A TPU particle purification precision control method and system based on GPC online detection

Abstract

The present application relates to the technical field of TPU car clothing particle purification, in particular to a TPU particle purification precision control method and system based on GPC online detection. The present application realizes the prospective control of potential degradation or cross-linking side reactions by using the molecular chain dynamics database to predict the change of molecular chain in the subsequent devolatilization section, generates a set of directional control parameters based on the dynamic prediction results, adjusts the devolatilization conditions and additive injection in real time, effectively inhibits the widening of molecular weight distribution and the generation of high molecular weight gel, further corrects the whole process parameters through closed loop optimization, realizes the synchronous optimization of molecular chain state and volatile residue, and through the construction of a full closed loop precision control model, the molecular weight and impurity volatilization in the TPU melting devolatilization process are synchronously controlled, the problem that the traditional process cannot capture the change of molecular chain in real time and causes the fluctuation of product performance is solved, and the product uniformity and process controllability are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of TPU car coating particle purification technology, and in particular to a precise control method and system for TPU particle purification based on GPC online detection. Background Technology

[0002] TPU car cover particle purification is a pretreatment technology for aliphatic TPU masterbatch specifically designed for car covers. Through processes such as dehydration, devolatilization, filtration, washing, drying, and grading, it removes moisture, residual monomers, oligomers, mechanical impurities, and gel particles, thereby improving the purity, molecular weight uniformity, and processing stability of the masterbatch. It is the core raw material pretreatment technology that ensures high light transmittance, no crystal points, anti-yellowing, weather resistance, and self-healing properties of car covers.

[0003] Current technologies primarily control the purification of TPU particles through melt devolatilization. The core mechanism is based on the significant difference in volatility between TPU polymers and impurities. By coupling high temperature, high vacuum, and strong surface renewal, volatile impurities are efficiently removed. Simultaneously, precise control of process parameters enables directional control of molecular weight and molecular weight distribution. However, melt devolatilization relies on offline sampling and detection, making it impossible to obtain TPU molecular weight distribution data in real time. During devolatilization, high temperature and high vacuum conditions may lead to molecular chain degradation or cross-linking side reactions, resulting in a wider molecular weight distribution or the formation of high molecular weight gels. These changes cannot be captured in time, leading to fluctuations in the final product performance. Summary of the Invention

[0004] The main objective of this invention is to provide a precise control method for TPU particle purification based on GPC online detection, aiming to solve the technical problems in the prior art.

[0005] This invention proposes a precise control method for TPU particle purification based on online GPC detection, applied to an online GPC detection system, comprising: Trace melt samples and real-time operating process parameters of multiple preset devolatilization stages in the entire TPU melt devolatilization process are obtained, and the real-time molecular chain characteristic parameters of TPU for the corresponding preset devolatilization stage are obtained through an online GPC detection system based on each trace melt sample. Based on the real-time molecular chain characteristic parameters of the current preset devolatilization section and the set operating process parameters of each subsequent preset devolatilization section, and combined with the preset TPU molecular chain reaction kinetics database, the predicted results of the molecular chain dynamic changes of the melt in each subsequent preset devolatilization section are obtained. Based on the predicted results of dynamic changes in each molecular chain and the preset target threshold of the molecular chain, the set of directional control parameters for the corresponding preset devolatilization section is obtained to make real-time closed-loop adjustments to the devolatilization process conditions and directional additive injection parameters for the corresponding preset devolatilization section. The real-time molecular chain characteristic parameters and real-time volatile residue data of each preset devolatilization stage after closed-loop adjustment are obtained to coordinately optimize and correct the directional control parameter set of the corresponding preset devolatilization stage and the devolatilization process parameters of the entire TPU melt devolatilization process, so as to obtain the closed-loop optimization control parameter set of the entire process. Based on the real-time molecular chain characteristic parameters, real-time operating process parameters, prediction results of molecular chain dynamic changes, and the full-process closed-loop optimization control parameter set, a full closed-loop precision control model for TPU melt devolatilization purification is constructed, so that the continuous TPU melt devolatilization production process can achieve real-time and precise control of the entire purification process according to the full closed-loop precision control model.

[0006] This application also provides a precise control system for TPU particle purification based on GPC online detection, applied to an online GPC detection system, including: The first acquisition module is used to acquire trace melt samples and real-time operating process parameters of multiple preset devolatilization stages in the entire TPU melt devolatilization process, and to acquire the real-time molecular chain characteristic parameters of TPU for the corresponding preset devolatilization stage through an online GPC detection system based on each trace melt sample. The second acquisition module is used to obtain the prediction results of the dynamic changes of the molecular chain of the melt in the subsequent preset devouring stages based on the real-time molecular chain characteristic parameters of the current preset devouring stage and the set operating process parameters of each subsequent preset devouring stage, combined with the preset TPU molecular chain reaction kinetics database. The closed-loop adjustment module is used to obtain the set of directional control parameters for the corresponding preset devolatilization section based on the prediction results of the dynamic changes of each molecular chain and the preset molecular chain target threshold, so as to make real-time closed-loop adjustments to the devolatilization process conditions and directional additive injection parameters of the corresponding preset devolatilization section. The optimization and correction module is used to obtain the real-time molecular chain characteristic parameters and real-time volatile residue data of each preset devolatilization section after closed-loop adjustment, so as to coordinately optimize and correct the directional control parameter set of the corresponding preset devolatilization section and the devolatilization process parameters of the entire TPU melt devolatilization process, and obtain the closed-loop optimization control parameter set of the entire process. The module is used to construct a full closed-loop precision control model for TPU melt devolatilization purification based on the real-time molecular chain characteristic parameters, real-time operating process parameters, molecular chain dynamic change prediction results, and the full-process closed-loop optimization control parameter set, so that the continuous TPU melt devolatilization production process can achieve real-time and precise control of the entire purification process according to the full closed-loop precision control model.

[0007] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described precise control method for TPU particle purification based on GPC online detection.

[0008] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method for precise control of TPU particle purification based on GPC online detection.

[0009] The beneficial effects of this invention are as follows: This invention utilizes a molecular chain dynamics database to predict changes in the molecular chain during the subsequent devolatilization process, enabling proactive control of potential degradation or cross-linking side reactions. Based on the dynamic prediction results, a set of directional control parameters is generated to adjust devolatilization conditions and additive injection in real time, effectively suppressing the broadening of molecular weight distribution and the formation of high molecular weight gels. Closed-loop optimization further corrects the parameters of the entire process, achieving simultaneous optimization of molecular chain state and volatile residues. By constructing a fully closed-loop precise control model, the molecular weight and impurity volatilization are simultaneously controlled during the TPU melt devolatilization process. This solves the problem of product performance fluctuations caused by the inability of traditional processes to capture changes in the molecular chain in real time, significantly improving product uniformity and process controllability. Attached Figure Description

[0010] Figure 1 This is a schematic diagram of a method flow according to an embodiment of the present invention.

[0011] Figure 2 This is a schematic diagram of the system structure according to an embodiment of the present invention.

[0012] Figure 3 This is a schematic diagram of the internal structure of a computer device according to an embodiment of this application.

[0013] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0014] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0015] like Figure 1 As shown, this application provides a precise control method for TPU particle purification based on online GPC detection, applied to an online GPC detection system, comprising: S1. Obtain trace melt samples and real-time operating process parameters for multiple preset devolatilization stages in the TPU melt devolatilization process, and obtain the real-time TPU molecular chain characteristic parameters for the corresponding preset devolatilization stage through an online GPC detection system based on each trace melt sample. The real-time molecular chain characteristic parameters include number-average molecular weight, weight-average molecular weight, molecular weight distribution index, oligomer content, and high molecular weight gel characteristic data. S2. Based on the real-time molecular chain characteristic parameters of the current preset devolatilization section and the set operating process parameters of each subsequent preset devolatilization section, and combined with the preset TPU molecular chain reaction kinetics database, obtain the prediction results of the dynamic changes of the molecular chain of the melt in each subsequent preset devolatilization section. S3. Based on the prediction results of the dynamic changes of each molecular chain and the preset molecular chain target threshold, obtain the set of directional control parameters for the corresponding preset devolatilization section, and adjust the devolatilization process conditions and directional additive injection parameters for the corresponding preset devolatilization section in real time in a closed loop according to each set of directional control parameters. S4. Obtain the real-time molecular chain characteristic parameters and real-time volatile matter residue data of each preset devolatilization section after closed-loop adjustment, and perform collaborative optimization and correction on the directional control parameter set of the corresponding preset devolatilization section and the devolatilization process parameters of the entire TPU melt devolatilization process based on each of the real-time molecular chain characteristic parameters and real-time volatile matter residue data to obtain the whole process closed-loop optimization control parameter set. S5. Based on the real-time molecular chain characteristic parameters, real-time operating process parameters, molecular chain dynamic change prediction results, and the full-process closed-loop optimization control parameter set, construct a full closed-loop precision control model for TPU melt devolatilization purification, so that the continuous TPU melt devolatilization production process can achieve real-time precision control of the entire purification process according to the full closed-loop precision control model.

[0016] As described in steps S1 and S5 above, the online GPC detection system includes a microfluidic mixing and dissolution unit, an online filtration unit, an ultra-high efficiency gel chromatography column, a differential refractive index detector, and a multi-angle laser light scattering detector. The microfluidic mixing and dissolution unit is the core innovative unit of the online GPC detection system, seamlessly connecting the microfluidic mixing and dissolution unit with the sampling valve and subsequent separation and detection units to achieve second-level online dissolution of TPU high-temperature melt. The online filtration unit is the sample injection protection and purification unit of the online GPC detection system. TPU melt from industrial production lines may contain trace amounts of gel and impurities, which can clog the chromatography column and interfere with the detection results. The online filtration unit is connected in series between the dissolution unit and the chromatography column to achieve continuous online purification of the sample, ensuring the stability and continuity of the detection. The ultra-high efficiency gel chromatography column is the core separation unit of the online GPC detection system. The differential refractive index detector and the multi-angle laser light scattering detector are the core quantitative detection units of the online GPC detection system. The combined detection mode can achieve accurate detection of the absolute molecular weight of TPU without the need for offline standard curve calibration. The pre-set TPU molecular chain reaction kinetics database refers to a structured quantitative relationship database designed for the closed-loop control of the entire TPU melt devolatilization process. It is the core underlying support connecting online GPC real-time detection data with molecular chain change prediction. Within the full range of industrial melt devolatilization conditions, it quantitatively characterizes the correspondence between the four core side reactions of TPU molecular chain thermal degradation, cross-linking branching, oligomer formation, and gel formation and devolatilization process parameters, initial characteristics of raw materials, and regulatory effects of additives. The core of the reaction kinetics database includes four layers of structured data: basic physical property layer, reaction kinetics layer, critical threshold layer, and regulation and adaptation layer. The steps for obtaining real-time TPU molecular chain characteristic parameters for the corresponding preset devolatilization section are as follows: Based on the section layout of the entire TPU melt devolatilization process, multiple segmented sampling nodes are set at the outlet of the pre-devolatilization section, the outlet of the multi-stage devolatilization exhaust section, and the inlet of the homogenization section of the extruder head. A trace melt sample is obtained from each sampling node corresponding to the devolatilization section. Based on each trace melt sample, the real-time melt temperature and melt viscosity characteristics of the corresponding preset devolatilization section are obtained. Matching high-temperature and high-pressure dissolution process parameters are then obtained based on the real-time melt temperature and melt viscosity characteristics. Based on the high-temperature and high-pressure dissolution process parameters, a micron-sized mixed droplet of melt and a preheated chromatographically good solvent is obtained through a microfluidic mixing and dissolution unit. The micron-sized mixed droplet... A homogeneous TPU polymer dilute solution meeting GPC testing requirements was prepared. Trace gel impurities were removed from the homogeneous TPU polymer dilute solution using an online filtration unit. The filtered homogeneous polymer dilute solution was then passed through an ultra-high performance gel chromatography column to obtain TPU molecular fractions separated by molecular hydrodynamic volume fractionation. The concentration and light scattering signals of the separated TPU molecular fractions were obtained using a differential refractive index detector and a multi-angle laser light scattering detector. Based on the concentration and light scattering signals, the number-average molecular weight, weight-average molecular weight, molecular weight distribution index, oligomer content, and high molecular weight gel characteristic data of the TPU in the corresponding preset devolatilization stage were obtained as real-time molecular chain characteristic parameters. The steps to achieve real-time and precise control of the entire TPU continuous melt devolatilization production process based on a fully closed-loop precision control model are as follows: First, obtain real-time control commands and overall process coordination constraint thresholds for each preset devolatilization stage based on the TPU melt devolatilization purification fully closed-loop precision control model. Second, obtain real-time input characteristic quantities for the model based on real-time molecular chain characteristic parameters and real-time operating process parameters. Third, obtain synchronous control signals for process additives in single stages based on real-time input characteristic quantities and real-time control commands. Fourth, perform cross-stage linkage matching verification based on the overall process coordination constraint thresholds and the synchronous control signals for process additives in single stages. Fifth, based on the verified synchronous control signals for process additives in single stages, perform real-time control of the temperature, vacuum degree, residence time, and shear rate of the corresponding preset devolatilization stage, and simultaneously match and control the injection rate, injection point, and injection concentration of directional additives to achieve real-time and precise control of the entire TPU melt devolatilization purification process. This invention, by deploying online GPC detection nodes at multiple pre-set devolatilization stages throughout the TPU melt devolatilization process, and simultaneously acquiring trace melt samples and real-time process parameters, can obtain real-time data on number-average molecular weight, weight-average molecular weight, molecular weight distribution index, oligomer content, and high molecular weight gel characteristics. This enables continuous and visual monitoring of the TPU molecular chain structure throughout the devolatilization process, solving the problem that existing offline detection methods cannot promptly capture molecular chain degradation, distribution broadening, and gelation side reactions under high-temperature and high-vacuum conditions. It provides a high-timeliness, high-resolution data foundation for subsequent dynamic prediction and closed-loop control. By coupling the real-time molecular chain characteristic parameters of the current stage with the set process parameters of subsequent stages, and combining this with pre-set TPU molecular chain reaction kinetics... The database predicts the subsequent evolution of the melt, and can identify trends such as molecular chain degradation, molecular weight distribution broadening, and high molecular weight gel formation in advance. This upgrades the control method from traditional post-processing feedback adjustment to feedforward predictive control for subsequent processes, effectively reducing irreversible performance fluctuations caused by the accumulation of side reactions under high temperature and high vacuum conditions. Based on the prediction results of molecular chain dynamic changes, a set of directional control parameters is constructed, and real-time closed-loop adjustments are made to devolatilization process conditions and directional additive injection parameters. Differential suppression can be implemented for different abnormal paths such as chain degradation, oligomer residue, and high molecular weight gel formation, thereby avoiding the control lag and insufficient correction caused by the coarse adjustment of traditional single process parameters, and achieving precise directional control of TPU molecular weight and molecular weight distribution. By jointly analyzing the real-time molecular chain characteristic parameters and real-time volatile residue data after closed-loop adjustment, the directional control parameters of each preset devolatilization stage and the overall devolatilization process parameters can be simultaneously corrected. This avoids the technical defects of simply pursuing volatile removal efficiency, which may lead to further degradation or cross-linking of molecular chains. It achieves synergistic optimization of devolatilization purification effect and molecular chain stability. By integrating real-time molecular chain characteristic parameters, real-time operating process parameters, dynamic prediction results, and overall closed-loop optimization control parameters, a precise closed-loop control model for TPU melt devolatilization purification is constructed. This enables the continuous melt devolatilization process to have adaptive real-time control capabilities. This invention, through online GPC multi-segment real-time detection, molecular chain dynamic prediction, directional closed-loop regulation, full-process synergistic optimization, and the construction of a full closed-loop control model, achieves for the first time real-time observation, prediction, and controllability of molecular weight, molecular weight distribution, oligomers, and gel byproducts during the TPU melt devolatilization purification process. This solves the problem that existing offline detection conditions cannot timely detect high-temperature and high-vacuum induced molecular chain degradation and cross-linking side reactions.

[0017] In one embodiment, step S2, which involves obtaining the predicted results of the dynamic changes of the molecular chains of the melt in subsequent preset devolatilization stages based on the real-time molecular chain characteristic parameters of the current preset devolatilization stage and the set operating process parameters of each subsequent preset devolatilization stage, combined with a preset TPU molecular chain reaction kinetics database, includes: S21. Obtain the number-average molecular weight, weight-average molecular weight, molecular weight distribution index, oligomer content, and high molecular weight gel characteristic data of the melt based on the real-time molecular chain characteristic parameters. S22. Obtain the set temperature, set vacuum degree, set residence time and set shear rate of each preset devouring section according to the set operating process parameters. S23. Obtain the thermal degradation rate constant and oligomer formation rate of the melt based on the number-average molecular weight, weight-average molecular weight, set temperature, set residence time, and preset TPU molecular chain reaction kinetics database. S24. Based on the molecular weight distribution index, initial characteristics of high molecular weight gel, set vacuum degree, set shear rate and preset TPU molecular chain reaction kinetics database, obtain the cross-linking branching rate constant of the melt and the critical threshold for gel formation. S25. Based on the thermal degradation rate constant, oligomer formation rate, cross-linking branching rate constant, and gel formation critical threshold, construct a molecular chain change dynamic model for each subsequent preset devolatilization stage; S26. Based on the molecular chain change dynamics model, obtain the predicted results of the molecular chain dynamic changes of the melt in each subsequent preset devolatilization stage, wherein the predicted results of the molecular chain dynamic changes include the molecular weight change trend, the molecular weight distribution fluctuation amplitude, the oligomer increment and the gel formation probability.

[0018] As described in steps S21 and S26 above, the thermal degradation rate constant is obtained by acquiring the initial molecular chain stability coefficient and temperature response coefficient of the melt based on the number-average molecular weight, weight-average molecular weight, set temperature, set residence time, and preset TPU molecular chain reaction kinetics database, and then obtaining the thermal degradation rate constant based on the molecular chain stability coefficient and temperature response coefficient. Finally, the oligomer formation gain factor is obtained through the thermal degradation rate constant, number-average molecular weight, weight-average molecular weight, set residence time, and preset TPU molecular chain reaction kinetics database, and the oligomer formation rate of the melt is obtained based on the thermal degradation rate constant and the oligomer formation gain factor. The branching triggering coefficient and shear regulation coefficient are obtained based on the molecular weight distribution index, initial characteristics of high molecular weight gel, set vacuum degree, set shear rate, and preset TPU molecular chain reaction kinetics database. The cross-linking branching rate constant of the melt is obtained based on the branching triggering coefficient and shear regulation coefficient. The critical calibration factor of the gel is obtained by combining the cross-linking branching rate constant, molecular weight distribution index, initial characteristics of high molecular weight gel, set vacuum degree, and preset TPU molecular chain reaction kinetics database. The critical threshold for melt gel formation is obtained based on the cross-linking branching rate constant and the critical calibration factor of the gel. This invention, by combining real-time molecular chain characteristic parameters such as number-average molecular weight and weight-average molecular weight with process settings and a TPU molecular chain reaction kinetics database, can accurately predict the rate constant and oligomer formation rate of the melt during thermal degradation. It can predict the degradation behavior of molecular chains in a timely manner, effectively avoiding molecular weight inhomogeneity or the formation of high molecular weight gels due to improper processes, thereby improving the stability and consistency of the product. By utilizing the real-time molecular weight distribution index and high molecular weight gel characteristics in conjunction with the TPU molecular chain reaction kinetics database, and obtaining the cross-linking branching rate constant and the critical threshold for gel formation by setting the vacuum degree and shear rate, the predictive ability of the melt cross-linking branching process is improved. This can effectively avoid over-cross-linking or gel formation problems, thereby reducing the undesirable formation of high molecular weight gels and avoiding their negative impact on product performance. By constructing a molecular chain change dynamics model based on multiple key parameters, the evolution trend of molecular chains in the melt during the subsequent devolatilization stage can be predicted. This provides an important reference for real-time optimization of process parameters, reduces the interference of uncertain factors during melt devolatilization, and ensures the stability of molecular chain structure, thereby guaranteeing the consistency and high performance of the final product. Through dynamic change prediction based on the molecular chain change dynamics model, the change trend of molecular weight, fluctuation of molecular weight distribution, increase of oligomers, and probability of gel formation can be predicted in real time during the melt devolatilization process. This solves the problem that existing technologies cannot monitor molecular chain changes in real time, effectively preventing molecular chain degradation or cross-linking side reactions caused by improper process parameters such as temperature and vacuum degree, improving the stability of molecular weight distribution during production, and thus ensuring a high degree of consistency in the quality of the final product.

[0019] In one embodiment, step S25, which involves constructing a molecular chain change kinetic model for each subsequent preset devolatilization stage based on the thermal degradation rate constant, oligomer formation rate, crosslinking branching rate constant, and gel formation critical threshold, includes: S251. Based on the thermal degradation rate constant and cross-linking branching rate constant, combined with the set temperature and set residence time of each subsequent preset devolatilization stage, the two-way coupled kinetic constitutive relationship of TPU molecular chain breakage degradation and branching cross-linking is obtained. S252. Based on the oligomer formation rate and the set vacuum degree and set shear rate of each subsequent preset devolatilization stage, obtain the dynamic mass balance relationship between in-situ oligomer formation and vacuum removal. S253. Obtain the critical boundary constraint conditions for cross-linking and gelation of TPU molecular chains based on the critical threshold for gel formation, the cross-linking branching rate constant, and the set residence time. S254. Obtain the initial boundary conditions of the model based on the real-time molecular chain characteristic parameters of the current preset devolatilization section. S255. The two-way coupled dynamic constitutive relation, dynamic mass balance relation, and critical boundary constraint conditions are coupled in multiple fields. The molecular chain change dynamic model of each preset devolatilization section is constructed with the initial boundary conditions as the calculation starting point and the set operating process parameters of each preset devolatilization section as the process constraint.

[0020] As described in steps S251 and S255 above, the mathematical expression for the weight-average molecular weight two-way coupled kinetic equation in the two-way coupled kinetic constitutive relation is: ;in, This represents the instantaneous rate of change of weight-average molecular weight with devouring time. express The weight-average molecular weight of TPU at any given time. Represents the thermal degradation rate constant. This represents the crosslinking branching rate constant. This indicates the initial weight-average molecular weight of the current devolatilization stage. This represents the Heaviside step function. The expression within the parentheses is 1 when true and 0 when false, and is used to limit the triggering conditions of the crosslinking reaction. This represents the critical molecular weight for the crosslinking reaction, derived from a pre-defined TPU molecular chain reaction kinetics database. The mathematical expression for the dynamic equilibrium equation of oligomer mass fraction in the dynamic mass balance relationship is: ;in, This represents the instantaneous rate of change of oligomer mass fraction with devolatilization time. Indicates the oligomer formation rate. express The oligomer mass fraction at a given time. This represents the mass transfer coefficient of oligomer vacuum removal, a correlation function for setting absolute pressure and shear rate in subsequent processes, derived from a pre-set TPU molecular chain reaction kinetics database; The mathematical expression for the gel formation kinetics equation under critical boundary constraints is: ;in, This represents the instantaneous rate of change of gel mass fraction with devolatilization time. express The weight-average molecular weight of TPU at any given time. This represents the crosslinking branching rate constant. express The gel mass fraction at time [time]. This indicates the critical threshold for gel formation; This invention obtains a two-way coupled kinetic constitutive relationship between TPU molecular chain breakage degradation and branching crosslinking by combining the thermal degradation rate constant, the crosslinking branching rate constant, and the set temperature and residence time of each subsequent preset devolatilization stage. This allows for dynamic prediction of the impact of temperature and residence time on the molecular chain structure, avoiding side reactions caused by high temperature and high vacuum conditions, and providing real-time feedback in process control. This reduces the broadening of molecular weight distribution or the formation of high molecular weight gels. By combining the oligomer formation rate with vacuum degree and shear rate, a dynamic mass balance relationship between in-situ oligomer formation and vacuum removal is obtained, which can precisely control the distribution of oligomers, avoid excessive accumulation of oligomers, and thus prevent problems such as impurity residue or molecular chain breakage. Through the synergistic effect of vacuum and shear, the impact on molecular chain degradation can be effectively reduced, further optimizing the molecular weight distribution. By combining the critical threshold for gel formation with the crosslinking branching rate constant, the critical boundary constraint conditions for TPU molecular chain crosslinking and gelation are obtained, which can effectively control the occurrence of gelation and prevent the formation of high molecular weight gels due to excessive crosslinking. This not only helps to improve the performance consistency of TPU but also avoids functional differences caused by uneven product structure. By acquiring the initial boundary conditions of the model based on the real-time molecular chain characteristic parameters of the current pre-set devolatilization stage, precise control of each stage during the devolatilization process can be achieved. Real-time acquisition of molecular chain characteristic data provides scientific starting conditions for subsequent stages based on the current molecular chain state, avoiding the drawbacks of traditional processes that rely on offline sampling and untimely feedback. This ensures the real-time nature and accuracy of the process, and helps to better control molecular weight distribution and molecular chain integrity. By coupling bidirectional kinetic constitutive relations, dynamic mass balance relations, and critical boundary constraints in multiple fields, and combining the initial boundary conditions with set process parameters to construct a kinetic model, multiple influencing factors such as thermal degradation, crosslinking, oligomer formation, and gelation can be considered simultaneously. This comprehensively optimizes molecular chain changes during the TPU devolatilization process, ensuring product performance stability and consistency. The model's real-time calculation and feedback further enhance the flexibility and adaptability of process control, avoiding performance fluctuations and inhomogeneities in traditional technologies.

[0021] In one embodiment, step S3, which involves obtaining the set of directional control parameters for the corresponding preset devolatilization stage based on the predicted dynamic changes of each molecular chain and a preset molecular chain target threshold, to perform real-time closed-loop adjustment of the devolatilization process conditions and directional additive injection parameters for the corresponding preset devolatilization stage, includes: S31. Based on the prediction results of dynamic changes in each molecular chain, obtain the molecular weight change deviation value, oligomer residue prediction value and gel formation risk level of the corresponding preset devolatilization stage, and obtain the allowable fluctuation range of molecular weight, upper limit value of oligomer residue and zero risk constraint condition for gel formation of the corresponding preset devolatilization stage based on the preset molecular chain target threshold. S32. Based on the molecular weight change deviation value and the allowable fluctuation range of molecular weight, obtain the basic control parameters of the corresponding preset devolatilization section, wherein the basic control parameters include temperature, vacuum degree and residence time; S33. Obtain the auxiliary control parameters for the corresponding preset devolatilization stage based on the oligomer residue prediction value and the oligomer residue upper limit value, wherein the auxiliary control parameters include the shear rate. S34. Obtain the basic injection parameters of the directional anti-crosslinking agent based on the gel formation risk level and the zero-risk constraint condition for gel formation, wherein the basic injection parameters include the agent injection rate, injection site and injection concentration. S35. Construct a multi-dimensional synergistic control matrix for process additives based on the basic control parameters, auxiliary control parameters, and injection basic parameters, and obtain a set of directional control parameters for the corresponding preset devolatilization stage based on the multi-dimensional synergistic control matrix for process additives, wherein the set of directional control parameters includes devolatilization process control sub-parameters and directional additive injection control sub-parameters; S36. Adjust the temperature, vacuum, residence time and shear rate of the preset devolatilization section in real time in a closed loop according to the devolatilization process control sub-parameters, and adjust the additive injection rate, injection point and injection concentration in real time in a coordinated manner according to the directional additive injection control sub-parameters to complete the real-time closed-loop control of the preset devolatilization section.

[0022] As described in steps S31 and S36 above, the step of obtaining the directional control parameter set based on the multi-dimensional synergistic control matrix of process additives involves obtaining the devolatilization process dimension control range and the directional additive dimension control range based on the multi-dimensional synergistic control matrix of process additives, locking the core constraint boundary of the process dimension control range based on the basic control parameters and auxiliary control parameters, and locking the safety constraint boundary of the additive dimension control range based on the injection basic parameters; obtaining the bidirectional coupling constraint conditions of process additives based on the core constraint boundary and the safety constraint boundary, and obtaining the matching verification rules between process parameters and additive parameters based on the bidirectional coupling constraint conditions; and applying the matching verification rules to the process dimension control range. The candidate process parameter groups within the time interval are prioritized, and the candidate auxiliary agent parameter groups within the auxiliary agent dimension control interval are screened for matching degree to obtain multiple sets of collaboratively matched process auxiliary agent parameter pairs; based on the preset optimal devolatilization efficiency target and optimal molecular chain stability target, the multiple sets of collaboratively matched process auxiliary agent parameter pairs are optimized to obtain the optimal process parameter combination and the optimal auxiliary agent parameter combination; devolatilization process control sub-parameters are generated based on the optimal process parameter combination, and directional auxiliary agent injection control sub-parameters are generated based on the optimal auxiliary agent parameter combination; the devolatilization process control sub-parameters and directional auxiliary agent injection control sub-parameters are combined and encapsulated to obtain the directional control parameter set of the corresponding preset devolatilization section; This invention dynamically controls molecular weight distribution during devolatilization by adjusting basic control parameters such as temperature, vacuum level, and residence time in real time, avoiding molecular chain degradation or excessive crosslinking caused by improper processes. By comparing the predicted value and the upper limit value of oligomer residue, the auxiliary control parameter of shear rate can be obtained and adjusted in real time, which not only optimizes the removal effect of oligomers but also reduces the possibility of oligomer residue under high temperature conditions. By combining the risk level of gel formation and the zero-risk constraint, the injection rate, injection point, and injection concentration of anti-crosslinking agent can be precisely controlled. Without affecting the main process flow, the amount of agent used and the injection method can be precisely adjusted to effectively avoid gel formation or excessive polymerization caused by crosslinking reaction. By synergistically regulating multi-dimensional process parameters and additive injection parameters, a comprehensive process and additive synergistic regulation matrix is ​​constructed. Through precise combination of basic regulation parameters, auxiliary regulation parameters, and additive injection parameters, different process factors can be adjusted in real time during devolatilization. This ensures that the risks of molecular chain degradation, cross-linking side reactions, and gel formation are effectively suppressed under the process conditions at each moment, improving the flexibility and adaptability of the process. While ensuring the consistency of product quality, it also improves the optimization of the production process. By constructing a closed-loop regulation system that combines devolatilization process regulation sub-parameters and additive injection regulation sub-parameters, synchronous and synergistic adjustment of process parameters and additive injection processes can be achieved. This allows for automatic adjustment of process conditions based on feedback information, ensuring that changes in temperature, vacuum, residence time, and shear rate during devolatilization do not cause molecular chain degradation or cross-linking side reactions. At the same time, it ensures that the additive injection rate, injection point, and injection concentration are consistent with the process conditions.

[0023] In one embodiment, step S4, which involves obtaining real-time molecular chain characteristic parameters and real-time volatile residue data of each preset devolatilization stage after closed-loop adjustment to collaboratively optimize and correct the directional control parameter set of the corresponding preset devolatilization stage and the devolatilization process parameters of the entire TPU melt devolatilization process, and obtaining the closed-loop optimization control parameter set for the entire process, includes: S41. Based on each of the real-time molecular chain characteristic parameters, obtain the actual deviation value of the molecular chain in the corresponding preset devolatification section and the consistency deviation value of the molecular chain throughout the process; and based on each of the real-time volatile matter residue data, obtain the volatile matter removal deviation value in the corresponding preset devolatification section and the volatile matter residue gradient deviation value throughout the process. S42. Obtain the relative deviation rate of the molecular chain based on the actual deviation value of the molecular chain, obtain the relative deviation rate of volatile matter removal based on the deviation value of volatile matter removal, and calculate the single-section control correction coefficient for the corresponding preset devolatification section based on the relative deviation rate of volatile matter removal and the relative deviation rate of the molecular chain, wherein the calculation formula is: ;in, This represents the adjustment correction coefficient for a single work section. Indicates the molecular chain quality weight. This represents the relative deviation rate of the molecular chain. This indicates the weight of volatile matter removal. This indicates the relative deviation rate of volatile matter removal; S43. Obtain the molecular chain fluctuation coefficient for the entire process based on the molecular chain consistency deviation value for the entire process, obtain the devolatilization load balance coefficient for the entire process based on the volatile matter residual gradient deviation value for the entire process, and calculate the process synergistic correction coefficient for the entire TPU melt devolatilization process based on the devolatilization load balance coefficient and the molecular chain fluctuation coefficient for the entire process. The calculation formula is as follows: ;in, Indicates the process synergy correction factor. This represents the consistency weight of the molecular chain throughout the entire process. This represents the molecular chain fluctuation coefficient throughout the entire process. Indicates the weight of load balance during the destocking process. This represents the load balance coefficient for the entire process of desorption and resorption. S44. Based on the single-section control correction coefficient, the directional control parameter set of the corresponding preset devolatilization section is modified and optimized parameter by parameter to obtain the section-level optimized control parameters. S45. Based on the process coordination correction coefficient, the devolatilization process parameters of the entire TPU melt devolatilization process are matched and corrected across different processes to obtain the basic process parameters at the whole process level. S46. Based on the section-level optimization control parameters and the full-process-level basic process parameters, perform multi-section coupling verification and conflict elimination to obtain multiple parameter-free collaborative matching parameter groups; S47. Based on the multiple sets of collaborative matching parameters, an integrated encapsulation is performed to obtain a closed-loop optimized control parameter set for the entire TPU melt devolatilization process.

[0024] As described in steps S41 and S47 above, the parameter-by-parameter correction optimization of the directional control parameter set using the single-section control correction coefficient is performed by multiplying the single-section control correction coefficient with the parameter values ​​in the directional control parameter set. Similarly, the cross-section matching correction of the devolatilization process parameters using the process coordination correction coefficient is performed by multiplying the process coordination correction coefficient with the devolatilization process parameters of the entire TPU melt devolatilization process. Multi-section parameter coupling constraint rules are established based on section-level optimized control parameters and full-process-level basic process parameters, including single-parameter boundary constraints (safe upper and lower thresholds for each parameter), process additive coupling constraints (matching rules between the effective action window of the additive and the process parameters), and cross-section linkage constraints (parameter matching rules between adjacent sections). First, the compliance of single-parameter boundaries is verified to eliminate threshold conflicts; then, the coupling matching between the process and additives within the section is verified to eliminate interference from additive failure and effect cancellation; finally, the upstream and downstream linkage matching between adjacent sections is verified to eliminate cross-section parameter fluctuations and load conflicts. This invention generates a single-stage control correction coefficient by calculating the relative deviation rate of molecular chains and volatiles. This coefficient can accurately compensate for the differences between each devolatilization stage and dynamically adjust the devolatilization stage parameters based on real-time data. This effectively avoids the shortcomings of traditional processes that cannot respond to changes in molecular chains or volatiles in real time during actual production, reduces the occurrence of molecular chain degradation or cross-linking side reactions, and ensures the stability of molecular weight distribution. By calculating the molecular chain consistency deviation and volatile residual gradient deviation throughout the entire process, a process coordination correction coefficient is obtained, which can coordinate between different stages of the entire melt devolatilization process, ensuring the consistency of process parameters and load balance between each stage. By accurately adjusting the process parameters, performance fluctuations caused by parameter inconsistencies between different stages can be avoided, and the quality stability of the product can be further improved. By correcting each stage parameter by parameter, the directional control parameters of each preset devolatilization stage can more accurately reflect the actual production needs. Real-time and precise control can be achieved in each stage, avoiding molecular chain degradation or cross-linking side reactions caused by dynamic changes under high temperature and high vacuum conditions, and effectively solving the problem of operational lag in traditional technologies. By matching and correcting across different processes, the devolatilization process parameters throughout the entire process can be unified and coordinated, solving the performance fluctuation problem caused by the mismatch of process parameters between different processes in existing technologies. By optimizing the process parameters throughout the entire process, the negative impact of parameter differences between different processes on the final product quality can be effectively avoided, and the stability of the entire process can be improved. Through multi-process coupling verification and conflict elimination, the coordination and consistency between process parameters are ensured. By resolving conflicts between parameters in different processes, the stability of the devolatilization process is further improved, avoiding product quality instability caused by process conflicts, thereby improving overall production efficiency and the quality of the final product. Through integrated packaging, a closed-loop optimized control parameter set for the entire process is obtained, enabling the entire melt devolatilization process to run in a complete closed-loop system. This allows for real-time dynamic optimization control of the entire process, avoiding the lag problems caused by offline detection and manual intervention in traditional technologies, ensuring high consistency and stability of product quality, thereby improving production efficiency and reducing the risk of process fluctuations.

[0025] In one embodiment, step S5, which involves constructing a fully closed-loop precise control model for TPU melt devolatilization purification based on the real-time molecular chain characteristic parameters, real-time operating process parameters, predicted results of dynamic changes in the molecular chain, and the full-process closed-loop optimization control parameter set, includes: S51. Obtain the real-time input feature matrix based on the real-time molecular chain characteristic parameters and the real-time operating process parameters, and obtain the feedforward constraint feature matrix based on the prediction result of the dynamic change of the molecular chain. S52. Obtain the full-process target output matrix and boundary constraints based on the full-process closed-loop optimization control parameter set, and obtain the label truth set based on the full-process target output matrix; S53. Obtain the training sample set and the validation sample set based on the real-time input feature matrix, the feedforward constraint feature matrix and the label truth set; S54. Construct a dual-closed-loop network architecture that couples the feedforward prediction branch and the feedback correction branch based on the training sample set and boundary constraints, and iteratively optimize the weight parameters of the dual-closed-loop network architecture based on the verification sample set and the full-process target output matrix to obtain the initial control model. S55. Obtain the synergistic control response function of process additives for each preset devolatilization stage based on the initial control model, and modify and optimize the cross-stage coupling constraints of the initial control model based on the response function to obtain the TPU melt devolatilization purification full closed-loop precise control model. The mathematical expression of the TPU melt devolatilization purification full closed-loop precise control model is: ;in, express The time-based model outputs a matrix of directional control parameters for each stage of the entire process. express Time of the first Synergistic gain coefficient of process additives in a pre-defined devolatilization stage This represents the feedforward branch weight matrix. This represents the feedforward constraint feature matrix, derived from the prediction results of dynamic changes in the molecular chain. This represents the feedback branch weight matrix. This represents the feedback feature matrix, derived from real-time molecular chain feature parameters and real-time operating process parameters. This represents the bias correction weight matrix. This represents the overall target output matrix, derived from the entire closed-loop optimization control parameter set, and serves as the core benchmark for model convergence. This represents the matrix of actual values ​​output by real-time control.

[0026] As described in steps S51 and S55 above, the process additive synergistic regulation response function quantifies the synergistic gain effect of synchronously adjusting process parameters and additive parameters within a preset devolatilization section, compared to adjusting the process or additive parameters individually. This is achieved by locking the optimal matching interval between the two, thus eliminating parameter conflicts. The mathematical expression of the process additive synergistic regulation response function is: ;in, express Time of the first Synergistic gain coefficient of process additives in a pre-defined devolatilization stage express Time of the first The actual comprehensive control effect when the process and additives of each section are adjusted simultaneously includes both molecular weight stability and oligomer removal rate as indicators. express Time of the first The effect of single-stage control when only process parameters and additive parameters are adjusted in a single process section while remaining unchanged. express Time of the first The effect of a single control method when only the auxiliary parameters are adjusted and the process parameters remain unchanged in a single section; This invention captures the instantaneous changes in molecular weight and molecular weight distribution during the TPU melt devolatilization process by using real-time molecular chain characteristic parameters, enabling dynamic monitoring of the molecular chain state. By introducing the predicted results of dynamic changes in the molecular chain, a feedforward constraint feature matrix is ​​formed, allowing the control model to predict and adjust operating parameters before molecular chain degradation or cross-linking side reactions occur, reducing the risk of abnormal molecular weight distribution. By establishing a full-process target output matrix, the molecular weight and molecular weight distribution of the final product are used as the core benchmark for model convergence, ensuring the scientific nature and quantifiability of the control target. Boundary constraints ensure that the operating parameters of each stage during the regulation process are within a safe and reasonable range, preventing over-adjustment that could lead to molecular chain degradation or cross-linking side reactions. By constructing training and validation sample sets, the control model can learn the dynamic change law of the molecular chain and its response characteristics to the devolatilization process, achieving predictive regulation. The design of the sample set, combined with real-time data and target output, ensures that the model can effectively capture the precursor signals of molecular chain degradation, cross-linking, and other side reactions under complex nonlinear conditions. By constructing a feedforward prediction branch, future trends can be predicted based on the dynamic changes of molecular chains, allowing for advance adjustment of process parameters. The feedback correction branch corrects deviations in real time, achieving closed-loop correction. The coupling optimization of the dual closed-loop architecture ensures that the control model can still converge quickly when facing nonlinear changes in molecular chains under high temperature and high vacuum environments, reducing fluctuations in molecular weight distribution. The synergistic response function of process additives reveals the parameter coupling law between different processes, enabling cross-process synergistic optimization and control, preventing molecular chain degradation or cross-linking side reactions caused by local adjustments. By optimizing the initial control model through coupling constraints, the sensitivity and accuracy of the whole-process control model to changes in molecular weight distribution are improved, ensuring the consistency of the final TPU particle performance. Through the coupling of the feedforward branch, feedback branch, and deviation correction weight matrix, integrated control of dynamic prediction and real-time correction is achieved, solving the defect of uncontrollable molecular weight distribution caused by offline monitoring delay in the existing technology.

[0027] like Figure 2 As shown, this application also provides a precise control system for TPU particle purification based on GPC online detection, applied to an online GPC detection system, including: The first acquisition module is used to acquire trace melt samples and real-time operating process parameters of multiple preset devolatilization stages in the entire TPU melt devolatilization process, and to acquire the real-time molecular chain characteristic parameters of TPU for the corresponding preset devolatilization stage through an online GPC detection system based on each trace melt sample. The second acquisition module is used to obtain the prediction results of the dynamic changes of the molecular chain of the melt in the subsequent preset devouring stages based on the real-time molecular chain characteristic parameters of the current preset devouring stage and the set operating process parameters of each subsequent preset devouring stage, combined with the preset TPU molecular chain reaction kinetics database. The closed-loop adjustment module is used to obtain the set of directional control parameters for the corresponding preset devolatilization section based on the prediction results of the dynamic changes of each molecular chain and the preset molecular chain target threshold, so as to make real-time closed-loop adjustments to the devolatilization process conditions and directional additive injection parameters of the corresponding preset devolatilization section. The optimization and correction module is used to obtain the real-time molecular chain characteristic parameters and real-time volatile residue data of each preset devolatilization section after closed-loop adjustment, so as to coordinately optimize and correct the directional control parameter set of the corresponding preset devolatilization section and the devolatilization process parameters of the entire TPU melt devolatilization process, and obtain the closed-loop optimization control parameter set of the entire process. The module is used to construct a full closed-loop precision control model for TPU melt devolatilization purification based on the real-time molecular chain characteristic parameters, real-time operating process parameters, molecular chain dynamic change prediction results, and the full-process closed-loop optimization control parameter set, so that the continuous TPU melt devolatilization production process can achieve real-time and precise control of the entire purification process according to the full closed-loop precision control model.

[0028] In one embodiment, the building module includes: The first acquisition unit is used to acquire a real-time input feature matrix based on the real-time molecular chain feature parameters and real-time operating process parameters, and to acquire a feedforward constraint feature matrix based on the prediction result of the dynamic change of the molecular chain. The second acquisition unit is used to acquire the full-process target output matrix and boundary constraints based on the full-process closed-loop optimization control parameter set, and to acquire the label truth set based on the full-process target output matrix; The third acquisition unit is used to acquire training sample set and verification sample set based on the real-time input feature matrix, feedforward constraint feature matrix and label truth set; The construction unit is used to construct a dual-closed-loop network architecture that couples the feedforward prediction branch and the feedback correction branch according to the training sample set and boundary constraints, and to iteratively optimize the weight parameters of the dual-closed-loop network architecture according to the verification sample set and the full-process target output matrix to obtain the initial control model. The correction and optimization unit is used to obtain the process additive synergistic regulation response function of each preset devolatilization section according to the initial control model, and to correct and optimize the cross-section coupling constraints of the initial control model according to the response function, so as to obtain the full closed-loop precise control model for TPU melt devolatilization purification.

[0029] It should be noted that each module and unit in the precision control system for TPU particle purification based on GPC online detection corresponds one-to-one with the steps in the precision control method for TPU particle purification based on GPC online detection.

[0030] like Figure 3 As shown, this application also provides a computer device, which can be a server, and its internal structure can be as follows: Figure 3 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores all data required for the process of the precise control method for TPU particle purification based on GPC online detection. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the precise control method for TPU particle purification based on GPC online detection.

[0031] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer equipment on which the present application is applied.

[0032] An embodiment of this application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements any of the above-described methods for precise control of TPU particle purification based on GPC online detection.

[0033] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in this application and in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0034] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0035] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A precise control method for TPU particle purification based on online GPC detection, applied to an online GPC detection system, characterized in that, include: Trace melt samples and real-time operating process parameters of multiple preset devolatilization stages in the entire TPU melt devolatilization process are obtained, and the real-time molecular chain characteristic parameters of TPU for the corresponding preset devolatilization stage are obtained through an online GPC detection system based on each trace melt sample. Based on the real-time molecular chain characteristic parameters of the current preset devolatilization section and the set operating process parameters of each subsequent preset devolatilization section, and combined with the preset TPU molecular chain reaction kinetics database, the predicted results of the molecular chain dynamic changes of the melt in each subsequent preset devolatilization section are obtained. Based on the predicted results of dynamic changes in each molecular chain and the preset target threshold of the molecular chain, the set of directional control parameters for the corresponding preset devolatilization section is obtained to make real-time closed-loop adjustments to the devolatilization process conditions and directional additive injection parameters for the corresponding preset devolatilization section. The real-time molecular chain characteristic parameters and real-time volatile residue data of each preset devolatilization stage after closed-loop adjustment are obtained to coordinately optimize and correct the directional control parameter set of the corresponding preset devolatilization stage and the devolatilization process parameters of the entire TPU melt devolatilization process, so as to obtain the closed-loop optimization control parameter set of the entire process. Based on the real-time molecular chain characteristic parameters, real-time operating process parameters, prediction results of molecular chain dynamic changes, and the full-process closed-loop optimization control parameter set, a full closed-loop precision control model for TPU melt devolatilization purification is constructed, so that the continuous TPU melt devolatilization production process can achieve real-time and precise control of the entire purification process according to the full closed-loop precision control model.

2. The method for precise control of TPU particle purification based on GPC online detection according to claim 1, characterized in that, The step of obtaining the predicted results of the dynamic changes of the molecular chains of the melt in the subsequent preset devouring stages based on the real-time molecular chain characteristic parameters of the current preset devouring stage and the set operating process parameters of each subsequent preset devouring stage, combined with the preset TPU molecular chain reaction kinetics database, includes: Based on the real-time molecular chain characteristic parameters, the number-average molecular weight, weight-average molecular weight, molecular weight distribution index, oligomer content, and high molecular weight gel characteristic data of the melt are obtained. The set temperature, set vacuum degree, set residence time, and set shear rate of each preset devouring section are obtained according to the set operating process parameters. The thermal degradation rate constant and oligomer formation rate of the melt are obtained based on the number-average molecular weight, weight-average molecular weight, set temperature, set residence time, and preset TPU molecular chain reaction kinetics database. Based on the molecular weight distribution index, initial characteristics of high molecular weight gel, set vacuum degree, set shear rate, and preset TPU molecular chain reaction kinetics database, the cross-linking branching rate constant of the melt and the critical threshold for gel formation are obtained; Based on the thermal degradation rate constant, oligomer formation rate, crosslinking branching rate constant, and gel formation critical threshold, a molecular chain change dynamic model is constructed for each subsequent preset devolatilization stage; The molecular chain change dynamics model is used to obtain the predicted results of the molecular chain dynamic changes of the melt in each subsequent preset devolatilization stage. The predicted results of the molecular chain dynamic changes include the molecular weight change trend, the molecular weight distribution fluctuation amplitude, the oligomer increment and the gel formation probability.

3. The method for precise control of TPU particle purification based on GPC online detection according to claim 2, characterized in that, The step of constructing the molecular chain change dynamics model for each subsequent preset devolatilization stage based on the thermal degradation rate constant, oligomer formation rate, crosslinking branching rate constant, and gel formation critical threshold includes: The bidirectional coupled kinetic constitutive relationship of TPU molecular chain breakage degradation and branching crosslinking is obtained by combining the thermal degradation rate constant and the crosslinking branching rate constant with the set temperature and set residence time of each subsequent preset devolatilization stage. The dynamic mass balance relationship between in-situ oligomer generation and vacuum removal is obtained by combining the oligomer generation rate with the set vacuum degree and set shear rate of each subsequent preset devolatilization stage. The critical boundary constraints for cross-linking and gelation of TPU molecular chains are obtained by combining the critical threshold for gel formation with the cross-linking branching rate constant and the set residence time. The initial boundary conditions of the model are obtained based on the real-time molecular chain characteristic parameters of the current preset devouring section. The two-way coupled dynamic constitutive relation, dynamic mass balance relation, and critical boundary constraint conditions are coupled in multiple fields. The molecular chain change dynamic model of each preset devolatilization section is constructed with the initial boundary conditions as the calculation starting point and the set operating process parameters of each preset devolatilization section as the process constraint.

4. The method for precise control of TPU particle purification based on GPC online detection according to claim 1, characterized in that, The step of obtaining the set of directional control parameters for the corresponding preset devolatilization stage based on the predicted dynamic changes of each molecular chain and the preset target threshold of the molecular chain, and then adjusting the devolatilization process conditions and directional additive injection parameters for the corresponding preset devolatilization stage in real time in a closed loop, includes: Based on the predicted results of dynamic changes in each molecular chain, the molecular weight change deviation value, the predicted value of oligomer residue, and the risk level of gel formation are obtained. Based on the preset molecular chain target threshold, the allowable fluctuation range of molecular weight, the upper limit of oligomer residue, and the zero-risk constraint condition for gel formation are obtained. The basic control parameters are obtained based on the molecular weight change deviation value and the allowable fluctuation range of molecular weight, and the auxiliary control parameters are obtained based on the oligomer residue prediction value and the oligomer residue upper limit value. Based on the gel formation risk level and the zero-risk constraint condition for gel formation, the basic injection parameters are obtained, and a multi-dimensional synergistic control matrix for process aids is constructed based on the basic control parameters, auxiliary control parameters, and basic injection parameters. A set of directional control parameters is obtained based on the multi-dimensional synergistic control matrix of the process additives, wherein the set of directional control parameters includes devolatilization process control sub-parameters and directional additive injection control sub-parameters; The temperature, vacuum, residence time, and shear rate of the preset devolatilization section are adjusted in real-time using the devolatilization process control sub-parameters in a closed loop. The injection rate, injection point, and injection concentration of the additives are adjusted synchronously and in real-time using the directional additive injection control sub-parameters in a closed loop, thereby completing the real-time closed-loop control of the preset devolatilization section.

5. The method for precise control of TPU particle purification based on GPC online detection according to claim 1, characterized in that, The step of obtaining real-time molecular chain characteristic parameters and real-time volatile residue data of each preset devolatilization stage after closed-loop adjustment to collaboratively optimize and correct the directional control parameter set of the corresponding preset devolatilization stage and the devolatilization process parameters of the entire TPU melt devolatilization process, and obtaining the closed-loop optimization control parameter set of the entire process, includes: The actual deviation value of the molecular chain and the consistency deviation value of the molecular chain throughout the entire process are obtained based on each of the real-time molecular chain characteristic parameters, and the volatile matter removal deviation value and the volatile matter residue gradient deviation value throughout the entire process are obtained based on each of the real-time volatile matter residue data. The single-stage control correction coefficient is obtained based on the actual deviation value of the molecular chain and the deviation value of volatile matter removal, and the process coordination correction coefficient of the entire TPU melt devolatilization process is obtained based on the molecular chain consistency deviation value and the volatile matter residual gradient deviation value of the entire process. Based on the single-section control correction coefficient, the directional control parameter set of the corresponding preset devolatilization section is modified and optimized parameter by parameter to obtain the section-level optimized control parameters. Based on the process synergy correction coefficient, the devolatilization process parameters of the entire TPU melt devolatilization process are matched and corrected across different processes to obtain the basic process parameters at the whole process level. Based on the section-level optimized control parameters and the full-process-level basic process parameters, multi-section coupling verification is performed to obtain multiple collaborative matching parameter groups; The TPU melt-devouring process closed-loop optimization control parameter set is obtained by integrating and encapsulating the collaborative matching parameter set.

6. The method for precise control of TPU particle purification based on GPC online detection according to claim 1, characterized in that, The step of constructing a precise closed-loop control model for TPU melt devolatilization purification based on the real-time molecular chain characteristic parameters, real-time operating process parameters, predicted results of dynamic changes in the molecular chain, and the full-process closed-loop optimization control parameter set includes: The real-time input feature matrix is ​​obtained based on the real-time molecular chain characteristic parameters and the real-time operating process parameters, and the feedforward constraint feature matrix is ​​obtained based on the prediction results of the dynamic changes of the molecular chain. The full-process closed-loop optimization control parameter set is used to obtain the full-process target output matrix and boundary constraints, and the label truth set is obtained based on the full-process target output matrix. The training sample set and the validation sample set are obtained based on the real-time input feature matrix, the feedforward constraint feature matrix and the label truth set. Based on the training sample set and boundary constraints, a dual closed-loop network architecture coupling the feedforward prediction branch and the feedback correction branch is constructed. The weight parameters of the dual closed-loop network architecture are iteratively optimized based on the verification sample set and the full-process target output matrix to obtain the initial control model. Based on the initial control model, the synergistic control response function of process additives for each preset devolatilization stage is obtained, and the cross-stage coupling constraint of the initial control model is modified and optimized based on the response function to obtain a fully closed-loop precise control model for TPU melt devolatilization purification.

7. A precise control system for TPU particle purification based on GPC online detection, applied to an online GPC detection system, characterized in that, include: The first acquisition module is used to acquire trace melt samples and real-time operating process parameters of multiple preset devolatilization stages in the entire TPU melt devolatilization process, and to acquire the real-time molecular chain characteristic parameters of TPU for the corresponding preset devolatilization stage through an online GPC detection system based on each trace melt sample. The second acquisition module is used to obtain the prediction results of the dynamic changes of the molecular chain of the melt in the subsequent preset devouring stages based on the real-time molecular chain characteristic parameters of the current preset devouring stage and the set operating process parameters of each subsequent preset devouring stage, combined with the preset TPU molecular chain reaction kinetics database. The closed-loop adjustment module is used to obtain the set of directional control parameters for the corresponding preset devolatilization section based on the prediction results of the dynamic changes of each molecular chain and the preset molecular chain target threshold, so as to make real-time closed-loop adjustments to the devolatilization process conditions and directional additive injection parameters of the corresponding preset devolatilization section. The optimization and correction module is used to obtain the real-time molecular chain characteristic parameters and real-time volatile residue data of each preset devolatilization section after closed-loop adjustment, so as to coordinately optimize and correct the directional control parameter set of the corresponding preset devolatilization section and the devolatilization process parameters of the entire TPU melt devolatilization process, and obtain the closed-loop optimization control parameter set of the entire process. The module is used to construct a full closed-loop precision control model for TPU melt devolatilization purification based on the real-time molecular chain characteristic parameters, real-time operating process parameters, molecular chain dynamic change prediction results, and the full-process closed-loop optimization control parameter set, so that the continuous TPU melt devolatilization production process can achieve real-time and precise control of the entire purification process according to the full closed-loop precision control model.

8. The precise control system for TPU particle purification based on GPC online detection according to claim 7, characterized in that, The building module includes: The first acquisition unit is used to acquire a real-time input feature matrix based on the real-time molecular chain feature parameters and real-time operating process parameters, and to acquire a feedforward constraint feature matrix based on the prediction result of the dynamic change of the molecular chain. The second acquisition unit is used to acquire the full-process target output matrix and boundary constraints based on the full-process closed-loop optimization control parameter set, and to acquire the label truth set based on the full-process target output matrix; The third acquisition unit is used to acquire training sample set and verification sample set based on the real-time input feature matrix, feedforward constraint feature matrix and label truth set; The construction unit is used to construct a dual-closed-loop network architecture that couples the feedforward prediction branch and the feedback correction branch according to the training sample set and boundary constraints, and to iteratively optimize the weight parameters of the dual-closed-loop network architecture according to the verification sample set and the full-process target output matrix to obtain the initial control model. The correction and optimization unit is used to obtain the process additive synergistic regulation response function of each preset devolatilization section according to the initial control model, and to correct and optimize the cross-section coupling constraints of the initial control model according to the response function, so as to obtain the full closed-loop precise control model for TPU melt devolatilization purification.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

Images