Textile process protection method and system based on furnace temperature curve model

Abstract

The invention discloses a textile process protection method based on a furnace temperature curve model, and the method comprises the steps: installing a plurality of temperature sensors on a heat treatment furnace, and collecting the real-time temperature data of the heat treatment furnace through the temperature sensors; by taking the real-time temperature data as input, constructing a furnace temperature curve model comprising a heating stage, a maintaining stage and a cooling stage, adjusting and optimizing the model, and outputting a temperature change curve of the heat treatment furnace; and the temperature change curve is compared with the real-time temperature data, and the heat treatment furnace is adjusted through the central controller based on the comparison result. The temperature change in the spinning process can be monitored in real time, the temperature fluctuation can be predicted and adjusted through the furnace temperature curve model, it is ensured that the temperature is always kept within the ideal range, and damage to the spinning material due to the temperature change is avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of textile processes, and particularly relates to a textile process protection method and system based on a furnace temperature curve model. Background Art

[0002] In the textile industry, temperature is one of the key factors affecting the quality of textile materials. Whether it is processes such as spinning, weaving, dyeing, and heat treatment, strict requirements are imposed on temperature changes. Excessive or too low temperature will have an adverse impact on the structure, morphology, and properties of textiles. For example, if the temperature is not properly controlled during the dyeing process of textiles, it may lead to uneven dye adsorption and affect the color stability; while during the heat treatment process, temperature fluctuations may cause fiber shrinkage or breakage.

[0003] Existing temperature control systems mostly perform single temperature monitoring, lacking precise dynamic adjustment and prediction and control of complex process temperature curves. In order to effectively manage temperature, especially in processes with high-precision temperature control requirements, it is necessary to introduce a furnace temperature curve model to monitor and adjust temperature changes in real time, so as to ensure the integrity of materials and the consistency of processes. Summary of the Invention

[0004] Object of the Invention: The object of the present invention is to provide a textile process protection method and system based on a furnace temperature curve model. The present invention can not only monitor the temperature changes in the textile process in real time, but also predict and adjust temperature fluctuations through the furnace temperature curve model to ensure that the temperature always remains within the ideal range and avoid damage to textile materials caused by temperature changes.

[0005] Technical Solution: A textile process protection method based on a furnace temperature curve model of the present invention includes the following steps:

[0006] Step 1: Install a number of temperature sensors on the heat treatment furnace, and collect real-time temperature data of the heat treatment furnace through the temperature sensors;

[0007] Step 2: Use the real-time temperature data as input, construct a furnace temperature curve model including a heating stage, a holding stage, and a cooling stage, adjust and optimize the model, and output the temperature change curve of the heat treatment furnace;

[0008] Step 3: Compare the temperature change curve with the real-time temperature data, and based on the comparison result, adjust the heat treatment furnace through the central controller.

[0009] Further, step 1 is specifically as follows: Install a number of temperature sensors at five positions, namely, at the furnace inlet, the upper part of the furnace chamber, the middle part of the furnace chamber, the lower part of the furnace chamber, and the furnace outlet of the heat treatment furnace; Monitor the initial temperature at the furnace inlet; Monitor the heating zone temperature at the upper part of the furnace chamber; Monitor the core heating zone temperature at the middle part of the furnace chamber; Monitor the cooling zone temperature at the lower part of the furnace chamber; Monitor the final temperature at the furnace outlet.

[0010] Further, in step 2, the heating stage is specifically as follows: By analyzing the heat source in the furnace, the heating efficiency, and the heat conduction characteristics of the textile material, predict the change of temperature with time. In the heating stage, the temperature change is simulated by a function similar to exponential growth;

[0011]

[0012] where, T(t) is the furnace temperature at time t, T start is the initial temperature, T target is the target temperature, and τ is a time constant related to the heat conduction of the furnace body and the heating efficiency.

[0013] Further, in step 2, the maintaining stage is specifically as follows: After the temperature reaches the target value, the furnace temperature enters a stable maintaining stage; In this stage, the furnace temperature remains constant, and a target temperature range is set according to the furnace temperature curve model. When the actual temperature deviates from the target range, start the heating or cooling equipment for adjustment;

[0014] T steady = T target

[0015] where, T steady is the constant target temperature in the maintaining stage.

[0016] Further, in step 2, the cooling stage is specifically as follows: When the temperature exceeds the target range, or when the process requires cooling, the cooling stage starts; In the cooling stage, the rate of temperature drop is affected by environmental factors and conforms to the exponential decay law;

[0017]

[0018] where, T final is the temperature at the end of cooling, T current is the current temperature, and α is a constant related to the cooling rate.

[0019] Further, in step 2, the adjustment and optimization of the model are specifically as follows: According to the thermal inertia of the heat treatment furnace, the heat capacity of the material, and the change of heating power, optimize the temperature curve in real time through PID control, fuzzy control, or neural network learning.

[0020] When adopting PID control, the heating power and cooling rate are dynamically adjusted according to the proportional, integral, and differential components of the real-time temperature deviation. The specific formula is as follows:

[0021]

[0022] Among them, u(k) is the control output, and K p 、K i 、K d are the proportional, integral, and differential coefficients respectively, and e(k) is the real-time temperature deviation;

[0023] When adopting fuzzy control, fuzzy inference is performed on the temperature deviation and the rate of change of deviation based on the fuzzy rule base, and the adjustment amount of heating or cooling is output;

[0024] When adopting neural network learning, the neural network model is trained using historical temperature data and process parameters, the optimal temperature curve is predicted, and the model weights are updated in real time through online learning to adapt to the thermal inertia of the furnace body and changes in the external environment.

[0025] Furthermore, step 3 is specifically as follows: After the central controller builds the furnace temperature curve model, it calculates the target temperature curve in real time and compares it with the actual temperature data. When the deviation between the temperature detected by the sensor and the target curve is too large, the controller issues an instruction to adjust the operating state of the relevant equipment;

[0026] When the temperature is too low, the controller starts the heating device to accelerate the temperature rise; when the temperature is too high, the controller starts the cooling equipment to cool down, ensuring that the temperature is always controlled within the safe range.

[0027] The present invention also discloses a textile process protection system based on the furnace temperature curve model, including a plurality of temperature sensors, a controller, a temperature adjustment actuator, and a real-time feedback and alarm system;

[0028] The temperature sensors are arranged at various positions of the textile equipment and are used to detect the temperature changes during the textile process;

[0029] The controller is used to receive the data from the temperature sensors and calculate the ideal temperature curve according to the furnace temperature curve model;

[0030] The temperature adjustment actuator is used to adjust the temperature of the equipment according to the instructions of the controller;

[0031] The real-time feedback and alarm system is used to provide temperature data and temperature anomaly alarms to the operator.

[0032] Furthermore, the temperature adjustment actuator includes a cooling system and a heating system, which are used to adjust the temperature during the textile process.

[0033] Furthermore, the textile process protection system further includes a remote control and data analysis system for remote monitoring and data.

[0034] Advantages: Compared with the prior art, the present invention has the following remarkable advantages:

[0035] 1. Multi-stage dynamic optimization: The present invention realizes the full-process dynamic optimization of temperature through a staged furnace temperature curve model (heating, maintaining, cooling), combined with PID control, fuzzy control or neural network algorithms, significantly improving the control accuracy.

[0036] 2. Adaptive adjustment ability: The model of the present invention can adjust parameters in real time according to the thermal inertia of the furnace body, the heat capacity of the material and environmental factors, adapt to different process scenarios, and reduce the need for manual intervention.

[0037] 3. Energy efficiency improvement: The present invention reduces energy waste and production costs by accurately predicting temperature changes and optimizing heating / cooling strategies.

[0038] 4. Intelligent alarm and feedback: The system of the present invention combines real-time feedback with remote monitoring functions, can quickly respond to abnormal working conditions, and avoid large-scale production losses caused by temperature fluctuations.

[0039] 5. Compatibility and scalability: The present invention supports a variety of control algorithms (such as PID, fuzzy control, neural network) and remote data analysis, facilitating subsequent process upgrades and system integration. Description of the Drawings

[0040] Figure 1 It is a flowchart of the textile process protection system based on temperature sensors and furnace temperature curve models of the present invention;

[0041] Figure 2 It is a schematic diagram of temperature change of the furnace temperature curve model; Detailed Embodiments

[0042] The technical solutions of the present invention will be further described below with reference to the accompanying drawings.

[0043] Taking the heat treatment process as an example, in a heat treatment furnace, multiple temperature sensors are respectively installed at different positions in the furnace cavity to collect temperature data in real time. The controller compares these data with the furnace temperature curve model to generate an ideal temperature curve. If the temperature in the furnace is too high, the controller will instruct the cooling system to start to keep the temperature within the set range; if the temperature is too low, the heating system will be enabled to adjust the temperature. The system will also regularly send feedback reports to the operator, showing the temperature change curve in the furnace and the deviation from the target curve in real time. The following will detail specific embodiments of the textile process temperature control and protection system based on temperature sensors and furnace temperature curve models, with a focus on the construction and application process of the furnace temperature curve model.

[0044] Example: Application Scenario of Heat Treatment Furnace

[0045] 1. Installation and Data Acquisition of Temperature Sensors

[0046] In this example, a typical heat treatment furnace is selected as the application scenario. During the heat treatment process, the stability of the furnace temperature is crucial for the quality of textile materials. Therefore, the system installs multiple high-precision temperature sensors (such as thermocouple sensors) at various key positions in the heat treatment furnace (such as the furnace cavity entrance, the middle of the furnace cavity, and the outlet). These temperature sensors can collect the temperature data inside the furnace in real time and transmit it to the central controller in a wired or wireless manner.

[0047] In this embodiment, a total of 5 temperature sensors are arranged and installed at the following positions:

[0048] Position 1: At the furnace entrance, monitoring the initial temperature;

[0049] Position 2: At the upper part of the furnace cavity, monitoring the temperature in the heating zone;

[0050] Position 3: In the middle of the furnace cavity, monitoring the temperature in the core heating zone;

[0051] Position 4: At the lower part of the furnace cavity, monitoring the temperature in the cooling zone;

[0052] Position 5: At the furnace outlet, monitoring the final temperature

[0053] 2. Construction of Furnace Temperature Curve Model

[0054] The furnace temperature curve model is the core of the system. Based on the real-time data obtained by the temperature sensors, the central controller calculates and predicts the ideal temperature change curve according to the furnace temperature curve model. This model not only considers the temperature increase process inside the furnace but also includes the control of the temperature maintenance stage and the cooling stage.

[0055] 2.1 Model Principle

[0056] The furnace temperature curve model adopts a dynamic temperature prediction model based on thermodynamics principles. The model mainly considers the following factors:

[0057] Heating stage: By analyzing the heat source inside the furnace, the heating efficiency, and the heat conduction characteristics of the textile material, the temperature change over time is predicted. During the heating stage, the temperature change can be simulated by a function similar to exponential growth, where the relationship between temperature and time is affected by factors such as the furnace temperature rise rate, the air flow inside the furnace, and the heater power.

[0058]

[0059] Among them, T(t) is the furnace temperature at time t, T start is the initial temperature, Ttarget is the target temperature, and τ is a time constant related to the heat conduction and heating efficiency of the furnace body.

[0060] Maintenance stage: After the temperature reaches the target value, the furnace temperature enters a stable maintenance stage. During this stage, the furnace temperature should remain constant to avoid any temperature fluctuations. Therefore, the controller sets a target temperature range according to the furnace temperature curve model. When the actual temperature deviates from the target range, the heating or cooling equipment is started for adjustment.

[0061] T steady = T target

[0062] where is the constant target temperature during the maintenance stage.

[0063] Cooling stage: When the temperature exceeds the target range or the process requires cooling, the cooling stage begins. During the cooling stage, the rate of temperature decrease is usually affected by environmental factors (such as the flow rate of external cooling air, humidity inside the furnace, etc.) and follows the exponential decay law.

[0064]

[0065] where T final is the temperature at the end of cooling, T current is the temperature at the current moment, and α is a constant related to the cooling rate.

[0066] 2.2 Adjustment and Optimization of the Curve Model

[0067] The furnace temperature curve model is dynamically adjusted according to historical data and real-time feedback. During different process stages, the change of the temperature inside the furnace is affected by various factors, including the thermal inertia of the furnace body, the heat capacity of the material, the change of heating power, etc. Therefore, the model optimizes the temperature curve in real time through online learning algorithms (such as PID control, fuzzy control or neural network) to ensure that the furnace temperature curve always meets the production requirements.

[0068] For example, during a certain production process, the model may detect that after the furnace body temperature reaches the set value, due to heat loss, the temperature drops slightly. The system will automatically adjust the power output of the heater according to the model to supplement heat in real time and maintain the temperature within the set range.

[0069] 3. Controller and Adjustment Module

[0070] After the central controller incorporates the furnace temperature curve model, it starts to calculate the target temperature curve in real time and compares it with the actual temperature data. When the temperature detected by the sensor deviates too much from the target curve, the controller issues an instruction to adjust the operating state of the relevant equipment.

[0071] Specifically, if the temperature is too low, the controller will activate the heating device (such as an electric heater, steam heating system, etc.) to accelerate the temperature rise. If the temperature is too high, the controller will activate the cooling equipment (such as an air-cooling system, water-cooling system, etc.) to lower the temperature and ensure that the temperature is always controlled within a safe range.

[0072] 3.1 Temperature Regulation Example

[0073] Suppose during a heat treatment process, the target temperature is 180 °C, and the furnace temperature curve model predicts that it will take 30 minutes to reach the target temperature in the heating stage. However, real-time sensor data shows that the temperature is rising slowly. The controller calculates through the model that the heating power needs to be increased to compensate for this difference. By intelligently adjusting the heating system, the actual temperature will reach 180 °C within 25 minutes, avoiding process failure caused by insufficient temperature.

[0074] 3.2 Cooling Stage Regulation

[0075] In another scenario, suppose it is necessary to quickly lower the temperature to 150 °C. The controller will, according to the cooling law of the furnace temperature curve model, ensure that the temperature quickly drops to the target value within the specified time by adjusting the cooling equipment (such as increasing the wind speed or turning on the cooling water spraying system).

[0076] 4. Real-time Feedback and Alarm System

[0077] During the entire temperature control process, the system monitors the temperature data in real-time and provides feedback to the operator. When the temperature deviates too much from the target curve, the system sends an alarm through the display screen, audio alarm, or remote platform push, prompting the operator to intervene. For abnormal temperature situations, the system will also record the deviation information for the operator to analyze and optimize the process parameters later.

[0078] 5. Remote Monitoring and Data Storage

[0079] This system supports remote monitoring and data storage on the cloud platform or local server. Management personnel can view the temperature curves of each process link through the remote platform and compare and analyze them with historical data. By learning and analyzing the data of multiple production processes, the system can continuously optimize the furnace temperature curve model and improve the temperature control accuracy.

[0080] By combining the furnace temperature curve model with the real-time feedback of the temperature sensor, the present invention can achieve precise temperature control and regulation, ensuring the quality and consistency of materials in the textile process. The furnace temperature curve model not only considers the temperature change law in different process stages but also can be dynamically adjusted according to real-time data to ensure the accuracy of temperature control. This system is applicable to various textile processing equipment with strict temperature control requirements, can effectively avoid the damage caused by temperature fluctuations to textile materials, improve production efficiency, and reduce energy consumption.

[0081] The object of the present invention is to provide a textile process protection system based on temperature sensors and furnace temperature curve models. This system can not only monitor the temperature changes in the textile process in real time, but also predict and adjust temperature fluctuations through the furnace temperature curve model to ensure that the temperature always remains within the ideal range and avoid damage to textile materials caused by temperature changes. The technical solution of the present invention includes the following parts:

[0082] Temperature sensor module:

[0083] Multiple high-precision temperature sensors are installed in the system and distributed at key positions of textile equipment (such as heat treatment furnaces, dyeing machines, spinning machines, etc.). These sensors monitor the temperature of the equipment and textile materials in real time and transmit the data to the central controller.

[0084] Furnace temperature curve model:

[0085] The central controller is embedded with a furnace temperature curve model. Based on the principles of physics and thermodynamics and combined with the actual production process, this model can predict and calculate the law of temperature change in the furnace over time and generate an ideal temperature curve in real time. The furnace temperature curve model dynamically adjusts the rising, maintaining, and falling stages of the temperature according to the actual situation to ensure that the temperature control accuracy is consistent with the production process.

[0086] Controller and adjustment module:

[0087] Based on the real-time collected temperature data and the furnace temperature curve model, the controller analyzes the deviation between the current temperature and the ideal temperature curve. If the temperature deviates from the preset range, the controller will issue an adjustment instruction to start the temperature adjustment device (such as heating system, cooling system, air cooling system, etc.) for adjustment to ensure that the temperature returns to the target range.

[0088] Real-time feedback and alarm system:

[0089] The system provides real-time feedback of the temperature data of each process stage to the operator through a display screen or a remote control platform, and automatically issues an alarm to prompt the operator to take corresponding measures when the temperature deviates from the ideal curve. In addition, the system supports remote monitoring and data storage, and can obtain historical temperature curves and their deviation information in real time through a cloud platform or a local server, providing a basis for later process optimization and production adjustment.

[0090] In this specification, the use of terms such as "embodiment", "specific embodiment", or "certain embodiments" is intended to indicate that the specific features, materials, structures, or characteristics described in connection with these embodiments or examples are applied in at least one embodiment of the present invention. However, the exemplary expressions of the terms do not necessarily refer to the same embodiment. In addition, the specific features, materials, structures, or characteristics described herein can be combined in any appropriate manner in one or more embodiments.

[0091] Although the embodiments of the present invention have been elaborated and described in detail, those skilled in the art should understand that these embodiments can be modified, substituted, or variously deformed without departing from the basic principles and spirit of the present invention. The protection scope of the present invention shall be subject to the scope of the appended claims and their equivalent technical features.

Claims

1. A textile process protection method based on a furnace temperature curve model, characterized in that: The steps include: Step 1: Install several temperature sensors on the heat treatment furnace, and collect real-time temperature data of the heat treatment furnace through the temperature sensors; Step 2: Using real-time temperature data as input, construct a furnace temperature curve model including heating stage, maintenance stage and cooling stage, adjust and optimize the model, and output the temperature change curve of the heat treatment furnace; Step 3: Compare the temperature change curve with the real-time temperature data, and adjust the heat treatment furnace through the central controller based on the comparison result.

2. The textile process protection method based on the furnace temperature curve model according to claim 1 is characterized in that: Step 1 is specifically as follows: a number of temperature sensors are installed at five locations of the heat treatment furnace, namely, the furnace entrance, the upper part of the furnace cavity, the middle part of the furnace cavity, the lower part of the furnace cavity and the furnace exit; the initial temperature is monitored at the furnace entrance; the heating zone temperature is monitored at the upper part of the furnace cavity; the core heating zone temperature is monitored in the middle part of the furnace cavity; the cooling zone temperature is monitored at the lower part of the furnace cavity; and the final temperature is monitored at the furnace exit.

3. The textile process protection method based on the furnace temperature curve model according to claim 1 is characterized in that: In step 2, the heating stage is specifically as follows: by analyzing the heat source in the furnace, the heating efficiency and the heat conduction characteristics of the textile material, the temperature change over time is predicted. In the heating stage, the temperature change is simulated by a function similar to exponential growth; Where T(t) is the furnace temperature at time t, T start is the initial temperature, T target is the target temperature, and τ is a time constant related to the heat conduction and heating efficiency of the furnace.

4. The textile process protection method based on the furnace temperature curve model according to claim 1 is characterized in that: In step 2, the maintenance phase is specifically as follows: after the temperature reaches the target value, the furnace temperature enters a stable maintenance phase; in this phase, the furnace temperature remains constant, and a target temperature range is set according to the furnace temperature curve model. When the actual temperature deviates from the target range, the heating or cooling equipment is started for adjustment; T steady =T target Among them, T steady is the constant target temperature during the maintenance phase.

5. The textile process protection method based on the furnace temperature curve model according to claim 1 is characterized in that: In step 2, the cooling stage is specifically: when the temperature exceeds the target range, or the process needs to be cooled, the cooling stage begins; in the cooling stage, the rate of temperature drop is affected by environmental factors and conforms to the exponential decay law; Among them, T final is the temperature at the end of cooling, T current is the current temperature, and α is a constant related to the cooling rate.

6. The textile process protection method based on the furnace temperature curve model according to claim 1 is characterized in that: In step 2, the model is adjusted and optimized in detail by optimizing the temperature curve in real time through PID control, fuzzy control or neural network learning according to the thermal inertia of the heat treatment furnace, the heat capacity of the material and the change of heating power; When PID control is used, the heating power and cooling rate are dynamically adjusted according to the proportional, integral and differential components of the real-time temperature deviation. The specific formula is: Among them, u(k) is the control output, K p , K i , K d are the proportional, integral and differential coefficients respectively, and e(k) is the real-time temperature deviation; When fuzzy control is adopted, fuzzy reasoning is performed on the temperature deviation and the rate of change of the deviation based on the fuzzy rule base, and the adjustment amount of heating or cooling is output; When using neural network learning, historical temperature data and process parameters are used to train the neural network model, predict the optimal temperature curve, and update the model weights in real time through online learning to adapt to changes in the thermal inertia of the furnace and the external environment.

7. The textile process protection method based on the furnace temperature curve model according to claim 1 is characterized in that: Step 3 is as follows: after the central controller has a built-in furnace temperature curve model, it calculates the target temperature curve in real time and compares it with the actual temperature data. When the temperature detected by the sensor deviates too much from the target curve, the controller will issue a command to adjust the operating status of the relevant equipment; When the temperature is too low, the controller will start the heating device to speed up the temperature rise; when the temperature is too high, the controller will start the cooling equipment to cool it down to ensure that the temperature is always controlled within a safe range.

8. A textile process protection system based on a furnace temperature curve model, used to implement the method according to claim 1, characterized in that: It includes several temperature sensors, controllers, temperature regulating actuators and real-time feedback and alarm systems; The temperature sensors are arranged at various positions of the textile equipment to detect temperature changes during the textile process; The controller is used to receive data from the temperature sensor and calculate an ideal temperature curve according to the furnace temperature curve model; The temperature adjustment actuator is used to adjust the temperature of the device according to the instructions of the controller; The real-time feedback and alarm system is used to provide temperature data and abnormal temperature alarms to the operator.

9. A textile process protection system based on a furnace temperature curve model according to claim 8, characterized in that: The temperature regulating actuator comprises a cooling system and a heating system, which are used to regulate the temperature during the weaving process.

10. The textile process protection system based on the furnace temperature curve model according to claim 8, characterized in that: The textile process protection system also includes a remote control and data analysis system for remote monitoring and data.

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