Simulation method and system of AEM electrolytic hydrogen production equipment based on fuzzy theory

Abstract

The invention provides a fuzzy theory-based simulation method and system for AEM electrolytic hydrogen production equipment, and relates to the technical field of electrolytic hydrogen production. The method comprises the following steps: constructing a geometric model and an electrochemical reaction model of an AEM electrolytic tank, wherein the geometric model comprises structural parameters of an anode, a cathode and a membrane; inputting working conditions and setting current and voltage parameters to obtain an electrochemical reaction model; calculating the current efficiency, the hydrogen generation rate and the energy efficiency by using a numerical calculation method; monitoring the ionic conductivity change of the membrane and the dynamic distribution of reaction substances based on the geometric model; analyzing the changes by applying a fuzzy logic controller to obtain a fuzzy theory analysis result; analyzing the dynamic characteristics and thermodynamic characteristics of the electrolytic reaction; and finally, optimizing equipment design and operation conditions according to dynamic characteristics, thermodynamic characteristics, current efficiency and the like. The problem that in the prior art, the operation reliability and stability of equipment are low due to degradation of a membrane material is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electrolytic hydrogen production, and in particular to a simulation method and system for AEM electrolytic hydrogen production equipment based on fuzzy theory. Background Art

[0002] With the transformation of the global energy mix, the demand for hydrogen is rapidly increasing, particularly for methods to produce clean hydrogen. Traditional hydrogen production methods (such as natural gas reforming and water electrolysis) are often associated with high energy consumption and environmental pollution. However, anion exchange membrane (AEM) electrolysis, which utilizes renewable energy to achieve green hydrogen production, has gradually become a research hotspot. With its low voltage and efficient hydrogen production capacity, AEM electrolysis shows promising application prospects.

[0003] The research and development of AEM (Atomic Energy Membrane Electrolysis) hydrogen production equipment is increasingly moving toward high efficiency, low cost, and environmental friendliness. Currently, many researchers are working to improve the conductivity of membrane materials and the activity of catalysts to enhance electrolysis efficiency. Furthermore, the introduction of digital and automated technologies has enhanced the real-time monitoring and control of the electrolysis process, driving electrolysis equipment towards intelligentization.

[0004] Despite significant progress in AEM electrolysis hydrogen production technology, several shortcomings remain. First, the reliability and stability of existing technology still need to be improved, particularly regarding the degradation of membrane materials during long-term operation. Second, existing simulation methods are often limited to short-term testing under laboratory conditions and lack comprehensive analysis of actual operating conditions, making it difficult to provide a scientific basis for large-scale application. Summary of the Invention

[0005] In order to overcome the shortcomings of the existing technology, the purpose of the present invention is to provide a simulation method and system for AEM electrolysis hydrogen production equipment based on fuzzy theory. The present invention solves the problems in the existing technology of low equipment operation reliability and stability caused by the degradation of membrane materials during long-term operation and lack of comprehensive analysis of actual working conditions.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A simulation method for AEM electrolysis hydrogen production equipment based on fuzzy theory, comprising:

[0008] Constructing a geometric model and an electrochemical reaction model of the AEM electrolytic cell, wherein the geometric model includes structural parameters of the anode, cathode, and membrane;

[0009] Inputting working condition parameters into the chemical reaction model and setting current parameters and voltage parameters to obtain an electrochemical reaction model to be operated;

[0010] According to the electrochemical reaction model to be operated, the current efficiency, hydrogen generation rate and energy efficiency of the AEM electrolytic cell under corresponding conditions are calculated using numerical calculation methods;

[0011] Based on the geometric model, monitoring the changes in the ionic conductivity of the membrane and the dynamic distribution of the reaction species during the electrolysis process;

[0012] According to the change of the ionic conductivity of the membrane and the dynamic distribution of the reaction substances, a fuzzy logic controller is applied to perform analysis to obtain a fuzzy theory analysis result;

[0013] Analyzing the kinetic characteristics and thermodynamic properties of the electrolysis reaction based on the changes in ionic conductivity, the dynamic distribution of the reaction substances, and the results of fuzzy theory analysis;

[0014] The equipment design and operating conditions are optimized based on the kinetic characteristics, thermodynamic properties, current efficiency, hydrogen generation rate and energy efficiency.

[0015] Preferably, the construction of the geometric model and electrochemical reaction model of the AEM electrolytic cell includes:

[0016] The shape of the geometric model of the AEM electrolytic cell was established using computer-aided design software; wherein the anode was rectangular in shape and had a thickness ranging from 0.5 mm to 5 mm; the cathode was rectangular in shape and had a thickness ranging from 0.5 mm to 5 mm; and the membrane had a thickness ranging from 20 μm to 200 μm.

[0017] Determining the material properties of the geometric model with the determined shape to obtain a final geometric model, wherein the materials of the anode and the cathode are both platinum, and the membrane is a polymer electrolyte membrane;

[0018] The electrochemical reaction model is integrated according to the final geometric model.

[0019] Preferably, integrating the electrochemical reaction model according to the final geometric model comprises:

[0020] extracting structural parameters of the anode, cathode, and membrane;

[0021] calculating the electrode interface based on the structural parameters of the anode, cathode and membrane;

[0022] determining a current chemical reaction type and determining an electrochemical reaction model based on the electrode interface;

[0023] Wherein, the expression of the electrochemical reaction model is:

[0024]

[0025] Where j is the current density, j0 is the exchange current density, E is the actual electrode potential, and E eq is the equilibrium potential, k is the adjustment factor used to control the effect of free energy on current, ΔG is the free energy change of the reaction, and D is the ion diffusion coefficient of the membrane. is the gradient of ion concentration in the membrane.

[0026] Preferably, the working condition parameters include:

[0027] The concentration of the electrolyte, the electrolyte temperature and the electrolyte flow rate.

[0028] Preferably, the current efficiency, hydrogen generation rate and energy efficiency of the AEM electrolytic cell under corresponding conditions are calculated using a numerical calculation method based on the electrochemical reaction model to be operated, including:

[0029] According to the electrochemical reaction model to be worked on, the mathematical model of the AEM electrolytic cell is established using the finite element method;

[0030] According to the electrochemical reaction model, appropriate boundary conditions and initial conditions are set for the mathematical model of the AEM electrolytic cell; the boundary conditions and initial conditions include: current density at the electrode interface, cathode current density, initial concentration of the electrolyte, and set operating temperature and pressure.

[0031] COMSOL Multiphysics was used to solve the established mathematical model to determine the current efficiency, hydrogen generation rate and energy efficiency of the AEM electrolytic cell.

[0032] Preferably, the mathematical model of the AEM electrolytic cell comprises:

[0033] a first mathematical model, a second mathematical model, and a third mathematical model;

[0034] The expression of the first mathematical model is:

[0035]

[0036] The expression of the second mathematical model is:

[0037]

[0038] The expression of the third mathematical model is:

[0039]

[0040] Among them, η c is the current efficiency, I actual is the actual current, I theoreticalis the theoretical current, v is the hydrogen generation rate, A is the reaction area, F is the Faraday constant, η e is the energy efficiency, E useful is the output energy, E input The input electrical energy.

[0041] Preferably, the fuzzy logic controller is used to analyze the change in the ionic conductivity of the membrane and the dynamic distribution of the reaction substances to obtain the fuzzy theory analysis results, including:

[0042] A fuzzy logic controller is constructed according to the change of the ionic conductivity of the membrane and the dynamic distribution of the reactant concentration, wherein the fuzzy logic controller includes: an input variable, an output variable and a membership function;

[0043] Construct corresponding fuzzy rules;

[0044] Combining the membership of input variables with fuzzy rules, we can get fuzzy output;

[0045] The fuzzy output is defuzzified to obtain the fuzzy theory analysis results.

[0046] Preferably, the defuzzification of the fuzzy output to obtain the fuzzy theory analysis result includes:

[0047] Determine the deblurring algorithm;

[0048] calculating, according to the adaptive mechanism, a membership function of the input variable according to the fuzzy rule;

[0049] The membership function of the input variable is calculated according to the determined defuzzification algorithm to obtain a fuzzy theory analysis result.

[0050] Preferably, the fuzzy rules are a multi-level fuzzy rule base.

[0051] A simulation system for AEM electrolysis hydrogen production equipment based on fuzzy theory, including:

[0052] A construction module for constructing a geometric model and an electrochemical reaction model of the AEM electrolytic cell, wherein the geometric model includes structural parameters of the anode, cathode, and membrane;

[0053] An input module, used to input working condition parameters into the chemical reaction model and set current parameters and voltage parameters thereof, so as to obtain an electrochemical reaction model to be operated;

[0054] a calculation module for calculating the current efficiency, hydrogen generation rate, and energy efficiency of the AEM electrolytic cell under corresponding conditions using a numerical calculation method based on the electrochemical reaction model to be operated;

[0055] a monitoring module for monitoring changes in the ionic conductivity of the membrane and the dynamic distribution of the reaction species during the electrolysis process based on the geometric model;

[0056] A fuzzy module is used to apply a fuzzy logic controller to analyze the changes in the ionic conductivity of the membrane and the dynamic distribution of the reaction substances to obtain fuzzy theoretical analysis results;

[0057] An analysis module for analyzing the kinetic characteristics and thermodynamic properties of the electrolysis reaction based on the change in ionic conductivity, the dynamic distribution of the reaction substances, and fuzzy theory analysis results;

[0058] An adjustment module is used to optimize equipment design and operating conditions based on the kinetic characteristics, thermodynamic properties, current efficiency, hydrogen generation rate and energy efficiency.

[0059] The present invention discloses the following technical effects:

[0060] The present invention provides a simulation method and system for an AEM electrolysis hydrogen production device based on fuzzy theory, the method comprising: constructing a geometric model and an electrochemical reaction model of an AEM electrolytic cell, wherein the geometric model includes structural parameters of an anode, a cathode, and a membrane; inputting working condition parameters into the chemical reaction model and setting current parameters and voltage parameters thereof to obtain an electrochemical reaction model to be operated; calculating, according to the electrochemical reaction model to be operated, the current efficiency, hydrogen generation rate, and energy efficiency of the AEM electrolytic cell under corresponding conditions using a numerical calculation method; monitoring, based on the geometric model, changes in the ionic conductivity of the membrane and the dynamic distribution of reactants during the electrolysis process; applying a fuzzy logic controller to perform analysis based on the changes in the ionic conductivity of the membrane and the dynamic distribution of reactants to obtain fuzzy theory analysis results; analyzing the kinetic characteristics and thermodynamic properties of the electrolysis reaction based on the changes in the ionic conductivity, the dynamic distribution of reactants, and the fuzzy theory analysis results; and optimizing the equipment design and operating conditions based on the kinetic characteristics, thermodynamic properties, current efficiency, hydrogen generation rate, and energy efficiency. The simulation method for the AEM electrolysis hydrogen production equipment provided by the present invention, by constructing a comprehensive geometric model and electrochemical reaction model, can truly reflect the operation of the equipment under actual working conditions, effectively monitor the changes in the ionic conductivity of the membrane and the dynamic distribution of the reactants, and solve the problem of low equipment operation reliability and stability caused by membrane material degradation in the prior art. By using a fuzzy logic controller for comprehensive analysis, the electrolysis parameters can be optimized under different working conditions, and the current efficiency, hydrogen generation rate and energy efficiency can be improved, thereby providing a scientific basis for efficient, low-cost and environmentally friendly hydrogen production. This method not only improves the real-time monitoring capability of the electrolysis process, but also provides data support for the design of equipment for long-term stable operation, promoting the further development of AEM electrolysis hydrogen production technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0062] Figure 1 A flow chart of a simulation method for an AEM electrolysis hydrogen production device based on fuzzy theory is provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0064] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0065] like Figure 1 As shown, the present invention provides a simulation method for AEM electrolysis hydrogen production equipment based on fuzzy theory, comprising:

[0066] Step 100: Constructing a geometric model and an electrochemical reaction model of the AEM electrolytic cell, wherein the geometric model includes structural parameters of the anode, cathode, and membrane;

[0067] Step 200: Inputting working condition parameters into the chemical reaction model and setting current parameters and voltage parameters to obtain an electrochemical reaction model to be operated;

[0068] Step 300: Calculate the current efficiency, hydrogen generation rate, and energy efficiency of the AEM electrolytic cell under corresponding conditions using a numerical calculation method based on the electrochemical reaction model to be operated;

[0069] Step 400: Monitoring changes in the ionic conductivity of the membrane and the dynamic distribution of reactants during electrolysis based on the geometric model;

[0070] Step 500: applying a fuzzy logic controller to analyze the change in ionic conductivity of the membrane and the dynamic distribution of the reaction substances to obtain a fuzzy theory analysis result;

[0071] Step 600: Analyzing the kinetic characteristics and thermodynamic properties of the electrolysis reaction based on the change in ionic conductivity, the dynamic distribution of the reaction species, and the fuzzy theory analysis results;

[0072] Step 700: Optimize equipment design and operating conditions based on the kinetic characteristics, thermodynamic properties, current efficiency, hydrogen generation rate, and energy efficiency.

[0073] Furthermore, the geometric model and electrochemical reaction model of the AEM electrolytic cell are constructed, including:

[0074] The shape of the geometric model of the AEM electrolytic cell was established using computer-aided design software; wherein the anode was rectangular in shape and had a thickness ranging from 0.5 mm to 5 mm; the cathode was rectangular in shape and had a thickness ranging from 0.5 mm to 5 mm; and the membrane had a thickness ranging from 20 μm to 200 μm.

[0075] Determining the material properties of the geometric model with the determined shape to obtain a final geometric model, wherein the materials of the anode and the cathode are both platinum, and the membrane is a polymer electrolyte membrane;

[0076] The electrochemical reaction model is integrated according to the final geometric model.

[0077] Specifically, enhancing current density and reactivity: Choosing rectangular anodes and cathodes provides a larger surface area, which helps to increase current density. This effectively increases the electrolysis reaction area and improves the hydrogen production rate.

[0078] Flexible Adjustment and Optimization: The fixed thickness range allows researchers to flexibly adjust the thickness of the anode and cathode, thereby reducing material consumption and costs while maintaining electrolysis efficiency. This flexibility enables optimization for different operating conditions.

[0079] Membrane ion conductivity: The range of membrane thickness provides room for adjusting ion conductivity. Thinner membranes can reduce the energy required for electrolysis, but must be tested to ensure their mechanical strength and service life.

[0080] Optimized material selection: Platinum is used as the anode and cathode materials. Its excellent conductivity and catalytic activity ensure efficient reaction. In addition, the choice of polymer electrolyte membrane minimizes energy loss during the electrolysis process, further improving efficiency.

[0081] Seamless integration with electrochemical models: Once the geometry and material properties are determined, the electrochemical reaction model can be efficiently integrated. This integration reduces computational complexity, making electrolytic cell performance analysis more efficient and enabling more accurate predictions of various parameters in the electrolysis process.

[0082] Improved Simulation and Accuracy: Geometric models created using computer-aided design software can be subjected to detailed digital simulations, providing empirical data for subsequent research. This not only improves the realism of the model but also helps predict device performance under actual operating conditions.

[0083] Furthermore, integrating the electrochemical reaction model according to the final geometric model includes:

[0084] extracting structural parameters of the anode, cathode, and membrane;

[0085] calculating the electrode interface based on the structural parameters of the anode, cathode and membrane;

[0086] determining a current chemical reaction type and determining an electrochemical reaction model based on the electrode interface;

[0087] Wherein, the expression of the electrochemical reaction model is:

[0088]

[0089] Where j is the current density, j0 is the exchange current density, E is the actual electrode potential, and E eq is the equilibrium potential, k is the adjustment factor used to control the effect of free energy on current, ΔG is the free energy change of the reaction, and D is the ion diffusion coefficient of the membrane. is the gradient of ion concentration in the membrane.

[0090] Furthermore, the working condition parameters include:

[0091] The concentration of the electrolyte, the electrolyte temperature and the electrolyte flow rate.

[0092] Specifically, the concentration of the electrolyte: The concentration of the electrolyte generally refers to the mass concentration of the electrolyte in the solvent, usually expressed in moles per liter (M).

[0093] Influencing factors:

[0094] Ionic conductivity: A higher concentration can increase the ion concentration in the electrolyte, enhance conductivity, and help improve current efficiency. Ions can participate in electrochemical reactions, thereby accelerating the rate of hydrogen production.

[0095] Reaction kinetics: Changes in concentration affect the frequency of collisions between reactant molecules, thereby changing the reaction rate. Depending on the reaction type and concentration, the rate equation can be expressed as the relationship between concentration and production rate.

[0096] OH- ion generation: In the cathode reaction, the concentration of OH- ions directly determines the rate of hydrogen generation. That is, when the current density is low, the concentration of OH- needs to be maintained at an appropriate level to ensure the effective progress of the reaction.

[0097] The generally recommended concentration range is 1M to 5M to ensure balanced electrolytic performance.

[0098] Electrolyte temperature: The temperature of the electrolyte refers to the actual operating temperature of the electrolyte, usually expressed in degrees Celsius.

[0099] Influencing factors:

[0100] Ion mobility: Increasing temperature increases the rate of ion migration in the electrolyte. Higher temperatures can reduce electrolyte viscosity, preventing ion conduction from being affected and improving current efficiency.

[0101] Electrode Reaction Activity: Increasing temperature helps reduce the activation energy of the electrode reaction, thereby promoting the reaction rate. Combined with Arrhenius's law, the reaction rate increases with increasing temperature.

[0102] The operating temperature is typically set between 30°C and 80°C, with a common operating temperature of 60°C, to balance current efficiency and overall system safety.

[0103] Electrolyte flow rate: The electrolyte flow rate refers to the flow rate of the electrolyte in the electrolytic cell, usually expressed in meters per second (m / s).

[0104] Influencing factors:

[0105] Material transfer efficiency: A higher flow rate can promote contact between the electrolyte and the electrodes, increase the transfer rate of reactants, and thus enhance the rate of hydrogen generation. The flow rate can also reduce the accumulation of gas bubbles and improve battery stability.

[0106] Temperature control: Good flow rate configuration helps to evenly distribute the temperature during the reaction process, reduce the risk of local overheating, ensure the homogenization of the electrolyte, and improve overall efficiency.

[0107] Degassing effect: Through appropriate flow rate, the generated hydrogen and oxygen can be effectively taken away, reducing the adverse effects of foaming on the reaction and maintaining stable operation of the equipment.

[0108] The recommended flow rate range is 0.1m / s to 1.0m / s, which can be adjusted according to the electrolytic cell design and liquid physical properties, with 0.5m / s being widely considered an effective operating flow rate.

[0109] Furthermore, the current efficiency, hydrogen generation rate and energy efficiency of the AEM electrolytic cell under corresponding conditions are calculated using a numerical calculation method based on the electrochemical reaction model to be operated, including:

[0110] According to the electrochemical reaction model to be worked on, the mathematical model of the AEM electrolytic cell is established using the finite element method;

[0111] According to the electrochemical reaction model, appropriate boundary conditions and initial conditions are set for the mathematical model of the AEM electrolytic cell; the boundary conditions and initial conditions include: current density at the electrode interface, cathode current density, initial concentration of the electrolyte, and set operating temperature and pressure.

[0112] COMSOL Multiphysics was used to solve the established mathematical model to determine the current efficiency, hydrogen generation rate and energy efficiency of the AEM electrolytic cell.

[0113] Specifically, the electrochemical reaction model is determined based on the reaction mechanism and actual electrolysis conditions, including the electro-oxidation and electro-reduction reactions at the anode and cathode.

[0114] The electrochemical reaction model is expressed in the form of a mathematical formula, and the relationship between it and current density, exchange current density and electrode potential is established.

[0115] The steps to establish the mathematical model of the finite element method are as follows:

[0116] A three-dimensional geometric model of the electrolytic cell was established using CAD software, and the geometric dimensions of the anode, cathode, and membrane were precisely set to accommodate subsequent numerical simulations. The geometric model was meshed finely, and the mesh density was rationally allocated to ensure sufficient resolution in key areas (such as the electrode surface and membrane layer) and the accuracy of the numerical solution. The current density, mass transfer, and electric field equations were incorporated into the mathematical model to form a solvable set of equations.

[0117] Furthermore, the mathematical model of the AEM electrolytic cell includes:

[0118] a first mathematical model, a second mathematical model, and a third mathematical model;

[0119] The expression of the first mathematical model is:

[0120]

[0121] The expression of the second mathematical model is:

[0122]

[0123] The expression of the third mathematical model is:

[0124]

[0125] Among them, η c is the current efficiency, I actual is the actual current, I theoretical is the theoretical current, v is the hydrogen generation rate, A is the reaction area, F is the Faraday constant, η e is the energy efficiency, Euseful is the output energy, E input The input electrical energy.

[0126] Furthermore, the fuzzy logic controller is used to analyze the changes in the ionic conductivity of the membrane and the dynamic distribution of the reaction substances to obtain fuzzy theoretical analysis results, including:

[0127] A fuzzy logic controller is constructed according to the change of the ionic conductivity of the membrane and the dynamic distribution of the reactant concentration, wherein the fuzzy logic controller includes: an input variable, an output variable and a membership function;

[0128] Construct corresponding fuzzy rules;

[0129] Combining the membership of input variables with fuzzy rules, we can get fuzzy output;

[0130] The fuzzy output is defuzzified to obtain the fuzzy theory analysis results.

[0131] Specifically, based on the changes in the membrane's ionic conductivity and the dynamic distribution of reactant concentrations, a fuzzy logic controller is used for intelligent regulation to ensure the efficiency and stability of the hydrogen generation process. The specific implementation steps are as follows:

[0132] Input variables include: changes in the ionic conductivity of the membrane and reactant concentrations;

[0133] The output variable is the desired current regulation value to optimize the efficiency of the electrolysis reaction;

[0134] A triangular or trapezoidal membership function is used to describe the relationship between input variables and output variables. Specific examples are as follows:

[0135] For the change of ionic conductivity of the membrane, three states of "low", "medium" and "high" can be set and described by a triangular membership function.

[0136] For the reactant concentration, the “dilute”, “moderate” and “rich” states are set and described using a trapezoidal membership function.

[0137] Construction of fuzzy rules (fuzzy rules are multi-level fuzzy rule base):

[0138] Based on the relationship between the input and output variables, corresponding fuzzy rules are constructed. The goal of building a fuzzy rule base is to derive appropriate current regulation values ​​by combining different states of the input variables. The following is a series of fuzzy rules based on different combinations of membrane ionic conductivity and reactant concentrations.

[0139] Fuzzy rule examples:

[0140] If the ionic conductivity of the membrane is "low" and the reactant concentration is "dilute", the current regulation value is "low".

[0141] If the ionic conductivity of the membrane is "low" and the reactant concentration is "moderate", the current regulation value is "low".

[0142] If the ionic conductivity of the membrane is "low" and the reactant concentration is "rich", the current regulation value is "medium".

[0143] If the ionic conductivity of the membrane is "medium" and the reactant concentration is "dilute", the current regulation value is "low".

[0144] If the ionic conductivity of the membrane is "medium" and the reactant concentration is "moderate", the current regulation value is "medium".

[0145] If the ionic conductivity of the membrane is "medium" and the reactant concentration is "rich", the current regulation value is "high".

[0146] If the ionic conductivity of the membrane is "high" and the reactant concentration is "dilute", the current regulation value is "medium".

[0147] If the ionic conductivity of the membrane is "high" and the reactant concentration is "moderate", the current regulation value is "high".

[0148] If the ionic conductivity of the membrane is "high" and the reactant concentration is "rich", the current regulation value is "high".

[0149] If the ionic conductivity of the membrane is "medium" and the reactant concentration is "dilute", the current regulation value is "low".

[0150] By designing different combinations of input variables, these fuzzy rules ensure that all possible operating conditions are considered and can adapt to changes in membrane performance and reactant concentrations.

[0151] The actual value of the input variable is calculated to determine its membership, and combined with the corresponding fuzzy rules to obtain the fuzzy output. This process will evaluate all the rules and find the corresponding output membership.

[0152] The fuzzy output is defuzzified by the center of gravity method or the maximum membership method to obtain the final current regulation value.

[0153] Furthermore, the defuzzification of the fuzzy output to obtain the fuzzy theory analysis result includes:

[0154] Determine the deblurring algorithm;

[0155] calculating, according to the adaptive mechanism, a membership function of the input variable according to the fuzzy rule;

[0156] The membership function of the input variable is calculated according to the determined defuzzification algorithm to obtain a fuzzy theory analysis result.

[0157] Specifically, a suitable defuzzification algorithm is selected to convert the fuzzy output into a specific value, including the centroid method and the maximum membership method.

[0158] In this implementation, the centroid method is preferably used, which determines the final current regulation value by calculating the centroid position of the fuzzy output membership function graph;

[0159] According to the real-time monitored input variables, the membership function of each input variable is calculated through the fuzzy rule base. The specific calculation steps are as follows:

[0160] For the ionic conductivity and reactant concentration of the membrane, its membership in low, medium and high states is determined according to its actual values; the membership function formula is adopted, for example, the ionic conductivity of the membrane can be in the form of a triangular membership function to describe the gradual change between its states.

[0161] The fuzzy rule base is used to combine the membership of each input variable to obtain the corresponding fuzzy output. This process summarizes the membership of the input variables (membrane ionic conductivity and reactant concentration) based on the fuzzy rule calculation results to obtain the fuzzy output of the current regulation value.

[0162] According to the above membership function and the selected defuzzification algorithm, calculations are performed and the final fuzzy theory analysis results are obtained.

[0163] This embodiment also provides a simulation system for an AEM electrolysis hydrogen production device based on fuzzy theory, including:

[0164] A construction module for constructing a geometric model and an electrochemical reaction model of the AEM electrolytic cell, wherein the geometric model includes structural parameters of the anode, cathode, and membrane;

[0165] An input module, used to input working condition parameters into the chemical reaction model and set current parameters and voltage parameters thereof, so as to obtain an electrochemical reaction model to be operated;

[0166] a calculation module for calculating the current efficiency, hydrogen generation rate, and energy efficiency of the AEM electrolytic cell under corresponding conditions using a numerical calculation method based on the electrochemical reaction model to be operated;

[0167] a monitoring module for monitoring changes in the ionic conductivity of the membrane and the dynamic distribution of the reaction species during the electrolysis process based on the geometric model;

[0168] A fuzzy module is used to apply a fuzzy logic controller to analyze the changes in the ionic conductivity of the membrane and the dynamic distribution of the reaction substances to obtain fuzzy theoretical analysis results;

[0169] An analysis module for analyzing the kinetic characteristics and thermodynamic properties of the electrolysis reaction based on the change in ionic conductivity, the dynamic distribution of the reaction substances, and fuzzy theory analysis results;

[0170] An adjustment module is used to optimize equipment design and operating conditions based on the kinetic characteristics, thermodynamic properties, current efficiency, hydrogen generation rate and energy efficiency.

[0171] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0172] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A simulation method for AEM electrolysis hydrogen production equipment based on fuzzy theory, characterized in that: include: Constructing a geometric model and an electrochemical reaction model of the AEM electrolytic cell, wherein the geometric model includes structural parameters of the anode, cathode, and membrane; Inputting working condition parameters into the chemical reaction model and setting current parameters and voltage parameters to obtain an electrochemical reaction model to be operated; According to the electrochemical reaction model to be operated, the current efficiency, hydrogen generation rate and energy efficiency of the AEM electrolytic cell under corresponding conditions are calculated using numerical calculation methods; Based on the geometric model, monitoring the changes in the ionic conductivity of the membrane and the dynamic distribution of the reaction species during the electrolysis process; According to the change of the ionic conductivity of the membrane and the dynamic distribution of the reaction substances, a fuzzy logic controller is applied to perform analysis to obtain a fuzzy theory analysis result; Analyzing the kinetic characteristics and thermodynamic properties of the electrolysis reaction based on the changes in ionic conductivity, the dynamic distribution of the reaction substances, and the results of fuzzy theory analysis; The equipment design and operating conditions are optimized based on the kinetic characteristics, thermodynamic properties, current efficiency, hydrogen generation rate and energy efficiency.

2. The simulation method of AEM electrolysis hydrogen production equipment based on fuzzy theory according to claim 1 is characterized in that: The geometric model and electrochemical reaction model of the AEM electrolytic cell are constructed, including: The shape of the geometric model of the AEM electrolytic cell was established using computer-aided design software; wherein the anode was rectangular in shape and had a thickness ranging from 0.5 mm to 5 mm; the cathode was rectangular in shape and had a thickness ranging from 0.5 mm to 5 mm; and the membrane had a thickness ranging from 20 μm to 200 μm. Determining the material properties of the geometric model with the determined shape to obtain a final geometric model, wherein the materials of the anode and the cathode are both platinum, and the membrane is a polymer electrolyte membrane; The electrochemical reaction model is integrated according to the final geometric model.

3. The simulation method of AEM electrolysis hydrogen production equipment based on fuzzy theory according to claim 2 is characterized in that: The integrating the electrochemical reaction model according to the final geometric model comprises: extracting structural parameters of the anode, cathode, and membrane; calculating the electrode interface based on the structural parameters of the anode, cathode and membrane; determining a current chemical reaction type and determining an electrochemical reaction model based on the electrode interface; Wherein, the expression of the electrochemical reaction model is: Where j is the current density, j0 is the exchange current density, E is the actual electrode potential, and E eq is the equilibrium potential, k is the adjustment factor used to control the effect of free energy on current, ΔG is the free energy change of the reaction, and D is the ion diffusion coefficient of the membrane. is the gradient of ion concentration in the membrane.

4. The simulation method of AEM electrolysis hydrogen production equipment based on fuzzy theory according to claim 1 is characterized in that: The working condition parameters include: The concentration of the electrolyte, the electrolyte temperature and the electrolyte flow rate.

5. The simulation method of AEM electrolysis hydrogen production equipment based on fuzzy theory according to claim 1 is characterized in that: The current efficiency, hydrogen generation rate and energy efficiency of the AEM electrolytic cell under corresponding conditions are calculated using a numerical calculation method based on the electrochemical reaction model to be operated, including: According to the electrochemical reaction model to be worked on, the mathematical model of the AEM electrolytic cell is established using the finite element method; According to the electrochemical reaction model, appropriate boundary conditions and initial conditions are set for the mathematical model of the AEM electrolytic cell; the boundary conditions and initial conditions include: current density at the electrode interface, cathode current density, initial concentration of the electrolyte, and set operating temperature and pressure. COMSOL Multiphysics was used to solve the established mathematical model to determine the current efficiency, hydrogen generation rate and energy efficiency of the AEM electrolytic cell.

6. The simulation method of AEM electrolysis hydrogen production equipment based on fuzzy theory according to claim 5 is characterized in that: The mathematical model of the AEM electrolytic cell includes: a first mathematical model, a second mathematical model, and a third mathematical model; The expression of the first mathematical model is: The expression of the second mathematical model is: The expression of the third mathematical model is: Among them, η c is the current efficiency, I actual is the actual current, I theoretical is the theoretical current, v is the hydrogen generation rate, A is the reaction area, F is the Faraday constant, η e is the energy efficiency, E useful is the output energy, E input The input electrical energy.

7. The simulation method of AEM electrolysis hydrogen production equipment based on fuzzy theory according to claim 5 is characterized in that: The fuzzy logic controller is used to analyze the change in the ionic conductivity of the membrane and the dynamic distribution of the reaction substances to obtain the fuzzy theory analysis results, including: A fuzzy logic controller is constructed according to the change of the ionic conductivity of the membrane and the dynamic distribution of the reactant concentration, wherein the fuzzy logic controller includes: an input variable, an output variable and a membership function; Construct corresponding fuzzy rules; Combining the membership of input variables with fuzzy rules, we can get fuzzy output; The fuzzy output is defuzzified to obtain the fuzzy theory analysis results.

8. The simulation method of AEM electrolysis hydrogen production equipment based on fuzzy theory according to claim 7 is characterized in that: The defuzzification process of the fuzzy output to obtain the fuzzy theory analysis result includes: Determine the deblurring algorithm; calculating, according to the adaptive mechanism, a membership function of the input variable according to the fuzzy rule; The membership function of the input variable is calculated according to the determined defuzzification algorithm to obtain a fuzzy theory analysis result.

9. The simulation method of AEM electrolysis hydrogen production equipment based on fuzzy theory according to claim 8 is characterized in that: The fuzzy rules are a multi-level fuzzy rule base.

10. A simulation system for AEM electrolysis hydrogen production equipment based on fuzzy theory, characterized in that: include: A construction module for constructing a geometric model and an electrochemical reaction model of the AEM electrolytic cell, wherein the geometric model includes structural parameters of the anode, cathode, and membrane; An input module, used to input working condition parameters into the chemical reaction model and set current parameters and voltage parameters thereof, so as to obtain an electrochemical reaction model to be operated; a calculation module for calculating the current efficiency, hydrogen generation rate, and energy efficiency of the AEM electrolytic cell under corresponding conditions using a numerical calculation method based on the electrochemical reaction model to be operated; a monitoring module for monitoring changes in the ionic conductivity of the membrane and the dynamic distribution of the reaction species during the electrolysis process based on the geometric model; A fuzzy module is used to apply a fuzzy logic controller to analyze the changes in the ionic conductivity of the membrane and the dynamic distribution of the reaction substances to obtain fuzzy theoretical analysis results; An analysis module for analyzing the kinetic characteristics and thermodynamic properties of the electrolysis reaction based on the change in ionic conductivity, the dynamic distribution of the reaction substances, and fuzzy theory analysis results; An adjustment module is used to optimize equipment design and operating conditions based on the kinetic characteristics, thermodynamic properties, current efficiency, hydrogen generation rate and energy efficiency.

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