A method for predicting apparent density and a device for measuring the same

Abstract

The application provides a method for predicting apparent density and a measuring device thereof, and relates to the technical field of detection devices. The method predicts the accurate apparent density value of different batches of materials by measuring the batches of materials in real time and according to the actual particle size distribution of the materials, so that more accurate apparent density information is provided for the determination of an optimal formula, and better finished products are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of detection devices, in particular to a method for predicting apparent density considering the influence of material particle size and a device for measuring the apparent density. BACKGROUND

[0002] In the building materials, chemical industry and other industries, the formula of the related finished products (such as imitation stone bricks, concrete, etc.) needs to use the apparent density information of the materials when the different materials are proportioned. For example, in the building materials industry, the apparent density of fine aggregate or coarse aggregate needs to extract part of the material sample and determine it according to the "Standard for Quality and Test Methods of Sand and Stone for Ordinary Concrete" JGJ52, and then determine the proportioning of the amount of coarse and fine aggregate according to the volume method. In actual production and use, the particle size of the material is often inconsistent (i.e. it changes within a certain range), and the corresponding apparent density value also changes within a certain range. The material sample used for experimental determination of the apparent density is necessarily different in particle size from the material of a certain batch used in actual production. Therefore, the apparent density value determined by the experiment has a certain error from the true apparent density value corresponding to the batch of material, that is, the apparent density value determined by the experiment cannot consider the difference in particle size distribution between the material sample and the material used in actual production, resulting in an error in the apparent density value and affecting the determination of the optimal formula. SUMMARY

[0003] The present application discloses a method for predicting apparent density considering the influence of material particle size, aiming to improve the above-mentioned problems.

[0004] The present application adopts the following scheme:

[0005] The present application provides a method for predicting apparent density, comprising the following steps:

[0006] S1: extract a material sample and stack it, then take a picture with a camera to obtain a surface image of the material;

[0007] S2: obtain the particle contour of the material in the surface image based on image processing;

[0008] S3: determine the corresponding envelope rectangular frame of each particle according to its contour; then obtain the long side and short side of the rectangular frame; and then take half of the long side as the particle size of the particle;

[0009] S4: classify the particle size according to the particles obtained in S3, obtain the percentage of the particles in different particle size ranges according to the ratio of the number of particles in different particle size ranges to the total number of particles in the surface image, and measure the apparent density according to the industry standard or national standard;

[0010] S5: repeating S1-S4 to obtain the particle size range distribution data corresponding to different batches of materials and the apparent density measurement value C={c i} T wherein i represents the i-th batch of material; j represents the j-th row of data; T represents matrix transposition;

[0011] S6: establishing a mapping relationship between the particle size range distribution data B and the apparent density measurement value C, and obtaining the mapping relationship by a function F, that is, C=F(B), to obtain accurate apparent density information.

[0012] Further, the mapping relationship between the particle size range distribution data and the apparent density measurement value is established by a neural network or a response surface method or a Kriging model.

[0013] Further, in step S1, when shooting, the camera is perpendicular to the material plane.

[0014] The application also provides a measuring device for implementing the prediction method of apparent density described in any one of the above, comprising a camera connected with a processing system, the camera is suitable for shooting towards the plane where the material is located, and the image shot by the camera is identified and processed by the processing system.

[0015] Further, the camera is suitable to be arranged outside the silo, a transparent window is arranged on the silo, and the camera is suitable for shooting towards the transparent window to realize real-time measurement during the material falling process of the silo.

[0016] Further, the transparent window is provided with an observation plate made of wear-resistant transparent material.

[0017] Further, a silo is included, a conveying belt is arranged below the silo, and the camera is arranged above the conveying belt to shoot the material falling into the conveying belt.

[0018] Beneficial effects:

[0019] By analyzing the surface graph of the material in different batches or different states, the apparent density corresponding to different batches of materials is obtained, the influence of the particle size distribution difference of different batches of materials is considered when measuring the material density, so that the corresponding accurate apparent density value is obtained, and then more accurate apparent density information is provided for the proportioning of different materials on the production line, so that the performance of the finished product on the production line is improved. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 is the picture of the piled material shot by the embodiment of the application;

[0021] Figure 2 isFigure 1 A schematic diagram of the profile of a material particle after being processed by the processing system;

[0022] Figure 3 is Figure 2 A rectangular frame determined according to the profile of any material particle;

[0023] Figure 4 is a structural schematic diagram of a measuring device according to an embodiment of the present application;

[0024] Figure 5 is another structural schematic diagram of a measuring device according to an embodiment of the present application;

[0025] Figure legend: 1-camera, 2-transparent window, 3-bunker. DETAILED DESCRIPTION

[0026] Embodiment 1

[0027] In combination Figures 1 to 5 with the drawings, the present embodiment provides a prediction method for apparent density, including the following steps:

[0028] S1: extract a material sample, and stack it, then take a picture by camera 1 to obtain a surface image of the material (such as Figure 1 );

[0029] S2: based on picture processing, obtain the particle profile of the material in the surface image (such as Figure 2 );

[0030] S3: determine the corresponding envelope rectangular frame of each particle according to its profile; then obtain the long side and short side of the rectangular frame; then take half of the long side as the particle size of the particle (such as Figure 3 );

[0031] S4: classify the particle size according to the particle obtained in S3, obtain the corresponding percentage of the particle in different particle size range distribution according to the ratio of the number of particles in different particle size range to the total number of particles in the surface image; according to the industry standard or national standard, perform experiments to measure the apparent density;

[0032] S5: repeat S1-S4 to obtain the particle size range distribution data and the apparent density measurement value C={c i} T , wherein i represents the material of the ith batch; j represents the data of the jth row; T represents matrix transposition;

[0033] S6: establish the mapping relationship between the particle size range distribution data B and the apparent density measurement value C, and the obtained mapping relationship is represented by a function F, that is, C=F(B), to obtain accurate apparent density information.

[0034] In this embodiment, in step S1, the camera 1 is perpendicular to the material plane during shooting. Of course, in other embodiments, the camera 1 can also be arranged to be inclined to the plane formed by the material to obtain a more realistic and accurate image to improve the accuracy of the determination. In step S3, the corresponding envelope rectangular frame of each particle is determined according to the contour of the particle; then the long side b and the short side a of the rectangular frame are obtained; then half of the long side b is taken as the particle size of the particle, i.e. the particle size r = b / 2;

[0035] The particle size is classified. Here, the classification does not need to be divided into equal parts. The particle size range is set according to the type of the material to improve the applicability to different products and different materials. Taking the particle size range r1-r2 as an example, r1 and r2 are the particle size values of two particles of different sizes, where r1 < r2. The number of particles with a particle size between r1 and r2 is defined as p, and the total number of particles in the surface image is defined as N. The percentage of the particles in the range r1-r2 in the surface image is p / N*100%, and the percentage of the particles in each particle size range is calculated. Different batches of materials are replaced, and the steps S1 to S4 are repeated to obtain the particle size range distribution data corresponding to different batches of materials. Further, the apparent density measurement value C = {c i} T It should be noted that the different batches of materials here can be materials selected at different times or different positions in the same batch of materials.

[0036] When establishing the mapping relationship, the neural network or the response surface method or the Kr i gi ng model can be used to establish the mapping relationship between the particle size range distribution data and the apparent density measurement value.

[0037] Through the scheme, the apparent density of the materials at different positions or in different states in the same batch of materials can be determined to obtain more accurate apparent density measurement values of the materials.

[0038] Embodiment 2

[0039] In the comparative embodiment, a batch of materials is divided into two groups, and the apparent density is determined according to the industry standard or the national standard. Different industries have their corresponding apparent density measurement standards. For example, the standard for quality and testing method of ordinary concrete sand and stone JGJ52 stipulates the apparent density measurement standard of coarse and fine aggregates in the building materials industry.

[0040] The first group of materials, as shown in Table 1, Table 1 represents the percentage of the first group of materials in different particle size range distribution, and the apparent density measured according to the standard JGJ52 is 2463kg / m 3 .

[0041] Table 1

[0042] Particle size range (mm) Percentage (%) 1.2～2.5 4.5 2.5～5.0 20.4 5.0～10 75.4

[0043] The second group of materials is divided into batches, and the size and weight of each batch of materials are the same or similar, and a total of 50 groups of materials are measured, so as to obtain Table 2, Table 2 represents the percentage of the materials in different particle size range distribution, and according to the particle size range distribution data in Table 2 and the corresponding apparent density C={c i} T , wherein i=1~50 represents the material of the ith group; j=1~3 represents the data in the jth row of Table 1. The Kriging model is used for modeling, and the mapping relationship between the particle size range distribution data and the apparent density C={c i} T is obtained. Here, the obtained mapping relationship is represented by a function F, that is: C=F(B).

[0044] Table 2

[0045] Particle size range (mm) Percentage (%) 1.2～2.5 3.8 2.5～5.0 18.9 5.0～10 86.7

[0046] The apparent density value C' of the second group of materials is predicted, that is: C'=F(B'). Wherein, B' selects the data in the second column of Table 2. C' represents the apparent density of the second group of materials. According to the industry standard or the national standard, the apparent density value C' is calculated to be 2587kg / m 3 .

[0047] Through actual measurement and calculation of the batch of materials, the value of C' is more accurate.

[0048] Embodiment 3

[0049] Combined Figure 4 and Figure 5 , the present application also provides a measuring device for realizing the prediction method of the apparent density of any one of the above-mentioned embodiments, which comprises a camera 1 connected with a processing system, the camera 1 is suitable for shooting towards the plane where the materials are located, and the image shot by the camera 1 is identified and processed through the processing system.

[0050] In the embodiment, the camera 1 can be arranged outside the bin 3, and a transparent window 2 is arranged on the bin 3. The camera 1 is adapted to shoot towards the transparent window 2 to determine the apparent density in real time during the material falling process of the bin 3. Through the arrangement, the material in different positions of the same batch can be determined during the material continuously falling from the bin 3, and then the apparent density value is obtained according to the mapping relationship. Through the dynamic monitoring mode, the accuracy of the determined value is improved. In the embodiment, the processing system can be integrated in the control system. The processing system for image analysis and recognition is prior art, which will not be described here.

[0051] In an embodiment, the transparent window 2 is provided as an observation plate made of wear-resistant transparent material, so that the falling material can be observed in real time. The transparent window 2 can be inclined or perpendicular to the horizontal plane, and the shooting direction of the camera 1 is perpendicular to the transparent window 2 to obtain a more real and accurate surface image.

[0052] In other embodiments, the determination device can be arranged outside the bin 3. Specifically, a conveying belt is arranged below the bin 3, and the camera 1 is arranged above the conveying belt to shoot the material falling into the conveying belt, that is, to shoot and analyze the already fallen material to obtain the apparent density measurement value.

[0053] It should be noted that the arrangement of the camera 1 is only one or several embodiments, including but not limited to the above.

[0054] It should be understood that: the above is only the preferred embodiment of the present application, and the protection scope of the present application is not limited to the above-mentioned embodiments. Any technical solution falling within the scope of the present application is within the protection scope of the present application.

[0055] The above description of the drawings used in the embodiments only shows some embodiments of the present application, and should not be considered as limiting the scope. For those skilled in the art, other related drawings can be obtained from the drawings without creative labor.

Claims

1. A method of predicting apparent density, characterized by, The method comprises the following steps: S1: extracting a material sample, and piling up, and then taking a picture by a camera to obtain a surface image of the material; S2: obtaining a grain outline of the material in the surface image based on picture processing; S3: determining a corresponding envelope rectangular frame of each grain according to the outline of the grain; then obtaining a long side and a short side of the rectangular frame; and then taking half of the long side as the particle size of the grain; S4: classifying the particle size according to the grains obtained in S3, obtaining a corresponding percentage of the grains in different particle size range distribution according to the ratio of the number of grains in different particle size range to the total number of grains in the surface image; and measuring the apparent density according to an industry standard or a national standard; S5: repeat S1-S4 to obtain the particle size range distribution data B = {b i j} and apparent density measurement value C = {c i} T , where i represents the i-th batch of material; j represents the j-th row of data; T represents matrix transposition. S6: establishing a mapping relationship between the particle size range distribution data B and the apparent density measurement value C, and obtaining the mapping relationship by a function F, that is, C=F(B), to obtain accurate apparent density information.

2. The method of predicting apparent density according to claim 1, wherein, The mapping relationship between the particle size range distribution data and the apparent density measurement value is established by a neural network or a response surface method or a Kriging model.

3. The method of predicting apparent density according to claim 1, wherein, In step S1, the camera is perpendicular to the material plane during shooting.

4. An apparatus for measuring the apparent density of a powder, for use in the method of predicting the apparent density according to any one of claims 1 to 3, characterized in that The camera is connected with a processing system, and the camera is suitable for shooting toward the plane where the material is located, and the image shot by the camera is recognized and processed by the processing system.

5. The assay device of claim 4, wherein, The camera is suitable for being arranged outside the silo, a transparent window is arranged on the silo, and the camera is suitable for shooting toward the transparent window to measure in real time during the material falling process of the silo.

6. The assay device of claim 5, wherein The transparent window is provided with an observation plate made of wear-resistant transparent material.

7. The assay device of claim 4, wherein The silo is provided with a conveying belt below, and the camera is arranged above the conveying belt to shoot the material falling into the conveying belt.

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