A heat recovery energy-saving control system for textile setting machines

Abstract

This invention relates to the field of energy-saving control technology, and more particularly to a heat recovery energy-saving control system for textile stenters. The system includes a heat capture module, a multi-stage heat recovery module, an energy storage module, an adaptive control module, and an energy allocation module. The heat capture module captures various heat sources from the stenter, including steam and exhaust gas, through a heat exchanger. The multi-stage heat recovery module utilizes segmented temperature difference technology to recover heat energy on demand, extracting energy step by step. The energy storage module adaptively adjusts its storage mode and capacity according to real-time demand. The adaptive control module uses deep learning algorithms to optimize energy management and improve system efficiency. The energy allocation module dynamically adjusts energy distribution through cloud computing technology to achieve efficient energy utilization. The monitoring module monitors the system status in real time, providing data support for maintenance and optimization. This invention improves energy recovery efficiency, optimizes energy management, enhances system adaptability and maintainability, and reduces environmental impact.

Description

Technical Field

[0001] This invention relates to the field of energy-saving control technology, and more particularly to a heat recovery energy-saving control system for a textile setting machine. Background Technology

[0002] Textile setting machines are primarily used for setting textiles. By controlling heat treatment conditions (such as temperature, time, and humidity), they enable textiles to achieve the desired physical and chemical properties, ensuring dimensional stability, shape memory, and hand feel. Traditional setting machines typically use steam or electric heating systems, which generate significant heat loss during operation. Currently, the following problems exist: existing textile setting machines experience substantial heat loss during production, leading to significant overall energy waste; traditional thermal management systems lack effective energy recovery technologies and tiered utilization strategies, failing to allocate and optimize energy according to real-time demand, resulting in low energy efficiency and an inability to meet the precise energy requirements of complex production processes; existing systems are not responsive enough to changes in energy demand during production, lacking effective adaptive control mechanisms and unable to adjust in real-time according to actual working conditions and environmental variables, impacting production efficiency and energy utilization. Summary of the Invention

[0003] To address the above problems, this invention provides a heat recovery energy-saving control system for textile setting machines.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0005] A heat recovery energy-saving control system for a textile setting machine includes a heat capture module, a multi-stage heat recovery module, an energy storage module, an adaptive control module, an energy allocation module, and a monitoring module that are connected in sequence via communication.

[0006] The heat capture module is used to capture and convert heat energy from multiple heat sources of the stenter through a heat exchanger, including steam, exhaust, electric heating, eddy current, and air heating.

[0007] The multi-stage heat recovery module is used to employ segmented temperature difference utilization technology, which sets different recovery levels to correspond to different energy demands, thereby achieving gradual energy extraction.

[0008] The energy storage module is used to employ a hybrid energy storage system to adaptively adjust the energy storage mode and capacity based on real-time energy demand and the status of recovered heat energy.

[0009] The adaptive control module is used to automatically adjust the parameters of energy capture and recovery based on real-time data using deep learning algorithms, while predicting system load changes, automatically optimizing operation strategies, reducing energy consumption and improving response speed.

[0010] The energy allocation module is used to dynamically adjust the energy allocation priority and path through real-time data analysis and processing technology based on cloud computing.

[0011] The monitoring module is used to monitor the working status and environmental variables of various parts of the system in real time through IoT sensing technology, and to provide maintenance warnings and performance optimization suggestions through cloud data analysis.

[0012] Furthermore, the segmented temperature difference utilization technology specifically sets multi-level temperature difference thresholds to perform refined classification processing on the heat energy captured from multiple heat sources in the textile setting machine. By deploying dedicated heat exchangers within each set temperature difference range, the captured heat energy is graded and recovered using a temperature difference management algorithm.

[0013] Furthermore, the formula for the temperature difference management algorithm is as follows:

[0014]

[0015] Where, ΔT i T represents the target temperature difference at level i. s，i T represents the current temperature of the i-th heat source, which is the temperature of multiple heat sources in the textile setting machine; a，i This represents the ambient or post-recovery temperature corresponding to the i-th stage, the target temperature after energy transfer, or the ambient temperature, which depends on the specific application and efficiency of the thermal energy conversion; P d，i P represents the actual energy demand of the current i-th stage; max This represents the system's maximum energy requirement.

[0016] Furthermore, the hybrid energy storage system specifically includes a high-temperature thermal storage device and an electrochemical storage device; the high-temperature thermal storage device is used to store high-temperature thermal energy captured by the thermal energy capture module, and the electrochemical storage device is used to store electrical energy obtained through thermoelectric conversion or grid supplementation.

[0017] Furthermore, the adaptive control module includes a deep learning optimization unit and an operation strategy optimization unit;

[0018] The deep learning optimization unit is used to adjust the parameters of energy capture and recovery in real time using deep learning algorithms, as well as to predict changes in system load.

[0019] The operation strategy optimization unit is used to automatically optimize the operation strategy based on the results generated by the deep learning optimization unit, thereby reducing energy consumption and improving system response speed and efficiency.

[0020] Furthermore, the operation of the deep learning optimization unit includes the following steps:

[0021] The real-time data of temperature, energy demand and ambient temperature of multiple heat sources of the textile setting machine are collected and fused together, and time series analysis technology is used to track and predict the thermal dynamics of the system.

[0022] Based on data collected from multiple heat sources, a convolutional neural network is used to identify temperature difference patterns and dynamically adjust the temperature difference threshold of the multi-stage heat recovery module.

[0023] By combining the collected data with the energy storage status, a deep reinforcement learning algorithm is used to optimize the heat recovery and allocation strategy to achieve the best energy efficiency and minimize energy loss.

[0024] By analyzing system operation data and prediction results, the energy storage mode and capacity of the hybrid energy storage system are adjusted in real time.

[0025] Furthermore, the formula for the deep reinforcement learning algorithm is as follows:

[0026]

[0027] Among them, Q π (s, a) represents the expected total reward of taking action a in state s and following policy π, i.e., the optimal heat recovery allocation policy; s represents the current system state; a represents the action taken in a given state s, including adjusting heat recovery parameters, changing the temperature difference threshold, selecting energy storage mode and capacity; r represents the reward obtained immediately after taking action a from state s, based on energy recovery efficiency and energy consumption reduction; γ represents the discount factor, a value between 0 and 1; P(s′|s, a) represents the probability of transitioning to state s′ after taking action a in state s; max a′ Qπ(s′,a′) represents the maximum expected reward of all possible actions in the new state s′.

[0028] Furthermore, the energy allocation module includes a priority setting unit and a routing decision unit;

[0029] The priority setting unit dynamically adjusts the priority of each energy demand point based on real-time data received from the adaptive control module and the monitoring module.

[0030] The routing decision unit uses graph theory and optimal path algorithms to calculate the most efficient energy allocation route based on the current energy supply status and the priority of each energy demand point.

[0031] Furthermore, the monitoring module includes a sensing component and an analysis component;

[0032] The sensing component is used to collect the working status data, environmental parameters and energy consumption data of the stenter;

[0033] The analysis component uses a cloud computing platform and machine learning algorithms to perform in-depth analysis of the collected data, generate energy consumption reports in real time, and provide energy-saving suggestions.

[0034] The beneficial effects of this invention are as follows:

[0035] This invention utilizes a heat capture module and a heat exchanger to capture heat energy from multiple heat sources, effectively improving energy utilization efficiency. This method reduces energy waste and converts previously lost heat energy into reusable energy, thereby lowering production costs and reducing environmental pollution. The multi-stage heat recovery module employs segmented temperature difference utilization technology, setting different recovery levels for different energy demands, making energy recovery more precise and efficient. This step-by-step energy extraction method maximizes heat energy utilization and provides suitable energy support for different process requirements.

[0036] In this invention, the energy storage module employs a hybrid energy storage system that adaptively adjusts the energy storage mode and capacity based on real-time energy demand and the status of recovered heat energy. This dynamic energy storage adjustment strategy can cope with fluctuations in energy demand during production, ensuring stable system operation. The adaptive control module uses deep learning algorithms to automatically adjust the parameters of energy capture and recovery based on real-time data, predict changes in system load, and automatically optimize operating strategies. This not only reduces energy consumption but also improves the overall system's response speed and adaptability.

[0037] In this invention, the energy allocation module utilizes cloud computing technology to analyze and process real-time data, dynamically adjusting energy allocation priorities and paths. This approach allows for more rational resource allocation and improves overall energy efficiency. The monitoring module leverages IoT sensing technology to monitor the system's operating status and environmental variables in real time, and provides maintenance alerts and performance optimization suggestions through cloud data analysis. This monitoring and early warning mechanism helps to promptly identify potential system problems, reduce downtime, and improve overall system performance and reliability. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the structure of a heat recovery energy-saving control system for a textile setting machine according to the present invention.

[0039] Figure 2 This is a flowchart illustrating the operation of the deep learning optimization unit in this invention. Detailed Implementation

[0040] Please see Figure 1-2 As shown, the present invention relates to a heat recovery energy-saving control system for a textile setting machine.

[0041] Example

[0042] A heat recovery energy-saving control system for a textile setting machine includes a heat capture module, a multi-stage heat recovery module, an energy storage module, an adaptive control module, an energy allocation module, and a monitoring module that are connected in sequence via communication.

[0043] The heat capture module is used to capture and convert heat energy from multiple heat sources of the stenter through a heat exchanger, including steam, exhaust, electric heating, eddy current, and air heating.

[0044] It should be noted that the main heat sources in textile setting machines include steam, exhaust gas, electric heating, eddy currents, and air heating. Each heat source has different temperature characteristics and heat energy content, therefore requiring detailed analysis separately.

[0045] Steam: Typically has high temperature and high heat capacity, and is the main source of heat energy recovery.

[0046] Exhaust gas contains a large amount of residual heat. Although its temperature is lower than that of steam, it still has enough heat energy to be recovered.

[0047] Electric heating: Heat is generated through resistance or electromagnetic induction, resulting in high temperatures.

[0048] Eddy currents: generated from the conversion of mechanical energy during machine operation, and may have a moderate temperature.

[0049] Wind-heated: Heat generated by airflow at a lower temperature, but can also be used for energy recovery if effectively captured.

[0050] Furthermore, in order to effectively capture thermal energy from these heat sources, the heat capture module uses efficient heat exchange technologies, including plate heat exchangers for heat exchange of steam and high-temperature exhaust gas, which are widely used due to their efficient heat transfer performance and easy-to-maintain structure; shell and tube heat exchangers for electric heating and eddy current heat sources, capable of handling high pressure and temperature conditions; and cross-flow heat exchangers for capturing wind heat, especially in environments with high wind speeds and dispersed heat.

[0051] The multi-stage heat recovery module is used to employ segmented temperature difference utilization technology, which sets different recovery levels to correspond to different energy demands, thereby achieving gradual energy extraction.

[0052] Specifically, the segmented temperature difference utilization technology sets multi-level temperature difference thresholds to perform fine-grained classification processing on the heat energy captured from multiple heat sources in the textile setting machine. By deploying dedicated heat exchangers in each set temperature difference range, the captured heat energy is graded and recovered using a temperature difference management algorithm.

[0053] Furthermore, the formula for the temperature difference management algorithm is as follows:

[0054]

[0055] Where, ΔT i T represents the target temperature difference at level i. s，i T represents the current temperature of the i-th heat source, which is the temperature of multiple heat sources in the textile setting machine; a，i This represents the ambient or post-recovery temperature corresponding to the i-th stage, the target temperature after energy transfer, or the ambient temperature, which depends on the specific application and efficiency of the thermal energy conversion; P d，i P represents the actual energy demand of the current i-th stage; max This represents the system's maximum energy requirement.

[0056] It's important to note that the core technology of the multi-stage heat recovery module is segmented temperature difference utilization. This means dividing the heat recovery process into multiple temperature segments, each utilizing a different temperature difference threshold for heat recovery. This method allows for setting appropriate recovery levels based on the specific temperature range of the heat source, thereby achieving gradual energy extraction.

[0057] By setting multiple temperature levels, each level corresponds to a specific heat recovery process. For example, the highest level might handle the highest temperature steam heat, the intermediate level handles the lower temperature exhaust heat, and the lowest level handles the lowest temperature air heat. Dedicated heat exchangers are deployed for each temperature level, such as plate heat exchangers for the high-temperature level and shell-and-tube heat exchangers, which are more resistant to high flow rates, for the low-temperature level.

[0058] Specifically, the selection of temperature difference thresholds is typically based on the temperature distribution of the waste heat source and the heat requirements of the target recovery application. For example, in a textile setting machine, the highest waste heat temperature might reach 200°C, while the lowest might be around 50°C. The set thresholds might include 150°C, 120°C, 90°C, 60°C, and 30°C. This segmented setting helps cover various needs from high to low temperatures, such as heating, preheating, and ambient heating. Different types of heat exchangers are selected for different temperature difference ranges; for example, plate heat exchangers are suitable for high-temperature applications, while shell-and-tube heat exchangers may be more suitable for low-temperature ranges.

[0059] The energy storage module is used to employ a hybrid energy storage system to adaptively adjust the energy storage mode and capacity based on real-time energy demand and the status of recovered heat energy.

[0060] Specifically, the hybrid energy storage system includes a high-temperature thermal storage device and an electrochemical storage device; the high-temperature thermal storage device is used to store high-temperature thermal energy captured by the thermal energy capture module, and the electrochemical storage device is used to store electrical energy obtained through thermoelectric conversion or grid supplementation.

[0061] Specifically, high-temperature thermal storage devices typically utilize phase change materials (PCMs) or high-temperature salt solutions. These materials can store large amounts of thermal energy when a specific temperature is reached and release the energy when the temperature drops. High-temperature thermal storage devices are designed as container or tank structures, filled internally with thermal storage materials and externally equipped with insulation to minimize heat loss. The stored thermal energy can be used directly for heating or converted into electrical energy via thermoelectric conversion mechanisms to supply power to textile machinery. Electrochemical storage devices include, but are not limited to, lithium-ion batteries, lead-acid batteries, or other types of rechargeable battery systems. They also store electrical energy converted from thermoelectricity or directly supplemented from the grid, providing the necessary power support for the system.

[0062] The adaptive control module is used to automatically adjust the parameters of energy capture and recovery based on real-time data using deep learning algorithms, while predicting system load changes, automatically optimizing operation strategies, reducing energy consumption and improving response speed.

[0063] The adaptive control module includes a deep learning optimization unit and an operation strategy optimization unit.

[0064] The deep learning optimization unit is used to adjust the parameters of energy capture and recovery in real time using deep learning algorithms, as well as to predict changes in system load.

[0065] The operation strategy optimization unit is used to automatically optimize the operation strategy based on the results generated by the deep learning optimization unit, thereby reducing energy consumption and improving system response speed and efficiency.

[0066] Specifically, the deep learning optimization unit collects a large amount of system operation data (such as temperature, pressure, and energy flow) and uses this data to train a deep learning model. Model training aims to understand and predict system behavior, thereby providing more accurate control decision support. Based on the trained model, this unit can adjust the energy capture and recovery parameters in the system in real time, such as adjusting the operating intensity of the heat exchanger and adjusting the energy flow direction, to adapt to real-time system demands and changes in the external environment. The deep learning model is also used to predict future system loads and potential efficiency changes, allowing control strategies to be adjusted in advance to avoid potential efficiency degradation or excessive equipment load.

[0067] The operation strategy optimization unit, based on the output of the deep learning optimization unit, generates specific operation strategies, including prioritization of energy use, resource allocation, and preventative maintenance prompts. By continuously optimizing these strategies, the unit helps reduce unnecessary energy consumption, improve energy efficiency, and ensure that energy demands during production are consistently met. Upon detecting changes in demand or system anomalies, the operation strategy optimization unit can react quickly, adjusting system configuration to address these changes, thereby improving the overall system responsiveness and reliability.

[0068] The operation of the deep learning optimization unit includes the following steps:

[0069] The real-time data of temperature, energy demand and ambient temperature of multiple heat sources of the textile setting machine are collected and fused together, and time series analysis technology is used to track and predict the thermal dynamics of the system.

[0070] Based on data collected from multiple heat sources, a convolutional neural network is used to identify temperature difference patterns and dynamically adjust the temperature difference threshold of the multi-stage heat recovery module.

[0071] By combining the collected data with the energy storage status, a deep reinforcement learning algorithm is used to optimize the heat recovery and allocation strategy to achieve the best energy efficiency and minimize energy loss.

[0072] By analyzing system operation data and prediction results, the energy storage mode and capacity of the hybrid energy storage system are adjusted in real time.

[0073] Furthermore, the formula for the deep reinforcement learning algorithm is as follows:

[0074]

[0075] Among them, Q π (s, a) represents the expected total reward of taking action a in state s and following policy π, i.e., the optimal heat recovery allocation policy; s represents the current system state; a represents the action taken in a given state s, including adjusting heat recovery parameters, changing the temperature difference threshold, selecting energy storage mode and capacity; r represents the reward obtained immediately after taking action a from state s, based on energy recovery efficiency and energy consumption reduction; γ represents the discount factor, a value between 0 and 1; P(s′|s, a) represents the probability of transitioning to state s′ after taking action a in state s; max a′ Q π (s′,a′) represents the maximum expected reward of all possible actions under the new state s′.

[0076] The energy allocation module is used to dynamically adjust the energy allocation priority and path through real-time data analysis and processing technology based on cloud computing.

[0077] The energy allocation module includes a priority setting unit and a routing decision unit.

[0078] The priority setting unit dynamically adjusts the priority of each energy demand point based on real-time data received from the adaptive control module and the monitoring module.

[0079] The routing decision unit uses graph theory and optimal path algorithms to calculate the most efficient energy allocation route based on the current energy supply status and the priority of each energy demand point.

[0080] Specifically, the priority setting unit uses high-speed data processing technology to collect and analyze energy usage data from various parts of the system, including but not limited to energy consumption rates, critical operation periods, and historical energy usage patterns. It utilizes decision trees, neural networks, or other machine learning algorithms to learn and predict the priority of each energy point. The algorithm can automatically adjust priorities based on energy availability and the urgency of demand. The system can automatically adjust priorities based on changes in external conditions (such as changes in production line speed, seasonal changes in energy demand, etc.) and internal conditions (such as equipment maintenance status, energy storage levels, etc.). During peak energy demand periods, such as when production is running at high speed, the priority setting unit will increase the energy supply priority for these periods to ensure that production is not affected by insufficient energy. During low demand periods, such as at night or non-production times, the system will decrease the energy supply priority, prioritizing the use of low-cost or renewable energy sources.

[0081] The routing decision unit uses graph theory algorithms, such as the shortest path algorithm and the maximum flow minimum cut theorem, to determine the optimal energy transmission path. The routing decision unit can receive real-time updates from the monitoring module and dynamically adjust the energy flow direction based on real-time changes in energy supply and demand. In the event of a sudden equipment failure requiring rapid shutdown, the routing decision unit can quickly reallocate its energy to other parts that need it, avoiding energy waste and ensuring the stable operation of other production lines. Based on the actual utilization efficiency of each part, it optimizes energy distribution, reduces energy loss during transmission, and improves overall energy efficiency.

[0082] The monitoring module is used to monitor the working status and environmental variables of each part of the system in real time through IoT sensing technology, and to provide maintenance warnings and performance optimization suggestions through cloud data analysis;

[0083] The monitoring module includes a sensing component and an analysis component;

[0084] The sensing component is used to collect the working status data, environmental parameters and energy consumption data of the stenter;

[0085] The analysis component uses a cloud computing platform and machine learning algorithms to perform in-depth analysis of the collected data, generate energy consumption reports in real time, and provide energy-saving suggestions.

[0086] Specifically, the sensing component mainly consists of various sensors distributed in key parts of the stenter to monitor the following parameters in real time:

[0087] Temperature sensors are installed at key locations such as the heat exchanger inlet and outlet, storage equipment, and inside the machine to monitor temperature changes at each point.

[0088] Pressure sensors are used to monitor pressure levels in the heat capture module and multi-stage heat recovery module to ensure that the system operates within safe limits.

[0089] Flow sensors measure the flow rate of heat media (such as steam and hot water) and cooling media to assess the heat exchange efficiency of a system.

[0090] Power is monitored by sensors to assess the power consumption of the entire system, helping to evaluate the efficiency and load response of electrochemical storage devices.

[0091] Environmental sensors monitor the temperature, humidity, and other relevant conditions of the surrounding environment to ensure that the system can adapt to changes in different environmental conditions.

[0092] The cloud platform within the analytics component is responsible for storing, processing, and analyzing the massive amounts of data from the sensing component. The platform possesses high-performance computing capabilities, enabling it to run complex data processing and machine learning algorithms. The analytics component uses machine learning algorithms, such as decision trees, random forests, and neural networks, to perform in-depth analysis of the data. These algorithms can identify patterns and trends in the data, predict potential system performance issues, and suggest possible optimization measures. The cloud platform not only generates real-time energy consumption reports but also automatically adjusts the system's energy allocation and operating parameters based on real-time data analysis results, optimizing energy consumption. For example, if lower energy demand is predicted for a certain period, the system can automatically reduce energy input to avoid unnecessary energy consumption. The analytics component also includes an intelligent maintenance and early warning system that predicts equipment maintenance needs based on real-time operating status and historical performance data, automatically issuing reminders for maintenance or component replacement. This helps prevent equipment failures and reduce unexpected downtime.

[0093] In summary, this application captures waste heat from multiple heat sources (such as steam, exhaust, electric heating, eddy current, and air heating) in a textile setting machine, converting previously dissipated heat energy into usable energy and improving overall energy efficiency. The system's multi-stage heat recovery module effectively utilizes heat sources at different temperatures by setting different recovery levels to correspond to different energy demands. This tiered utilization significantly reduces energy waste, thereby lowering energy costs. It also reduces dependence on fossil fuels and carbon dioxide emissions, making it environmentally friendly. By recovering and reusing heat energy, the system mitigates the environmental impact of industrial activities, contributing to enterprises' sustainable development goals.

[0094] This application's adaptive control module uses deep learning algorithms to adjust energy capture and recovery parameters in real time, automatically optimizing operating strategies and improving response speed and system efficiency. This intelligent control reduces human error and ensures efficient and stable system operation. The hybrid energy storage system of the energy storage module can adaptively adjust the energy storage mode and capacity according to real-time energy demand and the status of recovered heat energy. This improves energy availability and reliability, ensuring sufficient energy support during peak demand periods. By monitoring the system's operating status and environmental variables in real time, the monitoring module can detect potential problems early and perform maintenance, which helps avoid sudden failures and downtime, reducing maintenance costs and operational risks.

[0095] The above embodiments are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A heat recovery energy-saving control system for a textile setting machine, characterized in that, It includes a heat capture module, a multi-stage heat recovery module, an energy storage module, an adaptive control module, an energy allocation module, and a monitoring module that are connected in sequence via communication. The heat capture module is used to capture and convert heat energy from multiple heat sources of the stenter through a heat exchanger, including steam, exhaust, electric heating, eddy current, and air heating. The multi-stage heat recovery module is used to employ segmented temperature difference utilization technology, which sets different recovery levels to correspond to different energy demands, thereby achieving gradual energy extraction. The energy storage module is used in a hybrid energy storage system to adaptively adjust the energy storage mode and capacity based on real-time energy demand and the status of recovered heat energy. The adaptive control module is used to automatically adjust the parameters of energy capture and recovery based on real-time data using deep learning algorithms, while predicting system load changes, automatically optimizing operation strategies, reducing energy consumption and improving response speed. The energy allocation module is used to dynamically adjust the energy allocation priority and path through real-time data analysis and processing technology based on cloud computing. The monitoring module is used to monitor the working status and environmental variables of various parts of the system in real time through IoT sensing technology, and to provide maintenance warnings and performance optimization suggestions through cloud data analysis.

2. The heat recovery energy-saving control system for a textile setting machine according to claim 1, characterized in that, The segmented temperature difference utilization technology specifically involves setting multiple temperature difference thresholds to perform refined grading of the heat energy captured from multiple heat sources in the textile setting machine. By deploying dedicated heat exchangers within each set temperature difference range, the captured heat energy is graded and recovered using a temperature difference management algorithm.

3. The heat recovery energy-saving control system for a textile setting machine according to claim 2, characterized in that, The formula for the temperature difference management algorithm is as follows: Where, ΔT i T represents the target temperature difference at level i. s，i T represents the current temperature of the i-th heat source, which is the temperature of multiple heat sources in the textile setting machine; a,i This represents the ambient or post-recovery temperature corresponding to the i-th stage, the target temperature after energy transfer, or the ambient temperature, which depends on the specific application and efficiency of the thermal energy conversion; P d，i P represents the actual energy demand of the current i-th stage; max This represents the system's maximum energy requirement.

4. The heat recovery energy-saving control system for a textile setting machine according to claim 1, characterized in that, The hybrid energy storage system specifically includes a high-temperature thermal storage device and an electrochemical storage device; the high-temperature thermal storage device is used to store high-temperature thermal energy captured by the thermal energy capture module, and the electrochemical storage device is used to store electrical energy obtained through thermoelectric conversion or grid supplementation.

5. The heat recovery energy-saving control system for a textile setting machine according to claim 1, characterized in that, The adaptive control module includes a deep learning optimization unit and an operation strategy optimization unit; The deep learning optimization unit is used to adjust the parameters of energy capture and recovery in real time using deep learning algorithms, as well as to predict changes in system load. The operation strategy optimization unit is used to automatically optimize the operation strategy based on the results generated by the deep learning optimization unit, thereby reducing energy consumption and improving system response speed and efficiency.

6. The heat recovery energy-saving control system for a textile setting machine according to claim 5, characterized in that, The operation of the deep learning optimization unit includes the following steps: The real-time data of temperature, energy demand and ambient temperature of multiple heat sources of the textile setting machine are collected and fused together, and time series analysis technology is used to track and predict the thermal dynamics of the system. Based on data collected from multiple heat sources, a convolutional neural network is used to identify temperature difference patterns and dynamically adjust the temperature difference threshold of the multi-stage heat recovery module. By combining the collected data with the energy storage status, a deep reinforcement learning algorithm is used to optimize the heat recovery and allocation strategy to achieve the best energy efficiency and minimize energy loss. By analyzing system operation data and prediction results, the energy storage mode and capacity of the hybrid energy storage system are adjusted in real time.

7. The heat recovery energy-saving control system for a textile setting machine according to claim 6, characterized in that, The formula for the deep reinforcement learning algorithm is as follows: Among them, Q π (s, a) represents the expected total reward of taking action a in state s and following policy π, i.e., the optimal heat recovery allocation policy; s represents the current system state; a represents the action taken in a given state s, including adjusting heat recovery parameters, changing the temperature difference threshold, selecting energy storage mode and capacity; r represents the reward obtained immediately after taking action a from state s, based on energy recovery efficiency and energy consumption reduction; γ represents the discount factor, a value between 0 and 1; P(s′|s,a) represents the probability of transitioning to state s′ after taking action a in state s; max a′ Q π (s′,a′) represents the maximum expected reward of all possible actions under the new state s′.

8. The heat recovery energy-saving control system for a textile setting machine according to claim 1, characterized in that, The energy allocation module includes a priority setting unit and a routing decision unit; The priority setting unit dynamically adjusts the priority of each energy demand point based on real-time data received from the adaptive control module and the monitoring module. The routing decision unit uses graph theory and optimal path algorithms to calculate the most efficient energy allocation route based on the current energy supply status and the priority of each energy demand point.

9. The heat recovery energy-saving control system for a textile setting machine according to claim 1, characterized in that, The monitoring module includes a sensing component and an analysis component; The sensing component is used to collect the working status data, environmental parameters and energy consumption data of the stenter; The analysis component uses a cloud computing platform and machine learning algorithms to perform in-depth analysis of the collected data, generate energy consumption reports in real time, and provide energy-saving suggestions.

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