Estimation device, filtration processing system, estimation method and program

Abstract

To provide an estimation device and an estimation method for estimating a water permeation amount of a membrane module having one or a plurality of hollow fiber membranes, and a filtration processing system having the device.SOLUTION: An estimation device includes: an operation data acquisition part for acquiring operation data in backwashing of a membrane module; a reference information acquisition part for acquiring predetermined reference information on a water permeation amount of the membrane module; and an estimation part for estimating the water permeation amount of the membrane module, on the basis of the operation data in back washing and the reference information. An estimation method for estimating a water permeation amount of a membrane module having one or a plurality of hollow fiber membranes includes the steps of: acquiring operation data in back washing of the membrane module; acquiring predetermined reference information on a water permeation amount of the membrane module; and estimating the water permeation amount of the membrane module, on the basis of the operation data in back washing and the reference information.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present invention relates to an estimation device, a filtering processing system, an estimation method, and a program. [Background technology]

[0002] Patent Document 1 describes "a membrane separation device and a membrane filtration method that can suppress an increase in transmembrane pressure difference." [Prior art document] [Patent Documents] [Patent Document 1] JP 2007-296500 A [Patent Document 2] JP 2022-171037 A [Patent Document 3] Patent No. 7052926 Summary of the Invention

[0003] In a first aspect of the present invention, there is provided an estimation device for estimating the water permeation rate of a membrane module having one or more hollow fiber membranes, the estimation device comprising: an operating data acquisition unit for acquiring operating data during backwashing of the membrane module; a reference information acquisition unit for acquiring predetermined reference information regarding the water permeation rate of the membrane module; and an estimation unit for estimating the water permeation rate of the membrane module based on the operating data during backwashing and the reference information.

[0004] In the estimation device, the operating data acquisition unit may acquire operating data for a latter half of a backwash period of the membrane module.

[0005] In any of the above estimation devices, the operational data may include a transmembrane pressure difference of the membrane module.

[0006] In any of the above estimation devices, the estimation unit may estimate the water permeation rate of the membrane module using a moving average of the transmembrane pressure difference.

[0007] In any of the above estimation devices, the operating data may include a liquid temperature of a filtrate used for backwashing the membrane module.

[0008] In any of the above estimation devices, the operational data may include a flow rate of filtrate used for backwashing the membrane module.

[0009] In any of the above estimation devices, the operating data may include module information relating to properties of the membrane module.

[0010] In any of the above estimation devices, the estimation unit may include a calibration curve acquisition unit that acquires a calibration curve generated based on the reference information, and the estimation unit may estimate the water permeation rate of the membrane module using the operating data and the calibration curve.

[0011] In any of the above estimation devices, the estimation unit may estimate the water permeation rate of the membrane module using a machine learning model trained by machine learning based on the reference information to output a water permeation rate corresponding to the operating data during backwashing, and the operating data acquired by the operating data acquisition unit.

[0012] Any of the above estimation devices may be provided with a warning unit that notifies the user of the timing to replace the membrane module, the timing to clean the membrane module, or the change in the cleaning method for the membrane module based on the water permeation rate of the membrane module estimated by the estimation unit.

[0013] Any of the above estimation devices may include a display unit that displays information about the water permeation rate of the membrane module based on the water permeation rate of the membrane module estimated by the estimation unit.

[0014] In any of the above estimation devices, the one or more hollow fiber membranes may be produced by a thermally induced phase separation method.

[0015] In a second aspect of the present invention, there is provided a filtration treatment system including any one of the above estimation devices and a filtration treatment device having the membrane module.

[0016] The filtration treatment system may include a control device that controls the operation of the filtration treatment device based on the water permeation rate of the membrane module estimated by the estimation device.

[0017] In any of the above filtration treatment systems, the control device may adjust chemical cleaning conditions for the membrane module based on the water permeation rate of the membrane module estimated by the estimation device.

[0018] In a third aspect of the present invention, there is provided a method for estimating the water permeation rate of a membrane module having one or more hollow fiber membranes, the method comprising the steps of acquiring operating data during backwashing of the membrane module, acquiring predetermined reference information regarding the water permeation rate of the membrane module, and estimating the water permeation rate of the membrane module based on the operating data during backwashing and the reference information.

[0019] In a fourth aspect of the present invention, there is provided a program that is executed by a computer to cause the computer to function as an estimation device that estimates the water permeation rate of a membrane module having one or more hollow fiber membranes, the estimation device comprising: an operating data acquisition unit that acquires operating data during backwashing of the membrane module; a reference information acquisition unit that acquires predetermined reference information regarding the water permeation rate of the membrane module; and an estimation unit that estimates the water permeation rate of the membrane module based on the operating data during backwashing and the reference information.

[0020] The above summary of the invention does not list all of the features of the present invention, and subcombinations of these features may also be inventions. [Brief explanation of the drawings]

[0021] [Figure 1A] 1 shows an example of the configuration of a filtration processing system 10. [Figure 1B] An example of the configuration of the membrane module 150 is shown. [Figure 2A] An example of the filtration operation of the filtration treatment system 10 will be described. [Figure 2B] An example of a backwash operation of the filtration treatment system 10 will be described. [Figure 3A] FIG. 2 is a schematic diagram showing an example of the inside of a membrane module 150 during filtration operation. [Figure 3B] FIG. 2 is a schematic diagram showing an example of the inside of a membrane module 150 during backwashing operation. [Figure 4] An example of the configuration of the estimation device 300 is shown together with the filtration processing device 100 and the control device 200. [Figure 5] An example of the operation data is shown below. [Figure 6] An example of reference information and a calibration curve is shown below. [Figure 7] A modified example of the configuration of the estimation device 300 is shown together with the filtration processing device 100 and the control device 200. [Figure 8] 1 illustrates an example computer 1000 in which aspects of the present invention may be embodied in whole or in part. DETAILED DESCRIPTION OF THE INVENTION

[0022] The present invention will be described below through embodiments of the invention, but the following embodiments do not limit the scope of the invention according to the claims. Furthermore, not all of the combinations of features described in the embodiments are necessarily essential to the solution of the invention.

[0023] 1A shows an example of the configuration of a filtration processing system 10. Note that the blocks shown are functionally separated functional blocks and do not necessarily correspond to the actual device configuration. That is, a block shown as one block in this diagram does not necessarily have to be configured by one device. Also, blocks shown as separate blocks in this diagram do not necessarily have to be configured by separate devices.

[0024] The filtration system 10 includes an undiluted liquid tank 20 and a filtrate tank 30. The filtration system 10 may also include a concentrated liquid tank 40, a chemical tank 60, and an air tank 70. The filtration system 10 may also include a filtration treatment device 100, a control device 200, and an estimation device 300.

[0025] The filtration treatment system 10 of this example is a system for removing turbidity and / or parasites contained in the water to be treated by filtration. The water to be treated is not particularly limited, and may be river water, groundwater, seawater, brine, secondary sewage treatment water, RO concentrated water, or industrial wastewater. However, the filtration treatment system 10 is not limited thereto. The filtration treatment system 10 may be a system for concentrating valuable substances in the liquid to be treated (e.g., soy sauce) or a system for removing impurities from the liquid to be treated (e.g., wine).

[0026] The raw liquid tank 20 stores the raw liquid 22. In this example, the raw liquid 22 is water to be treated to produce drinking water. For example, the raw liquid 22 may be river water or groundwater, and is not particularly limited. The filtrate tank 30 stores the filtrate 32 recovered by the filtration treatment device 100 filtering the raw liquid 22. The concentrated liquid tank 40 stores the concentrated liquid 42 that remains unfiltered by the filtration treatment device 100. Note that the filtration treatment system 10 does not need to include the concentrated liquid tank 40. In this case, the concentrated liquid 42 that remains unfiltered by the filtration treatment device 100 may be returned to the raw liquid tank 20. In other words, the concentrated liquid tank 40 and the raw liquid tank 20 may refer to the same entity. Alternatively, the filtration treatment system 10 may include both the raw liquid tank 20 and the concentrated liquid tank 40, with a portion of the concentrated liquid 42 stored in the concentrated liquid tank 40 and the remaining concentrated liquid 42 returned to the raw liquid tank 20. The chemical tank 60 stores a chemical 62 used for backwashing. As an example, the chemical 62 includes sodium hypochlorite. However, the type of chemical included in the chemical 62 is not limited to this. The air tank 70 stores air for air cleaning. Details of air cleaning will be described later.

[0027] In this example, the filtration system 10 is shown with one each of the stock solution tank 20, concentrated solution tank 40, filtrate tank 30, chemical tank 60, and air tank 70, but the number of each tank and the ratio of the number of each tank in the filtration system 10 are not particularly limited. As an example, the filtration system 10 may have multiple chemical tanks 60.

[0028] The filtration processing device 100 may filter the stock solution 22. The filtration processing device 100 may separate the filtrate 32 recovered by filtering the stock solution 22 from the concentrated solution 42 that remains unfiltered. The filtration processing device 100 may supply the filtrate 32 to the filtrate tank 30 and supply the concentrated solution 42 to the concentrated solution tank 40. However, the filtration processing device 100 may supply a portion of the concentrated solution 42 to the concentrated solution tank 40 and return the remaining concentrated solution 42 to the stock solution tank 20, or may return all of the concentrated solution 42 to the stock solution tank 20.

[0029] The filtration treatment device 100 includes a membrane module 150. The filtration treatment device 100 may include a raw liquid supply pump 124, a raw liquid pressure measurement unit 128, a raw liquid flow rate measurement unit 129, a backwash liquid supply pump 134, a filtrate temperature measurement unit 135, a filtrate pressure measurement unit 138, a filtrate flow rate measurement unit 139, a concentrate pressure measurement unit 148, a chemical supply pump 164, an air pressure measurement unit 178, and an air flow rate measurement unit 179. The filtration treatment device 100 may include a raw liquid piping 120, a filtrate piping 130, a backwash liquid piping 132, a concentrate piping 140, a chemical piping 160, and an air piping 170.

[0030] In this example, one membrane module 150 is illustrated as the membrane module 150 included in the filtration processing device 100, but the number of membrane modules 150 included in the filtration processing device 100 is not limited to this. The filtration processing device 100 may have multiple membrane modules 150. As an example, the filtration processing device 100 may have 16 membrane modules 150 per set of pumps. Furthermore, the filtration processing device 100 does not necessarily have all of the pumps, valves, measuring units, and piping, and may have multiple components of each type depending on the purpose, and the ratio of the numbers of pumps, valves, measuring units, and membrane modules included in the filtration processing device 100 is not particularly limited.

[0031] The membrane module 150 has hollow fiber membranes for filtering the raw liquid 22. Details of the membrane module 150 will be described later.

[0032] The membrane module 150 may be connected to the stock solution tank 20 via a stock solution pipe 120. The membrane module 150 may be connected to the filtrate tank 30 via a filtrate pipe 130 and a backwash liquid pipe 132. The membrane module 150 may be connected to the concentrate tank 40 via a concentrate pipe 140. However, if the filtration treatment system 10 does not include the concentrate tank 40, the membrane module 150 may be connected to the stock solution tank 20 via the concentrate pipe 140.

[0033] The raw liquid supply pump 124 may be a driving source for supplying the raw liquid 22 from the raw liquid tank 20 to the membrane module 150. The raw liquid side pressure measuring unit 128 may measure the pressure on the raw liquid piping 120 side. The raw liquid side pressure measuring unit 128 may supply the measured pressure value to the estimation device 300. The raw liquid side flow rate measuring unit 129 may measure the flow rate on the raw liquid piping 120 side. The raw liquid side flow rate measuring unit 129 may supply the measured flow rate value to the estimation device 300. The raw liquid piping 120 may be provided with a raw liquid valve 126 for opening and closing the raw liquid piping 120.

[0034] The backwash liquid supply pump 134 may be a driving source for supplying the filtrate 32 from the filtrate tank 30 to the membrane module 150. The filtrate-side temperature measurement unit 135 may measure the temperature of the filtrate 32. The filtrate-side temperature measurement unit 135 may supply the measured liquid temperature to the estimation device 300. The filtrate-side temperature measurement unit 135 may measure the liquid temperature during filtration and during backwashing. Details of the filtration operation and the backwashing operation will be described later. The filtrate-side pressure measurement unit 138 may measure the pressure on the filtrate piping 130 and the backwash liquid piping 132 side. The filtrate-side pressure measurement unit 138 may supply the measured pressure to the estimation device 300. The filtrate-side pressure measurement unit 138 may measure the pressure during filtration and the pressure during backwashing. The filtrate-side flow rate measurement unit 139 may measure the flow rate on the filtrate piping 130 and the backwash liquid piping 132 side. The filtrate-side flow rate measuring unit 139 may supply the measured value of the flow rate to the estimation device 300. The filtrate-side flow rate measuring unit 139 may measure the flow rate during filtration and the flow rate during backwashing. The filtrate piping 130 may be provided with a filtrate valve 136 for opening and closing the filtrate piping 130. The backwash piping 132 may be provided with a backwash valve 137 for opening and closing the backwash piping 132.

[0035] The concentrate-side pressure measuring unit 148 may measure the pressure on the concentrate pipe 140 side. The concentrate-side pressure measuring unit 148 may supply the measured pressure value to the estimation device 300. The concentrate-side pressure measuring unit 148 may measure the pressure during filtration and the pressure during backwashing. The concentrate pipe 140 may be provided with a concentrate valve 146 for opening and closing the concentrate pipe 140.

[0036] The chemical supply pump 164 may be a driving source for supplying the chemical 62 from the chemical tank 60 to the membrane module 150 via the chemical piping 160. The chemical piping 160 may be provided with a chemical valve 166 for opening and closing the chemical piping 160. The chemical 62 may be supplied to the membrane module 150 during backwashing. The chemical piping 160 may be connected to the raw solution piping 120, the filtrate piping 130, or the concentrate piping 140. There is no particular limitation on which piping the chemical piping 160 is connected to. The chemical tank 60 may be connected to the raw solution piping 120, the filtrate piping 130, or the concentrate piping 140. There is no particular limitation on which piping the chemical tank 60 is connected to. The chemical tank 60 may receive a supply of chemicals from another chemical tank. For example, a chemical with a higher concentration than the chemical stored in the chemical tank 60 may be supplied from another chemical tank, and the chemical for cleaning the module may be adjusted by supplying the filtrate 32 from the filtrate tank 30.

[0037] The compressor 174 may be a driving source for supplying air from the air tank 70 to the membrane module 150 via the air piping 170. The air piping 170 may be provided with an air valve 176 for opening and closing the air piping 170. The compressor 174 may supply air to the membrane module 150 during air cleaning. The air pressure measuring unit 178 may measure the pressure of the air supplied from the air tank 70. The air flow rate measuring unit 179 may measure the flow rate of the air supplied from the air tank 70.

[0038] In this example, only the filtrate-side temperature measuring unit 135 that measures the temperature of the filtrate 32 is shown, but the location of the temperature measuring unit that measures the liquid temperature is not limited to this. A concentrate-side temperature measuring unit that measures the liquid temperature of the concentrate 42 may be provided, and a concentrate-side temperature measuring unit that measures the temperature of the concentrate 42 may be provided. It may be assumed that the liquid temperatures of the concentrate 42, the filtrate 32, and the concentrate 42 are almost the same.

[0039] The estimation device 300 estimates the water permeation rate of the membrane module 150. The water permeation rate indicates the flow rate per unit time per unit membrane area of ​​the membrane module 150, and is expressed in units of L / m. 2 / h (sometimes expressed as LMH). If the initial permeation rate of the membrane module 150 is known, the estimator 300 may calculate the permeation rate maintenance rate (%) from the quotient of the permeation rate and the initial permeation rate. The estimator 300 may supply the estimation result to the controller 200. Details of the estimator 300 will be described later.

[0040] The control device 200 may control the operation of the filtration treatment device 100. For example, the control device 200 controls the opening and closing of the raw liquid valve 126, the filtrate valve 136, the backwash liquid valve 137, and / or the concentrate valve 146 depending on the filtration operation or the backwash operation. The control device 200 may control the operation of the filtration treatment device 100 based on the permeation rate of the membrane module 150 estimated by the estimator 300. For example, the control device 200 controls the pump pressure of the raw liquid supply pump 124 and / or the backwash liquid supply pump 134 based on the permeation rate of the membrane module 150 estimated by the estimator 300. As another example, the control device 200 may control the duration and / or interval of the filtration operation and / or the backwash operation based on the permeation rate of the membrane module 150 estimated by the estimator 300.

[0041] The control device 200 may adjust the chemical cleaning conditions for the membrane module 150 based on the water permeation rate of the membrane module 150 estimated by the estimation device 300. For example, the control device 200 selects the chemical 62 to be used for chemical cleaning based on the water permeation rate of the membrane module 150 and adjusts the timing of the chemical cleaning.

[0042] 1B shows an example of the configuration of a membrane module 150. The membrane module 150 has one or more hollow fiber membranes 1510. The membrane module 150 may have a case 1500 and a potting portion 1520.

[0043] The case 1500 may be provided with an opening 1502, an opening 1504, and an opening 1506. The membrane module 150 may be connected to the raw liquid piping 120 through the opening 1502, to the filtrate piping 130 and the backwash liquid piping 132 through the opening 1504, and to the concentrate piping 140 through the opening 1506. That is, the raw liquid 22 may flow into the membrane module 150 through the opening 1502, the filtrate 32 may flow in and out of the membrane module 150 through the opening 1504, and the concentrate 42 may flow out of the membrane module 150 through the opening 1506.

[0044] The hollow fiber membrane 1510 may function as a filter for filtering the raw liquid 22. The hollow fiber membrane 1510 may be a porous membrane with a predetermined pore size. In this example, the hollow fiber membrane 1510 is a porous membrane produced by a thermally induced phase separation (TIPS) method. However, the method for producing the hollow fiber membrane 1510 is not limited to this. The hollow fiber membrane 1510 may also be produced by a non-solvent induced phase separation (NIPS) method.

[0045] The hollow fiber membrane 1510 produced by the TIPS method is more durable and less likely to be destroyed than a porous membrane produced by the NIPS method. This allows the estimation device 300 to more accurately estimate the water permeation rate of the membrane module 150 having the hollow fiber membrane 1510. In other words, because the hollow fiber membrane 1510 produced by the TIPS method is less likely to deteriorate, there is no need to frequently reacquire the reference information described below, and accurate estimation of the water permeation rate of the membrane module 150 can be continued based on the same reference information.

[0046] The potting section 1520 may fix one or more hollow fiber membranes 1510. The material of the potting section 1520 may include a thermosetting resin. For example, the material of the potting section 1520 may include at least one of urethane or epoxy. A liquid guide tube 1522 may be provided in the potting section 1520 on the stock solution piping 120 side. The stock solution 22 that flows into the membrane module 150 through the opening 1502 may flow through the liquid guide tube 1522 into the region where the hollow fiber membranes 1510 are provided and may be filtered by the hollow fiber membranes 1510.

[0047] 2A shows an example of the filtration operation of the filtration system 10. The thick arrows in the figure indicate the flow of liquid.

[0048] During filtration operation of the filtration system 10, the stock solution 22 in the stock solution tank 20 is supplied to the membrane module 150 by the stock solution supply pump 124. The stock solution 22 supplied to the membrane module 150 may be filtered by passing through the membrane wall of one or more hollow fiber membranes 1510 of the membrane module 150 from the outer surface to the inner surface, or may be filtered by passing through the membrane wall from the inner surface to the outer surface. In this example, the stock solution 22 is filtered by passing through the membrane wall of the hollow fiber membranes 1510 from the outer surface to the inner surface. The filtrate 32 recovered by filtration is supplied to the filtrate tank 30 through the filtrate piping 130, and the remaining concentrated solution 42 is supplied to the concentrated solution tank 40 through the concentrated solution piping 140. During filtration operation of the filtration system 10, the stock solution valve 126 may be open, the filtrate valve 136 may be open, the backwash liquid valve 137 may be closed, and the concentrated solution valve 146 may be open.

[0049] The raw liquid side pressure measurement unit 128, the filtrate side pressure measurement unit 138, and the concentrate side pressure measurement unit 148 may measure the pressure during filtration. The raw liquid side pressure measurement unit 128, the filtrate side pressure measurement unit 138, and the concentrate side pressure measurement unit 148 may supply the pressure measurement values ​​to the estimation device 300. However, the pressure values ​​measured during filtration do not have to be supplied to the estimation device 300.

[0050] 2B shows an example of a backwash operation of the filtration system 10. The thick arrows in the figure indicate the flow of liquid.

[0051] During backwash operation of the filtration system 10, the backwash liquid supply pump 134 supplies the filtrate 32 in the filtrate tank 30 to the membrane module 150. The filtrate 32 supplied to the membrane module 150 passes through the membrane walls from the inner surface to the outer surface of one or more hollow fiber membranes 1510 included in the membrane module 150. This cleans the hollow fiber membranes 1510. The liquid that has passed to the outside of the hollow fiber membranes 1510 is supplied to the concentrate tank 40 through the concentrate piping 140. During backwash operation of the filtration system 10, the raw liquid valve 126 may be closed, the filtrate valve 136 may be closed, the backwash liquid valve 137 may be open, and the concentrate valve 146 may be open.

[0052] During the backwash operation of the filtration system 10, the chemical 62 in the chemical tank 60 may be supplied to the membrane module 150 by the chemical supply pump 164. This can promote cleaning of the hollow fiber membranes 1510.

[0053] The raw liquid side pressure measuring unit 128, the filtrate side pressure measuring unit 138, and the concentrate side pressure measuring unit 148 measure the pressure during backwashing. The raw liquid side pressure measuring unit 128, the filtrate side pressure measuring unit 138, and the concentrate side pressure measuring unit 148 supply the pressure measurement values ​​to the estimation device 300.

[0054] During the backwash operation of the filtration treatment system 10, air cleaning may be performed simultaneously by the compressor 174 supplying air from the air tank 70. Air cleaning may be performed either before or after the backwash liquid supply pump 134 operates, or both. When air cleaning is performed, the air valve 176 may be in an open state. In air cleaning, the hollow fiber membrane 1510 may be cleaned by supplying air from the opening 1502 side of the membrane module 150.

[0055] The filtration treatment system 10 may repeat the filtration operation described in Fig. 2A and the backwash operation described in Fig. 2B. The control device 200 may control the duration and interval of each of the filtration operation and the backwash operation based on the water permeation rate of the membrane module 150 estimated by the estimating device 300.

[0056] 3A is a schematic diagram showing an example of the inside of the membrane module 150 during filtration operation. In this figure, one hollow fiber membrane 1510 is described as an example, but the same applies to the other hollow fiber membranes 1510 of one or more hollow fiber membranes 1510 included in the membrane module 150.

[0057] During filtration operation of the filtration treatment system 10, the raw liquid 22 supplied to the membrane module 150 is filtered by passing through the membrane wall 1512 from the outer surface 1516 of the hollow fiber membrane 1510 toward the inner surface 1514, as indicated by the dashed arrow. Water molecules 50 contained in the raw liquid 22 can pass through the membrane wall 1512, but turbidity matters 52 cannot pass through the membrane wall 1512. As a result, the filtered filtrate 32 is collected inside the hollow fiber membrane 1510, and a concentrated liquid 42 with an increased concentration of turbidity matters 52 remains outside the hollow fiber membrane 1510.

[0058] During the filtration operation of the filtration system 10, turbidity 52 that cannot pass through the membrane wall 1512 of the hollow fiber membrane 1510 may adhere to the vicinity of the surface of the hollow fiber membrane 1510. When the hollow fiber membrane 1510 becomes contaminated in this manner, the effective area of ​​the hollow fiber membrane 1510 decreases, and the water permeability of the membrane module 150 decreases. Therefore, it is necessary to clean the hollow fiber membrane 1510 by performing a backwash operation of the filtration system 10 and / or air cleaning, etc.

[0059] Furthermore, during the filtration operation of the filtration system 10, the structure of the hollow fiber membrane 1510 may be destroyed due to abrasion by the raw liquid 22, etc., and the pores of the hollow fiber membrane 1510 may become clogged. Such clogging also reduces the effective area of ​​the hollow fiber membrane 1510 and decreases the water permeability of the membrane module 150. Therefore, it is necessary to clean the hollow fiber membrane 1510 by performing a backwash operation and / or air cleaning of the filtration system 10.

[0060] 3B is a schematic diagram showing an example of the inside of the membrane module 150 during backwashing operation. In this figure, one hollow fiber membrane 1510 is used as an example for explanation, but the same applies to the other hollow fiber membranes 1510 of one or more hollow fiber membranes 1510 included in the membrane module 150.

[0061] During backwash operation of the filtration treatment system 10, the filtrate 32 supplied to the membrane module 150 passes through the membrane wall 1512 from the inner surface 1514 of the hollow fiber membrane 1510 toward the outer surface 1516, as indicated by the dashed arrow. As the water molecules 50 pass through the membrane wall 1512, the turbid matter 52 attached near the surface of the hollow fiber membrane 1510 is detached, thereby cleaning the hollow fiber membrane 1510. Note that while this example describes the cleaning of dirt caused by the turbid matter 52, clogging due to structural destruction of the hollow fiber membrane 1510 can also be cleaned in a similar manner.

[0062] During the backwashing operation of the filtration system 10, the turbidity 52 adhering near the surface of the hollow fiber membrane 1510 is detached, thereby cleaning the hollow fiber membrane 1510. However, not all of the turbidity 52 adhering near the surface of the hollow fiber membrane 1510 is necessarily detached. Therefore, even if the filtration system 10 repeats the filtration operation and the backwashing operation, the hollow fiber membrane 1510 may gradually become fouled. The estimation device 300 may estimate the water permeation rate of the membrane module 150 to detect the degree of such fouling.

[0063] 4 shows an example of the configuration of the estimation device 300 together with the filtration processing device 100 and the control device 200. Note that the blocks shown are functionally separated functional blocks and may not necessarily correspond to the actual device configuration. That is, blocks shown as one block in this diagram may not necessarily be configured by one device. Also, blocks shown as separate blocks in this diagram may not necessarily be configured by separate devices.

[0064] The estimation device 300 includes a driving data acquisition unit 310, a reference information acquisition unit 320, and an estimation unit 330. The estimation device 300 may also include a warning unit 340 and a display unit 350.

[0065] The operating data acquisition unit 310 acquires operating data during backwashing of the membrane module 150. The operating data acquisition unit 310 may acquire operating data during backwashing of the membrane module 150 from the filtration treatment device 100. For example, the operating data acquisition unit 310 may acquire pressure values ​​during backwashing from the raw liquid side pressure measurement unit 128, the filtrate side pressure measurement unit 138, and the concentrate side pressure measurement unit 148. The operating data acquisition unit 310 may acquire the pressure values ​​directly from each pressure measurement unit, or may acquire them indirectly via another configuration. However, the type of operating data acquired by the operating data acquisition unit 310 and the method of acquisition thereof are not limited to this. Details of the operating data will be described later.

[0066] The operating data acquisition unit 310 may correct the acquired pressure value. For example, the operating data acquisition unit 310 corrects the pressure value based on the liquid temperature measured by the filtrate-side temperature measurement unit 135. The operating data acquisition unit 310 may correct the pressure value according to the location of each pressure measurement unit, or may correct the pressure value based on the flow rate measured by each flow rate measurement unit. However, the operating data acquisition unit 310 may also acquire a pressure value that has been corrected in advance using another configuration.

[0067] The reference information acquisition unit 320 acquires predetermined reference information related to the permeation rate of the membrane module 150. The reference information may include information correlating the permeation rate with data corresponding to the operating data. For example, if the operating data includes a pressure value, the reference information may include information correlating the pressure value with the permeation rate. Details of the reference information will be described later.

[0068] The estimation unit 330 estimates the water permeation rate of the membrane module 150 based on the operating data and reference information during backwashing. For example, if the operating data includes a pressure value, the estimation unit 330 may estimate the water permeation rate associated with the pressure value included in the operating data as the water permeation rate of the membrane module 150 by referring to the reference information.

[0069] The estimation unit 330 may include a calibration curve acquisition unit 332 that acquires a calibration curve generated based on the reference information. The calibration curve acquisition unit 332 may generate and acquire a calibration curve based on the reference information, or may acquire a calibration curve generated outside the calibration curve acquisition unit 332. The calibration curve may be generated by multiple regression analysis or other multivariate analysis. Details of the calibration curve will be described later.

[0070] The estimation unit 330 may use the operating data and the calibration curve to estimate the water permeation rate of the membrane module 150. However, the method for estimating the water permeation rate of the membrane module 150 by the estimation unit 330 is not limited to this.

[0071] The estimation device 300 of this example estimates the permeation rate of the membrane module 150 based on the operational data and reference information of the membrane module 150. This makes it possible to manage the performance of the membrane module 150 at the operational site of the filtration treatment system 10 without having to move the membrane module 150 from the operational site to an experimental facility.

[0072] When estimating the permeation rate of the membrane module 150 based on operational data during filtration of the membrane module 150, the quality of the raw liquid 22 may not be constant and the permeation rate of the membrane module 150 may not be accurately estimated. On the other hand, the estimating device 300 of this example estimates the permeation rate of the membrane module 150 based on operational data during backwashing of the membrane module 150. Because the filtrate 32 that has already been filtered and has stable liquid quality is used for backwashing the membrane module 150, the estimating device 300 can accurately estimate the permeation rate of the membrane module 150. Therefore, the estimating device 300 of this example can more accurately manage the performance of the membrane module 150 compared to when estimating the permeation rate of the membrane module 150 based on operational data during filtration of the membrane module 150.

[0073] The estimation unit 330 may provide the estimated permeation rate of the membrane module 150 to the control device 200. The control device 200 may control the operation of the filtration treatment device 100 based on the estimated permeation rate of the membrane module 150. For example, the control device 200 may control the pump pressure of the raw solution supply pump 124 and / or the backwash liquid supply pump 134 based on the estimated permeation rate of the membrane module 150. The control device 200 may set the pump pressure according to the estimated permeation rate of the membrane module 150 so that filtration and / or backwashing can be performed at a predetermined flow rate. As another example, the control device 200 may control the duration and / or interval of the filtration operation and / or backwashing operation based on the estimated permeation rate of the membrane module 150. The control device 200 may increase the frequency of backwashing when the estimated permeation rate is lower than a predetermined reference value. The control device 200 may increase the duration of backwashing when the estimated permeation rate is lower than a predetermined reference value. The control device 200 may increase the flow rate during backwashing when the estimated permeation rate is lower than a predetermined reference value. The control device 200 may add chemicals 62 to the water flowing through the membrane module 150 during backwashing when the estimated permeation rate is lower than a predetermined reference value. The control device 200 may reduce the frequency of backwashing, reduce the backwashing period, or reduce the flow rate during backwashing when the estimated permeation rate is higher than a predetermined reference value. If the filtration treatment device 100 has multiple membrane modules 150, the control device 200 may adjust the number of membrane modules 150 used depending on the estimated permeation rate.

[0074] The control device 200 may adjust the chemical cleaning conditions for the membrane module 150 based on the estimated water permeation rate of the membrane module 150. For example, the control device 200 selects the chemical 62 to be used for chemical cleaning based on the water permeation rate of the membrane module 150 and adjusts the timing of the chemical cleaning. When the estimated water permeation rate of the membrane module 150 is higher than a predetermined reference value, the control device 200 may adjust the chemical cleaning conditions to select a chemical 62 with low cleaning ability or to select a reduced amount of the chemical 62 used. When the estimated water permeation rate of the membrane module 150 is lower than a predetermined reference value, the control device 200 may adjust the chemical cleaning conditions to select a chemical 62 with high cleaning ability or to select an increased amount of the chemical 62 used. When the estimated water permeation rate of the membrane module 150 is lower than a predetermined reference value, the control device 200 may adjust the chemical cleaning conditions so that chemical cleaning is performed. The control device 200 may circulate the chemical 62 by flowing the chemical 62 from the chemical tank 60 into the membrane module 150 and then returning the chemical 62 to the chemical tank 60. Here, the chemical cleaning conditions, such as the type of chemical 62 used, the order in which the chemicals 62 are used, the number of times the chemicals 62 are used or the combination of the chemicals 62 used, and the concentration of the chemicals 62, are not limited. The control device 200 may adjust the frequency of chemical cleaning, the leaving time after introducing the chemical 62, or the heating after introducing the chemical 62, based on the estimated water permeation rate of the membrane module 150. However, the chemical cleaning conditions adjusted by the control device 200 are not limited to these.

[0075] The estimation device 300 of this example manages the performance of the membrane module 150 at the operation site by estimating the permeation rate of the membrane module 150 based on the operating data and reference information of the membrane module 150. The control device 200 of this example then adjusts the control conditions and cleaning conditions of the filtration treatment device 100 based on the permeation rate of the membrane module 150 estimated at the operation site. This allows the filtration treatment system 10 of this example to continue operating in a state where the permeation rate of the membrane module 150 is within a preferred range. As a result, the filtration treatment system 10 of this example can suppress an increase in transmembrane pressure during operation and reduce the pump pressure required to maintain the filtration flow rate, thereby reducing power consumption in the filtration treatment system 10.

[0076] The warning unit 340 may notify the user of the timing for replacing the membrane module 150, the timing for cleaning the membrane module 150, or a change in the cleaning method for the membrane module 150, based on the water permeation rate of the membrane module 150 estimated by the estimation unit 330. For example, the warning unit 340 may notify the user of the timing for replacing the membrane module 150 when the ratio of the estimated water permeation rate of the membrane module 150 to the initial water permeation rate of the membrane module 150 falls below a predetermined threshold. As an example, the warning unit 340 may notify the user of the timing for replacing the membrane module 150 when the ratio of the estimated water permeation rate to the initial water permeation rate falls below 60%. However, the predetermined threshold is not limited to 60%. The initial water permeation rate of the membrane module 150 may be the water permeation rate of the membrane module 150 when it is assumed that no fouling occurs in the membrane module 150. For example, the initial permeability of the membrane module 150 may be the permeability designed when the membrane module 150 was first produced, or may be the permeability of the membrane module 150 estimated immediately after the filtration treatment system 10 began operation.

[0077] The warning unit 340 may notify the user of the time to replace the membrane module 150 via a display device such as a monitor, via an audio device such as a speaker, or via a sending device such as email. However, the method by which the warning unit 340 notifies the user of the time to replace the membrane module 150 is not limited to these. The warning unit 340 may notify the user of the time to replace the membrane module 150 by any method that can notify the user of the time to replace the membrane module 150.

[0078] The warning unit 340 may notify the user that the membrane module 150 should be cleaned if the water permeation rate of the membrane module 150 falls below a predetermined threshold. The warning unit 340 may notify the user that the cleaning method should be changed to a stronger cleaning method if the water permeation rate of the membrane module 150 falls below a predetermined threshold. The warning unit 340 may notify the user that the cleaning method should be changed to a weaker cleaning method if the water permeation rate of the membrane module 150 exceeds a predetermined threshold.

[0079] The display unit 350 displays information related to the water permeation rate of the membrane module 150 based on the water permeation rate of the membrane module 150 estimated by the estimation unit 330. As one example, the display unit 350 displays the estimated water permeation rate of the membrane module 150 itself. As another example, the display unit 350 may display the ratio of the estimated water permeation rate of the membrane module 150 to the initial water permeation rate of the membrane module 150. However, the information related to the water permeation rate of the membrane module 150 displayed by the display unit 350 is not limited to this.

[0080] FIG. 5 shows an example of operational data. The operational data in this example is time-series data of the transmembrane pressure when the liquid temperature is T (°C) and the flow rate is F (L / H). The solid and dotted lines during the backwashing period each show an example of the behavior that the transmembrane pressure can assume. However, the behavior that the transmembrane pressure can assume during the backwashing period is not limited to these. Details of the behavior of the transmembrane pressure during the backwashing period will be described later.

[0081] The operating data may include the transmembrane pressure of the membrane module 150. The transmembrane pressure of the membrane module 150 is a pressure difference calculated based on the pressure on the feed liquid 22 side, the pressure on the filtrate 32 side, and the pressure on the concentrate 42 side of the membrane module 150. That is, the transmembrane pressure of the membrane module 150 may be calculated based on the pressure measured by the feed liquid side pressure measurement unit 128, the pressure measured by the filtrate side pressure measurement unit 138, and the pressure measured by the concentrate side pressure measurement unit 148. The operating data acquisition unit 310 may calculate the transmembrane pressure of the membrane module 150 based on the pressure values ​​acquired from the feed liquid side pressure measurement unit 128, the filtrate side pressure measurement unit 138, and the concentrate side pressure measurement unit 148. Alternatively, the transmembrane pressure of the membrane module 150 may be calculated in the filtration processing device 100, and the operating data acquisition unit 310 may acquire the calculated transmembrane pressure.

[0082] The operating data may include the temperature of the filtrate 32 used for backwashing the membrane module 150. For example, the temperature of the filtrate 32 is the temperature measured by the filtrate-side temperature measuring unit 135.

[0083] The operating data may include the flow rate of the filtrate 32 used for backwashing the membrane module 150. For example, the flow rate of the filtrate 32 is a flow rate measured by the filtrate-side flow rate measuring unit 139. As another example, the flow rate of the filtrate 32 may be acquired based on the pump pressure of the backwash liquid supply pump 134 or the power supplied to the backwash liquid supply pump 134. However, the method of acquiring the flow rate of the filtrate 32 is not limited to these.

[0084] The operating data may be corrected using position information of the concentrate side pressure measurement unit 128, the filtrate side pressure measurement unit 138, and the concentrate side pressure measurement unit 148. For example, the pressure values ​​measured by each of the concentrate side pressure measurement unit 128, the filtrate side pressure measurement unit 138, and the concentrate side pressure measurement unit 148 may be converted to values ​​that take into account the hydraulic head, using the distance between each of the positions of the concentrate side pressure measurement unit 128, the filtrate side pressure measurement unit 138, and the concentrate side pressure measurement unit 148 and a predetermined reference point. However, the correction method is not limited to this.

[0085] The operating data may include module information regarding the properties of the membrane module 150. Examples of the module information include the total area of ​​one or more hollow fiber membranes 1510, the fixing method of one or more hollow fiber membranes 1510, the type of hollow fiber membrane 1510, the material of the hollow fiber membrane 1510, additives contained in the hollow fiber membrane 1510, the processing method applied to the hollow fiber membrane 1510, the thickness of the hollow fiber membrane 1510, the inner and outer diameter and / or their ratio of the hollow fiber membrane 1510, the production method of the hollow fiber membrane 1510, the size of the holes in the hollow fiber membrane 1510, the presence or absence of a case 1500, the material of the case 1500, the material of the nut for fixing the case 1500, the material of the potting portion 1520, the length and / or thickness of the membrane module 150, and the grade of the membrane module 150.

[0086] For example, the module information may be assigned a module information ID corresponding to a combination of information included in the module information, and the module information ID may be linked to the driving data. The driving data acquisition unit 310 may acquire the module information included in the driving data by acquiring the module information ID linked to the driving data.

[0087] The operating data may include the flow rate of air used for air cleaning or the pressure for supplying air, the operating time or the proportion of each operation of filtration operation, backwash operation, or air cleaning operation, any of the conditions of chemical cleaning in chemical cleaning, such as the concentration of chemical 62 or the frequency of chemical cleaning, the length or diameter of the piping in the filtration processing system 10, the number of membrane modules 150, etc.

[0088] The operating data acquisition unit 310 may acquire operating data for the latter half of the backwashing period of the membrane module 150. The latter half of the backwashing period may be the period from the middle of the backwashing period between the start and end of the backwashing period to the end of the backwashing period. The latter half of the backwashing period may be the period indicated by hatching in FIG. 5.

[0089] As an example of the behavior of the transmembrane pressure during the backwashing period, as shown by the solid line in Figure 5, the absolute value of the transmembrane pressure changes from a large value to a small value, and the transmembrane pressure stabilizes in the latter half of the backwashing period. Because turbidity 52 adheres near the surface of the hollow fiber membrane 1510 during the filtration period, the absolute value of the transmembrane pressure is large at the start of the backwashing period. When the backwashing period begins, the turbidity 52 adhered near the surface of the hollow fiber membrane 1510 is removed by backwashing, and the absolute value of the transmembrane pressure decreases. Once the removal of removable turbidity 52 by backwashing is completed, the absolute value of the transmembrane pressure stabilizes. By having the operating data acquisition unit 310 acquire operating data for the membrane module 150 during the latter half of the backwashing period, operating data for the period when the absolute value of the transmembrane pressure stabilizes can be acquired, allowing the permeation rate of the membrane module 150 to be accurately estimated.

[0090] As another example of the behavior of the transmembrane pressure during the backwash period, as shown by the dotted line in Figure 5, the absolute value of the transmembrane pressure at the start of the backwash period is smaller than the absolute value of the transmembrane pressure in the latter half of the backwash period. For example, if the flow rate of the filtrate 32 sent from the backwash liquid supply pump 134 is unstable, the absolute value of the transmembrane pressure at the start of the backwash period may be smaller than the absolute value of the transmembrane pressure in the latter half of the backwash period. In the latter half of the backwash period, the operation of the backwash liquid supply pump 134 stabilizes, thereby stabilizing the absolute value of the transmembrane pressure. Even in this case, by having the operating data acquisition unit 310 acquire operating data for the membrane module 150 in the latter half of the backwash period, operating data for the period when the absolute value of the transmembrane pressure is stable can be acquired, allowing the permeation rate of the membrane module 150 to be accurately estimated.

[0091] Furthermore, at the start of the backwashing period, the above-mentioned factors and / or other factors may overlap, causing the transmembrane pressure to be unstable or may not fluctuate significantly during the backwashing period. In other words, the transmembrane pressure may behave in ways other than those shown by the solid and dotted lines in FIG. 5 . Even in these cases, the absolute value of the transmembrane pressure will stabilize in the latter half of the backwashing period. Therefore, by having the operating data acquisition unit 310 acquire operating data for the membrane module 150 in the latter half of the backwashing period, it is possible to acquire operating data for a period in which the absolute value of the transmembrane pressure is stable, thereby enabling accurate estimation of the permeation rate of the membrane module 150.

[0092] The timing of acquiring the operating data by the operating data acquiring unit 310 is not limited to this. For example, the operating data acquiring unit 310 may acquire operating data for a period from a point a predetermined period prior to the end of the backwashing period to the end of the backwashing period. In this case, the predetermined period may be determined based on the behavior of the transmembrane pressure. In other words, the predetermined period may be set to a period during which the absolute value of the transmembrane pressure is stable.

[0093] The operating data acquired by the operating data acquisition unit 310 is not limited to only the operating data during the backwashing period. The operating data acquisition unit 310 may also acquire operating data during the filtration period. In this case, the estimation unit 330 may estimate the permeation rate of the membrane module 150 based on the operating data during backwashing. For example, the operating data acquisition unit 310 acquires operating data at predetermined time intervals throughout the backwashing period and the filtration period. The operating data acquisition unit 310 may acquire operating data every 5 seconds, every 10 seconds, every 30 seconds, or every 60 seconds. However, the time intervals at which the operating data acquisition unit 310 acquires operating data are not limited to these. The estimation unit 330 may estimate the permeation rate of the membrane module 150 based on the operating data during backwashing, among the operating data acquired by the operating data acquisition unit 310 at predetermined time intervals throughout the backwashing period and the filtration period.

[0094] The estimation unit 330 may use a moving average of the transmembrane pressure difference to estimate the water permeation rate of the membrane module 150. This makes it possible to accurately estimate the water permeation rate of the membrane module 150 even if there is an error in the operational data itself and / or an error in the timing of acquiring the operational data.

[0095] An example of reference information and a calibration curve is shown in Figure 6. This figure shows a calibration curve that represents the relationship between transmembrane pressure and water permeation rate when the liquid temperature is T (°C) and the flow rate is F (L / H).

[0096] The reference information may include information correlating data corresponding to the operating data with the water permeation rate. In this example, the reference information includes information on the water permeation rate for multiple transmembrane pressure differences when the liquid temperature is T (°C) and the flow rate is F (L / H). The reference information may be obtained in advance by experiments on the membrane module 150 or by simulations using a simulator that simulates the membrane module 150. In this example, reference information is shown for a case where the liquid temperature and flow rate are fixed, but similar reference information may be obtained in advance for multiple liquid temperatures and multiple flow rates. The reference information may also include module information regarding the properties of the membrane module 150.

[0097] The calibration curve may be generated based on reference information. For example, the calibration curve may be generated by regression analysis. As one example, the calibration curve may be generated by multiple regression analysis using the transmembrane pressure, liquid temperature, flow rate, and module information as explanatory variables and the water permeation rate as a response variable. As another example, the calibration curve may be generated by simple regression analysis using the transmembrane pressure as an explanatory variable and the water permeation rate as a response variable when the liquid temperature, flow rate, and module information are fixed.

[0098] The estimation unit 330 may use the operating data and the calibration curve to estimate the water permeation rate of the membrane module 150. For example, assume that the operating data acquisition unit 310 has acquired operating data in which the liquid temperature is T (°C), the flow rate is F (L / H), and the transmembrane pressure is −P (kPa) (P indicates the absolute value of the transmembrane pressure, and P≧0). In this case, the estimation unit 330 may estimate that the water permeation rate of the membrane module 150 is L (LMH) by substituting −P (kPa) for the transmembrane pressure in the calibration curve of FIG. 6 when the liquid temperature is T (°C) and the flow rate is F (L / H).

[0099] As described above, the estimation device 300 of this example estimates the permeation rate of the membrane module 150 based on the operating data of the membrane module 150 by the calibration curve acquisition unit 332 acquiring the calibration curve generated based on the reference information. This makes it possible to manage the performance of the membrane module 150 at the operating site of the filtration treatment system 10 without having to move the membrane module 150 from the operating site to an experimental facility.

[0100] 7 shows a modified example of the configuration of an estimation device 300 together with a filtration processing device 100 and a control device 200. The estimation device 300 of this example differs from the embodiment of FIG. 4 in that the estimation unit 330 uses a machine learning model 334. In this example, differences from the embodiment of FIG. 4 will be particularly described, and the rest may be the same as the embodiment of FIG. 4.

[0101] The estimation unit 330 may estimate the water permeation rate of the membrane module 150 using a machine learning model 334 that has been trained by machine learning based on reference information so as to output a water permeation rate corresponding to the operating data during backwashing, and the operating data acquired by the operating data acquisition unit 310. For example, the estimation unit 330 inputs the operating data during backwashing acquired by the operating data acquisition unit 310 to the machine learning model 334. The machine learning model 334 may output a water permeation rate corresponding to the input operating data during backwashing, and supply it to the estimation unit 330. The estimation unit 330 may estimate the water permeation rate output by the machine learning model 334 as the water permeation rate of the membrane module 150.

[0102] As an example, if the operational data includes the transmembrane pressure, the temperature of the filtrate 32, the flow rate of the filtrate 32, and module information, the reference information includes information correlating the transmembrane pressure, the temperature of the filtrate 32, the flow rate of the filtrate 32, and the module information with the permeation rate. In this case, the machine learning model 334 may be a model in which the explanatory variables are the transmembrane pressure, the temperature of the filtrate 32, the flow rate of the filtrate 32, and the module information, and the objective variable is the permeation rate. When operational data including the transmembrane pressure, the temperature of the filtrate 32, the flow rate of the filtrate 32, and the module information are input, the machine learning model 334 may output a permeation rate corresponding to the operational data. The machine learning model 334 may be generated by any existing learning method.

[0103] The machine learning model 334 may include multiple machine learning models 334 when at least some of the operating data is fixed. For example, the machine learning model 334 includes multiple machine learning models 334 when the temperature of the filtrate 32, the flow rate of the filtrate 32, and the module information are fixed. In this case, the estimation unit 330 may identify the machine learning model 334 that corresponds to the temperature of the filtrate 32, the flow rate of the filtrate 32, and the module information included in the operating data acquired by the operating data acquisition unit 310. The estimation unit 330 may then input the transmembrane pressure included in the operating data acquired by the operating data acquisition unit 310 into the identified machine learning model 334, and estimate the water permeation rate output by the identified machine learning model 334 as the water permeation rate of the membrane module 150. Note that the number and types of operating data to be fixed are not limited to these.

[0104] The machine learning model 334 may be generated outside the estimation device 300 and stored in an external server or the like. However, the generation location and storage location of the machine learning model 334 are not limited to this. The machine learning model 334 may be generated by the reference information acquisition unit 320 and stored in the reference information acquisition unit 320, or may be generated by the reference information acquisition unit 320 and stored in the estimation unit 330, or may be generated by the reference information acquisition unit 320 and stored in an external server or the like. Any other combination of the generation location and storage location of the machine learning model 334 may be adopted.

[0105] As described above, the estimation device 300 of this example uses the machine learning model 334 that has been machine-learned based on the reference information to estimate the permeation rate of the membrane module 150 based on the operating data of the membrane module 150. This makes it possible to manage the performance of the membrane module 150 at the operating site of the filtration treatment system 10 without having to move the membrane module 150 from the operating site to an experimental facility.

[0106] Various embodiments of the present invention may be described with reference to flowcharts and block diagrams, where the blocks may represent (1) stages of a process in which operations are performed or (2) sections of an apparatus responsible for performing the operations. Particular stages and sections may be implemented by dedicated circuitry, programmable circuitry provided with computer-readable instructions stored on a computer-readable medium, and / or a processor provided with computer-readable instructions stored on a computer-readable medium. Dedicated circuitry may include digital and / or analog hardware circuitry, and may include integrated circuits (ICs) and / or discrete circuits. Programmable circuitry may include reconfigurable hardware circuitry, including logical AND, OR, XOR, NAND, NOR, and other logical operations, flip-flops, registers, memory elements such as field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), and the like.

[0107] A computer-readable medium may include any tangible device capable of storing instructions that are executed by an appropriate device, such that the computer-readable medium having instructions stored thereon comprises an article of manufacture containing instructions that can be executed to create means for performing the operations specified in the flowcharts or block diagrams. Examples of computer-readable media may include electronic, magnetic, optical, electromagnetic, and semiconductor storage media. More specific examples of computer-readable media may include floppy disks, diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), electrically erasable programmable read-only memory (EEPROM), static random access memory (SRAM), compact disc read-only memory (CD-ROM), digital versatile disc (DVD), Blu-ray (RTM) disc, memory stick, integrated circuit card, and the like.

[0108] The computer readable instructions may include either assembler instructions, Instruction Set Architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, or source or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk®, JAVA®, C++, etc., and conventional procedural programming languages ​​such as the “C” programming language or similar programming languages.

[0109] The computer-readable instructions may be provided to a processor or programmable circuit of a programmable data processing device, such as a computer, locally or over a wide area network (WAN) such as a local area network (LAN) or the Internet, and the computer-readable instructions may be executed to create means for performing the operations specified in the flowcharts or block diagrams. Here, the computer may be a personal computer (PC), a tablet computer, a smartphone, a workstation, a server computer, a general-purpose computer, a special-purpose computer, or the like, or may be a computer system in which multiple computers are connected. Such a computer system in which multiple computers are connected is also called a distributed computing system, and is a broad definition of computer. In a distributed computing system, the multiple computers collectively execute a program by each executing a portion of the program and passing data between the computers as needed during program execution.

[0110] Examples of processors include a computer processor, a central processing unit (CPU), a processing unit, a microprocessor, a digital signal processor, a controller, a microcontroller, etc. A computer may have one or more processors. In a multiprocessor system with multiple processors, each processor executes a portion of a program and passes data between processors as needed during program execution, allowing the multiple processors to collectively execute a program. For example, in multitasking, each of the multiple processors may execute a portion of each task in small chunks by switching tasks at time slice intervals. In this case, which portion of a program each processor executes changes dynamically. Which portion of a program each of the multiple processors executes may also be statically determined by multiprocessor-aware programming.

[0111] 8 shows an example of a computer 1000 in which aspects of the present invention may be embodied, in whole or in part. A program installed on the computer 1000 may cause the computer 1000 to function as or perform operations associated with an apparatus or one or more sections of the apparatus according to embodiments of the present invention, and / or to perform a process or steps of a process according to embodiments of the present invention. Such a program may be executed by the CPU 1012 to cause the computer 1000 to perform specific operations associated with some or all of the blocks of the flowcharts and block diagrams described herein.

[0112] A computer 1000 according to this embodiment includes a CPU 1012, a RAM 1014, a graphics controller 1016, and a display device 1018, which are interconnected by a host controller 1010. The computer 1000 also includes input / output units such as a communication interface 1022, a hard disk drive 1024, a DVD-ROM drive 1026, and an IC card drive, which are connected to the host controller 1010 via an input / output controller 1020. The computer also includes legacy input / output units such as a ROM 1030 and a keyboard 1042, which are connected to the input / output controller 1020 via an input / output chip 1040.

[0113] The CPU 1012 operates according to programs stored in the ROM 1030 and RAM 1014, thereby controlling each unit. The graphics controller 1016 acquires image data generated by the CPU 1012 into a frame buffer or the like provided in the RAM 1014 or into the graphics controller itself, and causes the image data to be displayed on the display device 1018.

[0114] The communication interface 1022 communicates with other electronic devices via a network. The hard disk drive 1024 stores programs and data used by the CPU 1012 in the computer 1000. The DVD-ROM drive 1026 reads programs or data from a DVD-ROM 1027 and provides the programs or data to the hard disk drive 1024 via the RAM 1014. The IC card drive reads programs and data from an IC card and / or writes programs and data to an IC card.

[0115] The ROM 1030 stores therein a boot program and the like that is executed by the computer 1000 upon activation, and / or programs that depend on the hardware of the computer 1000. The input / output chip 1040 may also connect various input / output units to the input / output controller 1020 via a parallel port, a serial port, a keyboard port, a mouse port, and the like.

[0116] The programs are provided by a computer-readable medium such as a DVD-ROM 1027 or an IC card. The programs are read from the computer-readable medium, installed in the hard disk drive 1024, RAM 1014, or ROM 1030, which are also examples of computer-readable media, and executed by the CPU 1012. Information processing described in these programs is read by the computer 1000, and causes cooperation between the programs and the various types of hardware resources described above. An apparatus or method may be configured by implementing information manipulation or processing in accordance with the use of the computer 1000.

[0117] For example, when communication is performed between the computer 1000 and an external device, the CPU 1012 may execute a communication program loaded into the RAM 1014 and instruct the communication interface 1022 to perform communication processing based on the processing described in the communication program. Under the control of the CPU 1012, the communication interface 1022 reads transmission data stored in a transmission buffer processing area provided in the RAM 1014, the hard disk drive 1024, the DVD-ROM 1027, or a recording medium such as an IC card, and transmits the read transmission data to the network, or writes reception data received from the network to a reception buffer processing area or the like provided on the recording medium.

[0118] The CPU 1012 may also cause all or a necessary portion of a file or database stored on an external recording medium such as a hard disk drive 1024, a DVD-ROM drive 1026 (DVD-ROM 1027), an IC card, etc. to be read into the RAM 1014, and perform various types of processing on the data on the RAM 1014. The CPU 1012 then writes back the processed data to the external recording medium.

[0119] Various types of information, such as various types of programs, data, tables, and databases, may be stored on the recording medium and may undergo information processing. The CPU 1012 may perform various types of processing on data read from the RAM 1014, including various types of operations, information processing, conditional judgment, conditional branching, unconditional branching, information search / replacement, etc., as described throughout this disclosure and specified by the instruction sequences of the programs, and write the results back to the RAM 1014. The CPU 1012 may also search for information in a file, database, etc. on the recording medium. For example, if multiple entries, each having an attribute value of a first attribute associated with an attribute value of a second attribute, are stored on the recording medium, the CPU 1012 may search for an entry that matches a condition specified by the attribute value of the first attribute from among the multiple entries, read the attribute value of the second attribute stored in the entry, and thereby obtain the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.

[0120] The above-described programs or software modules may be stored in a computer-readable medium on or near the computer 1000. A recording medium such as a hard disk or RAM provided in a server system connected to a dedicated communication network or the Internet can also be used as a computer-readable medium, thereby providing the programs to the computer 1000 via the network.

[0121] Although the present invention has been described above using embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It will be apparent to those skilled in the art that various modifications and improvements can be made to the above embodiments. It is clear from the claims that such modifications and improvements can also be included within the technical scope of the present invention.

[0122] It should be noted that the execution order of each process, such as operations, procedures, steps, and stages, in the devices, systems, programs, and methods shown in the claims, specifications, and drawings is not specifically stated as "before," "prior to," etc., and that the processes can be performed in any order unless the output of a previous process is used in a subsequent process. Even if the operational flow in the claims, specifications, and drawings is described using "first," "next," etc. for convenience, this does not mean that the processes must be performed in this order. [Explanation of symbols]

[0123] 10 Filtration treatment system, 20 Raw liquid tank, 22 Raw liquid, 30 Filtrate tank, 32 Filtrate, 40 Concentrate tank, 42 ​​Concentrate, 50 Water molecules, 52 Suspended matter, 60 Chemical tank, 62 Chemicals, 70 Air tank, 100 Filtration treatment device, 120 Raw liquid piping, 124 Raw liquid supply pump, 126 Raw liquid valve, 128 Raw liquid side pressure measurement unit, 129 Raw liquid side flow rate measurement unit, 130 Filtrate piping, 132 Backwash liquid piping, 134 Backwash liquid supply pump, 135 Filtrate side temperature measurement unit, 136 Filtrate valve, 137 Backwash liquid valve, 138 Filtrate side pressure measurement unit, 139 Filtrate side flow rate measurement unit, 140 Concentrate piping, 146 Concentrate valve, 148 Concentrate side pressure measurement unit, 150 Membrane module, 160 Chemical piping, 164 Chemical supply pump, 166 Chemical valve, 170 Air piping, 174 Compressor, 176 Air valve, 178 Air pressure measurement unit, 179 Air flow measurement unit, 200 Control device, 300 Estimation device, 310 Operation data acquisition unit, 320 Reference information acquisition unit, 330 Estimation unit, 332 Calibration curve acquisition unit, 334 Machine learning model, 340 Warning unit, 350 Display unit, 1000 Computer, 1010 Host controller, 1012 CPU, 1014 RAM, 1016 Graphics controller, 1018 Display device, 1020 Input / output controller, 1022 Communication interface, 1024 Hard disk drive, 1026 DVD-ROM drive, 1027 DVD-ROM, 1030 ROM, 1040 Input / output chip, 1042 Keyboard, 1500 Case, 1502 Opening, 1504 opening, 1506 opening, 1510 hollow fiber membrane, 1512 membrane wall, 1514 inner surface, 1516 outer surface, 1520 potting portion, 1522 liquid guide tube

Claims

1. An estimation device for estimating a water permeation rate of a membrane module having one or more hollow fiber membranes, an operating data acquisition unit that acquires operating data during backwashing of the membrane module; a reference information acquisition unit that acquires predetermined reference information related to the water permeation rate of the membrane module; an estimation unit that estimates a water permeation rate of the membrane module based on the operating data during backwashing and the reference information; Equipped with Estimation device.

2. The operation data acquisition unit acquires operation data for the latter half of the backwash period of the membrane module. The estimation device according to claim 1 .

3. The operational data includes a transmembrane pressure difference of the membrane module. The estimation device according to claim 1 .

4. The estimation unit estimates the water permeation rate of the membrane module using a moving average of the transmembrane pressure difference. The estimation device according to claim 3 .

5. The operating data includes a liquid temperature of a filtrate used for backwashing the membrane module. The estimation device according to claim 1 .

6. The operational data includes a flow rate of filtrate used for backwashing the membrane module. The estimation device according to claim 1 .

7. The operational data includes module information regarding the properties of the membrane module. The estimation device according to claim 1 .

8. The estimation unit a calibration curve acquisition unit that acquires a calibration curve generated based on the reference information; The water permeation rate of the membrane module is estimated using the operating data and the calibration curve. The estimation device according to claim 1 .

9. The estimation unit estimates the water permeation rate of the membrane module using a machine learning model trained based on the reference information so as to output a water permeation rate corresponding to the operating data during backwashing, and the operating data acquired by the operating data acquisition unit. The estimation device according to claim 1 .

10. and a warning unit for notifying the user of the timing for replacing the membrane module, the timing for cleaning the membrane module, or a change in the cleaning method for the membrane module based on the water permeation rate of the membrane module estimated by the estimation unit. The estimation device according to claim 1 .

11. a display unit that displays information about the water permeation rate of the membrane module based on the water permeation rate of the membrane module estimated by the estimation unit; The estimation device according to claim 1 .

12. The one or more hollow fiber membranes are produced by a thermally induced phase separation method. The estimation device according to claim 1 .

13. An estimation device according to any one of claims 1 to 12; a filtration treatment device having the membrane module; Equipped with Filtration treatment system.

14. a control device that controls the operation of the filtration treatment device based on the water permeation rate of the membrane module estimated by the estimation device; The filtration system of claim 13.

15. The control device adjusts chemical cleaning conditions for the membrane module based on the water permeation rate of the membrane module estimated by the estimation device. The filtration system of claim 14.

16. A method for estimating a water permeation rate of a membrane module having one or more hollow fiber membranes, comprising: Acquiring operational data during backwashing of the membrane module; obtaining predetermined reference information regarding the water permeability of the membrane module; estimating a water permeation rate of the membrane module based on the operating data during backwashing and the reference information; Equipped with Estimation method.

17. When executed by a computer, the computer An estimation device for estimating a water permeation rate of a membrane module having one or more hollow fiber membranes, an operating data acquisition unit that acquires operating data during backwashing of the membrane module; a reference information acquisition unit that acquires predetermined reference information related to the water permeation rate of the membrane module; an estimation unit that estimates a water permeation rate of the membrane module based on the operating data during backwashing and the reference information; The device functions as an estimation device having program.

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