Temperature control method and system for corrosion-resistant refrigeration pipe with intelligent temperature control coating

Abstract

The invention discloses a temperature control method and system for a corrosion-resistant refrigeration pipe with an intelligent temperature control coating, and relates to the technical field of refrigeration process control. According to the method, the temperature and the flow in each electric refrigeration section pipe and the external wind speed and the temperature in the refrigeration process are collected, and a coupling heat balance equation of Fourier law and convective heat transfer is combined; quantifying radial and axial heat conduction to calculate heat absorption capacity; designing a physical embedded meta learning graph neural network to map the heat absorption capacity into target voltage, designing a voltage coupling conduction function by cooperatively considering the influence of the power-on voltage of the electric refrigeration sections on the other electric refrigeration sections in order to meet the target voltage of each electric refrigeration section, and iteratively determining the optimal power-on voltage through a swarm intelligence algorithm; parameters are optimized according to temperature difference feedback; the system comprises an acquisition module, a regulation and control module and a feedback module which are used for executing data acquisition, voltage regulation and control and parameter optimization decision making respectively, and aims at accurately controlling the temperature, eliminating inter-segment interference and meeting the cooling requirements under multiple working conditions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of refrigeration process control, in particular to a temperature control method and system of a corrosion-resistant refrigeration pipe with an intelligent temperature control coating. BACKGROUND

[0002] As a core component in the fields of cold chain transportation and industrial precision cooling, the refrigeration pipe needs to meet the dual requirements of corrosion resistance and high-precision temperature control. However, there are still significant defects in the existing technology in terms of temperature control technology. On the one hand, the existing temperature control method does not comprehensively consider the key factors such as the flow of the medium in the pipe and the external environmental wind speed and temperature in the calculation of the heat loss of the refrigeration pipe, resulting in a large deviation in the calculation of heat loss and an inability to accurately reflect the actual heat absorption. On the other hand, although some technologies introduce an electric cooling section, they use fixed algorithms or simple neural networks to determine the voltage, without quantifying the mutual influence of the voltages of each electric cooling section, which easily leads to mismatch between the voltage and the heat absorption due to the coupling interference between sections, resulting in local overcooling or insufficient refrigeration capacity, making it difficult to meet the high-precision scene requirements such as precision instrument cooling, and thus restricting the application boundary of the corrosion-resistant refrigeration pipe. SUMMARY

[0003] The purpose of the present application is to provide a temperature control method and system of a corrosion-resistant refrigeration pipe with an intelligent temperature control coating, which calculates the heat absorption by coupling the heat balance equation, maps the voltage by physically embedding the meta-learning graph neural network, and quantifies the influence between sections by the voltage coupling conduction function to determine the optimal energizing voltage, achieving the purposes of precise temperature control, eliminating interference between sections, and adapting to multiple working condition cooling requirements.

[0004] The technical solution to achieve the purpose of the present application is as follows:

[0005] On the one hand, the present application provides a temperature control method of a corrosion-resistant refrigeration pipe with an intelligent temperature control coating, comprising the following steps:

[0006] During the refrigeration process, the temperature and flow rate in each electric cooling section are collected, and the external wind speed and temperature are measured synchronously.

[0007] A coupled heat balance equation is constructed according to the Fourier law and the convective heat transfer formula to calculate the heat absorption of each electric cooling section. The heat absorption of each electric cooling section is converted and mapped to the target voltage by physically embedding the meta-learning graph neural network. In order to meet the target voltage of each electric cooling section, the influence of the energizing voltage of the electric cooling section on the remaining electric cooling sections is considered, and a voltage coupling conduction function is designed. The optimal energizing voltage is determined by iterative calculation using a swarm intelligence algorithm, and control is applied accordingly.

[0008] The absolute temperature difference between the temperature in each electric cooling section and the preset target temperature is calculated, and the absolute temperature difference is compared with the temperature difference threshold to determine whether to simulate and optimize the optimal parameters of the coupled heat balance equation and the physically embedded meta-learning graph neural network.

[0009] Further, by physically embedding the meta-learning graph neural network combined with the cooperative calculation of the voltage coupling conduction function, the heat absorption is converted into the optimal energizing voltage, including the following steps:

[0010] The heat absorption, pipe temperature, pipe flow, and external wind speed and external temperature of each electro-cooling section calculated based on the coupled heat balance equation are input into the physically embedded meta-learning graph neural network pre-trained by multi-working condition historical data. The neural network has been pre-trained by running data covering different pipe flow, pipe temperature, external wind speed, and external temperature scenarios, and the physical constraint term obtained by transforming the coupled heat balance equation is embedded in the network construction. Finally, the node feature vector is linearly mapped to the target voltage of each electro-cooling section without considering the coupling between the sections by the network output layer;

[0011] According to the thermal conductivity of the corrosion-resistant base pipe and the electro-cooling coating, the axial length of the electro-cooling section, the coating resistance, and the axial thermal contact area, the axial equivalent thermal conductivity is first calculated by combining the axial thermal cross-sectional area of the base pipe and the coating. The axial equivalent thermal conductivity is obtained by multiplying the base pipe thermal conductivity by the base pipe axial thermal cross-sectional area and the coating thermal conductivity by the coating axial thermal cross-sectional area, and then dividing the sum by the sum of the base pipe and coating axial thermal cross-sectional areas. Then, according to the Fourier heat conduction law and the Ohm's law, the voltage coupling coefficient of the voltage of a certain electro-cooling section to the temperature of any other electro-cooling section is calculated by introducing the inter-section voltage influence attenuation coefficient. Based on the voltage coupling coefficient, the voltage coupling conduction function is further constructed.

[0012] Subsequently, the initial energizing voltage of each electro-cooling section, with the initial value preliminarily set based on the target voltage, is substituted into the above voltage coupling conduction function to calculate the external coupling partial pressure generated by the energizing voltage of all other electro-cooling sections on each electro-cooling section. Among them, the electro-cooling section closer to the target section has a higher external coupling partial pressure value due to the larger inter-section voltage influence attenuation coefficient, and the electro-cooling section farther away has a significantly reduced partial pressure influence on the target section due to the smaller attenuation coefficient.

[0013] The superposition value of the actual energizing voltage and the external coupling partial pressure is used as the core optimization target to approximate the target voltage output by the physically embedded meta-learning graph neural network. At the same time, the energy consumption constraint term based on the Joule law is introduced to construct a complete voltage optimization objective function. At the same time, combined with the upper and lower limits of the safe working voltage of the electro-cooling coating, the constraint condition is set, and the group intelligence algorithm is used to iteratively update the target function until the target function value tends to be stable and the deviation between the superposition value of the actual energizing voltage and the external coupling partial pressure and the target voltage meets the pre-set precision requirement. At this time, the actual energizing voltage of each electro-cooling section obtained is the optimal energizing voltage.

[0014] Further, the coupled heat balance equation is constructed and the heat absorption of each electro-cooling section is calculated, including the following steps:

[0015] According to the total length of the refrigeration pipe and the axial length of a single electro-cooling section, the refrigeration pipe is divided into a plurality of independent electro-cooling sections, and heat of each electro-cooling section is transferred in the radial direction and the axial direction;

[0016] The total radial thermal resistance and the axial coupling thermal resistance, that is, the resistance of heat transfer in the radial direction and the axial direction, are calculated respectively; and the radial heat transfer and the axial net heat transfer are calculated based on the relationship between the thermal resistance and the temperature difference;

[0017] According to the principle of energy conservation, the heat absorption provided by the electro-cooling coating needs to completely offset the total heat loss of the section, and a coupled heat balance equation is constructed to calculate the heat absorption that needs to be compensated for each electro-cooling section.

[0018] Further, considering the problem that a pure data-driven neural network may violate physical laws, a physical embedded meta-learning graph neural network is constructed to generate a target voltage, including the following steps:

[0019] The heat absorption of each electro-cooling section, the temperature and flow rate in the pipe, the external wind speed and the external temperature are integrated into an input feature vector;

[0020] The coupled heat balance equation is mathematically transformed to obtain a physical constraint term reflecting the principle of energy conservation, and the physical constraint term is added to the input feature vector to generate a physical enhanced feature;

[0021] Each electro-cooling section is taken as a node of the graph neural network, and the axial heat conduction capacity between adjacent electro-cooling sections is taken as the edge weight between the nodes, which reflects the heat transfer correlation strength between the sections, and an adjacency matrix is constructed to complete the topology construction of the graph neural network, wherein the heat conduction capacity is inversely proportional to the axial coupling thermal resistance;

[0022] Each electro-cooling section is taken as a node of the graph neural network, and the axial heat conduction capacity between the sections is taken as the edge weight between the nodes, and an adjacency matrix is constructed to complete the topology construction of the graph neural network;

[0023] The historical multi-condition data covering different pipe flow rates, pipe temperatures, external wind speeds and external temperature scenarios are used to pre-train the constructed graph neural network, and the common law of the heat absorption-voltage mapping under each condition is fitted through pre-training to generate pre-training meta-parameters of the network;

[0024] The physical enhanced feature is input into the graph neural network containing the pre-training meta-parameters, and through a three-layer feature propagation mechanism, the node features are updated based on the edge weight in each layer to realize the information interaction between the sections and perform multiple rounds of iterative training to optimize the network parameters;

[0025] The graph neural network after training and fitting performs linear mapping on the feature vector of the last layer node through the output layer to obtain the target voltage of each electro-cooling section without considering the coupling between the sections.

[0026] Further, the heat of each electro-cooling section is transferred in radial and axial directions, wherein the radial transfer is a one-way steady-state transfer along the fluid in the tube, the corrosion-resistant base tube, the electro-cooling coating, and the external air, the heat diffuses from the tube outward or penetrates from the outside to the tube, the axial transfer is through the corrosion-resistant base tube and the electro-cooling coating, and is transferred between two adjacent electro-cooling sections, the transfer direction is from the section with a higher temperature to the section with a lower temperature.

[0027] Further, the radial total thermal resistance is formed in series by four parts, i.e., the convection heat resistance in the tube, the radial conduction heat resistance of the corrosion-resistant base tube, the radial conduction heat resistance of the electro-cooling coating, and the external convection heat resistance, and the total resistance value of the series heat resistance is the sum of the resistance values of the four parts, wherein the convection heat resistance in the tube is obtained by using the Dittus-Boelter empirical formula to obtain the convection heat transfer coefficient in the tube, to determine the convection heat transfer area in the tube, i.e., the contact area between the fluid in the tube and the inner wall of the corrosion-resistant base tube, and the convection heat resistance in the tube is obtained by taking the reciprocal of the product of the heat transfer coefficient and the heat transfer area, the radial conduction heat resistance of the corrosion-resistant base tube is calculated by using the heat conduction thermal resistance formula of the cylindrical wall according to the heat conduction characteristics of the cylindrical wall, by using the inner diameter, the outer diameter of the base tube, and the heat conduction coefficient of the base tube material, the radial conduction heat resistance of the corrosion-resistant base tube is obtained, since the thickness of the electro-cooling coating is much smaller than the outer diameter of the corrosion-resistant base tube, the radial heat conduction of the coating can be simplified according to the heat conduction characteristics of the flat wall, the radial conduction heat resistance of the electro-cooling coating is obtained by dividing the thickness of the coating by the product of the heat conduction coefficient of the coating and the heat transfer area of the coating, and the external convection heat resistance is calculated by first using the Churchill-Bernstein empirical formula to calculate the external convection heat transfer coefficient, then determining the external convection heat transfer area, i.e., the contact area between the outer wall of the electro-cooling coating and the external air, and taking the reciprocal of the product of the heat transfer coefficient and the heat transfer area to obtain the external convection heat resistance.

[0028] Further, the axial coupling thermal resistance reflects the hindering of the axial heat transfer between adjacent sections, and the calculation method is based on the parallel resistance principle and includes the axial thermal resistance of the corrosion-resistant base tube and the axial thermal resistance of the electro-cooling coating.

[0029] The calculation method of the axial coupling thermal resistance is the distance between the sections divided by the product of the axial heat conduction coefficient of the base tube and the axial heat conduction cross-sectional area of the base tube, wherein the axial heat conduction cross-sectional area of the base tube refers to the cross-sectional area of the base tube perpendicular to the axial direction, and the area is the effective area for the axial heat transfer of the base tube;

[0030] The calculation method of the axial thermal resistance of the electro-cooling coating is the distance between the sections divided by the product of the axial heat conduction coefficient of the coating and the axial heat conduction cross-sectional area of the coating, wherein the axial heat conduction cross-sectional area of the coating refers to the cross-sectional area of the coating perpendicular to the axial direction, and the area is the effective area for the axial heat transfer of the coating;

[0031] Since the base pipe and the coating belong to parallel heat transfer paths, the inverse of the axial coupling total thermal resistance is equal to the sum of the inverse of the axial thermal resistance of the base pipe and the inverse of the axial thermal resistance of the coating. Since the structures of all electro cooling segments, i.e. the base pipe size and the coating thickness, are consistent, the axial coupling total thermal resistances between all adjacent segments are equal.

[0032] Further, the radial heat transfer amount is calculated by subtracting the external temperature from the temperature in the pipe and then dividing by the radial total thermal resistance. When the calculation result is positive, it means that the fluid in the pipe releases heat to the external environment, and at this time, the electro cooling coating needs to absorb heat to make up for it. If the calculation result is negative, it indicates that the external environment transfers heat to the fluid in the pipe, and the electro cooling coating needs to adjust the heat absorption intensity to maintain the target temperature. The axial net heat transfer amount is calculated by subtracting the heat transferred to the adjacent front segment from the heat transferred out of the adjacent rear segment for the electro cooling segments from the second segment to the last segment. The heat transferred in or out is calculated by subtracting the target segment temperature from the temperature of the adjacent segment and then dividing by the axial coupling thermal resistance. Heat is transferred from the segment with a higher temperature to the segment with a lower temperature. For the first segment, there is no left adjacent segment, so the heat transferred in from the left is zero. For the last segment, there is no right adjacent segment, so the heat transferred out to the right is zero.

[0033] Further, the corrosion-resistant refrigeration pipe with an intelligent temperature control coating comprises, from the inside to the outside, a corrosion-resistant base pipe, an electro cooling coating, and an insulation protective layer. The electro cooling coating is uniformly attached to the outer wall of the corrosion-resistant base pipe. Electrode groups are arranged at intervals along the axial direction of the refrigeration pipe, and the electro cooling segments are formed between adjacent electrode groups.

[0034] In a second aspect, the present application provides a temperature control system for a corrosion-resistant refrigeration pipe with an intelligent temperature control coating, which comprises a collection module, a control module, and a feedback module.

[0035] The collection module collects the temperature and flow rate in the pipe of each electro cooling segment and synchronously measures the external wind speed and temperature in each period.

[0036] The control module constructs a coupled heat balance equation according to the Fourier law and the convection heat transfer formula in each period, calculates the heat absorption amount of each electro cooling segment, maps the heat absorption amount of each electro cooling segment to the target voltage through a physically embedded meta-learning graph neural network, designs a voltage coupling conduction function by considering the influence of the energized voltage of an electro cooling segment on the remaining electro cooling segments, and determines the optimal energized voltage through a group intelligence algorithm and applies control accordingly.

[0037] The feedback module compares the absolute temperature difference between the temperature of each electro cooling segment and the preset target temperature with the temperature difference threshold in each period to decide whether to simulate and optimize the optimal parameters of the coupled heat balance equation and the physically embedded meta-learning graph neural network in the current period.

[0038] The present application has the following advantages compared with the prior art:

[0039] 1. The heat absorption of each electrocooling section is quantified by coupling the heat balance equation with the radial and axial heat conduction, and the model parameters are dynamically adjusted according to the actual operating state.

[0040] 2. The heat absorption and the energizing voltage are accurately mapped and the inter-stage coupling interference is eliminated through the collaborative calculation of the physically embedded meta-learning graph neural network and the voltage coupling conduction function, effectively reducing the temperature fluctuation amplitude and significantly improving the temperature control accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 Flow chart of the temperature control method of the corrosion-resistant refrigeration pipe with intelligent temperature control coating;

[0042] Figure 2 Flow chart of the construction of the coupled heat balance equation and the heat absorption calculation in the present application;

[0043] Figure 3 Flow chart of the determination of the optimal energizing voltage in the present application;

[0044] Figure 4 Flow chart of the temperature difference comparison and parameter optimization decision in the present application. DETAILED DESCRIPTION

[0045] The present application will be further described in detail below in conjunction with the drawings and examples.

[0046] Example 1

[0047] As shown in Figure 1 , the present application discloses a temperature control method of a corrosion-resistant refrigeration pipe with intelligent temperature control coating, comprising the following steps:

[0048] During the refrigeration process, the pipe temperature, pipe flow of each electrocooling section are collected, and the external wind speed and external temperature are measured synchronously;

[0049] The coupled heat balance equation is constructed according to the Fourier law and the convection heat transfer formula, the heat absorption of each electrocooling section is calculated, the heat absorption of each electrocooling section is converted and mapped into the target voltage by physically embedding the meta-learning graph neural network, the voltage coupling conduction function is designed by considering the influence of the energizing voltage of the electrocooling section on the remaining electrocooling sections, the optimal energizing voltage is determined by the group intelligence algorithm and is applied correspondingly.

[0050] The absolute temperature difference between the pipe temperature of each electrocooling section and the preset target temperature is calculated, and the absolute temperature difference is compared with the temperature difference threshold to decide whether to simulate and optimize the optimal parameters of the coupled heat balance equation and the physically embedded meta-learning graph neural network.

[0051] Furthermore, the corrosion-resistant refrigeration pipe with intelligent temperature control coating consists of, from the inside out, a corrosion-resistant base pipe, an electro-cooling coating, and an insulating protective layer. The structure and arrangement of each part are as follows:

[0052] The electro-cooling coating uses a heat-absorbing material known in the art, the specific material composition and basic working principle of which are existing technologies and will not be elaborated here. The electro-cooling coating is uniformly applied to the outer wall of the corrosion-resistant base tube, spaced at predetermined intervals along the axial direction of the refrigeration tube. Electrode groups are set up to achieve segmented energization control of the electrocooling coating, and an electrocooling section is formed between two adjacent electrode groups.

[0053] Temperature sensors are embedded in the inner wall of the corrosion-resistant base pipe corresponding to each electrocooling section to monitor the pipe temperature of that section. A flow meter is connected in series at the input end of the cooling pipe to collect the fluid flow rate inside the pipe. Meanwhile, wind speed sensors and ambient temperature sensors are installed at predetermined intervals outside the refrigeration pipes to measure the wind speed and temperature of the external environment and record them as the external wind speed. and external temperature .

[0054] like Figure 2 As shown, further, a coupled heat balance equation is constructed based on Fourier's law and the convective heat transfer formula to calculate the heat absorption of each electrocooling section, including the following steps:

[0055] Based on the total length of the refrigeration pipe and the axial length of a single electro-cooling section Refrigeration pipes can be divided into The first section of the electrocooling section, denoted as the fluid delivery end within the refrigeration pipe, is... The electrocooling section near the receiving end is In the In each electrocooling section Heat transfer includes radial and axial heat transfer. Radial heat transfer is a unidirectional steady-state transfer along the fluid inside the pipe, the radial direction of the corrosion-resistant base pipe, the radial direction of the electro-cooling coating, and the external air. Axial heat transfer is carried out between adjacent sections through the axial direction of the corrosion-resistant base pipe and the axial direction of the electro-cooling coating. The direction of heat transfer is from the electro-cooling section with a higher temperature to the electro-cooling section with a lower temperature.

[0056] Calculate the total radial thermal resistance, where the first... Total radial thermal resistance of each electrocooling section The calculation formula is as follows:

[0057] ,

[0058] in, For the convection thermal resistance inside the tube, The calculation formula of the radial conduction thermal resistance of the corrosion-resistant base pipe is as follows: The calculation formula of the radial conduction thermal resistance of the electric cooling coating is as follows: The calculation formula of the external convection thermal resistance and the internal convection thermal resistance of the pipe is as follows: The calculation formula of the radial conduction thermal resistance of the corrosion-resistant base pipe is as follows:

[0059] ,

[0060] wherein, The calculation formula of the internal convection heat exchange coefficient of the first section pipe is as follows: The calculation formula of the internal convection heat exchange coefficient of the first section pipe is as follows: The calculation formula of the radial heat exchange area of the corrosion-resistant base pipe of the first section is as follows, that is, the contact area of the internal fluid of the pipe and the inner wall of the corrosion-resistant base pipe: , The calculation formula of the radial heat exchange area of the corrosion-resistant base pipe of the first section is as follows, that is, the contact area of the internal fluid of the pipe and the inner wall of the corrosion-resistant base pipe: The calculation formula of the internal diameter of the corrosion-resistant base pipe is as follows: The calculation formula of the internal convection heat exchange coefficient of the first section pipe is as follows: The calculation formula of the radial conduction thermal resistance of the corrosion-resistant base pipe is as follows:

[0061] ,

[0062] wherein, The calculation formula of the internal convection heat exchange coefficient of the first section pipe is as follows: The calculation formula of the internal convection heat exchange coefficient of the first section pipe is as follows: The calculation formula of the internal convection heat exchange coefficient of the first section pipe is as follows: The calculation formula of the internal convection heat exchange coefficient of the first section pipe is as follows: , The calculation formula of the internal convection heat exchange coefficient of the first section pipe is as follows: The calculation formula of the internal convection heat exchange coefficient of the first section pipe is as follows: The calculation formula of the internal convection heat exchange coefficient of the first section pipe is as follows: The calculation formula of the internal convection heat exchange coefficient of the first section pipe is as follows: The calculation formula of the radial conduction thermal resistance of the corrosion-resistant base pipe is as follows:

[0063] ,

[0064] wherein, The calculation formula of the internal convection heat exchange coefficient of the first section pipe is as follows: The calculation formula of the internal convection heat exchange coefficient of the first section pipe is as follows: The calculation formula of the internal convection heat exchange coefficient of the first section pipe is as follows: The calculation formula of the internal convection heat exchange coefficient of the first section pipe is as follows: The calculation formula of the internal convection heat exchange coefficient of the first section pipe is as follows: The calculation formula of the internal convection heat exchange coefficient of the first section pipe is as follows: The calculation formula is as follows:

[0065] ,

[0066] in, For the thickness of the electrocooling coating, The radial thermal conductivity of the electrocooling coating is... For the first The radial heat transfer area of ​​the corrosion-resistant base pipe-electrothermal coating section, i.e., the contact area between the outer wall of the corrosion-resistant base pipe and the electrothermal coating, varies depending on the thickness of the electrothermal coating. Much smaller than the outer diameter of the corrosion-resistant base pipe The outer wall area of ​​the electrocooling coating is approximately equal to External convection thermal resistance The calculation formula is as follows:

[0067] ,

[0068] in, For the first The external convective heat transfer coefficient of the section was calculated using the Churchill-Bernstein empirical formula.

[0069] Calculate axial coupling thermal resistance That is, the first Section and the Inter-segment heat transfer is hindered. Axial heat between segments is transferred simultaneously through the corrosion-resistant base tube and the electro-cooling coating, which are connected in parallel. When heat is transferred through multiple parallel paths, the calculation method for the total thermal resistance is similar to that for parallel resistance. This is because the reciprocal of thermal resistance is similar to electrical conductance. In the case of parallel connection, the total thermal conductance is the sum of the thermal conductances of each branch. Therefore, the formula for calculating axial coupling thermal resistance is as follows:

[0070] ,

[0071] in, To improve the axial thermal resistance of the corrosion-resistant base tube, The axial thermal resistance of the electrocooled coating is, where , The axial heat-conducting cross-sectional area of ​​the corrosion-resistant base pipe is the effective area for heat transfer between sections through the axial direction of the corrosion-resistant base pipe. The thermal conductivity of the corrosion-resistant base pipe material and the axial thermal resistance of the electro-cooling coating are considered. , The axial heat-conducting cross-sectional area of ​​the electrocooling coating is the effective area through which heat is transferred axially between sections. The thermal conductivity of the electrothermal coating material is given. Since all sections have the same structure, the axial coupling thermal resistance between all adjacent sections is equal. And let the axial coupling thermal resistance between adjacent segments be denoted as . ;

[0072] Calculate the radial heat transfer and axial coupled heat transfer of each segment. Radial heat transfer refers to the heat transferred radially outward. Based on the relationship between total radial thermal resistance and temperature difference, the first... The formula for calculating radial heat transfer in a segment is shown below:

[0073] ,

[0074] in, For radial heat transfer, i.e., the heat transfer rate per unit time... The heat released by the segment to the external environment through the radial heat transfer link. For the first Temperature inside the pipe section For the external temperature, if the first pipe section temperature greater than the external temperature , If the temperature inside the tube is positive, meaning it's an exothermic process, and the temperature inside the tube is lower than the external temperature, then... A negative value indicates an endothermic process. Without interfering with the cooling transmission of the refrigerant tube, the greater the transmission distance, the more heat the refrigerant tube releases, and the lower the temperature of the fluid inside the refrigerant tube. This leads to the following: The axial coupling heat transfer between the segment and the adjacent segment is , ,in, For axial heat transfer, i.e. from the first The first segment is passed to the first Axial heat of the segment For axial heat transfer, i.e. from the first The first segment is passed to the first The axial heat flow rate of a section follows Fourier's law, is directly proportional to the temperature difference between adjacent sections and inversely proportional to the axial coupling thermal resistance, and is calculated using the following formula:

[0075] ,

[0076] ,

[0077] Among them, when At that time, there was no adjacent segment on the left. ,when At that time, there was no adjacent segment on the right. , For the first The temperature inside the pipe section For the first The temperature inside the pipe section, if , It is positive, that is, the first Duan Xiangdi Sectional heat transfer, if , It is positive, that is, the first Duan Xiangdi Section heat transfer, therefore the first Total heat transfer of the section The sum of radial heat transfer and axial net heat transfer is calculated using the following formula:

[0078] ,

[0079] in, For the first unit of time The total heat loss outwards is equal to the net heat loss axially. Subtract axial heat transfer That is, the heat lost axially outward from this section. A positive value indicates that there is heat loss in this section, which needs to be compensated by the heat absorption of the electrocooling coating.

[0080] Construct a coupled heat balance equation, calculate the heat absorption in each segment, and according to the law of conservation of energy, the first... The heat absorption provided by the segment electrocooling coating through the Peltier effect The total heat loss in this section must be completely offset. To maintain the temperature inside the tube Stable, for the first In this context, the energy balance relationship is as follows:

[0081] ,

[0082] The left side of the equation represents the heat absorbed by the electrocooling coating. Plus axial heat transfer The right side of the equation represents radial heat transfer. Plus axial heat transfer , sorted out Section heat absorption The calculation formula is as follows:

[0083] ,

[0084] Combining the total heat transfer formula, we can obtain the coupled heat balance equation, which is shown below:

[0085] ,

[0086] The heat absorption of each electrocooling section in the axial and radial directions can be quantified according to the coupled heat balance equation, which is also the cooling loss of each electrocooling section.

[0087] like Figure 3As shown, further, a physical embedding meta-learning graph neural network is used to convert the heat absorption of each electrocooling segment into voltage and dynamically determine the optimal energizing voltage of each electrocooling segment through voltage coupling conduction function, including the following steps:

[0088] Construct a physical embedded cloud learning graph neural network. In the network input layer, the input feature vector consists of the heat absorption and operating parameters of each electrocooling section. Segment input features The definition is as follows:

[0089] ,

[0090] in, For the first The heat absorption of the segment For the first The temperature inside the pipe section For the flow rate within the pipe, External wind speed, The input feature vector, which takes the external temperature as its reference, utilizes the multi-parameter coupling principle of the heat transfer process. By integrating the heat absorption, the temperature inside the pipe, and the internal and external boundary conditions, including the flow rate inside the pipe, the external wind speed, and the external temperature, it ensures that the network can fully capture the dynamic operating status of the refrigeration pipe.

[0091] To avoid physical inconsistencies in purely data-driven models, i.e., predictions that violate energy conservation, a mathematical transformation is used to embed the coupled thermal balance equation into the feature vector, generating physically enhanced features. The expression is as follows:

[0092] ,

[0093] in, For the first The total radial thermal resistance of the segment, This is a normalization coefficient, whose value is based on the magnitude analysis of each parameter in the feature vector. It ensures that the numerical range of the physical constraint term is consistent with other features, thus avoiding gradient imbalance during network training. This is a physical constraint term, derived from the transformation of the heat balance equation. The introduction of this constraint term ensures that the network satisfies the law of conservation of energy during the learning process, making the prediction results physically reasonable.

[0094] Considering the significant axial heat conduction within the electrocooling chamber, and the fact that the heat transfer processes in each section are not independent, a graph neural network is used to model the coupling relationships between the sections. Each electrocooling section is considered as shown in the diagram. For each node, the axial thermal conduction intensity between segments is used as the edge weight between nodes to construct an adjacency matrix. The expression is as follows:

[0095] ,

[0096] in, For the first Sectional electro-cooling section, The reciprocal of the inter-segment axial coupling thermal resistance Characterizing the thermal conductivity between adjacent segments, the lower the thermal resistance, the stronger the conductivity, and the larger the edge weight. Graph neural networks update node features through a three-layer propagation mechanism, realizing the interaction of coupled information between segments. The feature update formula for the layer is shown below:

[0097] ,

[0098] in, , For the first The section in Feature vectors of the layer, initial layer For physical enhancement features, For the first The weight matrix of the layer, For the first The bias vector, weight matrix, and bias vector of a layer are learnable parameters of the network, which are optimized through training to capture the non-linear relationships between features. The ReLU activation function is used to introduce nonlinear transformation capabilities, enabling the network to fit complex heat transfer-voltage mapping relationships. The summation term... To perform weighted aggregation of the features of adjacent nodes, the weight is the inter-segment heat conduction capability, thereby realizing the transmission of inter-segment coupling information;

[0099] To address the adaptability of refrigerant pipes under various operating conditions, the MAML meta-learning framework is introduced. This allows the network to quickly adjust parameters using a small amount of new data to adapt to changing operating conditions. Initial parameters shared across all operating conditions were obtained through pre-training on a large amount of historical operating data, including operational data for different pipe flow rates, pipe temperatures, external wind speeds, and external temperatures, characterizing the common patterns of each operating condition and the task parameters. For the first The adaptation parameters for this new operating condition are obtained by rapidly fine-tuning the original parameters; task parameters. and loss function The calculation formula is as follows:

[0100] ,

[0101] ,

[0102] in, For learning rate, For the first The inner loop loss function for this operating condition. Predict voltage for the network. This is the historical measured voltage. For the inner loop loss function with respect to the meta-parameters The gradient;

[0103] The output layer of a physics-embedded meta-learning graph neural network is used to predict the target voltage that meets the heat absorption requirements of this segment. The feature vector of the last layer of the graph neural network is then used to predict the target voltage. The target voltage of each electrocooling stage is obtained through linear mapping. The expression is as follows:

[0104] ,

[0105] in, The output layer parameters are obtained through meta-learning optimization. To quantify the influence of voltage from other segments on the voltage division of this segment during actual operation, a voltage coupling transfer function is constructed, and the external coupling voltage division is calculated. The calculation formula is as follows:

[0106] ,

[0107] in, For other segments of voltage to the first The coupling voltage division generated by the segment For the first The initial voltage applied to the segment, For the first segment voltage for the first The voltage coupling coefficient of the segment is derived based on Fourier's law of heat conduction and Ohm's law of electricity, and the calculation formula is shown below:

[0108] ,

[0109] in, The axial equivalent thermal conductivity of the electrocooling section is calculated from the thermal conductivity of the corrosion-resistant base tube and the electrocooling coating. The calculation formula is as follows: , For the thermal conductivity of the corrosion-resistant base pipe material, For the axial heat conduction cross-sectional area of ​​the corrosion-resistant base pipe, The radial thermal conductivity of the electrocooling coating is... The axial thermal conductivity cross-sectional area of ​​the electrocooling coating is... This refers to the axial length of a single electrocooling section. For electrocooled coating resistors, This is the inter-segment coupling attenuation coefficient, whose value decreases as the inter-segment distance increases. The attenuation factor was determined through fitting experimental data. The axial thermal contact area is where, The outer diameter of the corrosion-resistant base pipe;

[0110] The core objective is to approximate the target voltage output by the graph neural network with the superposition of the actual voltage and the externally coupled voltage divider, while also constraining energy consumption. The objective function is... The definition is as follows:

[0111] ,

[0112] Among them, the first item The second term characterizes the deviation between the actual voltage plus the externally coupled voltage divider and the target voltage output by the physical constraint learning graph neural network. This is an energy consumption term based on Joule's law, used to constrain excessive voltage increases. The weighting coefficients, determined experimentally, prioritize minimizing voltage deviation. To clarify the inter-segment coupling relationship, substituting the external coupling voltage divider formula into the objective function yields:

[0113] ,

[0114] To ensure the safe operation of the electrothermal coating, the voltage must meet the upper and lower limits, as shown below:

[0115] ,

[0116] in, This is the minimum voltage, corresponding to the non-operating state. The maximum voltage is determined based on the voltage withstand characteristics of the coating material to avoid damage to the coating due to overvoltage.

[0117] The Lagrange multiplier method is employed, incorporating constraints into the objective function through Lagrange multipliers to construct a Lagrange augmented function. A search space is then built using the energizing voltage of each electrocooling segment. With the objective of minimizing the Lagrange augmented function, a swarm intelligence algorithm is used to iteratively search within the search space to determine the optimal energizing voltage for each electrocooling segment. This swarm intelligence algorithm includes ant colony optimization, particle swarm optimization, bat optimization, wolf pack optimization, and fruit fly optimization. Since swarm intelligence algorithms are existing technologies and not the focus of this application, they will not be elaborated upon further here.

[0118] like Figure 4 As shown, further, comparing the absolute temperature difference and temperature difference threshold between the temperature of each electrocooling section and the preset target temperature to decide whether to simulate and optimize to obtain the optimal parameters of the coupled thermal balance equation and the meta-learning graph neural network includes the following steps:

[0119] The absolute temperature difference between the internal temperature of each electrocooling section and the preset target temperature is calculated using the following formula:

[0120] ,

[0121] in, For the first The absolute temperature difference of the section reflects the degree of deviation between the temperature inside the pipe and the preset target temperature. For the first time collected Temperature inside the pipe section Preset target temperature;

[0122] By comparing the absolute temperature difference of each segment with the temperature difference threshold, a three-level decision logic is formed. If the absolute temperature difference of all segments is less than or equal to the temperature difference threshold, it is determined that the current parameters are suitable for the working condition and the operation is maintained without optimization. If the absolute temperature difference of less than 1 / 3 of the total number of segments is greater than the temperature difference threshold, it is determined that there is a local parameter mismatch. Only the task parameters of the meta-learning graph neural network are fine-tuned. That is, the operating data based on the current working condition data, including the flow rate in the pipe, the temperature in the pipe, the external wind speed, and the external temperature, are updated quickly without adjusting the coupled heat balance equation. If the absolute temperature difference of more than or equal to 1 / 3 of the segments is greater than the temperature difference threshold, it is determined that there is an overall parameter mismatch, and the global optimization of the coupled heat balance equation and the meta-learning graph neural network is triggered simultaneously.

[0123] For the coupled thermal balance equation, the least squares algorithm is used to optimize the radial conduction thermal resistance and convective heat transfer coefficient. For the meta-learning graph neural network, the Adam optimizer is used to update the network weights, biases and meta-learning rate through the loss function of voltage prediction error. After optimization, the generalization ability needs to be verified through the current operating condition data. After the optimized parameters are deployed, if the temperature control requirements are still not met after three consecutive sampling periods, the system will automatically backtrack to the parameters before optimization and record the current operating condition data for subsequent offline training to ensure the system stability and the effectiveness of parameter optimization.

[0124] Example 2

[0125] This invention discloses a temperature control system for a corrosion-resistant refrigeration pipe with an intelligent temperature control coating, comprising a data acquisition module, a control module, and a feedback module;

[0126] The data acquisition module collects the tube temperature and flow rate of each electrocooling section in each cycle, and simultaneously measures the external wind speed and external temperature.

[0127] In each cycle, the control module constructs a coupled heat balance equation based on Fourier's law and the convective heat transfer formula, calculates the heat absorption of each electrocooling segment, and converts the heat absorption of each electrocooling segment into a target voltage through a physical embedded element learning graph neural network. With the aim of satisfying the target voltage of each electrocooling segment, the module collaboratively considers the influence of the voltage of the electrocooling segment on the other electrocooling segments to design a voltage coupling transfer function. The optimal voltage is determined iteratively through a swarm intelligence algorithm and corresponding control is applied.

[0128] The feedback module compares the absolute temperature difference between the temperature of each electrocooling section and the preset target temperature with the temperature difference threshold in each cycle to decide whether to simulate and optimize the optimal parameters of the coupled heat balance equation and the meta-learning graph neural network in this cycle.

[0129] This invention discloses a temperature control method and system for corrosion-resistant refrigeration pipes with intelligent temperature control coatings. It aims to solve the problems of large calculation deviations in heat loss calculations and insufficient temperature control accuracy caused by inter-segment voltage coupling interference in existing refrigeration pipes. By collecting the temperature inside the pipe, the flow rate inside the pipe, and the external wind speed and temperature in each electro-cooled section, and based on Fourier's law and convective heat transfer formulas, the radial total thermal resistance and axial coupling thermal resistance are calculated. Combined with the thermal resistance and temperature difference, the radial and axial net heat transfer is calculated. Based on energy conservation, a coupled heat balance equation is constructed to obtain the heat absorption that needs to be compensated for in each segment. Subsequently, a physical embedded element learning graph neural network is used, combined with the voltage coupling conduction function... The system focuses on approximating the target voltage by superimposing the actual voltage and coupled voltage divider. Energy consumption constraints and upper and lower limits for coating safety voltage are added. A swarm intelligence algorithm iteratively updates the voltage until the target function is stable and the deviation meets the standard, obtaining and applying the optimal energizing voltage. The Peltier effect is used to compensate for cooling losses. Finally, the absolute temperature difference and temperature difference threshold between each section of the tube and the target temperature are compared. A three-level logical decision-making process optimizes the coupled thermal balance equation and the parameters of the physical embedded element learning graph neural network. The system includes acquisition, control, and feedback modules, which respectively perform data acquisition, voltage control, and parameter decision-making, achieving precise temperature control of the refrigeration tube.

[0130] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A temperature control method for a corrosion-resistant refrigeration pipe with an intelligent temperature control coating, characterized in that, Includes the following steps: During the cooling process, the tube temperature and flow rate of each electrocooling section are collected, and the external wind speed and external temperature are measured simultaneously. Based on Fourier's law and the convective heat transfer formula, a coupled heat balance equation is constructed, and the heat absorption of each electrocooling segment is calculated. The heat absorption of each electrocooling segment is converted and mapped to the target voltage through a physical embedded element learning graph neural network. With the goal of satisfying the target voltage of each electrocooling segment, the voltage coupling transfer function is designed by considering the influence of the voltage of the electrocooling segment on the other electrocooling segments. The optimal voltage is determined iteratively through a swarm intelligence algorithm and corresponding control is applied. Calculate the absolute temperature difference between the tube temperature of each electrocooling section and the preset target temperature, and compare the absolute temperature difference with the temperature difference threshold to decide whether to simulate and optimize to obtain the optimal parameters of the coupled thermal balance equation and the physical embedded element learning graph neural network.

2. The temperature control method for the corrosion-resistant refrigeration pipe with intelligent temperature control coating as described in claim 1, characterized in that, By combining physical embedding meta-learning graph neural networks with the collaborative calculation of voltage-coupled conduction functions, the heat absorption is converted into the optimal energizing voltage, including the following steps: The heat absorption, tube temperature, tube flow rate, external wind speed, and external temperature of each electrocooling section are collected and input into the trained physical embedding meta-learning graph neural network to obtain the target voltage. Based on the thermal conductivity of the corrosion-resistant base tube and the electro-cooling coating, the axial length of the electro-cooling section, the coating resistance, and the axial heat conduction contact area, combined with Fourier's law of heat conduction and Ohm's law of electricity, the voltage coupling coefficient of a certain voltage segment to the temperature of other segments is calculated, and the voltage coupling conduction function is determined. Considering the mutual interference of voltages between different electrocooling sections, the external coupling voltage division caused by the energizing voltage of other electrocooling sections on the current electrocooling section is calculated by the voltage coupling conduction function. The core objective is to approximate the target voltage output by the physical embedded element learning graph neural network with the superposition value of the actual energized voltage and the externally coupled voltage divider. At the same time, an energy consumption constraint based on Joule's law is introduced to construct the objective function. Combined with the upper and lower limits of the safe operating voltage of the electrocooling coating, a swarm intelligence algorithm is used to guide the voltage iterative update until the objective function tends to stabilize and the voltage deviation meets the preset requirements, thus obtaining the optimal energized voltage of each electrocooling segment.

3. The temperature control method for the corrosion-resistant refrigeration pipe with intelligent temperature control coating as described in claim 1, characterized in that, The coupled heat balance equations are constructed and the heat absorption of each electrocooling section is calculated, including the following steps: Based on the total length of the refrigeration tube and the axial length of a single electrocooling section, the refrigeration tube is divided into several independent electrocooling sections, and the heat of each electrocooling section is transferred radially and axially. Calculate the total radial thermal resistance and the axial coupling thermal resistance, i.e. the obstacles to heat transfer in the radial and axial directions, respectively; and calculate the radial heat transfer and the axial net heat transfer based on the relationship between thermal resistance and temperature difference. According to the principle of energy conservation, the heat absorption required by the electrocooling coating must completely offset the total heat loss of the section. Based on this, a coupled heat balance equation is constructed and the heat absorption that each electrocooling section needs to compensate is calculated.

4. The temperature control method for a corrosion-resistant refrigeration pipe with an intelligent temperature control coating as described in claim 2, characterized in that, Constructing a physically embedded meta-learning graph neural network to generate the target voltage includes the following steps: The heat absorption, tube temperature, tube flow rate, external wind speed, and external temperature of each electrocooling section are integrated into an input feature vector. The coupled thermal equilibrium equation is transformed and used as a physical constraint term, which is then added to the input feature vector to generate physically enhanced features. Each electrocooling segment is used as a node in the graph neural network, and the axial thermal conductivity between segments is used as the edge weight between nodes. Based on this, an adjacency matrix is ​​constructed to complete the topology construction of the graph neural network. For the established graph neural network, historical multi-condition data covering different pipe flow, pipe temperature, external wind speed, and external temperature scenarios are used for pre-training to fit the common patterns under each condition and generate the pre-training meta-parameters of the network. The physical enhancement features are input into a graph neural network with pre-trained meta-parameters, and the node features are updated and iteratively trained through a three-layer feature propagation mechanism. The trained and fitted graph neural network, through the output layer of the graph neural network, linearly maps the feature vector of the last layer to the target voltage without considering inter-segment coupling.

5. The temperature control method for a corrosion-resistant refrigeration pipe with an intelligent temperature control coating as described in claim 3, characterized in that, Heat is transferred in both the radial and axial directions in each electrocooling section. Radial transfer is a unidirectional steady-state transfer along the fluid inside the pipe, the corrosion-resistant base pipe, the electrocooling coating, and the external air, with heat diffusing from the inside of the pipe to the outside or penetrating from the outside into the pipe. Axial transfer is carried out through the corrosion-resistant base pipe and the electrocooling coating between adjacent electrocooling sections, with the transfer direction from the section with higher temperature to the section with lower temperature.

6. The temperature control method for a corrosion-resistant refrigeration pipe with an intelligent temperature control coating as described in claim 3, characterized in that, The total radial thermal resistance is formed by four parts connected in series: the internal convection thermal resistance, the radial conduction thermal resistance of the corrosion-resistant base tube, the radial conduction thermal resistance of the electro-cooling coating, and the external convection thermal resistance. The total resistance of the series thermal resistance is the sum of the individual resistance values. The internal convection thermal resistance is calculated using the Dittus-Boelter empirical formula, the external convection thermal resistance is calculated using the Churchill-Bernstein empirical formula, the radial conduction thermal resistance of the corrosion-resistant base tube is calculated based on the thermal conductivity characteristics of the cylindrical wall, and the radial conduction thermal resistance of the electro-cooling coating is simplified by calculating the thermal conductivity characteristics of a flat wall because the coating thickness is much smaller than the outer diameter of the base tube.

7. The temperature control method for a corrosion-resistant refrigeration pipe with an intelligent temperature control coating as described in claim 3, characterized in that, The axial coupling thermal resistance is formed by the parallel connection of the axial thermal resistance of the corrosion-resistant base tube and the axial thermal resistance of the electro-cooling coating. The reciprocal of the total resistance of the parallel thermal resistance is equal to the sum of the reciprocals of the individual resistances, and the axial coupling thermal resistance of all adjacent electro-cooling sections is equal due to their identical structure.

8. The temperature control method for the corrosion-resistant refrigeration pipe with intelligent temperature control coating as described in claim 3, characterized in that, Radial heat transfer is obtained by dividing the temperature difference between the inside and outside of the pipe by the total radial thermal resistance. If the temperature inside the pipe is higher than the outside temperature, it is exothermic, and vice versa. The axial net heat transfer is the axial heat transfer of a certain electrocooling section, which is equal to the axial heat transfer out of the adjacent section on the right minus the axial heat transfer into the adjacent section on the left. The first section has no adjacent section on the left, and the last section has no adjacent section on the right, so there is no heat transfer in the corresponding direction.

9. The temperature control method for a corrosion-resistant refrigeration pipe with an intelligent temperature control coating as described in claim 1, characterized in that, The corrosion-resistant refrigeration tube with intelligent temperature control coating includes, from the inside out, a corrosion-resistant base tube, an electro-cooling coating, and an insulating protective layer. The electro-cooling coating is uniformly attached to the outer wall of the corrosion-resistant base tube. Electrode groups are arranged at preset intervals along the axial direction of the refrigeration tube, and the electro-cooling section is formed between adjacent electrode groups.

10. A temperature control system for corrosion-resistant refrigeration pipes with intelligent temperature control coating, characterized in that, It includes a data acquisition module, a control module, and a feedback module; The data acquisition module collects the tube temperature and flow rate of each electrocooling section in each cycle, and simultaneously measures the external wind speed and external temperature. In each cycle, the control module constructs a coupled heat balance equation based on Fourier's law and the convective heat transfer formula, calculates the heat absorption of each electrocooling segment, and converts the heat absorption of each electrocooling segment into a target voltage through a physical embedded element learning graph neural network. With the aim of satisfying the target voltage of each electrocooling segment, the module collaboratively considers the influence of the voltage of the electrocooling segment on the other electrocooling segments to design a voltage coupling transfer function. The optimal voltage is determined iteratively through a swarm intelligence algorithm and corresponding control is applied. The feedback module compares the absolute temperature difference between the temperature of each electrocooling section and the preset target temperature with the temperature difference threshold in each cycle to decide whether to simulate and optimize the optimal parameters of the coupled heat balance equation and the meta-learning graph neural network in this cycle.

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