A temperature control system of a carbon nanotube master batch production line

Abstract

The present application belongs to the technical field of temperature control, and provides a temperature control system for a carbon nanotube master batch production line, comprising: a process data acquisition module: in the process of carbon nanotube master batch production, acquiring process state data of the production line; a dynamic cooperative control module: based on the acquired process state data, taking maintaining a preset melt viscosity range and specific mechanical energy range as a control target, and when the process state data deviates from the control target, dynamically generating and outputting a cooperative adjustment instruction. A composite control mode of feedforward prevention and feedback fine adjustment is formed through disturbance feedforward compensation and feedback adjustment of the dynamic cooperative control, process parameter drift caused by internal and external interference is inhibited, the production line can still maintain high stability when facing frequent or unpredictable disturbances, and the unqualified rate of products is reduced.

Description

Technical Field

[0001] This invention belongs to the field of temperature control technology, specifically a temperature control system for a carbon nanotube masterbatch production line. Background Technology

[0002] As a key intermediate for high-performance conductive and thermally conductive composite materials, the core quality of carbon nanotube masterbatch depends on the degree of nanoscale dispersion and structural integrity of CNTs in the polymer matrix. The production process mainly relies on twin-screw extruders, which achieve the deagglomeration and dispersion of CNT agglomerates through the combined action of mechanical shearing and thermal energy.

[0003] However, the production of CNT masterbatch, especially when the filler content is high or the thermosensitive polymer matrix is ​​used, is extremely sensitive to temperature and the control is extremely complex. Currently, most production lines use PID temperature control systems based on cylindrical thermocouples to fix the temperature setpoint, which has significant drawbacks.

[0004] The core of CNT masterbatch production lies in the dynamic balance between mechanical shear and thermal management. However, the existing system cannot detect and compensate for the local peak temperature of the melt caused by high shear. It is slow and passive in responding to key disturbances such as raw material fluctuations and changes in ambient temperature. At the same time, the control of key parameters such as temperature and rotation speed is fragmented, and adjustments can easily cause system oscillations. As a result, in the production of CNT masterbatch with a very narrow process window, the product quality is unstable and the anti-interference ability is weak.

[0005] Therefore, the present invention provides a temperature control system for a carbon nanotube masterbatch production line. Summary of the Invention

[0006] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0007] The technical solution adopted by this invention to solve its technical problem is: a temperature control system for a carbon nanotube masterbatch production line, comprising:

[0008] Process data acquisition module: Acquires process status data of the production line during the production of carbon nanotube masterbatch;

[0009] Dynamic Cooperative Control Module: Based on the acquired process state data, the module aims to maintain the preset melt viscosity range and specific mechanical energy range as control targets. When the process state data deviates from the control targets, the module dynamically generates and outputs cooperative adjustment commands.

[0010] Disturbance feedforward compensation module: Real-time monitoring of external disturbance variables. If changes in external disturbance variables are detected, disturbance feedforward compensation instructions are generated by analyzing the historical impact data of the changed external disturbance variables. Finally, the final decision control instructions are generated based on the coordinated adjustment instructions combined with the disturbance feedforward compensation instructions.

[0011] Control command execution module: Based on the final decision control command, the module includes a drive unit that drives the main screw and a temperature control unit that regulates the temperature of the cylinder in each temperature zone.

[0012] As a further aspect of the present invention: the process status data includes the actual melt temperature, melt pressure, and specific mechanical energy calculated based on the host power and feed rate;

[0013] The specific mechanical energy is obtained by processing the ratio of the main engine power and the total feed mass flow rate;

[0014] The total feed mass flow rate is calculated by summing the feed rate of the main feeder and the feed rates of all side feeders.

[0015] As a further aspect of the present invention: the dynamic collaborative control module performs:

[0016] Based on the current actual melt temperature and polymer matrix type, the melt density is determined through a pre-stored temperature-density relationship database, and the melt volumetric flow rate is calculated in conjunction with the total feed mass flow rate.

[0017] Based on melt pressure, melt volumetric flow rate and preset flow channel geometric parameters, the real-time apparent viscosity of the melt is calculated using a melt rheological model.

[0018] The viscosity deviation value is obtained by comparing the real-time apparent melt viscosity with the preset melt viscosity target value, and the specific mechanical energy is obtained by comparing the real-time specific mechanical energy with the preset specific mechanical energy target value.

[0019] Based on the viscosity deviation value and specific mechanical energy deviation value, combined with the current main screw speed and the setpoint of each barrel temperature, the adjustment amount of the main screw speed and the adjustment amount of the setpoint of each relevant temperature zone barrel are calculated by a multivariate collaborative decision-making algorithm, and output as a collaborative adjustment command.

[0020] As a further aspect of the present invention:

[0021] The multivariate collaborative decision-making algorithm is a dynamic decoupling control algorithm based on constraint optimization. It solves for the adjustment amount by constructing and minimizing a cost function. The cost function includes normalized square terms of viscosity deviation and specific mechanical energy deviation, as well as a penalty term for the amplitude of control action.

[0022] As a further aspect of the present invention: the dynamic response coefficient of the system is estimated online in real time using the recursive least squares method. The dynamic response coefficient includes the partial derivatives of the screw speed and the temperature of each temperature zone with respect to the melt viscosity and specific mechanical energy.

[0023] A linear prediction model based on the dynamic response coefficient is established to predict the effect of adjustment on melt viscosity and specific mechanical energy.

[0024] Substituting the linear prediction model into the cost function and using an approximate solution method based on gradient projection, the adjustment amount of the main screw speed and the adjustment amount of the cylinder temperature setpoint for each relevant temperature zone are calculated.

[0025] As a further aspect of the present invention: the disturbance feedforward compensation module performs:

[0026] The monitored external disturbance variable data are filtered and processed by moving average, and the difference between the current moving average and the historical baseline moving average is calculated as the change.

[0027] When the change in any external disturbance variable exceeds the corresponding change threshold, a valid disturbance is determined and a compensation decision process is triggered.

[0028] As a further aspect of the present invention: the compensation decision-making process:

[0029] Extract the feature vector of the current disturbance change, including: the type of the changing variable, the direction of change, the magnitude of change, and the duration;

[0030] The similarity matching calculation between the currently extracted interference feature vector and the records in the historical database is performed to obtain the analysis reference set;

[0031] The historical database stores historically recorded valid interference events and corresponding successful compensation records in a structured format.

[0032] The arithmetic mean of all successful compensation records in the analysis reference set is calculated, and the result is used as the disturbance feedforward compensation command.

[0033] As a further aspect of the present invention: the acquisition of the analysis reference set: calculate the Euclidean distance between the current feature vector and the feature vector of each historical record, sum the Euclidean distance with 1, and then take the reciprocal as the similarity value;

[0034] Successful compensation records with similarity values ​​greater than the similarity threshold are extracted and used as the analysis reference set.

[0035] As a further aspect of the present invention:

[0036] The final decision control instruction:

[0037] The disturbance feedforward compensation command is multiplied by the feedforward compensation gain coefficient, and then added to the coordinated adjustment command to obtain the final decision control command.

[0038] As a further aspect of the present invention: the control instruction execution module executes:

[0039] Receive data packets from the final decision control command and extract the main screw speed adjustment amount and the cylinder temperature setpoint adjustment amount for each relevant temperature zone;

[0040] The new target speed setting value is calculated by adding the obtained main screw speed adjustment value to the current screw speed setting value.

[0041] For each temperature zone that needs adjustment, the temperature adjustment amount obtained from the analysis is added to the current cylinder temperature setting value for the corresponding temperature zone to calculate the new target temperature setting value.

[0042] The beneficial effects of this invention are as follows:

[0043] This invention directly monitors the actual temperature, pressure, and specific mechanical energy of the melt through a process data acquisition module, and makes decisions with the goal of maintaining the balance of the two core process parameters of melt viscosity and specific mechanical energy. This directly affects the essential factors that influence the dispersion quality and material integrity of CNTs, thereby ensuring the high performance of the product from the source.

[0044] This invention uses a disturbance feedforward compensation module to monitor and proactively compensate for key disturbances such as cooling water temperature, ambient temperature and humidity, raw material batch changes, and power grid fluctuations in real time. Based on a compensation strategy that matches historical big data similarity, it can proactively intervene before disturbances have a significant impact on core processes. This, combined with feedback adjustment of dynamic collaborative control, forms a composite control mode of feedforward prevention and feedback fine-tuning, which suppresses process parameter drift caused by internal and external disturbances. This allows the production line to maintain high stability when facing frequent or unpredictable disturbances, thereby reducing the product defect rate.

[0045] This invention addresses the inherent problem of a narrow process window in carbon nanotube masterbatch production. By employing a multivariate collaborative decision-making algorithm, it incorporates the deviations of viscosity and SME (Strain Equivalent Size) into the optimization problem simultaneously. This collaborative and decoupled control method avoids system oscillations caused by manual single-parameter adjustments. It can maintain the process state within a narrow target window with minimal control actions and the most collaborative adjustment combinations, thus optimizing the problem of tightrope-walking production control. Attached Figure Description

[0046] The invention will now be further described with reference to the accompanying drawings.

[0047] Figure 1 This is a modular architecture diagram of a temperature control system for a carbon nanotube masterbatch production line according to the present invention.

[0048] Figure 2 This is a flowchart of the steps of a temperature control system for a carbon nanotube masterbatch production line according to the present invention. Detailed Implementation

[0049] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0050] Example 1

[0051] Please see Figure 1 As shown in the embodiment of the present invention, a temperature control system for a carbon nanotube masterbatch production line addresses the fundamental problems exposed by existing passive PID control systems based on fixed temperature setpoints when dealing with the complex process of carbon nanotube masterbatch production, which involves multiple disturbances (real-time changes in disturbances during continuous production) and a narrow process window (determined by the physical nature of CNT dispersion and the intrinsic properties of the materials used). The system improves the production line's adaptability and robustness under complex disturbances through a temperature control system that dynamically regulates the balance between energy input and dissipation. The system includes the following modules:

[0052] Process data acquisition module: Acquires process status data of the production line during the production of carbon nanotube masterbatch;

[0053] The execution process is as follows: The process status data includes at least the actual melt temperature, melt pressure, and specific mechanical energy calculated based on the main machine power and feed rate;

[0054] Install a melt thermocouple or infrared temperature sensor in the melt flow channel at the front end of the extruder die or the rear end of the high-shear mixing zone to collect the real temperature of the melt in real time at a set sampling frequency (e.g., 10Hz).

[0055] Install a melt pressure sensor in the melt flow channel before the die inlet or in the critical shearing zone to monitor and collect melt pressure data in real time.

[0056] The main power of the motor can be read in real time through the frequency converter or power transmitter of the main drive motor of the extruder;

[0057] The feed rate of the main feeder and the side feeder (CNT premix) is obtained in real time through the controller of the loss-in-weight feeder;

[0058] All collected data are synchronized using a unified timestamp to ensure that the data is aligned on the timeline, and the collected data is filtered.

[0059] Based on the host power and total feed mass flow rate after time synchronization processing, the ratio of host power and total feed mass flow rate is processed to obtain specific mechanical energy;

[0060] The total feed mass flow rate is calculated by summing the feed rate of the main feeder and the feed rates of all side feeders.

[0061] The actual temperature, pressure, and calculated specific mechanical energy of the processed melt are packaged into a standardized data packet.

[0062] Dynamic Cooperative Control Module: Based on the acquired process state data, the module aims to maintain the preset melt viscosity range and specific mechanical energy range as control targets. When the process state data deviates from the control targets, the module dynamically generates and outputs cooperative adjustment commands.

[0063] Among them, the coordinated adjustment command includes at least the adjustment amount of the main screw speed and the barrel temperature setpoint;

[0064] The execution process is as follows: the melt viscosity is calculated based on the built-in melt rheological model;

[0065] Specifically, based on the current actual melt temperature and the type of polymer matrix used, the pre-stored "temperature-density relationship database" is queried, and the melt density at the current temperature is obtained through linear interpolation. The volumetric flow rate is then calculated based on the ratio of the total feed mass flow rate to the melt density.

[0066] The calculation formula for the melt rheological model: Calculate the apparent viscosity of the melt based on the Newtonian fluid definition. : ;in, For shear stress, To represent the shear rate;

[0067] The formula for calculating shear rate: Where D is the diameter of the orifice and Q is the volumetric flow rate;

[0068] Formula for calculating shear stress: in, Where L is the pressure difference and L is the length;

[0069] The calculated real-time apparent melt viscosity is compared with the preset melt viscosity range, and the viscosity deviation between the real-time apparent melt viscosity and the target melt viscosity value is calculated.

[0070] The real-time specific mechanical energy is compared with the preset specific mechanical energy range, and the specific mechanical energy deviation value between the real-time specific mechanical energy and the specific mechanical energy target value is calculated.

[0071] The target value for melt viscosity is the average of the maximum and minimum values ​​within the preset melt viscosity range;

[0072] The target value for specific mechanical energy is the average of the preset maximum and minimum values ​​within the specific mechanical energy range.

[0073] Based on the viscosity deviation and specific mechanical energy deviation, combined with the current main screw speed and the setpoints of each barrel temperature, the adjustment amount of the main screw speed and the adjustment amount of the setpoints of each relevant temperature zone are calculated through a multivariate collaborative decision-making algorithm.

[0074] The multivariate collaborative decision-making algorithm is a dynamic decoupling control algorithm based on constraint optimization. It maps the process state to an action space composed of actuators, solves a micro-optimization problem to obtain the adjustment value, and the process is as follows:

[0075] Maintain melt viscosity range and specific mechanical energy range The objective is transformed into minimizing the cost function;

[0076] Alternatively, the cost function to be minimized can be:

[0077] ;

[0078] in, This is the target value for melt viscosity. To achieve the target value of specific mechanical energy, To provide real-time melt viscosity. To compare mechanical energy in real time, The penalty for controlling the action is a fixed value preset according to the process stability requirements. This refers to the adjustment amount of the main screw speed. For the adjustment amount of the cylinder temperature setpoint in the i-th relevant temperature zone, This is a penalty for controlling the range of motion;

[0079] Based on current and historical process status data, the system dynamic response coefficients are estimated online in real time using the recursive least squares method, including the partial derivative of screw speed with respect to melt viscosity. Partial derivatives of melt viscosity with respect to temperature in each temperature zone The partial derivative of screw speed with respect to mechanical energy And the partial derivatives of mechanical energy relative to temperature in each temperature zone ;

[0080] Substitute the dynamic response coefficients into the following linear prediction model:

[0081] ;

[0082] ;

[0083] in, This represents the predicted change in the apparent viscosity of the melt. The predicted change in specific mechanical energy;

[0084] Substituting the above prediction model into the cost function, we obtain a quadratic function with the main screw speed adjustment and the setpoint adjustment of the cylinder temperature in each relevant temperature zone as variables;

[0085] An approximate solution method based on gradient projection is adopted to calculate the gradient of the cost function with respect to each adjustment amount. The calculation is performed iteratively along the negative gradient direction with an adaptive step size. The method is then corrected by combining the preset adjustment amount amplitude limit and adjustment direction conflict arbitration rules to obtain the optimal main screw speed adjustment amount and the cylinder temperature setpoint adjustment amount for each relevant temperature zone.

[0086] The optimal adjustment amount of the main screw speed and the adjustment amount of the setpoint of the barrel temperature in each relevant temperature zone are output as a coordinated adjustment command.

[0087] The dynamic collaborative control module calculates the melt's apparent viscosity and specific mechanical energy in real time, compares the deviation with a preset target range, and constructs an optimization problem for predicting the dynamic response of the fusion system. The aim is to identify the dynamic characteristics of the process system online using the recursive least squares method and solve for an optimal combination of instructions that can simultaneously and collaboratively adjust the main screw speed and the temperature setpoints of each temperature zone based on an efficient gradient projection algorithm. Its fundamental goal is to surpass traditional single-point temperature control and actively maintain a dynamic balance between the "mechanical energy input required for shear dispersion" and "process heat dissipation." In this complex production process of carbon nanotube masterbatch, which has a narrow process window and many interferences, it can achieve closed-loop, decoupled, and collaborative control of key process parameters, ultimately improving product consistency and the adaptive robustness of the production line.

[0088] Disturbance feedforward compensation module: Real-time monitoring of external disturbance variables. If changes in external disturbance variables are detected, disturbance feedforward compensation instructions are generated by analyzing the historical impact data of the changed external disturbance variables. Finally, the final decision control instructions are generated based on the coordinated adjustment instructions combined with the disturbance feedforward compensation instructions.

[0089] The execution process is as follows: Real-time monitoring of external disturbance variables, including at least:

[0090] The cooling water inlet temperature is collected in real time by a temperature sensor installed on the main cooling water circulation pipe.

[0091] Ambient temperature and humidity are collected by temperature and humidity sensors installed in key areas of the production line (such as the feeding area and the main machine area);

[0092] Raw material batch information is obtained through the production line manufacturing execution system interface or manual input by the operator to obtain the batch number of the currently used polymer matrix and CNT premix. The system automatically associates the batch number with the pre-stored process characteristic parameter compensation table of the raw material for that batch, including the differences between the key parameters affecting the process and the benchmark values ​​such as melt index deviation, moisture content, and CNT content calibration value.

[0093] The grid voltage / frequency is read in real time by power monitoring instruments to detect power fluctuations that may affect the output stability of the host drive motor;

[0094] Real-time data of monitored external interference variables are filtered and processed by moving average. For the filtered data sequence, the moving average method with a window length of N (e.g., N=60, corresponding to 1 minute of data) is used to calculate the moving average.

[0095] The change is calculated as the difference between the current moving average and the baseline moving average from a certain time ago (e.g., 5 minutes ago).

[0096] When the change in any external disturbance variable exceeds the corresponding change threshold, it is determined that a valid disturbance has occurred, triggering the compensation decision process.

[0097] The threshold for change is set based on a comprehensive consideration of the physical nature of the process system, the stability requirements of process control, and production economics.

[0098] Extract the feature vector of the current disturbance change, including: the type of the changing variable, the direction of change, the magnitude of change, and the duration;

[0099] Based on the extracted feature vectors of interference changes, historical impact data analysis is performed. The process is as follows:

[0100] Based on the built-in interference-compensation history database, which stores in a structured form the effective interference events recorded in history and their corresponding successful compensation records;

[0101] Each successful compensation record must include at least: the interference feature vector (type, direction, amplitude), the basic process state when the interference occurred (such as current output, main machine speed range), details of the feedforward compensation instructions taken (such as the adjustment amount of the barrel temperature setpoint for each temperature zone, the adjustment amount of the main machine screw speed), and the actual changes in process parameters (melt apparent viscosity, specific mechanical energy) over a period of time after the compensation is executed (such as whether it is stable within the target window).

[0102] The similarity matching calculation is performed between the currently extracted interference feature vector and the records in the historical database;

[0103] Optionally, the similarity matching algorithm is as follows: calculate the Euclidean distance between the current feature vector and the feature vector of each historical record, sum the Euclidean distance with 1, and then take the reciprocal as the similarity value;

[0104] Successful compensation records with similarity values ​​greater than the similarity threshold (85%) are extracted and used as the analysis reference set;

[0105] The arithmetic mean of all successful compensation records in the analysis reference set is calculated, and the result is used as the disturbance feedforward compensation command for this time.

[0106] For each actuator (including the setpoint of the cylinder temperature in each relevant temperature zone and the main screw speed), the arithmetic mean is calculated independently.

[0107] Before calculating the average value, the adjustment amount of the same actuator in the analysis reference set is checked for consistency in direction (sign).

[0108] If all historical adjustments are in the same direction (either all positive or all negative), then the arithmetic mean is calculated directly.

[0109] If there is a discrepancy in direction, a majority decision will be made: count the number of records for positive and negative adjustments, select the direction with the higher percentage of adoptions, determine the adjustment amount for the adopted direction, and take the average of the absolute values ​​of all historical adjustment amounts in that direction, with the sign consistent with the adopted direction;

[0110] If the number of records in the positive and negative directions is equal, the direction with the smaller absolute value of the adjustment is adopted to ensure the conservatism of the control action;

[0111] The calculated arithmetic mean is used as the disturbance feedforward compensation command, which includes a vector of specific adjustment amounts.

[0112] The disturbance feedforward compensation command is multiplied by the feedforward compensation gain coefficient, and then summed with the coordinated adjustment command to obtain the final decision control command.

[0113] It should be noted that the feedforward compensation gain coefficient is a constant preset based on process experience, usually taken as 0.8, used to fine-tune the strength of the feedforward compensation;

[0114] It is important to note that conflict checks and amplitude limits are performed on the synthesized final decision control instructions to ensure that the final adjustment amount of each actuator does not exceed its maximum allowable adjustment amount per step. If the direction of the synthesized instructions is opposite to the direction of the collaborative adjustment instructions (feedback), the feedforward compensation gain coefficient is temporarily set to zero or significantly reduced, so that the final decision control instructions are essentially degenerated into instructions based on collaborative adjustment instructions (feedback).

[0115] The disturbance feedforward compensation module monitors foreseeable external disturbance variables in real time and proactively generates pre-correction instructions before they significantly affect core process parameters. First, it filters and analyzes the trend of the disturbance signal. When the change exceeds the threshold set based on process physics, it is determined to be a valid disturbance and its feature vector is extracted. Then, it retrieves compensation cases that have successfully handled similar disturbances in the historical database through a similarity matching algorithm based on Euclidean distance. It performs quantitative analysis on the compensation instructions of the matching cases, generates specific feedforward compensation instructions, and merges them with the feedback instructions of the dynamic collaborative control module according to preset rules to output the final control decision.

[0116] Its function is to achieve proactive control, overcoming the inherent lag of traditional feedback control. It aims to transform the production line from passively responding to disturbances to actively defending against them. By utilizing historical experience data, it preemptively applies reverse corrections before the negative impacts of disturbances have fully transmitted to core processes, thereby:

[0117] It significantly improves the system's anti-interference robustness and effectively suppresses process parameter drift and quality fluctuation caused by common interferences such as environmental fluctuations and raw material batch differences;

[0118] By offsetting some of the interference effects through feedforward compensation, a more stable and tolerant operating environment is created for energy balance-based feedback collaborative control, reducing the difficulty of controlling the tightrope walk within a narrow process window.

[0119] With self-learning capabilities, the historical database is continuously enriched and optimized as successful compensation experience is accumulated, making the system's compensation strategies for dealing with repetitive or similar interferences increasingly accurate, thus achieving knowledge accumulation and autonomous performance improvement.

[0120] The disturbance feedforward compensation module, in close cooperation with the dynamic collaborative control module, forms a composite intelligent control system of "feedforward prevention + feedback fine-tuning", which is the key to ensuring stable, high-quality and continuous production of carbon nanotube masterbatch under high-requirement and multi-interference conditions.

[0121] Control command execution module: Based on the final decision control command, the module includes a drive unit that drives the main screw and a temperature control unit that regulates the temperature of the cylinder in each temperature zone;

[0122] The specific execution process is as follows: Receive the data packet containing the final decision control command, extract the specific adjustment amounts for each actuator, mainly including:

[0123] Adjustment amount of main screw speed and adjustment amount of cylinder temperature setpoint for each relevant temperature zone;

[0124] The new target speed setting value is calculated by adding the obtained main screw speed adjustment value to the current screw speed setting value.

[0125] The new target speed setpoint is sent to the frequency converter or servo drive that drives the main screw via a digital-to-analog conversion module or fieldbus communication (such as PROFINET, EtherCAT).

[0126] Based on the received target value, the driver controls the motor to smoothly transition to the new speed with a specified acceleration, ensuring the stability of the mechanical transmission system and avoiding impact on the material conveying and shearing process;

[0127] For each temperature zone that needs adjustment, the temperature adjustment amount obtained from the analysis is added to the current cylinder temperature setting value of the corresponding temperature zone to calculate the new target temperature setting value.

[0128] Send the information of each temperature zone to the corresponding temperature zone controller (usually an intelligent temperature controller with PID adjustment function or a temperature control module in a PLC).

[0129] Each temperature zone temperature controller adjusts the heat input to that section of the cylinder by controlling the duty cycle of the heater (such as a resistance heating coil) and the opening of the cooling solenoid valve according to the new set value, so as to drive the actual temperature to approach the target value.

[0130] After execution, the actual cylinder temperature is collected in real time by thermocouples installed on each temperature zone cylinder and fed back to the temperature controller, forming a closed-loop control for a single temperature zone.

[0131] In this embodiment, the process data acquisition module directly monitors the actual temperature, pressure and specific mechanical energy of the melt. The dynamic collaborative control module makes decisions with the goal of maintaining the balance of the two core process parameters of melt viscosity and specific mechanical energy. It directly acts on the essential factors (shear stress and thermal history) that affect the dispersion quality and material integrity of CNTs, thereby ensuring the high performance of the product from the source.

[0132] This embodiment uses a disturbance feedforward compensation module to monitor and proactively compensate for key disturbances such as cooling water temperature, ambient temperature and humidity, raw material batch changes, and power grid fluctuations in real time. Based on the compensation strategy of historical big data similarity matching, it can proactively intervene before the disturbance has a significant impact on the core process. Together with the feedback adjustment of dynamic collaborative control, it forms a composite control mode of "feedforward prevention + feedback fine adjustment", which suppresses the drift of process parameters caused by internal and external disturbances. This allows the production line to maintain high stability when facing frequent or unpredictable disturbances, and significantly reduces the product defect rate.

[0133] This embodiment addresses the inherent problem of a narrow process window in carbon nanotube masterbatch production. By employing a multivariate collaborative decision-making algorithm, the deviation between viscosity and SME is simultaneously incorporated into the optimization problem. This collaborative and decoupled control method avoids system oscillations caused by manual single-parameter adjustments. It can maintain the process state within a narrow target window with minimal control actions and the most collaborative adjustment combination, thus optimizing the problem of tightrope-walking production control.

[0134] Example 2

[0135] Based on the same inventive concept as the temperature control system of a carbon nanotube masterbatch production line in the foregoing embodiments, such as Figure 2 As shown, this application provides a temperature control method for a carbon nanotube masterbatch production line, which specifically includes the following steps:

[0136] Step 1: During the production of carbon nanotube masterbatch, acquire process status data of the production line;

[0137] Step 2: Based on the acquired process state data, with the control target being to maintain the preset melt viscosity range and specific mechanical energy range, when the process state data deviates from the control target, a coordinated adjustment command is dynamically generated and output.

[0138] Step 3: Monitor external disturbance variables in real time. If changes in external disturbance variables are detected, generate disturbance feedforward compensation instructions by analyzing the historical impact data of the changed external disturbance variables, and generate final decision control instructions based on the coordinated adjustment instructions combined with the disturbance feedforward compensation instructions.

[0139] Step 4: Based on the final decision control command, including the drive unit that drives the main screw and the temperature control unit that regulates the temperature of the cylinder in each temperature zone.

[0140] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A temperature control system for a carbon nanotube master batch production line, characterized by: Comprise: Process data acquisition module: in the process of carbon nanotube master batch production, the process state data of production line is acquired; Dynamic collaborative control module: based on the acquired process state data, the preset melt viscosity range and specific mechanical energy range are taken as control targets, when the process state data deviates from the control targets, the collaborative adjustment instructions are dynamically generated and outputted; The dynamic collaborative control module executes: Based on the current melt real temperature and the polymer matrix type, the melt density is determined through the pre-stored temperature-density relationship database, and the melt volume flow is calculated combined with the total feeding mass flow; Based on the melt pressure, the melt volume flow and the preset flow channel geometric parameters, the real-time melt apparent viscosity is calculated through the melt rheological model; The real-time melt apparent viscosity is compared with the preset melt viscosity target value to obtain the viscosity deviation value, and the real-time specific mechanical energy is compared with the preset specific mechanical energy target value to obtain the specific mechanical energy deviation value; Based on the viscosity deviation value and the specific mechanical energy deviation value, combined with the current host screw speed and the temperature setting value of each cylinder, the host screw speed adjustment amount and the temperature setting value adjustment amount of each related temperature zone cylinder are calculated through a multivariate collaborative decision algorithm, and are outputted as collaborative adjustment instructions; Disturbance feedforward compensation module: real-time monitoring of external disturbance variables, if the monitoring of external disturbance variables exists changes, then through the analysis of the historical influence data of the changed external disturbance variables, the disturbance feedforward compensation instructions are generated, and the final decision control instructions are generated based on the collaborative adjustment instructions combined with the disturbance feedforward compensation instructions; The disturbance feedforward compensation module executes: The monitored external disturbance variable data is filtered and moving average processed, and the difference between the current moving average value and the historical baseline moving average value is calculated as the change value; When the change value of any external disturbance variable exceeds the corresponding change value threshold, it is determined that an effective disturbance occurs and triggers the compensation decision process; The compensation decision process: Extract the feature vector of the current disturbance change, including: change variable type, change direction, change amplitude, duration; The similarity matching calculation is carried out between the current extracted disturbance feature vector and the records in the historical database to obtain the analysis reference set; The historical database stores the recorded effective disturbance events and the corresponding successful compensation records in a structured form in history; The arithmetic mean value of all successful compensation records in the analysis reference set is calculated, and the calculation result is taken as the disturbance feedforward compensation instruction; Control instruction execution module: based on the final decision control instruction, the module includes a driving unit for driving the host screw and a temperature control unit for regulating the temperature of the cylinder in each temperature zone; The final decision control instruction: The disturbance feedforward compensation instruction is multiplied by the feedforward compensation gain coefficient, and then added to the collaborative adjustment instruction to obtain the final decision control instruction.

2. The temperature control system of the carbon nanotube master batch production line according to claim 1, characterized in that: The process state data includes melt real temperature, melt pressure, and specific mechanical energy calculated based on host power and feeding amount; The specific mechanical energy is obtained by ratio processing of host power and total feeding mass flow; The total feeding mass flow is calculated by summing the feeding amount of the main feeder and the feeding amount of all side feeders. 3.The temperature control system of a carbon nanotube master batch production line according to claim 1, characterized in that: The multivariable collaborative decision algorithm is a dynamic decoupling control algorithm based on constraint optimization, which solves the adjustment amount by constructing and minimizing a cost function, the cost function including the normalized square terms of viscosity deviation value and specific mechanical energy deviation value, and a penalty term for control action amplitude. 4.The temperature control system of a carbon nanotube master batch production line according to claim 3, characterized in that: The recursive least squares method is used to estimate the system dynamic response coefficients in real time, including the partial derivatives of screw speed and temperature of each temperature zone to melt viscosity and specific mechanical energy; A linear prediction model is established based on the dynamic response coefficients, which is used to predict the influence of adjustment amount on melt viscosity and specific mechanical energy; The linear prediction model is substituted into the cost function, and the approximate solution method based on gradient projection is used to calculate the main screw speed adjustment amount and the adjustment amount of the temperature setting value of each related temperature zone. 5.The temperature control system of a carbon nanotube master batch production line according to claim 1, characterized in that: The acquisition of the analysis reference set: calculate the Euclidean distance between the current feature vector and the feature vector of each historical record, sum the Euclidean distance with 1, and take the reciprocal as the similarity value; Extract the successful compensation records with similarity value greater than the similarity threshold value as the analysis reference set.

6. The temperature control system for a carbon nanotube master batch production line according to claim 1, wherein: The control instruction execution module executes: Receive the final decision control instruction data packet, extract the main screw speed adjustment amount and the adjustment amount of the temperature setting value of each related temperature zone; Add the analyzed main screw speed adjustment amount to the current screw speed setting value to calculate the new target speed setting value; For each temperature zone that needs to be adjusted, add the analyzed temperature adjustment amount to the current barrel temperature setting value of the corresponding temperature zone to calculate the new target temperature setting value.

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