Method and system for optimizing tobacco leaf laying station distribution of redrying factory

Abstract

The invention discloses a method and system for optimizing tobacco leaf laying station distribution of a redrying factory, and the method comprises the following steps: S1, obtaining the nicotine content: sampling each grade of tobacco leaves in a raw tobacco warehouse, uniformly mixing the selected samples, and then obtaining the nicotine content data of each grade of raw tobacco in each batch of formula list; the tobacco leaves in each batch of formula sheet are divided into multiple types according to producing areas and grades and marked as {X1, X2,..., Xn}, n is the number of types of the tobacco leaves of different producing areas and grades, the number of actual leaf laying tables of a redrying factory is Xmax, and distribution treatment is conducted according to the stacking rule; the nicotine content is detected through near-infrared equipment in a laboratory, so that the accuracy and reliability of data are ensured, and a solid foundation is provided for subsequent distribution and optimization; and an optimization algorithm is used for solving, so that an optimal solution meeting constraint conditions can be found, and the scientificity and efficiency of distribution are improved.

Description

Technical Field

[0001] This invention relates to the field of tobacco processing technology, specifically to a method and system for optimizing the allocation of tobacco leaf laying positions in a re-drying plant. Background Technology

[0002] In the tobacco processing chain, nicotine, as the core component of tobacco, has a direct impact on the quality of tobacco leaves and consumer health. Therefore, ensuring the homogenization of the intrinsic quality of tobacco leaves is crucial for improving the overall quality and market competitiveness of tobacco products.

[0003] As a fundamental step in the cigarette production process, leaf threshing and re-drying plays a decisive role in the quality of the final product. In recent years, with the tobacco industry's increasing demands for raw material homogenization, multi-grade compound threshing technology has gradually gained attention and widespread application. However, this processing method faces the challenge of uneven internal quality distribution when handling diverse, small-batch tobacco leaves, making it difficult to meet the high standards of raw material homogenization required in cigarette production.

[0004] Specifically, in the process of multi-grade compound tobacco leaf processing, the significant differences in the physical and chemical properties of different grades of tobacco leaves, coupled with the diversity of compounding parameters (such as grade, proportion, and origin), pose challenges to the uniformity of material feeding in traditional manual leaf spreading and stacking methods. In practice, it is difficult to unify the operating speed of workers at different positions, leading to uneven feeding during the process. At the same time, the complexity of compounding parameters increases the difficulty of material preparation management and worker operation, further affecting production efficiency and product quality.

[0005] Furthermore, current re-drying plants often use a method of directly mixing tobacco leaves according to grade ratios when implementing formulations. This method is prone to problems such as insufficient leaf-laying stations and uneven mixing when there are many grades in the formulation and some grades have a very small proportion, thus affecting the homogeneity of the finished tobacco leaves.

[0006] In summary, multi-grade compounding tobacco processing technology still faces many challenges in achieving homogenization of the intrinsic quality of tobacco leaves. To improve tobacco processing quality and meet the demands of cigarette production for raw material homogenization, innovative solutions are urgently needed. This invention addresses this issue by proposing a novel nicotine homogenization and balancing method, aiming to improve the homogenization and overall quality of tobacco processing by optimizing the threshing and re-drying process. Summary of the Invention

[0007] The purpose of this invention is to provide a method and system for optimizing the allocation of tobacco leaf laying positions in a re-drying plant, so as to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing the allocation of tobacco leaf laying positions in a re-drying plant, comprising the following steps: S1, Obtaining nicotine content: Sampling tobacco leaves of each grade in the raw tobacco warehouse, uniformly mixing the selected samples, and then obtaining the nicotine content data of the raw tobacco of each grade in each batch formula sheet; S2, Stacking: The tobacco leaves in each batch formula sheet are divided into multiple categories according to the place of origin and grade, marked as {X1, X2,..., Xn}, where n is the number of types of tobacco leaves with different places of origin and grades, and the actual number of laying tables in the re-drying factory is Xmax, and the allocation process is carried out according to the stacking rules; S3, Allocating Xmax tables to multiple strip laying lines, and the tables on each laying line are represented by A + laying line number + table number, meeting the corresponding allocation criteria; S4, Objective function: Z = CV_L + CV_NI, In the above formula, Z is the comprehensive score, CV_L is the coefficient of variation of the feeding flow between each laying line, and CV_NI is the coefficient of variation of nicotine between each laying line; Among them, the calculation formula of the coefficient of variation of the feeding flow CV_L is: CV_L = σ_L / C_m * 100%, where σ_L is the standard deviation of the feeding flow of each laying line, and C_m is the average value of the feeding flow of each laying line; The calculation formula of the weighted coefficient of variation of nicotine CV_NI is: CV_NI = σ_NI / μ_NI * 100%, where σ_NI is the standard deviation of the total weighted value of nicotine of m laying lines, and μ_NI is the average value of the total weighted value of nicotine of m laying lines; Among them, the weighted value of nicotine is the product of the nicotine of the raw tobacco of this grade and the number of bales fed at this table, and the total weighted value of nicotine on each line is the sum of the weighted values of nicotine of the raw tobacco on each table on this laying line; S5, Optimization solution: Taking the stacking rules in S2 and the table allocation criteria in S3 as constraints, and the Z comprehensive score in S4 as the objective function, using an optimization algorithm to solve for the optimal result.

[0009] Preferably, in S1, the nicotine content of the sample is detected by using a laboratory near-infrared device.

[0010] Preferably, the stacking rules in S2 are: n = Xmax: The number of tables is equal to the number of tobacco leaf grades, and direct allocation is carried out; n < Xmax: Calculate the average feeding weight Xa of each table = Xw / Xmax, where Xw represents the total feeding weight (number of bales) of the tobacco leaves in each batch formula sheet, and Xa represents the average feeding weight of each table; The highest-grade raw tobacco from the largest production area is divided into multiple blends, ensuring that the weight of each blend is closest to Xa; the process of dividing and stoking is repeated until n = Xmax; n>Xmax: Perform stacking processing.

[0011] Preferably, in step S3, Xmax stations are allocated to several leaf-laying lines, and the stations on each leaf-laying line are represented by A + leaf-laying line number + station number. The station allocation criteria are as follows: (1) The total amount of material fed on each of the m leaf-laying lines {A1,A2,A3,A4, ..., Am} is closest to Xw / m, and the error in the total amount of material fed among the m leaf-laying lines does not exceed 1%; (2) {A1,A2,A3,A4, ..., Am}, the weighted total nicotine value of each of the m leaf-laying lines is the same, and the error of the weighted nicotine value between the m leaf-laying lines does not exceed 1%; where the weighted nicotine value is the product of the nicotine of the raw tobacco of that grade and the number of feed loads at that station, and the weighted total nicotine value of each line is the sum of the weighted nicotine values ​​of the raw tobacco at each station on that leaf-laying line.

[0012] A system for optimizing the allocation of tobacco leaf laying positions in a re-drying plant includes: Nicotine content detection module: used to sample tobacco leaves of various grades in the raw tobacco warehouse and use near-infrared equipment to detect the nicotine content of the samples, and obtain the nicotine content data of each grade of raw tobacco in each batch formula sheet; Slagging module: Classifies tobacco leaves according to their origin and grade, and splits or stacks them according to slagging rules to ensure that the weight of each station is as close as possible to the average value. Station allocation module: Used to allocate stations to multiple leaf-laying lines, ensuring that the total amount of feed and the weighted total amount of nicotine on each leaf-laying line are close, with an error of no more than 1%; Optimization Solution Module: Similar to using optimization algorithms to solve the station allocation scheme, the module aims to minimize the coefficient of variation of feed flow rate and nicotine weighted value to find the optimal station allocation scheme.

[0013] Compared with existing technologies, the beneficial effects of this invention are as follows: Detecting nicotine content using laboratory near-infrared equipment ensures data accuracy and reliability, providing a solid foundation for subsequent allocation and optimization; using optimization algorithms to solve the problem allows for finding the optimal solution under constraints, improving the scientific nature and efficiency of allocation; the stacking rule considers the relationship between the number of tobacco varieties from different origins and grades and the number of stacking tables, ensuring full utilization of stacking tables and uniform distribution of tobacco leaves through splitting or combining stacks; the stacking allocation criterion considers the balance between the total feed amount and the weighted total nicotine value, ensuring relatively consistent workload and nicotine content across each stacking line, improving production stability and product quality; the objective function Z comprehensively considers the coefficient of variation of feed flow rate and the coefficient of variation of nicotine, both of which directly reflect the uniformity and stability of the production process, highly aligning with the actual needs of production optimization; minimizing the Z value enables refined control of the production process, improving production efficiency and product quality. Attached Figure Description

[0014] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Please see Figure 1 The present invention provides a technical solution: A system for optimizing the allocation of tobacco leaf laying positions in a re-drying plant includes: a nicotine content detection module: used to sample tobacco leaves of various grades in the raw tobacco warehouse and use near-infrared equipment to detect the nicotine content of the samples, obtaining nicotine content data for each grade of raw tobacco in each batch of formulations; a stacking processing module: classifying tobacco leaves according to their origin and grade, and splitting or stacking the tobacco leaves according to stacking rules to ensure that the feeding weight of each position is closest to the average value; a position allocation module: used to allocate positions to multiple leaf laying lines, ensuring that the total feeding amount and the weighted total nicotine value on each leaf laying line are close, with an error not exceeding 1%; and an optimization solution module: using an optimization algorithm to solve the position allocation scheme, aiming to minimize the coefficient of variation of the feeding flow rate and the weighted nicotine value, and finding the optimal position allocation scheme.

[0017] It should be noted that the nicotine content detection module and the slagging processing module of this invention are unidirectionally connected, with data flowing from the detection module to the slagging processing module. The slagging processing module and the station allocation module are also unidirectionally connected, with data flowing from the slagging processing module to the station allocation module. The station allocation module and the optimization solution module are bidirectionally connected, allowing them to exchange data and feedback information. The optimization solution module and the output result are unidirectionally connected, with the optimization solution module outputting the final result without feedback. When the system is in operation: In the raw tobacco warehouse, the nicotine content detection module takes samples of tobacco leaves of each grade, mixes the samples evenly, and uses the laboratory's near-infrared equipment to detect the nicotine content of the samples, thereby obtaining the nicotine content data of each grade of raw tobacco in each batch of formula sheet; Based on the origin and grade of the tobacco leaves, the slagging module classifies the tobacco leaves and slagging them according to the set slagging rules to ensure that the feeding weight of each station is as close as possible to the average value. Especially when the number of tobacco leaf grades is less than the number of slagging stations, the information of the slagging tobacco leaves will be transmitted to the station allocation module. The station allocation module assigns each station to a different leaf-laying line to ensure that the total amount of feed and the weighted total value of nicotine on each leaf-laying line are as balanced as possible to meet production requirements. Through reasonable station allocation, the quality fluctuations caused by uneven feed can be effectively reduced. The optimization solution module uses optimization algorithms to solve the platform allocation scheme and find the optimal laying scheme. By comprehensively considering the coefficient of variation of feed flow rate and nicotine variation of each leaf-laying line, the best homogenization effect is achieved. The final output solution will be provided to the production line to guide actual operation. Based on the optimal solution output by the optimization solution module, the actual tobacco leaf laying operation is carried out. During the production process, the quality and homogenization of the tobacco leaves are continuously monitored, and feedback data is collected. The feedback data can be used for subsequent optimization and adjustment, forming a closed loop, which further improves the efficiency of the system and the quality of the products.

[0018] Example: A method for optimizing the allocation of tobacco leaf laying positions in a re-drying plant: S1 Nicotine Content Acquisition: 1.1 Samples of tobacco leaves of various grades were taken from the raw tobacco warehouse and the selected samples were mixed evenly.

[0019] 1.2 The nicotine content of the samples was detected using a laboratory near-infrared spectroscopy system to obtain the nicotine content data for each grade of raw tobacco in each batch of formulations. See Table 1 below; Table 1. Nicotine of various grades in a certain batch of formulations S2 points 2.1 Marking and Number of Laying Platforms: The tobacco leaves in this batch of formula are divided into 14 types according to origin and grade, marked as {X1, X2, ..., X14}. It is assumed that the actual number of laying platforms in the re-drying plant is 28.

[0020] 2.2 Slagging Rules: The tobacco leaves in this batch of formula are divided into 14 types according to origin and grade, which is less than the actual number of leaf-laying tables (28) in the re-drying plant. Therefore, slagging is required. At the same time, the average feed weight per table is calculated as 22032.08 (dan) / 28 = 786.86 (dan).

[0021] S3 split position: 3.1 Station allocation criteria: The 28 stations are allocated to 4 leaf-laying lines. The stations on each leaf-laying line are represented by A + leaf-laying line number + station number. For example, A23 represents station number 3 on line 2.

[0022] The following allocation criteria must be met: (1) {A1,A2,A3,A4}, the total amount of material fed on each of the four leaf-laying lines should be as close as possible to 22032.08 (dan) / 4 = 5508.02 (dan), and the error in the total amount of material fed among the four leaf-laying lines should not exceed 1%; (2) The weighted total nicotine value of each of the four lay-up lines {A1,A2,A3,A4} should be as close as possible, and the error of the weighted total nicotine value between the four lay-up lines should not exceed 1%; S4 Objective function: Z = CV_L + CV_NI In the above formula, Z is the comprehensive score, CV_L is the coefficient of variation of feed flow rate between each leaf-laying line, and CV_NI is the weighted coefficient of variation of nicotine between each leaf-laying line. As shown in Tables 2-5 below, the flow rates of the four lines are 5500.22, 5515.82, 5515.82, and 5500.22, respectively, and the corresponding weighted nicotine values ​​are 2.1, 2.12, 2.12, and 2.1. The standard deviation of CV_L is 9.01, and the mean is 5508.02. Therefore, the coefficient of variation of CV_L is 9.01 / 5508.02*100=0.16%. The standard deviation of CV_NI is 0.009, and the mean is 2.11. Therefore, the coefficient of variation of CV_NI is 0.009 / 2.11*100=0.44%. Z = CV_L + CV_NI = 0.6%. S5 Optimization Solution: Considering that there are no order constraints between the 28 stations and the four production lines, there are a total of 28! / 4! possibilities. Therefore, we cannot use a traversal algorithm to find the global optimal solution. Instead, we consider finding the local optimal solution.

[0023] Therefore, the above-mentioned S2 slab arrangement rule and S3 platform allocation criterion are used as constraints, and the Z-comprehensive score in S4 is used as the objective function. An optimization algorithm is used to solve for the optimal result. To find the optimal solution under the effect evaluation system within a finite space set, a computer is considered to traverse a large amount of data to find the highest score as the optimal solution. 10,000 leaf-laying arrangements are randomly generated, and the Z-comprehensive score is used to score them, recording the scores. The optimal solution is shown in Table 2-5 below. As can be seen from Table 2-5, the total amount of leaves laid on the four lines is roughly the same, and the weighted nicotine values ​​for the four conditions are also roughly the same.

[0024] Table 2. Leaf Laying Station Design for Line 1 of Leaf Laying Platform Table 3. Leaf Laying Station Design for Line 2 of Leaf Laying Platform Table 4. Leaf Laying Station Design for Line 3 Table 5. Design of Leaf Laying Stations for 4 Lines at the Leaf Laying Platform Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing the allocation of tobacco leaf laying platforms in a re-drying plant, characterized in that: Includes the following steps: S1. Obtaining Nicotine Content: Samples of tobacco leaves of various grades are taken from the raw tobacco warehouse, and the selected samples are mixed evenly. Then, the nicotine content data of each grade of raw tobacco in each batch formula is obtained. S2, Slagging: The tobacco leaves in each batch of formula are divided into multiple types according to origin and grade, marked as {X1, X2,..., Xn}, where n is the number of tobacco leaf types of different origin and grade. The actual number of leaf-laying tables in the re-drying plant is Xmax, which is allocated according to the slagging rules. S3. Allocate Xmax stations to multiple leaf roll lines. Each station on a leaf roll line is represented by A + leaf roll line number + station number, satisfying the corresponding allocation criteria. S4, Objective Function: Z = CV_L + CV_NI In the above formula, Z is the overall score, CV_L is the coefficient of variation of feed flow rate between each leaf-laying line, and CV_NI is the coefficient of variation of nicotine between each leaf-laying line. The formula for calculating the coefficient of variation of feed flow rate CV_L is: CV_L = σ_L / C_m*100%, where σ_L is the standard deviation of feed flow rate of each leaf-laying line and C_m is the mean of feed flow rate of each leaf-laying line. The formula for calculating the nicotine-weighted coefficient of variation CV_NI is: CV_NI = σ_NI / μ_NI*100%, where σ_NI is the standard deviation of the total weighted nicotine values ​​of the m leaf-laying lines, and μ_NI is the mean of the total weighted nicotine values ​​of the m leaf-laying lines. The weighted value of nicotine is the product of the nicotine content of the raw tobacco of that grade and the number of feed loads at that station. The total weighted value of nicotine on each line is the sum of the weighted values ​​of nicotine in the raw tobacco at each station on that leaf-laying line. S5. Optimization Solution: Using the above S2 partitioning rules and S3 platform allocation criteria as constraints, and the Z comprehensive score in S4 as the objective function, the optimization algorithm is used to solve for the optimal result.

2. The method for optimizing the allocation of tobacco leaf laying positions in a re-drying plant according to claim 1, characterized in that: In step S1, the nicotine content of the sample is detected using a laboratory near-infrared spectroscopy device.

3. The method for optimizing the allocation of tobacco leaf laying positions in a re-drying plant according to claim 1, characterized in that: The partitioning rule of S2 is as follows: n = Xmax: The number of tobacco tables is equal to the number of tobacco leaf grades, and is directly allocated; n < Xmax: Calculate the average feed weight for each station Xa = Xw / Xmax, where Xw: represents the total feed weight (number of loads) of tobacco leaves in each batch formula, and Xa: the average feed weight for each station; The highest-grade raw tobacco from the largest production area is divided into multiple blends, ensuring that the weight of each blend is closest to Xa; the process of splitting and stoking is repeated until n = Xmax; n > Xmax: Perform stacking processing.

4. The method for optimizing the allocation of tobacco leaf laying positions in a re-drying plant according to claim 3, characterized in that: In S3, Xmax stations are allocated to several leaf-laying lines. Each station on a leaf-laying line is represented by A + leaf-laying line number + station number. The station allocation criteria are as follows: (1) The total amount of material fed on each of the m leaf-laying lines {A1,A2,A3,A4, ..., Am} is closest to Xw / m, and the error in the total amount of material fed among the m leaf-laying lines does not exceed 1%; (2) {A1,A2,A3,A4, ..., Am}, the weighted total nicotine value of each of the m leaf-laying lines is the same, and the error of the weighted nicotine value between the m leaf-laying lines does not exceed 1%; where the weighted nicotine value is the product of the nicotine of the raw tobacco of that grade and the number of feed loads at that station, and the weighted total nicotine value of each line is the sum of the weighted nicotine values ​​of the raw tobacco at each station on that leaf-laying line.

5. A system for optimizing the allocation of tobacco leaf laying positions in a re-drying plant, characterized in that: include: Nicotine content detection module: used to sample tobacco leaves of various grades in the raw tobacco warehouse and use near-infrared equipment to detect the nicotine content of the samples, and obtain the nicotine content data of each grade of raw tobacco in each batch formula sheet; Slagging module: Classifies tobacco leaves according to their origin and grade, and splits or stacks them according to slagging rules to ensure that the weight of each station is as close as possible to the average value. Station allocation module: Used to allocate stations to multiple leaf-laying lines to ensure that the total amount of feed and the weighted total amount of nicotine on each leaf-laying line are as close as possible, with an error of no more than 1%; Optimization Solution Module: Similar to using optimization algorithms to solve the station allocation scheme, the module aims to minimize the coefficient of variation of feed flow rate and nicotine weighted value to find the optimal station allocation scheme.

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