Dehydration control system, control device, and processing system

Abstract

This invention provides a technology that enables control over the water content of a material after dewatering and the water quality of the dewatered filtrate separated from the material during the dewatering process of a material to be treated. [Solution] The dewatering control system comprises a dewatering device for dewatering a workpiece and a control device for controlling the operation of the dewatering device. The control device executes a predictive model that predicts the amount of the operating factor for the operating factor, which is created based on the amount of the operating factor for the operating factor of the dewatering device at multiple timings, the moisture content of the workpiece after dewatering by the dewatering device, and the water quality of the dewatered filtrate separated from the workpiece by the dewatering device.

Description

Technical Field

[0001] The present disclosure relates to a dehydration control system, a control device, and a processing system.

Background Art

[0002] As a method for treating a workpiece containing organic components (e.g., wastewater containing sludge), for example, a digestion treatment using anaerobic organisms is known. Before performing this digestion treatment, solubilization treatment may be performed on the dehydrated workpiece. In the solubilization treatment, for example, the solid content contained in the workpiece is hydrolyzed by heating and pressurizing the workpiece using high-temperature steam. The amount of steam required for the solubilization treatment depends on the water content of the workpiece after dehydration.

[0003] Regarding the water content of the workpiece, for example, Patent Document 1 and Patent Document 2 disclose techniques for controlling the water content of sludge after dehydration in a dehydrator.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] In the dehydration treatment of the workpiece, it is desired to control the water content of the workpiece after dehydration and the water quality of the dehydrated filtrate separated from the workpiece.

Means for Solving the Problems

[0006] The dewatering control system in this disclosure comprises a dewatering device for dewatering a workpiece and a control device for controlling the operation of the dewatering device, wherein the control device executes a predictive model for predicting the amount of the operating factor of the operating factor, which is created based on the amount of the operating factor of the dewatering device at multiple timings, the moisture content of the workpiece after dewatering by the dewatering device, and the water quality of the dewatered filtrate separated from the workpiece by the dewatering device. [Effects of the Invention]

[0007] According to the technology disclosed herein, in the dewatering treatment of a material to be treated, it becomes possible to control the water content of the material after dewatering and the water quality of the dewatered filtrate separated from the material to be treated. [Brief explanation of the drawing]

[0008] [Figure 1] Figure 1 is a diagram illustrating the configuration of a processing system 100 according to an embodiment. [Figure 2] Figure 2 is a schematic diagram illustrating the dewatering apparatus 2 according to this embodiment. [Figure 3] Figure 3 is a diagram illustrating the hardware configuration of the control device 6 according to the embodiment. [Figure 4] Figure 4 is a block diagram illustrating the functional configuration of the control device 6 according to this embodiment. [Figure 5] Figure 5 shows an example of the first correspondence information and the second correspondence information. [Figure 6] Figure 6 is a flowchart illustrating the model predictive control of the dewatering device 2 by the control device 6 according to this embodiment. [Figure 7] Figure 7 is a diagram illustrating the configuration of a processing system 100A according to a modified example of the embodiment 1. [Figure 8] Figure 8 is a block diagram illustrating the functional configuration of the control device 6A according to a modified example 1 of the embodiment. [Figure 9] Figure 9 is a diagram illustrating the configuration of the processing system 100B according to a modified example 2 of the embodiment. [Figure 10]Figure 10 is a block diagram illustrating the functional configuration of the control device 6B according to a modified example 2 of the embodiment. [Modes for carrying out the invention]

[0009] Embodiments of this disclosure will be described below with reference to the drawings. However, this description should not be interpreted as limiting, and will not limit the subject matter described in the claims. Furthermore, various changes, substitutions, and modifications can be made without departing from the spirit and scope of this disclosure. Different embodiments can also be combined as appropriate.

[0010] Furthermore, the following describes an application of the technology described herein to a sewage treatment facility, but the scope of application of the technology described herein is not limited to this. Also, in the embodiments described below, sludge containing organic components is used as the material to be treated, but the scope of treatment of the technology described herein is not limited to this.

[0011] [Processing System 100] Figure 1 is a diagram illustrating the configuration of a treatment system 100 according to an embodiment. The treatment system 100 is incorporated, for example, into a sewage treatment facility to treat sludge. Hereinafter, the treatment system 100 will be described assuming that the material to be treated is sludge.

[0012] As shown in Figure 1, the processing system 100 in this embodiment includes, for example, a storage tank 1, a dewatering device 2, a solubilizing device 3, a digester tank 4, a dewatering device 5, a control device 6, a pump P1, a water content meter S1, and a water quality meter S2. Hereinafter, the water content meter S1 and the water quality meter S2 will be collectively referred to simply as sensors. Furthermore, the configuration of the processing system 100 that includes the storage tank 1, the dewatering device 2, the control device 6, the pump P1, and sensors S1 and S2 will also be referred to as the dewatering control system 10.

[0013] [Storage Tank 1] The storage tank 1 stores, for example, the primary sedimentation sludge discharged from the primary sedimentation tank (not shown) in the sewage treatment facility, the excess sludge discharged from the final sedimentation tank (not shown), or the mixed raw sludge obtained by mixing the primary sedimentation sludge and the excess sludge. Hereinafter, the primary sedimentation sludge, the excess sludge, and the mixed raw sludge are collectively referred to simply as sludge.

[0014] [Pump P1] The pump P1 is provided, for example, on the line L1 for supplying the sludge stored in the storage tank 1 to the dehydration device 2, and pumps the sludge to the dehydration device 2.

[0015] [Dehydration device 2] The dehydration device 2 dehydrates the sludge supplied from the storage tank 1. The dehydration device 2 is an example of the "dehydration device" according to the present disclosure.

[0016] As the dehydration device 2, for example, various types of dehydrators such as a belt press type, a centrifugal separation type, and a screw press type can be adopted, and the type is not particularly limited. Hereinafter, as an example, the case where the dehydration device 2 is a belt press type dehydration device will be described.

[0017] FIG. 2 is a schematic configuration diagram for explaining the dehydration device 2 according to the embodiment. As shown in FIG. 2, the dehydration device 2 includes, for example, a coagulation mixing tank 21 for mixing the polymer flocculant F1 into the sludge supplied from the storage tank 1, and a concentration section arranged at the subsequent stage of the coagulation mixing tank 21. 22 (primary dehydration section) for gravity filtration (gravity concentration) of the sludge on the upper surface of the endless track running filter cloth belt (filter medium) 22a, and a pair of dehydration filter cloth belts for conveying and pressing the sludge concentrated in the concentration section 22. A dehydration section 23 (secondary dehydration section) sandwiched between 23a and 23c, and a first chemical injection device 24 for adding the polymer flocculant F1 to the sludge introduced into the coagulation mixing tank 21.

[0018] The first chemical injection device 24 adds a polymer flocculant F1 to the sludge to coagulate the solid components (organic matter) contained in the sludge. After the polymer flocculant F1 is added, the sludge is introduced into the coagulation and mixing tank 21, where it is thoroughly stirred and mixed by the stirring blades 21a to form flocs, and then introduced into the concentration section 22. Any generally known polymer flocculant F1 may be used, such as anionic polymer flocculants or cationic polymer flocculants.

[0019] The concentration unit 22 includes, for example, a concentration filter cloth belt 22a, multiple rollers 22b, multiple plows 22c, multiple consolidation devices 22d, multiple screws 22e, a pre-dewatering roller 22f, a second chemical injection device 22g, and a filtrate receiving tray 22h. The concentration unit 22, for example, gravity filters the sludge introduced from the coagulation mixing tank 21 onto the upper surface of the concentration filter cloth belt 22a, and then pressurizes and dewaters the gravity-filtered sludge using the pre-dewatering roller 22f before discharging it to the dewatering unit 23 below. Note that a dewatering device 2 may be a type that does not include a consolidation device 22d, screws 22e, pre-dewatering roller 22f, and a second chemical injection device 22g.

[0020] The concentration filter belt 22a is formed in an endless shape and is wrapped around a plurality of rollers 22b. At least one of the plurality of rollers 22b is configured as a drive roller that is rotationally driven by a drive source (not shown), such as a motor. The concentration filter belt 22a can travel in the direction of the arrow shown in Figure 2 by the rotational drive of the drive roller by the rotational control of the drive source by the control device 6 described later. That is, in Figure 2, the direction from the right side (upstream side) to the left side (downstream side) is the direction of sludge transport in the concentration section 22.

[0021] The sludge introduced from the coagulation and mixing tank 21 to the thickening section 22 is placed on the thickening filter belt 22a. The thickening filter belt 22a is then moved by the rotation control of the drive source by the control device 6, thereby transporting the sludge downstream. The thickening filter belt 22a is configured, for example, to allow water to pass through but not sludge. As a result, during the process of transporting the sludge by the thickening filter belt 22a, the water contained in the sludge is filtered and separated by gravity as it passes through the thickening filter belt 22a, and the dewatered filtrate, which is the water separated from the sludge, is collected by the filtrate receiving tray 22h.

[0022] Multiple plows 22c are obstacles that disperse the sludge being transported on the thickening filter belt 22a and promote drainage, and are erected on the upper surface of the thickening filter belt 22a. Multiple consolidation devices 22d are arranged on the upper surface of the thickening filter belt 22a and consolidate the sludge being transported on the thickening filter belt 22a by pressing it against the upper surface of the thickening filter belt 22a. Multiple screws 22e are arranged on the upper surface of the thickening filter belt 22a and consolidate the sludge being transported on the thickening filter belt 22a by moving it in a direction perpendicular to the transport direction and the direction of gravity (i.e., the width direction of the thickening filter belt 22a, which is the direction perpendicular to the plane of the paper in Figure 2), while reducing the width dimension of the sludge and increasing the height of the sludge.

[0023] Above the concentration filter cloth belt 22a, a second chemical injection device 22g is provided for adding, for example, an inorganic coagulant F2 to the sludge to increase its concentration. Any generally known inorganic coagulant F2 may be used, such as iron-based or aluminum-based agents. The sludge to which the inorganic coagulant F2 has been added is then thoroughly kneaded, for example, by a screw 22e.

[0024] The pre-dewatering roller 22f is positioned, for example, so that its outer circumferential surface is in pressure against the upper surface of the concentration filter belt 22a, and it clamps and pressurizes the sludge being conveyed on the concentration filter belt 22a between itself and the upper surface of the concentration filter belt 22a. The sludge that has been pressurized and dewatered by the pre-dewatering roller 22f is discharged to the dewatering section 23.

[0025] The dewatering section 23 includes, for example, a lower dewatering filter belt 23a, a plurality of lower rollers 23b, an upper dewatering filter belt 23c, a plurality of upper rollers 23d, and a filtrate receiving tray 23e.

[0026] The dewatering section 23 is located, for example, below the concentration section 22. It transports the sludge that falls from the concentration section 22 between the dewatering lower filter belt 23a and the dewatering upper filter belt 23c, and dewaters it under pressure, thereby discharging it as a dewatered cake. Hereinafter, the dewatering lower filter belt 23a and the dewatering upper filter belt 23c will be collectively referred to as a pair of dewatering filter belts 23a, 23c.

[0027] The pair of dewatering filter belts 23a and 23c are formed in an endless manner. The lower dewatering filter belt 23a is wrapped around a plurality of lower rollers 23b, and the upper dewatering filter belt 23c is wrapped around a plurality of upper rollers 23d. At least one of the plurality of lower rollers 23b and at least one of the plurality of upper rollers 23d are configured as drive rollers that are rotationally driven by a drive source (not shown), such as a motor. The pair of dewatering filter belts 23a and 23c can travel in the direction of the arrows shown in Figure 2 by the rotational drive of the drive rollers by the rotational control of the drive source by the control device 6. That is, in Figure 2, the direction from the left side (upstream side) to the right side (downstream side) is the direction of sludge transport in the dewatering section 23.

[0028] The sludge fed from the concentration section 22 to the dewatering section 23 is placed on the dewatering lower filter belt 23a. Then, the pair of dewatering filter belts 23a and 23c are driven by the rotation control of the drive source by the control device 6, and the sludge is transported downstream. The pair of dewatering filter belts 23a and 23c are configured, for example, to allow water to pass through but not sludge. As a result, during the process of sludge transport by the concentration filter belt 22a, the water contained in the sludge is filtered and separated by gravity as it passes through the concentration filter belt 22a, and the dewatered filtrate, which is the water separated from the sludge, is collected by the filtrate receiving tray 23e. The sludge compressed by the pair of dewatering filter belts 23a and 23c is discharged as a dewatered cake.

[0029] Hereinafter, the sludge dewatered by the dewatering device 2 will also be referred to as dewatered sludge (processed material after dewatering). As shown in Figure 1, the dewatered sludge is discharged from the dewatering device 2 by a pump (not shown) via line L2, for example, and supplied to the solubilizing device 3. The dewatered filtrate separated from the sludge is discharged from the dewatering device 2 by a pump (not shown) via return line L3, for example, and returned as return water to the upstream equipment (primary sedimentation tank, etc.).

[0030] [Solubilizer 3] Returning to Figure 1, the solubilizer 3 performs a steam solubilization treatment on the sludge (dewatered sludge) supplied from the dewatering unit 2, for example, under predetermined temperature or pressure conditions. Specifically, the solubilizer 3 heats and pressurizes the sludge with high-temperature steam supplied from an external source (not shown), for example, to perform thermal hydrolysis of the solids contained in the dewatered sludge. The hydrothermal reaction in thermal hydrolysis promotes the solubilization of the sludge. The solubilizer 3 may also perform hydrolysis of the solids contained in the sludge by adding ozone or the like to the sludge, for example. The solubilized sludge is then supplied from the solubilizer 3 to the digester 4 via line L4, for example, by a pump (not shown). The solubilizer 3 may be operated continuously or in batch mode.

[0031] [Digester tank 4] The digester 4 digests organic components contained in sludge that have been solubilized by the solubilizer 3, for example. The digester 4 has, for example, a stirring function. Inside the digester 4, for example, organic components contained in sludge supplied from the solubilizer 3 are anaerobically digested (decomposed) by anaerobic bacteria inside the digester 4. Digestion gas is generated during this digestion process. The digested liquid from the digester 4 is then supplied from the digester 4 to the dewatering device 5 via line L5, for example, by a pump (not shown). The digester 4 is an example of a "digestion device" according to this disclosure.

[0032] In the processing system 100, for example, a coagulant is added to the digestate flowing through line L5 to coagulate the solids (organic matter) contained in the digestate and form flocs. Alternatively, for example, dilution water may be supplied to the sludge flowing through line L4 to reduce the solid content. The processing system 100 may also be equipped with any temperature control means to adjust the temperature of the sludge supplied to the digester 4. For example, a cooler (not shown) may be placed between the solubilizer 3 and the digester 4 to cool the sludge supplied to the digester 4. Alternatively, a heat exchanger (not shown) may be placed to heat the sludge in the digester 4 with a fluid (e.g., hot water) supplied from an external heat source (not shown).

[0033] [Dehydration device 5] The dewatering device 5 dewaters the sludge contained in the digestate supplied from the digester 4, for example. The dewatered sludge is then supplied from the dewatering device 5 to downstream equipment such as an incinerator by a pump (not shown), for example. The dewatered filtrate separated from the sludge by the dewatering device 5 is returned as return water from the dewatering device 5 to upstream equipment (such as a primary sedimentation tank) by a pump (not shown), for example.

[0034] [Sensors S1, S2] The moisture content meter S1 is installed, for example, in line L2 and measures the moisture content of the dewatered sludge discharged from the dewatering device 2. The water quality meter S2 is installed, for example, in line L3 and measures the water quality of the dewatered filtrate discharged from the dewatering device 2. The water quality meter S2 is, for example, a TS concentration meter that measures the solids (TS) concentration as a water quality indicator. However, the water quality meter S2 is not limited to this and may measure other water quality indicators. The water quality meter S2 may be, for example, a turbidimeter that measures turbidity as a water quality indicator.

[0035] [Control device 6] The control device 6 controls, for example, the operation of the dewatering device 2. Specifically, the control device 6 performs model predictive control (MPC) of the dewatering device 2 using, for example, a predictive model. In model predictive control, for example, the control device 6 predicts future output based on the predictive model so that the water content of the dewatered sludge and the water quality of the dewatered filtrate approach target values, and determines the optimal controllable amount (input) for the dewatering device 2.

[0036] The target value for the water content of the dewatered sludge is set to a value that minimizes the combined operating cost of the dewatering unit 2 and the solubilizing unit 3. The target value for the water quality of the dewatered filtrate is set considering, for example, the load on the return water sent back to the upstream equipment of the dewatering unit 2 (e.g., the primary sedimentation tank) (e.g., the concentration of ammoniacal nitrogen and BOD, which are targets for biological treatment).

[0037] [Hardware configuration of control device 6] Figure 3 is a diagram illustrating the hardware configuration of the control device 6 according to the embodiment. As shown in Figure 3, the control device 6 is, for example, an electronic device having an electronic circuit. More specifically, the control device 6 is a computer device having, for example, a CPU 601 which is a processor, a memory 602, a communication device 603, and a storage medium 604. Each part is connected to the others via, for example, a bus 605.

[0038] The storage medium 604 has, for example, a program storage area (not shown) for storing a program 610 for performing model predictive control and other arbitrary information. The storage medium 604 also has, for example, an information storage area 620 for storing information used when performing model predictive control. The storage medium 604 may be, for example, an HDD (Hard Disk Drive) or an SSD (Solid State Drive).

[0039] The CPU 601 performs model predictive control processing, for example, by executing a program 610 loaded into memory 602 from storage medium 604.

[0040] The communication device 603 accesses, for example, an operating terminal (not shown) where an operator inputs necessary information via a network (not shown), such as the Internet.

[0041] Furthermore, the electronic circuitry of the control device 6 may be, for example, an FPGA (Field Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit). The model predictive control processing may also be performed, for example, on the FPGA or ASIC.

[0042] [Functions of Control Device 6] Figure 4 is a block diagram illustrating the functional configuration of the control device 6 according to the embodiment. The control device 6 includes, for example, an information acquisition unit 61, a storage unit 62, a model generation unit 63, a prediction unit 64, and a control unit 65. The control device 6 realizes various functions, including the information acquisition unit 61, the storage unit 62, the model generation unit 63, the prediction unit 64, and the control unit 65, through the organic cooperation of hardware such as a CPU 601 and memory 602 and a program.

[0043] [Information acquisition unit 61] The information acquisition unit 61 acquires information from, for example, sensors S1 and S2. Specifically, the information acquisition unit 61 acquires a measured value of the water content of the dewatered sludge discharged from the dewatering device 2 from the water content meter S1, and a measured value of the solids concentration, which indicates the water quality of the dewatered filtrate discharged from the dewatering device 2, from the water quality meter S2. Note that the water quality measurements acquired by the information acquisition unit 61 are not limited to solids concentration; they may also be measured values ​​of other water quality indicators, such as turbidity.

[0044] Furthermore, the information acquisition unit 61 acquires, for example, the operating amount of the operating factors set in the dewatering device 2. Here, as the operating factors of the dewatering device 2, any operating conditions that have a relationship with the water content of the dewatered sludge or the water quality of the dewatered filtrate can be adopted. Examples of operating factors include the residence time of the sludge in the coagulation mixing tank 21, the amount of sludge supplied from the coagulation mixing tank 21 to the dewatering device 2 (more specifically, the amount of sludge supplied per unit time), the feed speed of the thickening filter cloth belt 22a in the thickening section 22 of the dewatering device 2 (hereinafter also referred to as the thickening section feed speed), the feed speed of the pair of dewatering filter cloth belts 23a and 23c in the dewatering section 23 of the dewatering device 2 (hereinafter also referred to as the dewatering section feed speed), and Examples include the clamping pressure between the pair of dewatering filter cloth belts 23a and 23c in the water section 23 (hereinafter also referred to as the dewatering section belt pressure), the amount of sludge supplied to the dewatering device 2 (hereinafter also referred to as the sludge supply amount), the addition rate of the polymer flocculant F1 added to the sludge supplied to the dewatering device 2 (hereinafter also referred to as the flocculant addition rate), and the rotational speed of the stirring blades 21a that mix the polymer flocculant F1 and sludge in the flocculation mixing tank 21 (hereinafter also referred to as the stirring speed).

[0045] [Storage section 62] The memory unit 62 is implemented, for example, by an information storage area 620, and stores various types of information. Specifically, the memory unit 62 stores, for example, information acquired by the information acquisition unit 61, the prediction model 621 used by the prediction unit 64 (described later), the target value for the water content of the dewatered sludge, the target value for the water quality of the dewatered filtrate, and so on.

[0046] [Predictive Model 621] The prediction model 621 predicts the amount of the operating factors for the dewatering device 2, for example, using the water content of the dewatered sludge and the water quality of the dewatered filtrate as control variables. Specifically, the prediction model 621 takes the measured value of the water content of the dewatered sludge, the measured value of the water quality of the dewatered filtrate, the target value of the water content of the dewatered sludge, and the target value of the water quality of the dewatered filtrate as input values, calculates and outputs predicted values ​​for the amount of one or more operating factors necessary for the water content of the dewatered sludge and the water quality of the dewatered filtrate to meet predetermined conditions based on the target values.

[0047] The prediction model 621 can employ any model, such as a pre-trained model, regression model, regression equation, or classification model. The prediction model 621 may be a pre-trained model that has been trained on the relationship between the operating factors of the dewatering device 2 and the moisture content of the dewatered product, and the relationship between the operating factors of the dewatering device 2 and the water quality of the dewatered filtrate. In that case, the prediction model 621 may include, for example, a neural network that accepts input of measured values ​​and target values ​​and outputs the manipulated values ​​of the operating factors. Furthermore, any machine learning method can be employed, such as linear regression, support vector machine regression, Gaussian process regression, decision trees, or neural networks.

[0048] In the following explanation, the measured values ​​of the water content of the dewatered sludge and the water quality of the dewatered filtrate used to create the prediction model 621 will also be referred to as the first measured values, and the measured values ​​of the water content of the dewatered sludge and the water quality of the dewatered filtrate used as input values ​​for the prediction model 621 in model predictive control will also be referred to as the second measured values.

[0049] The predictive model 621 may include, for example, at least one of the following as operating factors: the feed rate of the concentration section in the dewatering device 2, the feed rate of the dewatering section, the belt pressure of the dewatering section, the amount of sludge supplied to the dewatering device 2, the rate of coagulant addition to the sludge, and the stirring speed of the coagulation mixing tank 21.

[0050] The prediction model 621 may include predetermined conditions based on, for example, target values ​​for the moisture content of the dewatered sludge or target values ​​for the water quality of the dewatered filtrate. As an example of a condition based on a target value for moisture content, if the difference between the second measured value and the target value of the moisture content of the dewatered sludge exceeds a predetermined threshold, the prediction model 621 calculates and outputs predicted values ​​for the manipulation amounts of one or more manipulation factors so that the difference becomes less than or equal to the predetermined threshold. Also, as an example of a condition based on a target value for water quality, if the second measured value of the solids concentration of the dewatered filtrate exceeds the target value, the prediction model 621 calculates and outputs predicted values ​​for the manipulation amounts of one or more manipulation factors so that the solids concentration becomes less than or equal to the target value.

[0051] This predictive model 621 is created, for example, based on the amount of the operating factors of the dewatering device 2 at multiple timings during the operation of the dewatering device 2, a first measured value of the water content of the dewatered sludge, and a first measured value of the water quality of the dewatered filtrate. Specifically, the predictive model 621 is created, for example, based on first correspondence information showing the correspondence between the operating factors of the dewatering device 2 and the water content of the material to be treated after dewatering, and second correspondence information showing the correspondence between the operating factors of the dewatering device 2 and the water quality of the dewatered filtrate.

[0052] The first correspondence information can be derived, for example, based on the amount of the operating factor and the first measured value of the water content of the dewatered sludge at multiple timings. The second correspondence information can be derived, for example, based on the amount of the operating factor and the first measured value of the water quality of the dewatered filtrate at multiple timings. Specifically, the first and second correspondence information can be derived, for example, by measuring the change in the water content of the dewatered sludge and the change in the water quality of the dewatered filtrate when the value of the operating factor is changed at multiple timings.

[0053] Figure 5 shows an example of the first and second correspondence information. In the table shown in Figure 5, the operating factors are sludge supply rate, coagulant addition rate, concentration feed rate, dewatering feed rate, and dewatering belt pressure. The first correspondence information shows the trend of change in the water content of dewatered sludge when the value of each operating factor is increased, and the second correspondence information shows the trend of change in the solids concentration (water quality) of the dewatered filtrate when the value of each operating factor is increased. In Figure 5, if the water content or solids concentration increases in response to an increase in the value of the operating factor, it is indicated as "increase," if it decreases, it is indicated as "decrease," and if there is little change, it is indicated as "small effect."

[0054] For example, if the second measured value of the water content of the dewatered sludge is greater than the target value and the difference between the second measured value and the target value exceeds a predetermined threshold, the prediction model 621 calculates and outputs the amount of manipulation (predicted value) of the manipulation factor so that at least one of the following is performed, according to the first correspondence information shown in Figure 5: a decrease in the sludge supply amount, an increase in the coagulant addition rate, a decrease in the feed rate of the thickening section, a decrease in the feed rate of the dewatering section, and an increase in the belt pressure. Conversely, if the second measured value of the water content of the dewatered sludge is less than the target value and the difference between the second measured value and the target value exceeds a predetermined threshold, the prediction model 621 calculates and outputs the amount of manipulation (predicted value) of the manipulation factor so that at least one of the following is performed, according to the first correspondence information shown in Figure 5: an increase in the sludge supply amount, a decrease in the coagulant addition rate, an increase in the feed rate of the thickening section, an increase in the feed rate of the dewatering section, and a decrease in the belt pressure. Furthermore, if the second measured value of the solids concentration in the dewatered filtrate is greater than the target value, the prediction model 621 calculates and outputs the manipulated amount (predicted value) of the manipulative factor so that at least one of the following is performed: a decrease in the sludge supply, an increase in the coagulant addition rate, and a decrease in the belt pressure, according to the second corresponding information shown in Figure 5.

[0055] The prediction model 621 may include constraints on the range of the manipulated variables it outputs. For example, for each manipulator, the manipulated variables may be calculated so that they fall within a predetermined range relative to the design value (e.g., within ±20% of the design value).

[0056] The prediction model 621 may calculate the manipulated amount of the manipulator by considering the response time (dead time and first-order lag) of the control amount (moisture content of dewatered sludge and water quality of dewatered filtrate) to changes in the manipulated amount of the manipulator. Specifically, the prediction model 621 may be created based on, for example, first and second correspondence information derived from the dead time, first-order lag, and changes in the moisture content of dewatered sludge and water quality of dewatered filtrate after convergence, which are measured in advance for each manipulator when the manipulated amount of the manipulator is changed by a unit amount.

[0057] Here, dead time is, for example, the time required for changes in the moisture content of dewatered sludge or the water quality of the dewatered filtrate to begin in response to changes in the manipulated amount of the manipulator. The first-order lag is, for example, the time required from the start of changes in the moisture content of dewatered sludge or the water quality of the dewatered filtrate in response to changes in the manipulated amount of the manipulator until convergence. For example, with respect to the moisture content of dewatered sludge, if the sludge supply amount is increased at time t1, the moisture content does not change until time t2 (delayed from time t1), the moisture content starts to increase from time t2, and continues to increase until time t3 (delayed from time t2), and the moisture content converges at time t3. In this case, the time from time t1 to time t2 corresponds to the dead time for the moisture content of dewatered sludge relative to the sludge supply amount, and the time from time t2 to time t3 corresponds to the first-order lag time for the moisture content of dewatered sludge relative to the sludge supply amount.

[0058] Furthermore, the prediction model 621 may weight multiple operating factors, for example, by prioritizing the change in the amounts of other operating factors over the coagulant addition rate. Specifically, the prediction model 621 may calculate and output predicted values ​​for the operating amounts such that, for example, the rate of change in the operating amounts of other operating factors is greater than the rate of change in the operating amount of the coagulant addition rate. For example, if a second measured value of the water content of the dewatered sludge is greater than the target value, and the difference between the second measured value and the target value exceeds a predetermined threshold, the prediction model 621 may calculate and output predicted values ​​for the operating amounts such that the rate of decrease in sludge supply and the rate of increase in belt pressure are greater than the rate of increase in the coagulant addition rate.

[0059] Furthermore, the prediction model 621 does not necessarily have to be stored in the control device 6 beforehand; for example, it may be stored on an external server (not shown) and deployed to the storage unit 62 via a network.

[0060] [Model generation unit 63] Returning to Figure 4, the model generation unit 63 generates a prediction model 621 based on, for example, the information acquired by the information acquisition unit 61.

[0061] The model generation unit 63 extracts training data from actual data, including the amount of the operating factor, a first measured value of the water content of the dewatered sludge, and a first measured value of the water quality of the dewatered filtrate, which are acquired by the information acquisition unit 61 at multiple periodic timings during the operation of the dewatering device 2 and stored in the storage unit 62. The model generation unit 63 extracts, for example, first correspondence information and second correspondence information as training data. Then, the model generation unit 63 generates a predictive model 621 by, for example, training the training data.

[0062] Furthermore, the model generation unit 63 may update the prediction model 621 (feedback control) based on the results of model prediction control, for example. Specifically, for example, in model prediction control, the model generation unit 63 may update the first correspondence information and the second correspondence information based on the amount of the operating factor at predetermined time intervals acquired by the information acquisition unit 61, the measured value of the water content of the dewatered sludge, and the measured value of the water quality of the dewatered filtrate, and then update the prediction model 621 by training the updated first correspondence information and the second correspondence information as training data.

[0063] [Prediction section 64] The prediction unit 64 determines the control factors to be manipulated and the amounts of those control factors by, for example, executing the prediction model 621. Specifically, the prediction unit 64 inputs a second measured value of the water content of the dewatered sludge, a second measured value of the water quality of the dewatered filtrate, a target value for the water content of the dewatered sludge, and a target value for the water quality of the dewatered filtrate as input values ​​into the prediction model 621, and outputs predicted values ​​for the amounts of one or more control factors.

[0064] [Control Unit 65] The control unit 65, for example, determines operating parameters such that the water content of the dewatered sludge and the water quality of the dewatered filtrate approach target values, based on the predicted values ​​of the manipulated amounts of the operating factors output from the prediction model 621, and controls the operation of the dewatering device 2.

[0065] Specifically, the control unit 65, for example, determines the set value of the control amount of the control factor corresponding to the predicted value according to the predicted value output from the prediction model 621, and controls the operation of the dewatering device 2. Here, "control of the operation of the dewatering device 2" includes not only the control of the dewatering device 2 itself, but also the control of other devices involved in the operation of the dewatering device 2 (for example, the pump P1 for supplying sludge to the dewatering device 2). For example, by controlling the dewatering device 2, the control unit 65 controls the feeding speed of the thickening unit 22, the feeding speed of the dewatering unit 23, the belt pressure of the dewatering unit, the coagulant addition rate to the sludge, or the stirring speed of the coagulation mixing tank 21, and by controlling the pump P1, it controls the amount of sludge supplied to the dewatering device 2.

[0066] [Processing of model predictive control] The following describes the model predictive control process of the dewatering device 2 by the control device 6. Figure 6 is a flowchart illustrating the model predictive control of the dewatering device 2 by the control device 6 according to this embodiment. The model predictive control process is repeatedly executed, for example, during the operation of the dewatering device 2.

[0067] The model generation unit 63 prepares a prediction model 621 in advance. The model generation unit 63 creates the prediction model 621 based on actual data stored in the storage unit 62, which includes the amount of the operating factors at multiple timings during the operation of the dewatering device 2, a first measured value of the water content of the dewatered sludge, and a first measured value of the water quality of the dewatered filtrate. The created prediction model 621 is stored in the storage unit 62, for example.

[0068] In model predictive control, for example, first the information acquisition unit 61 acquires a second measurement of the water content of the dewatered sludge and a second measurement of the water quality of the dewatered filtrate (step S11 in Figure 6).

[0069] Next, the prediction unit 64 inputs the second measured value of the water content of the dewatered sludge and the second measured value of the water quality of the dewatered filtrate, obtained in step S11, along with the target value of the water content of the dewatered sludge and the target value of the water quality of the dewatered filtrate stored in the storage unit 62, as input values ​​into the prediction model 621 and executes the prediction model 621, outputting the manipulated amount of the manipulative factor calculated by the prediction model 621 (step S12 in Figure 6).

[0070] Next, the control unit 65 determines the set value of the manipulated amount according to the manipulated amount (predicted value) of the operating factor output from the prediction model 621 in step S12, and controls the operation of the dewatering device 2 (step S13 in Figure 6).

[0071] Then, the model generation unit 63 updates the prediction model 621 based on the control results based on the set value of the manipulated variable (step S14 in Figure 6).

[0072] [Effects / Effects] As described above, the dewatering control system 10 according to this embodiment comprises a dewatering device 2 for dewatering sludge (material to be treated) and a control device 6 for controlling the operation of the dewatering device 2. The control device 6 then executes a predictive model 621 that predicts the amount of the operating factor of the operating factor, which is created based on the amount of the operating factor of the operating factor of the dewatering device 2 at multiple timings, the water content of the dewatered sludge (material to be treated after dewatering) dewatered by the dewatering device 2, and the water quality of the dewatered filtrate separated from the sludge by the dewatering device 2.

[0073] According to the dewatering control system 10 configured in this way, by executing the prediction model 621 to predict the amount of the operating factors of the dewatering device 2, it becomes possible to adjust the amount of the operating factors based on the prediction result (predicted value) and control the water content of the dewatered sludge and the water quality of the dewatered filtrate.

[0074] Here, for example, if the solubilizer 3 downstream of the dewatering unit 2 uses steam to solubilize the dewatered sludge, the amount of steam required for the solubilization process will depend on the moisture content of the dewatered sludge. For example, the higher the moisture content of the dewatered sludge, the more steam tends to be required for the solubilization process. Therefore, if the moisture content of the dewatered sludge is too high, an excessive amount of steam will be needed for the solubilization process, resulting in extra energy being used in the solubilizer 3. On the other hand, if the moisture content of the dewatered sludge is too low, the dewatering unit 2 will dewater the sludge more than necessary, resulting in extra energy and coagulant being used in the dewatering unit 2. In contrast, the dewatering control system 10 according to this embodiment can reduce the amount of steam required for the subsequent solubilization process, as well as the energy and coagulant used in the dewatering unit 2, by adjusting the moisture content of the dewatered sludge. As a result, energy savings can be achieved in both the solubilizer 3 and the dewatering unit 2. Furthermore, by adjusting the moisture content of the dewatered sludge and reducing fluctuations in moisture content, the operation of the solubilization device 3 can be stabilized.

[0075] Furthermore, for example, when the solubilization device 3 uses ozone to solubilize dewatered sludge, the amount of ozone required for the solubilization process depends on the moisture content of the dewatered sludge, and the amount of ozone used tends to increase as the moisture content increases. For example, if the moisture content of the dewatered sludge is excessive, an excessive amount of ozone will be required for the solubilization process. On the other hand, if the moisture content of the dewatered sludge is insufficient, the ozone and the organic matter in the sludge may not be able to come into sufficient contact during the solubilization process, potentially reducing the efficiency of the solubilization process. In contrast, the dewatering control system 10 according to this embodiment can optimize the amount of ozone used in the solubilization device 3 by adjusting the moisture content of the dewatered sludge, for example, thereby achieving an efficient solubilization process.

[0076] Furthermore, the dewatering control system 10 can reduce the load of the return water sent back to the upstream equipment (primary sedimentation tank, etc.) of the dewatering device 2 by, for example, controlling the water quality of the dewatered filtrate.

[0077] Furthermore, the dewatering control system 10 can automate the operation of the dewatering device 2 by performing model predictive control of the dewatering device 2 using the predictive model 621. As a result, management costs such as labor costs in the operation of the dewatering device 2 can be reduced.

[0078] Furthermore, the control device 6 according to this embodiment inputs a second measured value of the water content of the dewatered sludge, a second measured value of the water quality of the dewatered filtrate, a target value for the water content of the dewatered sludge, and a target value for the water quality of the dewatered filtrate into the prediction model 621, executes the prediction model 621, and outputs the amount of the control factor calculated by the prediction model 621. This makes it possible to predict the amount of the control factor that will bring the water content of the dewatered sludge and the water quality of the dewatered filtrate closer to their respective target values.

[0079] Furthermore, the prediction model 621 according to this embodiment calculates the amount of manipulation for the manipulation factor according to first correspondence information showing the correspondence between the manipulation factor and the water content of the dewatered sludge, and second correspondence information showing the correspondence between the manipulation factor and the water quality of the dewatered filtrate. This makes it possible to predict the amount of manipulation for the manipulation factor based on the water content of the dewatered sludge and the water quality of the dewatered filtrate.

[0080] Furthermore, the control device 6 according to this embodiment can optimize the amount of polymer flocculant F1 used by prioritizing the manipulation of other operating factors over the flocculant addition rate. As a result, the chemical cost in the dewatering device 2 can be reduced.

[0081] Furthermore, the relationship between the amount of the control factor and the water content of the dewatered sludge, and the relationship between the amount of the control factor and the water quality of the dewatered filtrate, change depending on, for example, the properties of the sludge before dewatering supplied to the dewatering device 2 (e.g., organic matter concentration, solid matter concentration, etc.). In response to this, the control device 6 can control the water content of the dewatered sludge and the water quality of the dewatered filtrate according to the properties of the sludge before dewatering by updating the prediction model 621 based on the control results based on the set values ​​of the control factors.

[0082] Furthermore, the control device 6 according to this embodiment predicts the amount of manipulation for the manipulation factor by considering the response time (dead time and first-order lag) of the water content of the dewatered sludge and the water quality of the dewatered filtrate to changes in the amount of manipulation for the manipulation factor. This makes it possible to achieve more accurate predictions.

[0083] [Modified examples of embodiments] The following describes processing systems 100A and 100B according to modified embodiments. In the following description of the modified embodiments, the differences from processing system 100 will be the main focus, and components similar to those in processing system 100 will be denoted by the same reference numerals, thus omitting detailed explanations.

[0084] [Example 1] Figure 7 is a diagram illustrating the configuration of the processing system 100A according to the first modified embodiment. Figure 8 is a block diagram illustrating the functional configuration of the control device 6A according to the first modified embodiment.

[0085] As shown in Figure 7, the dewatering control system 10A of the modified example 1 of the processing system 100A includes, for example, a liquid level meter S3. The liquid level meter S3 is installed, for example, in the storage tank 1 and measures the liquid level of the sludge stored in the storage tank 1.

[0086] The information acquisition unit 61 of the control device 6A according to Modified Example 1 acquires, for example, the moisture content measurement of the dewatered sludge discharged from the dewatering device 2 from the moisture content meter S1, the solids concentration measurement indicating the water quality of the dewatered filtrate discharged from the dewatering device 2 from the water quality meter S2, and the liquid level measurement of the sludge in the storage tank 1 (hereinafter also referred to as the storage tank liquid level) from the liquid level meter S3.

[0087] Furthermore, as shown in Figure 8, the storage unit 62 of the control device 6A according to Modified Example 1 stores, for example, a prediction model 621A. Prediction model 621A differs from the prediction model 621 described above in that, for example, in addition to the water content of the dewatered sludge and the water quality of the dewatered filtrate, it predicts the amount of operation for the operating factors of the dewatering device 2, using the storage tank liquid level as a control variable. Specifically, prediction model 621A according to Modified Example 1 takes as input values ​​a second measured value of the water content of the dewatered sludge, a second measured value of the water quality of the dewatered filtrate, a target value for the water content of the dewatered sludge, a target value for the water quality of the dewatered filtrate, a second measured value of the storage tank liquid level, and a target value for the storage tank liquid level. The prediction model 621A then outputs predicted values ​​for the amount of operation for one or more operating factors necessary for the water content of the dewatered sludge, the water quality of the dewatered filtrate, and the storage tank liquid level to satisfy predetermined conditions based on the target values.

[0088] The prediction model 621A may include, for example, predetermined conditions based on a target value for the storage tank liquid level. As an example of predetermined conditions, if the second measured value of the storage tank liquid level exceeds the target value, the prediction model 621A outputs predicted values ​​for the manipulated amounts of one or more manipulated factors so that the storage tank liquid level becomes below the target value.

[0089] The prediction model 621A is created, for example, based on the amount of the operating factors of the dewatering device 2 at multiple timings during the operation of the dewatering device 2, a first measurement of the water content of the dewatered sludge, a first measurement of the water quality of the dewatered filtrate, and a first measurement of the liquid level in the storage tank. Specifically, the prediction model 621A is created, for example, based on the first correspondence information described above, the second correspondence information described above, and a third correspondence information showing the correspondence between the operating factors of the dewatering device 2 and the liquid level in the storage tank.

[0090] Third-party correspondence information can be derived, for example, based on the manipulated amount of the operating factor and the first measured value of the storage tank liquid level at multiple timings. Specifically, third-party correspondence information can be derived, for example, by measuring the change in the storage tank liquid level when the value of the operating factor is changed at multiple timings. An example of third-party correspondence information is a negative correlation, where the storage tank liquid level decreases when the sludge supply value increases, and increases when the sludge supply value decreases.

[0091] For example, if the second measurement of the storage tank liquid level is greater than the target value, the prediction model 621A calculates and outputs the manipulated amount (predicted value) of the operating factor so that the sludge supply amount is reduced according to the third corresponding information.

[0092] Furthermore, the prediction model 621A may calculate the amount of the control factor by considering the response time of the storage tank liquid level to changes in the amount of the control factor (dead time and first-order lag).

[0093] In the model predictive control according to Modification 1, the prediction unit 64 of the control device 6A inputs the second measured value of the water content of the dewatered sludge, the second measured value of the water quality of the dewatered filtrate, the second measured value of the liquid level in the storage tank, the target value of the water content of the dewatered sludge, the target value of the water quality of the dewatered filtrate, and the target value of the liquid level in the storage tank as input values ​​to the prediction model 621A, executes the prediction model 621A, and outputs the manipulated amount of the operating factor calculated by the prediction model 621A. Next, the control unit 65 of the control device 6A determines the set value of the manipulated amount according to the manipulated amount (predicted value) of the operating factor output from the prediction model 621A, and controls the operation of the dewatering device 2.

[0094] According to Modification 1, the amount of the operating factor (e.g., sludge supply amount) of the dewatering device 2 can be adjusted according to the liquid level of sludge in the storage tank 1 upstream of the dewatering device 2, thereby controlling the liquid level in the storage tank. As a result, the liquid level in the storage tank 1 can be controlled automatically, which can reduce management costs such as labor costs.

[0095] In Modification 1, the water content of the dewatered sludge, the water quality of the dewatered filtrate, and the liquid level in the storage tank were controlled using the same prediction model. However, the control of the liquid level in the storage tank may be performed independently of the control of the water content of the dewatered sludge and the water quality of the dewatered filtrate. For example, a separate prediction model may be used to control the liquid level in the storage tank, in addition to the prediction models used to control the water content of the dewatered sludge and the water quality of the dewatered filtrate.

[0096] [Differentiation 2] Figure 9 is a diagram illustrating the configuration of the processing system 100B according to the modified embodiment 2. Figure 10 is a block diagram illustrating the functional configuration of the control device 6B according to the modified embodiment 2.

[0097] As shown in Figure 9, the dewatering control system 10B of the processing system 100B according to the modified example 2 includes, for example, an imaging device S4. The imaging device S4 is provided, for example, in the coagulation mixing tank 21 of the dewatering device 2 and captures images of the flocs formed in the coagulation mixing tank 21 by mixing sludge and a coagulant (polymer coagulant F1).

[0098] The information acquisition unit 61 of the control device 6B in the modified example 2 acquires, for example, a measured value of the water content of the dewatered sludge discharged from the dewatering device 2 from the water content meter S1, a measured value of the solid matter concentration indicating the water quality of the dewatered filtrate discharged from the dewatering device 2 from the water quality meter S2, and an image of the flocs from the imaging device S4. The information acquisition unit 61 calculates, for example, characteristic quantities of the flocs from the captured image and calculates an index value indicating the properties of the flocs as a measured value of the properties of the flocs based on the calculated characteristic quantities. The properties of the flocs are, for example, properties related to the aggregation state of the sludge, and include the particle size of the flocs and the amount of flocs.

[0099] Furthermore, as shown in Figure 10, the storage unit 62 of the control device 6B according to the modified example 2 stores, for example, a prediction model 621B. The prediction model 621B differs from the prediction model 621 described above in that, in addition to the water content of the dewatered sludge and the water quality of the dewatered filtrate, it predicts the amount of the operating factors of the dewatering device 2, using the properties of the flocs as control variables. Specifically, the prediction model 621B according to the modified example 2 takes as input values ​​a second measured value of the water content of the dewatered sludge, a second measured value of the water quality of the dewatered filtrate, a target value for the water content of the dewatered sludge, a target value for the water quality of the dewatered filtrate, a second measured value for the properties of the flocs, and a target value for the properties of the flocs. The prediction model 621B then outputs predicted values ​​for the amount of one or more operating factors necessary for the water content of the dewatered sludge, the water quality of the dewatered filtrate, and the properties of the flocs to satisfy predetermined conditions based on the target values.

[0100] The prediction model 621B may include, for example, predetermined conditions based on target values ​​for the properties of the floc. For example, the prediction model 621B outputs predicted values ​​for the manipulated amounts of one or more manipulated factors so that the properties of the floc satisfy predetermined conditions.

[0101] Predictive model 621B is created, for example, based on the amount of the operating factors of the dewatering device 2 at multiple timings during the operation of the dewatering device 2, a first measurement of the water content of the dewatered sludge, a first measurement of the water quality of the dewatered filtrate, and a first measurement of the properties of the flocs. Specifically, predictive model 621B is created, for example, based on the first correspondence information described above, the second correspondence information described above, and a fourth correspondence information showing the correspondence between the operating factors and the properties of the flocs.

[0102] The fourth correspondence information can be derived, for example, based on the amount of manipulation of the operating factors and the first measured value of the properties of the flocs at multiple timings. The operating factors used for the fourth correspondence information may include, for example, at least one of the following: the coagulant addition rate, the stirring speed in the coagulation mixing tank 21, and the sludge residence time in the coagulation mixing tank 21 (i.e., the amount of sludge treated). Specifically, for example, the fourth correspondence information can be derived by taking images of the flocs when the coagulant addition rate, stirring speed, and sludge treatment amount are changed at multiple timings, and measuring the amount of change in the properties of the flocs based on the images.

[0103] The prediction model 621B calculates and outputs the manipulated amount (predicted value) of the manipulative factor according to the fourth correspondence information, for example, so that the coagulant addition rate is controlled according to the properties of the flocs.

[0104] Furthermore, the prediction model 621B may calculate the manipulated amount of the manipulator by considering, for example, the response time of the flock's characteristics to changes in the manipulated amount of the manipulator (dead time and first-order lag).

[0105] In the model predictive control according to the modified example 2, the prediction unit 64 of the control device 6B inputs the second measured value of the water content of the dewatered sludge, the second measured value of the water quality of the dewatered filtrate, the second measured value of the properties of the flocs, the target value of the water content of the dewatered sludge, the target value of the water quality of the dewatered filtrate, and the target value of the properties of the flocs as input values ​​to the prediction model 621B, executes the prediction model 621B, and outputs the manipulated amount of the operating factor calculated by the prediction model 621B. Next, the control unit 65 of the control device 6B determines the set value of the manipulated amount according to the manipulated amount (predicted value) of the operating factor output from the prediction model 621B, and controls the operation of the dewatering device 2.

[0106] According to Modification 2, the properties of the flocs in the coagulation mixing tank 21 of the dewatering device 2 can be controlled by adjusting the amount of the operating factor of the dewatering device 2 according to the properties of the flocs. This allows for the optimization of the floc properties. Optimizing the properties of the flocs contributes to reducing the water content of the dewatered sludge and improving the quality of the dewatered filtrate. Therefore, controlling the properties of the flocs helps to control the water content of the dewatered sludge and the quality of the dewatered filtrate. Furthermore, for example, by adjusting the coagulant addition rate as an operating factor, the amount of polymer coagulant F1 used can be optimized, thereby reducing the chemical costs in the dewatering device 2.

[0107] In Modification 2, the water content of the dewatered sludge, the water quality of the dewatered filtrate, and the properties of the flocs were controlled using the same predictive model. However, the control of the properties of the flocs may be performed independently of the control of the water content of the dewatered sludge and the water quality of the dewatered filtrate. For example, a separate predictive model may be used to control the properties of the flocs, in addition to the predictive models used to control the water content of the dewatered sludge and the water quality of the dewatered filtrate.

[0108] The various modifications described above may be combined as appropriate.

[0109] Furthermore, although the above describes an embodiment in which the prediction model is executed in a control device, the prediction model may also be executed in an information processing device other than the control device. For example, the control device may execute a prediction model stored in another information processing device. [Explanation of symbols]

[0110] 1: Storage tank 2: Dehydration device 21:Agglomeration mixing tank 22: Concentration section 23: Dehydration section 3: Solubilization device 4: Digestion tank 5: Dehydration device 6: Control device 61: Information acquisition department 62: Storage part 621: Predictive Model 63: Model generation unit 64: Prediction Department 65: Control Unit 10: Dehydration control system 100: Processing System P1: Pump S1: Moisture content meter S2: Water quality meter S3:Liquid level gauge S4: Imaging device

Claims

1. A dewatering device for dewatering the material to be processed, The system includes a control device for controlling the operation of the dewatering device, The control device executes a predictive model that predicts the amount of the operating factor of the dewatering device at multiple timings, based on the amount of the operating factor of the dewatering device, the moisture content of the dewatered material after dewatering by the dewatering device, and the water quality of the dewatered filtrate separated from the material by the dewatering device. Dehydration control system.

2. The control device is The measured water content of the dehydrated material, the measured water quality of the dehydrated filtrate, the target water content of the dehydrated material, and the target water quality of the dehydrated filtrate are input into the prediction model and the prediction model is executed. The system outputs the manipulated amount of the operating factor calculated by the prediction model. The dehydration control system according to claim 1.

3. The predictive model calculates the amount of the operating factor according to a first correspondence information showing the correspondence between the operating factor and the water content of the dewatered material, and a second correspondence information showing the correspondence between the operating factor and the water quality of the dewatered filtrate. The dehydration control system according to claim 2.

4. The prediction model further calculates the amount of the operating factor according to a third correspondence information that shows the correspondence between the operating factor and the liquid level of the material to be processed in the storage tank that stores the material to be processed supplied to the dewatering device. The control device inputs the measured liquid level of the material to be processed in the storage tank and the target liquid level of the material to be processed in the storage tank to the prediction model and executes the prediction model. The system outputs the manipulated amount of the operating factor calculated by the prediction model. The dehydration control system according to claim 3.

5. A control device for controlling the operation of a dewatering device that dewaters a material to be processed, A predictive model is created to predict the amount of the operating factor of the dewatering device, based on the amount of the operating factor of the dewatering device, the moisture content of the dewatered material after dewatering by the dewatering device, and the water quality of the dewatered filtrate separated from the material by the dewatering device, and this model is then executed. Control device.

6. A dewatering device for dewatering the material to be processed, A solubilizing device that performs a solubilizing treatment on the material to be treated which has been dehydrated by the dehydrating device, A digestion device for digesting organic components contained in the material to be processed, which have been solubilized by the solubilization device, The system includes a control device for controlling the operation of the dewatering device, The control device executes a predictive model that predicts the amount of the operating factor of the dewatering device at multiple timings, based on the amount of the operating factor of the dewatering device, the moisture content of the dewatered material after dewatering by the dewatering device, and the water quality of the dewatered filtrate separated from the material by the dewatering device. Processing system.

Images