95 (discharge section) of the dehydrator 9 for compressing the liquid, and the aperture between the back pressure plate and the dehydrated cake discharge port 95, and is acquired from the control device 3. Among these, the measurement data that contributes significantly to the estimation accuracy of the estimation model 112 include the drive current of the screw and the aperture (particularly when the dehydrator 9 is controlled so that the back pressure is constant). The moisture content is manually analyzed and measured (actually measured) by an operator using a device external to the control system 100. For this reason, the measurement cannot be performed frequently.

[0037] FIG. 3 is a graph showing the variation in moisture content estimated by the estimation model 112 relative to the moisture content actually measured by an operator. In the example of FIG. 3, the explanatory variables are the operating time of the dehydrator 9, the rotation speed and drive current value of the screw of the dehydrator 9, the back pressure caused by the back pressure plate, and the opening between the back pressure plate and the dehydrated cake discharge port 95. Referring to FIG. 3, it can be seen that the variation is extremely small. Therefore, by using measurement data related to the operation of the dehydrator 9 for the estimation model 112, the moisture content at the time of measurement can be accurately and frequently estimated.

[0038] [Prediction model training method] Next, we will explain the method of learning the moisture content using the prediction model 111. As described above, the prediction model 111 is a model that uses measurement data at the time of measurement to predict the moisture content at a time point in the future from the time of measurement.

[0039] Fig. 4 is a model diagram showing the concept of creating training data for the prediction model 111. The horizontal axis of Fig. 4 is driving time (minutes). The vertical axis of Fig. 4 is the measurement value of each measurement data, and each measurement data is shown on an arbitrary scale.

[0040] The measurement data at the time of measurement, which serve as explanatory variables for the prediction model 111, are the supply flow rate per unit time of the liquid (sludge), the concentration of the liquid (sludge), the average concentration value of the flocs, the average unit area of ​​the gaps between the flocs, the rotation speed of the agitator blades in the coagulation tank 51, the operation time of the dehydrator 9, the rotation speed of the screw of the dehydrator 9, and the introduction pressure of the liquid (coagulated sludge) introduced into the dehydrator 9, as shown in the upper part of FIG.

[0041] The objective variable of the prediction model 111 is the moisture content at a given time point. When using an actual measurement value by an operator as this moisture content, there is an upper limit to the number of measurements an operator can take in one day, as shown by the white circles in the lower part of Figure 4. This limits the amount of training data, which is a combination of explanatory variables and objective variables, making it difficult to create a highly accurate prediction model.

[0042] Therefore, in this embodiment, instead of actual measurements by an operator, estimated values ​​by the estimation model 112 are used as the dependent variables of the training data. As a result, as shown by the gray circles in the lower part of Fig. 4, the number of dependent variables is no longer limited by the number of measurements that an operator can make, so that the amount of training data can be increased and a highly accurate prediction model 111 can be created.

[0043] The estimated value of the moisture content at the elapsed time point is calculated from the measurement data at the elapsed time point using the estimation model 112. Therefore, the measurement data at the measurement time point, which is the explanatory variable of the prediction model 111, and the measurement data at the elapsed time point, which is the explanatory variable of the estimation model 112, are used to train the prediction model 111.

[0044] FIG. 5 is a graph showing the variation in moisture content predicted by prediction model 111, which was trained using moisture content estimated by estimation model 112, relative to the actual moisture content measured by an operator. FIG. 6 is a comparative example, showing the variation in moisture content predicted by a prediction model trained using actual moisture content measured by an operator, relative to the actual moisture content measured by an operator. Referring to FIGS. 5 and 6, it can be seen that prediction model 111, trained using moisture content estimated by estimation model 112, has smaller variation and higher accuracy than a prediction model trained using actual moisture content measured by an operator. In fact, the mean absolute error (MAE) of the moisture content predicted and actual moisture content values ​​shown in FIG. 5 was 1.12%, and the MAE of the moisture content predicted and actual moisture content values ​​shown in FIG. 6 was 1.29%.

[0045] [Method for predicting moisture content using a prediction model] The prediction unit 102 predicts the moisture content of the dehydrated cake using the trained prediction model 111. That is, the prediction unit 102 inputs at least one of the explanatory variables, which are measurement data on the liquid supplied to the coagulation tank 51, measurement data on the chemicals supplied to the coagulation tank 51, measurement data on the liquid in the coagulation tank 51, measurement data on the operation of the coagulation tank 51, and measurement data on the operation of the dehydrator 9, into the prediction model 111, thereby predicting the objective variable, which is the moisture content of the dehydrated cake at the completion of dehydration.

[0046] At this time, the acquisition unit 101 may omit acquisition of measurement data that is an explanatory variable of the estimation model 112 but not an explanatory variable of the prediction model 111, since the prediction unit 102 does not use the measurement data. However, for the processing of the update unit 103 described later, it is desirable for the acquisition unit 101 to perform the above acquisition.

[0047] As described above, the information processing device 1 of this embodiment includes an acquisition unit 101 that acquires at least one of measurement data related to the liquid supplied to a coagulation tank 51, where a chemical agent for coagulating the suspended solids is added to the liquid containing suspended solids, measurement data related to the chemical agent supplied to the coagulation tank 51, measurement data related to the liquid in the coagulation tank 51, measurement data related to the operation of the coagulation tank 51, and measurement data related to the operation of the dehydrator 9 that dehydrates the liquid discharged from the coagulation tank 51 while transporting it; and a prediction unit 102 that uses a prediction model 111 to predict, from the measurement data acquired by the acquisition unit 101, the moisture content of the dehydrated cake discharged from the dehydrator 9 at a time point after the retention time during which the liquid remains in the dehydrator 9 has elapsed from the time of measurement of the measurement data. The prediction model 111 is a prediction model trained using the measurement data acquired by the acquisition unit 101 as an explanatory variable and an estimate by an estimation model 112 of the moisture content of the dehydrated cake discharged from the dehydrator 9 at a time point after the retention time has elapsed from the time of measurement of the measurement data as a response variable. The estimation model 112 is an estimation model trained using measurement data relating to the operation of the dehydrator 9 as explanatory variables and the moisture content of the dehydrated cake discharged from the dehydrator 9 at the time of measurement of the measurement data as a response variable.

[0048] According to the above configuration, the estimation model 112 measures the explanatory variable, that is, the measurement time (at the completion of dehydration) of the measurement data related to the operation of the dehydrator 9, and the objective variable, that is, the moisture content of the dehydrated cake discharged from the dehydrator 9 (at the completion of dehydration). Therefore, even if there is a small amount of training data including the measurement data and the moisture content, the accuracy of estimation using the trained estimation model 112 is good. Therefore, the estimation model 112 can be trained without increasing the burden on the operator in manually analyzing the moisture content. Furthermore, the estimation model 112 can be used to calculate an estimated moisture content for each measurement of the measurement data.

[0049] Meanwhile, with regard to prediction model 111, an estimated value of the moisture content at an elapsed time point can be obtained using estimation model 112 from measurement data related to the operation of dehydrator 9 measured at an elapsed time point (at the completion of spin-drying) after the retention time has elapsed since the measurement time point (when liquid is supplied) of the measurement data acquired by acquisition unit 101. Then, training data can be created as many times as the number of measurements of the measurement data acquired by the acquisition unit, with the acquired estimated value as the objective variable and the measurement data acquired by the acquisition unit at the measurement time point as the explanatory variable. Therefore, using prediction model 111 trained using the training data, it is possible to accurately predict the moisture content at an elapsed time point after the retention time has elapsed from the measurement time point of the measurement data acquired by acquisition unit 101, from the measurement data acquired by acquisition unit 101.

[0050] [About the update section] The update unit 103 performs a process of updating the prediction model 111 and the estimation model 112 during a period when the flocculation tank 51 and the dehydrator 9 are stopped (a shutdown period). Specifically, the update unit 103 updates the estimation model 112 during the shutdown period using, as training data, a pair of the measurement data acquired by the acquisition unit 101 during the operation period of the dehydrator 9 and the moisture content of the dehydrated cake actually measured by an operator.

[0051] Next, the update unit 103 updates the prediction model during the above-mentioned shutdown period using as training data a pair of the measurement data acquired by the acquisition unit 101 during the operation period of the coagulation tank 51 and the dehydrator 9 and an estimated value by the estimation model 112 for the moisture content of the dehydrated cake discharged from the dehydrator 9 at the point in time when the retention time has elapsed since the time the measurement data was measured.

[0052] Typically, the operating period is a set period of one day. Therefore, the update unit 103 performs an update once a day. Furthermore, it takes about one day for an operator to actually measure the moisture content of the dehydrated cake. Therefore, the estimation model 112 may be updated using measurement data from the operating period two days ago. On the other hand, it is desirable to update the prediction model 111 using measurement data from the most recent operating period.

[0053] Therefore, since the prediction model 111 is updated using the latest measurement data, it is possible to adapt the prediction using the prediction model 111 to the latest conditions of the coagulation tank 51 and the dehydrator 9. Similarly, since the estimation model 112 is updated using the latest measurement data and the actual measurements by the operator, it is possible to adapt the estimation using the estimation model 112 to the latest conditions of the dehydrator 9.

[0054] [Prediction processing] FIG. 7 is a flowchart showing an example of a process for predicting the moisture content of dehydrated cake (a moisture content prediction method) in the information processing device 1 configured as described above. This prediction process is executed during operation, as described above. As shown in FIG. 7, first, the acquisition unit 101 collects (acquires) various measurement data (S11, acquisition step). Next, the prediction unit 102 uses the prediction model 111 to predict, from the measurement data, a predicted value of the moisture content at a point in time when a residence time has elapsed since the measurement of the measurement data (S12, prediction step). Thereafter, the process returns to step S11 and repeats the above operation.

[0055] [Update process] 8 is a flowchart showing an example of the process of updating the prediction model 111 and the estimation model 112 in the information processing device 1. As described above, this update process is executed for each shutdown period.

[0056] 8, first, the acquisition unit 101 determines whether a new actual measurement value of the moisture content has been acquired by an operator (S21). If the new actual measurement value has not been acquired (NO in S21), the process proceeds to step S24. On the other hand, if the new actual measurement value has been acquired (YES in S21), the update unit 103 creates a pair of the newly acquired actual measurement value of the moisture content and the measurement data at the time of measurement of the new actual measurement value as new training data (S22), and updates the estimation model 112 using the created training data (S23).

[0057] Next, the update unit 103 uses the estimation model 112 to calculate an estimated value of the moisture content during the latest operating period from the measurement data during that operating period (S24). Next, for each measurement time point during the latest operating period, the update unit 103 creates new training data, which is a pair of the measurement data at that measurement time point and the estimated value at a time point when the retention time has elapsed since the measurement time point (S25). The update unit 103 then updates the prediction model 111 using the created training data (S26). Thereafter, the update process ends.

[0058] [Modification] The execution entity of each process described in the above embodiment is arbitrary and is not limited to the above example. For example, each step of the moisture content prediction method shown in Fig. 7 can be shared among multiple information processing devices. In other words, the moisture content prediction method may be executed by one information processing device 1 or multiple information processing devices.

[0059] [Software implementation example] The functions of the information processing device 1 (hereinafter referred to as the "device") can be realized by a program for causing a computer to function as the device, and a program (moisture content prediction program) for causing a computer to function as each control block of the device (particularly each part included in the control unit 10).

[0060] In this case, the device includes a computer having at least one control device (e.g., a processor) and at least one storage device (e.g., a memory) as hardware for executing the program. The control device and storage device execute the program, thereby realizing the functions described in each of the above embodiments.

[0061] The program may be non-transitory and may be recorded on one or more computer-readable recording media. The recording media may or may not be included in the device. In the latter case, the program may be supplied to the device via any wired or wireless transmission medium.

[0062] Furthermore, some or all of the functions of the control blocks can be realized by logic circuits. For example, an integrated circuit in which a logic circuit that functions as each of the control blocks is formed is also included in the scope of the present invention. In addition, the functions of the control blocks can also be realized by, for example, a quantum computer.

[0063] The present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of the present invention. [Explanation of symbols]

[0064] 1. Information processing equipment 9 Dehydrator 10 Control Unit 11 Storage section 51 Coagulation tank 52 Mixing blade 92 Screw 101 Acquisition Department 102 Prediction Department 103 Update section 111 Predictive Model 112 Estimation Model

Claims

1. an acquiring unit that acquires at least one of measurement data relating to a liquid supplied to a coagulation tank in which a chemical agent for coagulating suspended solids is added to the liquid containing the suspended solids, measurement data relating to the chemical agent supplied to the coagulation tank, measurement data relating to the liquid in the coagulation tank, measurement data relating to the operation of the coagulation tank, and measurement data relating to the operation of a dehydrator that dehydrates the liquid discharged from the coagulation tank while transporting it; a prediction unit that predicts, using a prediction model, from the measurement data acquired by the acquisition unit, the moisture content of a dehydrated cake that will be discharged from the dehydrator at a time when a retention time for the liquid to remain in the dehydrator has elapsed from the time when the measurement data was acquired, the prediction model is a prediction model trained using the measurement data acquired by the acquisition unit as an explanatory variable and an estimated value by an estimation model of the moisture content of the dehydrated cake discharged from the dehydrator at a time point when the retention time has elapsed since the time point when the measurement data was acquired as a dependent variable, the estimation model is an estimation model trained using measurement data related to the operation of the dehydrator as an explanatory variable and a moisture content of a dehydrated cake discharged from the dehydrator at the time of measurement of the measurement data as a response variable. Information processing device.

2. The information processing apparatus according to claim 1 , wherein the measurement data relating to the liquid supplied to the coagulation tank is at least one of a supply flow rate of the liquid per unit time and a concentration of the liquid.

3. The information processing device according to claim 1 , wherein the measurement data relating to the chemical agent to be supplied to the flocculation tank is a supply flow rate of the chemical agent per unit time.

4. The information processing device according to claim 1 , wherein the measurement data relating to the liquid in the coagulation tank is at least one of an average gray value of flocs and an average unit area of ​​gaps between flocs.

5. The information processing device according to claim 1 , wherein the measurement data relating to the operation of the coagulation tank is a rotation speed of an agitator blade in the coagulation tank.

6. the dehydrator is a screw press type dehydrator, 2. The information processing device of claim 1, wherein the measurement data regarding the operation of the dehydrator as an explanatory variable of the prediction model is at least one of the operating time of the dehydrator, the supply flow rate of the liquid to the dehydrator per unit time, the rotation speed of the screw of the dehydrator, and the supply pressure of the liquid fed to the dehydrator.

7. the dehydrator is a screw press type dehydrator, 2. The information processing device of claim 1, wherein the measurement data regarding the operation of the dehydrator as explanatory variables of the estimation model is at least one of the operating time of the dehydrator, the rotational speed of the screw of the dehydrator, the drive current value or torque value of the screw, the back pressure caused by a back pressure plate provided in the discharge section of the dehydrator to compress the liquid, and the opening degree between the back pressure plate and the discharge section.

8. The information processing apparatus according to claim 1 , wherein the prediction unit executes prediction using the prediction model each time the acquisition unit acquires new measurement data.

9. an updating unit that updates the prediction model during periods when the dehydrator is not in operation, using as training data a set of measurement data acquired by the acquiring unit during an operation period of the dehydrator and an estimated value, by the estimation model, of the moisture content of the dehydrated cake discharged from the dehydrator at a time when the retention time has elapsed since the time when the measurement data was acquired; The information processing device according to claim 1 .

10. A moisture content prediction method executed by one or more information processing devices, an acquiring step of acquiring at least one of measurement data relating to a liquid supplied to a coagulation tank in which a chemical agent for coagulating suspended solids is added to the liquid containing the suspended solids, measurement data relating to the chemical agent supplied to the coagulation tank, measurement data relating to the liquid in the coagulation tank, measurement data relating to the operation of the coagulation tank, and measurement data relating to the operation of a dehydrator that dehydrates the liquid discharged from the coagulation tank while transporting it; a prediction step of predicting, using a prediction model, the moisture content of a dehydrated cake discharged from the dehydrator at a time when a retention time for the liquid to remain in the dehydrator has elapsed from the time of measurement of the measurement data acquired in the acquisition step, the prediction model is a prediction model trained using the measurement data acquired in the acquiring step as an explanatory variable and an estimated value by an estimation model of the moisture content of the dehydrated cake discharged from the dehydrator at a time point when the retention time has elapsed since the time point when the measurement data was acquired as a dependent variable, the estimation model is an estimation model trained using measurement data related to the operation of the dehydrator as an explanatory variable and a moisture content of a dehydrated cake discharged from the dehydrator at the time of measurement of the measurement data as a response variable. Moisture content prediction method.

11. A moisture content prediction program for causing a computer to function as the information processing device according to claim 1, the moisture content prediction program causing a computer to function as the acquisition unit and the prediction unit.

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