Electrolytic bath foreign ion monitoring method, device, equipment, medium and product

Abstract

The invention relates to the technical field of water electrolysis hydrogen production system monitoring, and discloses an electrolytic bath impurity ion monitoring method which comprises the following steps: constructing a digital twin model of an electrolytic bath; acquiring operation parameters of the water electrolysis hydrogen production system and real-time concentration distribution data of various impurity ions, and synchronizing the operation parameters and the real-time concentration distribution data to the digital twinborn model; based on the real-time concentration distribution data, predicting concentration distribution of various impurity ions at the next moment by using a multi-physics field coupling model; and judging whether the concentration of each impurity ion exceeds a set threshold value or not, if so, determining an early warning level through an ion type and early warning level mapping table according to the type of the impurity ion exceeding the threshold value, and performing early warning. According to the invention, through the digital twinning and multi-physical field coupling model, real-time monitoring and prediction of foreign ions in the electrolytic cell are realized, so that the safety and efficiency of the water electrolysis hydrogen production system are improved.

Description

Technical Field

[0001] This invention belongs to the field of monitoring technology for water electrolysis hydrogen production systems, and particularly relates to a method, device, equipment, medium and product for monitoring impurity ions in an electrolyzer. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] The presence of impurity ions in the electrolyte has always been a significant factor affecting electrolysis efficiency and product quality. Impurity ions can lead to numerous negative impacts, including reduced electrolysis efficiency, accelerated electrode corrosion, and decreased product purity. Specifically, the higher the concentration of impurity ions, the greater the damage to the electrolysis process, which not only increases production costs but also reduces production efficiency and product quality.

[0004] Currently, methods for monitoring the concentration of impurity ions in electrolytes mainly rely on periodic sampling. However, sampling analysis cannot reflect real-time changes in impurity ion concentration within the electrolyzer, resulting in insufficient timeliness of monitoring results. Although online monitoring using sensors can achieve real-time monitoring, it cannot comprehensively cover all areas within the electrolyzer, only obtaining the ion concentration at the sensor's location. This can easily miss the presence of localized high concentrations of impurity ions, making it impossible to take timely and effective control measures. Summary of the Invention

[0005] To overcome the shortcomings of the prior art, the present invention provides a method, device, equipment, medium and product for monitoring impurity ions in an electrolytic cell, which improves the timeliness of monitoring based on a digital twin model.

[0006] To achieve the above objectives, one aspect of the present invention provides a method for monitoring impurity ions in an electrolyzer, comprising the following steps: Constructing a digital twin model of the electrolyzer; The operating parameters of the water electrolysis hydrogen production system and the real-time concentration distribution data of various impurity ions are obtained and synchronized to the digital twin model. Based on real-time concentration distribution data of various impurity ions, and based on a multi-physics coupling model, the concentration distribution of various impurity ions at the next moment is predicted. Determine whether the concentration of various impurity ions exceeds the set threshold. If it does, determine the warning level based on the type of impurity ion exceeding the set threshold and the warning level mapping table, and issue a warning.

[0007] In some embodiments, the method for constructing a digital twin model of an electrolyzer includes: performing three-dimensional geometric modeling of the electrolyzer; assigning material properties to the geometric model of the electrolyzer; and establishing a multiphysics coupling model, which mainly includes an electrochemical reaction process model, an electrolyte transport process model, and a multi-component diffusion model.

[0008] In some embodiments, based on a multiphysics coupling model, the prediction of the concentration distribution of various impurity ions at the next time step includes: For each type of impurity ion, the ion concentration distribution of the impurity ion at different locations in the electrolytic cell is obtained; Calculate the velocity field of the electrolyte based on the electrolyte transport process model; The convection and diffusion processes of ions in the electrolyte are simulated using a multi-component diffusion model to obtain the concentration distribution of impurity ions at the next moment.

[0009] In some embodiments, after obtaining the concentration distribution of various impurity ions at the next moment, the maximum concentration value in the concentration distribution is extracted for each type of impurity ion, and the maximum concentration value is compared with a set threshold for that type of impurity ion to determine whether it exceeds the set threshold.

[0010] In some embodiments, a mapping table of ion types, warning levels, and response measures is pre-configured; after determining the warning level, the corresponding response measures are obtained to control the operating parameters of the water electrolysis hydrogen production system.

[0011] In some embodiments, if the concentrations of two or more types of impurity ions exceed a set threshold at the same time, after determining the warning level, if the warning levels are different, the higher level warning will be issued; at the same time, the higher level response measures will be implemented first.

[0012] A second aspect of the present invention also provides an electrolytic cell impurity ion monitoring device, comprising: The digital twin model building module is configured to build a digital twin model of the electrolyzer. The real-time data synchronization module is configured to acquire the operating parameters of the water electrolysis hydrogen production system and the real-time concentration distribution data of various impurity ions, and synchronize them to the digital twin model. The impurity ion concentration prediction module is configured to predict the concentration distribution of various impurity ions at the next moment based on the real-time concentration distribution data of various impurity ions and a multi-physics coupling model. The impurity ion concentration early warning module is configured to determine whether the concentration of various impurity ions exceeds a set threshold. If it does, it determines the early warning level based on the type of impurity ion exceeding the set threshold and an early warning level mapping table, and issues an early warning.

[0013] A third aspect of the present invention also provides an electronic device including a processor and a memory, the memory storing computer instructions that, when executed by the processor, cause the electronic device to perform the method described thereon.

[0014] A fourth aspect of the invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method.

[0015] A fifth aspect of the present invention also provides a computer program product comprising a computer program that, when executed by a processor, implements the method described herein.

[0016] The above one or more technical solutions achieve real-time monitoring of the concentration of various impurity ions by applying digital twin models to monitor the concentration of impurity ions in the electrolytic cell. Furthermore, the concentration can be predicted through a multi-physics coupling model, improving the accuracy and timeliness of the prediction. In addition, based on the impact of different types of impurity ion concentration exceeding the standard on the electrolysis reaction, different warning levels are distinguished, realizing graded control. This can significantly improve the stability and efficiency of the electrolysis process and reduce the failure rate and maintenance costs. Attached Figure Description

[0017] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0018] Figure 1 This is an overall flowchart of the electrolytic cell impurity ion monitoring method in an embodiment of the present invention; Figure 2 This is a detailed flowchart of the electrolytic cell impurity ion monitoring method in an embodiment of the present invention; Figure 3 This is a module architecture diagram of the electrolytic cell impurity ion monitoring device in an embodiment of the present invention. Detailed Implementation

[0019] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While some embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this application. It should be understood that the drawings and embodiments of this application are for illustrative purposes only and are not intended to limit the scope of protection of this application.

[0020] In the description of the embodiments of this application, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on".

[0021] During the operation of a water electrolysis hydrogen production system, impurity ions will inevitably appear in the electrolyte. For example, under fluctuating operating conditions or long-term operation, component corrosion and catalyst degradation occur in the water electrolysis hydrogen production system, and the main pollutant ions generated include Na+. + Ni 2+ Fe 3+ For example, when polyvinyl chloride (PVC) sealing materials are used in electrolytic cells, these materials will age and decompose, releasing chloride ions, etc., due to factors such as high humidity, high temperature, and electrochemical environment during operation.

[0022] The presence of these impurity ions can cause numerous negative effects, such as reduced electrolysis efficiency, accelerated electrode corrosion, and decreased product purity. Regardless of the type of impurity ion, the higher the concentration, the greater the damage. Different types of ions have different effects on the performance of PEM electrolyzers. 1) Compared to hydrogen ions, Na+... + Ca 2+ Ni 2+ Fe 3+ These cations have a strong binding affinity to sulfonic acid groups in the proton exchange membrane, and can displace hydrogen ions, causing membrane damage. The presence of these metal cations promotes the generation of hydroxyl radicals, accelerating membrane degradation. Furthermore, under the influence of an electric field, cations migrate towards the cathode, leading to accumulation in the cathode region; 2) Cl - It can adsorb onto the catalyst surface, causing catalyst detachment, further accelerating free radical formation, and impairing the performance of the electrolyzer. Meanwhile, Cl... - This can lead to the degradation of the proton exchange membrane. Under the influence of an electric field, Cl... - Moving towards the anode, coupled with the oxidizing environment of the anode, it readily generates chlorine gas. If not removed in time, it will react with water to regenerate Cl. - ;3) F produced by the degradation of proton exchange membranes - It can dissolve the precious metal coating on the surface of the bipolar plate, and further, F - It will react with TiO2 to form TiF, causing irreversible degradation of the bipolar plate surface structure; 4) The anode side of the electrolytic cell is under strong oxidation, high potential and acidic conditions for a long time, releasing Ti 4+ Ti 4+ The gas migrates to the cathode and undergoes a reduction reaction, depositing and covering the active sites of the cathode catalyst, hindering hydrogen evolution and causing a decline in the performance of the electrolyzer.

[0023] Table 1. Effects of impurity ions on the electrolysis process

[0024] As described in the background section, current methods for detecting impurity ion concentrations suffer from deficiencies such as incompleteness, inaccuracy, and untimeliness. One or more embodiments of the present invention provide a method for monitoring impurity ions in an electrolyzer, applied to the monitoring of a water electrolysis hydrogen production system. The water electrolysis hydrogen production system comprises three main parts: an electrolyzer, a power supply, and a PLC. The electrolyzer contains the electrolyte and carries out electrochemical reactions. It is equipped with various sensors (such as ion-selective electrodes, temperature sensors, and flow sensors) to monitor key parameters of the electrolyte in real time, such as concentration, temperature, and flow rate. The power supply provides a stable current or voltage to the electrolyzer and can be dynamically adjusted via a controllable power module to adapt to changes during electrolyte circulation. It is also equipped with a power monitoring module to monitor output parameters in real time and feed them back to the PLC control system. The PLC control system receives sensor signals and makes real-time decisions based on preset control strategies. It connects to sensors and actuators (such as pumps, valves, and heating / cooling devices) via input / output modules to achieve precise control of electrolyte flow rate, direction, and temperature. Simultaneously, it communicates with a host computer (such as an industrial computer or SCADA system) via a communication module to achieve data transmission and remote monitoring. This enables real-time monitoring and dynamic adjustment of the electrolyte circulation, improving the operating efficiency and stability of the water electrolysis hydrogen production system. The method includes the following steps: S101: Construct a digital twin model of the electrolyzer; S102: Obtain the operating parameters of the water electrolysis hydrogen production system and the real-time concentration distribution data of various impurity ions, and synchronize them to the digital twin model; S103: Based on real-time concentration distribution data of various impurity ions, and based on a multi-physics coupling model, predict the concentration distribution of various impurity ions at the next moment. S104: Determine whether the concentration of various impurity ions exceeds the set threshold. If it does, determine the warning level based on the type of impurity ion that exceeds the set threshold.

[0025] By applying a digital twin model to monitor the concentration of impurity ions in an electrolyzer, real-time monitoring of various impurity ion concentrations was achieved. Furthermore, concentration prediction was performed using a multiphysics coupling model, improving the accuracy and timeliness of predictions. In addition, based on the impact of different types of impurity ion concentration exceedances on the electrolysis reaction, different warning levels were differentiated, enabling tiered control. This significantly improves the stability and efficiency of the electrolysis process, reducing failure rates and maintenance costs. The digital twin model allows for the monitoring of Na+ concentrations in the electrolyzer. + Ni 2+ Fe 3+ Ti 4+ Ca 2+ Cl - Accurate prediction and effective early warning of impurity ion distribution.

[0026] In step S101, a digital twin model of the electrolyzer is constructed based on its physical structure, electrochemical characteristics, and fluid dynamics. This model includes a geometric structure model, an electrochemical reaction model, a fluid flow model, and an impurity ion migration and diffusion model. The digital twin model can reflect the physicochemical state inside the electrolyzer in real time, providing a basis for impurity ion monitoring.

[0027] The specific methods for constructing digital twin models of electrolyzers include: S1011: Perform 3D geometric modeling of the electrolyzer. Based on the spatial characteristics and material properties of the working environment of the water electrolysis hydrogen production system, and considering dimensions and details, a 3D model is established.

[0028] First, a geometric model of the electrolyzer is established using computer-aided design (CAD) software. The internal structure of the electrolyzer is described in detail, including the structure of the electrodes, diaphragm, and electrolyte flow channels, as well as the electrode support structure, seals, and other structures. The electrode structure includes the shape, size, and position of the cathode and anode. The diaphragm structure includes the thickness, porosity, and position of the diaphragm. The electrolyte flow channel mechanism includes the inlet, outlet, and flow path of the electrolyte.

[0029] S1012: Assign material properties to the geometric model of the electrolyzer. For example, the conductivity, catalytic activity, and corrosion resistance of the electrode material; the permeability, mechanical strength, and chemical stability of the membrane material; and the conductivity, viscosity, density, and thermal conductivity of the electrolyte.

[0030] S1013: Establish a multiphysics coupling model, which mainly includes an electrochemical reaction process model, an electrolyte transport process model, and a multi-component diffusion model. Connect these different physical field models through appropriate boundary conditions and coupling conditions to form a complete multiphysics coupling model. Solve the model using numerical methods (such as the finite element method, finite volume method, etc.) to obtain the system response under different operating conditions. Taking the establishment of a multiphysics coupling model of an electrolytic cell using COMSOL Multiphysics as an example: (1) Select the physical field: Electrochemical reaction uses the “Electrochemical Reaction” interface in the “Electrochemistry” module; Electrolyte flow uses the equation in the “Fluid Dynamics” module; Ion transport uses the “Multi-component Diffusion” interface in the “Mass Transfer” module.

[0031] (2) Import or create a geometric model: Use CAD software to create a geometric model of the electrolytic cell, including the anode, cathode, electrolyte region, and diaphragm. Import the geometric file into COMSOL Multiphysics. Define the boundaries of each region, such as the interface between the anode and the electrolyte, and the interface between the cathode and the electrolyte.

[0032] (3) Add a physics interface: Add an "Electrochemical Reaction" interface to the anode and cathode surfaces. Define electrochemical reaction parameters, such as exchange current density and reaction kinetic parameters (using the Butler-Volmer equation). Set the material properties of the anode and cathode, such as conductivity and diffusion coefficient. In the electrolyte region, add equations to describe the electrolyte flow. Define fluid properties, such as viscosity and density. Set boundary conditions, such as the flow rate or pressure at the electrolyte inlet and outlet. In the electrolyte region, add a "Multi-Component Diffusion" interface. Define the diffusion coefficient and electromigration coefficient of each component. Set the initial concentration distribution and boundary conditions, such as the concentration gradient on the electrode surface.

[0033] (4) Setting coupling and boundary conditions: Set boundary conditions for the electrochemical reaction on the anode and cathode surfaces, such as electrode potential and exchange current density. Ensure that the electrochemical reaction is coupled with ion transport in the electrolyte. In the electrolyte region, couple the velocity field with the ion concentration field, considering the convection effect. Set the source term between the velocity field and the concentration field to ensure the conservation of momentum and concentration.

[0034] (5) Define the coupling conditions for multi-component diffusion: In the electrolyte region, ensure the conservation of concentration and charge of different ions. Use COMSOL's "Global Equation" interface to define global conservation conditions, such as total charge conservation.

[0035] (6) Mesh generation and solver setup: Select an appropriate mesh type (e.g., hexahedron) based on the geometric complexity of the model and the characteristics of the changing physical fields. Increase the mesh density in critical regions, such as the electrode surface and electrolyte flow areas, to improve computational accuracy. Set solver parameters, such as relative tolerance, absolute tolerance, and maximum number of iterations, to ensure the stability and accuracy of the solution.

[0036] (7) Set initial conditions and operating conditions: Set the initial conditions for each physical field, such as initial potential, initial flow rate, initial concentration distribution, etc. Ensure that the initial conditions are consistent with the initial state of the actual electrolytic cell. Define operating conditions: Set different operating conditions, such as constant current electrolysis, constant voltage electrolysis, etc. Define the time step and solution time range to ensure that the dynamic behavior of the system can be captured.

[0037] The main models involved in the above multiphysics coupling model are as follows: Electrochemical reaction process model: Electrochemical reactions are influenced by multiple factors. The electrochemical reactions at the anode and cathode of the electrolytic cell are described by the Butler-Volmer equation. Considering that the effect of temperature on current density is not negligible, a factor is introduced into the equation. :

[0038]

[0039] In the formula, , The specific reaction surface area of ​​the anode and cathode; , E represents the exchange current density of the anode and cathode catalyst layers. exc It is the activation energy of the electrode reaction; , represents the charge transport coefficients of the anode and cathode; It is the gas constant; It is Faraday's constant; For temperature; , This represents the activation overpotential of the anode and cathode.

[0040] Based on Ohm's law, the conservation equations for protons and electrons are:

[0041]

[0042] , It is the source term, representing the rate of gain and loss of protons and electrons in the reaction; Electrolyte conductivity; Electronic conductivity; Electrolyte potential; Electron potential.

[0043] Electrolyte transport process model In an electrolytic cell, fluid transport processes comply with the laws of conservation of mass and momentum:

[0044]

[0045] In the formula, ρ is the density of liquid water; υ is the velocity of liquid water; S m ε is the source term for liquid water participating in chemical reactions; μ is the porosity of the porous medium; p is the dynamic viscosity of the fluid; k is the permeability of the porous medium; Q is the source term for liquid water participating in chemical reactions. m It is a quality source, and M is the transposition symbol; F It is an external force.

[0046] Multi-component diffusion model The convection and diffusion of the components in porous media are described by the Maxwell-Stefan equations:

[0047] In the above formula, the left side is the convection term for ion x, which describes the transport of ions in the electrolyte flow; the right side is the diffusion term for ion x, which describes the diffusion behavior of ions under the concentration gradient. Porosity represents the proportion of space in which fluid can flow. ; This indicates the convection velocity of the component (ions) in the electrolyte. Indicates the concentration of ion x; This represents the diffusion coefficient of ion x; This is the temperature correction factor, representing the 1.5th power of the ratio of the actual temperature T to the reference temperature T0; This is the pressure correction factor, representing the ratio of the reference pressure p0 to the actual pressure p.

[0048] For different ions, the diffusion coefficient is affected by characteristics such as ion size, shape and charge. Here, the diffusion coefficient of a certain type of ion can be predefined for different impurity ions.

[0049] In step S102, various sensors deployed in the water electrolysis hydrogen production system are used to collect system operating parameters in real time, including electrolyzer voltage, current, temperature, pH value, conductivity, current density, and voltage. Simultaneously, ion-selective electrodes or online ion chromatographs are used to monitor the concentration distribution of various impurity ions in the electrolyte in real time, such as Fe. 2+ Fe 3+ Ni 2 + Cu 2+ Cl - The collected data is transmitted to the data processing center through the data acquisition system and simultaneously updated to the digital twin model of the electrolyzer, ensuring that the model's state is consistent with the actual electrolyzer's state.

[0050] In step S103, the multiphysics coupling model in the digital twin model is used, combined with the currently acquired impurity ion concentration distribution data and system operating parameters, to calculate and predict the concentration distribution of various impurity ions in the electrolyzer at the next moment (e.g., 5 minutes, 10 minutes, or 30 minutes later). The prediction process considers the influence of electrolyte flow, ion migration, diffusion, and electrochemical reactions on the impurity ion distribution.

[0051] Based on real-time concentration distribution data of various impurity ions, and using a multiphysics coupling model, the migration path, deposition location, and concentration changes of impurity ions in the electrolyzer are determined. S1031: For each type of impurity ion, obtain the ion concentration distribution of the impurity ion at different positions in the electrolytic cell; S1032: Calculation of the velocity field of electrolyte based on electrolyte transport process model; S1033: Based on a multi-component diffusion model, the convection and diffusion processes of ions in the electrolyte are simulated to obtain the concentration distribution of impurity ions at the next moment.

[0052] In multiphysics coupling problems, different physical fields are interdependent. For example, the velocity field of a fluid affects the convection and diffusion of ions, thus influencing the ion concentration distribution. ； The gases produced by electrochemical reactions (such as hydrogen and oxygen) alter the density and viscosity of fluids, thus affecting fluid flow. Ion concentration distribution also influences fluid flow through diffusion and convection processes. Due to these interdependent relationships, directly solving the coupled equations is often very difficult or impossible. Therefore, iterative methods are needed to gradually approximate the true solution until the solutions for each physical field no longer change significantly, i.e., convergence is achieved. For example, the electrolyte transport process model is solved, updating the velocity field and pressure; based on the current ion concentration and velocity field v, the multi-component diffusion equation is solved, updating the ion concentration; the electrolyte transport process model is solved again using the new concentration distribution, updating the velocity field and pressure; it is determined whether the change in at least one of the velocity field, pressure, and ion concentration between two consecutive iterations is less than a certain set threshold. If it is less, convergence is considered, and the iteration stops.

[0053] In step S104, the concentrations of various impurity ions are compared with a set threshold for that type of impurity ion to determine whether they exceed the set threshold. The set threshold is a critical value determined based on the safe operation requirements of the electrolyzer and the degree of influence of impurity ions on electrolysis efficiency. Specifically, for each type of impurity ion, the maximum concentration value in the predicted concentration distribution for the next time step is extracted from the predicted concentration distribution for that type of impurity ion. This maximum concentration value is then compared with the set threshold for that type of impurity ion to determine whether it exceeds the set threshold. The set threshold is a critical value determined based on the safe operation requirements of the electrolyzer and the degree of influence of impurity ions on electrolysis efficiency. For example, Fe... 3+ The set threshold for ions is 5 mg / L. If the predicted maximum concentration is 5.8 mg / L, it is considered to exceed the threshold; Cl - The set threshold for ions is 2 mg / L. If the predicted maximum concentration is 1.8 mg / L, it is determined that the threshold has not been exceeded.

[0054] When the concentration of a certain type of impurity ion exceeds a set threshold, the system determines the warning level based on an ion type and warning level mapping table. Warning levels are typically divided into three levels: low warning, medium warning, and high warning. Different impurity ions exceeding the limit correspond to different warning levels. For example, Fe... 3+ Excessive ion levels correspond to a level-two warning, while Cl... -Excessive ion levels trigger a three-tiered warning system. Once the warning level is determined, the system issues alerts via audible and visual alarms, SMS notifications, and system interface prompts. For example: Level 1 warning is indicated by a yellow light, Level 2 by a yellow light, and Level 3 by a red light.

[0055] S105: Based on the warning level, obtain the corresponding response measures and control the operating parameters of the water electrolysis hydrogen production system. Specifically, a mapping table of ion types, warning levels, and response measures is pre-configured. This mapping table details the warning levels and corresponding response measures for different types of impurity ions exceeding the standard. After determining the warning level, the system automatically obtains the corresponding response measures from the mapping table and controls the operating parameters of the water electrolysis hydrogen production system, such as adjusting the electrolyte flow rate, adjusting the electrolysis current, and starting the filtration system, to reduce the concentration of impurity ions and ensure the safe and stable operation of the system.

[0056] Table 2 Mapping Table of Ion Types, Warning Levels, and Response Measures

[0057] Based on this, corresponding response measures can be taken according to the warning level. For example, Level 1 warning: yellow light prompts, the touch screen displays the warning status, and relevant process parameters are automatically optimized; Level 2 warning: yellow light prompts, the touch screen displays the warning status, the electrolytic cell enters the load reduction program until it stops; Level 3 warning: red light prompts, accompanied by a buzzer, emergency stop, and it can only be restarted when the fault is reset and all parameters are monitored to be normal.

[0058] In another preferred embodiment, if the concentrations of two or more types of impurity ions simultaneously exceed a set threshold, the system will determine the warning level corresponding to each type of impurity ion. If these warning levels are different, the system will issue a warning according to the highest level and prioritize the execution of the highest-level response measures. For example, if Fe... 3+ Excessive ion levels correspond to a level-two warning, while Cl... - If the ion level exceeds the standard, corresponding to a Level 3 warning, the system will issue an alarm according to the Level 3 warning and prioritize the response measures corresponding to the Level 3 warning, such as shutting down the electrolytic cell.

[0059] By predicting the behavior of impurity ions using digital twin models and combining early warning levels with control strategies, corresponding response measures can be taken according to different early warning levels, providing a new method for optimizing electrolysis processes.

[0060] In one or more of the above embodiments, sensors collect key data and transmit it to a digital twin model to predict the distribution of impurity ions in the electrolyzer or electrolyte in advance. When the impurity ion concentration exceeds a threshold, an early warning signal is triggered, and feedback is simultaneously sent to the PLC for appropriate response measures. Through this technical approach, spatial prediction of impurity ion distribution and minute-level time-based early warning can be achieved, transforming electrolyzer maintenance from periodic inspections to precise predictive maintenance, significantly improving the operating efficiency and reliability of the water electrolysis hydrogen production system.

[0061] Based on the above method, one or more embodiments of the present invention also provide an electrolyzer impurity ion monitoring device, comprising: a digital twin model construction module 201 configured to construct a digital twin model of the electrolyzer; a real-time data synchronization module 202 configured to acquire the operating parameters of the water electrolysis hydrogen production system and the real-time concentration distribution data of various impurity ions, and synchronize them to the digital twin model; an impurity ion concentration prediction module 203 configured to predict the concentration distribution of various impurity ions at the next moment based on the real-time concentration distribution data of various impurity ions and a multi-physics coupling model; and an impurity ion concentration early warning module 204 configured to determine whether the concentration of various impurity ions exceeds a set threshold, and if so, determine the early warning level based on the type of impurity ions exceeding the set threshold and an early warning is issued according to the ion type and early warning level mapping table.

[0062] In a preferred embodiment, the device further includes an operating parameter control module: based on the warning level, it obtains corresponding response measures and controls the operating parameters of the water electrolysis hydrogen production system.

[0063] One or more embodiments of the present invention also provide an electronic device for implementing the cross-region electrolyzer impurity ion monitoring method described in the above embodiments. The electronic device includes one or more processors, one or more memories coupled to the processors, and a communication module coupled to the processors.

[0064] The memory may include one or more non-volatile memories and one or more volatile memories. Examples of non-volatile memories include, but are not limited to, at least one of the following: read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, hard disk, compact disc (CD), digital video disc (DVD), or other magnetic and / or optical storage. Examples of volatile memories include, but are not limited to, at least one of the following: random access memory (RAM), or other volatile memories that do not persist during the power-off period. The computer program may be stored in the ROM. When the processor executes the computer program, it implements the above-described cross-region electrolytic cell impurity ion monitoring method.

[0065] In some embodiments, the program may be tangibly contained in a computer-readable medium, which may include in a device (such as in memory) or other storage device accessible by the device. The program may be loaded from the computer-readable medium into RAM for execution. The computer-readable medium may include any type of tangible non-volatile memory, such as ROM, EPROM, flash memory, hard disk, whereby the computer-readable storage medium stores a computer program that, when executed by a processor, implements the aforementioned cross-region electrolytic cell impurity ion monitoring method.

[0066] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a server or terminal, they generate, in whole or in part, the processes or functions described in the embodiments of this application. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic cable, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to the server or terminal, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, and magnetic tape), an optical medium (e.g., digital video disk (DVD), etc.), or a semiconductor medium (e.g., solid-state drive).

[0067] Furthermore, although the operations are described in a specific order, this should be understood as requiring that such operations be performed in the specific order shown or in sequential order, or requiring that all illustrated operations be performed to achieve the desired result. In certain environments, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this application. Certain features described in the context of individual embodiments may also be implemented in combination in a single implementation. Conversely, various features described in the context of a single implementation may also be implemented individually or in any suitable sub-combination in multiple implementations.

[0068] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for monitoring impurity ions in an electrolytic cell, characterized in that, Includes the following steps: Constructing a digital twin model of the electrolyzer; The operating parameters of the water electrolysis hydrogen production system and the real-time concentration distribution data of various impurity ions are obtained and synchronized to the digital twin model. Based on real-time concentration distribution data of various impurity ions, and based on a multi-physics coupling model, the concentration distribution of various impurity ions at the next moment is predicted. Determine whether the concentration of various impurity ions exceeds the set threshold. If it does, determine the warning level based on the type of impurity ion exceeding the set threshold and the warning level mapping table, and issue a warning.

2. The method for monitoring impurity ions in an electrolytic cell as described in claim 1, characterized in that, The method for constructing a digital twin model of an electrolyzer includes: performing three-dimensional geometric modeling of the electrolyzer; assigning material properties to the geometric model of the electrolyzer; and establishing a multi-physics coupling model, which mainly includes an electrochemical reaction process model, an electrolyte transport process model, and a multi-component diffusion model.

3. The method for monitoring impurity ions in an electrolytic cell as described in claim 2, characterized in that, Based on a multiphysics coupling model, the concentration distribution of various impurity ions at the next time step is predicted to include: For each type of impurity ion, the ion concentration distribution of the impurity ion at different locations in the electrolytic cell is obtained; Calculate the velocity field of the electrolyte based on the electrolyte transport process model; The convection and diffusion processes of ions in the electrolyte are simulated using a multi-component diffusion model to obtain the concentration distribution of impurity ions at the next moment.

4. The method for monitoring impurity ions in an electrolytic cell as described in claim 1, characterized in that, After obtaining the concentration distribution of various impurity ions at the next moment, for each type of impurity ion, the maximum concentration value in the concentration distribution is extracted. By comparing the maximum concentration value with the set threshold for that type of impurity ion, it is determined whether the set threshold is exceeded.

5. The method for monitoring impurity ions in an electrolytic cell as described in claim 1, characterized in that, A mapping table of ion types, warning levels, and response measures is pre-configured; after determining the warning level, the corresponding response measures are obtained to control the operating parameters of the water electrolysis hydrogen production system.

6. The method for monitoring impurity ions in an electrolytic cell as described in claim 5, characterized in that, If the concentrations of two or more types of impurity ions exceed the set threshold at the same time, after determining the warning level, if the warning levels are different, the higher level will be used for the warning; at the same time, the higher level response measures will be implemented first.

7. A device for monitoring impurity ions in an electrolytic cell, characterized in that, include: The digital twin model building module is configured to build a digital twin model of the electrolyzer. The real-time data synchronization module is configured to acquire the operating parameters of the water electrolysis hydrogen production system and the real-time concentration distribution data of various impurity ions, and synchronize them to the digital twin model. The impurity ion concentration prediction module is configured to predict the concentration distribution of various impurity ions at the next moment based on the real-time concentration distribution data of various impurity ions and a multi-physics coupling model. The impurity ion concentration early warning module is configured to determine whether the concentration of various impurity ions exceeds a set threshold. If it does, it determines the early warning level based on the type of impurity ion exceeding the set threshold and an early warning level mapping table, and issues an early warning.

8. An electronic device, characterized in that, It includes a processor and a memory, the memory storing computer instructions that, when executed by the processor, cause the electronic device to perform the method of any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method according to any one of claims 1 to 6.

10. A computer program product, the computer program product comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 6.

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