Control method and system for insulating material production line

Abstract

The invention relates to the technical field of industrial control, in particular to a control method and system for an insulating material production line. The method comprises the steps of obtaining initial operation data of an insulating material production line in real time; calculating a transient specific energy index in combination with a standard plasticizing reference absolute temperature; performing smooth filtering on the machine head melt pressure to extract a time differential item to obtain a pressure change rate; deducing a dynamic thinning coefficient by combining the transient specific energy index, the previous sampling moment value, the pressure change rate and the machine head melt pressure; constructing a state matrix mismatch correction factor based on the dynamic thinning coefficient, a sliding data window historical maximum thinning limit value and a standard steady-state thinning reference value; and correcting the discrete state space model system matrix in a targeted manner by using the correction factor, and executing adaptive model prediction control to output an optimal control instruction. The method can effectively solve the mismatch problem of the static model under the nonlinear sudden change condition, improves the control precision and reliability, and guarantees the stable and efficient operation of a production line.

Description

Technical Field

[0001] This invention relates to the field of industrial control technology, and in particular to a control method and system for an insulating material production line. Background Technology

[0002] In the field of insulating material production, co-rotating parallel twin-screw extrusion granulation production lines are widely used in the compounding and processing of high-performance polymer materials such as cross-linked polyethylene and low-smoke halogen-free materials. Because insulating materials have extremely high requirements for electrical performance and purity, the insulating material production line is a complex, multi-variable system encompassing loss-in-weight feeding, multi-temperature zone heating and cooling, high-speed shearing by the main motor, and rear-end traction pelletizing. During the start-up, shutdown, or transition period when switching between different grades of insulating material formulations, the physical response times of the various actuators along the entire line vary significantly.

[0003] To coordinate the time lag differences of multiple variables, model predictive control algorithms are often introduced in industrial control to optimize the global trajectory of insulation material production lines. Traditional model predictive control algorithms establish a dynamic mathematical model of the extrusion production line, aiming to minimize the amount of waste generated, and solve online for the control action sequence that optimizes the transition period.

[0004] However, the predictive power of traditional model predictive control algorithms relies on internally preset mathematical models. During formula switching or start-up / shutdown transitions, insulation materials undergo not only physical mixing but also complex rheological changes. Melt viscosity, shear thinning index, and specific heat capacity can experience drastic nonlinear abrupt changes. Since existing model predictive control algorithms typically employ static linear models or simplified nonlinear models identified based on a fixed operating condition, they cannot accurately assess the actual physical changes inside the twin-screw extruder when faced with drastic nonlinear changes in material rheological properties. Furthermore, nonlinear transient changes can lead to severe model mismatch, causing deviations in the predicted trajectory of traditional model predictive control algorithms. This not only fails to reduce waste but may even result in extreme control commands due to incorrect predictions, leading to a sudden increase in die head pressure or instantaneous thermal degradation of the material, thus affecting the control accuracy of the insulation material production line. Summary of the Invention

[0005] To address the problem that traditional model predictive control algorithms suffer from model mismatch due to rheological abrupt changes during the transition period of insulating materials, leading to extreme control commands and material degradation, which affects the control accuracy of insulating material production lines, this invention provides a control method and system for insulating material production lines.

[0006] In a first aspect, the present invention provides a control method for an insulating material production line, which adopts the following technical solution: A control method for an insulation material production line includes: acquiring initial operating data of the insulation material production line in real time, and normalizing the initial operating data to obtain dimensionless operating data; calculating and constructing a transient specific energy index characterizing energy conversion efficiency based on the main motor torque, spindle speed, die head melt temperature, and total feed rate of the twin-screw extruder in the operating data, combined with a preset standard plasticizing reference absolute temperature; extracting the time derivative term after smoothing and filtering the die head melt pressure of the twin-screw extruder in the operating data to obtain the pressure change rate; and combining the transient... The specific energy index, the transient specific energy index at the previous sampling time, the pressure change rate, and the melt pressure at the die head are used to deduce and calculate the dynamic thinning coefficient, which reflects the degree of viscosity reduction of the material. Based on the dynamic thinning coefficient, the historical maximum thinning limit and the standard steady-state thinning benchmark value dynamically updated by the sliding data window, a state matrix mismatch correction factor is constructed. The state matrix mismatch correction factor is used to target and correct the system matrix in the preset discrete state space model to obtain the dynamic system matrix. Adaptive model predictive control is then executed based on the dynamic system matrix to output the optimal control command for the insulation material production line.

[0007] This invention achieves a quantitative assessment of energy conversion efficiency by calculating the transient specific energy index, accurately reflecting the combined effects of main motor torque, speed, melt temperature, and feeding rate, providing a reliable energy conversion characteristic basis for subsequent dynamic thinning coefficient calculation. By extracting the pressure change rate, it achieves an assessment and analysis of the dynamic characteristics of the melt pressure at the die head, accurately reflecting the changing trend of the melt flow state, providing a reliable pressure dynamic characteristic basis for the calculation of the dynamic thinning coefficient. By calculating the dynamic thinning coefficient, it achieves a real-time assessment of the degree of material viscosity change, accurately reflecting the nonlinear changes in melt rheological properties, providing a reliable rheological characteristic index for constructing the state matrix mismatch correction factor. By constructing the state matrix mismatch correction factor, it improves the traditional model predictive control algorithm, dynamically adjusting the system matrix according to the material rheological properties, effectively solving the mismatch problem of the static model under nonlinear abrupt conditions, and improving the accuracy and reliability of model predictive control.

[0008] Furthermore, the transient specific energy index satisfies: In the formula, For the insulation material production line at the current moment The transient specific energy index, For the current moment, The observation window length for the insulation material production line. For twin-screw extruders at the integral time Main motor torque, For twin-screw extruders at the integral time spindle speed, For the feeder at the integration time Total feeding rate For twin-screw extruders at the integral time The temperature of the melt at the die head, The preset standard plasticizing reference absolute temperature, To prevent the feed lower limit from having a denominator of 0, To prevent the lower bound of the natural logarithm term with a denominator of 0, It is a function with maximum value. It is the natural logarithm function.

[0009] This invention achieves a scientific evaluation of the transient specific energy index by constructing a time integral model that includes the ratio of torque to speed and the ratio of temperature to material handling capacity. This model more accurately reflects the comprehensive relationship between energy input and material handling capacity of a twin-screw extruder. The maximum value function ensures the numerical stability of the denominator, thereby effectively characterizing the average characteristics of energy conversion efficiency within a time window.

[0010] Furthermore, the observation window time length is obtained by performing an offline step test on the insulation material production line. The offline step test includes adding a preset proportion of given amount as a step excitation at the feeder; collecting time response data of the melt pressure at the die head after being subjected to the step excitation; fitting the dominant response time constant of the melt pressure at the die head based on the time response data; and setting the dominant response time constant as the observation window time length of the insulation material production line.

[0011] Furthermore, the dynamic thinning coefficient satisfies: In the formula, For the insulation material production line at the current moment The dynamic thinning coefficient, and For the insulation material production line at the current moment With time The transient specific energy index, For the twin-screw extruder at the current moment The melt pressure at the die head, For the twin-screw extruder at the current moment The rate of pressure change of the melt pressure sequence belonging to the die head. To prevent the lower limit of pressure from a denominator of 0, It is a function for maximizing the value.

[0012] This invention achieves a scientific assessment of the dynamic thinning coefficient by constructing a function that includes the product of transient specific energy index and pressure change rate and the ratio of pressure and energy change terms. This function more accurately reflects the impact of material viscosity changes on energy conversion and pressure response. The square root term corrects for the impact of transient energy fluctuations, thereby effectively assessing the degree of material rheology.

[0013] Furthermore, the state matrix mismatch correction factor satisfies: In the formula, For the insulation material production line at the current moment State matrix mismatch correction factor For the insulation material production line at the current moment The dynamic thinning coefficient, The historical maximum thinning limit is dynamically updated for the sliding data window. This is the standard steady-state thinning reference value. It is a natural exponential function. It is a hyperbolic cosine function.

[0014] This invention achieves a scientific evaluation of the state matrix mismatch correction factor by constructing a product model that includes an exponential decay term that represents the ratio of the dynamic thinning coefficient to the historical maximum thinning limit and a hyperbolic cosine term that represents the ratio of the dynamic thinning coefficient to the standard steady-state thinning benchmark value. This ensures that the correction factor tends to the minimum value when the dynamic thinning coefficient is close to the historical maximum value, and tends to 1 when the dynamic thinning coefficient is close to the steady-state benchmark value. This effectively balances the intensity of model correction and prevents control instability caused by over-correction.

[0015] Furthermore, the method for obtaining the historical maximum thinning limit is as follows: using a sliding data window of a preset length, all dynamic thinning coefficients within the sliding data window are collected, and the peak value among the dynamic thinning coefficients is used as the historical maximum thinning limit for dynamic updating.

[0016] Furthermore, the step of smoothing and filtering the die melt pressure of the twin-screw extruder in the operating data and extracting the time derivative to obtain the pressure change rate includes: using a Savitzky-Golay filter to smooth the die melt pressure sequence in the operating data to eliminate high-frequency noise and generate a smooth pressure sequence; extracting the time derivative from the smooth pressure sequence and multiplying the time derivative by the observation window length of the insulation material production line to generate the pressure change rate.

[0017] Furthermore, the step of using the state matrix mismatch correction factor to target and correct the system matrix in the preset discrete state space model to obtain the dynamic system matrix includes: constructing a correction mask matrix; assigning the state index diagonal elements corresponding to the melt pressure of the die head and the state index diagonal elements corresponding to the torque of the main motor in the correction mask matrix to the state matrix mismatch correction factor; assigning the state index diagonal elements corresponding to any temperature zone in the correction mask matrix to 1; assigning the off-diagonal elements in the correction mask matrix to 1; and performing a Hadamard product operation between the system matrix in the discrete state space model and the correction mask matrix to generate the targeted corrected dynamic system matrix.

[0018] This invention achieves targeted correction of the system matrix by constructing a modified mask matrix and performing Hadamard product operations. This ensures that only the state transition coefficients related to the melt pressure at the die head and the torque of the main motor are dynamically adjusted, while keeping other state transition coefficients unchanged. This more accurately reflects the impact of changes in material rheological properties on key state variables and effectively improves the adaptability and accuracy of model predictive control.

[0019] Furthermore, the step of performing adaptive model predictive control based on the dynamic system matrix to output the optimal control command for the insulation material production line includes: inputting the dynamic system matrix into the adaptive model predictive control algorithm to perform rolling optimization; solving the cost function with the dual objectives of minimizing the transition time and minimizing the control increment to generate the optimal control command for the next cycle, wherein the optimal control command includes the main motor speed setpoint of the twin-screw extruder in the insulation material production line, the temperature control setpoint of each temperature zone of the twin-screw extruder, and the feeding rate setpoint of the feeder.

[0020] Secondly, the present invention provides a control system for an insulating material production line, which adopts the following technical solution: A control system for an insulating material production line includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the control method for the insulating material production line described above is implemented.

[0021] By adopting the above technical solution, a computer program is generated from the control method of the above-mentioned insulation material production line and stored in a memory so that it can be loaded and executed by a processor. Terminal equipment is then made based on the memory and the processor for convenient use.

[0022] The present invention has the following technical effects: (1) In view of the problem that traditional model predictive control relies on a fixed static nonlinear model, which cannot cope with the drastic nonlinear changes in melt viscosity and shear thinning index during formula switching and start-up / shutdown transition periods, resulting in model mismatch, prediction trajectory deviation and even extreme control commands, this invention constructs a transient specific energy index by parameters such as main motor torque, main shaft speed and die head melt temperature, and deduces the dynamic thinning coefficient by combining the die head melt pressure change rate. Then, based on the dynamic thinning coefficient and the historical limit of sliding window update, a state matrix mismatch correction factor is constructed to target and correct the system matrix of the discrete state space model. This effectively breaks the limitations of the traditional fixed model and allows the system matrix to be dynamically adjusted in real time according to the rheological properties of the material, accurately adapting to the nonlinear transient changes during the transition period, thereby avoiding the prediction deviation caused by model mismatch and the risks of sudden increase in die head pressure and thermal degradation of materials.

[0023] (2) Breaking through the prediction limitations of traditional static system matrix, this invention uses a modified dynamic system matrix to perform adaptive model predictive control, making the internal predicted trajectory more closely match the actual physical mixing and rheological changes inside the twin-screw extruder. It can more accurately coordinate the time lag differences of multiple actuators such as loss-in-weight feeder, multi-temperature zone heating and cooling, and main motor shearing. The global trajectory optimization is more effective. Compared with the control inaccuracy of traditional model predictive control during the transition period, the optimal control command output by this invention is more stable and accurate. It not only ensures the plasticization quality of materials, but also avoids the fluctuation of the production line caused by the incoordination of multivariable responses, and improves the control accuracy and operational stability of the insulation material production line during the transition period.

[0024] (3) Through precise adaptive model predictive control, this invention enables more efficient coordination of various actuators during the transition period, reduces the amount of waste generated due to model mismatch and control deviation, and avoids the waste of high-value insulation materials. At the same time, it avoids instantaneous thermal degradation of materials, ensures the electrical performance and purity of insulation materials, and meets the requirements of insulation materials for product quality. In addition, stable transition period control shortens the start-up and shutdown time and formula switching time, improves the continuous operation efficiency of the production line, takes into account production efficiency, product quality and cost control, and enhances the overall competitiveness of insulation material production. Attached Figure Description

[0025] Figure 1 This is a flowchart of a control method for an insulating material production line according to an embodiment of the present invention.

[0026] Figure 2 This is a schematic diagram of the initial system matrix in a control method for an insulating material production line according to an embodiment of the present invention.

[0027] Figure 3 This is a schematic diagram of the correction mask matrix in a control method for an insulating material production line according to an embodiment of the present invention.

[0028] Figure 4 This is a schematic diagram of the dynamic system matrix in a control method for an insulating material production line according to an embodiment of the present invention.

[0029] Figure 5 This is a schematic diagram comparing the operating indicators of the system control stability assessment in a control method for an insulating material production line according to an embodiment of the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] This invention discloses a control method for an insulating material production line, referring to... Figure 1 This includes steps S001-S006: S001: Acquire the initial operating data of the insulation material production line in real time, and normalize the initial operating data to obtain dimensionless operating data.

[0032] Specifically, in complex and multivariable insulation material production line systems, the data collected by various sensors have different physical dimensions and orders of magnitude. To facilitate unified mathematical calculations and feature extraction, and to prevent parameters with large absolute values ​​from absorbing minute features, the initial operating data must be standardized and normalized. Through sensors and programmable logic controllers deployed on the twin-screw extruder and related pipelines, real-time field operation data is collected. The initial operating data specifically includes: the main motor torque of the twin-screw extruder (N·m), the spindle speed of the twin-screw extruder (rpm), the die melt pressure of the twin-screw extruder (MPa), the die melt temperature of the twin-screw extruder (K), and the total feed rate of the feeder (kg / h). All initial operating data are dimensionlessly normalized according to the rated steady-state baseline value of the corresponding process. The dimensionless operating data is equal to the initial operating data divided by the rated steady-state value of the process, based on historical steady-state operating data or industry standards for that process.

[0033] S002: Based on the main motor torque, spindle speed, die head melt temperature, and total feed rate of the twin-screw extruder in the operating data, combined with the preset standard plasticizing reference absolute temperature, calculate and construct the transient specific energy index characterizing energy conversion efficiency.

[0034] Specifically, the transient specific energy index satisfies: ; In the formula, For the insulation material production line at the current moment The transient specific energy index, For the current moment, The observation window length for the insulation material production line. For twin-screw extruders at the integral time Main motor torque, For twin-screw extruders at the integral time spindle speed, For the feeder at the integration time Total feeding rate For twin-screw extruders at the integral time The temperature of the melt at the die head, The preset standard plasticizing reference absolute temperature is obtained through the melting temperature of the processed insulating material. In this embodiment, if the processed material is cross-linked polyethylene insulating material, the value is 403.15K. This value is the melting temperature of cross-linked polyethylene insulating material, which is also the core reference temperature of its plasticizing process and conforms to the industry standard for the melting section temperature of cross-linked polyethylene insulation extrusion. To prevent the feed lower limit from being zero in the denominator, in this embodiment, the value is set to 0.05. This value corresponds to 5% of the rated steady-state feed. While conforming to the actual physical lower limit of the equipment operation, it can strictly prevent the denominator from being divided by zero during integral calculation. To prevent the lower limit of the natural logarithm term with a denominator of 0, in this embodiment, the value is set to 0.1. This value can strictly prevent the temperature logarithm term from fluctuating and diverging, thereby causing the denominator to be divided by zero abnormally during calculation. It is a function with maximum value. It is the natural logarithm function.

[0035] Specifically, the observation window duration is obtained as follows: An offline step test is performed on the insulation material production line. The offline step test includes adding a preset proportion of given amount as a step excitation at the feeder. Collect time response data of the melt pressure at the die head after a step excitation; The dominant time constant of the response to the melt pressure at the die head is fitted based on time response data; In this embodiment, the dominant response time constant is set as the observation window length of the insulation material production line. The range of values ​​is Second.

[0036] When the feeding rate is constant, an increase in the torque or speed of the main motor will drive... Increase; in the denominator The nonlinear suppression of the shear work capacity of temperature is characterized. When the melt temperature approaches or exceeds the plasticizing reference temperature, this suppression is enhanced, preventing the exponential divergence and conforming to the rheological law that the viscosity of polymer melts decreases with increasing temperature.

[0037] The above-mentioned relationship is based on the empirical model of specific mechanical energy and the thermodynamic law of conservation of energy in the field of polymer material processing. It aims to evaluate the energy conversion efficiency inside the extruder and more accurately reflect the combined effects of main motor torque, speed, melt temperature, and feed rate. Traditional specific mechanical energy models typically only calculate static mechanical work, i.e., main motor torque multiplied by speed and divided by feed rate, neglecting the nonlinear effects of thermal energy on polymer chain segment motion and melt viscosity. This invention introduces a factor based on the actual die head melt temperature into the traditional integral model. Plasticizing reference absolute temperature The natural logarithm of the ratio In the denominator The nonlinear suppression of the shear work capacity by temperature is characterized. This suppression intensifies as the melt temperature approaches or exceeds the plasticizing reference temperature, preventing exponential unbounded divergence and conforming to the rheological law that polymer melt viscosity decreases with increasing temperature. Simultaneously, it introduces… Functions and and As a cutoff lower limit, it effectively matches the actual physical lower limit of the equipment's operation and strictly prevents mathematical anomalies such as division by zero in the denominator during time integral calculation.

[0038] The initial operating data of the insulation material production line have been dimensionlessly normalized to the rated steady-state reference values ​​of the corresponding process before being input into the calculation. , , and All values ​​are dimensionless pure numerical values, and the time integral and the time dimension of the observation window length cancel each other out. Therefore, the overall transient specific energy index is... It is a dimensionless comprehensive energy assessment coefficient.

[0039] S003: After smoothing and filtering the melt pressure at the die head of the twin-screw extruder in the operating data, the time derivative term is extracted to obtain the pressure change rate.

[0040] Specifically, including: Savitzky-Golay filters are used to smooth the head melt pressure sequence in the operating data to eliminate high-frequency noise and generate a smooth pressure sequence. The time derivative term is extracted from the smoothed pressure sequence, and then multiplied by the observation window length of the insulation material production line to generate the pressure change rate.

[0041] S004: Combining the transient specific energy index, the transient specific energy index at the previous sampling time, the pressure change rate, and the melt pressure at the die head, the dynamic thinning coefficient, which reflects the degree of viscosity reduction of the material, is derived and calculated.

[0042] Specifically, the dynamic thinning coefficient satisfies: ; In the formula, For the insulation material production line at the current moment The dynamic thinning coefficient, and For the insulation material production line at the current moment With time The transient specific energy index, For the twin-screw extruder at the current moment The melt pressure at the die head, For the twin-screw extruder at the current moment The rate of pressure change of the melt pressure sequence belonging to the die head. To prevent the pressure lower limit from having a denominator of 0, in this embodiment, a value of 0.05 is used. This value corresponds to 5% of the normalized rated pressure. While conforming to the actual physical lower limit of equipment operation, it can strictly prevent the denominator from being divided by zero during calculation. It is a function for maximizing the value.

[0043] Among them, when the absolute value of the pressure change rate increases sharply and When the level is high, it indicates that the material is in a stage of intense molecular rearrangement. Significantly increased; the square root term in the denominator provides smooth damping for step jumps in energy dissipation; absolute value handling ensures... This aligns with the rheological principle that shear thinning is only related to the magnitude of the deformation rate and is independent of the direction.

[0044] The above-mentioned relationship is constructed based on rheological equations in non-Newtonian fluid dynamics, such as the mapping theory between power-law fluid characteristics and macroscopic response. This aims to achieve real-time dynamic assessment of the degree of viscosity reduction in materials, accurately reflecting the nonlinear changes in melt rheological properties. In the complex, enclosed extrusion barrel, it is difficult to directly measure the microscopic shear rate, but the melt pressure at the die head... The time derivative, i.e., the rate of change of pressure. It is a direct macroscopic characterization of fluid flow resistance and dynamic viscosity changes. This invention uses the transient specific energy index. Integrating with the dynamic characteristics of macroscopic pressure, when the absolute value of the rate of change of pressure increases sharply and When the level is high, it indicates that the material is in a stage of intense molecular rearrangement. Significantly increased; to suppress computational oscillations caused by energy mutations, a square root term was introduced at the tail. It provides smooth damping for step transitions that dissipate energy; the non-negativity handling mechanism, including absolute value and square, ensures... This aligns with the rheological essence that shear thinning is only related to the magnitude of the deformation rate and is independent of the direction.

[0045] Through global dimensionless normalization, the pressure in the relation is... Pressure change rate and the transient specific energy index calculated beforehand. All values ​​are dimensionless. After multiplication, division, and square root operations on each dimensionless variable, the final result is the dynamic thinning coefficient. It is a dimensionless pure numerical scalar.

[0046] S005: Based on the dynamic thinning coefficient, the historical maximum thinning limit and the standard steady-state thinning benchmark value dynamically updated by the sliding data window, a state matrix mismatch correction factor is constructed.

[0047] Specifically, the state matrix mismatch correction factor satisfies: ; In the formula, For the insulation material production line at the current moment State matrix mismatch correction factor For the insulation material production line at the current moment The dynamic thinning coefficient, The historical maximum thinning limit is dynamically updated for the sliding data window. The standard steady-state thinning reference value is set to 1 in this embodiment, which corresponds to the typical thinning strength of the material under steady-state plasticizing conditions, ensuring that the correction factor approaches 1 in steady state. It is a natural exponential function. It is a hyperbolic cosine function.

[0048] Specifically, the method for obtaining the historical maximum thinning limit is as follows: Using a sliding data window of preset length, collect all dynamic thinning coefficients within the sliding data window, and use the peak value among the dynamic thinning coefficients as the historical maximum thinning limit for dynamic updating.

[0049] Among them, the combined exponential decay and hyperbolic cosine compensation, when near At that time, the negative exponential term decays rapidly, suppressing overcompensation; the hyperbolic cosine term ensures that... around It provides symmetrical smoothing gain during fluctuations; the more drastic the material thinning, the better. The greater the deviation from 1, the stronger the nonlinear calibration weights provided to the MPC model.

[0050] The basic architecture of the above relationships originates from adaptive control theory, such as the time-varying robust weight design model in Model Predictive Control (MPC), which aims to scientifically construct a state matrix mismatch correction factor to solve the mismatch problem of static models under nonlinear abrupt changes. Traditional model predictive control algorithms rely on fixed static models and cannot cope with drastic nonlinear abrupt changes in material rheology. This invention uses an exponential decay function... With hyperbolic cosine function Composite construction was performed, and the dynamic thinning coefficient was adjusted. Approaching the historical maximum value of sliding window updates At that time, the negative exponential term decays rapidly, suppressing overcompensation; simultaneously, the hyperbolic cosine term ensures that... Around the standard steady-state thinning benchmark value It provides symmetric smoothing gain during fluctuations. Through function combination, it not only effectively balances the intensity of model correction and prevents control instability caused by over-correction, but also its global continuous differentiability smoothness can ensure the continuity of gradient descent direction when performing adaptive model predictive control rolling optimization solution, thus avoiding the algorithm from getting trapped in local optima.

[0051] Dynamic thinning coefficient and its corresponding historical maximum thinning limit Standard steady-state thinning benchmark value All are dimensionless pure numerical values, and the exponential term formed by dividing them is... and hyperbolic cosine term Also dimensionless; therefore, after mapping with the natural exponent and hyperbolic cosine function, the overall state matrix mismatch correction factor is obtained. It is a dimensionless weight multiplier that can be directly used for targeted correction of system matrix elements.

[0052] S006: The system matrix in the preset discrete state space model is targeted and corrected using the state matrix mismatch correction factor to obtain the dynamic system matrix. Adaptive model predictive control is then executed based on the dynamic system matrix to output the optimal control command for the insulation material production line.

[0053] Specifically, after obtaining the correction factor, the control model needs to be updated in a way that minimizes computational resource consumption and makes the disturbance to the original system most controllable in order to complete the closed loop. The preset discrete state space model of the twin-screw extrusion production line includes the system matrix, the control input matrix, and the output matrix. The system matrix in the preset discrete state-space model is targeted for correction using a state matrix mismatch correction factor; Construct a correction mask matrix, and assign the state index diagonal elements corresponding to the melt pressure of the machine head and the state index diagonal elements corresponding to the torque of the main motor in the correction mask matrix as state matrix mismatch correction factors respectively. Assign the value 1 to the diagonal element and all off-diagonal elements of the state index corresponding to any temperature zone. Perform a Hadamard product operation between the system matrix and the modified mask matrix to generate the dynamic system matrix after targeted correction. The dynamic system matrix is ​​input into the adaptive model predictive control algorithm to perform rolling optimization; Solve the cost function with the dual objectives of minimizing transition time and minimizing control increment to generate the optimal control command for the next cycle. The optimal control command includes the spindle speed setpoint of the twin-screw extruder, the temperature control setpoint of each temperature zone, and the total feed rate setpoint of the feeder.

[0054] like Figure 2 As shown, the initial system matrix has a dimension of 4×4. The row and column indices 0-3 correspond to the four core state variables of the system. Index 0 corresponds to the torque state of the main motor of the twin-screw extruder, index 1 corresponds to the melt pressure state of the die head, and indices 2 and 3 correspond to the temperature states of the two typical temperature zones of the twin-screw extruder, respectively. The values ​​of the matrix elements are obtained through system identification under steady-state conditions of the production line, representing the state transition coefficients between each state variable. The diagonal elements of the matrix are the autoregressive coefficients of each state variable, reflecting the dynamic inertia characteristics of the corresponding state itself. The off-diagonal elements are the cross-coupling coefficients between different state variables. In this embodiment, the off-diagonal coupling coefficients of the initial system matrix are all close to 0, which is consistent with the decoupling characteristics of the core state variables of the insulating material production line.

[0055] like Figure 3 As shown, a 4×4 correction mask matrix is ​​used to achieve targeted correction of the system matrix. The assignment of matrix elements strictly follows the correction rules of this invention. Specifically, the diagonal elements corresponding to indices 0 and 1 are assigned the state matrix mismatch correction factor calculated at the current moment, corresponding to the main motor torque and the melt pressure state of the die head, which are most significantly affected by the sudden change in material rheological properties. The diagonal elements corresponding to indices 2 and 3, as well as all off-diagonal elements of the matrix, are assigned a value of 1 to ensure that the temperature transfer coefficient of the temperature zone and the coupling coefficient between states are not corrected, thus avoiding unnecessary disturbances to the steady-state characteristics of the original system. The design of the correction mask matrix achieves precise targeting of the correction effect, dynamically adjusting only the core states affected by shear thinning, thus balancing the effectiveness of model correction and the stability of the control system.

[0056] like Figure 4As shown, this matrix is ​​the dynamic system matrix obtained by performing a Hadamard product operation on the initial system matrix and the correction mask matrix. It is a time-varying matrix that is updated in real time with the rheological properties of the material, providing accurate dynamic model support for adaptive model predictive control. The diagonal elements corresponding to indices 0 and 1 are the product of the initial value and the correction factor, realizing the dynamic calibration of the autoregressive coefficients of the core state and adapting to the model mismatch caused by material shear thinning. The diagonal elements corresponding to indices 2 and 3 and all off-diagonal elements are consistent with the initial system matrix and have not changed. The dynamic system matrix not only solves the mismatch problem of the traditional static model under nonlinear mutation, but also retains the steady-state characteristics of the original system identification model, ensuring the accuracy of model predictive control and the stability of the closed-loop system.

[0057] like Figure 5 As shown, the smaller the standard deviation of the control error, the smaller the fluctuation of the parameter control, and the higher the control accuracy and system stability. Compared with conventional control algorithms, this invention can effectively reduce the control fluctuation of core process parameters, improve the control accuracy and system stability under transition conditions, and thus solve the problems of model mismatch and large control deviation caused by nonlinear changes in the rheological properties of materials in traditional static models. This fully verifies the effectiveness of this invention in actual production.

[0058] This invention also discloses a control system for an insulating material production line, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, a control method for an insulating material production line according to the present invention is implemented.

[0059] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

[0060] The above are all preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A control method for an insulating material production line, characterized in that, include: The initial operating data of the insulation material production line is acquired in real time, and the initial operating data is normalized to obtain dimensionless operating data. Based on the main motor torque, spindle speed, die head melt temperature, and total feed rate of the twin-screw extruder from the operating data, combined with the preset standard plasticizing reference absolute temperature, the transient specific energy index characterizing energy conversion efficiency is calculated and constructed. After smoothing and filtering the melt pressure at the die head of the twin-screw extruder in the operating data, the time derivative term is extracted to obtain the pressure change rate; By combining the transient specific energy index, the transient specific energy index at the previous sampling time, the pressure change rate, and the melt pressure at the die head, the dynamic thinning coefficient, which reflects the degree of viscosity reduction of the material, is deduced and calculated. Based on the dynamic thinning coefficient, the historical maximum thinning limit and the standard steady-state thinning benchmark value dynamically updated by the sliding data window, a state matrix mismatch correction factor is constructed. The system matrix in the preset discrete state space model is targeted and corrected by the state matrix mismatch correction factor to obtain the dynamic system matrix. Adaptive model predictive control is then executed based on the dynamic system matrix to output the optimal control command for the insulation material production line.

2. The control method for an insulating material production line according to claim 1, characterized in that, The transient specific energy index satisfies: ； In the formula, For the insulation material production line at the current moment The transient specific energy index, For the current moment, The observation window length for the insulation material production line. For twin-screw extruders at the integral time Main motor torque, For twin-screw extruders at the integral time spindle speed, For the feeder at the integration time Total feeding rate For twin-screw extruders at the integral time The temperature of the melt at the die head, The preset standard plasticizing reference absolute temperature, To prevent the feed lower limit from having a denominator of 0, To prevent the lower bound of the natural logarithm term with a denominator of 0, It is a function with maximum value. It is the natural logarithm function.

3. The control method for an insulating material production line according to claim 2, characterized in that, The method for obtaining the observation window duration is as follows: An offline step test is performed on the insulation material production line. The offline step test includes adding a preset proportion of given amount as a step excitation at the feeder. Collect time response data of the melt pressure at the die head after a step excitation; The dominant time constant of the response to the melt pressure at the die head is fitted based on time response data; The dominant response time constant is set to the observation window length of the insulation material production line.

4. The control method for an insulating material production line according to claim 1, characterized in that, The dynamic thinning coefficient satisfies: ； In the formula, For the insulation material production line at the current moment The dynamic thinning coefficient, and For the insulation material production line at the current moment With time The transient specific energy index, For the twin-screw extruder at the current moment The melt pressure at the die head, For the twin-screw extruder at the current moment The rate of pressure change of the melt pressure sequence belonging to the die head. To prevent the lower limit of pressure from a denominator of 0, It is a function for maximizing the value.

5. The control method for an insulating material production line according to claim 1, characterized in that, The mismatch correction factor of the state matrix satisfies: ； In the formula, For the insulation material production line at the current moment The state matrix mismatch correction factor For the insulation material production line at the current moment The dynamic thinning coefficient, The historical maximum thinning limit is dynamically updated for the sliding data window. This is the standard steady-state thinning reference value. It is a natural exponential function. It is a hyperbolic cosine function.

6. A control method for an insulating material production line according to claim 1 or 5, characterized in that, The method for obtaining the historical maximum thinning limit is as follows: Using a sliding data window of preset length, collect all dynamic thinning coefficients within the sliding data window, and use the peak value among the dynamic thinning coefficients as the historical maximum thinning limit for dynamic updating.

7. The control method for an insulating material production line according to claim 1, characterized in that, The process of smoothing and filtering the melt pressure at the die head of the twin-screw extruder in the operating data, and then extracting the time derivative term to obtain the pressure change rate, includes: Savitzky-Golay filters are used to smooth the head melt pressure sequence in the operating data to eliminate high-frequency noise and generate a smooth pressure sequence. The time derivative term is extracted from the smoothed pressure sequence, and then multiplied by the observation window length of the insulation material production line to generate the pressure change rate.

8. The control method for an insulating material production line according to claim 1, characterized in that, The process of using a state matrix mismatch correction factor to target and correct the system matrix in a preset discrete state-space model to obtain a dynamic system matrix includes: Construct a correction mask matrix, and assign the state index diagonal elements corresponding to the melt pressure of the die head and the state index diagonal elements corresponding to the torque of the main motor in the correction mask matrix as state matrix mismatch correction factors respectively. Set the diagonal element of the state index corresponding to any temperature zone in the correction mask matrix to 1; The off-diagonal elements in the correction mask matrix will be assigned a value of 1. The system matrix in the discrete state-space model is subjected to the Hadamard product operation with the modified mask matrix to generate the dynamic system matrix after targeted correction.

9. The control method for an insulating material production line according to claim 8, characterized in that, The adaptive model predictive control based on dynamic system matrix execution, which outputs optimal control commands for the insulation material production line, includes: The dynamic system matrix is ​​input into the adaptive model predictive control algorithm to perform rolling optimization; Solve the cost function with the dual objectives of minimizing transition time and minimizing control increment to generate the optimal control command for the next cycle. The optimal control command includes the main motor speed setting value of the twin-screw extruder in the insulation material production line, the temperature control setting value of each temperature zone of the twin-screw extruder, and the feeding rate setting value of the feeder.

10. A control system for an insulating material production line, characterized in that, include: A processor and a memory, the memory storing computer program instructions that, when executed by the processor, implement a control method for an insulating material production line according to any one of claims 1-9.

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