Processing method of anti-explosion line corrugated board

Abstract

The invention belongs to the technical field of paperboard processing, and particularly relates to a processing method of an anti-explosion line corrugated paperboard, which comprises the following steps: monitoring the environment temperature, the environment humidity and the spraying amount in real time; recording target parameters of each batch of paperboards, wherein the target parameters comprise the gram weight, the water content and the water absorbability of the raw paper and the solid content of the adhesive; establishing a paperboard thermal coupling model, and establishing a moisture absorbent-environment response model; and based on the paperboard thermal coupling model and the moisture absorbent-environment response model, spraying parameters in the corrugated paperboard processing process are controlled. According to the method, variable parameters such as environment temperature, humidity and spraying amount are monitored in real time, and quantitative parameters of the body paper and the adhesive are combined, so that dynamic adjustment of the production process is realized, and environment fluctuation and material batch difference can be effectively coped with; the paperboard thermal coupling model and the moisture absorbent-environment response model are introduced, the moisture migration process and the action mechanism of the moisture absorbent can be simulated, and theoretical support is provided for accurate control of spraying parameters.

Description

Technical Field

[0001] This invention belongs to the field of paperboard processing technology, and in particular relates to a processing method for explosion-proof corrugated paperboard. Background Technology

[0002] In the paperboard production process, spraying is often required to achieve explosion-proof lines. However, precise control of spraying parameters is challenging, primarily due to the dynamic interactions between the environment, the base paper, and the adhesive. Firstly, fluctuations in environmental temperature and humidity lead to inaccurate moisture balance control: changes in temperature and humidity in the production workshop, such as a diurnal temperature difference of 10°C or humidity exceeding 80% during the rainy season, significantly alter the water absorption of the base paper and the spray evaporation rate. For instance, in high-temperature and high-humidity environments, traditional fixed-parameter spraying can cause moisture accumulation on the paperboard surface, creating localized "over-wet zones," while low-temperature and low-humidity conditions can lead to insufficient fiber wetting, both of which can cause interlayer adhesion failure.

[0003] Secondly, batch-to-batch variations in the properties of the base paper material amplify the control difficulty: kraft paper and corrugated paper from different suppliers or batches exhibit significant differences in fiber structure, surface energy, and initial moisture content. These parameters directly affect the penetration depth of the spray liquid and the moisture gradient after the paperboard is formed. Existing control systems are often based on "average parameters" and cannot match the specific characteristics of the base paper in real time.

[0004] In addition, the mismatch between the physicochemical properties of the adhesive and the spraying system exacerbates the instability of quality: differences in the viscosity and solid content of starch adhesive can change the spreadability of the spray film. In order to achieve the processing of explosion-proof lines inside corrugated cardboard, moisture-absorbing materials such as salt or urea are often added to the adhesive. These changes in parameters all affect the difficulty of accurately controlling the spraying parameters in the production process to a certain extent.

[0005] In view of the above problems, how to obtain a processing method for explosion-proof corrugated cardboard that can achieve precise control of spray parameters has become a technical problem that relevant technicians need to solve. Summary of the Invention

[0006] This invention provides a processing method for explosion-proof corrugated cardboard, which can effectively solve the problems in the background art.

[0007] To achieve the above objectives, the technical solution of the present invention is as follows: The processing methods for explosion-proof corrugated cardboard include: Real-time monitoring of ambient temperature, humidity, and spray volume, used as a set of variable parameters; Record the target parameters for each batch of paperboard. The target parameters include the basis weight, moisture content and absorbency of the base paper, as well as the solid content of the adhesive, as a set of quantitative parameters. The adhesive uses a hygroscopic agent, which is salt, urea, or a mixture of the two. A paperboard thermal coupling model was established to simulate the moisture migration process of paperboard under different sets of variable parameters and quantitative parameter sets. In addition, a hygroscopic agent-environmental response model was established to simulate the influence of the hygroscopic agent on the performance of the adhesive under different environments. Based on the aforementioned paperboard thermal coupling model and desiccant-environment response model, spray parameters during the corrugated paperboard processing are controlled.

[0008] Furthermore, the cardboard thermal coupling model is at least a coupling model of the following models: A heat conduction model is used to describe the distribution of the temperature field inside the cardboard. A moisture diffusion model is used to describe the migration process of moisture inside the cardboard. A stress field model is used to describe the internal stress generated in the cardboard during the migration process; A desiccant response model is used to describe the effect of the desiccant on the migration process.

[0009] Furthermore, the thermal coupling model of the cardboard is solved, including: The cardboard is discretized into finite elements, and the initial values ​​of temperature, moisture concentration, stress and desiccant concentration at the nodes of the discretized finite elements are output as initial conditions. The migration process is discretized into multiple time steps, and the heat conduction model, moisture diffusion model, stress field model and desiccant response model for each time step are solved step by step based on the initial conditions to obtain the initial values ​​for each time step. Based on the initial value of each time step, each model is solved by an iterative method to ensure the consistency between the models.

[0010] Furthermore, the migration of moisture inside the cardboard is described by Fick's diffusion law to obtain the moisture diffusion model, which includes a moisture diffusion coefficient. The desiccant response model is obtained by revising the moisture diffusion model. The revision method is to revise the moisture diffusion coefficient using a revision coefficient.

[0011] Furthermore, the migration of moisture inside the cardboard is described by Fick's diffusion law to obtain the moisture diffusion model, which includes a moisture diffusion coefficient. The desiccant response model is obtained by revising the moisture diffusion model. The revision method is to revise the moisture diffusion coefficient by revising the coefficient and to add a moisture source term. The moisture source term is used to reflect the active moisture absorption effect of the desiccant.

[0012] Furthermore, the revision coefficient and the moisture source term were obtained experimentally.

[0013] Furthermore, a desiccant-environment response model is established, including: The input feature data and output target data are determined. The input feature data includes environmental parameters, desiccant parameters, and adhesive parameters. The output target data includes desiccant response and adhesive performance. A neural network model is determined and trained and validated using the input feature data and output target data; During the training process, synthetic data is generated by combining experimental data with physical laws, and this synthetic data is also used in the training and verification.

[0014] Furthermore, the synthetic data is labeled, and differential weights are set during the training process.

[0015] Furthermore, based on the aforementioned paperboard thermal coupling model and desiccant-environment response model, the spraying parameters during the corrugated paperboard processing are controlled, including: The variable parameter set and quantitative parameter set are collected in real time; Based on the collected results, the paperboard thermal coupling model and the desiccant-environment response model are solved to obtain the moisture distribution, temperature field, stress field and desiccant concentration inside the paperboard. The set parameters in the paperboard thermal coupling model are dynamically updated according to the output of the desiccant-environment response model. The optimal spray parameters are calculated based on the model solution results, and the optimized spray parameters are sent to the spray control system to adjust the working status of the spray equipment in real time.

[0016] The technical solution of this invention can achieve the following technical effects: In this invention, by real-time monitoring of environmental temperature, humidity, spray volume, and other variable parameters, combined with quantitative parameters of the base paper and adhesive, dynamic adjustments to the production process are achieved, effectively addressing environmental fluctuations and batch-to-batch material variations. The introduction of a paperboard thermal coupling model and a desiccant-environment response model simulates the moisture migration process and the desiccant's mechanism of action, providing theoretical support for precise control of spray parameters. By simultaneously controlling moisture migration and desiccant response, the problem of bursting lines is solved, while also achieving lower energy consumption, higher production efficiency, and stronger environmental adaptability.

[0017] During implementation, by coupling the moisture diffusion model with the hygroscopic agent-environmental response model, the active intervention of the hygroscopic agent on the moisture migration path can be quantified. The spray parameters can be dynamically adjusted according to the model output, so that the moisture distribution and adhesive performance can be optimized simultaneously, replacing the repeated trial and error in the traditional process. The hygroscopic agent's response mechanism can actively offset the impact of environmental changes. Furthermore, by determining the base paper parameters and linking them with the hygroscopic agent-environmental response model, the characteristics of different batches of materials can be automatically adapted. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the processing method of explosion-proof corrugated cardboard. Figure 2 A flowchart for solving the thermal coupling model of cardboard; Figure 3 A flowchart for establishing a desiccant-environment response model; Figure 4 This is a flowchart for controlling the spraying parameters during the corrugated cardboard processing. Detailed Implementation

[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0022] Processing methods for explosion-proof corrugated cardboard, such as... Figure 1 As shown, it includes: S1: Real-time monitoring of ambient temperature, ambient humidity, and spray volume as a set of variable parameters; in this step, the acquisition of temperature and humidity can be achieved through existing methods, which will not be elaborated here, while the acquisition of spray volume can be achieved by measuring the flow rate of the spray pipe; after the data acquisition is completed, it can be aggregated to the industrial control computer through the set protocol; S2: Record the target parameters for each batch of paperboard. The target parameters include the basis weight, moisture content and absorbency of the base paper, as well as the solid content of the adhesive, as a set of quantitative parameters. The adhesive uses a hygroscopic agent, which is salt, urea, or a mixture of the two. The basis weight and moisture content can be measured by electronic balance and moisture analyzer, respectively. The water absorption can be measured by the base paper according to the relevant national standards. The solid content of the adhesive can be determined by taking an adhesive sample, drying it in an oven to constant weight, and calculating the percentage of solid content. In this embodiment, a desiccant is mixed into the adhesive, and its ratio can be flexibly and appropriately according to actual needs. S3: Establish a paperboard thermal coupling model to simulate the migration process of paperboard moisture under different sets of variable parameters and quantitative parameter sets; and establish a hygroscopic agent-environmental response model to simulate the influence of hygroscopic agents on adhesive performance under different environments. In this step, the paperboard thermal coupling model can simulate the internal state of the paperboard, and the output of the hygroscopic agent-environmental response model can specifically include the prediction of the hygroscopic agent's moisture absorption rate under different environments, as well as its impact on the adhesive performance, such as curing time and peel strength. S4: Based on the paperboard thermal coupling model and the desiccant-environment response model, spray parameters in the corrugated paperboard processing process are controlled.

[0023] In this invention, by real-time monitoring of environmental temperature, humidity, spray volume and other variable parameters, and combined with quantitative parameters of the base paper and adhesive, dynamic adjustment of the production process is achieved, which can effectively cope with environmental fluctuations and material batch differences. The introduction of a paperboard thermal coupling model and a desiccant-environment response model can simulate the moisture migration process and the action mechanism of the desiccant, providing theoretical support for the precise control of spray parameters.

[0024] By implementing the present invention, the simultaneous control of moisture migration and desiccant response solves the problem of burst lines while achieving lower energy consumption, higher production efficiency, and stronger environmental adaptability. Specifically, the moisture migration process directly affects the moisture content distribution of the paperboard, resulting in problems such as rapid edge drying and high humidity in the center. The desiccant can actively compensate for the differences in moisture gradient by regulating the hygroscopicity and curing characteristics of the adhesive. Based on the above, the present invention, by comprehensively considering the paperboard thermal coupling model and the desiccant-environmental response model, can dynamically balance the moisture gradient and adhesive strength, thereby synergistically optimizing the control accuracy of the burst line by controlling the spray parameters.

[0025] During implementation, by coupling the moisture diffusion model with the hygroscopic agent-environmental response model, the active intervention of the hygroscopic agent on the moisture migration path can be quantified. The spray parameters can be dynamically adjusted according to the model output, so that the moisture distribution and adhesive performance can be optimized simultaneously, replacing the repeated trial and error in the traditional process. The hygroscopic agent's response mechanism can actively offset the impact of environmental changes. Furthermore, by determining the base paper parameters and linking them with the hygroscopic agent-environmental response model, the characteristics of different batches of materials can be automatically adapted.

[0026] As a preferred embodiment of the above, the cardboard thermal coupling model is at least a coupling model of the following: The heat conduction model describes the distribution of the temperature field inside the cardboard; the moisture diffusion model describes the migration process of moisture inside the cardboard; and the stress field model describes the internal stress generated in the cardboard during the migration process. All three models can be constructed using existing methods, which will not be elaborated here. The model also includes a desiccant response model to describe the effect of desiccant on the migration process.

[0027] In this embodiment, as a specific implementation of the desiccant response model, the migration of moisture within the cardboard is described by Fick's diffusion law to obtain a moisture diffusion model. This model includes a moisture diffusion coefficient, which is a core parameter and part of the prior art. The difference between this embodiment and the prior art is that the desiccant response model is obtained by revising the moisture diffusion model. The revision method involves revising the moisture diffusion coefficient using a revision coefficient. This method of obtaining the desiccant response model has low computational complexity, fast simulation speed, and easy parameter calibration. It is suitable for scenarios where the desiccant's function is singular, and for implementations with minimal environmental fluctuations where the desiccant's function is primarily passively regulated. In this method, the revision of the moisture diffusion coefficient is preferably linear. Linear revision does not require solving complex equations, is suitable for real-time control, and only requires calibration of a small number of experimental points, shortening the production line debugging cycle and making it more suitable for controlling cardboard production costs.

[0028] Alternatively, as another specific implementation of the desiccant response model, the migration of moisture within the cardboard is similarly described using Fick's diffusion law to obtain a moisture diffusion model. This model includes a moisture diffusion coefficient. The desiccant response model is obtained by revising the moisture diffusion model, specifically by revising the moisture diffusion coefficient using a revision coefficient and adding a moisture absorption source term. This term reflects the active moisture absorption effect of the desiccant. This approach can more accurately simulate the active moisture absorption behavior of the desiccant and is suitable for scenarios with large environmental fluctuations and requiring high-precision control, such as pharmaceutical packaging cardboard.

[0029] In the above embodiments, the coupling of the heat conduction model, moisture diffusion model, stress field model, and desiccant response model can reflect the interactive effects of temperature field, humidity field, stress field, and desiccant action in real time, overcoming the limitations of traditional single-field models. By coupling the moisture diffusion model with the desiccant response model based on it, the active regulation of moisture migration by the desiccant can be accurately quantified, thereby significantly improving the control accuracy and process adaptability of the explosion-proof line in corrugated cardboard processing. Finally, the above equations can be solved using COMSOL Multiphysics software. In this method, the revision coefficients and moisture source terms can be obtained experimentally.

[0030] As a specific interaction method between the paperboard thermal coupling model and the desiccant-environmental response model, the parameters in the paperboard thermal coupling model can be updated through the output of the desiccant-environmental response model. In a specific implementation, the parameters are the moisture diffusion coefficient, revision coefficient, and moisture absorption source term as described in the above embodiments. During implementation, the output of the desiccant-environmental response model should be the desiccant performance parameters, such as the moisture absorption rate and its impact on adhesive performance. By dynamically updating the moisture diffusion coefficient, revision coefficient, and moisture absorption source term, the paperboard thermal coupling model can more accurately reflect the moisture distribution and stress changes in actual production. When there are fluctuations in ambient temperature and humidity or significant batch differences in materials, the mechanism of action of the desiccant will change. By dynamically updating the model parameters, these changes can be captured in a timely manner, avoiding the prediction bias caused by traditional fixed-parameter models.

[0031] Among them, the moisture diffusion coefficient, revision coefficient, and moisture source term are indirectly calculated through the performance parameters of the desiccant, which can be specifically calculated through physical equations, ultimately improving the interpretability of the scheme.

[0032] As a preferred embodiment of the above, such as Figure 2 As shown, the solution to the thermal coupling model of the cardboard includes: A1: Discretize the cardboard into finite elements and output the initial values ​​of temperature, moisture concentration, stress and desiccant concentration at the nodes of the discretized finite elements as initial conditions; A2: Discretize the migration process into multiple time steps, and solve the heat conduction model, moisture diffusion model, stress field model and desiccant response model for each time step based on the initial conditions to obtain the initial values ​​for each time step; A3: Based on the initial value at each time step, solve each model using an iterative method to ensure consistency between the models.

[0033] Finite element discretization and step-by-step iterative solution significantly improve simulation efficiency and accuracy, ensuring the feasibility of real-time control; specifically, cardboard can be discretized into units of a set size, which can capture minute moisture gradients.

[0034] As a preferred embodiment of the above, such as Figure 3 As shown, a desiccant-environment response model is established, including: Determine the input feature data and output target data. The input feature data includes environmental parameters, desiccant parameters, and adhesive parameters. The output target data includes desiccant response and adhesive performance. The neural network model is determined and trained and validated using input feature data and output target data. A suitable neural network structure needs to be selected, such as a multilayer perceptron (MLP), a convolutional neural network (CNN), or a long short-term memory network (LSTM), and the specific structure is adjusted according to the data characteristics and problem complexity. During the training process, synthetic data is generated by combining experimental data with physical laws, and the synthetic data is also used in the training and validation.

[0035] In this embodiment, by combining experimental data with physical laws to generate synthetic data, and constructing a desiccant-environment response model based on neural networks, high-precision and highly generalized desiccant performance prediction can be achieved.

[0036] As a preferred embodiment of the above, the synthetic data is labeled and differential weights are set during the training process, thereby further improving the accuracy, generalization ability and interpretability of the hygroscopic agent-environmental response model. Specifically, by labeling the synthetic data, a lower weight can be set for it during the training process to reduce its excessive influence on model training, thereby improving the accuracy of the model.

[0037] As a preferred embodiment of the above, such as Figure 4 As shown, based on the paperboard thermal coupling model and the desiccant-environment response model, the spraying parameters in the corrugated paperboard processing process are controlled, including: B1: Real-time acquisition of variable parameter sets and quantitative parameter sets; B2: Based on the collected results, solve the paperboard thermal coupling model and the desiccant-environment response model to obtain the moisture distribution, temperature field, stress field and desiccant concentration inside the paperboard, and dynamically update the set parameters in the paperboard thermal coupling model according to the output of the desiccant-environment response model. B3: Calculate the optimal spray parameters based on the model solution results, and send the optimized spray parameters to the spray control system to adjust the working status of the spray equipment in real time.

[0038] In the above embodiments, by precisely controlling the spraying parameters, uniform distribution of moisture inside the cardboard can be achieved, avoiding problems of local over-wetting or insufficient drying, significantly improving the compressive strength and bursting strength of the cardboard. By dynamically optimizing the adhesive performance, adhesive failure and bursting phenomena can be reduced, improving the quality consistency of corrugated cardboard. By collecting environmental parameters in real time and dynamically adjusting the model and spraying parameters, it can quickly respond to temperature and humidity fluctuations, adapt to production needs under different seasons and climate conditions, and automatically adjust the control strategy according to the differences in the characteristics of different batches of base paper and adhesives, reducing the impact of material batch differences on product quality.

[0039] Specifically, the optimal spray parameters are calculated based on the model solution results. A rule-based control strategy can directly map the model solution results onto the spray parameters through preset rules or empirical formulas. This method is simple to calculate and suitable for real-time control.

[0040] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A processing method for explosion-proof corrugated cardboard, characterized in that, include: Real-time monitoring of ambient temperature, humidity, and spray volume, used as a set of variable parameters; Record the target parameters for each batch of paperboard. The target parameters include the basis weight, moisture content and absorbency of the base paper, as well as the solid content of the adhesive, as a set of quantitative parameters. The adhesive uses a hygroscopic agent, which is salt, urea, or a mixture of the two. A paperboard thermal coupling model was established to simulate the moisture migration process of paperboard under different sets of variable parameters and quantitative parameter sets. In addition, a hygroscopic agent-environmental response model was established to simulate the influence of the hygroscopic agent on the performance of the adhesive under different environments. Based on the aforementioned paperboard thermal coupling model and hygroscopic agent-environmental response model, spray parameters during the corrugated paperboard processing are controlled.

2. The processing method of explosion-proof corrugated cardboard according to claim 1, characterized in that, The cardboard thermal coupling model is at least a coupling model of the following models: A heat conduction model is used to describe the distribution of the temperature field inside the cardboard; A moisture diffusion model is used to describe the migration process of moisture inside the cardboard. A stress field model is used to describe the internal stress generated in the cardboard during the migration process; A desiccant response model is used to describe the effect of the desiccant on the migration process.

3. The processing method of explosion-proof corrugated cardboard according to claim 2, characterized in that, Solving the thermal coupling model of the cardboard includes: The cardboard is discretized into finite elements, and the initial values ​​of temperature, moisture concentration, stress and desiccant concentration at the nodes of the discretized finite elements are output as initial conditions. The migration process is discretized into multiple time steps, and the heat conduction model, moisture diffusion model, stress field model and desiccant response model for each time step are solved step by step based on the initial conditions to obtain the initial values ​​for each time step. Based on the initial value of each time step, each model is solved by an iterative method to ensure the consistency between the models.

4. The processing method of explosion-proof corrugated cardboard according to claim 2, characterized in that, The migration of moisture inside the cardboard is described by Fick's diffusion law to obtain the moisture diffusion model, which includes a moisture diffusion coefficient. The desiccant response model is obtained by revising the moisture diffusion model. The revision method is to revise the moisture diffusion coefficient using a revision coefficient.

5. The processing method of explosion-proof corrugated cardboard according to claim 4, characterized in that, The moisture diffusion coefficient is revised, specifically by a linear revision.

6. The processing method of explosion-proof corrugated cardboard according to claim 2, characterized in that, The migration of moisture inside the cardboard is described by Fick's diffusion law to obtain the moisture diffusion model, which includes a moisture diffusion coefficient. The desiccant response model is obtained by revising the moisture diffusion model. The revision method is to revise the moisture diffusion coefficient by revising the coefficient and to add a moisture source term. The moisture source term is used to reflect the active moisture absorption effect of the desiccant.

7. The processing method of explosion-proof corrugated cardboard according to claim 6, characterized in that, The revision coefficients and moisture source terms were obtained experimentally.

8. The processing method of explosion-proof corrugated cardboard according to claim 1, characterized in that, Establish a desiccant-environment response model, including: The input feature data and output target data are determined. The input feature data includes environmental parameters, desiccant parameters, and adhesive parameters. The output target data includes desiccant response and adhesive performance. A neural network model is determined and trained and validated using the input feature data and output target data; During the training process, synthetic data is generated by combining experimental data with physical laws, and this synthetic data is also used in the training and verification.

9. The processing method of explosion-proof corrugated cardboard according to claim 8, characterized in that, The synthetic data is labeled, and differential weights are set during training.

10. The processing method of explosion-proof corrugated cardboard according to claim 1, characterized in that, Based on the aforementioned paperboard thermal coupling model and desiccant-environment response model, spray parameters during corrugated paperboard processing are controlled, including: The variable parameter set and quantitative parameter set are collected in real time; Based on the collected results, the paperboard thermal coupling model and the desiccant-environment response model are solved to obtain the moisture distribution, temperature field, stress field and desiccant concentration inside the paperboard. The set parameters in the paperboard thermal coupling model are dynamically updated according to the output of the desiccant-environment response model. The optimal spray parameters are calculated based on the model solution results, and the optimized spray parameters are sent to the spray control system to adjust the working status of the spray equipment in real time.

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