Heat exchange performance identification model construction method and heat exchange performance identification method of heat exchanger

Abstract

The invention relates to the technical field of heat exchangers, in particular to a heat exchange performance recognition model construction method and a heat exchange performance recognition method for a heat exchanger, and the method comprises the steps: obtaining historical operation data of the heat exchanger in a compressed air energy storage system under different operation conditions, the historical operation data comprises mass flow of cold and hot fluid, inlet and outlet temperatures and corresponding heat exchange efficiency; a linear heat exchange part and a non-linear heat exchange part of the compressed air energy storage system in the energy storage process are extracted from the historical operation data, the linear heat exchange part is used for representing the linear heat exchange process, and the non-linear heat exchange part is used for representing the non-linear heat exchange process; and a heat exchange performance recognition model of the heat exchanger is constructed based on the linear heat exchange part and the nonlinear heat exchange part, and the heat exchange performance recognition model is used for recognizing the heat exchange performance of the heat exchanger under different operation conditions. Therefore, the problems that an existing heat exchanger recognition model is poor in recognition prediction effect and precision and the like are solved.

Description

Technical Field

[0001] The present application relates to the technical field of heat exchangers, and in particular to a method for constructing a heat exchange performance identification model and a heat exchange performance identification method for a heat exchanger. Background Art

[0002] In the study of compressed air energy storage heat exchangers, modeling of complex dynamic systems is particularly challenging, mainly due to the diversity of heat exchanger operating conditions and the complexity of inherent characteristics.

[0003] Heat exchangers involve heat transfer between fluids, and their dynamic behavior often exhibits significant nonlinear characteristics, such as fluctuations in fluid velocity, pressure changes, and heat transfer efficiency. These nonlinear factors make it impossible for traditional linear models to accurately capture the actual behavior of the system, resulting in deviations in model predictions.

[0004] Existing methods such as linear system theory, traditional Hammerstein model and Wiener model are limited in effectiveness and accuracy when dealing with strong nonlinear or complex dynamic systems, and cannot accurately identify the heat transfer performance of the heat exchanger.

[0005] Accurate identification of heat exchanger performance is of great significance for the efficient operation of compressed air energy storage systems: first, it can support system energy efficiency optimization and guide parameter adjustment through real-time performance identification; second, it helps to timely discover performance degradation trends and achieve predictive maintenance; third, it provides necessary characteristic parameter support for the intelligent control of the system; and finally, it can optimize the operation strategy through performance evaluation and improve the overall efficiency of the system. However, there is currently a lack of a heat exchanger performance identification method that can simultaneously meet high-precision requirements and strong adaptability. Summary of the invention

[0006] The present application provides a method for constructing a heat exchange performance identification model of a heat exchanger and a heat exchange performance identification method to solve the problems of poor identification prediction effect and accuracy of existing heat exchanger identification models.

[0007] A first aspect of the present application provides a method for constructing a heat exchange performance identification model of a heat exchanger, comprising the following steps: obtaining historical operating data of a heat exchanger in a compressed air energy storage system under different operating conditions, wherein the historical operating data includes mass flow rates of cold and hot fluids, inlet and outlet temperatures, and corresponding heat exchange efficiencies; extracting a linear heat exchange portion and a nonlinear heat exchange portion of the compressed air energy storage system during the energy storage process in the historical operating data, wherein the linear heat exchange portion is used to represent a linear heat exchange process, and the nonlinear heat exchange portion is used to represent a nonlinear heat exchange process; constructing a heat exchange performance identification model of the heat exchanger based on the linear heat exchange portion and the nonlinear heat exchange portion, wherein the heat exchange performance identification model is used to realize the identification of the heat exchange performance of the heat exchanger under different operating conditions.

[0008] Optionally, a heat exchange performance identification model of the heat exchanger is constructed based on the linear heat exchange part and the nonlinear heat exchange part, including: determining the model parameters of the linear heat exchange part and the model parameters of the nonlinear heat exchange part of the heat exchanger based on the mass flow rates of the cold and hot fluids, the inlet and outlet temperatures and the corresponding heat exchange efficiencies of the heat exchanger; and combining the model parameters of the linear heat exchange part and the model parameters of the nonlinear heat exchange part to generate a heat exchange performance identification model.

[0009] Optionally, the linear heat exchange part introduces fractional-order derivatives and kernel functions, and adjusts the fractional-order parameters and kernel function parameters according to the characteristics of the compressed air energy storage system; the nonlinear heat exchange part introduces nonlinear functions.

[0010] Optionally, after constructing the heat exchange performance identification model of the heat exchanger based on the linear heat exchange part and the nonlinear heat exchange part, it also includes: optimizing the initial model parameters of the heat exchange performance identification model based on a fuzzy genetic algorithm; judging the prediction accuracy of the optimized heat exchange performance identification model; if the prediction accuracy meets the preset standard, stopping optimizing the model parameters of the heat exchange performance identification model.

[0011] Optionally, the initial model parameters of the heat exchange performance identification model are optimized based on a fuzzy genetic algorithm, including: creating fuzzy variables corresponding to each data in the historical operation data, and defining a fuzzy term set for each fuzzy variable; constructing fuzzy rules based on expert knowledge or historical operation data; and optimizing the initial model parameters of the heat exchange performance identification model based on the fuzzy rules and historical operation data.

[0012] A second aspect of the present application provides a method for identifying the heat exchange performance of a heat exchanger, comprising the following steps: obtaining current operating data of the heat exchanger in a compressed air energy storage system, wherein the current operating data includes the mass flow rates of cold and hot fluids and inlet and outlet temperatures of the heat exchanger; inputting the current operating data into a heat exchange performance identification model of the heat exchanger, the heat exchange performance identification model outputting the current heat exchange performance of the heat exchanger, wherein the heat exchange performance identification model is constructed based on the linear heat exchange part and the nonlinear heat exchange part of the compressed air energy storage system during the energy storage process in the historical operating data.

[0013] Optionally, after the heat exchange performance identification model outputs the current heat exchange performance of the heat exchanger, it also includes: obtaining a reference heat exchange performance of current operating data; if the current heat exchange performance is inconsistent with the reference heat exchange performance, re-optimizing the model parameters of the heat exchange performance identification model.

[0014] In a third aspect, an embodiment of the present application provides a device for constructing a heat exchanger performance identification model, comprising: obtaining historical operating data of a heat exchanger in a compressed air energy storage system under different operating conditions, wherein the historical operating data includes mass flow rates of cold and hot fluids, inlet and outlet temperatures, and corresponding heat exchange efficiencies; an extraction module for extracting a linear heat exchange portion and a nonlinear heat exchange portion of the compressed air energy storage system during the energy storage process in the historical operating data, wherein the linear heat exchange portion is used to represent a linear heat exchange process, and the nonlinear heat exchange portion is used to represent a nonlinear heat exchange process; a construction module for constructing a heat exchange performance identification model of the heat exchanger based on the linear heat exchange portion and the nonlinear heat exchange portion, wherein the heat exchange performance identification model is used to realize the identification of the heat exchange performance of the heat exchanger under different operating conditions.

[0015] Optionally, a heat exchange performance identification model of the heat exchanger is constructed based on the linear heat exchange part and the nonlinear heat exchange part, including: determining the model parameters of the linear heat exchange part and the model parameters of the nonlinear heat exchange part of the heat exchanger based on the mass flow rates of the cold and hot fluids, the inlet and outlet temperatures and the corresponding heat exchange efficiencies of the heat exchanger; and combining the model parameters of the linear heat exchange part and the model parameters of the nonlinear heat exchange part to generate a heat exchange performance identification model.

[0016] Optionally, the linear heat exchange part introduces fractional-order derivatives and kernel functions, and adjusts the fractional-order parameters and kernel function parameters according to the characteristics of the compressed air energy storage system; the nonlinear heat exchange part introduces nonlinear functions.

[0017] Optionally, it also includes: a first optimization module, which is used to optimize the initial model parameters of the heat exchange performance identification model based on a fuzzy genetic algorithm after constructing the heat exchange performance identification model of the heat exchanger based on the linear heat exchange part and the nonlinear heat exchange part; judge the prediction accuracy of the optimized heat exchange performance identification model; if the prediction accuracy meets the preset standard, stop optimizing the model parameters of the heat exchange performance identification model.

[0018] Optionally, the first optimization module is further used to: create fuzzy variables corresponding to each data in the historical operation data, and define a fuzzy term set for each fuzzy variable; construct fuzzy rules based on expert knowledge or historical operation data; and optimize the initial model parameters of the heat exchange performance identification model based on the fuzzy rules and historical operation data.

[0019] In a fourth aspect, an embodiment of the present application provides a device for identifying the heat exchange performance of a heat exchanger, comprising: a second acquisition module, used to acquire current operating data of the heat exchanger in a compressed air energy storage system, wherein the current operating data includes the mass flow rates of cold and hot fluids and inlet and outlet temperatures of the heat exchanger; an input module, used to input the current operating data into a heat exchange performance identification model of the heat exchanger, the heat exchange performance identification model outputs the current heat exchange performance of the heat exchanger, wherein the heat exchange performance identification model is constructed based on the linear heat exchange part characteristics and the nonlinear heat exchange part of the compressed air energy storage system during the energy storage process in the historical operating data.

[0020] Optionally, it also includes: a second optimization module, which is used to obtain the reference heat exchange performance of the current operating data after the heat exchange performance identification model outputs the current heat exchange performance of the heat exchanger; if the current heat exchange performance is inconsistent with the reference heat exchange performance, the model parameters of the heat exchange performance identification model are re-optimized.

[0021] The fifth aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to execute a method for constructing a heat exchanger performance identification model or a method for identifying the heat exchanger performance as described in the above-mentioned embodiment.

[0022] Therefore, this application has at least the following beneficial effects:

[0023] The embodiment of the present application can extract the linear heat exchange part and the nonlinear heat exchange part of the compressed air energy storage system in the energy storage process from the historical operation data of the heat exchanger in the compressed air energy storage system, build a heat exchange performance identification model of the heat exchanger based on the linear heat exchange part and the nonlinear heat exchange part, and use the heat exchange performance identification model to identify the heat exchange performance of the heat exchanger, fully considering the linear heat exchange part and the nonlinear heat exchange part in the heat exchange process, so that the heat exchange performance identification model more comprehensively reflects the complex behavior of the heat exchanger, and can accurately identify the heat exchange performance of the heat exchanger, and improve the recognition effect and accuracy of the heat exchange performance identification model. Therefore, the technical problems such as the poor recognition prediction effect and accuracy of the existing heat exchanger identification model are solved.

[0024] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0026] Figure 1 A flowchart of a method for constructing a heat exchange performance identification model of a heat exchanger provided in an embodiment of the present application;

[0027] Figure 2 A flow chart of a heat exchange performance identification method provided according to an embodiment of the present application;

[0028] Figure 3 A flowchart for constructing and optimizing a heat exchange performance identification model for a heat exchanger according to an embodiment of the present application;

[0029] Figure 4 An example diagram of a device for constructing a heat exchange performance identification model for a heat exchanger provided in an embodiment of the present application;

[0030] Figure 5 This is an example diagram of a heat exchange performance identification device provided according to an embodiment of the present application;

[0031] Figure 6 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0032] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0033] The following describes the heat exchange performance identification model construction method and heat exchange performance identification method of the heat exchanger of the embodiment of the present application with reference to the accompanying drawings. In view of the problem that the existing methods mentioned in the above background technology, such as linear system theory, traditional Hammerstein model and Wiener model, are limited in effect and accuracy when dealing with strong nonlinear or complex dynamic systems, the present application provides a heat exchange performance identification model construction method for a heat exchanger, in which the linear heat exchange part and the nonlinear heat exchange part of the compressed air energy storage system in the energy storage process in the historical operation data of the heat exchanger in the compressed air energy storage system can be extracted, and the heat exchange performance identification model of the heat exchanger is constructed according to the linear heat exchange part and the nonlinear heat exchange part, and the heat exchange performance identification model is used to identify the heat exchange performance of the heat exchanger, and the linear heat exchange part and the nonlinear heat exchange part in the heat exchange process are fully considered, so that the heat exchange performance identification model more comprehensively reflects the complex behavior of the heat exchanger, and can accurately identify the heat exchange performance of the heat exchanger, and improve the identification effect and accuracy of the heat exchange performance identification model. Therefore, the problems of poor identification prediction effect and accuracy of the existing heat exchanger identification model are solved.

[0034] Specifically, Figure 1 A schematic flow chart of a method for constructing a heat exchange performance identification model for a heat exchanger provided in an embodiment of the present application.

[0035] like Figure 1 As shown, the heat exchange performance identification model construction method of the heat exchanger includes the following steps:

[0036] In step S101, historical operating data of a heat exchanger in a compressed air energy storage system under different operating conditions is obtained.

[0037] Among them, the historical operation data includes the mass flow rate of cold and hot fluids, inlet and outlet temperatures and the corresponding heat exchange efficiency.

[0038] In addition, it should be noted that in order to ensure the accuracy and reliability of the data, this application has strict requirements for data collection: the sensor layout should comply with the GB / T 2624 standard, the temperature sensor should be arranged at no less than 3 measuring points along the flow direction, and the pressure sensor should be set at the inlet and outlet; the sampling frequency should not be less than 1Hz, the temperature measurement accuracy should be ±0.1℃, the pressure measurement accuracy should be ±0.1%, and the flow measurement accuracy should be ±0.5%; GPS timing should be used to ensure the synchronous collection of multi-sensor data, and the sampling time deviation should not exceed 0.1s; the data preprocessing adopts the three sigma criterion to eliminate outliers, and the Butterworth low-pass filter is used for data smoothing. The processed data is stored in a unified format, including standard information such as timestamp, measurement value, and unit.

[0039] It is understandable that the embodiments of the present application can be based on obtaining historical operating data of the heat exchanger in the compressed air energy storage system under different operating conditions, so as to subsequently distinguish between the linear heat exchange part and the nonlinear heat exchange part.

[0040] In step S102, the linear heat exchange part and the nonlinear heat exchange part of the compressed air energy storage system in the energy storage process are extracted from the historical operation data.

[0041] Among them, the linear heat exchange part is used to represent the linear heat exchange process, and the nonlinear heat exchange part is used to represent the nonlinear heat exchange process.

[0042] It can be understood that the embodiment of the present application can extract the linear heat exchange part and the nonlinear heat exchange part of the compressed air energy storage system during the energy storage process in the historical operation data, specifically analyzing the linear heat exchange part and the nonlinear heat exchange part between the input data and the output data.

[0043] In step S103, a heat exchange performance identification model of the heat exchanger is constructed based on the linear heat exchange part and the nonlinear heat exchange part, wherein the heat exchange performance identification model is used to realize the identification of the heat exchange performance of the heat exchanger under different operating conditions.

[0044] Among them, the heat exchange performance model can output the comprehensive performance characteristics of the heat exchanger, including the current heat exchange efficiency, heat transfer coefficient, pressure drop coefficient and resistance coefficient reflecting flow loss, temperature efficiency and pressure efficiency characterizing thermal performance, etc.

[0045] It can be understood that the embodiment of the present application can construct a heat exchange performance identification model of the heat exchanger based on the linear heat exchange part and the nonlinear heat exchange part. The heat exchange performance identification model is used to identify the heat exchange performance of the heat exchanger under different operating conditions, and fully considers the linear heat exchange part and the nonlinear heat exchange part in the heat exchange process, so that the heat exchange performance identification model can more comprehensively reflect the complex behavior of the heat exchanger and can accurately identify the heat exchange performance of the heat exchanger, so as to subsequently use the heat exchange performance identification results of the heat exchanger for system optimization control, predictive maintenance, fault diagnosis and operation strategy formulation.

[0046] In an embodiment of the present application, a heat exchange performance identification model of a heat exchanger is constructed based on a linear heat exchange part and a nonlinear heat exchange part, including: determining the model parameters of the linear heat exchange part and the model parameters of the nonlinear heat exchange part of the heat exchanger based on the mass flow rates of the cold and hot fluids, the inlet and outlet temperatures and the corresponding heat exchange efficiencies of the heat exchanger; and combining the model parameters of the linear heat exchange part and the model parameters of the nonlinear heat exchange part to generate a heat exchange performance identification model.

[0047] Among them, the model parameters of the linear heat exchange part can be determined by a linear regression method, such as the least squares method, and the model parameters of the nonlinear heat exchange part can be determined by a nonlinear regression method, such as the gradient descent method.

[0048] It can be understood that the embodiments of the present application can determine the model parameters of the linear heat exchange part and the model parameters of the nonlinear heat exchange part respectively according to the mass flow rates of the cold and hot fluids, the inlet and outlet temperatures and the corresponding heat exchange efficiencies under different operating conditions, and combine the model parameters of the linear heat exchange part and the model parameters of the nonlinear heat exchange part to generate a heat exchange performance identification model.

[0049] In an embodiment of the present application, the linear heat exchange part introduces fractional-order derivatives and kernel functions, and adjusts the fractional-order parameters and kernel function parameters according to the characteristics of the compressed air energy storage system; the nonlinear heat exchange part introduces nonlinear functions.

[0050] It can be understood that the present application can introduce fractional-order derivatives and kernel functions in the linear heat exchange part, and adjust the fractional-order parameters and kernel function parameters according to the characteristics of the compressed air energy storage system, so that the linear part can better adapt to the dynamic changes of the system to finely describe the complexity of the heat exchange process; introduce nonlinear functions in the nonlinear heat exchange part, such as polynomial functions or other nonlinear functions, to accurately simulate the nonlinear heat exchange process in the compressed air energy storage system.

[0051] Specifically, the embodiment of the present application preliminarily constructs a heat exchange performance identification model (also known as a fractional-order nonlinear Hammerstein controlled autoregressive (FO-NHCAR) model) mainly including: starting with defining the basic dynamic behavior and operating environment of the heat exchanger system. Using the collected inlet and outlet temperatures and real-time mass flow of the heat exchange fluid in the heat exchanger under different working conditions, the input-output relationship of the model is preliminarily analyzed to identify the key variables and potential nonlinear relationships that affect the heat exchange performance of the turbine. Subsequently, appropriate fractional-order derivatives and kernel functions are selected to describe the influence of the parameter settings of the inlet and outlet temperatures of the heat exchange fluid in the heat exchanger and the real-time mass flow on the working efficiency of the turbine and the working efficiency of the entire compressed air energy storage system. These parameters will directly affect the accuracy and complexity of the compressed air energy storage system model. The nonlinear part of the model is usually expressed by polynomials or other basis functions, while the fractional-order derivatives are used to describe the linear dynamic part. After completing these steps, the model parameters are preliminarily estimated to provide a starting point for the subsequent global optimization of the fuzzy genetic algorithm.

[0052] In an embodiment of the present application, after constructing a heat exchange performance identification model of a heat exchanger based on a linear heat exchange part and a nonlinear heat exchange part, it also includes: optimizing the initial model parameters of the heat exchange performance identification model based on a fuzzy genetic algorithm; judging the prediction accuracy of the optimized heat exchange performance identification model; if the prediction accuracy meets a preset standard, stopping optimizing the model parameters of the heat exchange performance identification model.

[0053] The prediction standard can be pre-set according to the specific situation, and there is no specific limitation for comparison. The measurement indicator of prediction accuracy can be the mean square error MSE. For example, if the MSE is less than 0.01, the optimization is stopped.

[0054] It can be understood that the embodiment of the present application can optimize the initial model parameters of the heat exchange performance identification model based on the fuzzy genetic algorithm, and stop optimizing the model parameters of the heat exchange performance identification model after the prediction accuracy of the optimized heat exchange performance identification model meets the preset standards.

[0055] In an embodiment of the present application, the initial model parameters of the heat exchange performance identification model are optimized based on a fuzzy genetic algorithm, including: creating fuzzy variables corresponding to each data in the historical operation data, and defining a fuzzy term set for each fuzzy variable; constructing fuzzy rules based on expert knowledge or historical operation data; and optimizing the initial model parameters of the heat exchange performance identification model based on the fuzzy rules and historical operation data.

[0056] It can be understood that the embodiment of the present application can create fuzzy variables for each data in the historical operating data, define a fuzzy term set, construct fuzzy rules based on expert knowledge or historical operating data, and then optimize the initial model parameters of the heat exchange performance identification model according to the fuzzy rules and historical operating data, so as to achieve high accuracy in the subsequent identification and prediction of the heat exchanger performance of the compressed air energy storage system under different operating conditions.

[0057] For example, identify and define the key input and output parameters that affect the performance of the heat exchanger, such as mass flow, temperature difference, efficiency, etc. For each key parameter, create corresponding fuzzy variables and define fuzzy term sets for these variables, such as "low", "medium", and "high". Then, based on expert knowledge or historical data, construct fuzzy rules to describe the relationship between these variables. For example, if the mass flow is "high" and the inlet temperature difference is "low", the efficiency may be "medium". Apply the fuzzy logic reasoning mechanism, the Mamdani model, to handle the fuzziness of the input data.

[0058] In general, the heat exchange performance identification model constructed in the embodiment of the present application uses fractional derivatives to enhance the description ability of nonlinear dynamic characteristics, and introduces fuzzy genetic algorithms to achieve parameter adaptive optimization. The heat exchange performance identification model can output the comprehensive performance characteristics of the heat exchanger after identification and analysis, including: current heat exchange efficiency with an accuracy better than ±2%, heat transfer coefficient with an accuracy better than ±5%, pressure drop coefficient and resistance coefficient reflecting flow loss, temperature efficiency and pressure efficiency characterizing thermal performance, and the identification results are used for compressed air energy storage system optimization control, predictive maintenance, fault diagnosis and operation strategy formulation, providing reliable support for the optimized operation of compressed air energy storage system, specifically, through real-time performance evaluation and parameter optimization, the system operation efficiency is improved by 3-5%; based on performance degradation trend analysis, predictive maintenance is achieved to extend the service life of equipment by 15-20%; intelligent control strategies are adopted to improve system operation reliability and reduce unplanned downtime by more than 30%; combined with maintenance decision optimization, maintenance costs are reduced by 15-25%.

[0059] In addition, this application is particularly suitable for the performance evaluation and optimization control of heat exchangers in the fields of compressed air energy storage systems, industrial heat exchange equipment, energy systems, etc. that require high-precision performance identification.

[0060] According to the method for constructing a heat exchange performance identification model of a heat exchanger proposed in an embodiment of the present application, the linear heat exchange part and the nonlinear heat exchange part of the compressed air energy storage system in the energy storage process can be extracted from the historical operation data of the heat exchanger in the compressed air energy storage system, and a heat exchange performance identification model of the heat exchanger is constructed based on the linear heat exchange part and the nonlinear heat exchange part, so that the heat exchange performance identification model can be used to identify the heat exchange performance of the heat exchanger in the future, and the linear heat exchange part and the nonlinear heat exchange part in the heat exchange process are fully considered, so that the heat exchange performance identification model can more comprehensively reflect the complex behavior of the heat exchanger, and can accurately identify the heat exchange performance of the heat exchanger, thereby improving the identification effect and accuracy of the heat exchange performance identification model.

[0061] The above-mentioned embodiments focus on describing the solution of the present application from the perspective of model construction, and the following embodiments focus on describing the solution of the present application from the perspective of model operation.

[0062] Figure 2 It is a flow chart of a method for identifying heat exchange performance of a heat exchanger according to an embodiment of the present application.

[0063] like Figure 2 As shown, the heat exchange performance identification method of the heat exchanger includes the following steps:

[0064] In step S201, current operating data of a heat exchanger in a compressed air energy storage system is obtained.

[0065] The current operating data includes the mass flow rates of the cold and hot fluids of the heat exchanger and the corresponding inlet and outlet temperatures.

[0066] In step S202, the current operating data is input into a heat exchange performance identification model of the heat exchanger, and the heat exchange performance identification model outputs the current heat exchange performance of the heat exchanger, wherein the heat exchange performance identification model is constructed based on the linear heat exchange part and the nonlinear heat exchange part of the compressed air energy storage system during the energy storage process in the historical operating data.

[0067] The construction of the heat exchange identification model has been described in the above embodiments and will not be repeated here.

[0068] It is understandable that the embodiment of the present application can input the current operating data into the heat exchange performance identification model of the heat exchanger, and use the heat exchange performance identification model to output the current heat exchange performance of the heat exchanger.

[0069] In an embodiment of the present application, after the heat exchange performance identification model outputs the current heat exchange performance of the heat exchanger, it also includes: obtaining a reference heat exchange performance of the current operating data; if the current heat exchange performance is inconsistent with the reference heat exchange performance, re-optimizing the model parameters of the heat exchange performance identification model.

[0070] Among them, the reference heat transfer performance can be obtained from historical operating data or expert knowledge.

[0071] It is understandable that the embodiment of the present application can re-optimize the model parameters of the heat exchange performance identification model when the current heat exchange performance is inconsistent with the reference heat exchange performance, so as to further optimize the model and ensure that the model can maintain high accuracy and robustness under different operating conditions.

[0072] According to the heat exchange performance identification method of the heat exchanger proposed in the embodiment of the present application, the current operating data of the heat exchanger in the compressed air energy storage system can be input into the heat exchange performance identification model of the heat exchanger, and the heat exchange performance identification model outputs the current heat exchange performance of the heat exchanger, thereby realizing the identification and prediction of the heat exchange performance of the heat exchanger.

[0073] The construction and optimization of the heat exchange performance identification model of the heat exchanger of the present application is described below through a specific embodiment. Figure 3 As shown, including:

[0074] Step 1: According to the specific requirements of the compressed air energy storage system heat exchanger, the input data include fluid inlet temperature, inlet pressure, flow rate, ambient temperature and operation time. The output data include fluid outlet temperature, outlet pressure and heat exchange efficiency. Based on these data, select a suitable fractional order, such as 0.5 or 0.9, and use a Gaussian kernel function to construct a FO-NHCAR model that can reflect the thermal dynamic characteristics of the heat exchanger.

[0075] Step 2: According to the heat exchange characteristics of compressed air in the energy storage process, fractional-order derivatives are introduced in the linear dynamic part of the heat exchanger, and the fractional-order parameters are adjusted according to the system characteristics to finely describe the complexity of the heat exchange process.

[0076] Step 3: Design a static nonlinear block and use polynomials or other nonlinear functions to accurately simulate the nonlinear heat exchange process in the compressed air energy storage system.

[0077] Step 4: Initialize the model parameters based on the operating data of the heat exchanger in the compressed air energy storage system. These parameters include fractional-order coefficients and nonlinear and linear parts to establish the baseline behavior of the model.

[0078] Step 5: Use fuzzy genetic algorithm to fine-tune the model parameters, monitor the optimization effect through the fitness function, and gradually improve the model parameters to achieve a high degree of accuracy in predicting the performance of the heat exchanger under variable operating conditions of compressed air energy storage.

[0079] Step 6: Conduct a comprehensive performance test on the adjusted model to compare the response of the compressed air energy storage system heat exchanger in actual operation with the model prediction to ensure that the model can maintain high accuracy and robustness under different operating conditions.

[0080] It is mainly divided into three parts: preliminary construction of heat transfer performance identification model, model parameter optimization and model verification and performance evaluation.

[0081] First, a fractional-order nonlinear Hammerstein controlled autoregressive (FO-NHCAR) model is constructed. First, by introducing fractional-order derivatives in the linear dynamic block of the Hammerstein model, the description of the thermal dynamic behavior of the heat exchanger during the energy storage process is optimized, so that the model can accurately capture the inherent complexity of the energy storage system and the long-term system memory characteristics. This process begins with defining the basic dynamic behavior and operating environment of the heat exchanger system. Using the collected inlet and outlet temperatures and real-time mass flow rates of the heat exchange fluid in the heat exchanger under different working conditions, the input-output relationship of the model is preliminarily analyzed to identify the key variables and potential nonlinear relationships that affect the heat transfer performance of the turbine. Subsequently, appropriate fractional orders and kernel functions are selected to describe the effects of the parameter settings of the inlet and outlet temperatures of the heat exchange fluid in the heat exchanger and the real-time mass flow rate on the working efficiency of the turbine and the working efficiency of the entire compressed air energy storage system. These parameters will directly affect the accuracy and complexity of the compressed air energy storage system model. The nonlinear part of the model is usually expressed by polynomials or other basis functions, while fractional-order derivatives are used to describe the linear dynamic part. After completing these steps, a preliminary estimate of the model parameters is made, which provides a starting point for the global optimization of the genetic algorithm.

[0082] It should be noted that in the process of model construction, this application comprehensively considers key factors such as the influence of heat exchanger structural characteristics on heat transfer, the nonlinear characteristics of working fluid physical parameters changing with temperature, the heat transfer enhancement effect under different flow patterns, the influence of fouling and scaling on heat transfer performance, and the dynamic response characteristics of system operating condition fluctuations. The comprehensive consideration of these factors ensures that the model can accurately reflect the performance characteristics of the heat exchanger under various working conditions.

[0083] Second, the fuzzy genetic algorithm is used to optimize the heat exchanger parameters in the FO-NHCAR model. The fuzzy genetic algorithm is used to optimize the parameter estimation process of the model. By simulating the natural selection and genetic variation mechanism, the adaptability and prediction accuracy of the heat exchanger model under variable operating conditions of the compressed air energy storage system are enhanced. Such a design not only overcomes the shortcomings of the traditional linear model in dealing with the complex nonlinear problems of the heat exchanger in the compressed air energy storage system, but also improves the practical value and performance in the context of grid-connected multi-scenario applications of the compressed air energy storage system. In this stage, first, identify and define the key input and output parameters that affect the performance of the heat exchanger, such as mass flow, temperature difference, efficiency, etc. For each key parameter, create the corresponding fuzzy variable, and define the fuzzy term set for these variables, such as "low", "medium", and "high". Then, based on expert knowledge or historical data, construct fuzzy rules to describe the relationship between these variables. For example, if the mass flow is "high" and the inlet temperature difference is "low", the efficiency may be "medium". The fuzzy logic reasoning mechanism, the Mamdani model, is applied to deal with the fuzziness of the input data. A fuzzy logic controller (FLC) is designed to adjust and optimize the model parameters. The controller receives real-time or predicted model input parameters and, through fuzzy reasoning, outputs parameter settings or adjustment suggestions that are most suitable for the current situation. The fuzzy output is converted into precise numerical values ​​through the bilateral maximum method defuzzification so that it can be actually applied to the system. Based on the defuzzified results, the parameters of the heat exchanger model are updated. These updates can be verified for effectiveness through further simulation and actual testing. This process is repeated to iteratively optimize the parameters until the predetermined performance target or steady state is reached.

[0084] In the implementation of the fuzzy genetic algorithm, the model parameters are directly encoded using real number coding, the fitness function is constructed by comprehensively considering the recognition accuracy and computational efficiency, and the roulette wheel and elite retention strategies are used for selection, and the optimization efficiency is improved by combining adaptive factorial crossover and non-uniform mutation operations. In addition, the algorithm also innovatively introduces fuzzy rules to dynamically adjust the genetic operation parameters, further improving the optimization performance.

[0085] 3. Model verification and performance evaluation. Apply the optimized model parameters to the heat exchanger system to simulate and predict real-time data to verify the effectiveness of the model. First, set up a simulation environment and use the latest real-time data or historical data to run the optimized model. Generate the predicted output of the system through the constructed heat exchanger, and compare these outputs with the actual operating data. The mean square error (MSE) statistical method is used to quantify the prediction error and prediction quality of the model. Finally, a sensitivity analysis is performed to understand the impact of changes in each parameter on the output, so as to further adjust and optimize the model parameters.

[0086] It should be noted that, in order to ensure the accuracy and reliability of the identification model, the heat exchange performance identification of this application not only establishes a performance benchmark value based on historical data, but also evaluates the performance change trend by calculating the deviation between the current performance and the benchmark value, thereby providing a basis for predicting maintenance time and optimization suggestions. This comprehensive performance evaluation system lays the foundation for the intelligent operation and maintenance of heat exchangers, and also provides a reliable basis for efficiency optimization.

[0087] This application adopts a three-level verification system to ensure the reliability of the recognition model: first, the model prediction accuracy is verified, and the prediction accuracy of the model under different working conditions is evaluated through the cross-validation method to ensure that the prediction error is controlled within the design indicator range; secondly, the reliability of the recognition result is verified, and the Monte Carlo method is used to evaluate the stability and robustness of the model and verify the confidence interval of the recognition result; finally, engineering application verification is carried out, and operation verification is carried out in the actual system for at least 3 months to comprehensively evaluate the actual effect of the model in energy efficiency improvement, fault warning, maintenance optimization, etc.

[0088] The method of this application has shown significant technical effects and economic value in practical applications: the error in heat exchange efficiency identification can be controlled within the design value, the accuracy of heat transfer coefficient identification meets the requirements of engineering applications, and can effectively warn of performance anomalies, improve system operation efficiency, and reduce maintenance costs. Through actual verification, this method has obvious advantages in heat exchanger performance identification, fault warning, and maintenance decision-making, providing reliable support for the efficient operation of compressed air energy storage systems.

[0089] In summary, the solution of the embodiment of the present application has the following advantages:

[0090] 1. Adopt the fractional-order nonlinear Hammerstein controlled autoregressive (FO-NHCAR) model. The theory of fractional calculus is used to enhance the heat exchanger model's ability to capture the thermal dynamics in the energy storage system, reflecting the complex dynamic behavior of the system caused by changes in fluid dynamics and heat transfer. This enables the heat exchanger model to not only more accurately simulate the heat exchange phenomenon during compression and expansion, but also improve its adaptability and prediction reliability under different working conditions.

[0091] 2. By using the fuzzy genetic algorithm combined with the global search capability of the genetic algorithm and the ability of fuzzy logic to handle uncertainty, an efficient and accurate optimization method for the parameter estimation process is proposed to estimate the optimal parameters for the implementation of the compressed air energy storage heat exchanger. This method effectively explores the complex multi-physical field coupling parameter space under multiple working states of the heat exchanger by simulating the natural selection, crossover and mutation process of biological evolution, and finds the parameter combination that optimizes the performance of the heat exchanger under the real-time working state. It is particularly suitable for dealing with heat exchanger optimization problems with multiple local optimal solutions, ensuring that the best parameter configuration can be obtained under different working conditions.

[0092] 3. By adopting an accurate model and an optimized parameter estimation method, the high robustness of the model under different operating environments is achieved. The fractional order model has low sensitivity to initial conditions and historical data, which enables the heat exchanger to effectively adapt to the rapid changes in the working state of the turbine and maintain the stability of system performance. In addition, the optimization process of the fuzzy genetic algorithm also takes into account the performance of the model under different working conditions, ensuring that the mass flow rate of the heat exchange medium and the inlet and outlet temperatures can be quickly adjusted to adapt to different working requirements, so that the present invention enables the heat exchanger to quickly adjust to follow the changes in the working state of the turbine.

[0093] 4. Comprehensive identification: The present invention not only identifies the heat exchange efficiency, but also includes a complete performance index system including heat transfer coefficient, resistance characteristics, etc., providing all-round support for system optimization.

[0094] 5. Strong real-time performance: Through strict data collection specifications and efficient calculation methods, real-time identification of heat exchanger performance can be achieved, supporting dynamic optimization control of the system.

[0095] 6. Good scalability: The identification method of the present invention can be easily extended to other types of heat exchangers and has broad application prospects.

[0096] Next, a device for constructing a heat exchange performance identification model for a heat exchanger and a device for identifying the heat exchange performance of a heat exchanger proposed in an embodiment of the present application will be described with reference to the accompanying drawings.

[0097] Figure 4 It is a block diagram of a device for constructing a heat exchange performance identification model for a heat exchanger according to an embodiment of the present application.

[0098] like Figure 4 As shown, the heat exchange performance identification model building device 10 of the heat exchanger includes: a first acquisition module 101, an extraction module 102 and a building module 103.

[0099] Among them, the first acquisition module 101 is used to obtain the historical operation data of the heat exchanger in the compressed air energy storage system under different operating conditions, wherein the historical operation data includes the mass flow rate of the cold and hot fluids, the inlet and outlet temperatures and the corresponding heat exchange efficiency; the extraction module 102 is used to extract the linear heat exchange part and the nonlinear heat exchange part of the compressed air energy storage system during the energy storage process in the historical operation data, wherein the linear heat exchange part is used to represent the linear heat exchange process, and the nonlinear heat exchange part is used to represent the nonlinear heat exchange process; the construction module 103 is used to construct a heat exchange performance identification model of the heat exchanger based on the linear heat exchange part and the nonlinear heat exchange part, wherein the heat exchange performance identification model is used to realize the identification of the heat exchange performance of the heat exchanger under different operating conditions.

[0100] In an embodiment of the present application, the construction module 103 is further used to: determine the model parameters of the linear heat exchange part and the model parameters of the nonlinear heat exchange part of the heat exchanger based on the mass flow rates of the cold and hot fluids, the inlet and outlet temperatures and the corresponding heat exchange efficiencies of the heat exchanger; and combine the model parameters of the linear heat exchange part and the model parameters of the nonlinear heat exchange part to generate a heat exchange performance identification model.

[0101] In an embodiment of the present application, the linear heat exchange part introduces fractional-order derivatives and kernel functions, and adjusts the fractional-order parameters and kernel function parameters according to the characteristics of the compressed air energy storage system; the nonlinear heat exchange part introduces nonlinear functions.

[0102] In the embodiment of the present application, the device 10 of the embodiment of the present application further includes: a first optimization module.

[0103] Among them, the first optimization module is used to optimize the initial model parameters of the heat exchange performance identification model based on the fuzzy genetic algorithm after constructing the heat exchange performance identification model of the heat exchanger based on the linear heat exchange part and the nonlinear heat exchange part; judge the prediction accuracy of the optimized heat exchange performance identification model; if the prediction accuracy meets the preset standard, stop optimizing the model parameters of the heat exchange performance identification model.

[0104] In an embodiment of the present application, the first optimization module is further used to: create fuzzy variables corresponding to each data in the historical operation data, and define a fuzzy term set for each fuzzy variable; construct fuzzy rules based on expert knowledge or historical operation data; and optimize the initial model parameters of the heat exchange performance identification model based on fuzzy rules and historical operation data.

[0105] It should be noted that the above explanation of the embodiment of the method for constructing a heat exchanger performance identification model is also applicable to the heat exchanger performance identification model construction device of this embodiment, and will not be repeated here.

[0106] According to the device for constructing a heat exchanger performance identification model for a heat exchanger proposed in an embodiment of the present application, the linear heat exchange part and the nonlinear heat exchange part of the compressed air energy storage system in the energy storage process can be extracted from the historical operation data of the heat exchanger in the compressed air energy storage system, and a heat exchange performance identification model of the heat exchanger is constructed according to the linear heat exchange part and the nonlinear heat exchange part. The heat exchange performance identification model is used to identify the heat exchange performance of the heat exchanger, and the linear heat exchange part and the nonlinear heat exchange part in the heat exchange process are fully considered, so that the heat exchange performance identification model more comprehensively reflects the complex behavior of the heat exchanger, and can accurately identify the heat exchange performance of the heat exchanger, thereby improving the identification effect and accuracy of the heat exchange performance identification model.

[0107] Figure 5 It is a block diagram of a device for identifying heat exchange performance of a heat exchanger according to an embodiment of the present application.

[0108] like Figure 5 As shown, the heat exchange performance identification device 20 of the heat exchanger includes: a second acquisition module 201 and an input module 202.

[0109] Among them, the second acquisition module 201 is used to obtain the current operating data of the heat exchanger in the compressed air energy storage system, wherein the current operating data includes the mass flow rate of the cold and hot fluids and the inlet and outlet temperatures of the heat exchanger; the input module 202 is used to input the current operating data into the heat exchange performance identification model of the heat exchanger, and the heat exchange performance identification model outputs the current heat exchange performance of the heat exchanger, wherein the heat exchange performance identification model is constructed based on the linear heat exchange part characteristics and the nonlinear heat exchange part of the compressed air energy storage system during the energy storage process in the historical operating data.

[0110] In the embodiment of the present application, the device 20 of the embodiment of the present application further includes: a second optimization module.

[0111] Among them, the second optimization module is used to obtain the reference heat exchange performance of the current operating data after the heat exchange performance identification model outputs the current heat exchange performance of the heat exchanger; if the current heat exchange performance is inconsistent with the reference heat exchange performance, the model parameters of the heat exchange performance identification model are re-optimized.

[0112] It should be noted that the explanation of the aforementioned embodiment of the method for identifying the heat exchange performance of a heat exchanger is also applicable to the heat exchange performance identification device of the heat exchanger of this embodiment, and will not be repeated here.

[0113] According to the heat exchange performance identification device of the heat exchanger proposed in the embodiment of the present application, the current operating data of the heat exchanger in the compressed air energy storage system can be input into the heat exchange performance identification model of the heat exchanger, and the heat exchange performance identification model outputs the current heat exchange performance of the heat exchanger, thereby realizing the identification and prediction of the heat exchange performance of the heat exchanger.

[0114] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0115] Memory 401 , processor 402 , and a computer program stored in the memory 401 and executable on the processor 402 .

[0116] When the processor 402 executes the program, the heat exchange performance identification model construction method of the heat exchanger or the heat exchange performance identification method of the heat exchanger provided in the above embodiments is implemented.

[0117] Furthermore, the electronic device further comprises:

[0118] The communication interface 403 is used for communication between the memory 401 and the processor 402 .

[0119] The memory 401 is used to store computer programs that can be executed on the processor 402 .

[0120] The memory 401 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0121] If the memory 401, the processor 402 and the communication interface 403 are implemented independently, the communication interface 403, the memory 401 and the processor 402 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0122] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can communicate with each other through an internal interface.

[0123] The processor 402 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0124] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0125] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0126] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0127] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of multiple of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0128] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

Claims

1. A method for constructing a heat exchange performance identification model of a heat exchanger, characterized in that: The following steps are involved: Acquire historical operating data of the heat exchanger in the compressed air energy storage system under different operating conditions, wherein the historical operating data includes mass flow rates, inlet and outlet temperatures, and corresponding heat exchange efficiencies of cold and hot fluids; Extracting a linear heat exchange part and a nonlinear heat exchange part of the compressed air energy storage system in the energy storage process from the historical operation data, wherein the linear heat exchange part is used to represent a linear heat exchange process, and the nonlinear heat exchange part is used to represent a nonlinear heat exchange process; A heat exchange performance identification model of the heat exchanger is constructed based on the linear heat exchange part and the nonlinear heat exchange part, wherein the heat exchange performance identification model is used to realize the identification of the heat exchange performance of the heat exchanger under different operating conditions.

2. The method for constructing a heat exchange performance identification model of a heat exchanger according to claim 1, characterized in that: The heat exchange performance identification model of the heat exchanger is constructed based on the linear heat exchange part and the nonlinear heat exchange part, comprising: Determine the model parameters of the linear heat exchange part and the model parameters of the nonlinear heat exchange part of the heat exchanger based on the mass flow rates of the cold and hot fluids of the heat exchanger, the inlet and outlet temperatures, and the corresponding heat exchange efficiencies; The model parameters of the linear heat exchange part and the model parameters of the nonlinear heat exchange part are combined to generate the heat exchange performance identification model.

3. The method for constructing a heat exchange performance identification model of a heat exchanger according to claim 2, characterized in that: The linear heat exchange part introduces fractional order derivatives and kernel functions, and adjusts fractional order parameters and kernel function parameters according to the characteristics of the compressed air energy storage system; the nonlinear heat exchange part introduces nonlinear functions.

4. The method for constructing a heat exchange performance identification model of a heat exchanger according to claim 1, characterized in that: After constructing the heat exchange performance identification model of the heat exchanger based on the linear heat exchange part and the nonlinear heat exchange part, the method further includes: Optimizing initial model parameters of the heat exchange performance identification model based on a fuzzy genetic algorithm; Determine the prediction accuracy of the optimized heat transfer performance identification model; If the prediction accuracy meets the preset standard, the optimization of the model parameters of the heat exchange performance identification model is stopped.

5. The method for constructing a heat exchange performance identification model of a heat exchanger according to claim 4, characterized in that: The optimization of the initial model parameters of the heat exchange performance identification model based on the fuzzy genetic algorithm includes: Creating a fuzzy variable corresponding to each data in the historical operation data, and defining a fuzzy term set for each fuzzy variable; constructing fuzzy rules based on expert knowledge or the historical operation data; The initial model parameters of the heat exchange performance identification model are optimized based on the fuzzy rules and the historical operation data.

6. A method for identifying the heat exchange performance of a heat exchanger, characterized in that: The following steps are involved: Acquire current operating data of a heat exchanger in a compressed air energy storage system, wherein the current operating data includes the mass flow rate of cold and hot fluids and the inlet and outlet temperatures of the heat exchanger; The current operating data is input into a heat exchange performance identification model of the heat exchanger, and the heat exchange performance identification model outputs the current heat exchange performance of the heat exchanger, wherein the heat exchange performance identification model is constructed based on the linear heat exchange part and the nonlinear heat exchange part of the compressed air energy storage system during the energy storage process in the historical operating data.

7. The heat exchange performance identification method of a heat exchanger according to claim 6, characterized in that: After the heat exchange performance identification model outputs the current heat exchange performance of the heat exchanger, the method further includes: Obtaining a reference heat exchange performance of the current operating data; If the current heat exchange performance is inconsistent with the reference heat exchange performance, the model parameters of the heat exchange performance identification model are re-optimized.

8. A device for constructing a heat exchange performance identification model for a heat exchanger, characterized in that: include: The first acquisition module is used to acquire the historical operation data of the heat exchanger in the compressed air energy storage system under different operating conditions, wherein the historical operation data includes the mass flow rate, inlet and outlet temperature and corresponding heat exchange efficiency of the cold and hot fluids; An extraction module, used to extract the linear heat exchange part and the nonlinear heat exchange part of the compressed air energy storage system in the energy storage process in the historical operation data, wherein the linear heat exchange part is used to represent the linear heat exchange process, and the nonlinear heat exchange part is used to represent the nonlinear heat exchange process; A construction module is used to construct a heat exchange performance identification model of the heat exchanger based on the linear heat exchange part and the nonlinear heat exchange part, wherein the heat exchange performance identification model is used to realize the identification of the heat exchange performance of the heat exchanger under different operating conditions.

9. A device for identifying heat exchange performance of a heat exchanger, characterized in that: include: A second acquisition module is used to acquire current operating data of a heat exchanger in a compressed air energy storage system, wherein the current operating data includes the mass flow rate of cold and hot fluids and the inlet and outlet temperatures of the heat exchanger; An input module is used to input the current operating data into a heat exchange performance identification model of the heat exchanger, and the heat exchange performance identification model outputs the current heat exchange performance of the heat exchanger, wherein the heat exchange performance identification model is constructed based on the linear heat exchange part and the nonlinear heat exchange part of the compressed air energy storage system during the energy storage process in the historical operating data.

10. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for constructing a thermal performance identification model of a heat exchanger as described in any one of claims 1 to 5, or the method for identifying the thermal performance of a heat exchanger as described in any one of claims 6 to 7.

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