Water treatment system and water treatment method

Abstract

Provided is a water treatment system and a water treatment method in which a prediction formula used in deriving the fractal dimension of an aggregate can be verified later. [Solution] The water treatment system 1 includes a coagulation tank 20 for forming coagulation by adding a coagulant to the water to be treated, a camera 20 for acquiring multiple images of the coagulation formed in the coagulation tank 20, an information derivation unit 31 for deriving numerical information on feature quantities related to the coagulation state of the coagulation from the multiple images, a fractal dimension prediction unit 33 for deriving the fractal dimension value of the coagulation by applying the feature quantities included in the numerical information to a prediction formula, a pump 13 for adjusting the amount of coagulant added to the water to be treated based on the derived fractal dimension value, and a membrane filtration unit 50 for membrane filtration of the water to be treated. The numerical information includes digitized information for capturing the coagulation in three dimensions based on multiple images of the coagulation.

Description

[Technical field]

[0001] The present invention relates to a water treatment system and a water treatment method. [Background technology]

[0002] Water treatment technology using membrane filtration is used in a variety of fields because it is safe, has high quality, and can separate impurities in water using a compact, low-cost process. At present, the following technical issues exist regarding membrane filtration:

[0003] First, it is necessary to be able to respond to changes in water quality. For example, when filtering river water, the quality of the river may change suddenly due to torrential rains caused by climate change. Even in such cases, there is a demand for technology that allows stable membrane filtration of river water.

[0004] Secondly, it is required to be able to handle highly difficult water. While membrane filtration has the filtration accuracy to provide high-quality treated water, there are cases where the membrane becomes clogged when filtering water containing a large amount of organic matter or fine particles. Therefore, there is a problem that it is difficult to apply membrane filtration to the treatment of such highly difficult water.

[0005] Thirdly, it is required to be able to handle filtration with high flux. Flux (permeation rate per unit area and unit time, L / m 2 / hr), the device can be made more compact and less expensive.

[0006] As a method for solving these problems, a flocculation membrane filtration method that combines the addition of a flocculant and membrane filtration can be considered. This method can provide the following solutions to the above problems.

[0007] For the first problem, by adjusting the amount of flocculant added to the water to be treated in response to changes in water quality, stable membrane filtration may be possible regardless of changes in water quality. For the second problem, even if the water to be treated contains a large amount of organic matter or fine particles, clogging of the membrane can be suppressed by filtering the water in a flocculated state by adding a flocculant. For the third problem, by appropriately controlling the characteristics of the flocculated flocs, high flux filtration conditions may be achieved.

[0008] In the conventional flocculation membrane filtration method, flocs are formed by adding a flocculant, and then the flocs are precipitated in a settling tank, and the supernatant water is then filtered. In this case, a relatively large water tank is required to ensure a sufficient water surface load (retention time) in the settling tank. This causes a problem of installation space. There is also a problem that the amount of flocculant to be added tends to be large, which tends to increase costs. The conventional flocculation membrane filtration method is based on the premise that after flocculation, the flocs are passed through a settling tank, and then membrane filtration is performed after settling, so control is performed according to the settling properties of the flocs. For example, Patent Document 1 describes a flocculation settling control device and control method capable of forming flocs with good settling properties as a control technology for the settling properties of flocs. Patent Document 2 also describes a method of evaluating the state of flocs in order to form flocs with good settling properties.

[0009] On the other hand, in the coagulation membrane filtration method, unlike the conventional coagulation sedimentation method, there is a coagulation direct membrane filtration method in which the flocs are not precipitated in a sedimentation tank, but are directly filtered through a membrane together with the coagulant injected. This coagulation direct membrane filtration method has the advantage that the space for a sedimentation tank is not required, and the amount of coagulant added is small, so that the initial cost and running cost of the overall water treatment facility can be reduced. However, in the coagulation direct membrane filtration method, the coagulant injection amount control device for coagulation direct membrane filtration that can stably operate the membrane filtration and the appropriate injection amount have not been fully considered. For example, Patent Document 3 proposes a technology to inject so that the ratio of the turbidity of the membrane filtration raw water and the metal ion concentration is equal to or higher than a certain level. In addition, Patent Document 4 proposes controlling the injection amount of the coagulant based on the fractal dimension of the aggregate. [Prior art documents] [Patent documents]

[0010] [Patent Document 1] Patent No. 6730467 [Patent Document 2] Patent No. 6262092 [Patent Document 3] JP 2020-131055 A [Patent Document 4] Patent Publication No. 2022-64136 Summary of the Invention [Problem to be solved by the invention]

[0011] In Patent Document 3, it is necessary to provide a washing section that performs chemical washing using an acidic chemical in order to inject the coagulant so that the ratio of the turbidity of the raw water to be filtered through the membrane and the metal ion concentration is at a certain level or higher, and periodic washing with the acidic chemical is also required. In contrast, in Patent Document 4, the amount of coagulant injected is controlled based on the fractal dimension of the coagulated flocs, so a washing section for performing chemical washing is not required.

[0012] Patent Document 4 discloses that image data of flocs is acquired, a prediction model trained by machine learning is used using the image data of the flocs and a known fractal dimension as training data, and a measurement value of the fractal dimension of the flocs is output from the image data of the flocs using the prediction model. However, since a convolutional neural network (CNN) based on artificial intelligence such as Alex net is used to create the trained model of machine learning, the details of the weighting of each piece of information obtained from the image data are black-boxed and cannot be understood by humans. In other words, since the criteria for determining whether the prediction model and the output fractal dimension were appropriate are black-boxed, in fields such as medicine and drinking water where an incorrect judgment can have a significant impact, it is problematic that it is not possible to explain to users whether the criteria were appropriate.

[0013] The present invention has been made in consideration of the above problems, and its purpose is to provide a water treatment system and a water treatment method that are capable of coagulation and direct membrane filtration of the water to be treated, and that allow a prediction equation used to derive the fractal dimension of the aggregates to be verified later. [Means for solving the problem]

[0014] In order to achieve the above object, the water treatment system according to the first invention includes an aggregate forming unit that forms aggregates in the water to be treated by adding a coagulant to the water to be treated, an image acquiring unit that acquires a plurality of images of the aggregates formed in the aggregate forming unit, an information derivation unit that derives numerical information on a feature quantity related to the aggregation state of the aggregates from the plurality of images acquired by the image acquiring unit, a fractal dimension prediction unit that applies the feature quantity included in the numerical information derived by the information derivation unit to a prediction formula stored in a storage unit to derive a fractal dimension value for the aggregates, an addition amount adjustment unit that adjusts the amount of coagulant added to the water to be treated based on the fractal dimension value derived by the fractal dimension prediction unit, and a membrane filtration unit that performs membrane filtration on the water to be treated containing the aggregates. The numerical information includes numerically converted information for capturing the aggregates three-dimensionally based on the plurality of images in which the aggregates are captured.

[0015] In the water treatment system, the prediction formula to which the numerical information derived by the information derivation unit is applied is not a black box, so that when the derived fractal dimension value and the amount of flocculant added are verified later, it is possible to verify which part of the prediction formula had a problem. Moreover, the numerical information used by the fractal dimension prediction unit to derive the fractal dimension value for the aggregate, that is, the numerical information on the feature amount related to the aggregation state of the aggregate derived by the information derivation unit, includes digitized information for capturing the aggregate three-dimensionally based on a plurality of images in which the aggregate is captured. Therefore, even if only two-dimensional information is obtained from each of the plurality of images acquired by the image acquisition unit, it is possible to capture the aggregate three-dimensionally by relating the information obtained from these plurality of images. Therefore, the fractal dimension prediction unit is able to derive the value of the fractal dimension based on the aggregate captured three-dimensionally, so that the value of the fractal dimension according to the aggregation state of the aggregate is closer to the three-dimensional shape of the aggregate.

[0016] The second invention is a water treatment system according to the first invention, wherein the image acquisition unit includes a camera that captures an image of the water to be treated containing the aggregates, and the water treatment system further includes at least two markers that are provided within a range of the camera and arranged at different distances from the camera. In this case, the numerical information includes information indicating at least one characteristic amount obtained from an original image of the water to be treated or a processed image obtained by processing the original image, the size of the aggregates, the position of the center of gravity of the area of ​​the floc particles constituting the aggregates, the number of the floc particles, the color or shade or deviation of the color or shade or deviation of the color or shade of the color ... shade of the color or deviation of the color or shade of the color or shade of the color or deviation of the color or shade of the color or shade of the color or deviation of the color or shade of the color or shade of the color or shade of the color or deviation of the color or shade of the color or shade of the color or shade of the color or shade of the color or shade of the color or shade of the color or shade of the color or shade of the color or shade of the color or shade of the color or shade of the color or shade of the color or shade of the color or shade of the color or shade of the color or shade of the color or shade of the color or shade of the color or shade of the color or shade of the color or shade of the color or

[0017] In this aspect, when the numerical information includes information on the size of the aggregate as a feature amount, the feature amount is derived in a state associated with information indicating the degree of focus on at least two markers arranged at different distances from the camera. That is, by referring to information indicating the degree of focus on at least two markers, information indicating which position in the depth direction is in focus is obtained. Therefore, information for three-dimensionally capturing the size, etc. of the aggregate is obtained from each image taken at different focal positions in the depth direction. For example, when information indicating the size of an aggregate when a certain aggregate is photographed multiple times with the focal position changed in the depth direction is referred to, this information can be used as information for three-dimensionally capturing the aggregate in terms of its size. In addition, when information on the position of the areal center of gravity of the flock particles constituting the aggregate is included, this information can be used as information for three-dimensionally capturing the flock particles in terms of their size and shape. In addition, the amount of change in color or shade or deviation of the flock particles between images taken at multiple focal positions different in the depth direction can be used as information for three-dimensionally capturing the aggregate in terms of its shape and density. Furthermore, the change in edge length (perimeter) of a flock particle, or its color, or its shade, or its deviation between images captured at different focal positions in the depth direction can be used as information for three-dimensionally grasping the shape, size, and density of the aggregate. The number of flock particles may be compared between aggregates in different aggregated states. Even in this case, it can be used as information for three-dimensionally grasping each of the aggregates in different aggregated states. The size of the aggregate, the position of the areal center of gravity of the flock particle, the color of the flock particle, or the edge length of the flock particle may also be compared between aggregates in different aggregated states.

[0018] Furthermore, even when at least one of information indicating the size of the agglomerate, information indicating the position of the areal center of gravity of the flock particles that make up the agglomerate, information indicating the color or shade or deviation of the flock particles, and information indicating the edge length or the color or shade or deviation of the flock particles is referenced for a certain focal position, and at least one other of these pieces of information is referenced for a different focal position, the information can be used as information for capturing the agglomerate in three dimensions.

[0019] A third invention is a water treatment system according to the first or second invention, wherein the prediction formula specifies the relationship between the fractal dimension and the feature quantity using at least one of a coefficient and an order representing data weighting.

[0020] In this embodiment, the weighting of the features is expressed by the coefficients and orders in the prediction formula, so that when verifying the derived fractal dimension value and the amount of flocculant to be added later, it is possible to verify which coefficient or order in the prediction formula had a problem.

[0021] A fourth invention is a water treatment system according to any one of the first to third inventions, wherein the addition amount adjustment unit adjusts the addition amount of the coagulant so that the fractal dimension value of the agglomerates is such that the reversible fouling index value when membrane filtration is performed in the membrane filtration unit is 0 g / h or more and less than 33 g / h, and the irreversible fouling index value when membrane filtration is performed in the membrane filtration unit is 0 Pa / h or more and less than 88 Pa / h.

[0022] In this embodiment, reversible fouling and irreversible fouling can be suppressed even when the treated water containing aggregates is directly filtered through a membrane. That is, the amount of accumulation of suspended solids (SS) components on the membrane surface of the membrane module in the membrane filtration section can be suppressed, and clogging of the membrane of the membrane module can be suppressed.

[0023] A fifth invention is a water treatment system according to the fourth invention, wherein the reversible fouling index is an index indicating the amount of change in mass of SS components accumulating per unit time on the membrane surface of a membrane module during membrane filtration by the membrane filtration section.

[0024] A sixth invention is a water treatment system according to the fourth or fifth invention, wherein the irreversible fouling index is an index showing the amount of change per unit time in the transmembrane pressure detected at the earliest timing after repeated physical cleaning of the membrane filtration section.

[0025] A water treatment method according to a seventh aspect of the present invention includes an aggregate formation step of forming aggregates in the water to be treated by adding a coagulant to the water to be treated, an image acquisition step of acquiring a plurality of images of the aggregates formed in the aggregate formation step, an information derivation step of deriving numerical information on feature quantities related to the aggregation state of the aggregates from the plurality of images acquired in the image acquisition step, a fractal dimension prediction step of applying the feature quantities included in the numerical information derived in the information derivation step to a prediction formula stored in a storage unit to derive a value of the fractal dimension of the aggregates, an addition amount adjustment step of adjusting an addition amount of the coagulant to the water to be treated based on the value of the fractal dimension derived in the fractal dimension prediction step, and a membrane filtration step of performing membrane filtration on the water to be treated containing the aggregates. The numerical information includes numerically converted information for capturing the aggregates three-dimensionally based on the plurality of images in which the aggregates are captured.

[0026] An eighth invention is a water treatment method according to the seventh invention, wherein in the image acquiring step, the multiple images of the aggregates are acquired using a camera that captures images of the water to be treated containing the aggregates, and at least two markers that are provided within the range of photography by the camera and positioned at different distances from the camera, and the numerical information includes information indicating at least one characteristic amount among the size of the aggregate, the position of the center of gravity of the area of ​​the floc particles constituting the aggregate, the number of the floc particles, the color or shade or deviation of the floc particles, and the edge length of the floc particles, or the color or shade or deviation of the edge length of the floc particles, which is obtained from an original image of the water to be treated or a processed image obtained by processing the original image, and wherein in the information derivation step, the numerical information is derived from the multiple images captured by the camera at different focal lengths, and in the numerical information, the at least one characteristic amount is associated with a degree of focus for the at least two markers.

[0027] A ninth invention is a water treatment method according to the seventh or eighth invention, wherein the prediction formula specifies the relationship between the fractal dimension and the feature amount using at least one of a coefficient and an order representing data weighting.

[0028] A tenth invention is a water treatment method according to any one of the seventh to ninth inventions, wherein in the addition amount adjustment step, the amount of coagulant added is adjusted so that the fractal dimension value of the agglomerates is such that the reversible fouling index value when membrane filtration is performed in the membrane filtration step is 0 g / h or more and less than 33 g / h, and the irreversible fouling index value when membrane filtration is performed in the membrane filtration step is in the range of 0 Pa / h or more and less than 88 Pa / h. Effect of the Invention

[0029] As described above, according to the present invention, it is possible to perform coagulation direct membrane filtration of the water to be treated, and further, it is possible to derive the fractal dimension of the coagulation using a prediction formula that can be verified later. [Brief description of the drawings]

[0030] [Figure 1] 1 is a diagram illustrating a schematic configuration of a water treatment system according to an embodiment. [Diagram 2] FIG. 2 is a diagram illustrating a schematic configuration of a membrane filtration unit provided in the water treatment system. [Diagram 3] FIG. 2A is a characteristic diagram showing the relationship between the fractal dimension and the irreversible fouling index, and FIG. 2B is a characteristic diagram showing the relationship between the fractal dimension and the reversible fouling index. [Figure 4] FIG. 2 is a diagram for explaining a water treatment method performed by the water treatment system. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0031] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

[0032] <Water treatment system> First, the configuration of a water treatment system 1 according to this embodiment will be described with reference to Figures 1 and 2. The water treatment system 1 adds a flocculant to the water to be treated to flocculate impurities in the water to be treated, and obtains treated water by membrane filtration of the water to be treated containing the flocculates of impurities. Examples of the water to be treated include, but are not limited to, river water, lake water, groundwater, and industrial wastewater.

[0033] As shown in FIG. 1, the water treatment system 1 mainly comprises a coagulation tank 20 for forming coagulants in the water to be treated, a coagulant addition section 10 for introducing a coagulant into the coagulation tank 20, a membrane filtration section 50 for membrane filtering the water to be treated containing the coagulants, and a controller 30.

[0034] The coagulation tank 20 is a tank for coagulating impurities such as organic matter and fine particles contained in the water to be treated, and the water to be treated before coagulation treatment is introduced into the coagulation tank 20. The coagulation tank 20 is provided with an agitation mechanism 21 for agitating the water to be treated stored in the coagulation tank 20.

[0035] The flocculant adding unit 10 adds a flocculant to the water to be treated in the coagulation tank 20. The flocculant adding unit 10 includes a flocculant source 11, an addition line 12 connecting the flocculant source 11 and the coagulation tank 20, and a pump 13 installed on the addition line 12. When the flocculant is added to the water to be treated by the flocculant adding unit 10, impurities contained in the water to be treated are coagulated in the coagulation tank 20 to form coagulants (flocs). That is, a large number of floc particles gather to form coagulants. The flocculant adding unit 10 and the coagulation tank 20 function as a coagulant forming unit that forms coagulants in the water to be treated by adding a coagulant to the water to be treated.

[0036] The pump 13 is configured to be adjustable in rotation speed, and the rotation speed is adjusted by a command from the controller 30 (addition amount control unit 34). This adjusts the addition amount of the flocculant sent to the flocculation tank 20 through the flocculant addition unit 10.

[0037] The type of flocculant is not particularly limited, but examples that can be used include polyaluminum chloride, aluminum sulfate, ferric chloride, ferric sulfate, polysilica, etc. In the flocculation tank 20, the water to be treated after the addition of the flocculant is stirred by the stirring mechanism 21, so that flocs (flocs) are formed in the water to be treated. In addition, the flocculation tank 20 may further be provided with a pH adjuster adding section that adds a predetermined pH adjuster.

[0038] A connection line L1 is connected to the coagulation tank 20. The other end of the connection line L1 is connected to the membrane filtration section 50. The water to be treated, which contains the coagulation material obtained in the coagulation tank 20, is sent to the membrane filtration section 50 through the connection line L1. That is, the coagulation tank 20 is installed in the upstream stage of the membrane filtration section 50, and no settling tank for settling the coagulated flocs is installed between the coagulation tank 20 and the membrane filtration section 50.

[0039] A collection line L2 is connected to the connection line L1 for collecting a portion of the untreated water flowing through the connection line L1. The untreated water that has flowed through the collection line L2 is returned to the coagulation tank 20.

[0040] The collection line L2 is provided with a monitoring unit 24 made of a transparent material, which allows the state of the water to be treated flowing through the collection line L2 to be viewed from the outside. The monitoring unit 24 has a flow path with, for example, a rectangular cross section, and has a front surface 24a and a rear surface 24b that face each other in one direction. The front surface 24a and the rear surface 24b are each made of a transparent flat plate, and are separated by a predetermined distance in the one direction.

[0041] Markers 25a and 25b are provided on the front surface portion 24a and the rear surface portion 24b, respectively. These markers 25a and 25b are arranged so as to be within the imaging range of camera 27, which is arranged on the front surface portion 24a side, when the water to be treated in monitoring unit 24 is imaged by camera 27. Camera 27 constitutes an image acquisition unit that acquires multiple images of the aggregates.

[0042] The camera 27 photographs the monitoring unit 24 in the one direction, and the photographed image is stored in the controller 30. At this time, when the camera 27 focuses on a floc particle or aggregate in the water to be treated, information on the degree of focus on the markers 25a, 25b on the front and back sides is also stored in the controller 30 for each photographed image.

[0043] The membrane filtration unit 50 performs membrane filtration of the water to be treated that contains aggregates, and is configured, for example, by an external pressure type hollow fiber membrane module. Note that the membrane filtration unit 50 is not limited to the hollow fiber membrane module.

[0044] Fig. 2 is a schematic diagram showing the internal configuration of the membrane filtration unit 50. As shown in Fig. 2, the membrane filtration unit 50 includes a hollow fiber membrane bundle 53 having a plurality of bundle-shaped hollow fiber membranes 52, and a housing 51 that accommodates the hollow fiber membrane bundle 53.

[0045] The housing 51 is a hollow cylindrical container that is long in the vertical direction, and includes a cylindrical side portion 51B, an upper surface portion 51A that closes the upper end opening of the side portion 51B, and a lower surface portion 51C that closes the lower end opening of the side portion 51B. A fixing member 54 is provided inside the housing 51. The fixing member 54 is fixed to the side portion 51B, so that the space inside the housing 51 is liquid-tightly divided into two spaces. The water to be treated that has flowed through the connection line L1 is introduced into the first space S1 located below the fixing member 54, as described later. The water to be treated that has passed through the hollow fiber membrane 52 flows into the second space S2 located above the fixing member 54, as described later.

[0046] The lower surface portion 51C of the housing 51 is provided with an inlet port 46 to which the downstream end of the connection line L1 is connected. Therefore, the water to be treated is introduced into the first space S1 through the inlet port 46. A primary side air inlet 45 for introducing air and a drain outlet 47 for drainage are provided in a portion of the side surface portion 51B immediately above the lower surface portion 51C. Therefore, by introducing air into the first space S1 through the primary side air inlet 45, bubbling cleaning of the hollow fiber membrane 52 can be performed. In addition, water can be drained from the first space S1 through the drain outlet 47.

[0047] The upper surface portion 51A is provided with an outlet port 55 for discharging the treated water from the second space S2. The outlet port 55 is connected to the upstream end of the delivery line L3. The treated water that has been membrane filtered in the membrane filtration portion 50 is delivered to a predetermined destination through the delivery line L3.

[0048] The delivery line L3 is provided with a secondary air inlet 48. When the hollow fiber membrane bundle 53 is backwashed, air is introduced into the second space S2 through the secondary air inlet 48 from an air compressor (not shown).

[0049] The hollow fiber membrane bundle 53 is of a one-end free type that is arranged in the first space S1 while being suspended from a fixing member 54. That is, each hollow fiber membrane 52 includes an open upper end 52B and a sealed lower end 52A, and while the upper end 52B is fixed to the fixing member 54, the lower end 52A is not fixed. In the one-end free type hollow fiber membrane bundle 53, each hollow fiber membrane 52 can swing independently, so that turbidity components are less likely to accumulate. In addition, for example, epoxy resin or urethane resin can be used as the resin for sealing the hollow fiber membrane 52. In addition, the hollow fiber membrane bundle is not limited to the one-end free type, and may be a both-end fixed type in which both ends are fixed.

[0050] The upper end 52B opens into the second space S2. Therefore, the space inside the hollow fiber membrane 52 communicates with the second space S2. Therefore, the water to be treated that contains aggregates and flows into the first space S1 flows into the internal space of the hollow fiber membrane 52 through the membrane holes of the hollow fiber membrane 52. At this time, the aggregates are filtered, and the treated water from which the aggregates have been removed flows into the second space S2. In other words, the hollow fiber membrane bundle 53 is of an external pressure filtration type.

[0051] The controller 30 is composed of a microcomputer having a CPU for executing arithmetic processing, a ROM for storing processing programs and data, and a RAM for temporarily storing data, etc. By executing the processing program, the controller 30 functions as an information derivation unit 31, a storage unit 32, a fractal dimension prediction unit 33, and an addition amount control unit 34.

[0052] The information derivation unit 31 derives numerical information on feature quantities related to the aggregation state of the aggregate from a plurality of images captured by the camera 27. Specifically, the feature quantities related to the aggregation state include, for example, the shape, size, density, number, and color of the aggregate or flock particles. The numerical information on the feature quantities is information that indicates, in numerical form, various feature quantities that are input to a prediction formula described later when deriving the value of the fractal dimension of the aggregate. Examples of the feature quantities include the particle diameter of the flock particles, the size of the aggregate, the position of the areal center of gravity of the flock particles that compose the aggregate, the number of flock particles, the color shade of the flock particles, the edge length of the flock particles, and the like. However, it is not necessary for all of these feature quantities to be included in the numerical information, and it is sufficient that at least some of the feature quantities are included.

[0053] In addition, the feature amount included in the numerical information is associated with the degree of focus on both markers 25a and 25b so that the aggregate can be captured three-dimensionally. That is, in each image captured at different focal positions in the depth direction, information on the degree of focus on the marker 25a on the front surface 24a and information on the degree of focus on the marker 25b on the rear surface 24b are associated with information indicating the feature amount included in the numerical information. Since each image is an image captured at different focal positions in the depth direction, it is possible to capture the feature amount related to the aggregation state of the aggregate three-dimensionally by matching the numerical information on the feature amount obtained in each image. Note that each image captured at different focal positions in the depth direction does not mean an image of an aggregate in a different aggregation state, but an image of one aggregate in a certain aggregation state. In other words, it means multiple images of an aggregate having a certain fractal dimension. If the aggregation state is different, the fractal dimension of the aggregate is also different, so that multiple images are obtained for an aggregate in the same aggregation state so that the fractal dimension of the aggregate can be specified. However, images of agglomerates in different aggregation states may also be prepared so that feature quantities can be compared between agglomerates in different aggregation states (aggregates having different fractal dimensions).

[0054] It can be said that the information on the degree of focus on each of the markers 25a, 25b is substituted for information on the focal position of the camera 27 in the depth direction. For this reason, the amount of change in particle diameter of flock particles between images when multiple images are joined together when the focus is set to different positions in the depth direction can be used as information for three-dimensionally capturing the aggregate in terms of the size of the flock particles. Furthermore, the amount of change in size of the aggregate between multiple images when the focus is set to different positions in the depth direction can be used as information for three-dimensionally capturing the size of the aggregate. Furthermore, the amount of change in position of the area center of gravity of the flock particles between multiple images when the focus is set to different positions in the depth direction can be used as information for three-dimensionally capturing the aggregate in terms of the size and shape of the flock particles. Furthermore, the amount of change in a numerical value representing the color shade of the flock particles between multiple images when the focus is set to different positions in the depth direction can be used as information for three-dimensionally capturing the aggregate in terms of the shape and density of the aggregate. In addition, the edge length (perimeter) of the floc particles between multiple images when focused at different positions in the depth direction can be used as information to capture the aggregate in three dimensions in terms of its shape, size and density.

[0055] Furthermore, a comparison may be made between aggregates in different agglomeration states in terms of feature quantities related to the agglomeration state. For example, the number of floc particles may be compared between aggregates in different agglomeration states. In this case, the information can be used to three-dimensionally capture each of the aggregates in different agglomeration states. Similarly, a comparison may be made between aggregates in different agglomeration states in terms of shape, size, density, or color.

[0056] A prediction formula for deriving the value of the fractal dimension of the aggregate is stored in the storage unit 32. This prediction formula is used to calculate the fractal dimension of the aggregate from the feature amount derived by the information derivation unit 31.

[0057] The prediction formula was obtained by a preliminary experiment. In the preliminary experiment, water to be treated containing flocculants with various levels of fractal dimension was prepared by changing the flocculation conditions such as the amount of flocculant added and pH. The water to be treated containing the flocculants was then photographed using an image processing device and a camera (OMRON FH-2050), and the feature values ​​obtained from the image data were quantified. At this time, image processing mounted on the image processing device may be applied to emphasize the feature values. Possible image processing methods used at this time include designation of the processing range, expansion processing of floc particles, contraction processing of floc particles, and color adjustment filter processing.

[0058] "Fractal dimension" is a concept that expresses the overall density of flocs. In Water Research, 114, (2017), 88-103, the relationship between the effective density and size of flocs when kaolin and polyaluminum chloride are used is examined. As the size of the flocs increases, the effective density decreases, and as the size of the flocs decreases, the effective density increases. Fractal dimension is a concept that expresses the slope of the relationship between the size and effective density of flocs. When the fractal dimension is large, flocs with high effective density are likely to be formed regardless of particle size. On the other hand, when the fractal dimension is small, flocs with low effective density are likely to be formed regardless of particle size.

[0059] The fractal dimension (reference fractal dimension) of the water to be treated may be measured using a particle size distribution measuring device. That is, in the particle size distribution measuring device, a laser beam having a wavelength of 650 nm or 405 nm is incident on the water to be treated, and the scattering intensity I and the scattering vector q of the forward small angle scattering at that time are measured. The scattering vector q of the forward small angle scattering is calculated by the following formula (1): q = (2π / λ) sin(θ / 2) (1) where λ is the wavelength of the incident light and θ is the scattering angle. The scattering intensity I and the scattering vector q of small angle forward scattering are expressed by the following equation (2): I(q) ∝ qD (2) For this reason, by plotting the scattering intensity I and the scattering vector q on a log-log graph and linearly approximating them, the slope (D) can be calculated as the reference fractal dimension.

[0060] When a sufficient amount of image data is prepared as image data of the water to be treated, which is associated with a reference fractal dimension value, a prediction formula is created by associating the feature values ​​obtained from the image data with the calculated fractal dimension. The feature values ​​used at this time include the size (area) of the aggregates (aggregated flocs), the position of the area center of gravity of the floc particles, the number of floc particles having a predetermined size, the color of the floc particles and its shade or the deviation of the shade, the edge length or color of the floc particles and its shade, etc., which are obtained from the image data of the original image or the image data of the processed image that has been subjected to at least one type of processing. In addition, parameters obtained as a result of performing arithmetic operations using at least one of these feature values ​​may also be used when creating the prediction formula. Of the feature values, it is preferable to use a combination of the size (area) of the aggregates (aggregated flocs), the number of aggregates, and the shade of the color of the aggregates, etc., obtained from the image data and the image data that has been subjected to one type of processing. More preferably, the number of aggregates having a predetermined size, the number of aggregates having a predetermined size in an image (processed image) where the aggregates have been expanded, the number of aggregates having a predetermined size in an image (processed image) where the aggregates have been contracted, the color intensity of the aggregates, the color intensity of the aggregates in an image (processed image) where the aggregates have been expanded, the color deviation of the aggregates, etc. are preferably used. However, these feature quantities need to be associated with information indicating the degree of focus on the markers 25a and 25b. In addition, in order to summarize to a controllable number of digits, each feature quantity may be multiplied by N or 1 / N (N is a natural number) and used to create the prediction formula. The prediction formula may be expressed, for example, as the number of flock particles x coefficient (or order) + size of flock particle x coefficient (or order) + color intensity of flock particle x coefficient (or order) + focus 1 x coefficient (or order) + focus 2 x coefficient (or order) + offset value. In other words, the prediction formula only needs to clearly state the relationship between the fractal dimension and the quantified feature amount using at least one of a coefficient and an order that indicate the weighting of the data.

[0061] In addition, since the creation of a prediction formula requires the processing of a huge amount of information, machine learning software or the like may be used. Specifically, free software such as Rapid Miner Studio may be used. In creating a prediction formula, it is often more efficient to use machine learning to process big data.

[0062] The fractal dimension prediction unit 33 applies the feature amount included in the numerical information derived by the information derivation unit 31 to a prediction formula to derive the value of the fractal dimension for the aggregate.

[0063] The addition amount control unit 34 controls the pump 13 based on the value of the fractal dimension predicted by the fractal dimension prediction unit 33. The addition amount control unit 34 and the pump 13 constitute an addition amount adjustment unit 37 that adjusts the amount of flocculant added to the coagulation tank 20.

[0064] The storage unit 32 may store correlation data between the fractal dimension of the aggregates and the reversible fouling and irreversible fouling when membrane filtration is performed in the membrane filtration unit 50. For example, the storage unit 32 may store correlation data between the fractal dimension and the irreversible fouling index as shown in Fig. 3(a), and may store correlation data between the fractal dimension and the reversible fouling index as shown in Fig. 3(b).

[0065] The irreversible fouling index is the amount of change per hour in the transmembrane pressure detected at the earliest timing after repeated physical cleaning during membrane filtration operation. In short, it is an index that represents the amount of change in the transmembrane pressure immediately after each repeated cleaning of the hollow fiber membrane. This index is the transmembrane pressure measured immediately after the SS components accumulated on the membrane surface are removed by physical cleaning, so it is the transmembrane pressure in the state where the SS components accumulated on the membrane surface are the least. In other words, it does not indicate the transmembrane pressure due to the fouling components attached to the membrane surface, so it indicates the degree to which the membrane pores themselves are clogged. The irreversible fouling index is preferably 0 Pa / h or more and 88 Pa / h or less, more preferably 0.8 Pa / h or more and 88 Pa / h or less, and most preferably 0.8 Pa / h or more and 44 Pa / h or less. Under conditions where there is clogging of 88 Pa / h or more, stable operation for a long period of time (several months, several years) is impossible. On the other hand, the less the membrane clogging, the more stable the operation, and it is most preferable that there is no clogging (0 Pa / h).

[0066] The transmembrane pressure (TMP) of the hollow fiber membrane module is obtained from the difference between the pressure detected by the primary side pressure sensor P1, which is provided in the connection line L1 and detects the primary side pressure of the membrane filtration section 50, and the pressure detected by the secondary side pressure sensor P2, which is provided in the delivery line L3 and detects the secondary side pressure of the membrane filtration section 50.

[0067] On the other hand, the reversible fouling index is the time change (g / h) (mass change) of the weight of the SS components accumulated in the hollow fiber membrane bundle 53 by the membrane filtration operation. Specifically, when a continuous filtration operation is performed under actual operating conditions, the change in the weight of the SS components adhering to and accumulating in the hollow fiber membrane bundle 53 is measured, and the change per unit time is quantified. The reversible fouling index is preferably less than 33 g / h and 0 g / h or more, more preferably less than 33 g / h and 0.8 g / h or more, and most preferably less than 25 g / h and 0.8 g / h or more. Under conditions where there is an accumulation of 33 g / h or more, stable operation over a long period (several months or years) is impossible. On the other hand, the less the accumulation, the more stable the operation, and no accumulation (0 kg) is most preferable. The reversible fouling index does not necessarily have to be a measurement of the time change in the mass of the module, and another index that has a correlation, such as a change in the amount of accumulation predicted from log data, may be used.

[0068] The addition amount control unit 34 may control the pump 13 so as to obtain a value of the fractal dimension of the aggregates such that the irreversible fouling index falls within a region that satisfies both 0 Pa / h or more and less than 88 Pa / h, and the reversible fouling index falls within a region that satisfies 0 g / h or more and less than 33 g / h. That is, the fractal dimension may be controlled so that the value is preferably 1.9 or more and 2.2 or less, and most preferably 1.93 or more and 2.13 or less. In other words, it has been found that both the reversible fouling index and the irreversible fouling index are highly correlated with the fractal dimension of the aggregates. Therefore, by using the fractal dimension of the aggregates as an index, it is possible to operate in the preferred ranges of the reversible fouling index and the irreversible fouling index.

[0069] <Water treatment method> Next, a water treatment method using the water treatment system 1 according to this embodiment will be described with reference to FIG.

[0070] First, a flocculant is added to the water to be treated that contains impurities to form flocculants (flocculant formation step, step ST10). Specifically, the pump 13 of the flocculant addition unit 10 is operated at a predetermined rotation speed to add the flocculant to the water to be treated in the flocculation tank 20. The water to be treated is then stirred by the stirring mechanism 21 to cause the impurities to flocculate, and flocculants (flocs) are formed in the water to be treated.

[0071] The water to be treated in which flocculants have been formed in the flocculation tank 20 is sent to the membrane filtration section 50 through the connection line L1. This water to be treated is membrane filtered in the membrane filtration section 50 (membrane filtration step, step ST20). The treated water filtered in the membrane filtration section 50 is sent to a demand destination through the delivery line L3. In this method, after a flocculant is added to the water to be treated, the water to be treated is directly membrane filtered without performing a precipitation operation for the flocculants.

[0072] A part of the water to be treated flowing through the connection line L1 flows into the collection line L2. In the monitoring section 24 of the collection line L2, the camera 27 acquires image data of the water to be treated flowing through the monitoring section 24 (image acquisition step, step ST31). This image data is sent to the controller 30. At this time, the camera 27 captures multiple images of the same aggregate by changing the focal length.

[0073] The information derivation unit 31 of the controller 30 derives numerical information on feature quantities related to the shape, size, and density of the aggregate from a plurality of images among the acquired image data (information derivation step, step ST32). The derived numerical information is stored in the controller 30. The task of deriving the numerical information in this step ST32 may be performed manually, or the controller 30 may be configured to derive the numerical information from the images.

[0074] The numerical information is information that indicates various feature quantities numerically, and examples of the feature quantities include the particle diameter of the floc particles, the size of the aggregate, the position of the areal center of gravity of the floc particles that compose the aggregate, the number of floc particles, the color shade of the floc particles, the edge length of the floc particles, etc. These feature quantities are derived in association with the degree of focus on both markers 25a and 25b.

[0075] The fractal dimension prediction unit 33 applies the feature amount included in the numerical information derived by the information derivation unit 31 to the prediction formula stored in the storage unit 32 to derive the value of the fractal dimension of the aggregate (fractal dimension prediction step, step ST33).

[0076] Based on the value of the fractal dimension of the aggregates derived in step ST33, the amount of the flocculant added to the water to be treated by the flocculant adding unit 10 is adjusted (addition amount adjustment step, step ST34). In this step ST34, the addition amount control unit 34 controls the pump 13 of the flocculant adding unit 10 so that the value of the fractal dimension of the aggregates falls within a region where the value of the reversible fouling index when membrane filtration is performed in the membrane filtration unit 50 is 0 g / h or more and less than 33 g / h, and the value of the irreversible fouling index when membrane filtration is performed in the membrane filtration unit 50 is 0 Pa / h or more and less than 88 Pa / h.

[0077] Then, when a predetermined filtration time has elapsed (YES in step ST40), the filtration operation is temporarily stopped, and the membrane filtration section 50 is backwashed (step ST41). In this step ST41, the hollow fiber membrane module is backwashed by pressurizing air from the secondary side. Specifically, air is introduced into the housing 51 from the secondary side air inlet 48 (FIG. 2), and the treated water in the second space S2 is pushed into the first space S1 by pressurizing air. In this way, by using air in the backwashing, not only is the water recovery rate increased and running costs reduced, but the cleaning effect (the effect of peeling off SS from the membrane surface) can also be improved. Thereafter, bubbling cleaning or the like is further performed in the first space S1, and then the filtration operation is resumed.

[0078] As described above, in this embodiment, the prediction formula to which the numerical information derived by the information derivation unit 31 is applied is not a black box, so that when the derived fractal dimension value and the amount of flocculant added are verified later, it is possible to verify which part of the prediction formula had a problem. Moreover, the numerical information on the feature amount related to the aggregation state of the aggregate by the information derivation unit 31, which is used when the fractal dimension prediction unit 33 derives the fractal dimension value of the aggregate, includes quantified information for capturing the aggregate three-dimensionally based on a plurality of images in which the aggregate is captured. Therefore, even if only two-dimensional information is obtained from each of the plurality of images acquired by the camera 27, it is possible to capture the aggregate three-dimensionally by relating the information obtained from these plurality of images. Therefore, the fractal dimension prediction unit 33 is able to derive the value of the fractal dimension based on the aggregate captured three-dimensionally, so that the value of the fractal dimension according to the aggregation state of the aggregate becomes closer to the three-dimensional shape of the aggregate.

[0079] In this embodiment, information indicating the size of the aggregate is associated with information indicating the degree of focus on the two markers 25a, 25b arranged at different distances from the camera 27. That is, by referring to information indicating the degree of focus on the two markers 25a, 25b, information indicating which position in the depth direction is in focus is obtained, and information indicating the size of the aggregate at this position is obtained. Therefore, information for capturing the size of the aggregate three-dimensionally is obtained from each image taken at different focal positions in the depth direction. For example, information for capturing the size of the aggregate three-dimensionally is obtained from each image taken at different focal distances in the depth direction.

[0080] In addition, in this embodiment, the prediction formula expresses the weighting of features by coefficients or orders, so that when later verifying the derived fractal dimension value and the amount of flocculant to be added, it is possible to verify which coefficient or order in the prediction formula had a problem.

[0081] In this embodiment, the addition amount control unit 34 controls the pump 13 so that the fractal dimension of the aggregates is such that the reversible fouling index value during membrane filtration is 0 g / h or more and less than 33 g / h, and the irreversible fouling index value is 0 Pa / h or more and less than 88 Pa / h. Therefore, even when the treated water containing aggregates is directly subjected to membrane filtration, reversible fouling and irreversible fouling can be suppressed. That is, the amount of accumulation of SS (Suspended Solid) components on the membrane surface of the membrane module in the membrane filtration unit 50 can be suppressed, and clogging of the membrane of the membrane module can be suppressed.

[0082] <Example 1> Here, an example will be described in which the water to be treated was actually treated and a prediction equation was created.

[0083] In Example 1, surface water of the Takahashi River was used as the water to be treated. First, the flocculation conditions, such as the amount of flocculant added and pH, were changed to prepare water of various fractal dimensions as the water to be treated. Specifically, the amount of flocculant added to the water to be treated in the flocculation tank 20 was changed by changing the rotation speed of the pump 13 of the flocculant addition unit 10. Alternatively, the pH was changed by changing the amount of pH adjuster added to the water to be treated in the flocculation tank 20 by changing the rotation speed of the pump of the pH adjustment unit. Then, the water to be treated was stirred by the stirring mechanism 21 to form flocs (flocs) of each flocculation condition in the water.

[0084] Next, the fractal dimension, which serves as a reference for each aggregate, was measured for the treated water containing aggregates under each aggregation condition using a particle size distribution measuring device. Specifically, a SALD-7500nano (Shimadzu Corporation) was used as the particle size distribution measuring device. This device can measure the angle and scattered light intensity of the light scattered when a laser beam is irradiated onto the aggregated flocs contained in the treated water. From this data, the fractal dimension of the aggregates under each aggregation condition can be calculated. This calculation result can be said to be the true fractal dimension, and is therefore used as the fractal dimension (reference).

[0085] In addition, for the same water to be treated as that for which the reference fractal dimension was measured, the aggregates contained in the water to be treated were photographed by a camera, and the feature quantities of the aggregates obtained from the photographed image data were quantified. Specifically, the water to be treated was photographed using an OMRON FH-2050 camera. At this time, the same aggregate was photographed at multiple different focal positions in the depth direction, and multiple images of the aggregate were obtained. Then, the feature quantities of the aggregates obtained from the photographed image data were quantified.

[0086] The parameters representing the feature amounts of the aggregates were recorded as follows: size of the aggregate, number of aggregates having a predetermined size, number of floc particles, position of the areal center of gravity of the floc particles, number of aggregates having a predetermined size in an image (processed image) in which the aggregate (aggregated floc) was expanded, number of aggregates having a predetermined size in an image (processed image) in which the aggregate was contracted, color intensity of the aggregate, color intensity of the aggregate in an image (processed image) in which the aggregate was expanded, color deviation of the aggregate, edge length of the floc particles or its color or its shade or its deviation, and focus with respect to the markers 25a and 25b. When creating a prediction formula using at least a part of each of the recorded parameters, each parameter value was multiplied by 1 / 1000.

[0087] Next, a linear regression equation (prediction equation) was created based on the data obtained above using a machine learning trained model. Specifically, using free software Rapid Miner Studio, the fractal dimension (reference) of the treated water was linked to each of the above parameters obtained from the image of the treated water for all of the prepared treated water and input. Based on the input data, the software created a linear regression equation (prediction equation) that estimates the fractal dimension from the feature amount of the aggregates.

[0088] As a result, the following correlation equation 1 was obtained.

[0089]

number

[0090] Here, "number" refers to the number of flock particles whose cross-sectional area in a cross section including the focal position at the time of shooting is the number of pixels of the stated value (for example, "number of 6", it is 6 pixels). Note that, since the parameter value is multiplied by 1 / 1000 when creating the prediction formula, if the number of flock particles on the image is 1000, the number of block particles in the prediction formula will be 1. "Expansion" refers to an image that has been expanded, and "contraction" refers to an image that has been contracted. Focus 1 and 2 are the focus points for the markers 25a and 25b, "average density" refers to the average density of the color of the aggregate, and "average deviation" refers to the average deviation of the color of the aggregate. For example, "expansion 6 number" refers to the number of flock particles whose cross-sectional area in a cross section including the focal position at the time of shooting is 6 pixels in the expanded image.

[0091] When the error between the fractal dimension obtained by this correlation equation 1 and the fractal dimension (standard) measured using a particle size distribution measuring device was examined, the RSME (root mean square error) was 0.028 ± 0.002. In other words, it can be predicted with an error of 0.1 or less.

[0092] <Example 2> A more accurate fractal dimension prediction formula was created using parameters representing more feature quantities from the data used in Example 1. As a result, the following correlation formula 2 was obtained.

[0093]

number

[0094] Here, "number" refers to the number of floc particles whose cross-sectional area in a cross section including the focal position at the time of shooting is the number of pixels of the stated value (for example, "number of 6" is 6 pixels), "expansion" refers to an image after expansion processing, "contraction" refers to an image after contraction processing, "area" refers to the cross-sectional area of ​​the floc particles in a cross section including the focal position at the time of shooting, "concentration" refers to the color intensity of the aggregate, focus 1 and 2 refer to the focus of markers 25a and 25b, "average concentration" refers to the average color intensity of the aggregate, and "average deviation" refers to the average deviation of the color of the aggregate. For example, "number of expanded 6" refers to the number of floc particles whose cross-sectional area in a cross section including the focal position at the time of shooting is 6 pixels in the expanded image.

[0095] When the error between the fractal dimension obtained by this correlation equation 2 and the fractal dimension (standard) measured using a particle size distribution measuring device was examined, the RSME (root mean square error) was 0.015 ± 0.001. In other words, it can be predicted with an error of 0.1 or less.

[0096] <Example 3> In Example 3, industrial water received at the Kashima Plant of Kuraray Co., Ltd. was used as the water to be treated. First, flocculation conditions such as the amount of flocculant added and pH were changed to prepare water of various fractal dimensions as the water to be treated. Specifically, raw water was collected in a laboratory and weighed, and the amount of flocculant added to this raw water was changed, or the amount of pH adjuster added was changed to change the pH. Then, the water to be treated was stirred by the stirring mechanism 21 to form flocs (flocs) of each flocculation condition in the water.

[0097] Next, the fractal dimension (reference) of each aggregate was measured for the treated water containing aggregates under each aggregation condition using a particle size distribution measuring device. Specifically, a SALD-7500nano (Shimadzu Corporation) was used as the particle size distribution measuring device. This device can measure the angle and scattered light intensity of the light scattered when a laser beam is irradiated onto the aggregates contained in the treated water. From this data, the fractal dimension of the aggregates under each aggregation condition can be calculated. This calculation result can be said to be the true fractal dimension, and is therefore used as the reference fractal dimension.

[0098] In addition, for the same water to be treated as that for which the reference fractal dimension was measured, the aggregates contained in the water to be treated were photographed by a camera, and the feature quantities of the aggregates obtained from the photographed image data were quantified. Specifically, the water to be treated was photographed using an OMRON FH-2050 camera. At this time, the same aggregate was photographed at multiple different focal positions in the depth direction, and multiple images of the aggregate were obtained. Then, the feature quantities of the aggregates obtained from the photographed image data were quantified.

[0099] As parameters representing the feature amount of the aggregates, the number of aggregates having a predetermined size (e.g., 4 pixels, 6 pixels, 8 pixels, 10 pixels, etc.), the number of aggregates having a predetermined size in the image subjected to expansion processing, the number of aggregates having a predetermined size in the image subjected to contraction processing, the color intensity of the aggregates, and the average color intensity of the aggregates were recorded. When creating a prediction formula using each of these recorded parameters, each parameter value was multiplied by 1 / 1000.

[0100] Next, a linear regression equation (prediction equation) was created based on the data obtained above using a machine learning trained model. Specifically, using free software Rapid Miner Studio, the fractal dimension (reference) of the treated water was linked to each of the above parameters obtained from the image of the treated water for all of the prepared treated water and input. Based on the input data, the software created a linear regression equation (prediction equation) that estimates the fractal dimension from the feature amount of the aggregates, and the following correlation equation 3 was obtained.

[0101]

number

[0102] Furthermore, both the data of the water to be treated obtained in Example 3 and the data of the water to be treated obtained in Example 1 were input into the software, and a prediction equation was created in the same manner. As a result, the following correlation equation 4 was obtained.

[0103]

number

[0104] Here, "number" refers to the number of flock particles whose cross-sectional area in a cross section including the focal position at the time of shooting is the number of pixels of the stated value (for example, "number of 6" is 6 pixels). "Expansion" refers to an image that has been expanded, and "contraction" refers to an image that has been contracted. "Area" refers to the cross-sectional area of ​​the flock particles in a cross section including the focal position at the time of shooting. "Concentration" refers to the color intensity of the aggregate, focus 1 and 2 are the focus points relative to markers 25a and 25b, "average concentration" refers to the average color intensity of the aggregate, and "average deviation" refers to the average deviation of the color of the aggregate. For example, "expansion number of 6" indicates that the flock particles occupy 6 pixels in the expanded image.

[0105] When the error between the fractal dimension obtained by this correlation equation 4 and the true fractal dimension measured using a particle size distribution measuring device was examined, the RSME (root mean square error) was 0.080 ± 0.014. In other words, it can be predicted with an error of 0.1 or less.

[0106] It should be noted that the embodiments disclosed herein are illustrative in all respects and should not be considered restrictive. The present invention is not limited to the above-described embodiments, and various modifications and improvements are possible without departing from the spirit of the present invention. For example, in the above-described embodiment, one camera 27 and two markers 25a and 25b are used to obtain information on the depth direction of the aggregate in the image, but instead, two cameras may be used to photograph the aggregate from different directions to obtain information on the depth direction of the aggregate. [Explanation of symbols]

[0107] 1: Water treatment system 25a: Marker 25b: Marker 27: Camera 31: Information derivation part 32: Storage section 33: Fractal dimension prediction section 37: Addition amount adjustment section 50: Membrane filtration section ST10: Aggregation formation step ST31: Image acquisition step ST32: Information derivation step ST33: Fractal dimension prediction step ST34: Addition amount adjustment step ST20: Membrane filtration step

Claims

1. An aggregate forming section that forms aggregates in the water to be treated by adding a flocculant to the water to be treated; an image acquisition unit that acquires a plurality of images of the aggregate formed in the aggregate formation unit; an information derivation unit that derives numerical information on a feature amount related to an aggregation state of the aggregate from the plurality of images acquired by the image acquisition unit; a fractal dimension prediction unit that applies the feature amount included in the numerical information derived by the information derivation unit to a prediction formula stored in a storage unit to derive a value of a fractal dimension for the aggregate; An addition amount adjustment unit that adjusts the amount of flocculant added to the water to be treated based on the value of the fractal dimension derived by the fractal dimension prediction unit; A water treatment system comprising: a membrane filtration unit that performs membrane filtration of the water to be treated containing the aggregates, The image acquisition unit includes a camera that captures an image of the treatment water containing the aggregates, The water treatment system further includes at least two markers provided within a range of the camera and arranged at different distances from the camera; the numerical information includes digitized information for capturing the aggregate in three dimensions based on the plurality of images capturing the aggregate, The numerical information includes, as the digitized information, information indicating at least one characteristic amount among the size of the aggregate, the position of the areal center of gravity of the floc particles constituting the aggregate, the number of the floc particles, the color or shade or deviation of the color of the floc particles, and the edge length, color, shade or deviation of the color of the floc particles, which are obtained from an original image of the water to be treated or a processed image obtained by processing the original image, The information derivation unit derives the numerical information from the plurality of images taken by the camera at different focal lengths of the aggregate; A water treatment system, wherein in the numerical information, the at least one feature is associated with a degree of focus for the at least two markers.

2. The water treatment system according to claim 1 , wherein the prediction formula specifies a relationship between the fractal dimension and the feature amount using at least one of a coefficient and an order representing weighting of data.

3. An aggregate forming section that forms aggregates in the water to be treated by adding a coagulant to the water to be treated; an image acquisition unit that acquires a plurality of images of the aggregate formed in the aggregate formation unit; an information derivation unit that derives numerical information on a feature amount related to an aggregation state of the aggregate from the plurality of images acquired by the image acquisition unit; a fractal dimension prediction unit that applies the feature amount included in the numerical information derived by the information derivation unit to a prediction formula stored in a storage unit to derive a value of a fractal dimension for the aggregate; An addition amount adjustment unit that adjusts the amount of flocculant added to the water to be treated based on the value of the fractal dimension derived by the fractal dimension prediction unit; A membrane filtration unit that performs membrane filtration of the water to be treated containing the aggregates, the numerical information includes digitized information for capturing the aggregate in three dimensions based on the plurality of images capturing the aggregate, The water treatment system, wherein the addition amount adjustment unit adjusts the amount of the coagulant added so that the fractal dimension value of the agglomerates is such that the reversible fouling index value when membrane filtration is performed in the membrane filtration unit is 0 g / h or more and less than 33 g / h, and the irreversible fouling index value when membrane filtration is performed in the membrane filtration unit is 0 Pa / h or more and less than 88 Pa / h.

4. The water treatment system according to claim 3 , wherein the reversible fouling index is an index indicating a mass change amount of SS components accumulating on a membrane surface of a membrane module per unit time during membrane filtration by the membrane filtration section.

5. The water treatment system according to claim 3 , wherein the irreversible fouling index is an index indicating a change per unit time in a transmembrane pressure detected at the earliest timing after physical cleaning is repeatedly performed on the membrane filtration section.

6. An aggregate formation step of forming aggregates in the treated water by adding a coagulant to the treated water; an image acquisition step of acquiring a plurality of images of the aggregate formed in the aggregate formation step; an information derivation step of deriving numerical information on a feature quantity related to an aggregation state of the aggregate from the plurality of images acquired in the image acquisition step; a fractal dimension prediction step of applying the feature amount included in the numerical information derived in the information derivation step to a prediction formula stored in a storage unit to derive a value of the fractal dimension of the aggregate; an addition amount adjustment step of adjusting an addition amount of a flocculant to the water to be treated based on the value of the fractal dimension derived in the fractal dimension prediction step; A membrane filtration step of membrane-filtering the treated water containing the aggregates, In the image acquisition step, a camera is used to capture an image of the water to be treated containing the aggregates, and at least two markers are provided within the range of the camera and positioned at different distances from the camera to capture the images of the aggregates, the numerical information includes digitized information for capturing the aggregate in three dimensions based on the plurality of images capturing the aggregate, The numerical information includes, as the digitized information, information indicating at least one characteristic amount among the size of the aggregate, the position of the areal center of gravity of the floc particles constituting the aggregate, the number of the floc particles, the color or shade or deviation of the color of the floc particles, and the edge length, color, shade or deviation of the color of the floc particles, which are obtained from an original image of the water to be treated or a processed image obtained by processing the original image, In the information deriving step, the numerical information is derived from the plurality of images taken by the camera at different focal lengths; A water treatment method, wherein in the numerical information, the at least one feature amount is associated with a degree of focus for the at least two markers.

7. The water treatment method according to claim 6 , wherein the prediction formula specifies a relationship between the fractal dimension and the feature amount using at least one of a coefficient and an order representing weighting of data.

8. A flocculation formation step of forming flocs in the treated water by adding a flocculant to the treated water; an image acquisition step of acquiring a plurality of images of the aggregate formed in the aggregate formation step; an information derivation step of deriving numerical information on a feature quantity related to an aggregation state of the aggregate from the plurality of images acquired in the image acquisition step; a fractal dimension prediction step of applying the feature amount included in the numerical information derived in the information derivation step to a prediction formula stored in a storage unit to derive a value of the fractal dimension of the aggregate; an addition amount adjustment step of adjusting an addition amount of a flocculant to the water to be treated based on the value of the fractal dimension derived in the fractal dimension prediction step; A membrane filtration step of membrane-filtering the treated water containing the aggregates, the numerical information includes digitized information for capturing the aggregate in three dimensions based on the plurality of images capturing the aggregate, A water treatment method in which the amount of the coagulant added is adjusted in the addition amount adjustment step so that the fractal dimension value of the agglomerates falls within a range in which the reversible fouling index value when membrane filtration is performed in the membrane filtration step is 0 g / h or more and less than 33 g / h, and the irreversible fouling index value when membrane filtration is performed in the membrane filtration step is 0 Pa / h or more and less than 88 Pa / h.

Images