Energy-saving cable extrusion production process control system

Abstract

The invention relates to the technical field of cable production and manufacturing and automation control, in particular to an energy-saving cable extrusion production process control system, which comprises a data acquisition module for acquiring real-time working condition parameters of an extrusion production line; the rheological thermal coupling analysis module is used for resolving the shear heat contribution rate under the current working condition; the heat engine response decoupling module is used for determining a heat engine response lag time difference between mechanical shear heat and machine barrel heat conduction; the energy efficiency optimization calculation module is configured to generate a dynamic heat balance control strategy aiming at an unsteady state working condition according to the heat engine response lag time difference and a preset unit output ratio energy consumption target; the self-adaptive execution module is configured to output a cooperative control instruction for a heating and cooling system and a screw driving system based on the dynamic heat balance control strategy so as to realize minimization of system energy consumption; according to the method, the common energy confrontation phenomenon of alternate operation of heating and cooling in traditional control is solved, and the energy consumption in the transition process is greatly reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cable production and automation control, in particular to a control system for energy-saving cable extrusion production process. BACKGROUND

[0002] In the current cable extrusion production field, as the core processing equipment, the energy efficiency of the extruder is closely related to the resistance energy consumption of the barrel heating system and the mechanical work conversion of the screw driving system. During the extrusion process, the high molecular material will generate significant viscous dissipation heat under the action of screw shearing. This internal heat generation fluctuates dynamically with the changes of material flow characteristics, screw speed and process temperature. To achieve temperature control in the extrusion process, the existing scheme generally uses an independent control architecture based on conventional PID algorithm, that is, the barrel temperature ring and the screw speed ring are decoupled. Although this scheme has certain temperature control ability in steady-state operation scenarios, it fails to establish a coupled quantitative model of mechanical shear heat and external compensation heat, and ignores the time phase difference between rapidly changing mechanical energy input and slowly responding medium heat conduction, resulting in energy confrontation phenomenon of alternating operation of electric heating and forced cooling in non-steady-state conditions such as start-up speed-up and rule-down. In addition, the existing strategy lacks effective adaptive compensation mechanism for environmental temperature difference interference and material batch fluctuation, often causing excessive heat supply, melt temperature overshoot and decline in product outer diameter size stability. Therefore, how to realize precise decoupling of thermal-mechanical response in the extrusion process and improve system energy efficiency through deep optimization of energy structure while ensuring product quality constraints has become a technical problem to be solved. SUMMARY

[0003] To solve the above technical problems, the present application provides a control system for energy-saving cable extrusion production process. Specifically, the technical scheme of the present application comprises: A data acquisition module configured to obtain real-time working condition parameters of the extrusion production line and call preset material flow characteristic data, the real-time working condition parameters including screw speed, barrel temperature in each zone, melt temperature, main motor power, extrusion flow and environmental temperature; A rheological heat coupling analysis module configured to calculate the shear heat contribution rate under the current working condition based on the screw speed, the material flow characteristic data and the main motor power; A thermal-mechanical response decoupling module configured to determine the thermal-mechanical response lag time difference between mechanical shear heat and barrel heat conduction according to the shear heat contribution rate and a preset system thermal inertia model; An energy efficiency optimization calculation module configured to generate a dynamic thermal balance control strategy for non-steady-state conditions according to the thermal-mechanical response lag time difference and a preset unit output specific energy consumption target; An adaptive execution module configured to output a coordinated control instruction for the heating and cooling system and the screw drive system based on the dynamic thermal balance control strategy to achieve minimization of system energy consumption.

[0004] Preferably, based on the screw rotation speed, the material rheological property data and the main motor power, a shear heat contribution rate under the current working condition is calculated, including: The screw rotation speed and the material rheological property data are called; A viscous dissipation model based on non-Newtonian fluid characteristics is constructed to calculate the internal heat power converted by mechanical friction; The ratio of the internal heat power to the total enthalpy change power required by the melting process is calculated to generate a shear heat contribution rate representing the proportion of mechanical energy converted into heat energy.

[0005] Preferably, according to the shear heat contribution rate and a preset system thermal inertia model, a thermal-mechanical response lag time difference between mechanical shear heat and barrel heat conduction is determined, including: The time series fluctuation frequency characteristics of the shear heat contribution rate are extracted; The preset system thermal inertia model is called to obtain the heat conduction time constant of the barrel heating and cooling system; By comparing the fluctuation frequency characteristics with the heat conduction time constant, the thermal-mechanical response lag time difference between the melt temperature change caused by screw rotation speed adjustment and the barrel wall temperature response is calculated.

[0006] Preferably, according to the thermal-mechanical response lag time difference and a preset unit output specific energy consumption target, a dynamic thermal balance control strategy for non-steady state working conditions is generated, including: The thermal-mechanical response lag time difference is called; During the non-steady state stage of the production line in the speed-up or speed-down stage, the melt temperature overshoot at the future time is predicted based on the model predictive control algorithm; A target function is constructed to minimize the unit output specific energy consumption as the core, and the melt temperature deviation is combined as a soft constraint penalty term, and the optimal heating power compensation value within the thermal-mechanical response lag time difference is reversely solved to generate the dynamic thermal balance control strategy.

[0007] Preferably, the generation process of the dynamic thermal balance control strategy further includes process capability index constraint logic: The statistical distribution state of the key geometric dimension of the product is monitored in real time; The current process capability index is calculated; The relationship between the process capability index and the preset quality threshold is judged: If the process capability index is lower than the quality threshold, a dimensional stability priority signal is generated, the optimal heating power compensation value is modified, and a dimensional priority control strategy that guarantees product dimensional stability is generated as priority; If the process capability index is higher than or equal to the quality threshold, an energy efficiency priority signal is generated, and a dynamic thermal balance control strategy that targets optimal energy efficiency is maintained.

[0008] Preferably, the system further comprises an environmental disturbance compensation module configured to: collect the ambient temperature in real time; calculate the correlation between the ambient temperature change and the barrel heat dissipation rate, and generate an environmental thermal influence coefficient; input the environmental thermal influence coefficient as a feedforward variable into the energy efficiency optimization calculation module, and modify the dynamic thermal balance control strategy to offset external heat exchange disturbances.

[0009] Preferably, the rheological thermal coupling analysis module further comprises a material property self-learning unit configured to: record the corresponding relationship data of actual pressure, flow rate and temperature during production; use the corresponding relationship data to correct the pre-stored material rheological property data online; update the rheological model parameters used to calculate the shear heat contribution rate to adapt to the physical property fluctuations of different batches of materials.

[0010] Preferably, the adaptive execution module outputs a cooperative control instruction, which specifically includes: decomposes the total thermal energy demand into mechanical shear heat component and resistance heating component; under the premise of ensuring that the total thermal energy demand meets the melting condition, adjusts the screw rotation speed to increase the proportion of the mechanical shear heat component; synchronously reduces the set value of the resistance heating component, and sends a cooling or heating suppression instruction in advance according to the thermal-mechanical response lag time difference to prevent temperature overshoot.

[0011] Compared with the prior art, the present application has the following beneficial effects: 1. The present application realizes precise coupling of thermal-mechanical response and optimizes energy supply structure; the system breaks the state of temperature loop and speed loop being split in traditional control, and converts unmeasurable mechanical shear heat into explicit control variables through the establishment of a rheological thermal coupling analysis mechanism; this enables the system to accurately identify the contribution of endogenous heat generated by screw rotation to total thermal energy, thereby maximizing the use of mechanical energy to replace high-energy-consumption resistance heating under the premise of ensuring melt quality, and solving the problem of power waste caused by unreasonable energy structure. 2、The application eliminates the energy consumption of non-steady state conditions, and improves the dynamic control precision. By introducing a thermal engine response decoupling module, the system can accurately capture the time misalignment between the rapidly changing mechanical energy input and the slow response of the medium heat conduction. During the non-steady state stage such as start-up speed-up or speed-down, the system uses a predictive control algorithm to adjust the heating power in advance, skillfully uses the upcoming shear heat to fill the heat demand, and solves the energy confrontation phenomenon of the traditional control method of alternating heating and cooling, which greatly reduces the energy consumption of the transition process. 3、The application constructs a quality constraint protection wall to balance energy efficiency and product precision. The system introduces a constraint logic based on process capability index, which monitors the statistical distribution state of product size in real time while pursuing optimal energy efficiency. This self-examination mechanism can dynamically switch the control priority according to the production stability: aggressive energy saving when the process is stable, and automatically degrading to a high-precision constant temperature mode to prioritize product pass rate when the process fluctuates. This ensures that the energy saving strategy does not sacrifice product size stability, greatly enhancing the robustness of the system in actual industrial environments. 4、The application has full environmental self-adaptability and ensures long-period operation stability. The system integrates environmental disturbance compensation and material property self-learning functions, which can automatically offset the influence of seasonal temperature difference or day-night fluctuation on the heat dissipation of the cylinder, and online correct the rheological calculation deviation caused by the physical property fluctuation of different batches of raw materials. This closed-loop evolution capability solves the control failure problem caused by preset data distortion, ensures the continuous accuracy of shear heat calculation, and enables the system to maintain efficient and stable operation in different production environments and raw material conditions. BRIEF DESCRIPTION OF DRAWINGS

[0012] The application will be further explained in conjunction with the accompanying drawings and embodiments: Figure 1 is a structural diagram of the system of the application. DETAILED DESCRIPTION

[0013] In order to make the purpose, technical scheme and advantages of the application more clear and explicit, the application will be further described in detail in conjunction with specific embodiments.

[0014] Embodiment 1: Please refer to Figure 1 An energy-saving cable extrusion production process control system, comprising: A data acquisition module configured to obtain real-time working condition parameters of the extrusion production line and call preset material rheological property data, wherein the real-time working condition parameters include screw rotation speed, cylinder temperature in each zone, melt temperature, main motor power, extrusion flow rate and environmental temperature; A rheological heat coupling analysis module configured to calculate the shear heat contribution rate under the current working condition based on the screw rotation speed, the material rheological property data and the main motor power; The thermomechanical response decoupling module is configured to determine the thermomechanical response lag time difference between mechanical shear heat and barrel heat conduction based on the shear heat contribution rate and the preset system thermal inertia model. The energy efficiency optimization calculation module is configured to generate a dynamic thermal balance control strategy for unsteady-state conditions based on the heat engine response lag time difference and a preset unit output energy consumption target. The adaptive execution module is configured to output coordinated control commands to the heating and cooling system and the screw drive system based on the dynamic thermal balance control strategy, so as to minimize the system energy consumption.

[0015] This embodiment details the hardware architecture and data flow logic of the control system. The system relies on an industrial PC or PLC as the core computing carrier and constructs a high-frequency communication link through Profinet or EtherCAT bus. The data acquisition module obtains raw data from the field sensor group through a high-frequency sampling interface, for example, with a period of 100ms. Among them, the screw speed comes from the pulse feedback of the rotary encoder, the temperature of each zone of the barrel and the melt temperature come from the thermocouple array arranged in the key temperature zone, the main motor power comes from the communication message of the frequency converter, the extrusion flow rate comes from the real-time reading of the meter weight controller or metering pump, and the ambient temperature is collected by an externally deployed temperature sensor. Based on this, the system retrieves the material rheological property data of this batch of cable material from the associated database, mainly including the mapping curve of shear rate and viscosity; the rheology-thermal coupling analysis module introduces the above physical quantities into the rheological model, aiming to quantify the unmeasurable mechanical heat generation; the thermomechanical response decoupling module identifies the time misalignment between rapidly changing mechanical energy input and slow-responding medium heat conduction; the energy efficiency optimization calculation module no longer simply pursues constant temperature, but searches for the point of lowest energy consumption under the premise of meeting the process window; the adaptive execution module converts the strategy into specific voltage or frequency commands and distributes them to the heating coil, fan and motor driver; This embodiment constructs a control architecture based on the rheological thermal coupling mechanism. In the cable extrusion scenario, the system breaks the separation between the temperature loop and the speed loop in the traditional PID control. By quantifying shear heat in real time and introducing thermomechanical response decoupling, the system can accurately identify the contribution of the endogenous heat generated by the screw rotation to the total heat energy demand. Thus, while ensuring the quality of the melt, it maximizes the use of mechanical energy to replace expensive electric heating energy, thereby optimizing the energy structure.

[0016] Example 2: Based on the screw speed, the material rheological properties data, and the main motor power, the shear heat contribution rate under the current operating condition is calculated, including: Call the screw rotation speed and the material rheological property data; A viscous dissipation model based on the properties of non-Newtonian fluids is constructed to calculate the internal heat power converted from mechanical friction. The ratio of the internal thermal power to the total enthalpy change power required for the melting process is calculated to generate the shear heat contribution rate, which characterizes the proportion of mechanical energy converted into thermal energy.

[0017] This embodiment details the quantitative calculation process of mechanical shear heat, aiming to transform the implicit mechanical frictional heat into an explicit control variable; the system calls the screw speed. and includes consistency coefficient Power Law Index Material rheological property data; construct a viscous dissipation model based on non-Newtonian fluid properties to calculate the internal heat power converted from mechanical friction. The calculation formula is as follows: in, The source is calculated, and its physical meaning is the effective shear viscosity at the current shear rate, with units of Pa·s. The specific calculation is based on the power-law equation. in, The consistency coefficient of the material. Both are non-Newtonian exponents and are drawn from a pre-built rheological property database. The value is derived from calculations based on the screw's geometry and rotational speed; its physical meaning is the equivalent shear rate, expressed in units of 1 / s; the calculation formula is... ; in, The diameter of the homogenization section of the screw. To equalize the depth of the spiral groove in the section. The value is the screw speed, and the unit for calculation is r / s; The source is preset equipment parameters; its physical meaning is the effective volume of the screw homogenization section, and the unit is... ; The source is a correction value based on the comparison between the main motor power and the theoretical shaft power. Its physical meaning is the conversion efficiency coefficient of mechanical energy to heat energy, which is dimensionless. The system calculates the ratio of internal thermal power to the total enthalpy change power required for the melting process, generating the shear heat contribution rate. The formula is as follows: in, The mass flow rate in the denominator is sourced from extrusion mass flow rate data collection, and the unit is kg / s; The source is the material's thermophysical parameters, and its physical meaning is specific heat capacity, with units of J / (kg·°C). Joules are used here to ensure consistency with the power unit in the numerator, which is watts, and to maintain dimensionality. The data source is the sensor at the feed inlet, and its physical meaning is the initial temperature of the material, expressed in °C. This calculation process outputs in real time the percentage of mechanical energy that contributes to the heat of the material melting process. This embodiment achieves precise measurement of passive heat generation in continuous extrusion production by constructing a refined viscous dissipation model. This technique enables the control system to clearly distinguish between active electric heating demand and passive mechanical heat surplus, providing a quantitative basis for replacing high-energy-consuming resistance heating by increasing the mechanical shear share without reducing output. This effectively solves the problem of excessive cooling waste caused by neglecting shear heat in traditional control.

[0018] Example 3: Based on the shear heat contribution rate and the preset system thermal inertia model, the thermomechanical response lag time difference between mechanical shear heat and barrel heat conduction is determined, including: Extract the time-series fluctuation frequency characteristics of the shear heat contribution rate; The preset system thermal inertia model is invoked to obtain the heat conduction time constant of the barrel heating and cooling system; By comparing the fluctuation frequency characteristics with the heat conduction time constant, the thermomechanical response lag time difference between the melt temperature change caused by screw speed adjustment and the barrel wall temperature response is calculated.

[0019] This embodiment further refines the specific algorithm for decoupling the thermo-mechanical response, aiming to solve the problem of asynchronous mechanical action and thermal response in the time domain. The system performs a Fast Fourier Transform (FFT) on the shear heat contribution rate data sequence within the most recent time window to extract the main fluctuation frequency caused by speed adjustment or load fluctuation. Extracting the main fluctuation frequency The specific operator is: ,in The spectrum amplitude array of the shear heat contribution rate sequence after FFT; the preset system thermal inertia model is invoked, which includes the barrel heat conduction time constant measured through step response experiments. Based on this, the lag time difference of the heat engine response is calculated by comparing the fluctuation frequency with the heat conduction characteristics. The calculation logic is as follows: in, To adjust the coefficient based on experience The phase lag angle obtained by mapping is in rad. This division operator is used to convert the frequency domain information into the time domain offset, ensuring that the units on both sides of the equation are unified in seconds. The source is the preset thermodynamic model parameters, and its physical meaning is the time constant required for heat to be conducted from the heating element to the inner wall of the barrel and the melt, with the unit being seconds; The source is the frequency domain analysis result of real-time data, and its physical meaning is the main frequency of the shear heat input fluctuation, with the unit being Hz; The source is an empirical correction coefficient, which physically means a phase correction factor used to compensate for nonlinear thermal diffusion effects. It is dimensionless. The system outputs this time lag difference as the time reference for subsequent predictive control. This embodiment utilizes a combination of frequency domain analysis and thermal inertia model to accurately capture the time misalignment between mechanical energy input and temperature field response in a dynamic variable speed extrusion scenario. This technique not only considers the static heat conduction delay but also incorporates dynamic shear frequency characteristics, thereby accurately predicting the precise moment when shear heat causes the melt to heat up. This eliminates the risk of temperature overshoot caused by control lag and greatly improves the control accuracy of unsteady-state processes.

[0020] Example 4: Based on the heat engine response lag time difference and a preset unit output energy consumption target, a dynamic thermal balance control strategy for unsteady-state conditions is generated, including: Utilize the aforementioned heat engine response lag time difference; When the production line is in a non-steady-state phase of speed increase or decrease, the melt temperature overshoot is predicted at future moments based on the model predictive control algorithm. A dynamic thermal balance control strategy is generated by constructing an objective function that minimizes the energy consumption per unit output and incorporates melt temperature deviation as a soft constraint penalty term. The optimal heating power compensation value within the time difference of the thermodynamic response lag is solved in reverse.

[0021] This embodiment details the strategy generation process based on Model Predictive Control (MPC) for non-steady-state phases such as startup acceleration and deceleration during specification changes. The process prediction model built into the MPC algorithm is a discretized linear state-space model. , where the state vector Melt temperature Control variables This is the heating power compensation value, and the disturbance term. Shear heat power ;matrix and perturbation transfer matrix From the system's thermal inertia time constant The shear heat conduction gain is obtained through bilinear transformation, where Weights used to characterize the influence of mechanical shear heat on the melt temperature state; the thermomechanical response lag time difference calculated in the system call pre-step steps. In response to the production line entering a non-steady-state phase, the system takes the current moment as the starting point and predicts the time domain. Internally, based on the process model, the trajectory of melt temperature change is simulated to predict potential temperature overshoot. On this basis, an objective function is constructed that minimizes energy consumption per unit output, incorporating melt temperature deviation as a soft constraint penalty term. The formula is as follows: in, The source is the preset algorithm control parameters, and its physical meaning is the number of steps in the model prediction time domain. Its time span corresponds to the lag time difference of the heat engine response and is dimensionless. The source is model prediction calculation, and the physical meaning is the unit output energy consumption at the k-th prediction time, with the unit being kWh / kg; The source is the process setting, and the physical meaning is the ideal energy consumption target value, with the unit being kWh / kg; The source is the adaptive adjustment algorithm, and its physical meaning is the weighting coefficient of the energy consumption optimization term and the temperature stability term, which is dimensionless; among which... This item serves as a soft constraint penalty factor to ensure that energy consumption optimization is carried out within the temperature control accuracy allowed by the process. The source is model prediction calculation, and the physical meaning is the melt temperature at the k-th prediction time, in °C. The source is the process setting, and its physical meaning is the target value of the melt temperature, in °C. The system solves this objective function in reverse to generate a set of optimal heating power compensation values ​​including the time dimension. The reverse solution uses a quadratic programming algorithm to satisfy the upper and lower limits of heater power constraints. Under the premise of calculating the objective function Minimize control sequence That is, a dynamic thermal balance control strategy; This embodiment introduces a forward-looking model predictive control algorithm. In non-steady-state operating scenarios, the calculated lag time difference is used as a prediction window. This strategy enables the system to reduce heating power or start micro-cooling in advance when the temperature trend is determined but has not actually changed. It cleverly utilizes the upcoming mechanical shear heat to naturally fill the heat demand, thereby completely eliminating the alternating resistance between heating and cooling that is common in traditional PID control and significantly reducing the energy consumption of the transition process.

[0022] Example 5: The generation process of the dynamic thermal balance control strategy also includes process capability index constraint logic: Real-time monitoring of the statistical distribution of key geometric dimensions of the product; calculation of the current process capability index; Determine the relationship between the process capability index and the preset quality threshold: if the process capability index is lower than the quality threshold, generate a size stability priority signal, correct the optimal heating power compensation value, and prioritize the generation of a size priority control strategy to ensure product size stability. If the process capability index is higher than or equal to the quality threshold, an energy efficiency priority signal is generated to maintain a dynamic thermal balance control strategy aimed at optimal energy efficiency.

[0023] This embodiment adds quality constraint logic to the energy efficiency optimization, acting as a firewall to ensure product qualification rate; the system monitors the cable outer diameter data fed back by the online diameter measuring instrument in real time and statistically analyzes its distribution; and calculates the current process capability index. The formula is as follows: The source is the product specification sheet; its physical meaning is the upper and lower tolerance limits of the product dimensions, and the unit is mm. The source is real-time statistical analysis, and the physical meaning is the mean and standard deviation of the current sample. System execution logic judgment: in response to If the quality value is below a preset quality threshold, such as 1.33, it indicates significant process fluctuations. The system immediately generates a priority signal for dimensional stability, and the specific correction logic is as follows: Let... At the same time Set to the current nominal value of the energy efficiency item This causes the gradient contribution of the energy efficiency term to disappear when the optimization operator is searching for the extremum, resulting in the algorithm degenerating into a form where... For hard-target PID equivalent steady-state control, the optimal heating power compensation value is corrected, and a size-priority control strategy that ensures product dimensional stability is prioritized, degenerating into high-precision constant temperature control; conversely, in response to... If the value is higher than or equal to the threshold, the system generates an energy efficiency priority signal and maintains a strategy aimed at achieving optimal energy efficiency. This embodiment introduces Statistical Process Control (SPC) indicators as feedback constraints, constructing a dynamic trade-off mechanism between pursuing ultimate energy efficiency and ensuring product quality. This self-examination capability ensures that energy-saving strategies do not come at the expense of product qualification rates, enabling aggressive energy saving when processes are stable and conservative quality maintenance when processes fluctuate, greatly enhancing the robustness and usability of the system in actual industrial production.

[0024] Example 6: The system also includes an environmental disturbance compensation module. The configuration is as follows: Real-time acquisition of the ambient temperature; Calculate the correlation between the ambient temperature change and the heat dissipation rate of the barrel to generate the ambient thermal influence coefficient; The environmental thermal influence coefficient is input as a feedforward variable into the energy efficiency optimization calculation module to modify the dynamic heat balance control strategy in order to offset external heat exchange disturbances.

[0025] This embodiment describes in detail the compensation mechanism for external environmental interference; the system collects ambient temperature data in real time through sensors installed around the equipment. Based on a modified model of Newton's law of cooling, the correlation between ambient temperature change and barrel heat dissipation rate is calculated, generating an environmental thermal influence coefficient. The calculation logic is as follows: The source is a thermal parameter database; the physical meaning is the natural convection heat transfer coefficient, and the unit is... ; The source is the equipment's geometric parameters; its physical meaning is the effective heat dissipation surface area of ​​the casing exposed to the environment, measured in units of... ; The source is a barrel wall temperature sensor, which physically represents the barrel outer shell temperature, measured in °C; the calculated temperature will be... As a feedforward variable, it is directly input to the energy efficiency optimization calculation module, where changes in heat dissipation power caused by the environment are explicitly added or subtracted when calculating heating demand, thereby correcting the dynamic heat balance control strategy. The specific mathematical implementation of the correction is as follows: in each cycle of the MPC control output... In the middle, directly accumulate feedforward compensation terms. ,Right now Reverse power compensation is used to offset the heat loss of the barrel wall caused by the ambient temperature difference; This embodiment employs a feedforward compensation mechanism to address environmental thermal disturbances. In production scenarios spanning day and night or seasons, the system can accurately distinguish whether barrel temperature fluctuations are caused by internal heat absorption or external environmental heat dissipation. This design effectively avoids excessive cooling oscillations caused by slow heat dissipation in summer and insufficient heating caused by rapid heat dissipation in winter, significantly improving the control model's adaptability and stability to environmental changes.

[0026] Example 7: The rheological-thermal coupling analysis module also includes a material property self-learning unit. The configuration is as follows: record the actual pressure, flow rate, and temperature correlation data during the production process; The pre-stored material rheological property data are corrected online using the corresponding relationship data; The rheological model parameters used to calculate the shear heat contribution rate are updated to accommodate the property fluctuations of different batches of materials.

[0027] This embodiment endows the system with adaptive learning capabilities to material property fluctuations; during the production process, the system continuously records the actual head pressure at specific rotational speeds and temperatures. The system uses extrusion flow rate and temperature data to form a measured dataset; it then uses an inversion algorithm to perform online correction on the pre-stored material rheological property data; specifically, the system is based on a simplified die head flow equation. ,in Calculate the current theoretical pressure value for the nose section geometry constant; calculate the deviation ratio between the actual pressure and the theoretical pressure. The consistency coefficient is updated using the exponentially weighted moving average (EWMA) method. The updated formula is: in The preset learning rate, for example, 0.05, is used to smooth noise; the system will update the... The parameters are written to the rheological model parameter library in real time for calculating the shear heat contribution rate in the next cycle, to accommodate the property fluctuations of different batches of materials; Note: This is to distinguish it from the power symbol used in the previous embodiments. In this embodiment, pressure symbols are all adopted. express; This embodiment solves the calculation deviation problem caused by the distortion of preset rheological data by using online inversion correction technology in scenarios where raw material batches are changed or recycled materials are used; this mechanism gives the system the ability to evolve online, ensuring the continuous accuracy of shear heat calculation, thereby guaranteeing the reliability of energy efficiency control strategy in long-term operation.

[0028] Example 8: The adaptive execution module outputs coordinated control commands, specifically including: decomposing the total heat demand into mechanical shear heat components and resistance heating components; increasing the proportion of the mechanical shear heat component by adjusting the screw speed while ensuring that the total heat demand meets the melting conditions; simultaneously reducing the set value of the resistance heating component; and sending cooling or heating suppression commands in advance based on the thermomechanical response lag time difference to prevent temperature overshoot.

[0029] This embodiment details the final execution logic of the control commands, which is the key to achieving energy-saving goals; the system will calculate the total thermal energy demand. Decomposed into mechanical shear heat components and resistance heating component While ensuring complete melting of the material without thermal degradation, the system actively fine-tunes and increases the screw speed to increase the proportion of efficient mechanical shear heat; simultaneously reducing the resistance heating component. With screw speed increment The following energy equivalence mapping relationship is satisfied: ,in, The preset electromechanical-thermal conversion efficiency constant ( The system synchronously reduces the set value of the resistance heating component, and based on the aforementioned calculated thermodynamic response lag time difference... Execution timing control: For example, in response to the prediction that shear heat will occur A few seconds later, the temperature rises. The system immediately sends a heating suppression command at that moment, and... The fan is pre-started at a low speed at a specific time before arrival to prevent temperature overshoot. This embodiment achieves a high-efficiency operation mode in actual operation by actively reconfiguring the energy source ratio, which is mainly based on internal mechanical self-heating and supplemented by external resistance heating. With the help of precise timing control based on time difference, this solution not only significantly reduces the energy consumption of electric heating, but also improves the plasticizing quality by utilizing shear heat, truly achieving dual optimization of process and energy efficiency.

[0030] 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 control system for an energy-saving cable extrusion production process, characterized in that, include: The data acquisition module is configured to acquire real-time operating parameters of the extrusion production line and call preset material rheological property data. The real-time operating parameters include: screw speed, temperature of each zone of the barrel, melt temperature, main motor power, extrusion flow rate and ambient temperature. The rheological-thermal coupling analysis module is configured to calculate the shear heat contribution rate under the current working condition based on the screw speed, the material rheological property data, and the main motor power. The thermomechanical response decoupling module is configured to determine the thermomechanical response lag time difference between mechanical shear heat and barrel heat conduction based on the shear heat contribution rate and the preset system thermal inertia model. The energy efficiency optimization calculation module is configured to generate a dynamic thermal balance control strategy for unsteady-state operating conditions based on the heat engine response lag time difference and a preset unit output energy consumption target, specifically including: Utilize the aforementioned heat engine response lag time difference; When the production line is in a non-steady-state phase of speed increase or decrease, the melt temperature overshoot is predicted at future moments based on the model predictive control algorithm. A dynamic thermal balance control strategy is generated by constructing an objective function that minimizes the energy consumption per unit output and incorporates the melt temperature deviation as a soft constraint penalty term. The optimal heating power compensation value within the time difference of the thermodynamic response lag is solved in reverse. The adaptive execution module is configured to output coordinated control commands to the heating and cooling system and the screw drive system based on the dynamic thermal balance control strategy, so as to minimize the system energy consumption.

2. The control system for an energy-saving cable extrusion production process according to claim 1, characterized in that, Based on the screw speed, the material rheological properties data, and the main motor power, the shear heat contribution rate under the current operating condition is calculated, including: Call the screw rotation speed and the material rheological property data; A viscous dissipation model based on the properties of non-Newtonian fluids is constructed to calculate the internal heat power converted from mechanical friction. The ratio of the internal thermal power to the total enthalpy change power required for the melting process is calculated to generate the shear heat contribution rate, which characterizes the proportion of mechanical energy converted into thermal energy.

3. The control system for an energy-saving cable extrusion production process according to claim 1, characterized in that, Based on the shear heat contribution rate and the preset system thermal inertia model, the thermomechanical response lag time difference between mechanical shear heat and barrel heat conduction is determined, including: Extract the time-series fluctuation frequency characteristics of the shear heat contribution rate; The preset system thermal inertia model is invoked to obtain the heat conduction time constant of the barrel heating and cooling system; By comparing the fluctuation frequency characteristics with the heat conduction time constant, the thermomechanical response lag time difference between the melt temperature change caused by screw speed adjustment and the barrel wall temperature response is calculated.

4. The control system for an energy-saving cable extrusion production process according to claim 1, characterized in that, The generation process of the dynamic thermal balance control strategy also includes process capability index constraint logic: Real-time monitoring of the statistical distribution of key geometric dimensions of the product; Calculate the current process capability index; Determine the relationship between the process capability index and the preset quality threshold: If the process capability index is lower than the quality threshold, a size stability priority signal is generated to correct the optimal heating power compensation value and to prioritize the generation of a size priority control strategy that ensures product size stability. If the process capability index is higher than or equal to the quality threshold, an energy efficiency priority signal is generated to maintain a dynamic thermal balance control strategy aimed at optimal energy efficiency.

5. The control system for an energy-saving cable extrusion production process according to claim 1, characterized in that, The system also includes an environmental disturbance compensation module, configured as follows: The ambient temperature is collected in real time; Calculate the correlation between the ambient temperature change and the heat dissipation rate of the barrel to generate the ambient thermal influence coefficient; The environmental thermal influence coefficient is input as a feedforward variable into the energy efficiency optimization calculation module to modify the dynamic heat balance control strategy in order to offset external heat exchange disturbances.

6. The control system for an energy-saving cable extrusion production process according to claim 1, characterized in that, The rheological-thermal coupling analysis module also includes a material property self-learning unit, configured as follows: Record the actual pressure, flow rate, and temperature correlation data during the production process; The pre-stored material rheological property data are corrected online using the corresponding relationship data; The rheological model parameters used to calculate the shear heat contribution rate are updated to accommodate the property fluctuations of different batches of materials.

7. The control system for an energy-saving cable extrusion production process according to claim 1, characterized in that, The adaptive execution module outputs cooperative control commands, specifically including: The total heat energy demand is decomposed into mechanical shear heat component and resistance heating component. Under the premise of ensuring that the total heat energy demand meets the melting conditions, the proportion of the mechanical shear heat component is increased by adjusting the screw speed; The set value of the resistance heating component is reduced synchronously, and a cooling or heating suppression command is sent in advance based on the time difference of the thermodynamic response to prevent temperature overshoot.

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