Control method of water-based resin desolventizing process

Abstract

The invention relates to the technical field of automatic control, in particular to a control method for a water-based resin desolventizing process. The method comprises the steps that parameter data are monitored and preprocessed, and preprocessed parameter data are obtained; dynamically adjusting equipment parameters based on the preprocessed parameter data; based on environmental factors and parameter data monitored in real time, dynamically optimizing the dynamically adjusted equipment parameters to obtain optimized equipment parameters; and controlling the water-based resin desolventizing process based on the optimized equipment parameters. The technical problems that a traditional water-based resin desolventizing control mode is low in control precision and poor in stability are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic control, and particularly to a control method for the desolventization process of waterborne resin. Background Art

[0002] Waterborne resin is widely used in industrial fields such as coatings and adhesives. As an environmentally friendly material, the desolventization process of waterborne resin plays a crucial role in its performance and the quality of the final product. However, during the desolventization process of waterborne resin, it is affected by various factors such as temperature, humidity, and solvent concentration. Therefore, process fluctuations often occur, resulting in unstable quality of the final product and even defects. In addition, traditional control methods for waterborne resin desolventization rely mostly on manual experience, fail to make full use of modern intelligent control means, and have poor adaptability to environmental changes and equipment aging, easily leading to problems such as low production efficiency, high energy consumption, and frequent equipment failures.

[0003] In summary, traditional control methods for waterborne resin desolventization have technical problems of relatively low control accuracy and poor stability. Summary of the Invention

[0004] The present invention provides a control method for the desolventization process of waterborne resin to solve the technical problems of relatively low control accuracy and poor stability during the desolventization process of waterborne resin.

[0005] A control method for the desolventization process of waterborne resin according to the present invention specifically includes the following technical solutions: A control method for the desolventization process of waterborne resin includes the following steps: S1. Monitor parameter data and perform preprocessing to obtain preprocessed parameter data; based on the preprocessed parameter data, dynamically adjust equipment parameters; S2. Based on environmental factors and the parameter data monitored in real time, dynamically optimize the equipment parameters after dynamic adjustment to obtain optimized equipment parameters; based on the optimized equipment parameters, control the desolventization process of waterborne resin.

[0006] Preferably, the S1 specifically includes: Based on the preprocessed parameter data, introduce an adaptive optimization control algorithm to dynamically adjust equipment parameters to obtain dynamically adjusted equipment parameters.

[0007] Preferably, the S1 specifically includes: During the implementation of the adaptive optimization control algorithm, based on the preprocessed parameter data, calculate the change rate of the preprocessed parameter data over time and construct a dynamic change model.

[0008] Preferably, the S1 specifically includes: In the implementation process of the adaptive optimization control algorithm, the error is calculated based on the preprocessed parameter data at the current moment; the error is dynamically corrected by combining a nonlinear function to obtain the dynamically corrected error, and the dynamically corrected error is used as the current error.

[0009] Preferably, the S1 specifically includes: Based on the current error and combined with the error at the historical moment, the adaptive control increment is calculated; the formula for the adaptive control increment is as follows: , where, is the adaptive control increment at time ; is the adjustment coefficient of the current error; is the error dynamically corrected at time ; is the adjustment coefficient of the cumulative error; is the error dynamically corrected at time ; is the time integral variable; is the nonlinear feedback coefficient of the current error; is the nonlinear feedback coefficient of the error change rate; is the error at time .

[0010] Preferably, the S1 specifically includes: Based on the dynamic change model, the device parameters are analyzed and calculated by the state feedback control method; the device parameters are gradually adjusted based on the adaptive control increment to obtain the dynamically adjusted device parameters.

[0011] Preferably, the S2 specifically includes: The environmental factors and the parameter data of the real-time monitoring are quantified to obtain the influence quantification index of the environmental factors and the quantization feedback of the parameter data of the real-time monitoring, and a multi-dimensional adaptive feedback optimization algorithm is introduced to dynamically optimize the dynamically adjusted device parameters to obtain the optimized device parameters.

[0012] Preferably, the S2 specifically includes: In the implementation process of the multi-dimensional adaptive feedback optimization algorithm, based on the quantization feedback of the parameter data of the real-time monitoring and the influence quantification index of the environmental factors, the current error is adjusted to obtain the comprehensive error.

[0013] Preferably, the S2 specifically includes: Based on the comprehensive error, combining the influence quantization index of environmental factors and the quantization feedback of real-time monitored parameter data, a feedback optimization iteration mechanism is introduced to perform feedback optimization processing on the device parameters after dynamic adjustment, and the optimized device parameters are obtained. The specific formula is as follows: , where, is the optimized device parameter; is the device parameter at time , representing the device parameter after dynamic adjustment; is the feedback adjustment factor; is the comprehensive error at time ; is the error adjustment coefficient; is the feedback influence coefficient; is the influence quantization index of environmental factors at time ; is the quantization feedback of real-time monitored parameter data at time ; The optimized device parameters are converted into control signals through the PID control algorithm; based on the control signals, the desolvation process of the waterborne resin is controlled.

[0014] The beneficial effects of the technical solution of the present invention are as follows: 1. By introducing adaptive control algorithms such as dynamic modeling, lag compensation, and non-linear feedback correction, precise adjustment of device parameters is achieved; the introduction of the influence coefficient of the lag effect and the dynamic attenuation coefficient effectively describes the time delay characteristics of the waterborne resin during the desolvation process to provide more delicate control capabilities; by dynamically correcting errors and calculating the adaptive control increment, the device parameters are adjusted in real time to achieve continuous optimization of the process, thereby improving production efficiency and reducing human intervention and errors.

[0015] 2. Through the multi-dimensional adaptive feedback optimization algorithm, the device parameters can be optimized under the changes of different environmental factors (such as temperature, humidity, etc.). The multi-dimensional adaptive feedback optimization algorithm can not only eliminate the interference of the external environment, but also adaptively process the state fluctuations during long-term operation, improving the stability of the waterborne resin desolvation process and reducing the quality fluctuations caused by environmental changes. Description of the Drawings

[0016] Figure 1 is a flowchart of a control method for the desolvation process of a waterborne resin according to the present invention. Detailed Embodiments

[0017] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0019] The following specifically describes the specific solution of a control method for the desolventization process of an aqueous resin provided by the present invention in conjunction with the accompanying drawings.

[0020] Refer to the attached Figure 1 , which shows a flowchart of a control method for the desolventization process of an aqueous resin provided by an embodiment of the present invention. The method includes the following steps: S1. Monitor parameter data and perform preprocessing to obtain the preprocessed parameter data; based on the preprocessed parameter data, dynamically adjust the equipment parameters; Use high-precision sensors (such as temperature and humidity sensors, flow meters, pressure sensors, solvent concentration sensors, etc.) to monitor various parameter data during the desolventization process of the aqueous resin, including temperature, humidity, solvent concentration, air flow rate, pressure, etc.; perform preprocessing on the parameter data such as denoising, normalization, and missing data filling to obtain the preprocessed parameter data. The technical means adopted for the preprocessing are all well-known prior arts to those of ordinary skill in the art and will not be elaborated herein.

[0021] Furthermore, introduce an adaptive optimization control algorithm to dynamically adjust the equipment parameters. The adaptive optimization control algorithm is based on the preprocessed parameter data and gradually adjusts the working state of the equipment through multiple methods such as dynamic modeling, lag compensation, non-linear feedback correction, and adaptive control, so that the desolventization process of the aqueous resin can be continuously optimized. The specific implementation process is as follows: First, based on the preprocessed parameter data, construct a dynamic change model. For the lag effect in the desolventization process, describe the dynamic behavior of the desolventization process through a differential equation. Assume that the key parameters (preprocessed parameter data) in the desolventization process are , and the preprocessed parameter data is affected by various factors, not only depending on the current input but also closely related to the past state. To represent this time delay effect, the influence coefficient and dynamic attenuation coefficient of the lag effect are introduced, and the dynamic change model is as follows: , Among them, is the parameter data after pretreatment at time , such as temperature, humidity, solvent concentration, gas flow rate, pressure, etc., which changes with time ; is the change rate of the parameter data after pretreatment with time, which is estimated by the value difference between the previous and current moments ; is the parameter data after pretreatment at time ; is the equipment parameter (such as heating power); is the influence coefficient of the hysteresis effect, indicating the influence degree of the past state on the current state, which is obtained by the experimental method, and the reference value range is ; is the time lag (also known as time delay), reflecting the time delay of the equipment parameter on the response, which is determined by the expert experience method, and the reference value range is ; is the equipment parameter influence coefficient, indicating the direct influence degree of the equipment parameter on the dynamic behavior of the desolvation process, which is obtained by the experimental method, and the reference value range is ; is the dynamic attenuation coefficient, used to describe the natural attenuation or dissipation effect, reflecting the attenuation process without external control input, which is obtained by the experimental method, and the reference value range is ; is the time variable; Furthermore, dynamic error correction is carried out. The goal of the desolvation process is to make the actual desolvation state as close as possible to the desired state. Therefore, it is necessary to continuously calculate the error and make corrections. The error is the target value and the current actual value The gap between them can be expressed as: ; To avoid large errors, a weighted nonlinear correction function is introduced. This function combines nonlinear functions such as sigmoid and tanh to achieve dynamic correction of the error and obtain the dynamically corrected error. The specific form is: ; where is the dynamically corrected error and is used as the error at the current moment; is the first adjustment coefficient, used to adjust the weight or influence degree of the sigmoid function in error correction, which is determined by the experimental method, and the reference value range is ; It is an activation function commonly used in neural networks. Its shape is a smooth S-shaped curve. Its function is to adjust the control response according to the magnitude of the current error. Using the sigmoid function can control the error correction within a certain range and avoid over-adjustment; is the first amplification coefficient, which is used to control the error The amplification effect when input into the sigmoid function determines the sensitivity of the error to correction. It is determined by the existing control strategy, and the reference value range is ; is the second adjustment coefficient, which is used to adjust the weight or influence degree of the tanh function in error correction. It is determined by the experimental method, and the reference value range is ; is a non-linear activation function, similar to the sigmoid function, but its output range is , and it has stronger symmetry. It is another way of error correction provided by adjusting the relationship between the second adjustment coefficient and the error . Combining it with the sigmoid function can increase the non-linearity of control and enhance adaptability; is the second amplification coefficient, which determines the amplification effect of the error when passed to the tanh function. It is determined by the experimental method, and the reference value range is ; After the error correction is completed, based on the dynamically corrected error and combined with the error at the historical moment, the adaptive control increment is calculated to optimize the control input. The adaptive control increment formula is as follows: , where, is the adaptive control increment; is the adjustment coefficient of the current error, which is a weight factor used for control increment calculation and determines the influence degree of the current moment error on the adaptive control increment. It is determined by the expert experience method, and the reference value range is ; is the adjustment coefficient of the cumulative error, which is another weight factor used for control increment calculation and controls the influence of the historical accumulation of errors on the adaptive control increment. It is determined by the expert experience method, and the reference value range is ; is the accumulation of all dynamically corrected errors from the start to the current time . It reflects the overall trend of errors over a period of time, and has the effect of removing short-term fluctuations and smooth adjustment; is the time integration variable; is the non - linear feedback coefficient of the current error, which determines the influence degree of the error square term on the adaptive control increment. It is determined by the expert experience method, and the reference value range is ; is the non - linear feedback coefficient of the error change rate, which determines the influence degree of the square term of the error change rate (i.e., ) on the adaptive control increment. It is determined by the expert experience method, and the reference value range is . The above formula determines the adaptive control increment by weighting the current error and the cumulative error over a past period of time, so as to ensure that the adjustment of the device parameters is not only sensitive to the current error, but also can smooth the influence of historical errors, avoiding oscillation or over - reaction; Furthermore, entering the adaptive adjustment stage, the device parameters are gradually adjusted through the adaptive control increment, causing the working state of the device to change. The dynamically adjusted device parameters are: , where, is the device parameter at the next time , that is, the dynamically adjusted device parameter; is the device parameter at the current moment, which is obtained by analyzing and calculating through the existing state feedback control method based on the dynamic change model.

[0022] S2. Based on the environmental factors and the parameter data of real - time monitoring, dynamically optimize the dynamically adjusted device parameters to obtain the optimized device parameters; based on the optimized device parameters, control the desolvation process of the water - based resin.

[0023] Based on the environmental factors and the parameter data of real - time monitoring, dynamically optimize the dynamically adjusted device parameters by using the multi - dimensional adaptive feedback optimization algorithm to obtain the optimized device parameters; the specific implementation process is as follows: First, quantify the environmental factors and the parameter data of real - time monitoring to obtain the influence quantization index of the environmental factors and the quantization feedback of the parameter data of real - time monitoring; based on the quantization feedback of the parameter data of real - time monitoring and the influence quantization index of the environmental factors, adjust the current error to obtain the comprehensive error: , where, is the error after being adjusted by the environmental factors and real - time feedback at time , that is, the comprehensive error; is the environmental factor weighting coefficient, which is used to control the influence quantization index of the environmental factors The degree of influence represents the intensity of the impact of environmental changes on error adjustment, such as the effects of factors like temperature and humidity on the desolvation process of the water-based resin, which is obtained through experimental methods, and the reference value range is ; is a quantitative index of the influence of environmental factors, indicating the impact of environmental changes on the desolvation process of the water-based resin at time , which is obtained using existing regression analysis methods; is the feedback factor weighting coefficient, used to adjust the quantitative feedback of the parameter data monitored in real time on the degree of influence on the error, which is determined through experimental methods, and the reference value range is ; is the quantitative feedback of the parameter data monitored in real time at time , indicating the impact of parameter data such as temperature, humidity, and solvent concentration during the desolvation process on the desolvation process of the water-based resin, which is obtained using existing model predictive control techniques; Based on the comprehensive error , combining the quantitative index of the influence of environmental factors and the quantitative feedback of the parameter data monitored in real time, introducing a feedback optimization iteration mechanism, and performing feedback optimization processing on the dynamically adjusted equipment parameters to obtain the optimized equipment parameters: , wherein, is the optimized equipment parameter; is the equipment parameter at the next time , that is, the dynamically adjusted equipment parameter; is the feedback adjustment factor, used to determine the importance of the feedback information in the optimization of the equipment parameters, which is determined through experimental methods, and the reference value range is ; is the error adjustment coefficient, indicating the degree of influence of the error change rate (derivative of the comprehensive error) on the optimized equipment parameter, which can affect the sensitivity of the equipment response to the error change rate, and is determined through expert experience methods, and the reference value range is ; is the feedback influence coefficient, used to control the influence of environmental factors and real-time monitoring feedback on the optimization of the equipment parameters, determining the sensitivity of environmental factors and real-time monitoring feedback to the optimization of the equipment parameters, and is set through methods such as sensitivity analysis and deviation inversion modeling, and the reference value range is , and the larger the value, the more conservative it is for environmental disturbances; Finally, the optimized equipment parameters are converted into control signals through existing PID control algorithms, and the control of the desolvation process of the water-based resin is achieved based on the control signals.

[0024] To further verify the above embodiments, real-time data of temperature and solvent concentration were collected, and the experimental steps of the above adaptive optimization control algorithm were carried out. For the two control strategies of PID control and adaptive optimization control, the verification results are as follows: In terms of temperature response, the adaptive optimization control method can stabilize the temperature at the preset target temperature (such as 85°C) faster and with a smaller fluctuation range (within ±1°C); in contrast, the traditional PID control has a large initial overshoot and continuous oscillation, and the fluctuation amplitude is larger (about ±3.2°C); In terms of solvent concentration response, the adaptive optimization control method can achieve a rapid decrease and stabilization of the solvent concentration faster (such as dropping below 200 ppm within 15 minutes), while the traditional PID control has not fully converged within the same time period and there is a high tail residue; In terms of error decay, the error of the adaptive optimization control drops rapidly and stabilizes close to zero; the error decay process of the traditional PID control is slower and there are obvious errors; Therefore, the adaptive optimization control algorithm is significantly superior to the traditional PID control strategy in terms of accuracy, stability and response speed, verifying the effectiveness and engineering practical value of the present invention; using the adaptive optimization control method, the convergence speed of temperature and solvent concentration is significantly improved; the error decay speed is much faster than that of the traditional PID control strategy; the control response is smooth and there is no overshoot or oscillation phenomenon.

[0025] In summary, a control method for the desolventization process of waterborne resin is completed.

[0026] The sequence of the invention embodiments is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0027] Each embodiment in this specification is described in a progressive manner. The same or similar parts between each embodiment can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

[0028] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for controlling a water-based resin desolventizing process, characterized in that: The following steps are involved: S1. Monitor parameter data and preprocess them to obtain preprocessed parameter data; Dynamically adjust equipment parameters based on preprocessed parameter data; S2. Based on environmental factors and real-time monitored parameter data, dynamically optimize the dynamically adjusted device parameters to obtain optimized device parameters; Based on the optimized equipment parameters, the water-based resin desolventizing process is controlled.

2. The method for controlling a water-based resin desolventizing process according to claim 1, characterized in that: The S1 specifically includes: Based on the preprocessed parameter data, an adaptive optimization control algorithm is introduced to dynamically adjust the equipment parameters to obtain the dynamically adjusted equipment parameters.

3. The method for controlling a water-based resin desolventizing process according to claim 2, characterized in that: The S1 specifically includes: In the process of implementing the adaptive optimization control algorithm, based on the preprocessed parameter data, the rate of change of the preprocessed parameter data over time is calculated to construct a dynamic change model.

4. The method for controlling the desolventizing process of an aqueous resin according to claim 3, characterized in that: The S1 specifically includes: In the process of implementing the adaptive optimization control algorithm, the error is calculated based on the preprocessed parameter data at the current moment; the error is dynamically corrected in combination with a nonlinear function to obtain a dynamically corrected error, and the dynamically corrected error is used as the current error.

5. The method for controlling the desolventizing process of an aqueous resin according to claim 4, characterized in that: The S1 specifically includes: Based on the current error and the error at the historical moment, the adaptive control increment is calculated; the adaptive control increment formula is as follows: ， in, It's in time Time adaptive control increment; is the adjustment factor of the current error; It's in time The error after dynamic correction; is the adjustment factor for the cumulative error; It's in time The error after dynamic correction; is the time-integrated variable; is the nonlinear feedback coefficient of the current error; is the nonlinear feedback coefficient of the error change rate; It's time of error.

6. The method for controlling the desolventizing process of an aqueous resin according to claim 5, characterized in that: The S1 specifically includes: Based on the dynamic change model, the equipment parameters are analyzed and calculated through the state feedback control method; based on the adaptive control increment, the equipment parameters are gradually adjusted to obtain the dynamically adjusted equipment parameters.

7. The method for controlling the desolventizing process of an aqueous resin according to claim 6, characterized in that: The S2 specifically includes: The environmental factors and the parameter data of real-time monitoring are quantified to obtain the quantitative indicators of the impact of environmental factors and the quantitative feedback of the parameter data of real-time monitoring. A multi-dimensional adaptive feedback optimization algorithm is introduced to dynamically optimize the equipment parameters after dynamic adjustment to obtain the optimized equipment parameters.

8. The method for controlling the desolventizing process of an aqueous resin according to claim 7, characterized in that: The S2 specifically includes: In the implementation process of the multi-dimensional adaptive feedback optimization algorithm, the current error is adjusted based on the quantitative feedback of the real-time monitored parameter data and the quantitative indicators of the impact of environmental factors to obtain the comprehensive error.

9. The method for controlling the desolventizing process of an aqueous resin according to claim 8, characterized in that: The S2 specifically includes: Based on the comprehensive error, combined with the quantitative indicators of environmental factors and the quantitative feedback of real-time monitoring parameter data, a feedback optimization iteration mechanism is introduced to perform feedback optimization on the dynamically adjusted equipment parameters to obtain the optimized equipment parameters. The specific formula is: ， in, is the optimized device parameter; It's in time The device parameters represent the device parameters after dynamic adjustment; is a feedback regulator; It's in time The comprehensive error of is the error adjustment factor; is the feedback influence coefficient; It's in time Quantitative indicators of the impact of environmental factors; It's in time Quantitative feedback of parameter data monitored in real time; The optimized equipment parameters are converted into control signals through the PID control algorithm; based on the control signals, the water-based resin desolventizing process is controlled.

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