Repeated recycling process control method of regenerated polyethylene for tailing seepage prevention

Abstract

The invention relates to the technical field of process control, and discloses a multiple cyclic utilization process control method of regenerated polyethylene for tailing seepage prevention, which comprises the following steps: synchronously acquiring melt pressure, screw driving motor current and a control input sequence, calculating a cross-correlation function of the current and the pressure, and identifying physical transmission lag time; monitoring an uncommanded residual component of the current in real time, deducing an impending pressure fluctuation amplitude based on a cross-correlation function, and generating an inertia feedforward compensation amount; according to the method, a dual-time scale model is utilized to update parameters online and construct a disturbance observer to generate a feedback compensation control quantity, feedforward and feedback compensation are superposed to a basic output quantity to generate a final driving instruction, a heterogeneous signal time sequence correlation mechanism is utilized, reverse adjustment is carried out before pressure fluctuation is transmitted to a machine head through a screw, and the pressure fluctuation is reduced. And a control blind area caused by physical transmission lag is avoided.

Description

TECHNICAL FIELD

[0001] The application relates to a tailing anti-seepage recycled polyethylene multi-cycle utilization process control method, and belongs to the technical field of process control. BACKGROUND

[0002] In the current tailing pond anti-seepage engineering field, in order to reduce material cost and meet the requirements of environmental protection and recycling, recycled polyethylene is used as the main raw material for manufacturing anti-seepage membranes. For the extrusion production process of such raw materials, the dynamic stability of the melt pressure is the key to guaranteeing the consistency of the physical properties of the anti-seepage membrane. In industrial fields, a fixed parameter PID control strategy or a conventional model predictive control algorithm is generally used to adjust the screw speed or the feeding speed to maintain the melt pressure set value. Such control methods rely on the controlled object having a relatively determined static gain and time constant, and can achieve good steady-state control effect when processing homogeneous raw materials. However, when the above control methods are applied to the recycled polyethylene multi-cycle utilization process, the inherent limitations are revealed. The recycled material undergoes different degrees of photo-oxidation, shear degradation and multiple thermal history, and is mixed with complex heterogeneous impurities, resulting in random time-varying of the molecular weight distribution, crosslinking degree and rheological properties. The dramatic fluctuation of the raw material properties causes the process model parameters of the extruder controlled object to no longer maintain a constant value, and the parameters greatly drift with the feeding time of the same batch. Under this working condition, the traditional controller based on the linear constant assumption cannot sense the parameter changes of the object, and the response delay or overshoot oscillation often occurs due to parameter mismatch, resulting in uneven product thickness and even local overheating degradation.

[0003] The existing technology often focuses on maintaining production by optimizing the screw combination form or setting a static process window, and ignores the dynamic intervention of the non-steady-state process. For example, the Chinese invention patent with the authorization announcement number CN112390996B discloses a modified recycled polyethylene plastic for plastic pipes and a preparation method thereof. Although the second-order extrusion hardware configuration of the single-screw counter-tapered double-screw is limited, and the mixing effect of the base material is ensured by limiting the temperature and speed range, the control logic is still in the open-loop or simple constant value adjustment category. In the face of the dramatic rheological property drift between recycled material batches, such static control mode based on fixed formula and fixed parameters lacks real-time sensing and feedforward response mechanism of the process state, cannot predict and compensate before the melt pressure fluctuation, and is difficult to eliminate the transient impact caused by material heterogeneity, resulting in that the extrusion stability of the final product is difficult to be completely controlled.

[0004] Therefore, how to construct a control method that can identify the time-varying parameters of the controlled object in real time, avoid physical transmission lag and decouple mechanical noise, and realize high-precision stable control of the recycled polyethylene extrusion process under the condition of strong fluctuation of raw material properties, has become a technical problem to be solved by the present application. SUMMARY

[0005] To solve the problems presented in the background art, the technical solutions of the present application are as follows: A kind of tailings anti-infiltration with the recycling process control method of regenerated polyethylene, it is operated in the electronic control unit connected to extrusion production line, extrusion production line includes screw drive motor and melt pressure sensor installed at the end of head, method includes the following steps: Multi-dimensional heterogeneous data synchronous acquisition step, melt pressure sequence, driving current sequence of screw drive motor and current control input sequence output by melt pressure sensor are synchronously acquired with preset sampling frequency, and time stamp alignment reference between melt pressure sequence, driving current sequence and control input sequence is established; Physical transmission lag feature extraction step, the cross-correlation function of driving current sequence and melt pressure sequence is calculated, and the physical transmission lag time of load mutation feature in driving current sequence relative to pressure fluctuation feature in melt pressure sequence is identified based on the time offset of cross-correlation function peak; Advance disturbance targeting deduction step, driving current sequence is monitored in real time, and fundamental component caused by speed instruction change is filtered out to extract non-instruction residual component, when the amplitude of non-instruction residual component exceeds preset noise shielding threshold, the melt pressure fluctuation amplitude that will arrive at the end of head after the end of physical transmission lag time is deduced using physical transmission lag time and cross-correlation function; Time domain reverse compensation step, the opposite inertial feedforward compensation quantity is generated according to the deduced melt pressure fluctuation amplitude, and the inertial feedforward compensation quantity is superimposed into the basic control quantity generated by the electronic control unit to generate the final driving instruction, and the final driving instruction drives screw drive motor to perform reverse adjustment action before load mutation feature causes pressure fluctuation by physical transmission of screw to the end of head.

[0006] Preferably, the method further includes a double-time-scale model evolution step, while performing the time domain reverse compensation step, the coefficients of the discretized parameter model describing the dynamic characteristics of the extrusion process are updated online using the recursive least squares algorithm to process the melt pressure sequence and the control input sequence;The deviation between the theoretical model output and the measured melt pressure is calculated by using the updated discretized parameter model to construct a disturbance observer;The deviation output by the disturbance observer is processed by a low-pass filter to generate a feedback compensation control quantity, and the feedback compensation control quantity and the inertial feedforward compensation quantity are superimposed into the basic control quantity.

[0007] Preferably, in the double-time-scale model evolution step, the coefficient update of the discretized parameter model follows the following recursive operation rule containing forgetting factor: Where, is the model parameter vector at time , is the melt pressure measured value at time , is the model parameter vector at time , is the melt pressure measured value at time for a regression vector comprising historical input-output data, for a gain matrix, the calculation of the gain matrix introducing a preset forgetting factor for reducing the weight of historical data on the update of current model parameters to track process gain drift caused by component wear in the extrusion line.

[0008] Preferably, the deviation of the disturbance observer output needs to be subjected to periodic noise decoupling processing before entering the low-pass filter; the processing includes: acquiring the speed instruction of the screw driving motor in real time, and calculating the current device inherent pulsation frequency according to the screw geometric parameters; configuring a dynamic notch filter with a center frequency locked to the device inherent pulsation frequency in real time, and processing the deviation by using the dynamic notch filter to eliminate the periodic component synchronized with the screw rotation and retain the random disturbance component caused by the fluctuation of the raw material characteristics.

[0009] Preferably, the calculation of the cross-correlation function is based on data segments within a sliding time window; the physical transmission lag feature extraction step further includes: when the maximum correlation coefficient of the cross-correlation function is lower than a preset reliability threshold, it is determined that no raw material fluctuation transmission feature meeting the preset correlation requirement has occurred at present, and the physical transmission lag time identified at the last moment is maintained unchanged until the new maximum correlation coefficient exceeds the reliability threshold.

[0010] Preferably, the extraction of the non-instructional residual component is realized by: constructing an instruction response reference model of the screw driving motor, inputting the control input sequence into the instruction response reference model to generate a theoretical current response sequence; and calculating the difference between the driving current sequence and the theoretical current response sequence as the non-instructional residual component, which represents the load fluctuation caused by the change of the raw material rheological characteristics.

[0011] Preferably, the generation of the inertial feedforward compensation quantity follows an amplitude limiting logic; a maximum allowed compensation gradient is set, and when the compensation quantity change rate generated according to the deduction exceeds the maximum allowed compensation gradient, the inertial feedforward compensation quantity is subjected to slope limiting processing to prevent the screw speed change rate caused by the compensation action from exceeding the mechanical bearing limit of the screw driving motor.

[0012] Preferably, the final driving instruction includes the screw speed instruction and the feeding speed instruction; the time domain reverse compensation step proportionally distributes the inertial feedforward compensation quantity to the screw speed instruction and the feeding speed instruction by using a preset decoupling distribution matrix, thereby maintaining the mass conservation of the material in the extrusion line while maintaining the stability of the melt pressure.

[0013] Preferably, the method further comprises a melt temperature safety constraint step of monitoring the melt temperature and its rate of change at the end of the die head in real time; when the rate of change of the melt temperature exceeds a preset shear heat accumulation threshold, the gain coefficient of the inertial feedforward compensation is reduced, the control strategy is switched to a safety mode with priority to suppressing temperature rise, and thermal degradation of the recycled polyethylene caused by reverse regulation action is prevented.

[0014] Preferably, the control input sequence comprises a screw rotation speed set value, and the physical transmission lag time is defined as a variable dynamically adjusted with the screw rotation speed set value; the method comprises establishing an inverse mapping table of the physical transmission lag time and the screw rotation speed set value, and when the effective cross-correlation function cannot be calculated, the inverse mapping table is consulted according to the current screw rotation speed set value to obtain the estimated physical transmission lag time.

[0015] Compared with the prior art, the present application has the following beneficial effects: 1. The present application constructs a dual-time-scale parameter identification and disturbance observer architecture, solves the technical contradiction that the fixed parameters of the control model cannot adapt to the time-varying characteristics of the controlled object, and the control system uses a recursive algorithm to correct the process gain and time constant in real time in the slow time scale, locks the static characteristic drift caused by equipment wear or raw material batch switching, estimates and compensates the instantaneous pressure fluctuation caused by raw material impurities in the fast time scale, and decouples the time and frequency domains to ensure that the control system maintains the model base accuracy while having the ability to quickly suppress high-frequency nonlinear disturbances, ensuring that the melt pressure control system maintains dynamic balance under the condition that the rheological properties of the raw material flow randomly change, and eliminating the risk of system oscillation caused by model mismatch.

[0016] 2. The present application utilizes the time sequence cross-correlation characteristics of the screw driving motor current and the melt pressure, avoids the problem of physical transmission lag caused by simply relying on pressure feedback, calculates the lead time window of current fluctuation relative to pressure fluctuation, establishes a dynamic correlation model between heterogeneous signals, generates an inertial feedforward compensation amount before the pressure sensor senses the fluctuation, multiplexes the actuator load current signal as a lead sensing signal reflecting the sudden change of the rheological properties of the raw material, fills in the control blind area caused by fluid transmission on the time axis, and realizes zero-lag or even negative-lag response to sudden disturbance of the raw material.

[0017] 3. The present application introduces a dynamic frequency locking filter based on the screw rotation speed instruction, realizes frequency domain separation of the inherent pulsation noise of the equipment and the random disturbance of the raw material, calculates the characteristic frequency based on the real-time screw rotation speed, locks the center frequency of the dynamic notch filter to the characteristic frequency in real time, accurately filters out the pressure pulsation component caused by the mechanical period of screw rotation, ensures that the disturbance observer generates a compensation instruction only for the random deviation caused by the change in the properties of the raw material, avoids meaningless high-frequency regulation of the actuator to eliminate normal mechanical pulsation, improves the signal-to-noise ratio of the control signal, and reduces the energy consumption and mechanical wear of the driving mechanism. BRIEF DESCRIPTION OF DRAWINGS

[0018] Fig. 1 This is a flowchart of the collaborative control logic for dual-timescale model evolution and physical lag compensation in this invention. Fig. 2 This is a use case diagram illustrating the interaction between the multi-dimensional functional modules and hardware of the process control system of this invention. Fig. 3 This is a diagram showing the hardware topology and signal transmission architecture of the control system integrating heterogeneous sensing nodes in this invention. Detailed Implementation

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0020] This invention discloses a process control method for the multiple recycling of recycled polyethylene for tailings seepage prevention. The method operates on an electronic control unit connected to an extrusion production line, which includes a screw drive motor and a melt pressure sensor installed at the end of the die head. The electronic control unit performs a multi-dimensional heterogeneous data synchronous acquisition step, synchronously acquiring the melt pressure sequence output by the melt pressure sensor at a preset sampling frequency. Drive current sequence of screw drive motor and the current control input sequence The system establishes a melt pressure sequence. Drive current sequence With control input sequence The timestamp alignment benchmark between the two is used to eliminate communication transmission timing deviations; based on the extraction of physical transmission lag characteristics, the electronic control unit calculates the drive current sequence. With melt pressure sequence The drive current sequence is identified based on the time offset of the cross-correlation function within the sliding time window and the peak value of the cross-correlation function. Medium load mutation characteristics relative to melt pressure sequence Physical transmission lag time of medium pressure fluctuation characteristics When the maximum correlation coefficient of the cross-correlation function is lower than the preset confidence threshold, the system maintains the physical transmission lag time identified at the previous moment unchanged or consults the pre-established inverse mapping table based on the current screw speed setting to obtain the estimated physical transmission lag time.

[0021] In the advanced disturbance targeting simulation step, the electronic control unit monitors the drive current sequence in real time. To eliminate the fundamental frequency component caused by speed command changes, the system constructs a command response reference model for the screw drive motor, and incorporates the control input sequence... The model is inputted with the measured current response to calculate the theoretical current response The difference between the measured current response and the theoretical current response is used to extract the non-instructive residual component, when the amplitude of the non-instructive residual component exceeds a preset noise shielding threshold, the system uses the identified physical transmission lag time and the cross-correlation function to deduce the amplitude of the melt pressure fluctuation that will arrive at the end of the head after the physical transmission lag time ends, and generates an opposite inertial feed-forward compensation amount based on the deduced amplitude of the melt pressure fluctuation , and superimposes the inertial feed-forward compensation amount into the basic control amount, which drives the screw drive motor to perform a reverse adjustment action before the load mutation characteristics cause pressure fluctuations through screw physical transmission to the end of the head; the method performs a double-time-scale model evolution step, in the slow time scale, the electronic control unit processes the melt pressure sequence and the control input sequence , online updates the coefficients of the discrete parameter model describing the dynamic characteristics of the extrusion process, the model update follows a recursive operation rule containing a forgetting factor, and the forgetting factor is used to reduce the weight of historical data on the current model parameter update, so as to track the process gain drift caused by component wear or raw material batch switching in the extrusion production line; in the fast time scale, the updated discrete parameter model is used to construct a disturbance observer, and the deviation between the theoretical model output and the measured melt pressure is calculated, in order to realize periodic noise decoupling, the deviation needs to be processed by a dynamic notch filter before entering a low-pass filter, the electronic control unit obtains the speed command of the screw drive motor in real time, calculates the current equipment inherent pulsation frequency according to the screw geometric parameters, and locks the center frequency of the dynamic notch filter on the equipment inherent pulsation frequency in real time, uses the dynamic notch filter to remove the periodic component synchronized with the screw rotation in the deviation, and retains the random disturbance component caused by the fluctuation of raw material characteristics, the filtered deviation passes through the low-pass filter to generate a feedback compensation control amount , the feedback compensation control amount and the inertial feed-forward compensation amount are superimposed into the basic control amount to generate the final drive command.

[0022] The system runs melt temperature safety constraint logic, real-time monitors the melt temperature and the rate of change at the end of the head, when the rate of change of the melt temperature exceeds a preset shear heat accumulation threshold, the electronic control unit reduces the inertial feed-forward compensation amount The gain coefficient is adjusted to switch the control strategy to a safety mode that prioritizes suppressing temperature rise, preventing thermal degradation of recycled polyethylene due to excessive screw speed adjustment. The final drive command is proportionally distributed to the screw speed command and feed rate command through a preset decoupling allocation matrix, maintaining material mass conservation within the extrusion production line. Before closed-loop control, non-command residual component reference noise calibration is initiated. Under constant screw speed and stable thermal steady-state feed rate, the screw drive motor current sequence is continuously acquired at 50Hz for 300 seconds and detrended, and the statistical standard deviation of the residual signal is calculated. Noise shielding threshold Set as ,coefficient Using values ​​of 3 to 5, the statistical boundaries of background electromagnetic noise and load mutation are defined. The decoupling allocation matrix is ​​constructed following the open-loop step response identification procedure. During extrusion steady state, independent step excitations of 5% of the rated value are applied to the screw speed command and the feed speed command respectively. The melt pressure response curve is recorded, and the change after reaching the new steady state is extracted. Based on this, the screw speed sensitivity coefficient to pressure is calculated. and the sensitivity coefficient of feeding speed to pressure A two-dimensional decoupling matrix is ​​constructed, and the scalar inertial feedforward compensation amount is used by the matrix inverse. Decomposed into complementary screw speed adjustment vector Feeding speed adjustment vector Pressure is adjusted under the constraint of material mass conservation; the melt temperature safety constraint is based on offline calibration data of material thermal stability boundary. The calibration is based on ASTM D3895-03 (Standard Test Method for Determining Oxidation Induction Time of Polyolefins by Differential Scanning Calorimetry) to determine the oxidation induction period (OIT) of recycled polyethylene raw materials, and the relationship between OIT decay rate and melt temperature change rate is established. The nonlinear mapping defines the rate of temperature change corresponding to the OIT value falling to 80% of its initial value as the critical degradation threshold. The runtime shear heat accumulation threshold is limited to When the rate of temperature change exceeds the safety boundary, the gain decay logic is forcibly triggered to suppress the generation of shear heat.

[0023] Example 1: This example is set in a specific scenario of a continuous extrusion production line for tailings dam geomembrane. In this scenario, the production line uses recycled polyethylene containing randomly distributed hard impurities and with drastic fluctuations in melt flow index as raw material. The rheological properties of the raw material drift nonlinearly between batches and within batches, and the unpredictable mixing of hard particles causes sudden changes in screw load, resulting in lag and overshoot oscillation in the conventional feedback control system, which threatens the physical performance stability of the final geomembrane material.

[0024] Under this operating condition, the electronic control unit synchronously acquires the melt pressure sequence in real time. With screw drive motor current sequence Calculate and lock the physical transmission lag time of current fluctuations relative to pressure fluctuations. When high-viscosity, hard impurities enter the plasticizing section, causing a sudden change in screw load, the system compares the measured current with the output of the command response reference model, extracts the non-command residual component, and based on this residual component and... The system generates an inverse inertial feedforward compensation quantity before the melt pressure sensor detects the pressure fluctuation. This is then superimposed onto the drive command. Simultaneously, the disturbance observer utilizes a dynamic notch filter with its center frequency locked to the inherent pulsation frequency of the equipment to eliminate periodic mechanical noise synchronized with the screw speed in the melt pressure signal. Feedback compensation control quantities are then generated to address random disturbances caused by the raw material characteristics. The FF-RLS algorithm updates the coefficients of the discretized parameter model online on a slow time scale, tracking the process gain changes caused by the rheological state of the raw materials. This control mechanism uses the time lead characteristic of the current signal to fill the physical lag blind zone of the pressure feedback, and completes the speed counter-adjustment before impurities reach the die head and cause pressure fluctuations. The dynamic notch filter separates the inherent pulsation of the equipment and the random disturbance of the raw materials, preventing the actuator from making ineffective adjustments for mechanical noise. Under the constraint of keeping the melt temperature change rate below the shear heat accumulation threshold, the system maintains the melt pressure fluctuations in the recycled polyethylene extrusion process within the set range, ensuring the consistency of geomembrane product quality.

[0025] Example 2: This example aims to verify the effectiveness of the process control method of the present invention under high noise and strong nonlinear conditions through comparative experiments, and to empirically define the preferred numerical range of key control parameters. The experiment relies on an industrial-grade single-screw extrusion experimental platform, which is equipped with a sampling frequency of [missing information]. The high-frequency data acquisition system was used to construct a rigorous testing environment that closely approximates the actual tailings geomembrane production site. The experimental raw material was waste polyethylene shredded material that had undergone three thermal history cycles, with a melt flow index (MI) of [missing value]. to The values ​​exhibit drastic and random fluctuations, and the premixed mass fraction in the raw materials is... High-melting-point polypropylene particles were used to simulate unpredictable hard impurity disturbances. Simultaneously, to verify the system's ability to suppress signal noise, a signal-to-noise ratio of [value missing] was actively superimposed on the original signal channel of the melt pressure sensor. Gaussian white noise, coupled at a frequency of Power frequency interference signals.

[0026] To construct a legally valid chain of evidence, the experimental design incorporates a multi-dimensional control system with missing gradient parameters and partial features. Control group 1 uses a conventional fixed-parameter PID controller as the benchmark for existing technologies. Control group 2 employs a dual-timescale model evolution step but removes the inertial feedforward compensation based on current cross-correlation, aiming to verify the synergistic effect of heterogeneous signal fusion through partial missing features. Control groups 3 and 4 employ the complete control strategy, but respectively remove the forgetting factor from the recursive least squares algorithm. Set as and Located in the scope defined by this invention to Outside the scope, the aim is to verify the critical significance of the parameter range; finally, the sample group of the present invention is set to enable the complete technical solution, and... Set as Each group has its screw speed set to [value missing]. The target melt pressure is set to Continuous operation under the same working conditions During the test, the melt pressure and temperature sequences at the die head were recorded in real time, and the pressure standard deviation during steady-state operation was calculated. and maximum temperature deviation As a quantitative evaluation indicator.

[0027] Table 1: Comparison of Performance Indicators under Different Control Strategies

[0028] Referring to Table 1, the data from control group 1 show that under conditions where the rheological properties of the raw material change over time and are accompanied by impacts from hard impurities, the fixed-parameter PID controller cannot maintain pressure stability. Gundam Furthermore, accompanied by temperature fluctuations, data from control group 2 showed that although adaptive model updates were introduced, the system could not overcome physical transmission lag due to the lack of current-based inertial feedforward compensation. Consequently, when impurities caused sudden load changes through the screw, pressure fluctuations still reached [a certain level]. The comparison results between the sample group of this invention and control group 2 confirm that the time-domain inverse compensation step has an irreplaceable synergistic effect in suppressing transient disturbances; further comparison of the data of control groups 3 and 4 with the sample group of this invention shows that the forgetting factor The value of directly determines the stability and agility of the model's recognition. At times, the model forgets historical data too quickly, causing parameter estimates to fluctuate wildly due to measurement noise. Deteriorated to ;when At that time, the model was not sensitive enough to new data and could not track the process gain drift caused by raw material batch switching in a timely manner, resulting in a lag in control response. Only the sample of this invention could achieve this. Realize for The optimal control accuracy is achieved. Furthermore, spectrum analysis shows that the control commands output by the sample group of this invention do not contain any elements that appear in the control commands. The peak energy corresponding to the power frequency and the fundamental frequency of the screw rotation speed.

[0029] Example 3: This example combines Figs. 1 to 3 The process control method for multiple recycling of recycled polyethylene used for tailings seepage prevention is explained, such as... Fig. 1 As shown, this step establishes a timestamp alignment benchmark for pressure, current, and control sequences. The processing flow is divided into two parallel logical branches. The left branch performs dual-timescale model evolution to update process gain online and track the drift of raw material rheological properties. Then, it locks the inherent pulsation frequency of the equipment through a dynamic notch filter to eliminate periodic mechanical noise. Subsequently, it uses a disturbance observer to generate feedback compensation control quantity based on denoising deviation. The right branch performs physical transmission lag feature extraction to identify the lead time window of current fluctuations relative to pressure fluctuations. It performs targeted inference of advance disturbances to extract non-command residual components and infer the amplitude of pressure fluctuations. It performs time-domain inverse compensation to generate anti-phase inertial feedforward quantity to eliminate physical lag blind zone. The calculation results of the two branches are finally converged to generate drive command. This command is superimposed with compensation quantity and proportionally distributed to screw speed and feed rate. At the same time, the system executes melt temperature safety constraint logic in parallel, monitors the shear heat accumulation threshold in real time, and prevents thermal degradation of recycled material through forced yield reduction path when necessary.

[0030] like Fig. 2 As shown, the process operator is responsible for setting the process target parameters. The system performs multi-dimensional heterogeneous data synchronous acquisition and timestamp benchmark alignment by connecting the melt temperature sensor and the melt pressure sensor. The core processing functions include generating inertial feedforward compensation by identifying physical transmission lag and extracting non-command current residuals; generating feedback compensation control by updating the dual time scale model and decoupling periodic noise; and executing melt temperature safety constraints and triggering a safety degradation mode when the shear heat accumulation exceeds the threshold. The above functional modules work together and finally converge into the output drive command function, directly controlling the screw drive motor to perform corresponding adjustment actions; such as Fig. 3As shown, the system hardware topology is centered on the Electronic Control Unit (ECU). This unit integrates a dual-timescale evolution model, a cross-correlation hysteresis analysis algorithm, and a dynamic notch filter algorithm. It is connected to the human-machine interface terminal above via industrial Ethernet communication for parameter configuration and alarm recording. The system input is connected to a field sensing node consisting of a melt pressure sensor and a melt temperature sensor at the end of the die head, and a current transformer on the drive side. Signals are transmitted through a multi-dimensional heterogeneous data acquisition link. The system output is connected to a reverse adjustment node consisting of a screw drive motor servo driver and a feed speed regulating motor. The drive commands output by the ECU act on this adjustment node to implement closed-loop control of the tailings seepage-proof recycled polyethylene extrusion production line, which includes the screw, barrel, and physical transmission hysteresis characteristics.

[0031] Example 4: This example aims to systematically explain the calibration procedure for the noise shielding threshold and the construction logic of the decoupling allocation matrix involved in the process control method, in order to eliminate potential uncertainties in parameter setting and multivariable collaborative control, and ensure the reproducibility and stability of the control strategy under different equipment and operating conditions. After the extrusion production line completes preheating and enters thermal steady-state operation, the electronic control unit executes the reference noise calibration procedure for the non-command residual components. During this period, the system forcibly locks the screw speed command and the feed rate command, keeping them constant, and... The sampling frequency and continuous acquisition duration are The system performs detrending processing on the current data within the screw drive motor for a given time window to filter out extremely low-frequency components caused by temperature drift or mechanical break-in, and calculates the standard deviation of the remaining high-frequency random components. Based on statistical principles, the electronic control unit sets a preset noise shielding threshold. Set as this standard deviation Doubled times, that is (In this embodiment) Values This calibration procedure establishes a criterion based on measured noise levels to ensure that the system responds only to statistically significant load fluctuations.

[0032] Furthermore, the system executes the identification procedure of the decoupling allocation matrix to quantify the degree of coupling influence of the two input variables, screw speed and feed rate, on the output variable, melt pressure. In open-loop control mode, the system applies an amplitude of [missing value] to the screw speed command. The step signal was recorded while the feed rate remained constant. The melt pressure response curve was recorded and the change after reaching a new steady state was extracted. Based on this, the first sensitivity coefficient of screw speed to pressure was calculated. After the system returns to its initial equilibrium state, an amplitude of [value] is applied to the feeding speed command. While maintaining a constant screw speed, the step signal is recorded, the change in melt pressure is observed, and the second sensitivity coefficient of the feed rate to pressure is calculated. Based on these two measured sensitivity coefficients, the electronic control unit constructs a two-dimensional decoupling allocation matrix; during the execution of the time-domain reverse compensation step, when the system generates a scalar inertial feedforward compensation amount... Subsequently, this signal is input as a target pressure adjustment request to the decoupling allocation matrix, and the system accordingly... and The weighting ratio will The analysis decomposes the screw speed adjustment component. Adjust the amount of feed with the feeding speed This decomposition logic follows the material conservation constraint, meaning that while adjusting the pressure, the feed rate is adjusted in the opposite direction to offset the fluctuations in theoretical output caused by changes in screw speed, or the optimal solution is found under the constraint of maintaining a constant output. and Combining, the final generated contains and The vector drive commands are synchronously sent to their respective servo drives, thereby maintaining the dynamic balance of the extruder head discharge flow while suppressing melt pressure fluctuations.

[0033] Example 5: This example aims to construct a set of on-site pre-deployment calibration and commissioning procedures for different extrusion production lines and raw material characteristics. This procedure covers two core aspects: offline identification of the basic characteristics of the controlled object and adaptive initialization of control parameters. Before formally connecting the control system to the new extrusion production line, an offline identification program for basic characteristics is executed. With the extruder in an unloaded state, the system drives the screw motor at a preset stepped speed, covering the entire range from the lowest operating speed to the rated speed. The drive current value at each steady-state speed point is recorded, and a reference load current model is constructed accordingly. The model serves as a reference benchmark for subsequent extraction of non-command residual components. It can effectively eliminate the inherent nonlinear effects of mechanical friction and motor efficiency changes with speed. After the extruder is loaded with standard raw materials and enters thermal steady-state operation, an open-loop step response test is performed. The system applies small-amplitude step signals to the screw speed command and the feeding speed command respectively, and records the dynamic response curve of the melt pressure. By analyzing the time constant and steady-state gain of the response curve, the initial coefficients of the discretized parameter model are initially determined, and the initial value of the covariance matrix of the recursive least squares algorithm is set to accelerate the convergence speed of the online identification process.

[0034] To address the differences in rheological properties among different batches of recycled polyethylene raw materials, the system executes adaptive initialization logic for control parameters. In the initial stage of each batch of new raw materials being put into production, the system automatically runs a self-learning mode for a period of time. During this period, the control system only uses the dual-timescale model evolution step for parameter tracking and does not apply any active control actions. By monitoring the root mean square value of the model prediction error in real time, the system evaluates the degree of fit of the current model to the rheological properties of the new raw materials. When the prediction error converges to the preset allowable range, the system automatically calculates and sets the bandwidth of the disturbance observer and the gain coefficient of the feedforward compensation based on the identified model parameters. This ensures that the control system has completed the optimal parameter configuration for the current raw material characteristics before formally engaging in closed-loop control, avoiding initial control oscillations caused by parameter mismatch.

[0035] Example 6: This example provides a standardized destructive thermal stress calibration procedure and a quantitative execution logic for a safety degradation mode to address the thermal stability boundary of recycled polyethylene under extreme operating conditions. This quantitatively defines the melt temperature safety constraint characteristics, ensuring the physical safety of the control system during operation. Before formal production, a destructive thermal stress calibration procedure is executed to determine the preset shear heat accumulation threshold. With the extruder barrel temperature set to the standard process value and the feed rate constant, the system drives the screw to increase its speed in a stepped manner, with each speed increment being a fraction of the rated speed. The holding time is Minutes, rate of change of melt temperature in real-time computer head of electronic control unit Simultaneously, offline sampling of the extruded material at each speed step was performed, and its oxidation induction period (OIT) was tested according to ASTM D3895-03 (Standard Test Method for Determining Oxidation Induction Time of Polyolefins by Differential Scanning Calorimetry). When the OIT value of the sample dropped to the initial value of the raw material, the OIT was determined. When the rate of change of the melt temperature reaches the critical point of thermal degradation of the material, it is denoted as . Considering sensor noise and thermal inertia in industrial environments, the system sets a safety threshold during operation as follows: This calibration process maps the chemical degradation characteristics of materials into a rate of change of physical quantities that can be observed in real time, providing a clear quantitative basis for safety constraints.

[0036] Based on the above calibration The electronic control unit executes dynamic gain scheduling logic in real-time operation to achieve a safety degradation mode. The system defines the value range as follows: dynamic attenuation coefficient In each control cycle The system calculates the current rate of temperature change. ,like Then set The system performs full-amplitude inertial feedforward compensation if This indicates that the rate of accumulation of shear heat has approached the safety boundary, and the system immediately responds according to the formula. Calculate the attenuation coefficient, where The preset sensitivity gain (typical value is...) to The system will use the originally calculated inertial feedforward compensation amount. Revised to It outputs a smooth and uninterrupted switch from a pressure-priority to a temperature-priority control strategy by continuously and linearly reducing the control amplitude within the overheat risk zone.

[0037] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0038] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for controlling the multiple recycling process of recycled polyethylene for tailings seepage prevention, characterized in that, An electronic control unit connected to an extrusion production line, which includes a screw drive motor and a melt pressure sensor mounted at the end of the die head, is used in a method comprising the following steps: The multidimensional heterogeneous data synchronous acquisition step synchronously acquires the melt pressure sequence output by the melt pressure sensor, the drive current sequence of the screw drive motor, and the current control input sequence at a preset sampling frequency, and establishes a timestamp alignment reference between the melt pressure sequence, the drive current sequence, and the control input sequence. The physical transmission lag feature extraction step involves calculating the cross-correlation function between the driving current sequence and the melt pressure sequence, and based on the time offset of the peak value of the cross-correlation function, identifying the physical transmission lag time of the load mutation feature in the driving current sequence relative to the pressure fluctuation feature in the melt pressure sequence. The advanced disturbance targeted simulation step monitors the drive current sequence in real time and filters out the fundamental component caused by speed command changes to extract the non-command residual component. When the amplitude of the non-command residual component exceeds the preset noise shielding threshold, the amplitude of the melt pressure fluctuation that will reach the end of the machine head after the physical transmission lag time is simulated by using the physical transmission lag time and cross-correlation function. The time-domain reverse compensation step generates an inverse inertial feedforward compensation amount based on the derived melt pressure fluctuation amplitude, and superimposes the inertial feedforward compensation amount into the basic control amount generated by the electronic control unit to generate the final drive command. The final drive command drives the screw drive motor to perform reverse adjustment action before the load change characteristics are physically transmitted to the end of the die head and cause pressure fluctuations.

2. The method for controlling the multiple recycling process of recycled polyethylene for tailings seepage prevention according to claim 1, characterized in that, The method also includes a dual-timescale model evolution step. While performing the time-domain inverse compensation step, the recursive least squares algorithm is used to process the melt pressure sequence and control input sequence, and the coefficients of the discretized parameter model describing the dynamic characteristics of the extrusion process are updated online. The updated discretized parameter model is used to construct a perturbation observer and calculate the deviation between the theoretical model output and the measured melt pressure. A low-pass filter is used to process the deviation of the disturbance observer output to generate a feedback compensation control quantity, and the feedback compensation control quantity and the inertial feedforward compensation quantity are superimposed on the basic control quantity.

3. The method for controlling the multiple recycling process of recycled polyethylene for tailings seepage prevention according to claim 2, characterized in that, In the dual-timescale model evolution steps, the coefficient updates of the discretized parameter model follow the following recursive operation rules that include a forgetting factor: ,in, For a moment The model parameter vector, For a moment The measured value of the melt pressure. A regression vector containing historical input and output data. The gain matrix is ​​calculated using a pre-defined forgetting factor. This forgetting factor reduces the weight of historical data on the current model parameter updates to track process gain drift caused by component wear in the extrusion production line.

4. The method for controlling the multiple recycling process of recycled polyethylene for tailings seepage prevention according to claim 2, characterized in that, Before entering the low-pass filter, the deviation output by the disturbance observer needs to undergo periodic noise decoupling processing. This processing includes: acquiring the speed command of the screw drive motor in real time, calculating the current inherent pulsation frequency of the equipment based on the screw's geometric parameters, configuring a dynamic notch filter whose center frequency is locked to the inherent pulsation frequency of the equipment in real time, using the dynamic notch filter to process the deviation, eliminating the periodic component synchronized with the screw rotation, and retaining the random disturbance component caused by the fluctuation of raw material characteristics.

5. The method for controlling the multiple recycling process of recycled polyethylene for tailings seepage prevention according to claim 1, characterized in that, The cross-correlation function is calculated based on the data segment within the sliding time window; The physical transmission lag feature extraction step also includes: when the maximum correlation coefficient of the cross-correlation function is lower than the preset confidence threshold, it is determined that no raw material fluctuation transmission feature that meets the preset correlation requirements has appeared at present, and the physical transmission lag time identified at the previous moment remains unchanged until the new maximum correlation coefficient exceeds the confidence threshold.

6. The method for controlling the multiple recycling process of recycled polyethylene for tailings seepage prevention according to claim 1, characterized in that, The extraction of non-command residual components is achieved as follows: a command response reference model of the screw drive motor is constructed, and the control input sequence is input into the command response reference model to generate a theoretical current response sequence; the difference between the drive current sequence and the theoretical current response sequence is calculated as the non-command residual component, which characterizes the load fluctuation caused by the change in the rheological properties of the raw materials.

7. The method for controlling the multiple recycling process of recycled polyethylene for tailings seepage prevention according to claim 1, characterized in that, The generation of inertial feedforward compensation follows a single-value limit logic; a maximum allowable compensation gradient is set, and when the rate of change of the compensation generated according to the deduction exceeds the maximum allowable compensation gradient, the slope of the inertial feedforward compensation is limited to prevent the rate of change of screw speed caused by the compensation action from exceeding the mechanical bearing limit of the screw drive motor.

8. The method for controlling the multiple recycling process of recycled polyethylene for tailings seepage prevention according to claim 1, characterized in that, The final drive commands include screw speed commands and feed rate commands; the time-domain reverse compensation step uses a preset decoupling allocation matrix to proportionally distribute the inertial feedforward compensation amount to the screw speed commands and feed rate commands, maintaining material mass conservation within the extrusion production line while maintaining stable melt pressure.

9. The method for controlling the multiple recycling process of recycled polyethylene for tailings seepage prevention according to claim 1, characterized in that, The method also includes a melt temperature safety constraint step, which monitors the melt temperature and rate of change at the end of the die head in real time; when the rate of change of the melt temperature exceeds the preset shear heat accumulation threshold, the gain coefficient of the inertial feedforward compensation is reduced, and the control strategy is switched to a safety mode that prioritizes suppressing temperature rise.

10. The method for controlling the multiple recycling process of recycled polyethylene for tailings seepage prevention according to claim 1, characterized in that, The control input sequence includes the screw speed setpoint, and the physical transmission lag time is defined as a variable that is dynamically adjusted as the screw speed setpoint changes. The method includes establishing an inverse mapping table between the physical transmission lag time and the screw speed setpoint. When an effective cross-correlation function cannot be calculated, the estimated physical transmission lag time is obtained by consulting the inverse mapping table based on the current screw speed setpoint.

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