JPH02229598A - Method for recognizing image of activated sludge - Google Patents

Abstract

PURPOSE:To exactly quantitatively recognize activated sludge and to execute the stable operation control of an activated sludge process by subjecting the magnified images of the above-mentioned activated sludge to a prescribed image arithmetic processing. CONSTITUTION:The settling property of the activated sludge is decided by executing a 1st stage 25 in which the activated sludge is macrophotographed and the difference in the brightness values between the original image data 21 and the background image data 23 is computed, a 2nd stage 26 in which the image is subjected to a binarization processing, a 3rd stage 28 in which the image is subjected to the binarization processing after a line detection processing 27, 4th, 5th, 6th stages 29, 31, 32 in which a mask image is obtd. from the image data of the 2nd and 3rd stages and intensifier images are obtd. from the mask image and the image data of the 1st stage are subjected to the binarization processing, 7th, 10th stages 33, 34, 35 in which the images of the 6th stage are subjected to reduction and magnification processing and the area and volume of the activated sludge flocs are computed, 8th, 9th, 11th stages 36, 38, 39 in which the image of the 6th stages are subjected to a fine line processing, and the length and volume of molds are computed and 12th stage 40 in which the ratio of the length of the molds with respect to the area of the activated sludge flocs or the ratio of both the volumes is computed.

Description

[Detailed Description of the Invention] [Industrial Application Field] The present invention relates to an image recognition method for activated sludge, and in particular, the present invention relates to an image recognition method for activated sludge, and in particular, to an aeration process in equipment for purifying wastewater using the activated sludge method in sewage treatment, etc. The present invention relates to an image recognition processing method for recognizing and evaluating the properties of activated sludge in a tank.

[Prior Art] The activated sludge method is a water treatment process that is utilized in a wide range of fields such as sewage treatment. The requirements for implementing this method are to ensure the decomposition and assimilation reaction of organic pollutants by microorganisms in the presence of oxygen, and to ensure effective sedimentation and separation of the generated activated sludge flocs. Due to fluctuations, there are many situations in which good precipitation separation cannot be ensured, and conventionally, it has been considered one of the water treatment processes that is relatively difficult to manage.

In particular, sedimentation and separation failure caused by large fk generation of filamentous bacteria is a serious problem that can even be seen in the activated sludge process as a system. There was a need to make changes.

Conventionally, in managing the solid-liquid separation state, indicators such as SvI have been used to comprehensively (macroscopically) evaluate sedimentation properties and provide information for operational management. However, since the SV process index itself was based on manual analysis, it lacks real-time characteristics, and there is no clear threshold value for the value, so it cannot be used in correspondence with the quality of sedimentation separation. It has characteristics that make it difficult to automate or make it online, such as requiring the intervention of empirical judgment.

Furthermore, the increase in SVI, that is, the phenomenon of poor sedimentation and separation, includes the generation of filamentous bacteria mentioned above, as well as the swelling phenomenon caused by the dispersion of the flocculating bacteria themselves, which basically constitute activated sludge. Although it is necessary to take measures to
It was difficult to distinguish this using macro indicators such as I. For this reason, in the past, the properties of activated sludge have been evaluated by observing the properties with the naked eye using a microscope, but it requires a lot of effort to quantify this manually, so It was impossible to use this information as information, and there was a desire to develop an effective method.

Against this background, in recent years, attempts have been made to obtain microscopic information about activated sludge by taking enlarged images of activated sludge using a microscope, etc., using a television camera, and performing image processing. Considerations regarding the processing procedures have also begun to emerge. These targets are
Although filamentous bacteria and protozoa may be considered, even studies targeting filamentous bacteria and activated sludge flocs are insufficient to accurately separate and quantitatively measure both. In addition, each is measured separately, and it does not express that the sedimentation separation of live poured sludge is basically influenced by the relative quantity ratio of both, and the condition of live poured sludge is expressed appropriately. The information provided was extremely insufficient.

[Problem to be solved by the invention]

As mentioned above, in the prior art, it has not been possible to obtain sufficiently satisfactory information that suitably expresses the state of activated sludge.

Therefore, an object of the present invention is to provide an image recognition method that can accurately and quantitatively understand activated sludge.

[Means to solve the problem]

In order to achieve the above objective, the present inventors conducted intensive research and quantitatively counted activated sludge flocs and filamentous bacteria from images obtained by enlarging activated sludge and diagnosed treatment conditions. This led to the invention of a new method for image recognition of activated sludge, which can be used for automatic control.

That is, the present invention provides a method for enlarging activated sludge imaging and image processing, which includes: (1) a first step of calculating a difference between a brightness value of original image data and a brightness value of background image data; a second step of binarizing the image obtained in step 1; (3) a third step of binarizing the image obtained in the first step after line detection processing; (4) a fourth step of calculating the sum of the brightness values of the image data obtained in the second step and the nine Ill data obtained in the third step to obtain a mask image; and (5) the first step. a fifth step of calculating the sum of the brightness values of the image data obtained in the step and the image data obtained in the fourth step to obtain an enhanced image;
(6) A sixth step in which the image obtained in the fifth step is binarized.
(7) A seventh step of reducing and enlarging the painter obtained in the sixth step to obtain only an image of activated sludge flocs; (8) An image obtained in the sixth step. The eighth step is to thin the line.
(9) A ninth step of calculating the difference in brightness values between the image data obtained in the seventh step and the image data obtained in the eighth step to obtain only an image of the filamentous bacteria. , α1 Integrate the number of painter pixels obtained in the seventh step and calculate the area and/or volume of the activated sludge floc $10
(11) An eleventh step of integrating the number of pixels of the image obtained in the ninth step and calculating the length and/or volume of the filamentous bacteria; At least the ratio of the length of the filamentous bacteria obtained in the eleventh step to the area of the activated sludge flocs, or the ratio of the volume of the filamentous bacteria obtained in the eleventh step to the volume of the activated sludge flocs determined in the tenth step. an activated sludge image recognition method, comprising: a twelfth step of calculating one; and a thirteenth step of determining the sedimentability of the activated sludge from the ratio obtained in the twelfth step. be.

In the above, activated sludge is a general term for those formed by floc-forming bacteria, filamentous bacteria, protozoa, etc., and the floc-like objects formed by floc-forming bacteria are called activated sludge flocs, and are called activated sludge flocs. The filamentous objects extending radially from the floc or floc are called filamentous bacteria, and in the present invention, these are separated and woven in a controlled manner.

[Operation] The image recognition method according to the present invention can evaluate the composition, state, etc. of activated sludge in an activated sludge treatment process such as sewage treatment by constant filtration, thereby accurately determining the treated face. This function enables proper operation management and promotes automatic control.

[Examples]

Hereinafter, the present invention will be explained in more detail with reference to the drawings, but the present invention is not limited to these embodiments.

First, explanation will be given using FIG. FIG. 1 is an explanatory diagram showing an example of operation of the activated sludge image recognition processing method according to the present invention.

The raw water 1 is pretreated if necessary and then introduced into the aeration tank 2, where it is mixed with the activated sludge 12 returned from the settling tank 4. Air is supplied into the aeration tank 2 by a blower 3 in order to supply oxygen necessary for activated sludge activity. Activated sludge separates organic substances from wastewater while remaining in the aeration tank. The water flowing out from the aeration tank 2 is sent to the sedimentation tank 4.
The activated sludge is separated by precipitation to obtain clear treated water 7. A part of the precipitated activated sludge becomes return sludge 12, and the remainder is discharged outside the system as surplus sludge 15.

An underwater microscope 8 suitable for obtaining enlarged images of activated sludge is immersed in the aeration tank 2, and images of the activated sludge are periodically captured and transmitted to an image recognition system 9 according to the present invention. . This underwater microscope! As 1B, a device suitable for this purpose is selected, for example, as described in Japanese Patent Application No. 62-279056 previously filed by the present applicant.

The image recognition processing system 9 is composed of an image processing device, a computer, a monitor, a TV, etc., and an underwater microscope 8.
The area and/or volume of activated sludge flocs in activated sludge, the length and/or volume of filamentous bacteria, and their ratio are calculated by performing necessary arithmetic processing on the video signal from . The calculation results are converted into control signals for the aeration blower 3, return sludge pump 5, and surplus sludge bong 6 according to a predetermined method, and at least one of these is controlled as necessary via the controller 10. Control.

Here, the above-mentioned method may be a method of calculating a formula obtained in advance based on conventional knowledge, or a method of converting the knowledge into μ-y and storing it in a computer and converting the /I/ - A method of deriving the control signal for the blower, pump, etc. by referring to the 7tz group may be used, or any other method that can obtain the desired control signal may be adopted.

Next, the processing procedure of tS image data in the image recognition system 9 will be explained using the process diagram shown in FIG. 2 showing an example of the flow of the image recognition processing method of the present invention.

Underwater microscope! The input original image data 21 including the objects (activated sludge flocs, filamentous bacteria) from 18 is smoothed by multiple integral inputs 22 if necessary, and then the illumination unevenness of the input image is smoothed out. In order to remove the object, a difference in luminance value is calculated 25 for each corresponding pixel between the background image data 23 that does not include the object and an image obtained by similarly smoothing 24.
do. (First Step) Next, the image obtained in the first step is subjected to binarization processing 26 according to a predetermined luminance value.

(Second step) Separately, after performing line detection processing 27 using a Hupfuchian operator to emphasize intermediate brightness areas (filamentous bacteria, activated sludge floc periphery, etc.) of the image obtained in the first step. , performs binarization processing 28 according to a predetermined brightness value. (Third step) Here, the size of the Wodefusian Obe V Ichiyo is 5 x 3 matrix or more, preferably 9 x 9 matrix,
Preferably, 12 or more different weighting coefficients are prepared so that line segments with random directions can be emphasized regardless of the direction.

After filtering processing is performed using operators 12a and j5, a logical sum operation is performed to obtain the brightness value of each pixel. By calculating 29 the sum of the luminance values for each pixel between the obtained image and the image obtained in the second step, an image to be used as a mask is generated. (Fourth step) Furthermore, in this calculation,
If the sum of the brightness values exceeds the maximum brightness value (255 in the case of 8 bits), the maximum brightness value is substituted.

On the fire, I put this mask image in the 3rd inversion process, and the first one.
The sum of the luminance values for each pixel is calculated 31 using the same operation as in the fourth step to obtain an enhanced image. (fifth step), and then binarized according to a predetermined brightness value (sixth step) to remove noise, illumination noise, etc., and remove unclear filamentous bacteria and flocs. An accurate △ binarized image of the original image can be obtained, with the peripheral edges clearly defined.

Regarding the H image obtained in the sixth step, on the one hand, in order to eliminate filamentous bacteria, reduction processing 53 and subsequent enlargement processing 34 are performed the same number of times for each pixel. (Seventh step) This number of times may be sufficient to eliminate the bacteria, taking into consideration the magnification of the sample and the thickness of the filamentous bacteria.

With this process, an image of only activated sludge flocs without filamentous bacteria is obtained, so the area and/or volume of the flocs is measured 35 for this image.

(10th step) As for this measurement method, the area can be calculated based on the number of pixels composing the flock and the unit pixel area (area of a square made of 4 pixels), or A method of calculating as a ratio of the number of pixels constituting a flock to the total number of pixels can be adopted.

Regarding the volume, add the microscope plate L// to the obtained area value.
It can be determined by multiplying the distance between the slide glass and the cover which form (Fu}11).

On the other hand, after performing thinning processing 36 on the image obtained in the sixth step (eighth step), the image obtained by performing inversion processing 37 on the image obtained in the seventh step is different from the image obtained in the seventh step. In between, the difference in luminance value is calculated 38 for each pixel. (Ninth step) At this stage, since an image of only filamentous bacteria without flocs has been obtained, the length and/or volume of filamentous bacteria is calculated 39 for this image. (Eleventh step) As for the calculation method, the length may be calculated by multiplying the number of pixels constituting the filamentous bacteria by the distance between pixels, but more precisely, taking into account the directionality of the filamentous bacteria, A preferred method is to divide the filamentous bacteria into groups according to several directions and multiply the integrated value of each pixel by a predetermined distance.

The volume can be calculated by multiplying the obtained length by the thickness of the filamentous bacteria (this varies depending on the bacterial species, but it is 15 to 2 microns, and the thickness of each bacterial species is now known). .

In the twelfth step, . From the measurement results obtained in the tenth step and the eleventh step, the ratio of the length and/or volume of the filamentous bacteria to the floc area and/or volume is calculated 40. That is, the ratio of the length of filamentous bacteria to the area of activated sludge flocs, the ratio of the volume of filamentous bacteria to the volume of activated sludge flocs, or the ratio of both of these is calculated.

According to the research conducted by the present inventors, the quality of sedimentation of activated sludge is basically determined by the relative relationship between the amounts of floc-forming bacteria and filamentous bacteria present in the activated sludge mixture. Note that this relative relationship is preferably expressed, for example, as the ratio of the length and/or volume of the filamentous bacteria to the floc area and/or volume, as described above.

Fig. 5 shows an example of the relationship between the sludge capacity index 8'/I and the above-mentioned ratio. By creating a correlation diagram like this based on operational data and past research examples, and checking it every time you make an observation, you can determine whether the current sedimentation properties of sludge are caused by filamentous bacteria. It becomes possible to determine whether the problem is caused by other causes such as dispersion of flocs, etc. By changing the operating force based on the determination result, it becomes possible to always contribute to appropriate operation management.

〔Effect of the invention〕

As described above, according to the present invention, it is possible to quantitatively and quickly evaluate the condition of activated sludge flocs, which has been difficult to perform in the past, and to perform appropriate and economical operational management of activated sludge processes. , produces an effect that contributes to the promotion of automation.

[Brief explanation of the drawing]

Fig. 1 is a schematic explanatory diagram showing an example of operation of the activated sludge image recognition processing method of the tree invention, Fig. 2 is a process diagram showing an example of the flow of the present invention, and Fig. 3 is a sludge volume index and floc It is a graph showing the relationship between the ratio of filamentous bacteria to area.

Claims (1)

[Claims] 1. A method for enlarging and image-processing activated sludge, comprising: (1) a first step of calculating a difference between a brightness value of original image data and a brightness value of background image data; ) A second step in which the image obtained in the first step is binarized.
(3) After performing line detection processing on the image obtained in the first step,
(4) a fourth step of calculating the sum of the luminance values of the image data obtained in the second step and the image data obtained in the third step to obtain a mask image; (5) a fifth step of calculating the sum of the brightness values of the image data obtained in the first step and the image data obtained in the fourth step to obtain an enhanced image; and (6) a fifth step of obtaining an enhanced image. The sixth step is to perform binarization processing on the image obtained in step 5.
(7) A seventh step of reducing and enlarging the image obtained in the sixth step to obtain only an image of activated sludge flocs; (8) An image obtained in the sixth step. The eighth step is to thin the line.
(9) A ninth step of calculating the difference in brightness values between the image data obtained in the seventh step and the image data obtained in the eighth step to obtain only an image of the filamentous bacteria. , (10) Integrate the number of pixels of the image obtained in the seventh step,
a tenth step of calculating the area and/or volume of the activated sludge floc; (11) integrating the number of pixels of the image obtained in the ninth step;
an eleventh step of calculating the length and/or volume of the filamentous bacteria, and (12) calculating the ratio of the length of the filamentous bacteria obtained in the eleventh step to the area of the activated sludge floc obtained in the tenth step or a twelfth step of calculating at least one of the ratio of the volume of the filamentous bacteria obtained in the eleventh step to the volume of the activated sludge flocs obtained in the step ten; An image recognition method for activated sludge, comprising: a thirteenth step of determining sedimentation property of the sludge.