Alkaline electrolytic cell performance discreteness Monte Carlo analysis method and system

Abstract

The invention discloses an alkaline electrolytic cell performance discreteness Monte Carlo analysis method and system, and relates to the technical field of electrolytic hydrogen production, and the method comprises the following steps: constructing an electrochemical-thermodynamic-hydrodynamic multi-physics field coupling steady-state model of an alkaline electrolytic cell; based on a Monte Carlo method, probabilistic modeling is carried out on manufacturing tolerance, aging effect and operation parameter uncertainty; through large-scale random sampling simulation, quantizing discrete distribution characteristics of stack output voltage of the alkaline electrolytic cell and voltage of each small electrolytic chamber in the alkaline electrolytic cell; high-voltage abnormal cells and the influence of the high-voltage abnormal cells on the system performance are identified; and carrying out parameter sensitivity analysis, and identifying highly sensitive parameters and low sensitive parameters. The method can provide theoretical basis and method support for robust design, health monitoring, operation and maintenance optimization and manufacturing tolerance control of the alkaline electrolytic cell.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of electrolytic hydrogen production, and particularly relates to a Monte Carlo analysis method and system for performance discreteness of an alkaline electrolyzer. BACKGROUND

[0002] With the increasing demand for clean energy worldwide, hydrogen energy, as an efficient and clean energy carrier, has gradually become a research hotspot in the field of new energy. Industrial new energy hydrogen production technology, especially water electrolysis hydrogen production technology, is considered one of the key technologies for future energy transformation due to its high efficiency and environmental protection. In the industrial new energy hydrogen production system, the alkaline water electrolyzer (AWE) is currently the most mature and economic technology route in large-scale and centralized hydrogen production scenarios. As the scale of the electrolyzer expands to thousands of square meters or megawatts, there are a large number of electrolytic cell structures in series inside the electrolyzer.

[0003] In actual manufacturing and operation, due to manufacturing tolerances, assembly errors, catalyst activity attenuation, uneven distribution of electrolyte concentration, uneven temperature field, and other factors, there is parameter discreteness between each electrolytic cell. This discreteness can cause uneven voltage distribution, resulting in a "bucket effect", in which a small number of high-voltage "abnormal cells" will limit the power output of the entire stack, accelerate local aging, and even cause failure. Currently, most modeling research on alkaline electrolyzers focuses on unit scale or deterministic simulation based on ideal assumptions, and does not fully consider the impact of parameter uncertainty on the overall performance of the system.

[0004] Therefore, there is a need for an analysis method that can systematically quantify parameter discreteness, identify key sensitive parameters, and provide probabilistic design guidance for the manufacturing and operation of large electrolyzers. SUMMARY

[0005] To solve the problem of lack of systematic parameter discreteness analysis for alkaline electrolyzers in the prior art, and difficulty in quantifying the impact of manufacturing and aging uncertainty on system performance, the present application proposes a Monte Carlo analysis method and system for performance discreteness of an alkaline electrolyzer, which can accurately predict the voltage output, hydrogen production efficiency, and internal state distribution of the electrolyzer under complex operating conditions, and quantitatively evaluate the performance fluctuations caused by parameter uncertainty, providing a theoretical basis and engineering guidance for the robust design, manufacturing process optimization, and intelligent operation and maintenance of large electrolyzers.

[0006] To achieve the above-mentioned purpose, the technical scheme adopted by the present application is as follows:

[0007] A Monte Carlo analysis method for performance discreteness of an alkaline electrolyzer, comprising the following steps:

[0008] A multiphysics steady-state model of an alkaline electrolyzer is established, which integrates electrochemical, thermodynamic and fluid dynamic mechanisms, and introduces bubble coverage, electrolyte conductivity correction and temperature-pressure coupling effect.

[0009] Identify the key uncertainty parameters affecting the performance of alkaline electrolyzers and establish probability distribution models for these key uncertainty parameters;

[0010] The Monte Carlo method was used for large-scale random sampling and simulation to obtain the probability distribution of the output voltage of the alkaline electrolyzer stack and the voltage of each electrolysis chamber inside the alkaline electrolyzer.

[0011] Analyze the voltage dispersion of each electrolysis cell inside the alkaline electrolyzer to identify the high-voltage cells and their distribution characteristics;

[0012] Parameter sensitivity analysis was performed to identify high, medium, and low sensitivity parameters for analysis of alkaline electrolyzers.

[0013] This invention also provides a system for analyzing the performance dispersion of an alkaline electrolyzer, comprising the following modules:

[0014] The multiphysics model module is used to construct a multiphysics steady-state model that integrates electrochemical, thermodynamic, and fluid dynamic mechanisms, and to perform parameter calculations.

[0015] The parameter probabilistic module is used to set the probability distribution model for each key uncertainty parameter. It allows users to customize the distribution type of each key uncertainty parameter according to the actual manufacturing tolerance level or aging degree, historical aging data and operating conditions.

[0016] The Monte Carlo simulation module is used to perform large-scale random sampling, automatically generate parameter combinations, and call multiphysics steady-state models to complete simulation calculations.

[0017] The statistical analysis module is used to process the data output by the Monte Carlo simulation module;

[0018] The visualization module is used to observe the results of data processing.

[0019] The present invention also 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 perform the steps of the Monte Carlo analysis method for the discrete performance of an alkaline electrolyzer described above.

[0020] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method for Monte Carlo analysis of the discrete performance of an alkaline electrolyzer.

[0021] Beneficial effects:

[0022] 1. This invention is the first to systematically apply the Monte Carlo probability analysis method to the performance dispersion analysis of alkaline electrolyzers, which can effectively quantify the performance fluctuations caused by manufacturing tolerances and aging effects, and provide methodological support for the reliability analysis and robust design of alkaline electrolyzers.

[0023] 2. This invention identifies electrolyte concentration and catalyst activity as highly sensitive parameters through parameter sensitivity analysis, which should be strictly controlled in production and operation. Mechanical structure parameters are less sensitive, and manufacturing tolerances can be appropriately relaxed while meeting basic performance requirements, thereby reducing production costs.

[0024] 3. This invention can provide a theoretical basis for online monitoring, health status assessment and fault early warning of alkaline electrolyzer stacks. By monitoring the dispersion of the cell voltage in real time, performance degradation and potential faults can be detected early. Attached Figure Description

[0025] Figure 1 This is a diagram showing the internal structure of an alkaline electrolytic cell.

[0026] Figure 2 This is a flowchart of a Monte Carlo analysis method for the performance dispersion of an alkaline electrolyzer according to the present invention;

[0027] Figure 3 This is a schematic diagram of a system for analyzing the performance dispersion of an alkaline electrolyzer according to the present invention.

[0028] The attached diagram is labeled as follows: end plate 1, electrode frame 2, gasket 3, bipolar plate 4, electrolyte and gas outlet 5, electrolyte inlet 6, cathode electrode 7, diaphragm 8, anode electrode 9, and fixing screw 10. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0030] This invention uses a 1,000 cubic meter alkaline water electrolyzer as the analytical object, such as... Figure 2 As shown, the Monte Carlo analysis method for the performance dispersion of an alkaline electrolyzer in this embodiment includes the following steps:

[0031] S1. Construct a multiphysics steady-state model:

[0032] The internal structure of an alkaline electrolytic cell is as follows: Figure 1As shown, the alkaline electrolytic cell consists of several electrolytic chambers connected in series, all of which are fixed by end plates 1 and fixing screws 10. Each electrolytic chamber is where the water electrolysis reaction takes place, and includes an electrode frame 2, a gasket 3, a bipolar plate 4, a cathode electrode 7, a diaphragm 8, and an anode electrode 9. During the water electrolysis reaction, the electrolyte inlet 6 provides alkaline solution to each electrolytic chamber, and the reacted liquid and generated gas are discharged through the electrolyte / gas outlet 5.

[0033] First, a multiphysics steady-state model of the alkaline electrolyzer is established:

[0034] A steady-state model of the alkaline electrolyzer is established based on the principles of electrochemistry, thermodynamics, and fluid dynamics. The voltage of the alkaline electrolyzer is calculated as follows:

[0035] (1)

[0036] (2)

[0037] In the formula, This refers to the total voltage of the electrolytic cell; This refers to the voltage of a single electrolysis cell in the electrolytic cell; The open-circuit voltage for each electrolysis cell; and These are the activation overvoltages of the anode and cathode in each electrolysis chamber, respectively. The ohmic overvoltage for each electrolysis cell; The concentration overvoltage for each electrolysis cell; is the total number of electrolytic cells in the electrolytic cell, and k represents the index value of the electrolytic cell.

[0038] Open circuit voltage The formula, calculated according to the Nernst equation, is as follows:

[0039] (3)

[0040] In the formula, This is a standard reversible overvoltage. It is the ideal gas constant; It is Faraday's constant; and These are the temperature and pressure during the operation of the alkaline electrolytic cell, respectively. The pressure of the humid hydrogen and oxygen near the electrode; This represents the pressure of pure water vapor near the electrode.

[0041] Standard reversible overvoltage The effect is greatly affected by temperature; the calculation equation is as follows:

[0042] (4)

[0043] The activation overvoltage is calculated based on the Butler-Folmer equation, as follows:

[0044] (5)

[0045] (6)

[0046] In the formula, and These are the charge transfer coefficients at the anode and cathode, respectively; This refers to the current density during operation. and These represent the exchange current densities in the anode and cathode regions, respectively. This represents the coverage factor of bubbles near the electrode; This represents the hyperbolic sine function.

[0047] The formula for calculating the charge transfer coefficient is as follows:

[0048] (7)

[0049] (8)

[0050] The exchange current density between the anode and cathode regions and the coverage factor of bubbles near the electrodes are calculated as follows:

[0051] (9)

[0052] (10)

[0053] (11)

[0054] In the formula: This is the operating reference temperature for the alkaline electrolytic cell. The reference pressure for the alkaline electrolyzer is exp(), which represents an exponential function.

[0055] The ohmic overvoltage in an alkaline electrolyzer is the linear voltage drop generated during ion or electron transfer through internal resistive components such as the electrolyte, diaphragm, electrodes, and connectors. According to Ohm's law, the ohmic overvoltage... The calculation equation is as follows:

[0056] (12)

[0057] In the formula: This refers to the current in an alkaline electrolytic cell; For anode resistance; For cathode resistance; Resistance of the electrolyte; This refers to the diaphragm resistor.

[0058] The materials used for the anode and cathode of the alkaline electrolytic cell are: The resistance of the electrodes can be calculated as follows:

[0059] (13)

[0060] (14)

[0061] In the formula, for Electrode conductivity; and These represent the thicknesses of the anode and cathode, respectively. and These represent the effective areas of the anode and cathode, respectively.

[0062] According to Brugmann's theory, electrolyte resistance for:

[0063] (15)

[0064] In the formula, This indicates the conductivity of the KOH solution; and These represent the distance between the anode and the diaphragm, and the distance between the cathode and the diaphragm, respectively.

[0065] For a 0.5 mm thick diaphragm used in a 30 wt% KOH electrolyte, its resistance... It can be calculated as:

[0066] (16)

[0067] In the formula, This indicates the area of ​​the diaphragm.

[0068] The concentration overvoltage mainly comes from hydrogen and oxygen that are not dissipated from the alkaline solution in time. The calculation formula is as follows:

[0069] (17)

[0070] In the formula, and It refers to the concentrations of hydrogen and oxygen at the electrode interface. and It represents the concentrations of hydrogen and oxygen under standard conditions.

[0071] Faraday efficiency is calculated using an empirical formula related to current density and temperature:

[0072] (18)

[0073] (19)

[0074] In the formula, For Faraday efficiency, This represents the actual hydrogen production per unit time. This represents the theoretical hydrogen production per unit time. For the first The current in each electrolysis chamber.

[0075] S2. Identify key uncertainty parameters and establish a probability model:

[0076] Key uncertainty parameters were identified, assuming they all followed a normal distribution, and their distribution parameters are shown in Table 1.

[0077] Table 1. Probability distribution settings for uncertainty parameters

[0078]

[0079] S3. Perform the Monte Carlo simulation, including the following steps:

[0080] S3-1. Initialization: Set the total number of Monte Carlo simulations to N, and the total number of electrolysis chambers in the alkaline electrolyzer to [missing information]. .

[0081] S3-2, Perform the outer loop (the... (Simulation 1):

[0082] For each electrolysis cell in the alkaline electrolyzer, a set of parameter values ​​is generated by independently sampling from the probability distribution of the parameters defined in Table 1. The independent randomness of the parameters for each electrolysis cell is simulated.

[0083] The generated set of random parameters, along with the fixed operating conditions, are input into the multiphysics steady-state model established in this invention.

[0084] Calculate the voltage of the cell under the current random parameters. .

[0085] S3-3, Alkaline Electrolyzer Stack Voltage Calculation: Sum of all voltages in this simulation. The voltage of each electrolysis chamber is used to obtain the total voltage of the alkaline electrolysis cell stack under the i-th simulation with random parameter combinations. .

[0086] S3-4. Data Storage: Store the voltage of each electrolysis chamber in all alkaline electrolyzers used in this simulation. and alkaline electrolytic cell stack voltage .

[0087] S3-5, Termination Judgment: Repeat S3-2-S3-4 until N independent simulations are completed.

[0088] S3-6 provides visualization data for engineering design by including the output alkaline electrolyzer stack voltage distribution histogram, the voltage distribution diagram of each electrolysis chamber in the alkaline electrolyzer, the worst-case chamber voltage distribution diagram, and the parameter sensitivity bar chart.

[0089] S3-7. Based on the simulation results, the total voltage of the alkaline electrolyzer stack, the voltage dispersion of each electrolysis chamber in the alkaline electrolyzer, and the parameter sensitivity analysis can be performed.

[0090] like Figure 3 As shown, the present invention also provides a Monte Carlo analysis system for the performance dispersion of an alkaline electrolyzer, used to implement the aforementioned method, including the following:

[0091] On the user side, users can set the core parameters of the system according to the manufacturing tolerances, aging degree and operating conditions of the alkaline electrolyzer, and can view the design optimization requirements and control early warning requirements given by the system;

[0092] The multiphysics model module is used to construct a multiphysics steady-state model that integrates electrochemical, thermodynamic and fluid dynamic mechanisms, and to perform deterministic calculations of voltage, efficiency, operating conditions, electrolyzer geometric parameters and related internal state parameters.

[0093] The parameter probabilistic module is used to set the probability distribution model for each uncertain parameter, and supports users to customize the distribution type of the parameter according to the actual manufacturing tolerance level or aging degree, historical aging data and operating conditions.

[0094] The Monte Carlo simulation module is used to implement large-scale random sampling processes, automatically generate parameter combinations, and call multiphysics steady-state models to complete thousands of simulation calculations.

[0095] The statistical analysis module is used to process the data output by the Monte Carlo simulation module, including voltage distribution fitting, dispersion index calculation, extreme small cell identification, and parameter sensitivity quantification.

[0096] The visualization module allows users to observe the stack voltage of the alkaline electrolyzer and the voltage distribution of each electrolysis cell, cell voltage dispersion, extreme cell list, parameter sensitivity ranking, risk warning reports, design optimization requirements, and control warning requirements.

[0097] This invention provides a quantitative analysis tool for tolerance optimization in the design stage of alkaline electrolyzers, quality control in the manufacturing process, and status assessment and risk warning during operation.

[0098] The present invention also 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 implement the steps of the Monte Carlo analysis method for the discrete performance of an alkaline electrolyzer described above.

[0099] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method for Monte Carlo analysis of the discrete performance of an alkaline electrolyzer.

[0100] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0101] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0102] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0103] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0104] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0105] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A Monte Carlo method for analyzing the discrete performance of an alkaline electrolyzer, characterized in that, Includes the following steps: A multiphysics steady-state model of an alkaline electrolyzer is established, which integrates electrochemical, thermodynamic and fluid dynamic mechanisms, and introduces bubble coverage, electrolyte conductivity correction and temperature-pressure coupling effect. Identify the key uncertainty parameters affecting the performance of alkaline electrolyzers and establish probability distribution models for these key uncertainty parameters; The Monte Carlo method was used for large-scale random sampling and simulation to obtain the probability distribution of the output voltage of the alkaline electrolyzer stack and the voltage of each electrolysis chamber inside the alkaline electrolyzer. Analyze the voltage dispersion of each electrolysis cell inside the alkaline electrolyzer to identify the high-voltage cells and their distribution characteristics; Parameter sensitivity analysis was performed to identify high, medium, and low sensitivity parameters for analysis of alkaline electrolyzers.

2. The Monte Carlo method for analyzing the performance dispersion of an alkaline electrolyzer according to claim 1, characterized in that, The multiphysics steady-state model includes: The open-circuit voltage of a single electrolysis cell in an alkaline electrolyzer is calculated based on the Nernst equation, with corrections for temperature, pressure, and concentration. The activation overvoltage of a single electrolysis cell in an alkaline electrolyzer is calculated based on the Butler-Folmer equation, and corrections are made based on bubble coverage and exchange current density. The ohmic overvoltage of a single electrolysis cell in an alkaline electrolyzer is calculated based on Ohm's law, and the correction of electrolyte conductivity is introduced by the bubble effect. The concentration overvoltage of a single electrolysis chamber in an alkaline electrolyzer is calculated based on reaction kinetics. Faraday efficiency is calculated using empirical formulas related to current density and temperature.

3. The Monte Carlo method for analyzing the performance dispersion of an alkaline electrolyzer according to claim 1, characterized in that, The key uncertainty parameters include effective reaction area, membrane thickness, electrode thickness, electrolyte concentration, exchange current density, electrolyte flow rate, and operating temperature.

4. The Monte Carlo method for analyzing the performance dispersion of an alkaline electrolyzer according to claim 3, characterized in that, The key uncertainty parameters follow a normal distribution, and their standard deviation is set based on the degree of aging and manufacturing tolerance.

5. The Monte Carlo method for analyzing the performance dispersion of an alkaline electrolyzer according to claim 1, characterized in that, The Monte Carlo method includes: The parameters of each electrolysis cell were randomly sampled independently. Repeat the simulation multiple times and record the voltage of each electrolysis chamber in the alkaline electrolyzer and the total voltage of the alkaline electrolyzer stack. Statistical analysis was performed on the voltage of each electrolysis chamber in the alkaline electrolyzer and the total voltage of the alkaline electrolyzer stack.

6. The Monte Carlo method for analyzing the performance dispersion of an alkaline electrolyzer according to claim 5, characterized in that, The Monte Carlo method analysis includes: calculating the mean, calculating the standard deviation, distribution fitting, and outlier identification.

7. The Monte Carlo method for analyzing the performance dispersion of an alkaline electrolyzer according to claim 1, characterized in that, In the parameter sensitivity analysis, the parameters are divided into three levels: high, medium, and low, according to their sensitivity.

8. A Monte Carlo analysis system for the discrete performance of an alkaline electrolyzer, characterized in that, A Monte Carlo analysis method for the discrete performance of an alkaline electrolyzer according to any one of claims 1-7 includes the following modules: The multiphysics model module is used to construct a multiphysics steady-state model that integrates electrochemical, thermodynamic, and fluid dynamic mechanisms, and to perform parameter calculations. The parameter probabilistic module is used to set the probability distribution model for each key uncertainty parameter. It allows users to customize the distribution type of each key uncertainty parameter according to the actual manufacturing tolerance level or aging degree, historical aging data and operating conditions. The Monte Carlo simulation module is used to perform large-scale random sampling, automatically generate parameter combinations, and call multiphysics steady-state models to complete simulation calculations. The statistical analysis module is used to process the data output by the Monte Carlo simulation module; The visualization module is used to observe the results of data processing.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the Monte Carlo analysis method for the discrete performance of an alkaline electrolyzer as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of a Monte Carlo analysis method for the discrete performance of an alkaline electrolyzer as described in any one of claims 1 to 7.

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