PEM electrolytic membrane electrode thermoelectric coupling digital twin online early warning system and method

Abstract

The application provides a PEM electrolytic membrane electrode thermoelectric coupling digital twin online early warning system and method, relates to the technical field of proton exchange membrane water electrolysis, and comprises a physical entity sensing and collecting module, a thermoelectric coupling digital twin modeling module, a thermoelectric coupling fault discrimination and hierarchical early warning module and a visualization and man-machine interaction feedback module; the physical entity sensing and collecting module comprises a distributed micro-sensor array and an edge collection gateway; the thermoelectric coupling digital twin modeling module comprises a mechanism-driven thermoelectric coupling physical model and a data-driven PINN lightweight agent model double-model parallel operation module. The application breaks the traditional temperature and voltage independent monitoring technical paradigm by constructing a temperature-potential two-way coupling digital twin, and through the mechanism-driven physical model and data-driven PINN lightweight agent model double-model parallel architecture, the simulation accuracy of multiple physical fields and the real-time performance of online operation are considered.

Description

Technical Field

[0001] This invention relates to the field of proton exchange membrane electrolysis technology, and in particular to an online early warning system and method for PEM electrolysis membrane electrode thermoelectric coupling digital twin. Background Technology

[0002] Proton exchange membrane (PEM) electrolyzers have attracted much attention as a highly efficient and fast-response hydrogen production technology, especially suitable for coupling with fluctuating renewable energy sources such as wind and solar power. The membrane electrode assembly (MEA), the core component of a PEM electrolyzer, involves complex multiphysics coupling processes during operation, with strong nonlinear interactive coupling relationships between the temperature field, potential field, and mass transfer field. The distribution of temperature and current density / potential within the MEA has a crucial impact on the electrolyzer's performance, efficiency, and long-term operational stability.

[0003] However, existing monitoring and early warning technologies for PEM electrolyzers have the following problems: Traditional monitoring schemes only collect macroscopic lumped parameters such as total voltage and total temperature of the electrolyzer. Limited by cost and technology, actual industrial installations often only use the most simplified sensor configurations. Due to the lack of distributed microsensors embedded within the membrane electrode assembly (MEA), current technology cannot obtain the microscopic temperature and potential field distributions within the various functional layers, such as the proton exchange membrane, catalyst layer, and gas diffusion layer. Studies have shown that there is an in-plane temperature and current density non-uniform distribution within the PEM electrolyzer. This non-uniformity is a significant contributing factor to early problems such as localized hotspots and performance degradation. Because localized distribution information cannot be obtained, early-stage faults such as localized overheating, membrane drying, and flooding are difficult to identify in their nascent stages.

[0004] In existing diagnostic methods, temperature and voltage / current parameters are often collected and analyzed independently. However, localized high temperatures can lead to increased membrane resistance and voltage distortion; conversely, excessive local voltage can generate additional Joule heating, further exacerbating local overheating. Current technologies lack modeling and utilization of this thermoelectric coupling effect, resulting in high false alarm rates and a significant risk of missed alarms in alarm methods based on a single threshold.

[0005] While high-precision multiphysics simulation can describe the detailed physical field distribution inside the membrane electrode, its calculation time often reaches tens of seconds or even minutes, which cannot meet the timeliness requirements of online real-time monitoring and early warning.

[0006] A major technological advantage of PEM electrolyzers lies in their rapid dynamic response capability, enabling them to adapt to the fluctuating input of renewable energy sources. However, frequent power fluctuations from sources such as wind and solar power cause continuous dynamic changes in the operating parameters of the electrolyzer, such as voltage and temperature. Under these dynamic operating conditions, static early warning strategies based on fixed thresholds frequently result in false alarms or missed alarms.

[0007] To address this, a PEM electrolytic membrane electrode thermoelectric coupling digital twin online early warning system and method are proposed. Summary of the Invention

[0008] In view of this, the present invention provides a PEM electrolytic membrane electrode thermoelectric coupling digital twin online early warning system and method to solve or alleviate one of the technical problems existing in the prior art, and at least provides a beneficial option.

[0009] The technical solution of this invention is implemented as follows: A PEM electrolytic membrane electrode thermoelectric coupling digital twin online early warning system and method, comprising: The physical entity sensing and acquisition module is used to collect raw thermoelectric data of the entire PEM electrolytic membrane electrode. The physical entity sensing and acquisition module includes a distributed micro-sensor array and an edge acquisition gateway. The distributed micro-sensor array contains miniature temperature measuring points, high-frequency impedance measuring points, and local potential probes embedded in the membrane electrode, covering the proton exchange membrane, anode and cathode catalyst layers, and gas diffusion layer. It is used to collect multi-point membrane temperature, local ohmic voltage, activation overpotential, high-frequency internal resistance, cooling water inlet and outlet temperatures, inlet water flow rate, total stack current, and total voltage of a single tank. The edge acquisition gateway is used to synchronously read sensor and PLC / DCS operation data in real time, and upload the data via MQTT protocol after filtering, noise reduction, and timestamp alignment. At the same time, it locally caches historical thermoelectric coupling datasets. A thermoelectric coupling digital twin modeling module is used to construct a multi-field coupled virtual twin of the membrane electrode, realizing real-time mapping between the physical entity and the virtual model. This module includes a dual-model parallel computing module consisting of a mechanism-driven thermoelectric coupling physical model and a data-driven PINN lightweight proxy model. The mechanism-driven thermoelectric coupling physical model combines an electrochemical potential field model, a temperature field heat transfer model, and thermoelectric coupling correlation equations to output temperature distribution cloud maps, potential distribution cloud maps, and internal resistance distribution cloud maps for each region of the membrane electrode. The data-driven PINN lightweight proxy model uses a physical information neural network to reduce the order of the mechanism model, receiving real-time acquired data in the online stage and extrapolating the global temperature and potential distributions in milliseconds. The thermoelectric coupling fault detection and graded early warning module has a built-in thermoelectric coupling feature fault library, which is used to extract the coupling features of temperature gradient, potential difference and internal resistance change rate in each region, and output graded early warning signals after matching with the fault library. The visualization and human-computer interaction feedback module is used to render the membrane electrode temperature cloud map and potential cloud map in real time, highlight the fault area, and send the early warning and optimization instructions to the electrolysis DCS to form a closed-loop control.

[0010] Further preferred: The mechanism-driven thermoelectric coupling physical model includes: an electrochemical potential field model, constructed based on Butler-Wolmer reaction kinetics and Ohmic proton conduction loss; a temperature field heat transfer model, constructed based on electrochemical reaction heat generation, cooling water convection heat dissipation, and membrane solid-phase heat conduction; and a thermoelectric coupling correlation equation, characterizing the relationship between membrane internal resistance and temperature and water content, as well as the synchronous generation relationship of Joule heat with local voltage.

[0011] Further preferred: The data-driven PINN lightweight proxy model is constructed in the following way: a massive number of thermoelectric coupling samples are generated by offline multi-condition simulation to train the physical information neural network; after receiving real-time acquired data in the online stage, the global temperature distribution and potential distribution are inferred in milliseconds; the residuals of the actual thermoelectric data measured by the sensor and the output of the twin simulation are compared by the virtual-real synchronous correction module to automatically correct the model parameters of the membrane thermal conductivity and proton conductivity, and eliminate the operating condition drift error.

[0012] Further preferred: The thermoelectric coupling characteristic fault library includes the following four types of typical fault thermoelectric coupling abnormality characteristic judgment rules: Local hot spot overheating fault: local membrane temperature > 90℃ and the corresponding area potential rises sharply and internal resistance surges; Membrane dry water shortage fault: the internal resistance of the whole area rises significantly, the average voltage rises, and heat generation is concentrated; Anode and cathode water flooding fault: local potential decreases, temperature is low, and gas transmission loss increases; Catalyst layer decay fault: steady-state voltage continues to drift and heat distribution continues to be unbalanced under the same current.

[0013] Further preferred: The thermoelectric coupling fault identification and graded early warning module adopts a three-level early warning logic: Level 1 early warning: triggered when the local thermoelectric coupling deviation exceeds the standard range by 10%, generating parameter optimization suggestions; Level 2 early warning: triggered when the thermoelectric distortion area expands and the hot spot continues to spread, outputting an audible and visual alarm and linking the electrolysis system to reduce load; Level 3 early warning: triggered when a large area of ​​membrane dryness or over-temperature occurs, outputting an interlock shutdown signal to cut off the electrolysis power supply.

[0014] Further preferred: The thermoelectric coupling fault discrimination and hierarchical early warning module adopts the isolated forest multivariate anomaly detection algorithm, which uses the joint judgment of temperature gradient, electric potential gradient and internal resistance change rate to replace the single threshold judgment, so as to realize a low false alarm rate early warning under dynamic wind and solar fluctuation conditions.

[0015] In addition, the present invention also provides an online early warning method for PEM electrolytic membrane electrode thermoelectric coupling digital twin, characterized by comprising the following steps: Step S1: Collect full-domain thermoelectric data under the steady-state standard operating conditions of the electrolytic cell, complete the offline training of the mechanism model and the PINN proxy model, and establish a reference thermoelectric coupling standard field; Step S2: The edge gateway continuously reads the thermoelectric data of the membrane electrode distributed sensor, and uploads it to the twin platform after denoising and time alignment. Step S3: The PINN model calculates the global temperature and potential distribution in real time, and corrects the model parameters by comparing them with the measured values, so that the error between the virtual field and the physical film electrode is less than 2%. Step S4: Extract the coupling features of temperature gradient, potential difference and internal resistance change rate of each region and match them with the fault feature library; Step S5: Output alarms of the corresponding level according to the degree of coupling distortion, and simultaneously display the fault location, fault type and damage risk assessment; Step S6: When a Level 2 or Level 3 early warning is triggered, the system will automatically reduce load or shut down, and update the twin model operating condition database.

[0016] Further preferred: The model parameters corrected in step S3 include the membrane thermal conductivity and proton conductivity, which are dynamically corrected by real-time data acquisition to eliminate model drift caused by long-term operation and operating condition fluctuations.

[0017] In a further preferred embodiment: while outputting the corresponding level alarm in step S5, the visualization and human-computer interaction feedback module renders the full-domain temperature cloud map and potential cloud map of the membrane electrode in real time, and highlights the fault area.

[0018] The embodiments of the present invention have the following advantages due to the adoption of the above technical solutions: I. This invention breaks away from the traditional paradigm of independent temperature and voltage monitoring by constructing a temperature-potential bidirectional coupled digital twin. Distributed micro-temperature measuring points, high-frequency impedance measuring points, and local potential probes are embedded within the membrane electrode, covering the entire proton exchange membrane, anode and cathode catalytic layers, and gas diffusion layer. This enables precise acquisition of the microscopic temperature and potential field distribution across the entire membrane electrode. By using a mechanism-driven thermoelectric coupling physical model to simultaneously establish an electrochemical potential field model, a temperature field heat transfer model, and thermoelectric coupling correlation equations, the bidirectional coupling effect between temperature and potential is described. This allows for early identification of potential problems such as localized overheating, membrane dryness, and flooding of the membrane electrode, preventing irreversible damage and reducing maintenance and replacement costs.

[0019] II. This invention employs a dual-model parallel architecture—a mechanism-driven physical model and a data-driven PINN lightweight proxy model—to balance the accuracy of multiphysics simulation with real-time online computation. It utilizes a Physical Information Neural Network (PINN) to accelerate the high-precision mechanism model by reducing its order, and integrates Butler-Wolmer activation kinetics and electrochemical constraints such as ohmic impedance to achieve millisecond-level global thermoelectric field derivation. Simultaneously, a virtual-real synchronous correction module compares the residuals from sensor measurements with those from the twin simulation output, automatically correcting model parameters such as membrane thermal conductivity and proton conductivity, ensuring that the error between the virtual field and the physical membrane electrode is less than 2%. This overcomes the technical bottleneck of traditional simulations' inability to provide online early warning.

[0020] Third, this invention replaces the traditional single-threshold alarm method with a multi-dimensional coupled joint judgment mechanism based on temperature gradient, potential gradient and internal resistance change rate. It adopts the isolated forest multivariate anomaly detection algorithm, and replaces the single threshold judgment with the joint judgment of temperature gradient, potential gradient and internal resistance change rate. It can adaptively adapt to fluctuating input conditions such as wind power and photovoltaic, and reduce the false alarm rate and missed alarm rate under variable load conditions.

[0021] Fourth, this invention corrects the model's physical property parameters in real time through a virtual-real synchronous correction module, eliminating model deviations caused by long-term operating condition drift. It uses a recursive Bayesian filtering algorithm to continuously compare measured data with model prediction residuals and recursively update the posterior estimates of physical property parameters online, achieving progressive dynamic alignment between the twin model and the physical entity. At the same time, it renders the full-domain temperature cloud map and potential cloud map of the membrane electrode in real time through a 3D membrane electrode twin visualization interface, allowing maintenance personnel to intuitively locate the precise fault area and shorten the fault diagnosis time.

[0022] The above overview is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the invention will become readily apparent from the accompanying drawings and the following detailed description. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a system structure diagram of the present invention; Figure 2 This is a schematic diagram of the parallel operation principle of the thermoelectric coupling digital twin modeling module of the present invention. Figure 3 This is a flowchart of the method of the present invention. Detailed Implementation

[0025] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0026] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0027] Example 1

[0028] This embodiment provides a detailed description of the specific implementation of the physical entity perception and acquisition module.

[0029] In this embodiment, the distributed micro-sensor array employs flexible thin-film sensor technology, integrated into the membrane electrode assembly via attachment or embedding. Specifically, micro-temperature measuring points, high-frequency impedance measuring points, and local potential probes are arranged on both sides of the proton exchange membrane and at the interface between the anode and cathode catalytic layers and the gas diffusion layer. Each measuring point uses an array-style grid layout, with the spacing between adjacent measuring points set according to the effective area of ​​the membrane electrode assembly, generally controlled within the range of 5-15 mm, ensuring that the spatial resolution of the global thermoelectric field meets the fault location accuracy requirements. For effective areas of 100-300 cm²... 2 A typical industrial-grade membrane electrode has no fewer than 64 temperature measurement points, no fewer than 36 potential probes, and no fewer than 16 impedance measurement points.

[0030] Temperature measurement points employ miniature thermocouples or fiber Bragg grating sensors, covering a temperature range of -20℃ to 120℃ with an accuracy of ±0.5℃. High-frequency impedance measurement points utilize a four-electrode AC impedance measurement scheme with an excitation frequency range of 1Hz-10kHz, used to acquire the high-frequency internal resistance and charge transfer resistance of various local regions of the membrane electrode in real time. Local potential probes employ a miniature reference electrode array, arranged on both the anode and cathode sides, used to measure the activation overpotential and ohmic voltage drop in each region.

[0031] The edge acquisition gateway adopts an industrial-grade embedded hardware platform and runs a real-time operating system. Its data acquisition module supports multiple industrial communication protocols such as Modbus-TCP, Profibus-DP, and OPC-UA, and can simultaneously interface with the digital signal output of the distributed micro-sensor array and the analog signals of the original PLC / DCS system of the electrolytic cell. The edge acquisition gateway has a built-in lightweight time-series data preprocessing algorithm pipeline: the first stage is sliding window filtering with a window width of 50 sampling points to remove high-frequency electromagnetic interference noise; the second stage is multi-source data timestamp alignment based on the IEEE 1588 precise time protocol to ensure that data from different sensor channels have a unified time reference; the third stage is outlier removal based on the local outlier factor algorithm, which automatically marks and isolates measurements that exceed 3 times the standard deviation.

[0032] The preprocessed data is packaged at a fixed frequency of 1Hz, compressed, and then uploaded to the twin platform via the MQTT protocol. Simultaneously, the edge acquisition gateway is equipped with at least 512GB of local solid-state storage, which uses a circular buffer to cyclically cache at least 72 hours of thermocoupled raw data and preprocessed logs, ensuring no data loss during network interruptions and automatic retransmission upon network recovery.

[0033] Example 2

[0034] This embodiment provides a detailed explanation of the parallel operation principle of the dual-model modeling module for thermoelectric coupling digital twin modeling.

[0035] In this embodiment, the mechanism-driven thermoelectric coupling physical model specifically includes the following three major governing equation modules: 1. Electrochemical Potential Field Model. Based on the Butler-Wolmer reaction kinetics, the charge transfer process in the anode and cathode catalyst layers is described. Combined with Ohm's law to describe the proton conduction loss within the proton exchange membrane, a global potential distribution equation for the membrane electrode is established. The kinetics of the oxygen evolution reaction at the anode are described by the Butler-Wolmer equation:

[0036] in, For local current density, For exchange current density, and These are the anode and cathode transfer coefficients, respectively. For the number of transferred electrons, It is Faraday's constant. The gas constant is For local temperature, This is the activation overpotential. The ohmic overpotential is determined by the in-plane resistance and thickness of the proton exchange membrane.

[0037] 2. Temperature Field Heat Transfer Model. Taking into account heat generation from electrochemical reactions (including activation overpotential heat generation and ohmic heat generation), convective heat dissipation from cooling water, and heat conduction through the film solid phase, a three-dimensional unsteady-state heat transfer equation is established:

[0038] in, For density, For specific heat capacity, Thermal conductivity, The rate of heat production per unit volume, This represents the heat carried away by the cooling water. The model's inputs are the heat generation rate and cooling conditions in each local region, and its output is the transient temperature distribution across the entire membrane electrode area.

[0039] 3. Thermoelectric Coupling Equations. Establish the functional relationship between membrane resistance and local temperature and water content, as well as the coupling equation for Joule heat generated synchronously with the local voltage gradient. The equation relating membrane resistance and temperature is:

[0040] in, For activation energy, For reference temperature, This represents the membrane resistance as a function of water content at a reference temperature.

[0041] Solving the above three sets of equations simultaneously achieves bidirectional coupling between the temperature field and the potential field, and outputs a global temperature distribution cloud map, a potential distribution cloud map, and an internal resistance distribution cloud map of the membrane electrode.

[0042] The data-driven construction process of the PINN lightweight proxy model is as follows: First, the above mechanism model is used under different operating conditions (current density varies from 0.2 to 3.0 A / cm). 2 Offline simulations were conducted to generate massive amounts of thermoelectric coupling sample data under conditions of temperature variation (20-95℃) and water content variation (0.3-1.0%). Secondly, a physical information neural network incorporating physical constraints was constructed, embedding the electrochemical control equation and heat transfer control equation as physical regularization terms into the loss function.

[0043] in, For data fitting loss, For the residual loss of the physical equation, These are the physical constraint weights. High-precision training is achieved with a small amount of simulation data. During the online inference phase, the PINN model, after lightweight pruning and quantization acceleration, can complete a single global thermoelectric field simulation within 10 milliseconds, meeting the real-time requirements of online early warning.

[0044] The virtual-real synchronization correction module continuously operates using a recursive Bayesian filtering algorithm: it uses the measured temperature and voltage values ​​at multiple points by the sensor as observation variables, and the predicted thermoelectric field values ​​at the corresponding points output by the twin model as state variables, to recursively update the posterior estimates of the membrane thermal conductivity k and key physical properties such as proton conductivity online.

[0045] in, These are estimated values ​​of physical property parameters. These are the actual measured values ​​from the sensor. For the observation matrix, This is the Kalman gain. Through continuous calibration, a progressive dynamic alignment between the twin model and the physical entity is achieved, ensuring that the global mapping error between the virtual field and the physical membrane electrode is always less than 2%. When the residual between the measured value and the model prediction value continues to exceed a set threshold, the model parameter recalibration process is triggered.

[0046] Example 3

[0047] This embodiment provides a detailed description of the workflow of the thermoelectric coupling fault detection and hierarchical early warning module.

[0048] In this embodiment, the thermoelectric coupling characteristic fault database stores the following four types of typical faults and their multi-dimensional coupling determination rules: Localized hotspot overheating fault. The criteria for judgment are: local membrane temperature > 90℃, the corresponding region's potential increases by more than 5% compared to adjacent regions, and the local internal resistance increases by more than 20% within 5 minutes. The hazards of this fault are membrane dehydration, perforation, and catalyst layer dissolution.

[0049] Membrane dryness fault. The criteria for judgment are: the average internal resistance across the entire region increases by more than 30% compared to the reference value, the average voltage rises by more than 3%, and the heat generation concentration (temperature distribution variance) increases by more than 50%. The hazards of this fault are proton conduction failure and permanent membrane carbonization.

[0050] Anode and cathode flooding fault. The criteria for determination are: a local potential decrease of more than 4% compared to adjacent areas, a corresponding temperature drop of more than 5°C, and increased gas transport losses (abnormal change rate of the imaginary part of high-frequency impedance exceeding 15%). The hazards of this fault include blockage of reaction sites and a sharp drop in hydrogen production efficiency.

[0051] Catalyst layer degradation failure. The criteria for determination are: a sustained steady-state voltage drift exceeding 2% / 1000h under the same current, and a sustained imbalance in thermal distribution (temperature distribution skewness change exceeding 30%). This failure leads to a rapid decline in the lifespan of the electrolytic reactor.

[0052] The specific judgment process of the Level 3 early warning logic module is as follows: Level 1 Warning (Minor Potential Hazard): Triggered when a local thermoelectric coupling deviation exceeds the standard range by 10% but does not reach the threshold explicitly defined in the fault database. At this time, the system displays a pop-up window on the 3D visualization interface indicating the location of the abnormal area and details of the parameter deviation, and automatically generates parameter optimization suggestions (such as increasing the cooling water flow rate by 5%-15% and increasing the inlet water humidity by 3%-8%), without interfering with the normal operation of the electrolytic reactor.

[0053] Level 2 Warning (Moderate Risk): Triggered when the thermoelectric distortion area continues to expand, the hot spot temperature spreads at a rate exceeding 2℃ / min, or any two coupling characteristics in the fault database judgment rules simultaneously exceed the standard. At this time, the system outputs an audible and visual alarm, automatically pushes a maintenance work order containing the fault location, fault type, and suggested handling measures to the dispatch center, and simultaneously links the electrolysis DCS system to reduce the operating current by 10%-30% to slow down the fault development rate.

[0054] Level 3 warning (serious fault): Triggered when large-area membrane dryness (internal resistance increase of more than 50% in more than 20% of the membrane area), local overheating (membrane temperature at any measuring point >95℃ and lasting for more than 10 seconds), or multiple adjacent areas simultaneously trigger the fault rule. At this time, the system outputs an interlock shutdown signal, cuts off the electrolysis power supply, and initiates the membrane electrode safety protection program (including nitrogen purging and residual voltage discharge) to prevent irreversible damage to the membrane electrode.

[0055] The coupled feature fusion discrimination algorithm employs the isolated forest multivariate anomaly detection method: constructing a feature fusion discrimination algorithm based on temperature gradient, electric potential gradient, and other parameters. An isolated forest model is based on V, where the rate of change of internal resistance is a three-dimensional feature vector. By randomly selecting features and randomizing split values, the feature space is recursively divided into several subspaces. Outliers are more easily isolated, resulting in shorter path lengths within the isolated forest.

[0056] Example 4: This example provides a detailed description of the specific implementation of the visualization and human-computer interaction feedback module.

[0057] The 3D membrane electrode twin visualization interface is developed based on the WebGL 3D graphics engine and supports browser-based operation without plugins. The interface renders real-time temperature and potential cloud maps of the entire membrane electrode using different color mapping methods, with the color scale range dynamically adjusted according to measured data. Users can observe the multiphysics distribution inside the membrane electrode from any angle using mouse dragging and scroll wheel zooming, and can also view the detailed thermoelectric distribution of the proton exchange membrane, catalytic layer, and gas diffusion layer layer by layer along the membrane thickness direction. Fault areas are highlighted in flashing red, and an information window automatically pops up displaying the specific fault type, abnormal parameter values, and risk assessment level for that area.

[0058] The data traceability module runs continuously in the background, storing thermoelectric coupling curve data, early warning records, model calibration logs, and operation and maintenance records throughout the entire lifecycle in a time-series database. Maintenance personnel can use the timeline control to trace back the thermoelectric field distribution of the membrane electrode at any historical moment, view the evolution of various physical quantities before and after a fault, and support root cause analysis and post-fault diagnosis.

[0059] The closed-loop control interface uses the OPC-UA communication protocol to interface with the electrolysis DCS system. When the level 3 early warning logic module outputs a level 2 or level 3 early warning, the closed-loop control interface sends a structured command containing the control type, control amplitude, and execution time limit to the DCS. After receiving the command, the DCS automatically performs operations such as cooling water temperature adjustment, inlet water humidity adjustment, or output current reduction, and returns a confirmation signal after execution, forming a complete closed-loop control link.

[0060] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in the present invention, and these should all be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A PEM electrolytic membrane electrode thermoelectric coupling digital twin online early warning system, characterized in that, include: The physical entity sensing and acquisition module is used to collect raw thermoelectric data of the entire PEM electrolytic membrane electrode. The physical entity sensing and acquisition module includes a distributed micro-sensor array and an edge acquisition gateway. The distributed micro-sensor array contains miniature temperature measuring points, high-frequency impedance measuring points, and local potential probes embedded in the membrane electrode, covering the proton exchange membrane, anode and cathode catalyst layers, and gas diffusion layer. It is used to collect multi-point membrane temperature, local ohmic voltage, activation overpotential, high-frequency internal resistance, cooling water inlet and outlet temperatures, inlet water flow rate, total stack current, and total voltage of a single tank. The edge acquisition gateway is used to synchronously read sensor and PLC / DCS operation data in real time, and upload the data via MQTT protocol after filtering, noise reduction, and timestamp alignment. At the same time, it locally caches historical thermoelectric coupling datasets. A thermoelectric coupling digital twin modeling module is used to construct a multi-field coupled virtual twin of the membrane electrode, realizing real-time mapping between the physical entity and the virtual model. This module includes a dual-model parallel computing module consisting of a mechanism-driven thermoelectric coupling physical model and a data-driven PINN lightweight proxy model. The mechanism-driven thermoelectric coupling physical model combines an electrochemical potential field model, a temperature field heat transfer model, and thermoelectric coupling correlation equations to output temperature distribution cloud maps, potential distribution cloud maps, and internal resistance distribution cloud maps for each region of the membrane electrode. The data-driven PINN lightweight proxy model uses a physical information neural network to reduce the order of the mechanism model, receiving real-time acquired data in the online stage and extrapolating the global temperature and potential distributions in milliseconds. The thermoelectric coupling fault detection and graded early warning module has a built-in thermoelectric coupling feature fault library, which is used to extract the coupling features of temperature gradient, potential difference and internal resistance change rate in each region, and output graded early warning signals after matching with the fault library. The visualization and human-computer interaction feedback module is used to render the membrane electrode temperature cloud map and potential cloud map in real time, highlight the fault area, and send the early warning and optimization instructions to the electrolysis DCS to form a closed-loop control.

2. The online early warning system for PEM electrolytic membrane electrode thermoelectric coupling digital twin according to claim 1, characterized in that: The mechanism-driven thermoelectric coupling physical model includes: an electrochemical potential field model, constructed based on Butler-Wolmer reaction kinetics and Ohmic proton conduction loss; a temperature field heat transfer model, constructed based on electrochemical reaction heat generation, cooling water convection heat dissipation, and membrane solid-phase heat conduction; and thermoelectric coupling correlation equations, characterizing the relationship between membrane internal resistance and temperature and water content, as well as the synchronous generation relationship of Joule heat with local voltage.

3. The online early warning system for PEM electrolytic membrane electrode thermoelectric coupling digital twin according to claim 1, characterized in that: The data-driven PINN lightweight proxy model is constructed in the following way: offline multi-condition simulation is used to generate a large number of thermoelectric coupling samples to train the physical information neural network; after receiving real-time acquired data in the online stage, the global temperature distribution and potential distribution are inferred in milliseconds; the virtual-real synchronous correction module compares the sensor measured thermoelectric data with the twin simulation output residuals to automatically correct the model parameters of the membrane thermal conductivity and proton conductivity, and eliminate the operating condition drift error.

4. The online early warning system for PEM electrolytic membrane electrode thermoelectric coupling digital twin according to claim 1, characterized in that: The thermoelectric coupling characteristic fault database includes the following four types of typical fault thermoelectric coupling abnormality characteristic judgment rules: Local hot spot overheating fault: local membrane temperature > 90℃ and the corresponding area potential rises sharply and internal resistance surges; Membrane dry water shortage fault: the internal resistance of the whole area rises significantly, the average voltage rises, and heat generation is concentrated; Anode and cathode water flooding fault: local potential decreases, temperature is low, and gas transmission loss increases; Catalyst layer decay fault: steady-state voltage continues to drift and heat distribution continues to be unbalanced under the same current.

5. The online early warning system for PEM electrolytic membrane electrode thermoelectric coupling digital twin according to claim 1, characterized in that: The thermoelectric coupling fault detection and hierarchical early warning module adopts a three-level early warning logic: Level 1 early warning: triggered when the local thermoelectric coupling deviation exceeds the standard range by 10%, generating parameter optimization suggestions; Level 2 warning: Triggered when the thermoelectric distortion area expands and the hot spot continues to spread, outputting an audible and visual alarm and triggering the electrolysis system to reduce load; Level 3 warning: Triggered when large-area membrane dryness or overheating occurs, outputting an interlock shutdown signal to cut off the electrolysis power supply.

6. The online early warning system for PEM electrolytic membrane electrode thermoelectric coupling digital twin according to claim 1, characterized in that: The thermoelectric coupling fault discrimination and hierarchical early warning module adopts the isolated forest multivariate anomaly detection algorithm, which uses the combined judgment of temperature gradient, electric potential gradient and internal resistance change rate to replace the single threshold judgment, so as to achieve low false alarm rate early warning under dynamic wind and solar fluctuation conditions.

7. A PEM electrolytic membrane electrode thermoelectric coupling digital twin online early warning method, applied to the PEM electrolytic membrane electrode thermoelectric coupling digital twin online early warning system according to any one of claims 1-6, characterized in that, Includes the following steps: Step S1: Collect full-domain thermoelectric data under the steady-state standard operating conditions of the electrolytic cell, complete the offline training of the mechanism model and the PINN proxy model, and establish a reference thermoelectric coupling standard field; Step S2: The edge gateway continuously reads the thermoelectric data of the membrane electrode distributed sensor, and uploads it to the twin platform after denoising and time alignment. Step S3: The PINN model calculates the global temperature and potential distribution in real time, and corrects the model parameters by comparing them with the measured values, so that the error between the virtual field and the physical film electrode is less than 2%. Step S4: Extract the coupling features of temperature gradient, potential difference and internal resistance change rate of each region and match them with the fault feature library; Step S5: Output alarms of the corresponding level according to the degree of coupling distortion, and simultaneously display the fault location, fault type and damage risk assessment; Step S6: When a Level 2 or Level 3 early warning is triggered, the system will automatically reduce load or shut down, and update the twin model operating condition database.

8. The online early warning method for PEM electrolytic membrane electrode thermoelectric coupling digital twin according to claim 7, characterized in that: The model parameters corrected in step S3 include the membrane thermal conductivity and proton conductivity. The model drift caused by long-term operation and fluctuations in operating conditions is eliminated by dynamically correcting the parameters through real-time data acquisition.

9. The online early warning method for PEM electrolytic membrane electrode thermoelectric coupling digital twin according to claim 7, characterized in that: In step S5, while outputting the corresponding level alarm, the visualization and human-computer interaction feedback module renders the full-domain temperature cloud map and potential cloud map of the membrane electrode in real time, and highlights the fault area.

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