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

Abstract

To alleviate reduction in accuracy of prediction of a water content of a dehydrated cake in the case of change in an operation condition, and maintain the accuracy of the prediction in a normal case.SOLUTION: In an information processor (1), a prediction part (102) predicts a water content using a first prediction model from measurement data acquired by an acquisition part (101), and a prediction value by a second prediction model (112) for the water content at a passage time point when residence time passes from a measurement time point of the measurement data. The measurement data used for learning by the first prediction model (111) is measurement data for a predetermined period of an actual operation period of a dehydrator. The second prediction model (112) is learned with the measurement data in an operation period in which an operation condition is different from that of the actual operation period as an explanatory variable, and with the water content at the passage 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] Generally, the sludge treatment equipment is test-run under a wide variety of operating conditions to determine the optimum operating conditions. During the test run, the moisture content prediction model is generated using a wide variety of measured values ​​of the multiple parameters measured under the wide variety of operating conditions as training data.

[0006] The apparatus is then operated based on the determined operating conditions. During this operating period, the moisture content prediction model is updated using the most recent measured values ​​of the parameters as training data. This results in the moisture content prediction model being adapted to the most recent operating conditions. Therefore, under normal circumstances where there are no changes in the operating conditions, the accuracy of predictions made by the moisture content prediction model is maintained. However, there is a concern that the accuracy of predictions made by the moisture content prediction model may decrease when the operating conditions are changed.

[0007] In this regard, it is conceivable to update the moisture content prediction model using at least some of the various measurements and the most recent measurements as training data, but the influence of the various measurements usually results in a decrease in the accuracy of predictions made by the moisture content prediction model.

[0008] An object of one aspect of the present invention is to mitigate a decrease in the accuracy of the moisture content prediction when the operating conditions are changed, while maintaining the accuracy of the prediction under normal circumstances. [Means for solving the problem]

[0009] 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 discharged from the coagulation tank while transporting it; and a prediction unit that predicts, using a first prediction model, the measurement data acquired by the acquisition unit and the amount of the liquid discharged from the dehydrator at a time when a retention time for the liquid to remain in the dehydrator has elapsed since the time when the measurement data was measured. and a prediction unit that predicts the post-dehydration moisture content at a time when the retention time has elapsed from the time of measurement of the measurement data, based on a predicted value by a second prediction model for the post-dehydration moisture content, which is the moisture content of the dehydrated cake to be dehydrated, wherein the measurement data used by the first prediction model for learning is the measurement data acquired by the acquisition unit from a predetermined period before the learning time during the actual operation period of the dehydrator to the learning time, and the second prediction model is a prediction model that has been trained using the measurement data acquired by the acquisition unit during an operating period in which operating conditions are different from the actual operation period as an explanatory variable and the post-dehydration moisture content at a time when the retention time has elapsed from the time of measurement of the measurement data as a dependent variable.

[0010] Furthermore, 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, and includes 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 after being discharged from the coagulation tank; and a first prediction model for calculating a moisture content of the liquid after the measurement data acquired in the acquisition step and a time when a residence time for the liquid to remain in the dehydrator has elapsed since the time of measurement of the measurement data. and a prediction step of predicting the post-dehydration moisture content at a point when the retention time has elapsed from the time of measurement of the measurement data based on a predicted value by a second prediction model of the post-dehydration moisture content, which is the moisture content of the dehydrated cake discharged from the dehydrator. The measurement data used by the first prediction model for learning is the measurement data acquired in the acquisition step from a predetermined period before the learning point during the actual operation period of the dehydrator to the learning point, and the second prediction model is a prediction model trained using the measurement data acquired in the acquisition step during an operating period in which operating conditions are different from the actual operation period as an explanatory variable and the post-dehydration moisture content at a point when the retention time has elapsed from the time of measurement of the measurement data as a dependent variable. [Effects of the Invention]

[0011] According to one aspect of the present invention, it is possible to mitigate a decrease in the accuracy of the moisture content prediction when the operating conditions are changed, while maintaining the accuracy of the prediction under normal circumstances. [Brief explanation of the drawings]

[0012] [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 flowchart showing an example of a process for predicting the moisture content of a dehydrated cake in the information processing device. [Figure 4] 10 is a flowchart illustrating an example of a process of updating a first prediction model in the information processing device. [Figure 5] FIG. 4 is a diagram illustrating, in a table format, combinations of explanatory variables and response variables of a first prediction model in the first embodiment of the information processing device. [Figure 6] FIG. 10 is a diagram showing, in a table format, combinations of explanatory variables and response variables of the second prediction model in the first embodiment. [Figure 7] FIG. 10 is a diagram showing, in tabular form, combinations of explanatory variables and response variables of a fourth comparative model in Comparative Example 4. [Figure 8] 10 is a graph showing the variation in moisture content predicted by the first prediction model of Example 1 relative to the moisture content actually measured by the operator. [Figure 9] 10 is a graph showing variations in moisture content predicted values ​​by the first to fourth comparative models in Comparative Examples 1 to 4 relative to actual moisture content measurements by operators. [Figure 10] 1 is a diagram showing, in tabular form, the mean absolute error, the operating time parameter importance, and the sludge flow rate parameter importance for each of Example 1 and Comparative Examples 1 to 4. FIG. DETAILED DESCRIPTION OF THE INVENTION

[0013] [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.

[0014] 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.

[0015] 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.

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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).

[0024] [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.

[0025] 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."

[0026] The storage unit 11 stores a first prediction model 111 and a second prediction model 112. Details of these will be explained later in the sections "Regarding the First Prediction Model" and "Regarding the Second Prediction Model," respectively.

[0027] 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 first prediction model."

[0028] Prediction unit 102 predicts the moisture content of the dehydrated cake from the measurement data acquired by acquisition unit 101, using first prediction model 111 stored in memory unit 11. This dehydrated cake is the dehydrated cake that is discharged from dehydrator 9 at a point in time (hereinafter referred to as the "elapsed time") when the retention time for the flocculated sludge in dehydrator 9 has elapsed from the time of measurement of the measurement data. Prediction unit 102 performs a prediction using first prediction model 111 every time acquisition unit 101 acquires new measurement data. Therefore, prediction unit 102 can predict the moisture content of the dehydrated cake in real time, that is, continuously at short time intervals (for example, every minute).

[0029] [About the first forecast model] The first prediction model 111 is a prediction model trained using the measurement data acquired by the acquisition unit 101 at the time of measurement as an explanatory variable and the moisture content of the dehydrated cake discharged from the dehydrator 9 at the time when the retention time has elapsed from the time of measurement (hereinafter referred to as the "post-dehydration moisture content") as a dependent variable. In this embodiment, the first prediction model 111 has an additional explanatory variable, which is the predicted value of the post-dehydration moisture content from the measurement data by the second prediction model 112. The second prediction model 112 will be described later in the section "Regarding the Second Prediction Model."

[0030] The measurement data acquired by the acquisition unit 101 and used for prediction by the first prediction model 111 includes the following.

[0031] (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.

[0032] (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.

[0033] (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.

[0034] (4) Measurement data related to the operation of the coagulation tank 51. The measurement data is the rotation speed of the agitator blades 52 in the coagulation tank 51, and is acquired from the control device 3.

[0035] (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.

[0036] 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 predicted with high accuracy using multifaceted explanatory variables. Among the measurement data shown in (1) to (5) above, measurement data that have a large contribution (high importance) to the prediction accuracy of the first prediction model 111 include the operation time, the screw rotation speed, and the supply flow rate shown in (5) above, and the average floc color value shown in (3) above.

[0037] [About the second forecast model] The second prediction model 112 is a prediction model trained using the measurement data acquired by the acquisition unit 101 at the time of measurement as an explanatory variable and the post-spin moisture content at the time point when the retention time has elapsed since the measurement as a target variable. In this embodiment, the second prediction model 112 is a prediction model trained during a trial operation period before the start of the actual operation period of the dehydrator 9. The measurement data that serves as the explanatory variable of the second prediction model 112 may be the same as or different from the measurement data that serves as the explanatory variable of the first prediction model 111.

[0038] As described above, the information processing device 1 of this embodiment includes an acquisition unit 101 that acquires at least one of measurement data on the liquid supplied to a coagulation tank 51, where a chemical agent that coagulates suspended solids is added to the liquid containing the suspended solids, measurement data on the chemical agent 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 that dehydrates the liquid while transporting it, and a prediction unit 102 that uses a first prediction model 111 to predict the post-dehydration moisture content, which is the moisture content of the dehydrated cake discharged from the dehydrator 9 at a point in time after the retention time has elapsed from the time of measurement of the measurement data, based on the measurement data acquired by the acquisition unit 101 and a predicted value by a second prediction model 112 for the post-dehydration moisture content, which is the moisture content of the dehydrated cake discharged from the dehydrator 9 at a point in time after the retention time has elapsed from the time of measurement of the measurement data. The measurement data used by first prediction model 111 for learning is measurement data acquired by acquisition unit 101 from a predetermined period before the learning point in time during the actual operation period of dehydrator 9 to the learning point in time. Second prediction model 112 is a prediction model trained using, as explanatory variables, the measurement data acquired by acquisition unit 101 during a trial operation period, which is an operation period in which the operating conditions are different from those of the actual operation period, and the post-dehydration moisture content at the elapsed time point when the retention time has elapsed since the measurement of the measurement data, as a response variable.

[0039] According to the above configuration, second prediction model 112 is a prediction model that is trained using, as training data, measurement data acquired under a wide variety of operating conditions during the test run of dehydrator 9. Therefore, the predicted value by second prediction model 112 for the post-spin moisture content at the time when the retention time has elapsed since the measurement of the measurement data is a predicted value that is suitable for a wide variety of operating conditions.

[0040] On the other hand, first prediction model 111 is a prediction model trained using measurement data acquired from a predetermined period before the learning point during the actual operation of dehydrator 9 to the learning point and the predicted value by second prediction model 112 of the post-spin moisture content corresponding to the measurement data as explanatory variables, and the post-spin moisture content corresponding to the measurement data as a response variable. First prediction model 111 is trained using the most recent measurement data, and therefore is a prediction model adapted to the most recent operating conditions. Therefore, in normal cases where there are no changes in operating conditions, the accuracy of predictions by first prediction model 111 is maintained.

[0041] Furthermore, first prediction model 111 is trained using the predicted value of the post-spin moisture content predicted by second prediction model 112 using the most recent measurement data, and therefore is a prediction model that takes into account a wide variety of operating conditions. Therefore, even if the operating conditions are changed after a long period of time without any changes, it is possible to mitigate any decrease in the accuracy of prediction by first prediction model 111.

[0042] [About the update section] The update unit 103 performs a process of updating the first prediction model 111 during a period when the coagulation tank 51 and the dehydrator 9 are stopped (a shutdown period). Specifically, the update unit 103 uses the second prediction model 112 to calculate a predicted value of the post-dehydration moisture content at a point in time when the retention time has elapsed from the time of measurement of the measurement data acquired by the acquisition unit 101 during the operation period of the dehydrator 9. Then, the update unit 103 updates the first prediction model 111 using, as training data, a set of the measurement data, the predicted value of the post-dehydration moisture content, and the actual moisture content of the dehydrated cake measured by an operator.

[0043] Typically, the operating period is a set period of one day. Therefore, the updating unit 103 updates the data once a day. Furthermore, it takes time for an operator to actually measure the moisture content of the dehydrated cake. Therefore, instead of the actual moisture content measured by the operator, an estimated moisture content calculated using another model may be used as the objective variable of the training data.

[0044] Therefore, since the first prediction model 111 is updated using the latest measurement data, predictions using the first prediction model 111 can be adapted to the latest conditions of the coagulation tank 51 and the dehydrator 9.

[0045] [Prediction processing] FIG. 3 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. 3, first, the acquisition unit 101 collects (acquires) various measurement data (S11, acquisition step). Next, the prediction unit 102 predicts, from the measurement data, the moisture content at a point in time when a residence time has elapsed since the measurement of the measurement data, using the first prediction model 111 (S12, prediction step). Thereafter, the process returns to step S11 and repeats the above operations.

[0046] [Update process] 4 is a flowchart showing an example of the process of updating the first prediction model 111 in the information processing device 1. As described above, this update process is executed for each shutdown period.

[0047] 4, first, the update unit 103 calculates, using the second prediction model 112, a predicted value of the post-dehydration moisture content at a point in time when the retention time has elapsed from the time of measurement data acquired by the acquisition unit 101 during the operation period of the dehydrator 9 (S21). Next, the update unit 103 updates the first prediction model 111 using a set of the measurement data, the predicted value of the post-dehydration moisture content, and the actual moisture content of the dehydrated cake measured by an operator as training data (S22). Thereafter, the update process ends.

[0048] [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. 3 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.

[0049] [Additional Notes] In the above embodiment, the explanatory variables of the first prediction model 111 include the predicted value of the post-spin moisture content using the second prediction model 112, but may also include predicted values ​​of the post-spin moisture content using other models. In this way, multiple predicted values ​​using different models may be used as explanatory variables of the first prediction model 111.

[0050] In the above embodiment, the second prediction model 112 is not updated, but this is not limiting. For example, measurement data acquired by the acquisition unit 101 when an operating condition different from normal occurs during an actual operation period may be accumulated, and the accumulated measurement data may be used to update the second prediction model 112.

[0051] In the above-described embodiment, the trial operation period is used as an operation period in which the operating conditions are different from those of the actual operation period. However, the different operation period is not limited to the trial operation period, and may be any operation period in which measurement data can be obtained under a wide variety of operating conditions, including the range of operating conditions in the actual operation period. For example, the different operation period may be a test operation period performed after maintenance of the dehydrator 9.

[0052] [Example] An example of the information processing device 1 having the above configuration and a comparative example will be described with reference to FIGS.

[0053] Example 1 As described above, the first prediction model 111 of this embodiment is a model that predicts the post-spin moisture content at an elapsed time point using measurement data at the time of measurement and a predicted value of the post-spin moisture content at an elapsed time point calculated from the measurement data using the second prediction model 112. The first prediction model 111 is trained using the measurement data acquired during the most recent predetermined period of the actual operation period. The second prediction model 112 is trained using the measurement data acquired during the trial operation period. Therefore, the second prediction model 112 is not updated.

[0054] 5 is a diagram showing, in tabular form, combinations of explanatory variables and response variables of the first prediction model 111 in this embodiment. In the example of Fig. 5, the measurement data at the measurement time, which serve as explanatory variables for the first prediction model 111, are the supply flow rate (sludge flow rate) of the liquid (sludge) per unit time, the concentration of the liquid (sludge), the average concentration value of the flocs, the average unit area of ​​the gaps between the flocs, the supply flow rate of the chemical per unit time, 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.

[0055] Fig. 6 is a diagram showing, in tabular form, combinations of explanatory variables and dependent variables of the second prediction model 112. The second prediction model 112 shown in Fig. 6 is similar to the first prediction model 111 shown in Fig. 5 except that predicted values ​​by the second prediction model 112 are omitted. That is, in this example, the measurement data included in the explanatory variables of the first prediction model 111 and the measurement data included in the explanatory variables of the second prediction model 112 are the same.

[0056] (Comparative Example 1) The first comparative model, which is a comparative example of Example 1, has explanatory variables and response variables similar to those shown in Fig. 6. The first comparative model is trained using all measurement data measured in the past. For this reason, the time required for training is longer than in other cases.

[0057] (Comparative Example 2) The second comparative model, which is another comparative example to the first embodiment, has explanatory variables and response variables that are similar to those shown in Fig. 6. The second comparative model is trained using measurement data acquired during a predetermined period immediately preceding the actual operation period. Therefore, the second comparative model is similar to the first prediction model 111 of the first embodiment except that the predicted values ​​by the second prediction model 112 are omitted.

[0058] (Comparative Example 3) A third comparative model, which is yet another comparative example to Example 1, has explanatory variables and response variables similar to those shown in Fig. 6. The third comparative model is trained using a variety of measurement data, such as measurement data from a test run period, and the measurement data measured during the most recent predetermined period.

[0059] Comparative Example 4 FIG. 7 is a table showing combinations of explanatory variables and dependent variables of a fourth comparative model, which is yet another comparative example to Example 1. As shown in FIG. 7, the only explanatory variable of the fourth comparative model is the predicted value by the second prediction model 112. Therefore, the fourth comparative model is a univariate regression model that predicts the post-dehydration moisture content using the fourth comparative model from the predicted value of the post-dehydration moisture content predicted from the measurement data using the second prediction model 112. Furthermore, the fourth comparative model is trained using the measurement data measured during the most recent predetermined period.

[0060] (evaluation) Fig. 8 is a graph showing the variation in moisture content predicted by the first prediction model 111 of Example 1 relative to the actual moisture content measured by different workers. Fig. 9 is a graph showing the variation in moisture content predicted by the first to fourth comparative models relative to the actual moisture content measured by different workers. In Fig. 9, the graphs for the first to fourth comparative models are shown in the order of upper left, lower left, upper right, and lower right.

[0061] 10 is a diagram showing, in tabular form, the mean absolute error (MAE), the operating time parameter importance, and the sludge flow rate parameter importance for each of Example 1 and Comparative Examples 1 to 4. Here, parameter importance indicates the degree of influence that an explanatory variable in a model has on a target variable. Of the nine measurement data included in the explanatory variables in the first prediction model 111 and the first to third comparative models, the operating time and sludge flow rate tend to have the highest parameter importance.

[0062] Regarding the operating time, the dehydrator 9 continues to discharge dehydrated cake to prevent the cake remaining in the dehydrator 9 from sticking after the operation is stopped. Therefore, when the dehydrator 9 is next started, operation begins with some space inside the dehydrator 9. Then, the dehydrator 9 is filled with the supplied sludge, and the pressure distribution inside the dehydrator 9 fluctuates and stabilizes. This pressure distribution causes the dehydration of the cake to fluctuate.

[0063] Therefore, the pressure distribution has a high degree of parameter importance. However, it is currently difficult to include the pressure distribution in the explanatory variables. Therefore, instead of the pressure distribution, the operating time, which is closely related to the pressure distribution, has a high degree of parameter importance and is included in the explanatory variables.

[0064] On the other hand, regarding the sludge flow rate, in the dehydrator 9, the sludge flow rate (input amount) = the amount of dehydrated cake discharged by the screw + the amount of filtrate. Since the discharge amount is roughly constant if the screw rotation speed is constant, the better the dehydration, the greater the amount of filtrate and the greater the sludge flow rate. Therefore, the greater the sludge flow rate, the lower the moisture content after dehydration. Therefore, the sludge flow rate is a highly important parameter.

[0065] 8 to 10, it can be seen that the first prediction model 111 of Example 1 has smaller variations and the smallest mean absolute error compared to the first to fourth comparative models, and therefore has better accuracy.

[0066] 10, among Comparative Examples 1 to 4, Comparative Example 2 has the smallest mean absolute error and appears to have good accuracy. However, the importance of the operating time parameter is twice that of the sludge flow rate parameter. This means that the second comparative model is a model that makes predictions based only on the time from start-up indicated by the operating time, rather than on the operating state indicated by the sludge flow rate, etc. The reason for this is thought to be that the second comparative model is trained using only measurement data from the most recent specified period, which results in low diversity in the training data.

[0067] For example, in the measurement data for the most recent specified period, the screw rotation speed is a constant value (0.17 min -1 ) and the screw rotation speed was 0.19 min on the day of operation. -1 Consider the case where the screw rotation speed is changed to . Normally, the moisture content would worsen, but the second comparative model cannot fully consider the effect of the screw rotation speed. Therefore, it is difficult to use the second comparative model for various controls in the control system 100.

[0068] In contrast, in Example 1, the predicted value of the spinning rate by second prediction model 112 deteriorates, and therefore the predicted value of the spinning rate by first prediction model 111 also deteriorates. That is, first prediction model 111 can indirectly take into account the influence of the screw rotation speed. Therefore, first prediction model 111 can be used for various controls in control system 100.

[0069] [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).

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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]

[0074] 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 First Prediction Model 112 Second Prediction 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 first prediction model, the post-dehydration moisture content, which is the moisture content of a dehydrated cake discharged from the dehydrator at a time when a retention time has elapsed since the time when the measurement data was measured, from the measurement data acquired by the acquisition unit and a predicted value by a second prediction model for the post-dehydration moisture content, the moisture content being the moisture content of a dehydrated cake discharged from the dehydrator at a time when the retention time for the liquid to remain in the dehydrator has elapsed since the time when the measurement data was measured, the measurement data used by the first prediction model for learning is the measurement data acquired by the acquisition unit from a predetermined period before the learning point in time during an actual operation period of the dehydrator to the learning point in time, The second prediction model is a prediction model trained using the measurement data acquired by the acquisition unit during an operating period in which operating conditions are different from the actual operating period as an explanatory variable, and the post-dehydration moisture content at a time point when the residence time has elapsed since the measurement of the measurement data as a target 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 according to claim 1, wherein the measurement data relating to the operation of the dehydrator 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 into the dehydrator.

7. The information processing apparatus according to claim 1 , wherein the prediction unit performs prediction using the first prediction model every time the acquisition unit acquires new measurement data.

8. The present invention further includes an update unit that updates the first prediction model during periods when the dehydrator is not in operation, using as training data a set of measurement data acquired by the acquisition unit during an operation period of the dehydrator, a predicted value of the post-dehydration moisture content by the second prediction model at a time when the retention time has elapsed since the measurement of the measurement data, and the post-dehydration moisture content at a time when the retention time has elapsed since the measurement of the measurement data. The information processing device according to claim 1 .

9. 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 first prediction model, the post-dehydration moisture content, which is the moisture content of a dehydrated cake discharged from the dehydrator at a time when a retention time has elapsed since the time when the measurement data was measured, from the measurement data acquired in the acquisition step and a predicted value by a second prediction model for the post-dehydration moisture content, which is the moisture content of a dehydrated cake discharged from the dehydrator at a time when the retention time for the liquid to remain in the dehydrator has elapsed since the time when the measurement data was measured, the measurement data used by the first prediction model for learning is the measurement data acquired in the acquiring step from a predetermined period before the learning point in time during an actual operation period of the dehydrator to the learning point in time, The second prediction model is a prediction model trained using the measurement data acquired in the acquisition step during an operating period in which operating conditions are different from the actual operating period as an explanatory variable, and the post-dehydration moisture content at a time point when the residence time has elapsed since the time point at which the measurement data was acquired as a response variable. Moisture content prediction method.

10. 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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