A method and system for controlling the production of non-polar tissue paper

Abstract

The application discloses a kind of nonpolar soft paper production process control method and system.Method comprising: acquisition data collection;Based on real-time surface temperature data and dew point temperature data, through condensation risk calculation model, obtain condensation risk zone distribution map;Based on condensation risk zone distribution map, through preset gradient analysis model, obtain the target humidification of each section;Through gradient descent algorithm, the air supply temperature of each condensation risk zone is optimized, and the moisture content recovery target value is determined based on the optimized air supply temperature, and moisture content recovery target value and real-time moisture content monitoring data are compared point by point, to determine the final humidification control parameter;Based on final humidification control parameter, nonpolar soft paper production process is controlled.The application provides a kind of nonpolar soft paper production process control method and system, to solve the technical problem of the contradiction that existing dynamic coordination moisture content is fully recovered and inhibits condensation caused by nonpolar attenuation.

Description

Technical Field

[0001] This invention relates to the field of cotton paper production technology, and in particular to a method and system for controlling the production process of non-polar cotton paper. Background Technology

[0002] In the production of non-polar tissue paper (such as thin packaging paper or toilet paper with high water and oil resistance), the rehumidification process is a crucial step that determines the final quality of the paper. The core objective of this process is to restore the moisture content of the paper to a balanced state after high-temperature drying by precisely controlling humidification and ambient temperature and humidity, thereby improving the paper's dimensional stability, softness, and surface smoothness. Currently, the mainstream rehumidification control methods in the industry mainly rely on macroscopic adjustments to the overall ambient dew point temperature and average humidification rate. By setting fixed process parameters, such as constant humidification spray volume and air supply temperature, the aim is to ensure a uniform increase in the moisture content of the entire paper sheet.

[0003] However, in actual production, existing technologies have significant drawbacks. First, traditional methods ignore the microscopic differences in surface temperature distribution caused by uneven drying and variations in air permeability along the horizontal direction of the paper web. When uniform humidity control parameters are used, uncontrollable condensation films will form first in localized areas where the paper web surface temperature is lower than the ambient dew point temperature. If this film remains for too long, it will cause irreversible molecular flipping of the alkyl segments on the surface of non-polar sizing agents such as alkyl ketene dimers (AKD), causing the originally hydrophobic non-polar ester groups to turn inward, while the hydrophilic polar groups are exposed on the surface, resulting in a step-like decrease in the local contact angle of the paper, i.e., a permanent degradation of non-polar properties. Second, existing control logic contains a fundamental contradiction: excessive humidification in pursuit of dimensional stability often exacerbates the risk of condensation and accelerates the loss of non-polar properties; conversely, reducing humidification to avoid condensation leads to insufficient recovery of paper moisture content, resulting in problems such as dimensional instability, warping, or decreased softness. Therefore, the biggest drawback of existing technology is that it cannot dynamically coordinate the inherent contradiction between fully restoring the moisture content and suppressing the non-polar decay caused by condensation. It also lacks refined and coordinated control over the temperature difference in the horizontal direction of the micro-area and the threshold for condensation formation, resulting in poor dimensional stability of the final product, substandard functional indicators, and difficulty in meeting the needs of high-end applications. Summary of the Invention

[0004] This invention provides a method and system for controlling the production process of non-polar cotton paper, in order to solve the technical problem of the contradiction between dynamically coordinating the full recovery of moisture content and suppressing the non-polar decay caused by condensation.

[0005] The present invention discloses the following technical solutions: In a first aspect, embodiments of the present invention provide a method for controlling the production process of non-polar tissue paper, the method comprising: Acquire the collected data, which includes real-time surface temperature data of each point on the paper web, dew point temperature data of each section of the conditioning zone, and real-time moisture content monitoring data of the cotton paper. Based on the real-time surface temperature data and the dew point temperature data, a condensation risk zone distribution map is obtained through a condensation risk calculation model. Based on the condensation risk zone distribution map, the humidification amount of each segment is iteratively reduced according to the correlation between the humidification amount reduction gradient and the change in the area of ​​the condensation risk zone through a preset gradient analysis model until the predicted value of condensation risk is lower than the preset threshold, thereby obtaining the target humidification amount of each segment. Based on the condensation risk zone corresponding to the target humidification amount, the supply air temperature corresponding to the condensation risk zone is obtained. With the goal of minimizing the deviation between the supply air temperature and the process allowable range, the supply air temperature exceeding the process allowable range is optimized using a gradient descent algorithm to obtain an optimized supply air temperature sequence. Through a preset temperature-moisture content mapping model, the supply air temperature sequence is converted into a moisture content recovery target value for each point. Each moisture content recovery target value is then compared point by point with the real-time moisture content monitoring data to determine the final humidification control parameters. The non-polar cotton paper production process is controlled based on the final humidification control parameters.

[0006] Optionally, after controlling the non-polar cotton paper production process based on the final humidification control parameters, the method further includes: Obtain the actual execution data under the final humidification control parameters, wherein the actual execution data includes the actual supply air temperature and spray volume; Based on the actual execution data, the surface temperature estimate of each point on the paper web banner is updated using a preset temperature influence coefficient and a heat balance equation. The temperature difference distribution is recalculated based on the surface temperature estimate to obtain the temperature difference distribution for the next control cycle.

[0007] Optionally, the step of obtaining a condensation risk zone distribution map based on the real-time surface temperature data and the dew point temperature data through a condensation risk calculation model includes: Based on the real-time surface temperature data and the dew point temperature data of each segment, the temperature difference distribution is obtained by calculating the point-by-point difference between the surface temperature and dew point temperature at each point on the paper web. Based on the temperature difference distribution, the coordinate points corresponding to temperature differences below a preset temperature threshold are marked as the initial low temperature point set; The initial low temperature point set and temperature difference gradient are aggregated to obtain an aggregated region, and the aggregated region that is continuous and whose temperature difference is lower than the preset temperature threshold is identified as a condensation risk zone. Based on the contour coordinates of each condensation risk zone, the regional boundaries of each condensation risk zone are smoothed to obtain the risk zone boundaries. Based on each of the condensation risk zones and the corresponding risk zone boundaries, a condensation risk zone distribution map is generated.

[0008] Optionally, based on the condensation risk zone distribution map, the humidification amount of each segment is iteratively reduced according to the correlation between the decreasing humidification amount gradient and the change in the area of ​​the condensation risk zone using a preset gradient analysis model, until the predicted condensation risk value is lower than a preset threshold, to obtain the target humidification amount for each segment, including: Acquire spatial location data, current humidification volume data, and surface humidity field monitoring data for each conditioning section, wherein the conditioning section refers to multiple humidification control areas divided along the material conveying direction; Based on the current humidification data, the humidification decrease gradient value between adjacent conditioning sections is calculated along the material conveying direction; The humidification rate decrease gradient value and the change in the area ratio of the condensation risk zone are input into a preset gradient analysis model, and the condensation risk prediction value is output through linear regression. Based on the current humidification scheme, the condensation risk assessment problem is iteratively solved using the preset gradient analysis model until the condensation risk prediction value output by the current iteration meets the preset condition of being lower than a preset threshold. Then, the adjusted target humidification amount for each segment corresponding to the current iteration is output. Specifically, the humidification scheme in the first iteration is determined based on the condensation risk zone distribution map and the current humidification amount data for each segment. When the condensation risk prediction value is not lower than the preset threshold, a humidification reduction instruction is generated. In each iteration, the humidification amount of each segment in the humidification scheme of the previous iteration is reduced segment by segment according to the humidification reduction instruction to obtain the adjusted target humidification amount for each segment in the current iteration.

[0009] Optionally, the step involves obtaining the supply air temperature corresponding to the condensation risk zone segment corresponding to the target humidification amount, aiming to minimize the deviation between the supply air temperature and the process allowable range. This is achieved by optimizing the supply air temperature exceeding the process allowable range using a gradient descent algorithm to obtain an optimized supply air temperature sequence. A preset temperature-moisture content mapping model is then used to convert the supply air temperature sequence into a moisture content recovery target value for each point. Finally, each moisture content recovery target value is compared point-by-point with the real-time moisture content monitoring data to determine the final humidification control parameters, including: Based on the supply air temperature of each condensation risk zone, the supply air temperature distribution value in the conditioning section is obtained. Each supply air temperature value in the supply air temperature distribution is compared with the upper and lower limits of the process allowable range to determine whether the supply air temperature exceeds the process allowable range. The supply air temperature of each condensation risk zone is then optimized using a gradient descent algorithm to obtain an optimized supply air temperature sequence. Based on the optimized supply air temperature sequence, the target value for moisture content recovery at each point is determined; The target values ​​for moisture content recovery and the real-time moisture content monitoring data are compared point by point. When the real-time moisture content monitoring data is lower than the moisture content recovery target value, the humidification amount of each segment is adjusted to determine the final humidification control parameters.

[0010] Optionally, the step of comparing each supply air temperature value in the supply air temperature distribution with the upper and lower limits of the process allowable range one by one to determine whether the supply air temperature exceeds the process allowable range, and optimizing the supply air temperature of each condensation risk zone segment using a gradient descent algorithm to obtain an optimized supply air temperature sequence, including: Each air supply temperature value in the air supply temperature distribution value is compared with the upper and lower limits of the process allowable range one by one to determine whether the air supply temperature value exceeds the process allowable range. If the air supply temperature exceeds the permissible range of the process, then the excess value is determined; Based on the excess amplitude value and gradient descent algorithm, the optimization adjustment step size is iteratively calculated, and the supply air temperature is updated based on the optimization adjustment step size until the supply air temperature is within the process allowable range, and the optimized supply air temperature sequence is output.

[0011] Optionally, when the real-time moisture content monitoring data is lower than the moisture content recovery target value, the humidification amount of each segment is adjusted to determine the final humidification control parameters, including: The deviation amplitude value is calculated by subtracting the moisture content recovery target value from the real-time moisture content monitoring data. The humidification distribution is calculated by multiplying the deviation amplitude value by a preset humidity compensation factor. Based on the humidification distribution, a humidification amount sequence is obtained, and based on the humidification amount sequence, the final humidification control parameters are determined.

[0012] Secondly, embodiments of the present invention provide a non-polar cotton paper production process control system, comprising: The acquisition module is used to acquire collected data, including real-time surface temperature data of each point on the paper web, dew point temperature data of each section of the conditioning zone, and real-time moisture content monitoring data of the cotton paper. The calculation module is used to obtain a condensation risk zone distribution map based on the real-time surface temperature data and the dew point temperature data through a condensation risk calculation model. The analysis module is used to iteratively reduce the humidification amount of each segment based on the distribution map of the condensation risk area and a preset gradient analysis model, according to the correlation between the decreasing gradient of humidification amount and the change in the area of ​​the condensation risk area, until the predicted value of condensation risk is lower than a preset threshold, so as to obtain the target humidification amount of each segment. The optimization module is used to obtain the supply air temperature corresponding to the condensation risk zone segment corresponding to the target humidification amount, with the goal of minimizing the deviation between the supply air temperature and the process allowable range. The module optimizes the supply air temperature that exceeds the process allowable range using a gradient descent algorithm to obtain an optimized supply air temperature sequence. The module then converts the supply air temperature sequence into a moisture content recovery target value at each point using a preset temperature-moisture content mapping model. Finally, the module compares each moisture content recovery target value with the real-time moisture content monitoring data point by point to determine the final humidification control parameters. The control module is used to control the non-polar cotton paper production process based on the final humidification control parameters.

[0013] Thirdly, another embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the non-polar cotton paper production process control method described above.

[0014] Another embodiment of the present invention also provides a computer program product, including a computer program or instructions, which, when executed by a device, implement the steps of a non-polar cotton paper production process control method as described above.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This invention provides a data foundation for refined process control by real-time acquisition of paper web width, dew point temperature, and moisture content. A condensation risk calculation model generates a risk zone distribution map, transforming the invisible condensation risk into a visualized spatial distribution, providing clear spatial coordinate guidance for subsequent precise control. A gradient analysis model iteratively reduces the humidification rate, quantifying the correlation between the humidification rate reduction gradient and the condensation risk suppression effect. This minimizes the humidification rate while ensuring the elimination of condensation risk, alleviating the water film retention problem caused by excessive humidification at its source. A gradient descent algorithm optimizes the supply air temperature, ensuring it remains within the process-permissible range and guaranteeing the feasibility of subsequent moisture content recovery. Finally, a temperature-moisture content mapping model converts the optimized supply air temperature into a moisture content recovery target value, comparing it point-by-point with real-time monitoring data to achieve closed-loop correction of humidification control. This ensures uniform and sufficient recovery of paper web moisture content, thereby synergistically resolving the contradiction between moisture content recovery and non-polar performance degradation. Attached Figure Description

[0016] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a schematic flowchart of a non-polar cotton paper production process control method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the specific implementation process of a non-polar cotton paper production process control method provided in this embodiment of the invention; Figure 3 This is a schematic diagram of the condensation risk zone distribution map generation process of a non-polar cotton paper production process control method provided in this embodiment of the invention; Figure 4 This is a schematic diagram of the final humidification control parameter generation process of a non-polar cotton paper production process control method provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of a non-polar cotton paper production process control device provided in an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, 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.

[0019] 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 herein is for the purpose of describing particular embodiments only and is not intended to limit the invention; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the invention, are intended to cover non-exclusive inclusion.

[0020] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0021] See Figure 1 To address the contradiction between dynamically coordinating the full recovery of moisture content and suppressing non-polar degradation caused by condensation, an embodiment of the present invention provides a method for controlling the production process of non-polar cotton paper, comprising: S1, acquire collected data, wherein the collected data includes real-time surface temperature data of each point on the paper web, dew point temperature data of each section of the conditioning zone, and real-time moisture content monitoring data of the cotton paper.

[0022] The fundamental step of acquiring data includes: real-time acquisition of surface temperature data at various points on the paper web using online scanners deployed at key locations such as the dryer outlet; simultaneous acquisition of dew point temperature data for each section of the conditioning zone using environmental dew point temperature acquisition stations; and simultaneous acquisition of real-time moisture content monitoring data at various points on the cotton paper web using online monitoring equipment such as infrared moisture meters or microwave moisture detectors. These three types of data are time-aligned through a unified synchronization clock mechanism to ensure the accuracy and reliability of subsequent point-by-point temperature difference calculations, condensation risk identification, and moisture content control.

[0023] S2, Based on the real-time surface temperature data and the dew point temperature data, a condensation risk zone distribution map is obtained through a condensation risk calculation model.

[0024] The specific process of obtaining the condensation risk zone distribution map based on real-time surface temperature data and dew point temperature data through the condensation risk calculation model is as follows: First, for each measurement point on the paper web, the difference between its surface temperature and the corresponding dew point temperature of the conditioning section is calculated point by point; then, this difference is compared with a preset condensation risk threshold. If the difference at a certain point is lower than the threshold, it is determined that there is a condensation risk at that point; next, a spatial clustering algorithm, such as the connected component analysis method, is used to aggregate adjacent measurement points that all have risks into continuous risk areas, and the risk level is classified according to the area, the average temperature difference deviation, and its position on the paper web, such as high, medium, and low risk; finally, each risk area is mapped onto a two-dimensional coordinate graph of the paper web to generate a visualized condensation risk zone distribution map. This distribution map can dynamically reflect the spatiotemporal distribution characteristics of condensation risk in the conditioning area, providing a spatial positioning basis for subsequent segmented adjustment of humidification amount.

[0025] S3. Based on the distribution map of the condensation risk area, the humidification amount of each segment is iteratively reduced according to the correlation between the humidification amount reduction gradient and the change in the area of ​​the condensation risk area through a preset gradient analysis model, until the predicted value of condensation risk is lower than the preset threshold, and the target humidification amount of each segment is obtained.

[0026] The process involves extracting the current humidification data of the conditioning sections covered by each risk zone based on the risk zone distribution map, and calculating the humidification reduction gradient value between adjacent conditioning sections along the material conveying direction. This gradient value is then input as an independent variable into a pre-constructed gradient analysis model. The model uses linear regression to establish the correlation between the humidification reduction gradient and the condensation suppression effect. The condensation suppression effect is characterized by the change in the area ratio of the condensation risk zone or the improvement in the average temperature difference, and outputs a predicted condensation risk value. If this predicted value is higher than a preset condensation elimination threshold, the current gradient direction is determined. If the condensation is not effectively suppressed, the system generates a humidification reduction command, gradually reducing the humidification along the material flow direction, for example, by decreasing it in an arithmetic or geometric manner, and recalculating the new reduction gradient and the corresponding risk prediction value. The above iterative process is executed repeatedly, and each iteration updates the input of the gradient analysis model based on the latest condensation risk zone distribution map, until the condensation risk prediction value output by the model is lower than the preset threshold. At this point, the humidification of each segment corresponding to the current iteration is determined as the target humidification that can effectively eliminate the condensation risk, thereby achieving precise segmented control of the humidification while avoiding excessive formation of local water films.

[0027] S4. Based on the condensation risk zone corresponding to the target humidification amount, obtain the supply air temperature corresponding to the condensation risk zone. With the goal of minimizing the deviation between the supply air temperature and the process allowable range, optimize the supply air temperature that exceeds the process allowable range using a gradient descent algorithm to obtain an optimized supply air temperature sequence. Through a preset temperature-moisture content mapping model, convert the supply air temperature sequence into a moisture content recovery target value for each point, and compare each moisture content recovery target value with the real-time moisture content monitoring data point by point to determine the final humidification control parameters.

[0028] The process involves constructing an objective function with the sum of squares of air supply temperatures exceeding the permissible range. The gradient of the current temperature relative to the objective function is calculated, and the temperature values ​​exceeding the range are gradually adjusted along the negative gradient direction until the air supply temperatures of all segments converge to the permissible range, resulting in an optimized air supply temperature sequence. Subsequently, a temperature-moisture content mapping model, pre-established and validated through regression analysis of historical drying data of similar paper types, is used. This model, employing a linear function, takes the air supply temperature as input and the moisture content recovery target value as output, converting the optimized air supply temperatures of each segment into the corresponding moisture content recovery target value for the paper web. Finally, the moisture content recovery target value for each point is compared point-by-point with synchronously collected real-time moisture content monitoring data, and the deviation is calculated. If the real-time moisture content is lower than the target range, the humidification amount of the corresponding segment is corrected based on a preset humidity compensation factor. After multiple iterations, the final humidification control parameters are generated, thereby ensuring that the paper web moisture content is fully restored to the target level while eliminating the risk of condensation.

[0029] S5, control the non-polar cotton paper production process based on the final humidification control parameters.

[0030] The final humidification control parameters include the target humidification amount, the optimized air supply temperature, and the spray volume. The target humidification amount, the optimized air supply temperature, and the spray volume are sent to the field actuators to achieve segmented and precise humidification and temperature and humidity control within the conditioning area. At the same time, real-time data is continuously collected and fed back to the control system. If there is a risk of condensation or a deviation in moisture content, iterative optimization is automatically triggered to form a closed-loop adaptive control, ensuring a long-term balance between paper web dimensional stability and non-polar properties.

[0031] Furthermore, such as Figure 2As shown, this application provides a specific implementation flow of a non-polar cotton paper production process control method. First, real-time surface temperature data at each point on the cotton paper web and dew point temperature data at each section of the conditioning zone are acquired. This data is simultaneously collected using an online paper web surface temperature scanner and an environmental dew point temperature acquisition station. The point-by-point difference between the surface temperature and dew point temperature on the paper web is calculated to determine the temperature difference distribution. Then, based on the temperature difference distribution, local locations where the temperature difference is below a preset threshold are identified. These local locations are classified, and areas with insufficient temperature differences are marked as condensation risk zones, resulting in a condensation risk zone distribution map. Next, using the condensation risk zone distribution map, the current humidification amount of the conditioning section covered by each condensation risk zone is extracted. The inhibitory effect of the decreasing humidification amount gradient on condensation formation on the cotton paper surface is analyzed. The humidification amount is gradually adjusted to the level required to eliminate condensation risk, resulting in the adjusted humidification amount for each section. Finally, the adjusted humidification amount is used to determine the humidification amount for each section. In the process, the supply air temperature of the section where the condensation risk zone is located is obtained, and it is assessed whether the supply air temperature exceeds the process allowable range. The supply air temperature exceeding the range is optimized using a gradient descent algorithm to obtain an optimized supply air temperature sequence. Then, for the optimized supply air temperature sequence, the moisture content recovery target value of each point on the paper web is extracted, and the online monitoring data of the cotton paper moisture content is acquired simultaneously. The moisture content recovery target value is compared with the real-time moisture content. When the real-time moisture content is lower than the target range, the humidification amount of each section is adjusted to promote full moisture content recovery, and the final humidification control parameters are obtained. Finally, the actual supply air temperature and spray volume of each section in the conditioning area after the final humidification control parameters are executed are collected. The temperature difference distribution of each point on the paper web is updated through the execution data, realizing a complete closed-loop control from data acquisition, risk identification, humidification adjustment to parameter verification.

[0032] In one embodiment, after controlling the non-polar cotton paper production process based on the final humidification control parameters, steps S201 to S203 are further included, each step being as follows: S201, Obtain the actual execution data under the final humidification control parameters, wherein the actual execution data includes the actual air supply temperature and spray volume.

[0033] After the final humidification control parameters are executed, the actual air supply temperature and spray volume data are collected in real time by temperature sensors and flow meters deployed in each section of the conditioning zone. The actual air supply temperature can be obtained by temperature probes installed at the air outlets, and the spray volume can be measured by feedback signals from electromagnetic flow meters or atomizers. The data acquisition process uses a synchronous clock aligned with the central control system to ensure the temporal consistency and spatial matching of the data, thereby providing reliable and real execution feedback for subsequent updates to the temperature difference distribution of the paper web and horizontal banner, verification of control effects, and triggering closed-loop correction.

[0034] S202, based on the actual execution data, using a preset temperature influence coefficient, update the estimated surface temperature of each point on the paper banner through the heat balance equation.

[0035] The process involves using a preset temperature influence coefficient to characterize the local influence weight of the air supply temperature and spray volume on the surface temperature of the paper web in each conditioning section. Then, based on the actual air supply temperature, a heat balance equation is established by combining the correction term for the evaporative cooling effect of the spray volume. That is, the estimated surface temperature of a certain point on the paper web is equal to the temperature value at that point at the previous moment plus the convective heat transfer contribution caused by the air supply temperature minus the latent heat loss caused by spray evaporation. The estimated temperature value of each measurement point is calculated point by point through this equation, thereby realizing the dynamic updating of the surface temperature distribution of the paper web and providing an updated temperature benchmark for subsequent recalculation of temperature difference and reassessment of condensation risk.

[0036] S203, based on the surface temperature estimate, recalculate the temperature difference distribution to obtain the temperature difference distribution for the next control cycle.

[0037] Specifically, based on the updated surface temperature estimate, the point-by-point difference is recalculated with the dew point temperature synchronously collected in the corresponding conditioning section. That is, for each measurement point on the paper web, the surface temperature estimate updated in the current cycle is subtracted from the dew point temperature at the corresponding position to obtain a new point-by-point temperature difference value. These temperature difference values ​​are arranged according to the web coordinates to form the temperature difference distribution for the next control cycle. This distribution will serve as the input benchmark for subsequent condensation risk identification and iterative adjustment of humidification, thus forming a complete closed-loop control cycle.

[0038] For example, after humidification is performed in the first section of the conditioning zone, the actual air supply temperature is 42 degrees Celsius, and the spray volume is 15 liters per minute. The system calculates the temperature difference update for each horizontal point, first determining the temperature influence range, such as an influence width of 20% of the total width of the paper web, and then applying the difference formula to adjust the distribution to ensure that the temperature difference at the center point does not exceed 1 degree Celsius.

[0039] This embodiment collects the actual air supply temperature and spray volume after the final humidification control parameters are executed, and updates the surface temperature estimate of each point on the paper web using the temperature influence coefficient and heat balance equation. Then, it recalculates the temperature difference distribution, thus forming a complete closed-loop control from data acquisition, risk identification, humidification adjustment to parameter verification. This closed-loop feedback mechanism can correct the deviation between actual execution and theoretical setting in real time, making the temperature difference distribution in the next control cycle more accurate. This continuously suppresses the risk of condensation and stably restores the moisture content of the paper web, effectively improving the robustness and long-term stability of the non-polar cotton paper production process control.

[0040] In one embodiment, based on the real-time surface temperature data and the dew point temperature data, a condensation risk zone distribution map is obtained through a condensation risk calculation model, including steps S301 to S305, each step of which is as follows: S301, based on the real-time surface temperature data and the dew point temperature data of each segment, the temperature difference distribution is obtained by calculating the point-by-point difference between the surface temperature and dew point temperature of each point on the paper web.

[0041] Based on real-time surface temperature data and dew point temperature data for each section, the difference between the surface temperature of each point on the horizontal strip of the paper web and the dew point temperature of the corresponding conditioning section is calculated point by point using the synchronously collected data. The calculation formula is as follows: ΔT=T s -T d In the formula, T s For surface temperature, T d This is the dew point temperature.

[0042] Then, the differences of all measurement points are arranged according to the horizontal axis to form a temperature difference distribution curve or heat map. This distribution intuitively reflects the degree of dryness of each position of the paper web relative to the critical condensation condition, providing a quantitative basis for subsequent condensation risk identification.

[0043] S302, based on the temperature difference distribution, the coordinate points corresponding to the temperature difference values ​​below the preset temperature threshold are marked as the initial low temperature point set.

[0044] Based on the temperature difference distribution, the temperature difference value of each measurement point is compared with a preset temperature threshold. The preset temperature threshold is usually set by the process engineer according to the paper type, production speed and target moisture content. If the temperature difference value of a certain point is lower than the threshold, the coordinate position of the point on the paper web is marked as the initial low temperature point. All coordinate points that meet the conditions constitute the initial low temperature point set. This point set serves as the basis for subsequent aggregation and classification of condensation risk areas, and is used to locate weak locations where the surface temperature of the paper web is close to or lower than the dew point temperature.

[0045] S303, the initial low temperature point set and temperature difference gradient are aggregated to obtain an aggregated region, and the aggregated region that is continuous and whose temperature difference is lower than the preset temperature threshold is determined as a condensation risk zone.

[0046] In this process, when aggregating regions based on an initial set of low-temperature points and temperature difference gradients, a connected component analysis algorithm is used to merge adjacent low-temperature points with temperature differences all below a preset threshold into the same aggregation region. Simultaneously, the spatial gradient of temperature differences is considered; if the temperature gradient between adjacent points is gentle, they are further confirmed to belong to the same risk region. Finally, aggregation regions that satisfy continuous distribution and where the temperature difference of all internal points is below the threshold are identified as condensation risk zones, thereby achieving accurate location and spatial definition of potential condensation locations on the paper web. In specific applications, for high-speed toilet paper production lines, the risk threshold can be set to 3℃. When the difference ΔT between the surface temperature and dew point temperature at a certain point on the paper web is less than 3℃, the local area where that point is located is considered to have a condensation risk.

[0047] S304, Based on the contour coordinates of each of the condensation risk areas, the regional boundaries of each of the condensation risk areas are smoothed to obtain the risk area boundaries.

[0048] Based on the contour coordinates of each condensation risk zone, a boundary smoothing algorithm in image processing is used to smooth the irregular jagged boundary of the initial aggregation area, eliminating local bulges and depressions caused by measurement noise or discrete sampling points, thereby obtaining continuous, clear and geometrically regular risk zone boundaries, which facilitates the spatial mapping and visualization of subsequent humidification adjustments.

[0049] S305, Based on each of the condensation risk zones and the corresponding risk zone boundaries, generate a condensation risk zone distribution map.

[0050] Based on each condensation risk zone and its corresponding smooth boundary, all risk zones are mapped to the two-dimensional coordinate system of the paper banner according to their actual spatial location. Different colors or filling patterns are used to distinguish and mark each risk zone according to its risk level, such as high, medium, and low. At the same time, key parameters of each risk zone are marked, such as center coordinates, area, and average temperature difference deviation. Finally, a visualized condensation risk zone distribution map is generated. This distribution map can be displayed in real time on the central monitoring screen, intuitively reflecting the spatiotemporal distribution of condensation risk in the conditioning area and providing clear spatial guidance for segmented humidification adjustment.

[0051] This embodiment calculates the point-by-point difference between the surface temperature and dew point temperature of each point on the paper web and forms a temperature difference distribution. Based on a preset threshold, an initial low-temperature point set is marked. Combined with the temperature difference gradient, regional aggregation and smoothing are performed to finally generate a condensation risk zone distribution map. This series of operations can accurately locate the local positions on the paper web where the temperature difference is insufficient and a thin water film of condensation is about to form. It can also clearly delineate the boundaries of areas with different risk levels, thereby effectively avoiding irreversible flipping of alkyl segments on the surface of AKD sizing agent and permanent degradation of non-polar properties caused by prolonged coverage of local water film. This provides an intuitive and quantitative spatial basis for the targeted reduction of subsequent segmented humidification, significantly improving the precision of condensation risk prevention and control.

[0052] Furthermore, such as Figure 3 As shown, this application provides a process for generating a condensation risk zone distribution map in a non-polar cotton paper production process control method. First, based on the temperature difference distribution, the temperature difference at each point is compared with a preset threshold to identify all coordinate points whose temperature difference is lower than the preset threshold; these coordinate points constitute an initial low-temperature point set. Then, for the initial low-temperature point set, regions are aggregated based on coordinate proximity and temperature difference gradient; aggregated regions with continuous temperature differences all below the preset threshold are identified as condensation risk zones. Next, based on the contour coordinates of each condensation risk zone, the region boundaries are smoothed to obtain continuous and clear risk zone boundaries. Finally, all condensation risk zones and their corresponding smooth boundaries are integrated to generate a condensation risk zone distribution map.

[0053] In one embodiment, based on the condensation risk zone distribution map, a preset gradient analysis model is used to iteratively reduce the humidification amount of each segment according to the correlation between the decreasing humidification amount gradient and the change in the area of ​​the condensation risk zone, until the predicted condensation risk value is lower than a preset threshold, thereby obtaining the target humidification amount for each segment. This includes steps S401 to S404, each step of which is as follows: S401, acquire spatial location data of each conditioning section, current humidification data and surface humidity field monitoring data, wherein the conditioning section refers to multiple humidification control areas divided along the material conveying direction.

[0054] Based on the production line layout, the conditioning area is divided into multiple continuous humidification control sections along the material conveying direction, each section having a unique spatial coordinate range. Then, the real-time humidification setpoint or output value (unit: kg / h) of each section is read from the environmental control system, and surface humidity field monitoring data is collected from the humidity sensor array deployed in each section, thereby establishing the mapping relationship between the spatial location of each conditioning section and the process parameters, providing input for subsequent humidification reduction gradient analysis and condensation suppression effect evaluation.

[0055] S402, based on the current humidification data, calculate the humidification reduction gradient value between adjacent conditioning sections along the material conveying direction.

[0056] Among them, when calculating the humidification decrease gradient value between adjacent conditioning sections based on the current humidification data along the material conveying direction, the formula for calculating the adjusted humidification amount of the k-th section is as follows:

[0057] In the formula, As a decreasing percentage, This is the initial humidification amount for the starting section of the risk zone. This formula assigns a starting segment number to the risk zone. The decreasing ratio between adjacent segments can be extracted or inferred from the current humidification data, providing quantitative input for subsequent gradient analysis models.

[0058] S403, input the humidification rate decrease gradient value and the change in the area ratio of the condensation risk zone into a preset gradient analysis model, and output the condensation risk prediction value through linear regression.

[0059] The model is constructed using a linear regression method, and the regression equation is as follows:

[0060] In the formula, E is the condensation suppression effect index, α is the gradient influence coefficient, and β is the baseline suppression effect. α and β are estimated by the least squares method using historical production data.

[0061] After inputting the measured or set decreasing gradient value G and the corresponding change in the area ratio of the risk zone as E into the model, the model outputs a condensation risk prediction value R. This prediction value is used to compare with the preset risk elimination threshold to determine whether the current gradient scheme meets the requirements.

[0062] S404, based on the current humidification scheme, the condensation risk judgment problem is iteratively solved using the preset gradient analysis model until the condensation risk prediction value output by the current iteration meets the preset condition of being lower than a preset threshold, and the target humidification amount of each segment after adjustment corresponding to the current iteration is output; wherein, the humidification scheme in the first iteration is determined based on the condensation risk zone distribution map and the current humidification amount data of each segment; when the condensation risk prediction value is not lower than the preset threshold, a humidification reduction instruction is generated; in each iteration, the humidification amount of each segment in the humidification scheme of the previous iteration is reduced segment by segment according to the humidification reduction instruction to obtain the target humidification amount of each segment after adjustment in the current iteration.

[0063] Based on the distribution map of condensation risk areas and the current humidification data for each segment, the humidification scheme for the first iteration (i.e., the initial humidification for each segment) is determined. Then, the humidification reduction gradient value G of this scheme and the change in the area ratio of condensation risk areas ΔA are input into a preset gradient analysis model, which uses a linear regression equation. Output the predicted condensation risk value R; if R is lower than the preset threshold, the risk is determined to be eliminated, and the current scheme is directly output as the target humidification amount; if R is not lower than the preset threshold, a humidification amount reduction command is generated, based on the proportional reduction formula. The humidification amount of each segment in the previous iteration is gradually reduced, where r is the reduction ratio (unit: % / segment). This is the initial humidification amount for the starting section of the risk zone. The starting segment number, Let k be the adjusted humidification amount for the kth segment. The reduced humidification amount is used as the new scheme for the current iteration. The decreasing gradient is recalculated and input into the model. The above process is repeated until the condensation risk prediction value output by the model is lower than the preset threshold. At this time, the adjusted humidification amount for each segment corresponding to the current iteration is determined as the final target humidification amount.

[0064] This embodiment acquires the spatial location, current humidification amount, and surface humidity field monitoring data of each conditioning section, calculates the humidification reduction gradient value along the material conveying direction, and establishes the correlation between the reduction gradient and the change in the area of ​​the condensation risk zone using a gradient analysis model constructed by linear regression. Then, iteratively reduces the humidification amount of each section until the predicted condensation risk value is lower than the preset threshold. This process can dynamically match the humidification gradient with the actual distribution of condensation risk, avoiding excessive local water film formation and non-polar performance degradation caused by traditional uniform humidification or fixed gradient humidification. While effectively eliminating the condensation risk, it achieves precise segmented control of humidification amount, ensuring the stability of paper web dimensions and avoiding the rearrangement of sizing agent film surface segments caused by excessive humidification, significantly improving the economy and reliability of the non-polar cotton paper production process.

[0065] In one embodiment, based on the condensation risk zone corresponding to the target humidification amount, the supply air temperature corresponding to the condensation risk zone is obtained. With the goal of minimizing the deviation between the supply air temperature and the permissible process range, a gradient descent algorithm is used to optimize the supply air temperature exceeding the permissible process range, resulting in an optimized supply air temperature sequence. A preset temperature-moisture content mapping model is then used to convert the supply air temperature sequence into a moisture content recovery target value for each point. Each moisture content recovery target value is then compared point-by-point with the real-time moisture content monitoring data to determine the final humidification control parameters. This includes steps S501 to S505, each step of which is detailed below: S501, based on the supply air temperature of each condensation risk zone, obtain the supply air temperature distribution value in the conditioning section.

[0066] Specifically, from the control parameters corresponding to the adjusted humidification amounts of each section, the supply air temperature data of the conditioning section covered by each condensation risk zone is extracted and denoted as T. Where i is the conditioning section number; then, the supply air temperature of all relevant sections is aggregated through the environmental control module, and these temperature values ​​are arranged in section order to obtain the distribution value of supply air temperature within the conditioning section, denoted as set {T}. i1 ,T i2 ,…,T n This distribution value reflects the temperature setting along the material conveying direction in the condensation risk area, providing basic data for subsequent comparison with the process allowable range and gradient descent optimization.

[0067] S502, each supply air temperature value in the supply air temperature distribution value is compared with the upper and lower limits of the process allowable range one by one to determine whether the supply air temperature exceeds the process allowable range, and the supply air temperature of each condensation risk zone is optimized by gradient descent algorithm to obtain the optimized supply air temperature sequence.

[0068] Among them, each supply air temperature in the supply air temperature distribution value Each is related to the lower limit of the process license scope. and upper limit Compare one by one: If or If the temperature of the air supply section exceeds the permissible range of the process, the extent of the exceedance is calculated. For temperatures exceeding the range, an iterative optimization algorithm using gradient descent is employed, with the objective function defined as follows:

[0069] In the formula, For supply air temperature, This represents the lower limit of the permitted scope of the process. This represents the upper limit of the permitted scope of the process.

[0070] The gradient descent iterative formula is as follows:

[0071] In the formula, For the learning rate, when When, partial derivatives ,when When, partial derivatives Through multiple iterations, the supply air temperature that exceeds the range is gradually adjusted until it falls within [the range]. , Within the specified range, the optimized supply air temperature sequence is finally obtained.

[0072] For a standard toilet paper production line, the permissible range of the process can be preset by engineers according to the drying process requirements. If the obtained supply air temperature exceeds this range, it is determined that optimization is needed. For example, if the obtained supply air temperature is 23 degrees Celsius, while the permissible upper limit is 22 degrees Celsius, it is determined to be out of range. Assuming the initial temperature sequence is [23, 22.5, 24] degrees Celsius, the portion exceeding the range is adjusted to [22, 22, 22] degrees Celsius through algorithmic iteration. The system records the temperature change at each iteration step, forming a sequence list for subsequent control of the air conditioning equipment. This sequence not only meets the process requirements but also minimizes energy consumption.

[0073] S503, based on the optimized air supply temperature sequence, determine the target value for moisture content recovery at each point.

[0074] The target value for moisture content recovery is calculated using a preset temperature-moisture content mapping function f(T). This mapping function is based on the paper drying equilibrium moisture content curve and is as follows:

[0075] In the formula, The optimized supply air temperature for the i-th segment (unit: °C). The corresponding moisture content recovery target value (unit: %) is determined by regression analysis of historical drying data of similar paper.

[0076] Each temperature value in the optimized supply air temperature sequence By substituting the values ​​into the mapping function, the target moisture content recovery values ​​corresponding to each measurement point on the paper banner can be obtained, forming the target moisture content distribution, which provides a benchmark for subsequent point-by-point comparison with real-time moisture content monitoring data.

[0077] S504, compare each of the moisture content recovery target values ​​with the real-time moisture content monitoring data point by point.

[0078] The process involves comparing the target moisture content recovery value at each point with the synchronously collected real-time moisture content monitoring data point by point. For each measurement point i on the horizontal strip of the paper, the deviation is calculated. If the real-time moisture content is lower than the target value, it is determined that the humidification amount at that point needs to be increased. If the real-time moisture content is higher than or equal to the target value, the moisture content at that point is considered to meet the requirements. This point-by-point comparison provides a quantitative basis for subsequent adjustments to the humidification amount of each section based on the deviation magnitude.

[0079] S505, when the real-time moisture content monitoring data is lower than the moisture content recovery target value, the humidification amount of each segment is adjusted to determine the final humidification control parameters.

[0080] Specifically, when the real-time moisture content monitoring data is lower than the target moisture content recovery value, the humidification amount in each section needs to be adjusted to promote full moisture content recovery. The specific adjustment method is to use a preset humidity compensation factor. Multiply by the deviation amplitude to calculate the correction amount of humidification, and update the humidification distribution segment by segment. Through this point-by-point comparison and compensation adjustment, after multiple iterations or a one-time correction, the humidification sequence of each segment that meets the moisture content recovery requirements is obtained, which is the final humidification control parameter. This parameter ensures that the paper web moisture content can be fully restored to the target range, provided that the condensation risk is under control.

[0081] This embodiment compares the air supply temperature distribution values ​​of the section where condensation risk occurs with the process allowable range one by one, and uses a gradient descent algorithm to iteratively optimize temperatures that exceed the range, ensuring that the air supply temperature is precisely controlled while meeting process constraints. Then, a preset temperature-moisture content mapping model is used to convert the optimized air supply temperature sequence into the moisture content recovery target value of each point on the paper web, and the value is compared with the real-time moisture content monitoring data point by point. The humidification amount is dynamically adjusted for areas with insufficient moisture content, and finally, the humidification control parameters that take into account both condensation suppression and sufficient moisture content recovery are determined. This process effectively resolves the contradiction between dimensional stability improvement and non-polar performance degradation in the rehumidification process. While avoiding irreversible rearrangement of the sizing agent film surface segments, it accurately restores the moisture content of the paper, significantly improving the overall quality stability and process adaptability of non-polar cotton paper.

[0082] In one embodiment, each supply air temperature value in the supply air temperature distribution is compared one by one with the upper and lower limits of the process allowable range to determine whether the supply air temperature exceeds the process allowable range. Then, the supply air temperature of each condensation risk zone is optimized using a gradient descent algorithm to obtain an optimized supply air temperature sequence, including steps S601 to S603, each step of which is as follows: S601, compare each air supply temperature value in the air supply temperature distribution value with the upper and lower limits of the process allowable range one by one, and determine whether the air supply temperature value exceeds the process allowable range.

[0083] Among them, each supply air temperature in the supply air temperature distribution value Each is related to the lower limit of the process license scope. and upper limit Compare one by one: If or If the temperature exceeds the permissible range, the supply air temperature is determined to be outside the permissible range; otherwise, it is determined to be within the permissible range. This comparison result provides a basis for subsequent optimization of supply air temperatures exceeding the range using the gradient descent algorithm.

[0084] S602, if the air supply temperature exceeds the permissible range of the process, then the excess value is determined.

[0085] When the supply air temperature is determined to exceed the permissible range of the process, the extent of the exceedance needs to be further determined: if If the amplitude value is exceeded ;like If the amplitude value is exceeded This excess value is used to calculate the gradient of the objective function in the subsequent gradient descent algorithm, to guide the iterative optimization direction of the supply air temperature.

[0086] S603, based on the excess amplitude value and gradient descent algorithm, iteratively calculate the optimization adjustment step size, and update the air supply temperature based on the optimization adjustment step size until the air supply temperature is within the process allowable range, and output the optimized air supply temperature sequence.

[0087] For supply air temperatures exceeding the process allowable range, the magnitude of the deviation from the upper or lower limit is first calculated. Then, a gradient descent algorithm is used for iterative optimization. In each iteration, an adjustment step size is determined based on the current deviation magnitude, and the supply air temperature is gradually updated in the direction of reducing the deviation. If the temperature exceeds the upper limit, the temperature is reduced; if it is below the lower limit, the temperature is increased. The above iterative process is repeated until all supply air temperatures fall within the process allowable range. At this point, the optimized supply air temperature sequence is output.

[0088] This embodiment accurately locates out-of-range temperature values ​​and their extent by comparing each section's air supply temperature with the upper and lower limits of the process's permissible range. Then, a gradient descent algorithm is used to iteratively calculate and optimize the step size and gradually update the air supply temperature until all temperature values ​​fall within the permissible range. This process can accurately bring out-of-range air supply temperatures back to the process-permissible range in a fast-converging and highly stable manner, avoiding the risk of condensation or damage to the non-polar properties of the sizing agent film due to excessively high or low air supply temperatures. At the same time, it provides temperature input that meets process constraints for the accurate mapping of the subsequent moisture content recovery target value, thereby effectively improving the accuracy and reliability of temperature control in the non-polar cotton paper production process.

[0089] In one embodiment, when the real-time moisture content monitoring data is lower than the moisture content recovery target value, the humidification amount of each segment is adjusted to determine the final humidification control parameters. The method further includes steps S701 to S703, each step as follows: S701, subtract the moisture content recovery target value from the real-time moisture content monitoring data to calculate the deviation amplitude value.

[0090] The process involves subtracting the real-time moisture content monitoring data from the target moisture content recovery value point by point. Specifically, the actual measured moisture content is subtracted from the target moisture content for that point to calculate the deviation range. A negative deviation range indicates that the real-time moisture content is lower than the target value, requiring an increase in humidification. A positive deviation range indicates that the moisture content has reached or exceeded the target, requiring no additional humidification or a reduction in humidification. This deviation range provides a quantitative basis for subsequent adjustments to the humidification levels at each stage.

[0091] S702, the deviation amplitude value is multiplied by a preset humidity compensation factor to calculate the humidification distribution.

[0092] Specifically, if the deviation magnitude is negative, meaning the real-time moisture content is lower than the target value, multiplying by the compensation factor yields a positive increment in humidification, indicating that the humidification amount needs to be increased in that segment. If the deviation magnitude is positive, a negative increment or zero is obtained, indicating that no increase is needed or the amount should be appropriately reduced. In this way, the moisture content deviation is converted into a correction value for the humidification amount, forming an adjusted humidification distribution, which provides a basis for determining the final humidification control parameters.

[0093] S703, Based on the humidification distribution, a humidification amount sequence is obtained, and based on the humidification amount sequence, the final humidification control parameters are determined.

[0094] Based on the updated humidification distribution, the original humidification amount is superimposed with the adjustment amount to obtain the adjusted humidification amount sequence. Each value in this sequence corresponds to the target humidification amount of a conditioning section. Combined with previously optimized parameters such as air supply temperature, these parameters constitute the final humidification control parameters. These parameters are sent to the field actuators to guide the humidification operation in actual production, ensuring that the paper web moisture content is fully restored to the target range.

[0095] This embodiment obtains the deviation amplitude value by subtracting the moisture content recovery target value from the real-time moisture content monitoring data, and multiplies it by a preset humidity compensation factor to calculate the humidification distribution, thereby generating a humidification amount sequence to determine the final humidification control parameters. This process realizes the quantitative conversion of moisture content deviation into humidification amount correction, and can dynamically adjust the humidification amount of each segment according to the difference between the actual moisture content and the target value. This avoids paper web size instability caused by insufficient humidification, and prevents the risk of condensation and sizing agent film surface chain segment rearrangement that may be caused by excessive humidification. Thus, while ensuring sufficient moisture content recovery, it maintains the long-term stability of non-polar properties, significantly improving the accuracy of control parameters and the adaptability of the production process.

[0096] Furthermore, such as Figure 4As shown, this application provides a final humidification control parameter generation process for a non-polar cotton paper production process control method. First, the target moisture content recovery value for each point on the paper web is extracted from the optimized air supply temperature sequence. Simultaneously, online monitoring data of the cotton paper's moisture content is acquired, and the distribution range of the target moisture content recovery value within the production section is determined. Then, the target moisture content recovery value is compared point-by-point with the online monitoring data of the cotton paper's moisture content. If the online monitoring data of the cotton paper's moisture content is lower than the distribution range, the deviation amplitude is calculated by subtracting the target moisture content recovery value from the online monitoring data. Next, the humidification amount for each section is adjusted according to the deviation amplitude, and the humidification distribution is updated by multiplying the deviation amplitude value by a preset humidity compensation factor to obtain an adjusted humidification amount sequence. Finally, the final humidification control parameters are generated from the adjusted humidification amount sequence, and the applicable values ​​of the final humidification control parameters for sufficient moisture content recovery are determined.

[0097] Based on the same inventive concept, this application also provides a control system for implementing the aforementioned non-polar cotton paper production process. The solution provided by this system is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the non-polar cotton paper production process control device provided below can be found in the limitations of the non-polar cotton paper production process control method described above, and will not be repeated here.

[0098] In one exemplary embodiment, such as Figure 5 As shown, a non-polar tissue paper production process control system is provided, including: The acquisition module 801 is used to acquire collected data, wherein the collected data includes real-time surface temperature data of each point on the paper web, dew point temperature data of each section of the conditioning zone, and real-time moisture content monitoring data of the cotton paper. The calculation module 802 is used to obtain a condensation risk zone distribution map based on the real-time surface temperature data and the dew point temperature data through a condensation risk calculation model. The analysis module 803 is used to, based on the condensation risk zone distribution map, use a preset gradient analysis model to iteratively reduce the humidification amount of each segment according to the correlation between the humidification amount reduction gradient and the change in the area of ​​the condensation risk zone, until the predicted value of condensation risk is lower than a preset threshold, and obtain the target humidification amount of each segment. The optimization module 804 is used to obtain the supply air temperature corresponding to the condensation risk zone segment corresponding to the target humidification amount, with the goal of minimizing the deviation between the supply air temperature and the process allowable range, and to optimize the supply air temperature exceeding the process allowable range through a gradient descent algorithm to obtain an optimized supply air temperature sequence. Through a preset temperature-moisture content mapping model, the supply air temperature sequence is converted into a moisture content recovery target value at each point, and each moisture content recovery target value is compared with the real-time moisture content monitoring data point by point to determine the final humidification control parameters. The control module 805 is used to control the non-polar cotton paper production process based on the final humidification control parameters.

[0099] In one embodiment, a computer program product is provided, including a computer program or instructions that, when executed by a device, implement the steps of a non-polar cotton paper production process control method as described above.

[0100] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0101] This technical solution deeply integrates an online paper web surface temperature scanner with an environmental dew point temperature acquisition station, and links it to a condensation risk gradient analysis model to construct a dedicated system for the coordinated control of condensation risk and moisture content in the production process of non-polar cotton paper. Its core benefit lies in overcoming the limitations of existing technologies that only focus on dimensional stability or passively adjust humidity. It achieves the identification, classification, and control of condensation risk zones along the paper web width dimension. Users can actively adjust the humidification amount and supply air temperature baseline of each section of the conditioning zone through an intuitive monitoring interface, and observe in real time the impact of different control strategies on condensation suppression and the non-polar properties of the paper. This provides precise decision-making basis for high-quality cotton paper production, achieving a leap from passive adjustment to active control. Simultaneously, this solution fully leverages the respective advantages of the temperature scanner in real-time data acquisition, the dew point acquisition station in environmental humidity monitoring, and the gradient analysis model in data-driven decision optimization, solving the problem that traditional single humidity control platforms struggle to simultaneously address condensation risk prevention and control and maintain non-polar properties.

[0102] For the device embodiments, since they basically correspond to the method embodiments, the relevant details can be found in the descriptions of the method embodiments. The device embodiments described above are merely illustrative; components described as separate parts may or may not be physically separate, and components shown as units may or may not be physical units, meaning they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without any inventive effort.

[0103] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for controlling the production process of non-polar cotton paper, characterized in that, include: Acquire the collected data, which includes real-time surface temperature data of each point on the paper web, dew point temperature data of each section of the conditioning zone, and real-time moisture content monitoring data of the cotton paper. Based on the real-time surface temperature data and the dew point temperature data, a condensation risk zone distribution map is obtained through a condensation risk calculation model. Based on the condensation risk zone distribution map, the humidification amount of each segment is iteratively reduced according to the correlation between the humidification amount reduction gradient and the change in the area of ​​the condensation risk zone through a preset gradient analysis model until the predicted value of condensation risk is lower than the preset threshold, thereby obtaining the target humidification amount of each segment. Based on the condensation risk zone corresponding to the target humidification amount, the supply air temperature corresponding to the condensation risk zone is obtained. With the goal of minimizing the deviation between the supply air temperature and the process allowable range, the supply air temperature exceeding the process allowable range is optimized using a gradient descent algorithm to obtain an optimized supply air temperature sequence. Through a preset temperature-moisture content mapping model, the supply air temperature sequence is converted into a moisture content recovery target value for each point. Each moisture content recovery target value is then compared point by point with the real-time moisture content monitoring data to determine the final humidification control parameters. The non-polar cotton paper production process is controlled based on the final humidification control parameters.

2. The method for controlling the production process of non-polar cotton paper as described in claim 1, characterized in that, After controlling the non-polar cotton paper production process based on the final humidification control parameters, the process further includes: Obtain the actual execution data under the final humidification control parameters, wherein the actual execution data includes the actual supply air temperature and spray volume; Based on the actual execution data, the surface temperature estimate of each point on the paper web banner is updated using a preset temperature influence coefficient and a heat balance equation. The temperature difference distribution is recalculated based on the surface temperature estimate to obtain the temperature difference distribution for the next control cycle.

3. The method for controlling the production process of non-polar cotton paper as described in claim 1, characterized in that, The method for obtaining a condensation risk zone distribution map based on the real-time surface temperature data and the dew point temperature data using a condensation risk calculation model includes: Based on the real-time surface temperature data and the dew point temperature data of each segment, the temperature difference distribution is obtained by calculating the point-by-point difference between the surface temperature and dew point temperature at each point on the paper web. Based on the temperature difference distribution, the coordinate points corresponding to temperature differences below a preset temperature threshold are marked as the initial low temperature point set; The initial low temperature point set and temperature difference gradient are aggregated to obtain an aggregated region, and the aggregated region that is continuous and whose temperature difference is lower than the preset temperature threshold is identified as a condensation risk zone. Based on the contour coordinates of each condensation risk zone, the regional boundaries of each condensation risk zone are smoothed to obtain the risk zone boundaries. Based on each of the condensation risk zones and the corresponding risk zone boundaries, a condensation risk zone distribution map is generated.

4. The method for controlling the production process of non-polar cotton paper as described in claim 1, characterized in that, Based on the condensation risk zone distribution map, and using a preset gradient analysis model, the humidification amount of each segment is iteratively reduced according to the correlation between the decreasing humidification amount gradient and the change in the area of ​​the condensation risk zone, until the predicted condensation risk value is lower than a preset threshold, thus obtaining the target humidification amount for each segment, including: Acquire spatial location data, current humidification volume data, and surface humidity field monitoring data for each conditioning section, wherein the conditioning section refers to multiple humidification control areas divided along the material conveying direction; Based on the current humidification data, the humidification decrease gradient value between adjacent conditioning sections is calculated along the material conveying direction; The humidification rate decrease gradient value and the change in the area ratio of the condensation risk zone are input into a preset gradient analysis model, and the condensation risk prediction value is output through linear regression. Based on the current humidification scheme, the condensation risk assessment problem is iteratively solved using the preset gradient analysis model until the condensation risk prediction value output by the current iteration meets the preset condition of being lower than a preset threshold. Then, the adjusted target humidification amount for each segment corresponding to the current iteration is output. Specifically, the humidification scheme in the first iteration is determined based on the condensation risk zone distribution map and the current humidification amount data for each segment. When the condensation risk prediction value is not lower than the preset threshold, a humidification reduction instruction is generated. In each iteration, the humidification amount of each segment in the humidification scheme of the previous iteration is reduced segment by segment according to the humidification reduction instruction to obtain the adjusted target humidification amount for each segment in the current iteration.

5. The method for controlling the production process of non-polar cotton paper as described in claim 1, characterized in that, The step involves obtaining the supply air temperature corresponding to the condensation risk zone segment corresponding to the target humidification amount, aiming to minimize the deviation between the supply air temperature and the process allowable range. A gradient descent algorithm is used to optimize the supply air temperature exceeding the process allowable range, resulting in an optimized supply air temperature sequence. A preset temperature-moisture content mapping model is then used to convert the supply air temperature sequence into a moisture content recovery target value for each point. Each moisture content recovery target value is then compared point-by-point with the real-time moisture content monitoring data to determine the final humidification control parameters, including: Based on the supply air temperature of each condensation risk zone, the supply air temperature distribution value in the conditioning section is obtained. Each supply air temperature value in the supply air temperature distribution is compared with the upper and lower limits of the process allowable range to determine whether the supply air temperature exceeds the process allowable range. The supply air temperature of each condensation risk zone is then optimized using a gradient descent algorithm to obtain an optimized supply air temperature sequence. Based on the optimized supply air temperature sequence, the target value for moisture content recovery at each point is determined; The target values ​​for moisture content recovery and the real-time moisture content monitoring data are compared point by point. When the real-time moisture content monitoring data is lower than the moisture content recovery target value, the humidification amount of each segment is adjusted to determine the final humidification control parameters.

6. The method for controlling the production process of non-polar cotton paper as described in claim 5, characterized in that, The process involves comparing each supply air temperature value in the supply air temperature distribution with the upper and lower limits of the process allowable range to determine whether the supply air temperature exceeds the process allowable range. Then, a gradient descent algorithm is used to optimize the supply air temperature in each condensation risk zone segment, resulting in an optimized supply air temperature sequence, including: Each air supply temperature value in the air supply temperature distribution value is compared with the upper and lower limits of the process allowable range one by one to determine whether the air supply temperature value exceeds the process allowable range. If the air supply temperature exceeds the permissible range of the process, then the excess value is determined; Based on the excess amplitude value and gradient descent algorithm, the optimization adjustment step size is iteratively calculated, and the supply air temperature is updated based on the optimization adjustment step size until the supply air temperature is within the process allowable range, and the optimized supply air temperature sequence is output.

7. The method for controlling the production process of non-polar cotton paper as described in claim 5, characterized in that, When the real-time moisture content monitoring data is lower than the moisture content recovery target value, the humidification amount of each segment is adjusted to determine the final humidification control parameters, including: The deviation amplitude value is calculated by subtracting the moisture content recovery target value from the real-time moisture content monitoring data. The humidification distribution is calculated by multiplying the deviation amplitude value by a preset humidity compensation factor. Based on the humidification distribution, a humidification amount sequence is obtained, and based on the humidification amount sequence, the final humidification control parameters are determined.

8. A control system for the production process of non-polar cotton paper, characterized in that, The system includes: The acquisition module is used to acquire collected data, including real-time surface temperature data of each point on the paper web, dew point temperature data of each section of the conditioning zone, and real-time moisture content monitoring data of the cotton paper. The calculation module is used to obtain a condensation risk zone distribution map based on the real-time surface temperature data and the dew point temperature data through a condensation risk calculation model. The analysis module is used to iteratively reduce the humidification amount of each segment based on the distribution map of the condensation risk area and a preset gradient analysis model, according to the correlation between the decreasing gradient of humidification amount and the change in the area of ​​the condensation risk area, until the predicted value of condensation risk is lower than a preset threshold, so as to obtain the target humidification amount of each segment. The optimization module is used to obtain the supply air temperature corresponding to the condensation risk zone segment corresponding to the target humidification amount, with the goal of minimizing the deviation between the supply air temperature and the process allowable range. The module optimizes the supply air temperature that exceeds the process allowable range using a gradient descent algorithm to obtain an optimized supply air temperature sequence. The module then converts the supply air temperature sequence into a moisture content recovery target value at each point using a preset temperature-moisture content mapping model. Finally, the module compares each moisture content recovery target value with the real-time moisture content monitoring data point by point to determine the final humidification control parameters. The control module is used to control the non-polar cotton paper production process based on the final humidification control parameters.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program or instructions, which, when executed by a communication device, implement the non-polar cotton paper production process control method as described in any one of claims 1-7.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by the communication device, the non-polar cotton paper production process control method as described in any one of claims 1-7 is implemented.

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