A method and system for optimizing and adjusting running parameters of a papermaking machine based on an adaptive algorithm

Abstract

The application discloses a paper-making machine operation parameter optimization adjusting method and system based on an adaptive algorithm, and relates to the technical field of equipment control. The parameter optimization adjusting method comprises the following steps: collecting historical production data of the paper-making machine; and establishing associated data: according to the historical production data, the associated data is quantitatively expressed by a data-driven model to quantify the correlation degree between parameters. The application can provide effective theoretical support for subsequent data prediction by using comprehensive production data. The prediction model combines real-time detection data and theoretical detection data to realize prediction of subsequent data. Based on the prediction result, the equipment parameters are adjusted. The quality of paper production can be ensured by adjusting the equipment. After the adjustment of the equipment parameters, support can be provided for subsequent production, the stability of the equipment during production can be ensured, and the situation that the detection data exceeds the allowable range can be reduced.

Description

Technical Field

[0001] This invention relates to the field of equipment control technology, specifically to a method and system for optimizing and adjusting the operating parameters of a paper machine based on an adaptive algorithm. Background Technology

[0002] In the papermaking industry, the paper machine is one of the key pieces of equipment in paper production. Its performance and stability have a decisive impact on paper quality and production efficiency. Adaptive algorithms, as an advanced control method, can automatically adjust control parameters based on the real-time status and operating data of the system, ensuring that the system always remains in optimal working condition. Applying adaptive algorithms to the optimization and adjustment of the operating parameters of the paper machine can achieve precise control of the papermaking process, thereby ensuring the stability of the paper machine's production quality.

[0003] For example, patent publication number "CN118311871A", patent title "Governor Control Method and System Based on Adaptive Model Predictive Control Algorithm", step one: start the simulation environment module to simulate the actual operating state of the turbine and monitor the state data in real time; step two: input the monitored data into the adaptive model predictive control algorithm; this invention introduces an adaptive model predictive control algorithm, which can update the model parameters in real time according to the changes in the turbine's operating conditions, thereby improving the robustness and accuracy of speed control.

[0004] When the above methods are applied to paper machines, they suffer from a lack of flexibility despite their strong targeting capabilities. This is particularly evident when producing paper of varying thicknesses (or qualities). While the adjustment methods are generally effective for producing paper of a single thickness (or quality) due to the availability of rich historical production data, they must be changed when producing paper of different thicknesses (or qualities). For example, toilet paper's thickness standard needs to be controlled between 0.06 and 0.18 millimeters, and other types of paper also have their own different thickness requirements. This means that even for toilet paper, different thickness specifications such as 0.06 millimeters, 0.07 millimeters, and 0.08 millimeters require corresponding adjustment algorithms to ensure the final thickness after production.

[0005] In actual production, multiple dimensions of product quality (such as allowable thickness range, allowable roughness range, tensile strength, etc.) combine to form a wide range of production requirements. For some ordinary paper, the allowable production range is particularly wide. This may lead to a lack of corresponding historical production data as a reference in some cases. When faced with paper production tasks without historical data, the adjustment work becomes particularly difficult because there is a lack of experience to guide the setting of paper machine operating parameters.

[0006] Therefore, the diversity of paper quality production requirements directly leads to the continuous changes in the operating parameters of papermaking machines. As a result, the optimization and adjustment methods for different paper qualities have become more complex, and there is no historical data to rely on. To address this, an optimization and adjustment method and system for papermaking machine operating parameters based on an adaptive algorithm has been invented. Summary of the Invention

[0007] The purpose of this invention is to provide a method and system for optimizing and adjusting the operating parameters of a paper machine based on an adaptive algorithm, so as to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: the parameter optimization and adjustment method includes:

[0009] Basic data collection: Collect historical production data of the paper machine;

[0010] Establishing related data: Based on historical production data, a data-driven model is used to quantitatively express the degree of correlation between parameters in the related data. Based on historical production data and the degree of correlation, a correlation diagram is established between equipment, equipment operating parameters, paper parameters in the paper production process, and the quality parameters of the final product.

[0011] Establish and apply simulation models: Establish simulation models, simulate blank production data through simulation models to obtain simulated production data, and combine simulated production data with historical production data to obtain a complete set of simulated production data.

[0012] Input requirements: Users input the target theoretical final product quality data, and obtain the theoretical operating parameter data of the paper machine and the theoretical paper parameter data by retrieving from the complete set;

[0013] Establish allowable ranges: Establish allowable ranges based on the quality parameters in the input requirements. The allowable ranges include the allowable ranges for equipment operating parameters, paper parameters, and quality parameters.

[0014] Real-time monitoring data: Real-time monitoring of paper machine production data to obtain real-time equipment operating parameters, real-time parameter data, real-time paper parameter data, and real-time quality parameter data;

[0015] Establish and apply a predictive model: Establish a predictive model that combines real-time parameter data and theoretical parameter data to predict future paper parameter data and future quality parameter data for paper production, thereby obtaining predicted paper parameters and predicted quality parameters.

[0016] Adaptive adjustment: Based on the degree of correlation, correlation diagram and allowable range, establish parameter adjustment method and parameter fine-tuning method. When the predicted paper parameters exceed the allowable range, the parameter fine-tuning method is used, and when the predicted quality parameters exceed the allowable range, the parameter adjustment method is used.

[0017] Adaptive overall control: Establish a hierarchical control for adjusting equipment operating parameters. The hierarchical control is based on the comparison between real-time paper parameter data and the allowable range of paper parameters. The hierarchical control includes short feedback adjustment for fine-tuning the parameters of equipment ranked ahead of the production line and long feedback adjustment for overall optimization.

[0018] Repeated and continuous correction: Continuously monitor real-time data, and continuously apply predictive models, adjustments, and overall control based on real-time data until paper production is completed.

[0019] Furthermore, the simulation method of the simulation model includes: using historical production data as a reference, simulating the relationship between equipment parameters, paper parameters during production, and final product quality parameters through correlation; inputting the final quality parameters; setting the theoretical final product quality data in the simulation model; using the simulation model and correlation data to simulate the process of producing the theoretical final product quality data; the simulation model obtaining process data during the simulation process; the process data including simulated equipment parameter data and simulated paper parameter data; and combining the theoretical final product quality data, process data, and historical production data to obtain a complete set of simulated production data.

[0020] Furthermore, the prediction method of the prediction model includes: combining a complete set of simulated production data, using real-time paper parameter data as input to the prediction model, and inputting real-time paper parameters and theoretical paper parameters into the correlation degree to obtain the corresponding predicted quality parameter data.

[0021] Furthermore, the parameter adjustment method includes: obtaining predicted quality parameter data based on actual equipment parameter data and real-time paper parameter data, combined with a prediction model; obtaining the first difference between the predicted quality parameter data and the allowable range of the quality parameter; controlling the specific values ​​of the equipment operating parameters according to the degree of correlation; and, combined with the prediction model, ensuring that the paper parameter data is within the allowable range of the paper parameter, so that the adjusted predicted quality parameter is within the allowable range of the quality parameter.

[0022] Furthermore, the parameter fine-tuning method includes: obtaining a second difference between real-time paper parameters and the allowable range of paper parameters; controlling the specific values ​​of the corresponding equipment operating parameters based on the degree of correlation; and combining a prediction model to control the paper parameter data to be within the allowable range of paper parameters.

[0023] Furthermore, the comparison results include: obtaining the median value of the allowable range of paper parameters, obtaining the third difference between the real-time paper parameter data and the median value, and determining the positive or negative status of the third difference. If the actual paper parameter data exceeds the allowable range of paper parameters, long feedback adjustment is triggered. If the actual paper parameter data does not exceed the allowable range of paper parameters and the third difference remains unchanged, short feedback adjustment is triggered.

[0024] Furthermore, the long feedback adjustment includes: adjusting the parameter data of the equipment at the front of the sorting order in the production line, obtaining the corresponding equipment based on the degree of correlation and comparison results, adjusting the operating parameters of the corresponding equipment, obtaining the adjusted equipment operating parameters, predicting the paper parameters through a prediction model, obtaining the adjusted predicted paper parameter data, and the adjusted predicted paper parameter data being within the allowable range of the paper parameters.

[0025] Furthermore, the short feedback adjustment method includes: adjusting the parameter data of the equipment preceding the production line, adjusting the parameters of the preceding equipment based on the degree of correlation and comparison results, and combining the prediction model to obtain the adjusted predicted paper parameter data, obtaining the predicted third difference between the predicted paper parameter data and the median value, and controlling the specific value of the predicted third difference to be at the minimum value.

[0026] A paper machine operating parameter optimization and adjustment system based on an adaptive algorithm is provided, which adopts the aforementioned paper machine operating parameter optimization and adjustment method based on an adaptive algorithm.

[0027] Compared with the prior art, the beneficial effects of the present invention are:

[0028] This paper machine operating parameter optimization and adjustment method and system based on adaptive algorithms obtains simulated production data by simulating the production process, thus acquiring comprehensive production data. This comprehensive production data provides effective theoretical support for subsequent data prediction. The prediction model combines real-time detection data and theoretical detection data to predict subsequent data. Based on the prediction results, the equipment parameters are adjusted. This adjustment ensures the quality of paper production and provides support for subsequent production, guaranteeing the stability of the equipment during production and reducing the occurrence of detection data exceeding the allowable range.

[0029] Meanwhile, through adjustments and overall control settings, the parameter fine-tuning method and parameter adjustment method adjust the operating parameters of subsequent equipment based on the paper parameters at the detection point. By adjusting the operating parameters of subsequent equipment, the quality parameters of the paper at that point are made to meet the requirements. The overall control adjusts the equipment in front of the production line based on the paper parameters at the detection point. By adjusting the operating parameters of the equipment in front, the difference between the paper parameters at that point and the intermediate value of the allowable range of paper parameters is reduced, or the paper parameters at that point are prevented from exceeding the allowable range of paper parameters. This achieves comprehensive adjustment of the operating parameters of the paper machine while ensuring production quality.

[0030] Through the design of correlation degree and simulation model, the simulation model can simulate blank production data by means of correlation degree, thereby supplementing the production data. By simulating the complete set of production data, it can help the prediction of subsequent prediction model. At the same time, the use of the complete set can provide effective theoretical support for the subsequent adjustment process of equipment parameters. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the parameter optimization and adjustment method of the present invention;

[0032] Figure 2 This is a schematic diagram of the association diagram and the first association of the present invention;

[0033] Figure 3 This is a schematic diagram illustrating the adjustment of subsequent equipment operating parameters according to the present invention;

[0034] Figure 4 This is a schematic diagram illustrating the adjustment of the operating parameters of the preceding equipment according to the present invention. Detailed Implementation

[0035] 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.

[0036] like Figure 1 - Figure 4 As shown, the present invention provides a technical solution: a parameter optimization and adjustment method comprising:

[0037] Basic data collection: Collect historical production data of the paper machine;

[0038] Establishing related data: Based on historical production data, a data-driven model is used to quantitatively express the degree of correlation between parameters in the related data. Based on historical production data and the degree of correlation, a correlation diagram is established between equipment, equipment operating parameters, paper parameters in the paper production process, and the quality parameters of the final product.

[0039] Establish and apply simulation models: Establish simulation models, simulate blank production data through simulation models to obtain simulated production data, and combine simulated production data with historical production data to obtain a complete set of simulated production data.

[0040] Input requirements: Users input the target theoretical final product quality data, and obtain the theoretical operating parameter data of the paper machine and the theoretical paper parameter data by retrieving from the complete set;

[0041] Establish allowable ranges: Establish allowable ranges based on the quality parameters in the input requirements. Allowable ranges include allowable ranges for equipment operating parameters, paper parameters, and quality parameters.

[0042] Real-time monitoring data: Real-time monitoring of paper machine production data to obtain real-time equipment operating parameters, real-time parameter data, real-time paper parameter data, and real-time quality parameter data;

[0043] Establish and apply a predictive model: Establish a predictive model that combines real-time parameter data and theoretical parameter data to predict future paper parameter data and future quality parameter data for paper production, thereby obtaining predicted paper parameters and predicted quality parameters.

[0044] Adaptive adjustment: Based on the degree of correlation, correlation diagram and allowable range, establish parameter adjustment method and parameter fine-tuning method. When the predicted paper parameters exceed the allowable range, the parameter fine-tuning method is used, and when the predicted quality parameters exceed the allowable range, the parameter adjustment method is used.

[0045] Adaptive overall control: Establish hierarchical control for adjusting equipment operating parameters. Hierarchical control is adopted based on the comparison between real-time paper parameter data and the allowable range of paper parameters. Hierarchical control includes short feedback adjustment for fine-tuning the parameters of equipment ranked ahead of the production line and long feedback adjustment for overall optimization.

[0046] Repeated and continuous correction: Continuously monitor real-time data, and continuously apply predictive models, adjustments, and overall control based on real-time data until paper production is completed.

[0047] The simulation method of the simulation model includes: using historical production data as a reference, simulating the relationship between equipment parameters, paper parameters during production, and final product quality parameters through correlation; inputting the final quality parameters; setting the theoretical final product quality data in the simulation model; using the simulation model and correlation data to simulate the process of producing the theoretical final product quality data; obtaining process data during the simulation process; the process data includes simulated equipment parameter data and simulated paper parameter data; and combining the theoretical final product quality data, process data, and historical production data to obtain a complete set of simulated production data.

[0048] The prediction method of the prediction model includes: combining a complete set of simulated production data, using real-time paper parameter data as input to the prediction model, and inputting real-time paper parameters and theoretical paper parameters into the correlation degree to obtain the corresponding predicted quality parameter data.

[0049] The parameter adjustment method includes: obtaining predicted quality parameter data based on actual equipment parameter data and real-time paper parameter data, combined with a prediction model; obtaining the first difference between the predicted quality parameter data and the allowable range of the quality parameter; controlling the specific values ​​of the equipment operating parameters according to the degree of correlation; and combining the prediction model to ensure that the paper parameter data is within the allowable range of the paper parameter, so that the adjusted predicted quality parameter is within the allowable range of the quality parameter.

[0050] The parameter fine-tuning method includes: obtaining the second difference between the real-time paper parameters and the allowable range of paper parameters; controlling the specific values ​​of the corresponding equipment operating parameters according to the degree of correlation; and combining the prediction model to control the paper parameter data to be within the allowable range of paper parameters.

[0051] The comparison results include: obtaining the median value of the allowable range of paper parameters, obtaining the third difference between the real-time paper parameter data and the median value, and determining the positive or negative value of the third difference. If the actual paper parameter data exceeds the allowable range of paper parameters, long feedback adjustment is triggered. If the actual paper parameter data does not exceed the allowable range of paper parameters and the third difference remains unchanged, short feedback adjustment is triggered.

[0052] Long-term feedback adjustment includes: adjusting the parameter data of the equipment at the beginning of the sequence according to the sorting order in the production line; obtaining the corresponding equipment based on the degree of correlation and comparison results; adjusting the operating parameters of the corresponding equipment; obtaining the adjusted equipment operating parameters; predicting the paper parameters through a prediction model; obtaining the adjusted predicted paper parameter data; and ensuring that the adjusted predicted paper parameter data is within the allowable range of the paper parameters.

[0053] The short feedback adjustment method includes: adjusting the parameter data of the equipment preceding the production line, adjusting the parameters of the preceding equipment based on the degree of correlation and comparison results, and combining the prediction model to obtain the adjusted predicted paper parameter data, obtaining the predicted third difference between the predicted paper parameter data and the median value, and controlling the specific value of the predicted third difference to be at the minimum value.

[0054] A paper machine operating parameter optimization and adjustment system based on an adaptive algorithm is provided, which adopts the aforementioned paper machine operating parameter optimization and adjustment method based on an adaptive algorithm.

[0055] While historical production data may not cover the entire production range of paper quality, numerical representation of parameters and quality-related data facilitates subsequent processing and use. Processing historical production data after acquisition, such as data cleaning and deduplication, helps improve its reliability. Production data includes equipment operating parameters, paper parameters during the paper production process, and final product quality parameters. Equipment operating parameters refer to the equipment parameters during operation. Paper parameters are obtained by using detection devices to inspect the paper during production. Final quality parameters are obtained by using detection devices to inspect the paper after production is complete. Paper quality includes thickness and surface roughness. Since the paper machine operates in a production line manner, the subsequent and preceding equipment mentioned in this application refer to their relative positions on the production line.

[0056] There may be devices at the discharge end of the equipment that detect the same parameter. For example, there are devices to detect paper moisture at the discharge end of both pressing and drying equipment. However, since the two processes are at different locations, the properties are also different. Therefore, the two paper parameters obtained cannot be regarded as the same paper parameter.

[0057] The system monitors the paper machine's operating parameters and paper parameters during production in real time. It obtains real-time equipment parameters and theoretical parameters, and combines these with a predictive model to obtain predicted paper parameters. This process is continuous, constantly collecting real-time and paper parameter data as production progresses, allowing for continuous adjustments.

[0058] Regarding the issue of the same batch, there are two types: First, unformed products are moved from one process to the next as a whole, and the products processed in the current and next processes simultaneously are not from the same batch. Second, unformed products are moved continuously from one process to the next, and the products processed in the current and next processes simultaneously are from the same batch. For example, the papermaking process includes pulp, bulk pulp, high-consistency deslagging, refining, mixing, forming, pressing, drying, creping, winding, and base paper. Pulp, bulk pulp, high-consistency deslagging, refining, and mixing can be considered the first type of batch, while forming, pressing, drying, creping, winding, and base paper can be considered the second type. The specific parameter adjustment methods used in the two types will be significantly different. Because the first type involves moving the entire product within the process, the paper parameters can be detected in real time using a detection device. While it's possible to measure paper parameters, the operating parameters of the equipment or the content of raw materials inside the equipment can be changed to bring the paper parameters to the allowable range, or even the optimal data point within the allowable range. Once the optimal data point or allowable range is reached, the product can be moved to the next process. This application addresses the second type. In this process, when detecting the paper parameters at the output end of the equipment, if the paper parameters exceed the corresponding allowable range, the paper parameters cannot be corrected. Even when adjusting the equipment parameters, the paper parameters at the output end cannot be modified or adjusted. Furthermore, the adjustment of equipment parameters can only be applied to subsequent paper parameters. Therefore, if it is necessary to correct the paper parameters to ensure the quality parameters of the paper at that point, subsequent equipment parameters need to be adjusted to correct the quality parameters of the paper at that point.

[0059] Papermaking is a continuous process, from pulp formation to product completion. For example, a papermaking machine includes processes such as pulping, flow preparation, forming, pressing, drying, and rewinding, and the equipment used to implement these processes. The equipment is arranged according to a production line. At the discharge end of the equipment, there are detection devices to monitor the paper's parameters. These devices include an online near-infrared moisture analyzer, primarily used for detecting the moisture content during papermaking. This analyzer provides real-time, rapid moisture data to allow for timely adjustments to production parameters (such as pressing pressure, drying temperature, and speed) to ensure the paper's moisture content meets production standards. The machine also includes optical performance testing equipment. For paper whiteness and opacity, there are other detection devices. The existence of these devices depends on the specific type of data for the final quality parameters. Data-driven models are statistical models that rely on the data itself to find parameters and achieve preset goals. Through correlation analysis, a significant linear relationship can be found between pulp concentration and paper thickness, strength, and other indicators. Through regression analysis, a mathematical relationship between pulp concentration and paper quality can be established to predict and control paper quality. Using machine learning models, the impact of multiple parameters on paper quality can be comprehensively considered, and predicted values ​​or classification results can be output to support decision-making in the production process.

[0060] Both simulation and prediction models have existing technologies. The simulation model uses historical production data and uses a data-driven model based on the correlation diagram and historical production data to quantify the degree of correlation between parameters in the correlation data. It uses numerical values ​​to represent and simulates blank and missing production parameters, such as operating parameter a, paper parameter p, and quality parameter q, where a, p, and q all represent positive integers.

[0061] The method for calculating paper parameter p:

[0062] K11 running parameter 1 + K12 running parameter 2 + ... + K1a running parameter a = paper parameter 1;

[0063] K21 operating parameter 1 + K22 operating parameter 2 + ... + K2a operating parameter a = paper parameter 2;

[0064] …

[0065] Kp1 running parameter 1 + Kp2 running parameter 2 + ... + Kpa running parameter a = paper parameter p;

[0066] The method for calculating the mass parameter q:

[0067] H11 paper parameter 1 + H12 paper parameter 2 + ... + H1p paper parameter p = quality parameter 1;

[0068] H21 paper parameter 1 + H22 paper parameter 2 + ... + H2p paper parameter p = mass parameter 2;

[0069] …

[0070] Hq1 paper parameter 1 + Hq2 paper parameter 2 + ... + Hqp paper parameter p = mass parameter q;

[0071] Where K11, K12…Kpa are coefficients, and H11, H12…Hqp are coefficients. K and H coefficients are calculated using historical production data. These coefficients, along with the calculation methods for paper parameter p, are used to supplement the remaining blank production data. K and H coefficients may contain zero values. The input requires the desired quality parameter q. Based on the principle of similarity, similar paper parameters from historical production data are selected as references and substituted. Even if there is a deviation between the obtained quality parameter and the required quality parameter, the parameter with the fewest modifications is prioritized when changing one or more paper parameters. The principle of similarity is based on finding parameters with similar values ​​from historical production data for the quality parameter in the input requirements. In practice, the calculation methods for paper parameter p and quality parameter q do not always use linear parameters; other calculation methods can also be used, such as nonlinear regression models like multinomial regression, exponential regression, and logarithmic regression. These models can capture more complex nonlinear relationships and improve the accuracy of the calculation.

[0072] Numerical simulation is used to calculate and obtain the final quality data and process data of the simulated theoretical product. By obtaining a complete set of simulated production data, and through the simulation process of the data in the simulation model, a reference can be used to establish a prediction model. The simulation process generates blank production data based on historical production data, and the prediction model simulates the changes in quality parameters based on parameter changes. The simulation model combines historical production data, simulated equipment parameter data and simulated paper parameter data under the simulated final quality data. The combination of simulated data and historical production data results in a comprehensive and complete set of production data. The simulation model can simulate production data. First, the final quality data of the theoretical product is input into the simulation model. The simulation model simulates production based on the final quality data of the theoretical product to obtain process data, including simulated equipment parameter data and simulated paper parameter data. Then, the data is integrated to obtain a complete set of production data. The complete set can achieve correspondence and full coverage of the production range of quality parameters for subsequent prediction. At the same time, during actual production, the actual paper parameters, actual paper parameters, and actual quality parameters are obtained, which can correct the simulation data in the complete set of production data.

[0073] There is a natural correlation between equipment and its operating parameters. The primary purpose of establishing the primary correlation between equipment and its operating parameters is to obtain the order of events in the equipment and combine the order of events with the operating parameters, so that the order of events can be used as a reference for paper parameters.

[0074] In long feedback regulation, during actual production, when the equipment operating parameters, paper parameters, and final quality parameters are all within the allowable range, the system will not automatically apply the operating parameter optimization adjustment method. Only when the paper parameters exceed the allowable range of the intermediate value will long feedback regulation be implemented. Each piece of equipment has a detection device at its discharge end to monitor paper parameter data. This device monitors the paper parameter data in real time, obtaining data about the paper parameter at the discharge end of that equipment. This data is compared with the allowable range of paper parameters. When the data exceeds the allowable range, the optimization adjustment system operates, applying long feedback regulation to adjust the operating parameters of that equipment. Simultaneously, a predictive model predicts the paper parameter data at that location to predict subsequent paper parameter data and final quality parameters. When the final quality parameter data exceeds the allowable range, adjustments will be made to a series of subsequent equipment operating parameters.

[0075] In short feedback adjustment, the real-time detected paper parameter data may change, but since it remains within the allowable range, long feedback adjustment will not be triggered. However, as usage time increases, the changes in real-time paper parameter data will eventually trigger long feedback adjustment. Due to the complexity and excessive adjustment range of long feedback adjustment, short feedback adjustment is prioritized to correct the real-time detected paper parameter data, ensuring it remains within the allowable range. When the real-time paper parameter data is normal, but the final detected quality parameter data still exceeds its operating range, short feedback adjustment is applied to adjust the equipment parameter data in real time so that the paper parameters at the output end of the equipment are within the corresponding operating range. Short feedback adjustment adjusts the paper parameters by correcting the equipment parameters at the previous position, thereby adjusting the quality parameters of the final product. Short feedback adjustment causes the simulated quality parameters or simulated paper parameters to fluctuate around this intermediate value. By controlling the fluctuation of the specific values ​​around the intermediate value, this method ensures the stability of the paper machine's operation, thus guaranteeing the stability of paper machine production.

[0076] like Figure 3As shown, the actual paper parameter 1 is obtained through real-time detection. The actual paper parameter 1 and the subsequent theoretical paper parameter 3 are combined and a prediction model is used to obtain a prediction of the quality parameter. When it is determined that the specific value of the predicted quality parameter 1 exceeds the allowable range of the corresponding quality parameter, adjustments are made. Based on the correlation diagram and the degree of correlation, the equipment parameter 4 is adjusted to modify the paper parameter 3, thereby obtaining the modified final quality 1 and ensuring that the modified final quality 1 is within the allowable range of the corresponding quality parameter. Both the modified paper parameter 3 and the modified final quality 1 are obtained by the prediction model through prediction.

[0077] like Figure 4 As shown, when the actual paper parameter 3 is detected to be outside the allowable range of the corresponding paper parameter, long feedback adjustment and short feedback adjustment will be used to adjust the parameters of the equipment in front of the production line. Combined with the prediction model, the predicted paper parameter 3 can be adjusted to be within the allowable range of the corresponding paper parameter. At the same time, when the prediction model makes a prediction, it needs to ensure that the predicted final quality 1 is within the allowable range of the corresponding quality parameter.

[0078] Before production, the user inputs their requirements. The target theoretical product final quality data refers to the paper data the user wants to produce. Based on the requirements, the corresponding theoretical equipment operating parameters and theoretical paper parameters are obtained from the complete set of simulated production data. Ideally, by following the theoretical equipment operating parameters, the required paper can be obtained. The equipment operating parameters can be directly controlled, and the paper parameters and theoretical quality parameters can only be controlled through the operating parameters. When the detection device detects the actual paper parameters, it combines the actual paper parameters with the theoretical paper parameters and uses a predictive model to simulate the quality parameters, obtaining simulated quality parameters. The simulated quality parameters are compared with the allowable range of the quality parameters, using the median value within the allowable range as a reference. When the simulated quality parameters exceed the allowable range, long feedback adjustment is applied. If the specific data of the simulated quality parameters continuously deviates from the median value within the allowable range, short feedback adjustment is applied. Short feedback adjustment adjusts the equipment parameters, thereby causing the specific data of the simulated quality parameters to fluctuate at the median value within the allowable range. The use of parameter fine-tuning methods, parameter adjustment methods, short feedback adjustment, and long feedback adjustment is essentially the application of adaptive algorithms.

[0079] The parameter fine-tuning method and parameter adjustment method are based on the paper parameters at the detection point to adjust the operating parameters of the subsequent equipment. By adjusting the operating parameters of the subsequent equipment, the quality parameters of the paper at that point are made up to meet the requirements. The overall control method is based on the paper parameters at the detection point to adjust the equipment in front of the production line. By adjusting the operating parameters of the preceding equipment, the difference between the paper parameters at that point and the intermediate value of the allowable range of paper parameters is reduced, or the paper parameters at that point are prevented from exceeding the allowable range of paper parameters. This achieves comprehensive adjustment of the operating parameters of the paper machine while ensuring production quality.

[0080] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended embodiments and their equivalents.

Claims

1. A method for optimizing and adjusting the operating parameters of a paper machine based on an adaptive algorithm, characterized in that, The parameter optimization and adjustment method includes: Basic data collection: Collect historical production data of the paper machine; Establishing related data: Based on historical production data, a data-driven model is used to quantitatively express the degree of correlation between parameters in the related data. Based on historical production data and the degree of correlation, a correlation diagram is established between equipment, equipment operating parameters, paper parameters in the paper production process, and the quality parameters of the final product. Establish and apply simulation models: Establish simulation models, simulate blank production data through simulation models to obtain simulated production data, and combine simulated production data with historical production data to obtain a complete set of simulated production data. Input requirements: Users input the target theoretical final product quality data, and obtain the theoretical operating parameter data of the paper machine and the theoretical paper parameter data by retrieving from the complete set; Establish allowable ranges: Establish allowable ranges based on the quality parameters in the input requirements. The allowable ranges include the allowable ranges for equipment operating parameters, paper parameters, and quality parameters. Real-time monitoring data: Real-time monitoring of paper machine production data to obtain real-time equipment operating parameters, real-time parameter data, real-time paper parameter data, and real-time quality parameter data; Establish and apply a predictive model: Establish a predictive model that combines real-time parameter data and theoretical parameter data to predict future paper parameter data and future quality parameter data for paper production, thereby obtaining the predicted parameters and predicted quality parameters. Adaptive adjustment: Based on the degree of correlation, correlation diagram and allowable range, establish parameter adjustment method and parameter fine-tuning method. When the predicted paper parameters exceed the allowable range, the parameter fine-tuning method is used, and when the predicted quality parameters exceed the allowable range, the parameter adjustment method is used. Adaptive overall control: Establish a hierarchical control for adjusting equipment operating parameters. The hierarchical control is based on the comparison between real-time paper parameter data and the allowable range of paper parameters. The hierarchical control includes short feedback adjustment for fine-tuning the parameters of equipment ranked ahead of the production line and long feedback adjustment for overall optimization. Repeated and continuous correction: Continuously monitor real-time data, and continuously apply predictive models, adjustments and overall control based on real-time data until paper production is completed; The comparison results include: obtaining the median value of the allowable range of paper parameters, obtaining the third difference between the real-time paper parameter data and the median value, and determining the positive or negative value of the third difference. If the actual paper parameter data exceeds the allowable range of paper parameters, long feedback adjustment is triggered. If the actual paper parameter data does not exceed the allowable range of paper parameters and the third difference remains unchanged, short feedback adjustment is triggered. The long feedback adjustment includes: adjusting the parameter data of the equipment at the front of the sorting order in the production line, obtaining the corresponding equipment based on the degree of correlation and comparison results, adjusting the operating parameters of the corresponding equipment, obtaining the adjusted equipment operating parameters, predicting the paper parameters through a prediction model, obtaining the adjusted predicted paper parameter data, and the adjusted predicted paper parameter data being within the allowable range of the paper parameters. The short feedback adjustment method includes: adjusting the parameter data of the preceding equipment in the production line, adjusting the parameters of the preceding equipment based on the degree of correlation and comparison results, and combining the prediction model to obtain the adjusted predicted paper parameter data, obtaining the predicted third difference between the predicted paper parameter data and the median value, and controlling the specific value of the predicted third difference to be at the minimum value.

2. The method for optimizing and adjusting the operating parameters of a paper machine based on an adaptive algorithm according to claim 1, characterized in that: The simulation method of the simulation model includes: using historical production data as a reference, simulating the relationship between equipment parameters, paper parameters during production, and final product quality parameters through correlation; inputting the final quality parameters; setting the theoretical final product quality data in the simulation model; using the simulation model and correlation data to simulate the process of producing the theoretical final product quality data; obtaining process data during the simulation process; the process data includes simulated equipment parameter data and simulated paper parameter data; and combining the theoretical final product quality data, process data, and historical production data to obtain a complete set of simulated production data.

3. The method for optimizing and adjusting the operating parameters of a paper machine based on an adaptive algorithm according to claim 1, characterized in that: The prediction method of the prediction model includes: combining a complete set of simulated production data, using real-time paper parameter data as input to the prediction model, and inputting real-time paper parameters and theoretical paper parameters into the correlation degree to obtain the corresponding predicted quality parameter data.

4. The method for optimizing and adjusting the operating parameters of a paper machine based on an adaptive algorithm according to claim 1, characterized in that: The parameter adjustment method includes: obtaining predicted quality parameter data based on actual equipment parameter data and real-time paper parameter data, combined with a prediction model; obtaining the first difference between the predicted quality parameter data and the allowable range of the quality parameter; controlling the specific values ​​of the equipment operating parameters according to the degree of correlation; and, combined with the prediction model, ensuring that the paper parameter data is within the allowable range of the paper parameter, so that the adjusted predicted quality parameter is within the allowable range of the quality parameter.

5. The method for optimizing and adjusting the operating parameters of a paper machine based on an adaptive algorithm according to claim 1, characterized in that: The parameter fine-tuning method includes: obtaining the second difference between the real-time paper parameters and the allowable range of the paper parameters; controlling the specific values ​​of the corresponding equipment operating parameters according to the degree of correlation; and combining the prediction model to control the paper parameter data to be within the allowable range of the paper parameters.

6. A paper machine operating parameter optimization and adjustment system based on an adaptive algorithm, characterized in that: The paper machine operating parameter optimization and adjustment method based on an adaptive algorithm, as described in any one of claims 1-5, is adopted.

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