Method and process for determining the standard grain size of paddy rice based on probability analysis

Abstract

The application provides a method for determining the standard size of husked rice based on probability analysis, comprising the following steps: S1, counting the proportion of husked rice with different sizes and the average size of a test sample, and obtaining a content distribution curve of husked rice with different sizes; S2, screening the test sample to a critical value of husked rice content meeting the warehouse requirement, obtaining the content distribution curve of husked rice with different sizes after screening, taking out all the husked rice of the test sample, recording the total mass of the husked rice, and calculating the proportion of the mass of husked rice removed from the test sample to meet the warehouse requirement in the total mass of the husked rice; S3, calculating the standard boundary value of the test sample after removing the husked rice; S4, repeating steps S1-S3 by selecting multiple test samples, and obtaining the standard boundary value and the average size of the rice of each test sample; and S5, establishing a functional relationship between the average size of the test sample and the standard size boundary value. The application can quickly determine the screen size of the rice with excessive husked rice before entering the warehouse.

Description

Technical Field

[0001] This invention relates to the field of rice dehulling technology, specifically to a method and process for determining the standard particle size for rice dehulling based on probability analysis. Background Technology

[0002] my country is a major rice producer with a large planting area and an annual total output of 220 million tons, playing an important role in the national economy and serving as a cornerstone for ensuring national food security. Due to the increased mechanization of agriculture, rice grains are more easily dehulled and formed into brown rice after being subjected to impacts and friction during harvesting, threshing, drying, loading, unloading, and transportation.

[0003] National standards stipulate that rice with a brown rice content exceeding 2% cannot be accepted into grain depots as policy-related grain (because brown rice affects the storage performance of paddy). However, in most years, the brown rice content in rice is typically between 3% and 5%, reaching as high as around 8%, indicating that excessive brown rice content is quite common. Traditional methods for removing brown rice from paddy before storage lack specific methods and processes, leading to potential storage safety risks due to excessive brown rice content. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by providing a method and process for determining the standard particle size for rice dehulling based on probability analysis.

[0005] This invention provides a method for determining the standard grain size for rice hulling based on probability analysis, comprising:

[0006] S1. Take the total mass with excessive roughness as M. n Using a specific variety of rice as the experimental sample, the proportion of brown rice of different grain sizes and the average grain size A of the experimental sample were statistically analyzed. n Obtain the content distribution curves of brown rice with different grain sizes in the experimental samples. a (x);

[0007] S2. Sieve the test samples until the brown rice content meets the critical value 'a' for warehousing requirements. Calculate the percentage of brown rice of different particle sizes in the sieved test samples and obtain the brown rice content distribution curve 'f' for different particle sizes in the sieved test samples. b (x), remove all the brown rice from the test sample and record the total mass m of the brown rice. n2 Calculate the mass (t) of brown rice required to remove from the test sample to meet the storage requirements. n m of total brown rice n2 The proportion β n ;

[0008] S3, according to f a (x), β n f b(x) Calculate the standard boundary value for removing brown rice from the test sample.

[0009] S4. Select m test samples and repeat steps S1 to S3 to obtain the standard boundary values ​​for each test sample. And the average grain size of rice A1, A2, A3, ... A m ;

[0010] S5. Establish the boundary value between the average particle size A of the test samples and the standard particle size. The functional relationship between them.

[0011] This invention plots curve f based on the distribution characteristics of brown rice content at different grain sizes. a (x) and f b (x), according to f a (x), β n f b (x) Calculate the standard boundary value for removing brown rice from the test sample. Based on probability analysis, the average particle size A of multiple experimental samples is compared with the standard particle size boundary value. Establish a functional relationship between them, so that the standard grain size boundary value can be obtained by only needing to obtain the average grain size A of a certain rice grain. Standard particle size boundary value This refers to the sieve aperture size of the screening equipment. Rice that meets the storage standards can be screened through a sieve with this aperture size. It can quickly determine the sieve aperture size of rice with excessive roughness before storage, improve the screening speed of rice with excessive roughness before storage, and help ensure the safety of stored grain.

[0012] Further, step S3 includes: standard boundary values. x' n For curve f b The minimum value of the x-coordinate in (x), x n This represents the theoretical granularity boundary.

[0013] x n According to f a (x) The theoretical particle size boundary obtained by calculation is the brown rice with the smallest particle size in the experimental sample after screening, x' n For curve f b The minimum value of the horizontal axis in (x) is the smallest brown rice in the actual screening test sample. By averaging the experimental particle size boundary with the calculated theoretical particle size boundary, the error can be effectively reduced, making the standard particle size boundary value more accurate.

[0014] Furthermore, the theoretical granularity boundary x n The calculation methods include:

[0015] Calculate curve f a The total area S of (x),

[0016] Establish the particle size distribution curve f corresponding to the brown rice to be removed. a The formula for calculating the area S1 of (x) is as follows:

[0017] according to Calculate the theoretical granularity boundary x n ;

[0018] Where, x a0 For curve f a The minimum value of the x-coordinate in (x), x an For curve f a The maximum value of the x-coordinate in (x).

[0019] For the entire curve f a (x) and curve f a Integrating on the left side of (x) allows us to calculate the theoretical granularity boundary x. n .

[0020] Further, in step S2, according to Total mass of test sample M n Total mass of brown rice (m) n2 Calculate the mass (t) of brown rice required to remove from the test sample to meet the storage requirements. n ,calculate

[0021] Furthermore, in steps S1 and S2, the content of brown rice with different particle sizes and their corresponding contents are used as coordinate points, and regression analysis is used to fit the data to obtain f. a (x) and f b (x).

[0022] Further, step S1 includes: measuring the width of all brown rice in the test sample to obtain the minimum and maximum widths; uniformly setting multiple width intervals between the minimum and maximum widths; calculating the percentage of brown rice in each width interval relative to the total mass of brown rice in the test sample; taking the median of each width interval as the abscissa and the percentage of brown rice in each width interval relative to the total mass of brown rice in the test sample as the ordinate; and fitting the curve to obtain curve f. a (x).

[0023] By setting multiple width intervals, the width of brown rice within each width interval is represented by the median of that width interval, which facilitates the statistical analysis of coordinate points and subsequent curve fitting.

[0024] Furthermore, in step S5, Lagrange interpolation is used to obtain the boundary value between the average particle size A of the test sample and the standard particle size. Functional relationship between Where i≠j.

[0025] Furthermore, in step S4, the m test samples include multiple different varieties of rice.

[0026] By obtaining the average particle size A and the standard particle size boundary value of rice varieties. The functional relationship between the two allows the standard particle size boundary value of the rice variety to be directly calculated when the average particle size of the rice in the storage is known, thus providing a reference for the selection of the sieve.

[0027] The present invention also provides a method for determining the sieve aperture size of a rice dehulling and screening device, comprising: based on the average particle size A of the rice and the standard particle size boundary value. The functional relationship between the two is used to calculate the standard particle size boundary value of rice. Based on the standard particle size boundary value, the screen aperture size of the screening equipment is determined, and the screen aperture size of the screening equipment is equal to the standard particle size boundary value.

[0028] This invention also provides a rice dehulling process, comprising: classifying different varieties of rice with excessive hull content according to their average grain size A, and determining the boundary value between the average grain size A and the standard grain size. The functional relationship between the two is used to calculate the standard particle size boundary value corresponding to each variety of rice. Based on the standard particle size boundary value, the screen aperture size of the screening equipment corresponding to each variety of rice is determined. The various types of rice are screened and de-roughened using the corresponding screening equipment, and the material on the screen of the screening equipment is directly put into storage.

[0029] Furthermore, the process includes: the screened material enters a gravity paddy rice separator for further debrowning; the paddy rice separator separates the remaining paddy rice from the screened material and stores it directly in the warehouse; the separated brown rice enters the production line for processing into rice.

[0030] The beneficial effects of this invention are: curves f are plotted based on the distribution characteristics of brown rice content at different grain sizes. a (x) and f b (x), according to f a (x), β n f b (x) Calculate the standard boundary value for removing brown rice from the test sample. Based on probability analysis, the average particle size A of multiple experimental samples is compared with the standard particle size boundary value. Establish a functional relationship between them, so that the standard grain size boundary value can be obtained by only needing to obtain the average grain size A of a certain rice grain. Standard particle size boundary value This refers to the screen aperture size of the screening equipment. Rice that meets the storage standards can be screened through a screen with this aperture size. This can increase the screening speed of rice with excessive roughness before storage and help ensure the safety of stored grain. Attached Figure Description

[0031] Figure 1 This is a graph showing the change in brown rice content with particle size in the test samples of this invention.

[0032] Figure 2 This is a graph showing the change in brown rice content with particle size in the test samples before and after sieving in an embodiment of the present invention.

[0033] Figure 3 The average particle size A of the test samples in this invention is the boundary value between the standard particle size and the standard particle size. A graph showing the functional relationship between them;

[0034] Figure 4 This is a process flow diagram of the present invention. Detailed Implementation

[0035] To make the technical problems, technical solutions, and beneficial effects to be solved by this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and are not intended to limit the scope of this application.

[0036] like Figure 1 As shown, the average size of brown rice is smaller than that of paddy rice. The content of brown rice and paddy rice of different grain sizes has a natural property, that is, according to the grain size (taking the width of the brown rice as the standard), the content of brown rice and paddy rice increases first and then decreases along the grain size axis.

[0037] This invention provides a method for determining the standard particle size for rice hulling based on probability analysis:

[0038] 100g of Xianghan 45 indica rice was randomly weighed as test sample 1, and the mass of brown rice m was measured. 12 =4g, mass of rice m 11 =96g, the total sample mass is M1=100g, and the average width of Xianghan 45 rice is A1=2.57mm.

[0039] The width of all brown rice in the pre-sieve sample was measured, with a minimum width of 1.92 mm and a maximum width of 2.64 mm. Width ranges were set as follows: 1.85 mm to 1.95 mm, 1.95 mm to 2.05 mm, 2.05 mm to 2.15 mm, 2.15 mm to 2.25 mm, 2.25 mm to 2.35 mm, 2.35 mm to 2.45 mm, 2.45 mm to 2.55 mm, and 2.55 mm to 2.65 mm. The medians of each range were taken as 1.90 mm, 2.00 mm, 2.10 mm, 2.20 mm, 2.30 mm, 2.40 mm, 2.50 mm, and 2.60 mm. The percentage of brown rice in each width range to the total number of brown rice in the sample was calculated, as shown in Table 1.

[0040] Table 1

[0041]

[0042] By applying regression analysis and mapping the data measured in Table 1 one by one, the fitted curve function is obtained:

[0043] f1(x) = -65.385x 5 +735.52x 4 -3291.8x 3 +7324.4x 2 -8100x+3561.1, R 2 =0.9974, therefore the goodness of fit of the curve function model is considered to be high.

[0044] The test samples were sieved using a grading sieve. Sieving was stopped when the brown rice content on the sieve reached 2%. The width of all brown rice on the sieve was measured, with the minimum width being 2.10 mm and the maximum width being 2.62 mm. Width ranges were set as 2.10 mm to 2.20 mm, 2.20 mm to 2.30 mm, 2.30 mm to 2.40 mm, 2.40 mm to 2.50 mm, 2.50 mm to 2.60 mm, and 2.60 mm to 2.70 mm. The medians of each range were taken as 2.15 mm, 2.25 mm, 2.35 mm, 2.45 mm, 2.55 mm, and 2.65 mm. The percentage of brown rice in each width range relative to the total number of brown rice samples was calculated, as shown in Table 2.

[0045] Table 2

[0046]

[0047] By applying regression analysis and mapping the data measured in Table 2 one by one, the fitted curve function is obtained:

[0048] R 2=0.9983, therefore the goodness of fit of the curve function model is considered to be high.

[0049] The two fitted curves f1(x) above like Figure 2 As shown.

[0050] The mass of brown rice t1 required to be removed to meet the storage requirements of the test sample is calculated based on the critical value a = 2%: t1 = (4 - 0.02 × 100) / 0.98 ≈ 2g.

[0051] The required amount of brown rice to be removed, t1, as a percentage of the total amount of brown rice, m 12 The ratio β1 is: β1 = 2 / 4 = 0.5.

[0052] When calculating the critical value a = 2%, the theoretical grain size boundary x1 of brown rice needs to be removed. The specific calculation method is as follows:

[0053] Based on the function f1(x), the total area S corresponding to the function is calculated as follows:

[0054]

[0055] The ratio of area S1 to total area S represents the proportion of brown rice mass to be removed out of the total brown rice mass, i.e., S1 = β1S = 0.5 × 0.207 = 0.104.

[0056] The theoretical granularity boundary x1 will affect the function f a (x) is divided into two parts, left and right. The area of ​​the left part is S1, calculated using the following formula:

[0057]

[0058] The theoretical particle size boundary for removing brown rice was calculated to be x1 = 2.21 mm.

[0059] Based on the fitting curve and its function corresponding to the proportion of the width of the sieved brown rice, the abscissa value corresponding to the leftmost end of the curve is taken as the experimental particle size boundary x'1, x'1 = 2.15 mm.

[0060] Therefore, the standard particle size boundary value of test sample 1

[0061] 100g of Xiangnuo No. 28 glutinous rice was randomly weighed as test sample 2, and 100g of Xiushui No. 09 japonica rice was weighed as test sample 3. The mass m of brown rice in test samples 2 and 3 was measured respectively. 22 =4g,m 32 =4g, mass of rice m 21 =96g,m 31=96g, the total sample mass was M2=100g, M3=100g, the average width of Xiangnuo 28 rice was A2=2.78mm, and the average width of Xiushui 09 rice was A3=3.26mm. Repeating the above experiment, the theoretical particle size boundaries for critical brown rice removal of Xiangnuo 28 and Xiushui 09 were obtained as x2=2.42mm and x3=2.90mm, respectively; the experimental particle size boundaries were x'2=2.36mm and x'3=2.84mm, respectively; and the standard particle size boundary values ​​were...

[0062] With the average grain size An of rice as the independent variable and the standard boundary value n as the dependent variable, as shown in Table 3 (this experiment only uses three different varieties of rice as examples; increasing the number of rice varieties will improve the accuracy of the following functional relationship), the functional relationship between the average grain size of rice and the standard grain size boundary value after removing the husks in the rice can be obtained by applying Lagrange interpolation:

[0063] Table 3

[0064]

[0065]

[0066] Average grain size A of rice n Corresponding standard particle size boundary value Function curves as follows Figure 3 As shown.

[0067] The average width A of this rice variety was measured before the raw grain was put into storage. n Substituting these values ​​into the above formula, the standard particle size boundary value for removing brown rice from the paddy can be determined. Based on this standard particle size boundary value, the required sieve aperture size for sieving equipment to remove brown rice can be determined. All types of paddy are first sieved through a primary sieve for browning. The sieve aperture size corresponding to each type of paddy is the standard particle size boundary value for browning removal from the paddy. The material oversizes from the primary sieve is directly stored in a warehouse, while the material undersizes enters a gravity paddy-brown separator for further browning. The paddy-brown separator separates the remaining paddy from the undersize and stores it directly in a warehouse, while the separated brown rice is processed into rice on the production line. The paddy browning process in the warehouse is as follows: Figure 4 As shown.

[0068] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for determining the standard grain size of unhusked rice based on probability analysis, characterized by: Comprise: S1, taking total mass M containing excessive amount of unpolished rice n of a certain variety of paddy as a test sample, counting proportions of different sizes of unpolished rice in the test sample and average size A of the test sample n , obtaining a content distribution curve f a (x) of different sizes of unpolished rice in the test sample. S2, screen the test sample to a critical value a containing the amount of unpolished rice meeting the warehouse requirements, count the proportion of unpolished rice of different particle sizes in the test sample after screening, and obtain the content distribution curve f of unpolished rice of different particle sizes in the test sample after screening b (x), take out all the unpolished rice of the test sample, and record the total mass m of unpolished rice n2 , calculate the mass t of unpolished rice to be removed to meet the warehouse requirements of the test sample n , and the proportion β of the total mass m of unpolished rice n2 n ;​ S3、According to f a (x), β n , f b (x) calculating the standard boundary value of the test sample removing brown rice The step S3 comprises a standard boundary value x' n is the minimum value of the abscissa in the curve f b (x), x n is the theoretical particle size boundary; S4, repeating steps S1-S3 for m test samples to obtain standard boundary values for each test sample and average particle sizes A1, A2, A3,..., A of the rice grains m ; S5, building a function relationship between the average particle size A of the test sample and the standard particle size boundary value ; In step S5, a function relationship between the average particle size A of the test sample and the standard particle size boundary value is obtained by Lagrange interpolation where i≠j.​ 2. The method of determining the paddy unhusked standard grain size based on probability analysis according to claim 1, characterized in that: Theoretical granularity boundary x n The method of calculating includes: calculating the curve f a the total area S of (x), establishing a particle size distribution curve f corresponding to the unpolished rice to be removed a a formula for calculating the area S1 of (x), According to Theoretical particle size boundary x n ; where x a0 is the minimum value of the abscissa in the curve f a (x) and x an is the maximum value of the abscissa in the curve f a (x).

3. The method of determining the paddy unhusked standard grain size based on probability analysis according to claim 1, characterized in that: In the step S2, the total mass M of the test sample is calculated according to the total mass M of the test sample n the total mass m of the brown rice n2 the mass t of the brown rice to be removed for the test sample to meet the storage requirement n the mass t of the brown rice to be removed for the test sample to meet the storage requirement 4. The method of determining the paddy unhusked standard grain size based on probability analysis according to claim 1, characterized in that: The step S1 comprises: measuring the width of all the brown rice in the test sample, obtaining the minimum width and the maximum width, evenly setting a plurality of width intervals between the minimum width and the maximum width, counting the percentage of the brown rice in each width interval in the total mass of the brown rice in the test sample, taking the median of each width interval as the abscissa, the percentage of the brown rice in each width interval in the total mass of the brown rice in the test sample as the ordinate, performing fitting, and obtaining a curve f a (x).

5. The method of determining the paddy unhusked standard grain size based on probability analysis according to claim 1, characterized in that: In the step S4, the m test samples include rice of different varieties.

6. A method of determining the mesh size of a paddy dehulling and sizing apparatus, characterised in that: Comprise: S1, taking total mass M containing excessive amount of unpolished rice n of a certain variety of paddy as a test sample, counting the proportions of different sizes of unpolished rice in the test sample and the average size A of the test sample n , obtaining the content distribution curve f a (x) of different sizes of unpolished rice in the test sample. S2, screen the test sample to a critical value a containing the amount of unpolished rice meeting the warehouse requirements, count the proportion of unpolished rice of different particle sizes in the test sample after screening, and obtain the content distribution curve f of unpolished rice of different particle sizes in the test sample after screening b (x), take out all the unpolished rice of the test sample, and record the total mass m of unpolished rice n2 , calculate the mass t of unpolished rice to be removed to meet the warehouse requirements of the test sample n , and the proportion β of the total mass m of unpolished rice n2 n ;​ S3、According to f a (x), β n , f b (x) calculating the standard boundary value of the test sample removing brown rice The step S3 comprises a standard boundary value x' n is the minimum value of the abscissa in the curve f b (x), x n is the theoretical size boundary; S4, repeating steps S1-S3 for m test samples to obtain standard boundary values for each test sample and average particle sizes A1, A2, A3,..., A of the rice grains m ; S5. Establish the boundary value between the average particle size A of the test samples and the standard particle size. The functional relationship between them; based on the average grain size A of rice and the standard grain size boundary value. The functional relationship between the two is used to calculate the standard particle size boundary value of rice. Based on the standard particle size boundary value, the screen aperture size of the screening equipment is determined. The screen aperture size of the screening equipment is equal to the standard particle size boundary value. In step S5, the function relationship between the average particle size A of the test sample and the standard particle size boundary value is obtained by Lagrange interpolation where i≠j.​ 7. A process for dehulling of paddy characterized in that: Comprise: S1, taking total mass M containing excessive amount of unpolished rice n of a certain variety of paddy as a test sample, counting the proportions of different sizes of unpolished rice in the test sample and the average size A of the test sample n , obtaining the content distribution curve f a (x) of different sizes of unpolished rice in the test sample. S2. Sieve the test samples until the brown rice content meets the critical value 'a' for warehousing requirements. Calculate the percentage of brown rice of different particle sizes in the sieved test samples and obtain the brown rice content distribution curve 'f' for different particle sizes in the sieved test samples. b (x), remove all the brown rice from the test sample and record the total mass m of the brown rice. n2 Calculate the mass (t) of brown rice required to remove from the test sample to meet the storage requirements. n m of total brown rice n2 The proportion β n ; S3、According to f a (x), β n , f b (x) calculating the standard boundary value of the test sample removing brown rice The step S3 comprises a standard boundary value x' n is the minimum value of the abscissa in the curve f b (x), x n is the theoretical size boundary; S4, repeating steps S1-S3 for m test samples to obtain standard boundary values for each test sample and average particle sizes A1, A2, A3,..., A of the rice grains m ; S5. Establish the boundary value between the average particle size A of the test samples and the standard particle size. The functional relationship between them; different varieties of rice with excessive roughness are classified according to their average grain size A, and the boundary value between the average grain size A and the standard grain size is determined. The functional relationship between the two is used to calculate the standard particle size boundary value corresponding to each variety of rice. Based on the standard particle size boundary value, the screen size of the screening equipment used for each variety of rice is determined. All types of rice are screened and de-roughened using the corresponding screening equipment. The material on the screen of the screening equipment is directly put into storage. In step S5, a function relationship between the average particle size A of the test sample and the standard particle size boundary value is obtained by Lagrange interpolation where i≠j.​ 8. The rice dehulling process according to claim 7, characterized in that: Comprise: The undersize enters a gravity husked rice separator for further husking, the husked rice separator separates the residual rice in the undersize and directly stores the separated rice in a warehouse, and the separated husked rice enters the production line for processing into rice.

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