JPH01266813A - Device for controlling injector of flocculant - Google Patents

Abstract

PURPOSE:To maintain high settling efficiency by watching the water temp., alkalinity, turbidity, pH, etc., as the water quality, watching the flocculating amt., density, and number of flocs as the feature quality of the floc image, and controlling the amt. of flocculant to be injected. CONSTITUTION:Raw water is introduced into a rapid mixing basin 10. The temp., alkalinity, turbidity, pH, etc., of the raw water are measured by a water quality meter 5. The flocculant stored in a flocculant tank 11 is supplied by a flocculant injection pump 12. The water added with the flocculant and agitated is introduced into a flocculation tank 15. The flocs grown in the flocculation tank 15 are settled in a settling basin 16, and the supernatant liq. is filtered by a filter basin 17. A floc image pickup device 18 in a flocculating basin 15C is set at a position where collision with an agitating paddle 17C can be avoided, and the image is processed by an image processor 40. The injection rate of a flocculant is determined by the signals from the water quality meter 5 and the image processor 40.

Description

[Detailed Description of the Invention] [Industrial Application Field] The present invention is directed to controlling the injection of a flocculant in the flocculation process of suspended solids in water treatment plants, sewage treatment plants, and other industrial wastewater treatment. Regarding equipment.

[Conventional technology]

In water treatment, in the flocculation process, suspended substances are flocculated (by adding a flocculant) into flocs (hereinafter referred to as flocs), which are then removed. If +9ffi substances do not coagulate, turbidity flows out from the settling basin.

Since the quality of the inflow water fluctuates, the state of floc formation changes accordingly. Water quality factors that affect floc formation include water temperature, turbidity, particle size, and pH.

Alkalinity and other factors are known to be involved, as well as operating factors such as the amount of coagulant injected and the number of rotations of the stirring paddle, but the causal relationship between these factors has not been completely elucidated.

Therefore, flocculant injection control methods based on these factors have not yet achieved sufficient performance.

Therefore, it is essential to monitor whether or not flocs are actually formed, and the inventors have already devised a floc monitoring device using image processing. For example, the patent application 1986-23
At 1750, the floc density is calculated based on the turbidity and floc image monitoring results.

We have devised a method to automatically inject flocculant based on this density.

[Problem to be solved by the invention]

In Japanese Patent Application No. 59-231750, the density of flocs is calculated and a flocculant is automatically injected based on this density, but the inventors also conducted repeated research for practical application in order to achieve good floc formation. Ta. As a result, it was found that the sedimentation efficiency in the sedimentation tank could be maintained at a high level by injecting a flocculant based on the water quality and floc formation state.

[Means to solve the problem]

Water quality focuses on water temperature, alkalinity, turbidity, and pH.

In the image of flocs, m and t are the amount of floc formation and density.

The number of flocculants is determined by paying attention to these and injecting just the right amount of flocculant.

[Effect]

In the present invention, the degree to which water quality affects the amount of flocculant injection and the degree to which the feature amount of the floc image influences the amount of flocculant injection is quantified, and the flocculant injection rate is controlled based on this.
Just the right amount of flocculant is always injected, and as a result, the flocs can be efficiently settled in the settling tank.

〔Example〕

An embodiment of the present invention will be described with reference to the following drawings. The configuration and operation of the first section will be explained below. In FIG. 1, raw water from a river or lake (not shown) is introduced into a rapid mixing pond 10. A water quality meter 5 for measuring the quality of this raw water and a water quality meter 6 for measuring the water quality of the settling pond are installed. What about water quality?

These include water temperature, turbidity, alkalinity, pH, electrical conductivity, and residual chlorine concentration. a liquid polymer flocculant (polyaluminum chloride) stored in the flocculant tank 11;
Alternatively, an inorganic flocculant such as aluminum sulfate is supplied by a flocculant injection pump 12. Although not shown, calcium hydroxide or
An alkaline agent such as sodium carbonate is injected. In the rapid mixing basin 1o, stirring blades 14 are used to stir the mixture by a stirrer 13. This stirring causes the flocculant to diffuse into the raw water. Suspended particles are negative colloids whose particle surfaces are negatively charged, and a positively charged flocculant binds (agglomerates) the numerous suspended particles to each other. The residence time in the rapid mixing tank 100 is from 1 minute to 10 minutes, and during this time the suspended fine particles are aggregated to form micro flocs (floc cores) with a particle size of 10 to 100 μm.

The water into which the flocculant has been injected and stirred is led to a flocculation tank (hereinafter referred to as "floc formation pond") 15.

The floc formation pond 15 has three ponds 15A, 15B and 15.
rectifying walls 16A and 1 having a plurality of holes on the wall surface;
Separated by 6B. Wooden stirring paddles 17A, 17B and 17G are installed in the floc formation ponds 15A, 15B and 15C, respectively, and rotate gently at around 1 to IQ rpm (paddle peripheral speed = 0.15-0, sm/s). . The residence time in the floc formation ponds 15A, 15B and 15C is 5 to 15 minutes each (total of 15 to 45 minutes in three ponds).
minute). Since the flocculant is sufficiently supplied in the rapid mixing basin 100 and the flocculant is attached to the surface of the microflocs, the microflocs in the flocculation basin 15 collide or come into contact with each other due to stirring and coagulate. While the particles remain in the floc formation pond 15 for 15 to 45 minutes and are stirred, they grow into aggregates (hereinafter referred to as "puff flocs") having a particle size of 100 to 5000 μm.

The grown flocs settle in a sedimentation basin 16, and the supernatant liquid is filtered in a filtration basin 17. In the floc formation pond 15c, an aggregate imaging means 18 such as an underwater camera is installed at a position where the stirring paddle 17G does not collide with the floc formation pond 15c. The analog electrical signal of the grayscale image of the aggregate obtained from the aggregate imaging means 18 is subjected to image processing by the image processing device 4o. The signal obtained by the aggregate imaging means 18 is converted by an A/D converter (not shown), but in FIG.
Transducer not shown.

The image processing device 40 and the system processor 42 are connected by a system bus 52. An external storage device 54, such as a floppy disk, is connected to system bus 52. External storage device 54 stores data processed by system processor 42. A signal from the water quality meter 5 is input to the input/output boat 56 and sent to the system processor 42 . On the other hand, the signal obtained by the image processing device 40 is sent to the system processor 42 . system processor 4
2 determines the flocculant injection rate based on the signal from the water quality meter 5 and the signal obtained by the image processing device 40. In this way, the flocculant injection rate control signal obtained by the signal processing operation of the system processor 42 is sent to the actuator 12A to operate the flow rate of the flocculant injection pump 12.

Further, the knowledge base 60 and the inference mechanism 61 are connected to the system bus S
2 and is used to control the flocculant injection rate.

The image processing device 40 has a histogram processing function.

It consists of an image processing unit 401 having a labeling processing function, a feature extraction function, a convolution function, and other image processing functions, and one image storage unit 403 and 405. The image storage unit (a-light side image memory) 403 is a memory for the gray scale image obtained by the camera 38, and the image storage unit (binary image memory) 405 stores the binary image processed by the image processing unit 401. This is memory for storing data. Assuming that one screen image obtained by the aggregate imaging means 18 is composed of 256 pixels x 256 pixels,
The grayscale image storage unit 403 has a storage capacity of 256 pixels x 256 pixels x 8 bits.

The binary image storage unit 405 has a storage capacity of 256 pixels x 256 pixels x 1 bit. The image processing apparatus used by the inventors has four grayscale image storage units 403 and four binary image storage units 405 each.

FIG. 2 shows the processing steps of the aforementioned processing apparatus (consisting of 40 and 42). This flowchart shows the image processing device 4.
0 and system processor 42 processing operations. First, the processing start time and processing frequency are set using the keyboard 44. Based on this, the timer 501 sets a processing start time, and when this time comes, the following processing is executed. This frequency is approximately once every 5 minutes to once every hour. First, the number of repetitions n is set to 0 in step 402.

n is increased by one in step 504. Next, in step 506, the a light side image is read into the grayscale image memory 403. Next, in step 508, the grayscale image is converted to a binary image. The converted binary image is stored in the binary image memory 40.
It is stored in 5. The binarization method will be explained later. The grayscale image and the binary image are displayed on the monitor television 5o.

In step 51-0, a process (labeling process) of assigning a number to each binarized flock image is performed. In step 512, the area of each labeled floc is calculated. Here, the area is calculated from the number of pixels. The area calculation result is sent to the system processor 42.
Store it in memory located in . The subsequent steps are calculated by the system processor 42. Step 514
In step 516, the diameter is calculated from the area, and then in step 516, the volume is calculated from the diameter. These diameter and volume calculations are stored in memory located in system processor 42. In step 518, it is determined whether the number of repetitions n is a predetermined value M or not. The process returns to step 504 until the process is repeated a predetermined number of times M. If the number of repetitions n exceeds M, the process proceeds to step 520 and the particle size distribution is calculated. The calculation results are stored in memory located in system processor 42.

Particle size distribution is a calculation of how many flocs (expressed by volume) are included in the width of the corresponding particle size. In step 522, the floc volume of the particle size distribution is integrated to calculate the total floc formation amount Vf. Further, step 524 is a representative particle size (log mean diameter, etc.) of the particle size distribution.
Calculate.

In step 526, the floc formation amount vf calculated in step 522 and the turbidity Tui measured by the water quality meter 5B are determined.
The value ρ corresponding to the floc density is calculated using the value of ρ. In step 528, the flocculant injection rate C is calculated based on the floc density equivalent value ρ, the floc formation amount Vf, the water temperature O1 obtained by the water quality analyzer 5A, and the alkalinity AQ obtained by the water quality analyzer 5C.

Note that the values of the water quality meters 5B and 5C are sent from the input/output port 56 to the system processor 42 via the system bus 52. In step 530, the settling tank outlet turbidity Tus is obtained by the water quality meter 6 and the flocculant injection rate is corrected. A corrected flocculant injection rate control signal is sent from system bus 52 via input/output port 56 to actuator 12A to manipulate the flow rate of flocculant injection pump 12.

Note that the particle size distribution, the floc density equivalent value ρ, and the floc formation amount Vf stored in the memory of the system processor 42
, water temperature θ obtained with water quality meter 5A, alkalinity AQ obtained with water quality meter 5C, and settling tank outlet turbidity T obtained with water quality meter 6.
Values such as us are displayed on the display 46 and stored in the external storage device 54 as necessary.

Next, the information processing operation will be explained in detail using FIG.

In step 508, the grayscale image is converted to a binary image by first emphasizing the luminance portion of the flock, then making the bright portion the flock and the dark portion the background. A description of a method for selectively emphasizing the luminance of flocks is omitted in the present invention. Here, binarization refers to the process of converting the pixels of the flock to "I IT" and all the pixels of the background to "It OIT". The converted binary image is stored in the binary image memory 405. In step 512, labeling is performed. The area of each flock is calculated from the number of pixels of its LL i IT.In other words, since the area of one pixel is subtracted from the magnification factor in advance, multiplying the pixel by this area yields the area of the flock Si (i= 1 to the number of flocs).In step 514, assuming a circle with area Si,
) Calculate the diameter di using the formula.

di=f (77) (1) In step 516, the volume v1 is calculated from the diameter di using the equation (2).

Vi=π(di)3/6...
(2) In step 520, the floc particle size is classified to calculate the particle size distribution. The classification width is 0.1 mm, but this width may be set arbitrarily and can be changed depending on the magnification. In addition, the representative particle size of the distribution is calculated from the particle size distribution. For example, the use of a calculated mean diameter or a logarithmic mean diameter (geometric mean diameter) has already been made clear in Japanese Patent Application No. 82953/1983.

In step 522, the floc volume of the particle size distribution is integrated, and the total floc formation 11Vf is calculated using equation (3).

Vf=ΣVi...(3
) In step 526, a value ρ corresponding to the density of flocs is calculated using equation (4) using the floc formation amount Vf calculated in step 522 and the value of turbidity Tui measured by the water quality meter 5B.

p=Tui/Vf”
・(4) Note that the turbidity Tui is output as a value converted to kaolin, but in order to actually calculate the weight of suspended solids, the weight of the suspended solids is calculated using the following formula, taking into account the amount of flocculant that has become flocs. Quality weight M
Calculate f.

Mf=a-Tui+b-C...(5) Here, a, b
is a predetermined coefficient set in advance, and when formula (5) is used, formula (4) becomes formula (6).

ρ=Mui/Vf...
- (6) Since the calculation of equation (6) uses suspended solid weight instead of turbidity, the density equivalent value can be calculated more accurately.

Next, in step 528, the floc density equivalent value ρ, the floc formation amount Vf, the water temperature θ obtained by the water quality meter 5A, and
Based on the alkalinity AQ obtained by the water quality meter 50, the flocculant injection rate C is calculated using equation (7). Note that equation (7) was derived by statistically analyzing long-term data.

C=-0,50+0.5AQ+0.8Vf+0.5+ρ
-(7) Here, each value on the right side is 0. Weighting coefficients for Afl, Vf and ρ. Also, 0. A5Vf and ρ
The values are standardized so that the annual average value is 0 and the standard deviation is 1. Let a certain variable be X, and let the standardized variable be X-
Then, XI = (X - average value) / standard deviation ... (8)
The weighting coefficient in equation (7) has a negative value for the water temperature θ, a positive value for the alkalinity AQ, a positive value for the floc formation amount Vf, and a positive value for the floc density equivalent value ρ. It is a value. Under these conditions, when the water temperature θ is low, the alkalinity AQ is high, the floc formation amount Vf is large, and the floc density equivalent value ρ is high, the flocculant injection rate C
acts to increase.

This method considers the effects of water temperature and alkalinity on floc formation, and injects flocculant based on the amount and density of flocs formed, so flocs are formed in just the right amount. , and precipitation can be carried out efficiently.

In particular, the results of the experiments conducted by the inventors have revealed that flocculant injection can be carried out over a longer period of time in a more normal and effective manner than the conventionally used method that uses only raw water quality as an index.

To use the example of formula (7), water temperature is used as the water quality.

alkalinity, and turbidity. Turbidity is used to calculate the density equivalent value ρ.

Although the four factors shown in equation (7) were considered, if the alkalinity Afl is not constantly measured, the second term on the right side may be omitted, although the accuracy will be slightly lower. That is, C=-0,50+0.8Vf+0.5p+correction value...
(9) Furthermore, although the fourth term on the right side of equation (7) indicates a value equivalent to density, turbidity may be used instead. However, in this case, the value of 0.5 of the coefficient changes.

In step 530, various corrections are performed. In this embodiment, ΔC is added by equation (10) to C calculated by equation (7) only when the settling tank outlet turbidity Tus obtained by the water quality meter 6 is equal to or higher than a predetermined value.

c' = c + ΔC... (10) In other words, even if the flocculant injection rate is calculated using equation (7), if the turbidity at the outlet of the settling tank becomes high, the flocculant injection rate can be calculated using equation (10). increase. If the flocculant injection rate is increased, the turbidity at the outlet of the settling tank can be reduced, so the flocculant injection rate can be corrected in this way.

Further, in a method that focuses on an early increase in the sedimentation tank outlet turbidity Tus, the flocculant injection rate is increased in step 530 using the time gradient ΔTus/Δ of the sedimentation tank outlet turbidity Tus according to equation (11).

c' = c + ΔC(ΔTus/Δt) −(1
1) In this way, the flocculant injection rate can be set with high reliability by taking into consideration the turbidity at the outlet of the settling tank in addition to the image measurement of the flocs.

Furthermore, in order to reflect the floc formation state, in step 530, the representative particle size of the floc particle size distribution is set to a certain value D.
- If it is smaller, make corrections by increasing the flocculant injection rate. This size is about 0.5 to 0.6 nm.

This is based on the fact that the inventors have found an empirical rule that if the representative particle size is smaller than this value, the aggregation is poor. Therefore, if D≦D*, the flocculant injection rate is increased. That is, the flocculant injection rate is increased using equation (12).

If D≦D*, then c' = c + ΔC... (12) This method detects abnormalities in floc formation even earlier than correcting the market based on the sedimentation tank outlet turbidity Tus, and promptly takes countermeasures. can be done. Such knowledge and know-how can also be accumulated in the knowledge base 60, and a conclusion is drawn via the inference mechanism 61. Although not shown, the input and output of this information in step 530 is transmitted from the system bus 52. Note that knowledge and know-how are input into the knowledge base 60 using the keyboard 44. As a result, the flocculant injection rate can be determined using information related to empirical knowledge in addition to image information, making it possible to respond more flexibly to changes in water quality.

Next, another embodiment will be described using FIG. 3.

FIG. 3 depicts steps 528 and 530.

Note that the steps from steps 501 to 526 are the same as those shown in FIG. 2, so the explanation will be omitted.

In step 528, the floc density equivalent value ρ input in FIG. 2, floc formation tvf, and the water temperature O obtained by the water quality meter 5A
1 and the alkalinity AM obtained with the water quality meter 5C, the raw water turbidity Tu obtained with the water quality meter 5B, the pH 1 obtained with the water quality meter 5D, the processed flow rate Q obtained with the water quality meter 5E, and the Add the floc number concentration N, representative particle size D, and floc brightness K obtained in the process. Here, the flock luminance represents the brightness of the flock. Consider these factors and inject flocculant! $C (
13) Calculate using the formula.

C=-0,60+0.5A! 2+0.05Tui-0,
07pH+0.05 Q + 0, 5 V f +
0,5 p+0,3 N+0.18D-0,06
K...(13) Here, the weighting coefficient on the right side of equation (13) is a negative value for water temperature O, a positive value for alkalinity AQ, a positive value for raw water turbidity, p Negative value for H, positive value for processing U4itQ, positive value for floc formation amount Vf, positive value for floc density equivalent value ρ, number of flocs N
A positive value is set for the representative particle diameter, a positive value is set for the floc luminance K, and a negative value is set for the floc luminance K. Due to these conditions, when the water temperature 0 is low, the alkalinity AQ is high, the floc formation amount Vf is large, and the floc density equivalent value ρ is high, the flocculant injection 1IC increases accordingly. .

This method takes into account the influence of water quality over a wide range of conditions, and injects flocculant based on the quantity and quality of flocs formed, so it forms just the right amount of flocs and performs precipitation efficiently. be able to.

In particular, results carried out by one of the inventors have revealed that it is possible to carry out more accurate and effective flocculant injection over a long period of time than the conventionally used method that uses only raw water quality as an index. Note that the method shown in FIG. 3 provides substantially the same effect as the embodiment shown in FIG. 2.

In step 530, various corrections are performed. The method of correcting the flocculant injection rate C obtained from equation (13) is as follows:
Correction is made using the method of equation (10), which is corrected by
11) Correct the equation. In this way, the flocculant injection rate can be set with high reliability by taking into consideration the turbidity at the outlet of the sedimentation tank in addition to the image measurement of the flocs.

Furthermore, to reflect the floc formation state.

In step 530, if the representative particle size of the floc particle size distribution is smaller than a certain value DI, the flocculant injection rate is corrected (12
) By taking into consideration the method of the formula, the flocculant can be injected doubly safely.

In the embodiment shown in FIG. 3, since the flocculant is injected based on various water quality and floc formation conditions, good flocculation and sedimentation treatment can be performed regardless of water quality fluctuations.

In the embodiment shown in FIG.
8 is placed in the floc formation pond 15A or 15
Either B is fine. The idea of the present invention is to basically measure the quality of raw water and then quantitatively and qualitatively measure the state of the formed flocs to properly inject the flocculant. . Therefore, it goes without saying that the rapid mixing pond 100 may be used as the place to measure floc formation. FIG. 4 shows an embodiment in which the aggregate imaging means 18 is submerged in a rapid mixing basin 100. This diagram shows rapid mixing pond 1.
Although only the area around 0 is shown, the other parts are the same as in FIG. The advantage of image-measuring flocs in the rapid mixing basin 10 is that agglomeration effects can be detected at an early stage.

The floc in this case refers to micro floc. In other words, the formation state of microflocs, which form the core of the flocs, is image-measured. Here, if the micro flocs are sufficiently formed, the flocs are also well formed. Since micro flocs are smaller than flocs, the magnification of the aggregate imaging means 18 is set higher than in the embodiment shown in FIG. Fourth
In the embodiment shown in the figure, since the state of floc formation can be detected earlier than in the embodiment shown in FIG. 1, the flocculant injection rate can be controlled with a shorter time delay. However, since the actual state of floc formation in the floc formation pond 15 is not monitored, the state of floc formation must be predicted.

Although an embodiment in a water purification plant has been described, the present invention can be used for coagulation treatment in a sewage treatment plant, industrial wastewater treatment, and the like.

〔Effect of the invention〕

According to the present invention, since the flocculant injection rate is controlled based on various types of water quality and the quantitative and qualitative information on flocs obtained through five-image measurements, flocs are reliably formed and flocculation sedimentation is always performed properly. be able to.

[Brief explanation of the drawing]

FIG. 1 is a system diagram of one embodiment of the present invention, FIG. 2 is a flowchart of signal processing, FIG. 3 is a process diagram of signal processing of another embodiment of the present invention, and FIG. It is an explanatory diagram of an example. 5... Water quality meter, 10... Rapid mixing pond, 12... Coagulant injection pump, 15... Floc formation pond, 18...
- Aggregate imaging means, 40... Image processing device, 42...
- System processor, 56...input/output port,
52...System bus.

Claims (1)

[Scope of Claims] 1. A flocculation tank into which a flocculant is injected and which forms flocs of suspended matter by stirring the liquid, a water quality meter that measures the water quality of the liquid flowing into the flocculation tank, and an image of the flocs. a floc imaging means for converting the floc into an electrical signal; an image recognition means for recognizing the characteristic quantity of the floc based on the image signal obtained from the floc imaging means; A flocculant injection control device consisting of a control device that controls the amount of agent to be injected. 2. The flocculant injection control device according to claim 1, wherein the water quality is water temperature. 3. The flocculant injection control device according to claim 1, wherein the water quality is water temperature and alkalinity. 4. The flocculant injection control device according to claim 1, wherein the water quality is water temperature and turbidity. 5. The flocculant injection control device according to claim 1, wherein the water quality is water temperature, turbidity, and alkalinity. 6. The flocculant injection control device according to claim 1, wherein the characteristic quantity of the flocs is the amount of flocs formed. 7. The flocculant injection control device according to claim 1, wherein the characteristic quantity of the flocs is the density of the flocs. 8. The flocculant injection control device according to claim 1, wherein the characteristic quantities of the flocs are a floc formation amount and a floc density. 9. The flocculant injection control device according to claim 1, wherein the control device corrects the flocculant injection rate using turbidity at the outlet of the settling tank. 10. The flocculant injection control device according to claim 1, wherein the control device corrects the flocculant injection rate using a time rate of change in turbidity at the outlet of the settling tank. 11. The flocculant injection control device according to claim 1, wherein the control device corrects the flocculant injection rate using a representative particle size of the flocs. 12. A flocculant injection control device according to claim 1, characterized in that a knowledge processing rule and an inference mechanism are added. 13. The flocculant injection control device according to claim 1, wherein the control device corrects the flocculant injection rate using information obtained from the knowledge processing rule and the inference mechanism.