A temperature control management method, system, equipment and medium for electrolytic water tank

Abstract

The present invention provides a temperature control management method, system, equipment and medium for an electrolytic water tank. The method obtains electrolytic tank simulation indicators corresponding to an electrolytic tank to be simulated, establishes a mathematical model according to the electrolytic tank simulation indicators, obtains an electrolytic tank simulation model, analyzes and evaluates the electrolytic tank to be simulated through the electrolytic tank simulation model to obtain temperature optimization parameters of the electrolytic tank to be simulated, and adjusts the temperature of the electrolytic tank to be simulated based on the temperature optimization parameters. The method performs digital simulation of the electrolytic tank to be simulated by obtaining relevant parameters and simulation indicators of the electrolytic tank to be simulated, realizes the simulation goal of temperature distribution based on the simulated electrolytic tank, optimizes the simulation model and performs actual simulation by analyzing and evaluating the electrolytic tank simulation model, finally obtains the temperature optimization parameters to adjust the temperature of the electrolytic tank, quantitatively realizes temperature control of the electrolytic tank process, ensures safe and efficient operation of the entire process, and improves the quality of production products.

Description

Technical Field

[0001] The present application relates to the field of production control, and in particular to a temperature control management method, system, equipment and medium for an electrolytic water tank. Background Art

[0002] In the field of hydrogen production by electrolysis, multiple parameters, especially temperature, need to be precisely controlled to achieve efficient operation and safe operation. In the electrolytic water tank, the electrolyte solution is placed between two electrodes. When an external direct current passes through the electrolyte, water molecules decompose at the anode to produce oxygen, and at the cathode to produce hydrogen. This process is carried out at a certain temperature. The appropriate temperature can not only improve the efficiency of electrolysis and reduce energy consumption, but also affect the purity and yield of the product gas, and is crucial to the life of the equipment. However, improper temperature control may lead to safety risks, such as accidents caused by overheating. The usual process of controlling temperature can maintain the temperature in the electrolytic cell within a stable range by heating or cooling the electrolyte. Adjusting the current density and adjusting the applied voltage can also indirectly affect the temperature.

[0003] However, in practice, temperature control requires technicians to find the best operating parameters through repeated trials. This process not only consumes a lot of time and resources, but also may bring safety hazards, such as pressure increase or equipment failure caused by overheating. The complexity of temperature control lies in the fact that temperature is affected by many factors, including current density, voltage, electrolyte composition and flow rate, so it is not easy to find the best operating conditions. In addition, current technical means are not sufficient to accurately control temperature, especially for complex industrial-scale electrolyzers. The experience and technical level of operators have a significant impact on the temperature control effect. Improper temperature control may lead to safety problems such as equipment overheating, abnormal pressure increase, and even serious accidents such as explosions. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a temperature control management method, system, device and medium for an electrolytic water tank to solve the above technical problems.

[0005] The present invention provides a temperature control management method for an electrolytic water tank, which comprises the following steps: obtaining an electrolytic water tank simulation index corresponding to an electrolytic water tank to be simulated, wherein the electrolytic water tank simulation index at least includes the temperature distribution at different positions inside the electrolytic water tank; establishing a mathematical model according to the electrolytic water tank simulation index to obtain an electrolytic water tank simulation model; analyzing and evaluating the electrolytic water tank to be simulated through the electrolytic water tank simulation model to obtain the temperature optimization parameters of the electrolytic water tank to be simulated; and adjusting the temperature of the electrolytic water tank to be simulated based on the temperature optimization parameters.

[0006] In one embodiment of the present invention, after obtaining the electrolytic cell simulation model, the temperature control management method of the electrolytic water tank also includes: setting the simulation convergence conditions of the initial electrolytic cell simulation model, the simulation convergence conditions including the error between the simulation results and the preset test data, and the preset test data are the temperature distribution test data at different positions inside the electrolytic cell obtained by conducting an electrolysis experiment under the same electrolysis conditions.

[0007] In one embodiment of the present invention, the electrolytic cell simulation model is used to analyze and evaluate the electrolytic water tank, including: establishing an upper-level model with minimizing the error between the simulation result and the preset test data as the optimization target of the upper-level model, and establishing a lower-level model with minimizing the change of the simulation model adjustment variable as the optimization target of the lower-level model, and the simulation model adjustment variable includes at least one of the electrolysis voltage, current density and electrolyte solution temperature; constructing a double-layer optimization model for the initial electrolytic cell simulation model according to the upper-level model and the lower-level model; running the electrolytic cell simulation model to obtain simulation test data; solving the double-layer optimization model according to the preset test data and the simulation test data to obtain the temperature optimization parameters of the electrolytic water tank.

[0008] In one embodiment of the present invention, establishing an upper-level model with minimizing the error between the simulation results and the preset test data as the optimization target of the upper-level model includes: using at least one of the electrolysis voltage, current density and electrolyte solution temperature as the particle position of the particle swarm optimization algorithm, and using the error between the simulation results and the preset test data as the particle fitness value of the particle swarm optimization algorithm; and establishing the upper-level model of the double-layer optimization model according to the particle swarm optimization algorithm based on the particle position and the particle fitness value.

[0009] In one embodiment of the present invention, establishing a lower-level model with minimizing the change in the simulation model adjustment variable as the optimization target of the lower-level model includes: determining at least one of the electrolysis voltage, current density and electrolyte solution temperature as a population individual, and minimizing the change in the adjustment variable as a genetic optimization fitness value; and constructing the lower-level model based on a genetic algorithm according to the population individuals and the genetic optimization fitness value.

[0010] In one embodiment of the present invention, solving the double-layer optimization model according to preset test data and the simulation test data includes: initializing the position and velocity of the particles in the upper model based on the simulation test data to obtain the current particle state; calling the lower model, setting a preset number of population individuals based on the current particle state in the upper model, and performing genetic algorithm solution to obtain the change in the minimum adjustment variable corresponding to the current particle state in the upper model; the upper model simulates according to the change in the minimum adjustment variable and the current particle state to obtain the current simulation result, and calculates the error between the current simulation result and the preset test data; using the error between the current simulation result and the preset test data as the fitness value of the current particle information, and updating the optimal position of the particle and the global optimal position of the particle group according to the fitness value; iterating based on the double-layer model algorithm until the maximum preset number of iterations is reached or the preset iteration convergence condition is met, and the iteration is terminated to obtain the minimum error between the simulation result and the preset test data, and the corresponding minimum adjustment variable change; the minimum error between the simulation result and the preset test data, and the minimum adjustment variable change are determined as the temperature optimization parameters of the electrolytic cell to be simulated and output.

[0011] In one embodiment of the present invention, a mathematical model is established according to the electrolytic cell simulation indicators to obtain the electrolytic cell simulation model, including: obtaining the physical property parameters and simulation process condition information of the electrolytic cell to be simulated; constructing a three-dimensional digital model of the electrolytic cell to be simulated according to the physical property parameters; meshing the electrolysis region in the three-dimensional digital model, and setting the simulation process boundary conditions according to the simulation process condition information to obtain an initial simulation model; configuring the initial simulation model based on a preset temperature calculation function to obtain the electrolytic cell simulation model.

[0012] An embodiment of the present invention also provides a temperature control and management system for an electrolytic water tank, which includes: a simulation information acquisition module, used to obtain an electrolytic water tank simulation index corresponding to an electrolytic water tank to be simulated, wherein the electrolytic water tank simulation index at least includes the temperature distribution at different positions inside the electrolytic water tank; a simulation model construction module, used to establish a mathematical model according to the electrolytic water tank simulation index to obtain an electrolytic water tank simulation model; a model operation and optimization module, used to analyze and evaluate the electrolytic water tank to be simulated through the electrolytic water tank simulation model to obtain the temperature optimization parameters of the electrolytic water tank to be simulated; and an electrolytic water tank parameter adjustment module, used to adjust the temperature of the electrolytic water tank to be simulated based on the temperature optimization parameters.

[0013] An embodiment of the present invention also provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the electronic device implements the temperature control management method for an electrolytic water tank as described in any one of the above embodiments.

[0014] An embodiment of the present invention further provides a computer-readable storage medium having computer-readable instructions stored thereon. When the computer-readable instructions are executed by a processor of a computer, the computer executes the temperature control management method for an electrolytic water tank as described in any one of the above embodiments.

[0015] The present invention provides a temperature control management method, system, device and medium for an electrolytic water tank. The method obtains electrolytic tank simulation indicators corresponding to an electrolytic tank to be simulated, establishes a mathematical model according to the electrolytic tank simulation indicators, obtains an electrolytic tank simulation model, analyzes and evaluates the electrolytic tank to be simulated through the electrolytic tank simulation model to obtain temperature optimization parameters of the electrolytic tank to be simulated, and adjusts the temperature of the electrolytic tank to be simulated based on the temperature optimization parameters. The method performs digital simulation of the electrolytic tank to be simulated by obtaining relevant parameters and simulation indicators of the electrolytic tank to be simulated, realizes the simulation goal of temperature distribution based on the simulated electrolytic tank, optimizes the simulation model and performs actual simulation by analyzing and evaluating the electrolytic tank simulation model, finally obtains the temperature optimization parameters to adjust the temperature of the electrolytic tank, quantitatively realizes temperature control of the electrolytic tank process, ensures safe and efficient operation of the entire process, and improves the quality of production products.

[0016] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0018] Figure 1 is a schematic diagram of an exemplary system architecture shown in an exemplary embodiment of the present application;

[0019] Figure 2 is a flow chart of a temperature control management method of an electrolytic water tank shown in an exemplary embodiment of the present application;

[0020] Figure 3 is a comparative schematic diagram of a specific electrolytic water tank simulation process shown in an exemplary embodiment of the present application;

[0021] Figure 4 is a schematic diagram of a temperature control management system for an electrolytic water tank shown in an exemplary embodiment of the present application;

[0022] Figure 5 It is a structural diagram of a computer system of an electronic device shown in an exemplary embodiment of the present application.

[0023] In the drawings of the present application, the meanings of the various marks are as follows: 110 is the electrolytic cell to be simulated, 120 is the computer equipment, 401 is the simulation information acquisition module, 402 is the simulation model construction module, 403 is the model operation and optimization module, 404 is the electrolytic cell parameter adjustment module, 500 is the computer system, 501 is the CPU (central processing unit), 502 is the ROM (read-only memory), 503 is the RAM (random access memory), 504 is the bus, 505 is the I / O interface, 506 is the input part, 507 is the output part, 508 is the storage part, 509 is the communication part, 510 is the drive, and 511 is the removable medium. DETAILED DESCRIPTION

[0024] The following will describe the embodiments of the present invention with reference to the accompanying drawings and specific embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, not for limiting the scope of protection of the present invention.

[0025] It should be noted that the illustrations provided in the following embodiments are only used to illustrate the basic concept of the present invention in a schematic manner, and thus the illustrations only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0026] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0027] The "and / or" mentioned in this application describes the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship.

[0028] Figure 1 It is a schematic diagram of an exemplary system architecture shown in an exemplary embodiment of the present application.

[0029] Reference Figure 1 As shown, the system architecture may include an electrolyzer to be simulated 110 and a computer device 120. The computer device 120 obtains the electrolyzer simulation index corresponding to the electrolyzer to be simulated 110, and the electrolyzer simulation index at least includes the temperature distribution at different positions inside the electrolyzer. A mathematical model is established according to the electrolyzer simulation index to obtain an electrolyzer simulation model. The electrolyzer to be simulated is analyzed and evaluated by the electrolyzer simulation model to obtain the temperature optimization parameters of the electrolyzer to be simulated, and the temperature of the electrolyzer to be simulated 110 is adjusted based on the temperature optimization parameters. The computer device 120 may be at least one of a microcomputer, an embedded computer, a network computer, a single-chip microcomputer, etc.; the electrolyzer to be simulated 110 refers to a generating container for executing the alkaline water electrolysis (AWE) hydrogen production technology process, for example, including seven parts including an anode plate, an anode flow channel, an anode, a diaphragm, a cathode, a cathode flow channel, and a cathode plate.

[0030] Schematically, the computer device 120 obtains the electrolytic cell simulation index corresponding to the electrolytic cell 110 to be simulated, establishes a mathematical model according to the electrolytic cell simulation index, obtains the electrolytic cell simulation model, analyzes and evaluates the electrolytic cell to be simulated through the electrolytic cell simulation model to obtain the temperature optimization parameters of the electrolytic cell to be simulated, and adjusts the temperature of the electrolytic cell 110 to be simulated based on the temperature optimization parameters; the method performs digital simulation of the electrolytic cell to be simulated by obtaining relevant parameters and simulation indexes of the electrolytic cell to be simulated, realizes the simulation goal of temperature distribution based on the simulated electrolytic cell, and optimizes and actually simulates the simulation model by analyzing and evaluating the electrolytic cell simulation model, finally obtains the temperature optimization parameters to adjust the temperature of the electrolytic cell to be simulated, and quantitatively realizes the temperature control of the electrolytic cell process to be simulated, thereby ensuring the safe and efficient operation of the entire process and improving the quality of production products.

[0031] Figure 2 is a flow chart of a temperature control management method for an electrolytic water tank shown in an exemplary embodiment of the present application. The temperature control management method for an electrolytic water tank can be Figure 1 The implementation environment may be implemented in other implementation environments, and the above implementation environments are not specifically limited here. Figure 2 As shown, the flow chart of the temperature control management method of the electrolytic water tank includes at least steps S210 to S240, which are described in detail as follows:

[0032] In S210, an electrolytic cell simulation index corresponding to the electrolytic cell to be simulated is obtained.

[0033] In one embodiment of the present application, the electrolyzer simulation index at least includes the temperature distribution at different positions inside the electrolyzer. It is worth noting that the present application can be applied to alkaline solution electrolyzers, but is not limited thereto. The present application can also be applied to other electrolyzers, such as proton exchange membrane (PEM) electrolyzers, anion exchange membranes (AEM), solid oxide (SOEC), seawater hydrogen production electrolyzers or other electrolyzers.

[0034] In one embodiment of the present application, after determining the electrolytic cell to be simulated, the relevant parameters and information of the electrolytic cell to be simulated are obtained, wherein the relevant parameters and information are not limited to electrolysis voltage, current density, initial temperature of the electrolytic cell, electrolyte solution temperature, electrolyte solution type, electrolytic cell size information, electrolyte solution flow rate, electrolyte solution concentration, electrolytic cell outlet pressure, electrolytic cell wall heat transfer capacity, etc. It should be noted that the above parameters and information can be actually measured according to the experimental environment, or data can be retrieved according to historical production information, and the specific acquisition method of the relevant parameters and information of the above electrolytic cell to be simulated is not limited here. On the other hand, the parameters in the relevant parameters and information of the above electrolytic cell to be simulated are not all necessary parameters, and the relevant parameter types can be determined according to the requirements of the accuracy of the electrolytic cell simulation process. For example, when ignoring the simulation content related to the heat conduction of the electrolytic cell itself, it is not necessary to obtain the relevant information of the heat transfer capacity of the electrolytic cell wall. In some embodiments of the present application, the temperature distribution of the electrolytic cell to be simulated is simulated by changing the electrolyte solution temperature parameters by fixing the voltage and current density.

[0035] In S220, a mathematical model is established according to the electrolytic cell simulation index to obtain an electrolytic cell simulation model.

[0036] In one embodiment of the present application, the above-mentioned process of establishing a mathematical model according to the electrolytic cell simulation indicators is constructed based on simulation experiment tools, such as ANSYS engineering simulation platform, STAR-CCM+ simulation software, COMSOLMultiphysics, FLUENT, Altair AcuSolve, SimScale cloud simulation platform, etc. In the embodiment of the present application, STAR-CCM+ simulation software is selected as the technical support platform for establishing a mathematical model according to the electrolytic cell simulation indicators. STAR-CCM+ is a new generation of computational fluid dynamics (CFD) simulation software developed by Siemens, which integrates an integrated environment such as geometric modeling, model pre-processing, calculation execution, and post-processing and analysis of calculation results.

[0037] In one embodiment of the present application, after obtaining the electrolytic cell simulation model, the simulation convergence conditions of the initial electrolytic cell simulation model are set, wherein the simulation convergence conditions include the error between the simulation results and preset test data, and the preset test data are temperature distribution test data at different positions inside the electrolytic cell obtained by conducting an electrolysis experiment under the same electrolysis conditions.

[0038] In one embodiment of the present application, physical property parameters and simulation process condition information of the electrolytic cell to be simulated are obtained, a three-dimensional digital model of the electrolytic cell to be simulated is constructed according to the physical property parameters, the electrolysis region in the three-dimensional digital model is meshed, and the simulation process boundary conditions are set according to the simulation process condition information to obtain an initial simulation model, and the initial simulation model is configured based on a preset temperature calculation function to obtain an electrolytic cell simulation model.

[0039] In one embodiment of the present application, a specific implementation of the above construction process is as follows:

[0040] A. Use the modeling tools in STAR-CCM+ or directly import CAD files to create the geometric model of the electrolyzer and perform meshing. The meshing is related to the accuracy of the simulation. The more complex the model, the more meshes are required.

[0041] B. Set the boundary conditions of the electrolytic cell simulation model, for example, including (1) inlet boundary conditions: the initial temperature in the cell is 298 K (25°C), the flow rate is 0.1 m / s, the electrolyte solution is a 10%-50% alkaline solution, and the electrolyte solution temperature is 60-100°C; (2) outlet boundary conditions: the pressure is 1 atm (atmospheric pressure); (3) wall conditions: the initial potential of the electrode surface is 2 V, and the electrolytic cell wall is adiabatic; fixed voltage mode, set the voltage to 2 V; (4) physical model selection turbulence model, mass transfer model (for simulating the generation of hydrogen and oxygen), and thermodynamic model.

[0042] C. Select STAR-CCM+ built-in function field Temprature as the temperature calculation function;

[0043] D. Set the convergence condition as follows: the residual convergence standard between the simulation result and the preset test data is 1e-5, or the maximum number of iterations is reached, and the maximum number of iterations is, for example, 100-1000.

[0044] It should be noted that the above specific implementation method is only one implementation method of the solution of the present application. In some other embodiments, the solution of the present application can still be implemented by changing the boundary conditions and convergence conditions. The specific implementation parameters and conditions of the above solution are not limited here.

[0045] In S230, the electrolytic cell to be simulated is analyzed and evaluated by the electrolytic cell simulation model to obtain temperature optimization parameters of the electrolytic cell to be simulated.

[0046] In one embodiment of the present application, an upper model is established with the minimization of the error between the simulation result and the preset test data as the optimization target of the upper model, and a lower model is established with the minimization of the change of the simulation model adjustment variable as the optimization target of the lower model, and the simulation model adjustment variable includes at least one of the electrolysis voltage, current density and electrolyte solution temperature. A double-layer optimization model for the initial electrolytic cell simulation model is constructed according to the upper model and the lower model, and the electrolytic cell simulation model is run to obtain simulation test data. The double-layer optimization model is solved according to the preset test data and the simulation test data to obtain the temperature optimization parameters of the electrolytic cell to be simulated. The double-layer optimization model is a hierarchical optimization method, which is usually composed of two interrelated optimization layers: upper (high-level) optimization and lower (low-level) optimization. In such a model, the upper-level optimization is responsible for making high-level decisions or goals, while the lower-level optimization solves specific sub-problems according to the goals or constraints provided by the upper layer. This structure allows the model to better coordinate goals and constraints at different levels when solving complex problems, thereby achieving more efficient optimization. The two-level optimization model is widely used in various fields, such as supply chain management, system optimization, etc. It can effectively handle multi-objective or multi-stage optimization problems.

[0047] In one embodiment of the present application, at least one of the electrolysis voltage, current density and electrolyte solution temperature is used as the particle position of the particle swarm optimization algorithm, and the error between the simulation result and the preset test data is used as the particle fitness value of the particle swarm optimization algorithm. Based on the particle position and the particle fitness value, the upper model of the double-layer optimization model is established according to the particle swarm optimization algorithm. Particle Swarm Optimization (PSO) is a heuristic global optimization algorithm that simulates the behavior pattern of bird flocks foraging. In this algorithm, each potential solution is called a "particle", and these particles fly in a multidimensional search space to find the optimal solution by updating their own position and speed. The particle swarm optimization algorithm realizes global search through cooperation and competition between particles. Each particle adjusts its flight path according to its own best position and personal experience (called "personal best position") and the best position in the entire group (called "global best position"). By iteratively updating the position and speed of particles, the particle swarm optimization algorithm can gradually approach the optimal solution of the problem, and is suitable for solving various continuous and discrete optimization problems.

[0048] In one embodiment of the present application, at least one of the electrolysis voltage, current density and electrolyte solution temperature is determined as a population individual, the change amount of the minimized adjustment variable is used as the genetic optimization fitness value, and the lower model is constructed based on the genetic algorithm according to the population individual and the genetic optimization fitness value. Genetic Algorithm (GA) is a global optimization search algorithm that simulates the biological evolution process, which draws on Darwin's natural selection and genetics principles. This algorithm encodes potential solutions (called "chromosomes" or "individuals") and uses genetic operations such as selection, crossover (or recombination), and mutation to gradually evolve a better solution in the iterative process. Genetic algorithms can efficiently handle complex and nonlinear problems, and are particularly suitable for solving problems that are difficult to solve with traditional methods, such as function optimization, combinatorial optimization, machine learning, and other fields. Through continuous iteration, individuals in the population gradually tend to the best solution to the problem, thereby achieving the optimization goal.

[0049] In one embodiment of the present application, the position and velocity of the particles in the upper model are initialized based on the simulation test data to obtain the current particle state. Prior to this, it also includes determining the number of particles, and determining the number of particles based on computing resources, such as 30 particles. One way to initialize the position is to randomly initialize the position of each particle, and the position represents the adjustment variable in the simulation model, including at least one of the electrolysis voltage, current density, and electrolyte solution temperature. Another more optimized way to initialize the position is to initialize the position of the particles in the upper model based on the simulation test data after the model is built. In initializing the particle velocity, the velocity represents the direction and velocity of the particle movement.

[0050] The lower model is called, a preset number of population individuals are set based on the current particle state in the upper model, and a genetic algorithm is used to solve the problem to obtain the minimum change in the adjustment variable corresponding to the current particle state in the upper model.

[0051] Specifically, the population is initialized, that is, a set of random solutions is created as the initial population, where the number of random solutions is a preset number N (N is a positive integer), the fitness value of each individual is calculated, that is, the minimum change in the adjustment variable corresponding to the current particle state in the upper model, and a selection operation is performed. The tournament selection algorithm is selected to select M individuals from the population to enter the next stage (M is a positive integer and less than or equal to N), and a crossover operation is performed to perform a single-point crossover on the selected individuals to generate new offspring individuals, and each gene in the offspring individuals is mutated with a probability Pm (Pm is a preset mutation probability), and the newly generated offspring individuals are added to the next generation population, and the population size is kept unchanged, and the iteration is stopped when the predetermined number of iterations T is met or the preset quality threshold is reached.

[0052] After the genetic algorithm solves the lower model, the change in the minimum adjustment variable corresponding to the current particle state in the upper model is obtained.

[0053] The upper model performs simulation according to the change of the minimum adjustment variable and the current particle state to obtain the current simulation result, and calculates the error between the current simulation result and the preset test data. Among them, one possible implementation method of calculating the error between the simulation result and the preset test data includes solving the mean square error.

[0054] The error between the current simulation result and the preset test data is used as the fitness value of the current particle information, and the optimal position of the particle and the global optimal position of the particle group are updated according to the fitness value. The iteration is terminated when the maximum preset number of iterations is reached or the preset iteration convergence condition is met, and the minimum error between the simulation result and the preset test data and the corresponding minimum change of the adjustment variable are obtained. The minimum error between the simulation result and the preset test data and the minimum change of the adjustment variable are determined as the temperature optimization parameters of the electrolytic cell to be simulated and output.

[0055] like Figure 3 As shown, Figure 3 This is a comparative diagram of a specific electrolytic water tank simulation process shown in an exemplary embodiment of the present application. In the simulation test software, temperature simulation is performed based on the electrolytic tank simulation model, and the model simulation is started based on the initial boundary conditions. It can be seen that the temperature distribution in the initial electrolytic tank simulation model is uneven. After the variables are adjusted, the simulation purpose is achieved when the temperature distribution in the electrolytic tank is relatively uniform. The process variables are adjusted quantitatively and applied to the variable adjustment of the electrolytic tank to be simulated. Figure 3In the figure, the light and dark representation of different levels of the marked graph part reflects different temperature changes. For example, in the specific electrolytic water tank simulation process, the marked graph part represents the temperature range of 25°C-100°C, where 62.5°C is the middle value of the temperature range, which is convenient for determining the temperature value in the simulation diagram according to the marked graph part. It should be noted that the numerical value and light and dark representation of the marked graph part are only illustrative examples in one embodiment of the present application. In some other embodiments, the marked graph part can also be represented by range and legend through color changes and other temperature ranges.

[0056] The embodiment of the present application performs further double-layer optimization on the output of the constructed electrolytic cell simulation model based on the double-layer optimization model. On the premise of satisfying the minimum error between the simulation results and the preset test data, the minimum adjustment cost of the adjustment parameters of the adjustment variables is optimized and calculated, thereby achieving a more precise control and lower-cost temperature adjustment process.

[0057] In S240 , the temperature of the electrolytic cell to be simulated is adjusted based on the temperature optimization parameters.

[0058] In one embodiment of the present application, based on the change in the minimum adjustment variable, the adjustment parameters of the adjustment variable are determined under the premise of satisfying the minimum error between the simulation result and the preset test data, and the electrolytic cell to be simulated is controlled and adjusted according to the adjustment parameters to adjust the temperature of the electrolytic cell to be simulated.

[0059] The present invention provides a temperature control management method, system, equipment and medium for an electrolytic water tank. The method obtains electrolytic tank simulation indicators corresponding to an electrolytic tank to be simulated, establishes a mathematical model according to the electrolytic tank simulation indicators, obtains an electrolytic tank simulation model, analyzes and evaluates the electrolytic tank to be simulated through the electrolytic tank simulation model to obtain temperature optimization parameters of the electrolytic tank to be simulated, and adjusts the temperature of the electrolytic tank to be simulated based on the temperature optimization parameters. The method performs digital simulation of the electrolytic tank to be simulated by obtaining relevant parameters and simulation indicators of the electrolytic tank to be simulated, realizes the simulation goal of temperature distribution based on the simulated electrolytic tank, optimizes and actually simulates the simulation model by analyzing and evaluating the electrolytic tank simulation model, finally obtains the temperature optimization parameters to adjust the temperature of the electrolytic tank, quantitatively realizes the temperature control of the electrolytic tank process, ensures the safe and efficient operation of the whole process, and improves the quality of the produced products. In addition, the output of the constructed electrolytic tank simulation model is further optimized according to the double-layer optimization model, and the minimum adjustment cost of the adjustment parameter of the adjustment variable is optimized and calculated on the premise of satisfying the minimum error between the simulation result and the preset test data, thereby realizing a more precise control and a lower cost temperature adjustment process.

[0060] The following describes a system embodiment of the present application, which can be used to execute the temperature control management method of the electrolytic water tank in the above embodiment of the present application. For details not disclosed in the system embodiment of the present application, please refer to the embodiment of the temperature control management method of the electrolytic water tank in the above embodiment of the present application.

[0061] Figure 4 FIG. 1 is a schematic diagram of a temperature control management system for an electrolytic water tank shown in an exemplary embodiment of the present application. The system can be applied to Figure 2 The method implementation process shown in the figure can be based on Figure 1 The system is executed in the implementation environment shown in the figure, and may also be applicable to other exemplary implementation environments and specifically configured in other devices. This embodiment does not limit the implementation environment to which the system is applicable.

[0062] like Figure 4 As shown, the exemplary temperature control management system of the electrolytic water tank includes: a simulation information acquisition module 401, a simulation model construction module 402, a model operation and optimization module 403 and an electrolytic water tank parameter adjustment module 404.

[0063] Among them, the simulation information acquisition module 401 is used to obtain the electrolytic cell simulation indicators corresponding to the electrolytic cell to be simulated, and the electrolytic cell simulation indicators at least include the temperature distribution at different positions inside the electrolytic cell; the simulation model construction module 402 is used to establish a mathematical model according to the electrolytic cell simulation indicators to obtain the electrolytic cell simulation model; the model operation and optimization module 403 is used to analyze and evaluate the electrolytic cell to be simulated through the electrolytic cell simulation model to obtain the temperature optimization parameters of the electrolytic cell to be simulated; the electrolytic cell parameter adjustment module 404 is used to adjust the temperature of the electrolytic cell to be simulated based on the temperature optimization parameters.

[0064] An embodiment of the present application also provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, and when the one or more programs are executed by one or more processors, the electronic device implements the temperature control management method of the electrolytic water tank provided in the above-mentioned embodiments.

[0065] Figure 5 is a schematic diagram of the structure of a computer system of an electronic device according to an exemplary embodiment of the present application. It should be noted that: Figure 5 The computer system 500 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0066] like Figure 5As shown, computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to the program stored in read-only memory (ROM) 502 or the program loaded from the storage part to random access memory (RAM) 503, such as executing the method in the above embodiment. In RAM 503, various programs and data required for system operation are also stored. CPU 501, ROM 502 and RAM 503 are connected to each other through a bus. Input / output (I / O) interface 505 is also connected to bus 504.

[0067] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, a mouse, etc.; an output section 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section performs communication processing via a network such as the Internet. A drive is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed so that a computer program read therefrom is installed into the storage section 508 as needed.

[0068] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 509, and / or installed from a removable medium 511. When the computer program is executed by a central processing unit (CPU) 501, various functions defined in the system of the present application are executed.

[0069] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. A computer program contained on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0070] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. Among them, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0071] In the corresponding drawings of the above embodiments, connecting lines may indicate the connection relationship between the various components to indicate more constituent signal paths (constituent_signalpath) and / or one or more ends of some lines may have arrows to indicate the main information flow direction. Connecting lines, as a mark, are not a limitation to the scheme itself, but the use of these lines in combination with one or more exemplary embodiments helps to connect circuits or logic units more easily. Any represented signal (determined by design requirements or preferences) may actually include one or more signals that can be transmitted in either direction and implemented with any appropriate type of signal scheme.

[0072] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0073] Another aspect of the present application further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. The computer-readable storage medium may be included in the electronic device described in the above embodiment, or may exist independently without being assembled into the electronic device.

[0074] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0075] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the implementation methods of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation methods of the present application.

[0076] It should be noted that the present application can be used in many general or special computing system environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc.

[0077] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application.

[0078] It should be understood that the above content of the present application is only a preferred exemplary embodiment of the present application and is not intended to limit the implementation scheme of the present application. Ordinary technicians in the field can easily make corresponding changes or modifications based on the main concept and spirit of the present application. Therefore, the scope of protection of the present application shall be based on the scope of protection required by the claims.

Claims

1. A temperature control management method for an electrolytic water tank, characterized in that: The temperature control management method of the electrolytic water tank comprises: Obtaining an electrolytic cell simulation index corresponding to the electrolytic cell to be simulated, wherein the electrolytic cell simulation index at least includes temperature distribution at different positions inside the electrolytic cell; Establishing a mathematical model according to the electrolytic cell simulation index to obtain an electrolytic cell simulation model; The electrolytic cell to be simulated is analyzed and evaluated by the electrolytic cell simulation model to obtain the temperature optimization parameters of the electrolytic cell to be simulated, wherein an upper model is established with minimizing the error between the simulation result and the preset test data as the optimization target of the upper model, and a lower model is established with minimizing the change of the simulation model adjustment variable as the optimization target of the lower model, and the simulation model adjustment variable includes at least one of the electrolysis voltage, the current density and the electrolyte solution temperature; a double-layer optimization model for the initial electrolytic cell simulation model is constructed according to the upper model and the lower model; the electrolytic cell simulation model is run to obtain simulation test data; the double-layer optimization model is solved according to the preset test data and the simulation test data to obtain the temperature optimization parameters of the electrolytic cell to be simulated; Establishing an upper model with minimizing the error between the simulation result and the preset test data as the optimization target of the upper model includes taking at least one of the electrolysis voltage, current density and electrolyte solution temperature as the particle position of the particle swarm optimization algorithm, taking the error between the simulation result and the preset test data as the particle fitness value of the particle swarm optimization algorithm, and establishing the upper model of the double-layer optimization model according to the particle swarm optimization algorithm based on the particle position and the particle fitness value; establishing a lower model with minimizing the change amount of the simulation model adjustment variable as the optimization target of the lower model includes determining at least one of the electrolysis voltage, current density and electrolyte solution temperature as a population individual, taking the minimized change amount of the adjustment variable as the genetic optimization fitness value, and constructing the lower model based on the genetic algorithm according to the population individual and the genetic optimization fitness value; Solving the double-layer optimization model according to the preset test data and the simulation test data includes initializing the position and speed of the particles in the upper model based on the simulation test data to obtain the current particle state; calling the lower model, setting a preset number of population individuals based on the current particle state in the upper model, and performing genetic algorithm solution to obtain the change of the minimum adjustment variable corresponding to the current particle state in the upper model; the upper model simulates according to the change of the minimum adjustment variable and the current particle state to obtain the current simulation result, and calculates the error between the current simulation result and the preset test data; the error between the current simulation result and the preset test data is used as the fitness value of the current particle information, and the optimal position of the particle and the global optimal position of the particle group are updated according to the fitness value; iterative processing is performed based on the double-layer model algorithm until the maximum preset number of iterations is reached or the preset iteration convergence condition is met, and the iteration is terminated to obtain the minimum error between the simulation result and the preset test data, and the corresponding minimum adjustment variable change; the minimum error between the simulation result and the preset test data, and the minimum adjustment variable change are determined as the temperature optimization parameters of the electrolytic cell to be simulated and output; The temperature of the electrolytic cell to be simulated is adjusted based on the temperature optimization parameters.

2. The temperature control management method of the electrolytic water tank according to claim 1, characterized in that: After obtaining the electrolytic cell simulation model, the temperature control management method of the electrolytic water cell further includes: The simulation convergence conditions of the initial electrolytic cell simulation model are set, wherein the simulation convergence conditions include the error between the simulation result and the preset test data, wherein the preset test data is the temperature distribution test data at different positions inside the electrolytic cell obtained by conducting an electrolysis experiment under the same electrolysis conditions.

3. The temperature control management method of the electrolytic water tank according to claim 1, characterized in that: A mathematical model is established according to the electrolytic cell simulation index, and the electrolytic cell simulation model includes: Obtaining physical property parameters of the electrolytic cell to be simulated and simulation process condition information; Constructing a three-dimensional digital model of the electrolytic cell to be simulated according to the physical property parameters; Meshing the electrolysis occurrence area in the three-dimensional digital model, and setting the simulation process boundary conditions according to the simulation process condition information to obtain an initial simulation model; The initial simulation model is configured based on a preset temperature calculation function to obtain an electrolytic cell simulation model.

4. A temperature control management system for an electrolytic water tank, characterized in that: The temperature control management system of the electrolytic water tank comprises: A simulation information acquisition module, used to obtain an electrolytic cell simulation index corresponding to the electrolytic cell to be simulated, wherein the electrolytic cell simulation index at least includes the temperature distribution at different positions inside the electrolytic cell; A simulation model building module, used to establish a mathematical model according to the electrolytic cell simulation index to obtain an electrolytic cell simulation model; A model operation and optimization module is used to analyze and evaluate the electrolytic cell to be simulated through the electrolytic cell simulation model to obtain the temperature optimization parameters of the electrolytic cell to be simulated, wherein an upper model is established with the minimization of the error between the simulation result and the preset test data as the optimization target of the upper model, and a lower model is established with the minimization of the change of the simulation model adjustment variable as the optimization target of the lower model, and the simulation model adjustment variable includes at least one of the electrolysis voltage, current density and electrolyte solution temperature; a double-layer optimization model for the initial electrolytic cell simulation model is constructed according to the upper model and the lower model; the electrolytic cell simulation model is run to obtain simulation test data; according to the preset test data and the simulation The double-layer optimization model is solved by the test data to obtain the temperature optimization parameters of the electrolytic cell to be simulated; the upper-layer model is established with the optimization target of minimizing the error between the simulation result and the preset test data as the optimization target of the upper-layer model, including taking at least one of the electrolysis voltage, current density and electrolyte solution temperature as the particle position of the particle swarm optimization algorithm, taking the error between the simulation result and the preset test data as the particle fitness value of the particle swarm optimization algorithm, and establishing the upper-layer model of the double-layer optimization model according to the particle swarm optimization algorithm based on the particle position and the particle fitness value; the lower-layer model is established with the optimization target of minimizing the change of the simulation model adjustment variable as the optimization target of the lower-layer model, including taking the electrolysis voltage, current density and electrolyte solution temperature as the optimization target of the lower-layer model At least one of the population individuals is determined as a population individual, the change amount of the minimized adjustment variable is used as the genetic optimization fitness value, and the lower model is constructed based on the genetic algorithm according to the population individuals and the genetic optimization fitness value; solving the double-layer optimization model according to the preset test data and the simulation test data includes initializing the position and speed of the particles in the upper model based on the simulation test data to obtain the current particle state; calling the lower model, setting a preset number of population individuals based on the current particle state in the upper model, and performing genetic algorithm solving to obtain the minimum change amount of the adjustment variable corresponding to the current particle state in the upper model; the upper model is solved according to the change amount of the minimum adjustment variable and the current particle state Perform simulation to obtain the current simulation result, and calculate the error between the current simulation result and the preset test data; use the error between the current simulation result and the preset test data as the fitness value of the current particle information, and update the optimal position of the particle and the global optimal position of the particle group according to the fitness value; perform iterative processing based on the double-layer model algorithm until the maximum preset number of iterations is reached or the preset iteration convergence condition is met, and the iteration is terminated to obtain the minimum error between the simulation result and the preset test data, and the corresponding minimum change in the adjustment variable; determine the minimum error between the simulation result and the preset test data, and the minimum change in the adjustment variable as the temperature optimization parameter of the electrolytic cell to be simulated and output them; The electrolytic cell parameter adjustment module is used to adjust the temperature of the electrolytic cell to be simulated based on the temperature optimization parameters.

5. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, enables the electronic device to implement the temperature control management method of the electrolytic water tank as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that: Computer-readable instructions are stored thereon, and when the computer-readable instructions are executed by a processor of a computer, the computer is enabled to execute the temperature control management method for an electrolytic water tank as claimed in any one of claims 1 to 3.

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