JPS6031887A - Microorganism phase discriminating apparatus - Google Patents

Abstract

PURPOSE:To accurately and automatically detect the particle size and the particle size distribution of Zoogloea flocs, by judging an observed picture element as a picture element corresponding to a non-fibriform microorganism when the brightness level of the observed picture element is the one other than a set width. CONSTITUTION:When a definite amount of a liquid to be examined containing activated sludge is collected on a preparation and microscopically examined in the bright field of an optical microscope 2, an original image having contrast can be fabricated. In the original image, a dark part is present in a liquid phase part B being a bright background and is floc part Z and a fibriform microorganism part F. Image information obtained by a vidicon camera 3 is processed by an image information processing apparatus 9 and only the image of a Zooglea microorganism (floc) is displayed by CRT8. A brightness information processing circuit 4 performs the A/D conversion of the brightness information from the vidicon camera 3 into a digital signal.

Description

DETAILED DESCRIPTION OF THE INVENTION [Field of Application of the Invention] The present invention relates to a microbiota recognition device that automatically detects the type and number of microbiota that appear.

[Background of the invention]

Biological sewage treatment methods are known as a representative example that utilizes large amounts of microorganisms. The activated sludge method is a method of treating organic matter in sewage using the oxidative decomposition action of aerobic microorganisms. The process includes an aeration tank maintained in aerobic conditions;
It consists of a sedimentation tank that allows microorganisms to settle by gravity. The operational management of this process, and especially important, is the cultivation of microorganisms with good sedimentation properties. This is because when microorganisms with poor sedimentation properties appear, they not only flow out of the settling basin and deteriorate the quality of the treated water, but also cause a decrease in the amount of four microorganisms in the process system, making it impossible to treat the water.

By the way, it is said that there are generally about 50 types of microorganisms that often appear in the activated sludge method. Activated sludge, which is a general term for these microorganism groups, can be broadly classified into two types of microorganisms. One is zooglare, which has cohesive properties.
gloea) type microorganisms, which are immersed in sedimentation to form good flocs. The other type of microorganisms are filamentous microorganisms that prevent flocs from approaching each other and have poor sedimentation and compaction properties.

When these filamentous microorganisms proliferate abnormally, a bulking state (sludge swelling) occurs, and solid-liquid separation cannot be performed in the sedimentation tank, causing activated sludge to flow out.

The growth of these zooglai microorganisms and filamentous microorganisms is
It varies depending on the organic matter load in the aeration tank (amount of organic matter per unit weight of activated sludge) and aeration conditions. Therefore, in sewage treatment plants where inflow conditions vary greatly on a daily or seasonal basis, it is important to maintain and manage the microbiota that form stable flocs. For this purpose, it is necessary to understand the condition of activated sludge.

An optical microscope is usually used to understand the state of activated sludge. Microscopic images contain a lot of information, and by observing the types and numbers of microorganisms that appear, the condition of activated sludge can be determined. Conventionally, the types and numbers of microorganisms have been measured by visually observing the microscopic image itself, or by taking photographs and visually observing the imaging results. However, reading this information requires skill, and even experienced operators who are familiar with microorganisms spend a long time, making frequent observation difficult.

The particle size distribution is a factor that influences the settling properties of activated sludge flocs. That is, when the floc particle size is large, the settling property is usually good. However, even if the particle size is large, if a large number of filamentous fungi are planted and the particle size appears large, the settling property will be adversely affected. On the other hand, when the floc particle size is small, the sedimentation property is poor, and therefore the floc does not settle completely in the final sedimentation tank and flows out.

Therefore, accurately determining the particle size of zooglare flocs that tend to settle is extremely important in terms of process maintenance and management. - A known method for determining particle size distribution in particles is by image processing. However, when filamentous fungi proliferate, the particle size of the sludge flocs becomes larger than it appears, as shown in FIG. In other words, when zooglare sludge flocs are z1 and filamentous bacteria are F, the diameter of the flocs is measured as d due to the propagated filamentous bacteria even though the particle size of zooglare sludge floc Z is dz. . As described above, conventional image processing techniques have the disadvantage that the particle size of zooglare sludge flocs cannot be accurately measured when there are many filamentous bacteria.

[Purpose of the invention]

The present invention has been made in response to the conventional problems, and its purpose is to analyze information from microscopic images in a short time, and in particular, to accurately determine the particle size and particle size distribution of zooglare flocs. An object of the present invention is to provide a microbiota recognition device that automatically detects microbiota.

[Summary of the invention]

In the present invention, when a microscopic image is converted into brightness information, the density histograms differ between zooglare microorganisms and filamentous microorganisms, and by setting an arbitrary threshold level, the number of pixels corresponding to each microorganism is integrated, We experimentally found that there is a correlation between the number of pixels and the number of microorganisms.
This is due to the new discovery that it is possible to automatically detect the state of the microbiota of zooglare microorganisms and filamentous microorganisms.

[Embodiments of the invention]

Hereinafter, the present invention will be explained in detail by way of examples.

FIG. 2 shows an embodiment of the present invention. The image magnifying device 2 magnifies the microflora like an optical microscope. Collect a certain amount of the test solution containing activated sludge on preparation 1, and apply it to optical microscope 2.
When examined and mirrored in the decoy field of view, the original image with contrast shown in Figure 3 can be created.

In the original image, a dark portion exists in the liquid phase portion B, which is a bright background. The dark areas are the floc part Z and the filamentous microorganism part F. The vidicon camera 3 is a progressive scanning type imaging device that converts an original image into luminance information. The image information obtained by the vidicon camera 3 is processed by the image information processing device 9, and an image of only zooglare microorganisms (floc) is displayed on the CRT 8. Here, the process of signal processing in the image information processing device 9 will be explained below.

The brightness information processing circuit 4 A/D converts the brightness information from the vidicon camera 3 and processes it into a digital signal. FIG. 4 is a brightness histogram obtained by brightness information processing when scanning along line AA' in FIG. 3. In Figure 4, 8 pits)
(256 scale), the liquid phase part B shows a high value, the floc part Z shows a low value, and the filamentous microorganism part F is displayed in the middle. The determination circuit 5 is a brightness level determination circuit that extracts the maximum value 8b and minimum value St of the brightness level from the brightness histogram. Maximum value 8b is liquid phase part B
The target is the minimum value SL, and the flock part Z is the target, and the average value is used for each. The setting circuit 6 is a circuit for setting brightness levels S for detecting filamentous microorganisms. The brightness level 8 is selected to be higher than the signal level SL corresponding to the flock part Z. For example, S can be chosen according to the following equation:

Mixed=St+a.(Sk-8o)...Old...(1) Here, a is a constant, and a value of 0.05 to 0.3 is preferable. FIG. 4 shows an example when a=0.2.

Next, when the brightness shows a value lower than S, the discrimination circuit 7 considers that part to be a floc part, and on the other hand, when the brightness is higher than S, the discrimination circuit 7 considers that part to be a floc part. considered as a department. Like this 8. By selecting , it is possible to extract pixels only in the shift 0 section. According to this selection method, a flock portion and other portions are determined for each pixel.

Specifically, the rules are as follows.

As an example, an image having 256×256 pixels as shown in FIG. 5 and having pixels all in the cover row and column j is taken as an example.

For each S-th pixel, a flock portion and other portions are determined according to the following equation.

Here, fIJ-1 represents a flock part, while f
, , :Q indicates a part other than the flock part. This operation is carried out in the discrimination circuit 7, and the obtained one output signal is displayed as an image on Cl(T8).The output signal obtained by processing the image in FIG. 3 is shown in FIG. The output signal as a line is shown in Fig. 7. In this way, only the part of the flock part Z can be displayed.

Once the image has been transformed as shown in Figure 6,
The grain size of the flock part Z can be easily determined using known conventional techniques. For example, as a method for calculating the representative particle size,
There is a method represented by the one-way diameter d shown in the figure, and a method represented by the average value d1 when the longest diameter is dl, 8, and the shortest diameter is d2, 1 m, as shown in FIG. The calculation formula in this case is as follows.

d1=(tun-+d-1-)/2=(4) Also, the first
As shown in Figure 0, there is also a method in which the area Ax of the flock portion 2 is determined and represented by the diameter dc of a circle having an equal surface area.

A method for calculating the representative diameter dc is, for example, the following equation.

dc=V'4・λ2/π (5) Next, as an example of a general case, - the image when there are multiple activated sludge flocs in the screen is FIG. 12 shows the luminance level distribution along the AA' line at this time. FIG. 13 shows an image in which the flock portion 2 and other portions are distinguished and displayed according to the signal processing method described above, and FIG. 14 shows the output signal value on the AA' line at this time.

As shown in Figure 13, only the floc part can be extracted, so
The representative grain size of each of the floc parts can be determined with high precision.

〔Effect of the invention〕

According to the present invention, it is possible to extract and display only the pixels of the floc part excluding filamentous microorganisms in the image of the activated sludge floc (9), so that the particle size of the activated sludge floc can be measured with high accuracy.

[Brief explanation of the drawing]

Fig. 1 is a schematic diagram of an activated sludge floc, Fig. 2 is a diagram showing the configuration of an embodiment of the present invention, Figs. 3 to 7 are diagrams showing the signal processing process of the present invention, and Figs. Up to figure 10,
FIGS. 11 to 14 are diagrams showing how to determine the representative diameter, and are diagrams showing the signal processing process of the present invention. 1... Flehalato, 2... Image magnifying device, 3...
Vidicon camera, 4... Brightness information processing circuit, 5...
Judgment circuit, (10) Part I 20 30 40 5 60 712] 11 120 56 2 28

Claims (1)

[Claims] 1. In microbial utilization processes that utilize the metabolic abilities of microorganisms to remove harmful substances or produce useful substances,
A microorganism characterized in that when the brightness level of the observed pixel when the mirror image of the microorganism is converted into brightness information and displayed as a pixel is outside a set width, the observed pixel is determined to be a pixel corresponding to a non-filamentous microorganism. Phase recognition device.