Method for predicting actual bottle explosion rate of production line

Abstract

The invention is suitable for the technical field of quality control of glass packaging containers, and provides a method for predicting the actual bottle explosion rate of a production line, and the method comprises the steps: determining the model of a predicted beer bottle; extracting the glass bottles with different shoulder wear widths behind the bottle inspection machine, and detecting and respectively counting the bottle explosion rates of the glass bottles with different shoulder wear widths; sampling bottles are randomly extracted and recycled behind a bottle inspection machine, and the proportion of glass bottles with different shoulder abrasion widths is counted. Based on the proportions of different worn bottles and the laboratory detection bottle explosion rates of different wear widths, carrying out weighted fitting on the total laboratory bottle explosion rate; and obtaining the bottle explosion rate of the glass bottled beer filling line based on an empirical conversion coefficient between the total bottle explosion rate of the fitting laboratory and the actual bottle explosion rate of the filling line. According to the method for predicting the actual bottle explosion rate of the production line, the removal standard of the bottle inspection machine on the shoulder abrasion width of the recycled bottle can be guided and adjusted on the basis of the bottle explosion rate control requirement of the filling line, and the bottle explosion risk of glass bottled beer filling, storage and market circulation is reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of glass packaging container quality control, and in particular relates to a method for predicting an actual bottle explosion rate of a production line. Background Art

[0002] The main packaging material for beer in my country is glass. Glass bottles are favored by the beer industry for their stable material structure, transparency, good barrier properties, and low prices, accounting for 70% of the total usage. However, glass is brittle, and collisions and frictions during production, processing, transportation, filling, beer product circulation, and glass bottle recycling can easily lead to microcracks, causing the surface of the beer bottle to become rough and lose its luster. The strength of the beer bottle decays, which not only affects the image of the beer product, but also easily causes beer bottles to explode. In the process of beer production and processing, the bottle explosion rate is usually below 3‰, and a well-controlled filling line can control the bottle explosion rate below 20PPM (parts per million).

[0003] At present, beer factories mainly monitor the quality of recycled beer bottles at the factory and on the production line. Due to the complexity of beer bottle recycling channels and the unstable source of recycled bottles, the sampling representativeness of the test results is poor, and only bottles that are obviously damaged can be picked out. For bottle source quality monitoring on the production line, the sampling site is generally located behind the bottle inspection machine. The test results are affected by the uncertainty of the bottle source composition and the objective conditions of the test sampling volume and sampling frequency, which affect its reflection of the actual situation and the control of glass bottle risks is lagging. Summary of the invention

[0004] The object of the present invention is to provide a method for predicting the actual bottle explosion rate of a production line, aiming to solve the technical problems existing in the prior art identified in the background technology.

[0005] The present invention is implemented as follows: a method for predicting the actual bottle explosion rate of a production line, the method comprising:

[0006] Step 1: Select a glass beer bottle of a specified model as the prediction object;

[0007] Step 2: Take 100 glass bottles with different shoulder wear widths from the bottle inspection machine, test the samples based on the impact resistance and internal pressure resistance test conditions specified in GB4544, and calculate the bottle burst rate X of glass bottles with different shoulder wear widths. i ;

[0008] Step 3: Randomly select 200 sample bottles for recycling after the bottle inspection machine, and calculate the proportion of glass bottles with different shoulder wear widths W i .

[0009] Step 4: Weighted fitting of the total laboratory bottle burst rate based on the proportion of bottles with different wear and the laboratory test bottle burst rate with different wear widths. The fitting formula is: Y = ∑W i X i ;

[0010] Step 5: Based on the empirical conversion coefficient k between the total bottle burst rate in the fitting laboratory and the actual bottle burst rate of the filling line, the predicted value of the bottle burst rate of the filling line under the current beer bottle shoulder wear width ratio composition condition is obtained, and the conversion formula is: Z=kY.

[0011] As a further solution of the present invention, the selection of different shoulder wear widths in step 2 is based on a predetermined wear width range.

[0012] As a further solution of the present invention, in step 4, a linear regression method is used to perform weighted fitting on the proportion of bottles with different wear and tear and the bottle burst rate in laboratory tests with different wear widths.

[0013] As a further solution of the present invention, the fitting formula of step 4 includes weight allocation for different wear widths.

[0014] As a further solution of the present invention, the determination of the empirical conversion coefficient k in step five is based on historical data analysis.

[0015] The beneficial effects of the present invention are:

[0016] The method proposed in the present invention for predicting the actual bottle burst rate of a production line can guide the adjustment of the rejection standard of the bottle inspector for the wear width of the shoulder of recycled bottles based on the bottle burst rate control requirements of the filling line, thereby reducing the risk of bottle bursting of glass bottled beer during filling, storage and market circulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] A method for predicting the actual bottle explosion rate of a production line, the method comprising:

[0020] Step 1: Select a glass beer bottle of a specified model as the prediction object;

[0021] Step 2: Take 100 glass bottles with different shoulder wear widths from the bottle inspection machine, test the samples based on the impact resistance and internal pressure resistance test conditions specified in GB4544, and calculate the bottle burst rate X of glass bottles with different shoulder wear widths. i ;

[0022] Step 3: Randomly select 200 sample bottles for recycling after the bottle inspection machine, and calculate the proportion of glass bottles with different shoulder wear widths W i .

[0023] Step 4: Weighted fitting of the total laboratory bottle burst rate based on the proportion of bottles with different wear and the laboratory test bottle burst rate with different wear widths. The fitting formula is: Y = ∑W i X i ;

[0024] Step 5: Based on the empirical conversion coefficient k between the total bottle burst rate in the fitting laboratory and the actual bottle burst rate of the filling line, the predicted value of the bottle burst rate of the filling line under the current beer bottle shoulder wear width ratio composition condition is obtained, and the conversion formula is: Z=kY.

[0025] The specific embodiments are as follows:

[0026] Detect the bottle burst rate X of a specific 500mL bottle with different shoulder wear widths i .

[0027] Sampling plan: 100 beer bottles with shoulder wear ≤1mm, 2mm, 3mm, 4mm, 5mm, and ≥6mm were selected;

[0028] Testing method: Bottles with the same wear width are divided into two groups, each with 50 bottles. The number of bottles that burst is tested according to the impact resistance and internal pressure resistance test conditions of the national standard GB4544 "Beer Bottles". The total burst rate X under different widths is calculated. i ;

[0029] Table 1. Test results of impact resistance and internal pressure resistance of a 500mL glass bottle in workshop A

[0030] Wear width ≤1mm 2mm 3mm 4mm 5mm ≥6mm Number of impact-resistant bottles, only 0 0 0 3 2 5 Number of bottles resistant to internal pressure explosion, only 0 0 6 6 9 10 <![CDATA[Total explosion bottle rate X i > 0％ 0％ 6.0％ 9.0％ 11.0％ 15.0％

[0031] Statistics on the percentage of different wear widths of recycled bottles of this bottle type used in packaging workshop A of a beer factory W i :

[0032] Randomly select 200 beer bottles from the bottle inspection machine;

[0033] Measure the wear width of the beer bottle shoulder;

[0034] Statistics on the percentage of beer bottles with wear width ≤1mm, 2mm, 3mm, 4mm, 5mm, and ≥6mm i The results are as follows:

[0035] Table 2. Proportion of different wear widths of recycled bottles in workshop A

[0036] Wear width ≤1mm 2mm 3mm 4mm 5mm ≥6mm <![CDATA[Proportion W i > 19.61％ 22.55％ 14.22％ 19.10％ 20.59％ 3.94％

[0037] Fit the total bottle explosion rate Y in the laboratory.

[0038] According to the different wear width ratios in the workshop, based on Y = ∑W i X i (X i Represents the bottle burst rate of different widths, W i Represents the proportion of different widths, and Y represents the total bottle explosion rate) fits the total bottle explosion rate in the laboratory, and the total bottle explosion rate is 5.52%.

[0039] Establishment of a calculation model for the bottle explosion rate in workshop A

[0040] Select bottling workshop A to collect and analyze the data on the bottle burst rate of recycled bottles of this bottle type.

[0041] The number of exploded bottles and the reasons for the explosions on each machine in workshop A were counted. After excluding the samples of explosions due to abnormal factors, the actual explosion rate of the filling line is shown in the table below.

[0042] Table 3. Statistics of bottle explosion rate of actual production line in workshop A

[0043] workshop Total bottle explosion rate in production, PPM Wine filling machine,PPM Sterilizer, PPM Palletizing, PPM Workshop A 4.51 2.54 0.80 1.17

[0044] Fit the correlation between the bottle explosion rate in laboratory sampling and the total bottle explosion rate in production, and output the prediction curve equation:

[0045] Z=kY×10 5

[0046] Where Y is the laboratory fitting bottle explosion rate (%)

[0047] Z is the actual bottle explosion rate of the production line (PPM)

[0048] k is an empirical constant, 10 5 is the unit conversion factor.

[0049] The calculated k is 8.17×10 -6 Therefore, the output production bottle explosion rate calculation model is:

[0050] Z = 8.17 × 10 -6 ×Y×10 5 , simplified to Z = 0.817 × Y.

[0051] Based on the above prediction model, the workshop A of the factory is expected to improve the quality of recycled bottles and reduce the production bottle explosion rate to 4.0PPM. According to Z = 0.817 × Y, it can be inferred that the laboratory fitting bottle explosion rate should be controlled at 4.90%. Referring to the implementation case, the parameters of the bottle inspection machine should be adjusted to remove bottles with shoulder wear > 5mm width. To verify the accuracy of the solution, the specific implementation is as follows.

[0052] Application examples:

[0053] Statistics on the proportion of different wear widths of recycled bottles in workshop A

[0054] Randomly select 200 beer bottles from the bottle inspection machine;

[0055] Measure the wear width of the beer bottle shoulder;

[0056] The percentage of beer bottles with wear width ≤1mm, 2mm, 3mm, 4mm, 5mm, and ≥6mm were counted. The results are as follows:

[0057] Table 4. Proportion of different wear widths of recycled bottles in workshop A

[0058] Wear width ≤1mm 2mm 3mm 4mm 5mm ≥6mm <![CDATA[Percentage W i > 23.05％ 21.95％ 19.51％ 18.29％ 17.20％ 0％

[0059] Fit the total bottle explosion rate Y in the laboratory.

[0060] According to the different wear width proportion W of the workshop i (Table 4), and the bottle burst rate X of different shoulder wear widths for a specific 500mL bottle type i (Table 1), based on Y = ∑W i X i (X i Represents the bottle burst rate of different widths, W i Represents the proportion of different widths, and Y represents the total bottle explosion rate) fits the total bottle explosion rate in the laboratory, and the total bottle explosion rate is 4.71%, which meets the requirement of the estimated value of 4.90%.

[0061] Statistics of bottle explosion rate on each machine in workshop A

[0062] After the parameters of the bottle inspection machine in workshop A were adjusted, the number of bottles exploded and the reasons for the explosion were counted. After excluding the samples of bottle explosion due to abnormal factors, the actual bottle explosion rate of the filling line was 3.94PPM, which was consistent with the set standard of 4.0PPM.

[0063] Table 5. Statistics of bottle explosion rate of actual production line in workshop A

[0064] workshop Total bottle explosion rate in production, PPM Wine filling machine,PPM Sterilizer, PPM Palletizing, PPM Workshop A 3.94 2.51 0.54 0.89

[0065] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0066] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0067] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for predicting the actual bottle explosion rate of a production line, characterized in that: The method comprises: Step 1: Select a specified type of glass beer bottle as the prediction object; Step 2: Take 100 glass bottles with different shoulder wear widths from the bottle inspection machine, test the samples based on the impact resistance and internal pressure resistance test conditions specified in GB4544, and calculate the bottle burst rate X of glass bottles with different shoulder wear widths. i ; Step 3: Randomly select 200 sample bottles for recycling after the bottle inspection machine, and count the percentage of glass bottles with different shoulder wear widths W i . Step 4: Weighted fitting of the total laboratory bottle burst rate based on the proportion of bottles with different wear and the laboratory test bottle burst rate with different wear widths. The fitting formula is: Y = ∑W i X i ; Step 5: Based on the empirical conversion coefficient k between the total bottle burst rate in the fitting laboratory and the actual bottle burst rate of the filling line, the predicted value of the bottle burst rate of the filling line under the current beer bottle shoulder wear width ratio composition condition is obtained, and the conversion formula is: Z=kY.

2. The method according to claim 1, characterized in that: The selection of different shoulder wear widths in step 2 is based on a predetermined wear width range.

3. The method according to claim 2, characterized in that In step 4, the linear regression method is used to perform weighted fitting on the proportion of bottles with different wear and tear and the bottle burst rate in laboratory tests with different wear widths.

4. The method according to claim 3, characterized in that The fitting formula in step 4 includes the weight assignment for different wear widths.

5. The method according to claim 4, characterized in that The determination of the empirical conversion coefficient k in step five is based on historical data analysis.

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