CN114065483A - Hydrogen production system efficiency optimization method and device, computer equipment and storage medium - Google Patents
Hydrogen production system efficiency optimization method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a method, a device, computer equipment and a computer readable storage medium for optimizing the efficiency of a hydrogen production system, wherein the method comprises the steps of obtaining an operation parameter data set of an electrolytic cell in the hydrogen production system through a server according to a triggered efficiency optimization instruction of the hydrogen production system; performing optimization solution on a preset target function set based on an operation parameter data set through a multi-objective optimization algorithm to obtain an optimal solution of the target function set, wherein the target function set comprises an electrolytic cell efficiency function, an electrolytic cell hydrogen production rate function and an electrolytic cell voltage rise rate network model; and determining an optimization strategy of the hydrogen production system according to the optimal solution of the target function set, wherein the optimization strategy is used for optimizing the efficiency of the hydrogen production system. The hydrogen production system efficiency optimization method can perform multi-objective optimization solution according to multiple indexes of the electrolytic cell, so that various operation parameters of the electrolytic cell are optimized from different angles, and the efficiency of the hydrogen production system is improved.
Description
Technical Field
The present application relates to the field of optimization control technologies, and in particular, to a method and an apparatus for optimizing efficiency of a hydrogen production system, a computer device, and a storage medium.
Background
Hydrogen energy is an ideal choice for long-term, large-scale storage of renewable energy. At present, the hydrogen production method is commonly used for producing hydrogen by electrolyzing water, and the hydrogen production by electrolyzing water not only can produce hydrogen, but also can realize the utilization of renewable energy sources. The hydrogen production by water electrolysis needs an electrolytic cell (core component) and a water electrolysis hydrogen production system consisting of numerous accessories, such as a separation system, a circulating system, a purification system, a power supply system and the like, so that the water electrolysis hydrogen production system is a complex multi-system coordinated working system, and the demand of high-efficiency and accurate control on the water electrolysis hydrogen production system exists.
At present, the control of the water electrolysis hydrogen production system mostly adopts a mode of carrying out independent optimization control on the operation parameters of an electrolytic cell, and the control mode cannot improve the comprehensive performance of the water electrolysis hydrogen production system.
Disclosure of Invention
The application provides a hydrogen production system efficiency optimization method, a hydrogen production system efficiency optimization device, computer equipment and a computer readable storage medium, which can improve the efficiency of a water electrolysis hydrogen production system.
In a first aspect, a method for optimizing the efficiency of a hydrogen production system is provided, the method comprising:
responding to an efficiency optimization instruction triggered by a user, and acquiring an operation parameter data set of an electrolytic cell in the hydrogen production system;
performing optimization solution on a preset target function set based on an operation parameter data set through a multi-objective optimization algorithm to obtain an optimal solution of the target function set, wherein the target function set comprises an electrolytic cell efficiency function, an electrolytic cell hydrogen production rate function and an electrolytic cell voltage rise rate network model;
and determining an optimization strategy of the hydrogen production system according to the optimal solution of the target function set, wherein the optimization strategy is used for optimizing the efficiency of the hydrogen production system.
In a second aspect, there is provided a hydrogen production system efficiency optimizing device, comprising:
the acquisition module is used for responding to an efficiency optimization instruction triggered by a user and acquiring an operation parameter data set of an electrolytic cell in the hydrogen production system;
the calculation module is used for carrying out optimization solution on a preset target function set based on the operation parameter data set through a multi-objective optimization algorithm to obtain an optimal solution of the target function set, wherein the target function set comprises an electrolytic cell efficiency function, an electrolytic cell hydrogen production rate function and an electrolytic cell voltage rise rate network model;
and the determining module is used for determining an optimization strategy of the hydrogen production system according to the optimal solution of the target function set, and the optimization strategy is used for optimizing the efficiency of the hydrogen production system.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps of the method of any one of the above when executing the computer program:
a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any of the above.
According to the method, the device, the computer equipment and the computer readable storage medium for optimizing the efficiency of the hydrogen production system, the server obtains the operation parameter data set of the electrolytic cell in the hydrogen production system according to the triggered efficiency optimization instruction of the hydrogen production system; performing optimization solution on a preset target function set based on an operation parameter data set through a multi-objective optimization algorithm to obtain an optimal solution of the target function set, wherein the target function set comprises an electrolytic cell efficiency function, an electrolytic cell hydrogen production rate function and an electrolytic cell voltage rise rate network model; and determining an optimization strategy of the hydrogen production system according to the optimal solution of the target function set, wherein the optimization strategy is used for optimizing the efficiency of the hydrogen production system. The hydrogen production system efficiency optimization method can perform multi-objective optimization solution according to multiple indexes of the electrolytic cell, so that various operation parameters of the electrolytic cell are optimized from different angles, and the efficiency of the hydrogen production system is improved.
Drawings
FIG. 1 is a diagram of an environment in which a method for optimizing the performance of a hydrogen production system according to one embodiment may be implemented;
FIG. 2 is a schematic flow diagram of a process for optimizing the performance of a hydrogen production system in one embodiment;
FIG. 3 is a schematic flow diagram illustrating the performance optimization step of the hydrogen production system in one embodiment;
FIG. 4 is a schematic flow diagram of the performance optimization step of another embodiment of the hydrogen production system;
FIG. 5 is a schematic flow diagram illustrating the performance optimization step of a hydrogen production system in another embodiment;
FIG. 6 is a schematic flow diagram illustrating the performance optimization step of a hydrogen production system in another embodiment;
FIG. 7 is a block diagram of an apparatus for optimizing the performance of a hydrogen production system in accordance with an embodiment;
FIG. 8 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The hydrogen production system efficiency optimization method provided by the application can be applied to the application environment as shown in FIG. 1. Wherein hydrogen production system 102 communicates with server 104 via a network. The server 104 obtains various historical operating parameter data of the electrolytic cell in the hydrogen production system 102, trains based on a large amount of historical operating parameter data to obtain an electrolytic cell voltage increase rate network model, establishes an electrolytic cell efficiency function and an electrolytic cell hydrogen production rate function, assigns values to the operating parameters of the electrolytic cell based on the historical operating parameter data of the electrolytic cell, obtains an operating parameter data set according to a plurality of groups of operating parameters after assignment, substitutes data in the operating parameter data set into a function set comprising the electrolytic cell efficiency, the electrolytic cell hydrogen production rate and the electrolytic cell voltage increase rate network model through a multi-project optimization algorithm to perform iterative operation, finally obtains an optimal solution of the function set, and assigns an optimization strategy for the hydrogen production system 102 according to the optimal solution of the function set to optimize the hydrogen production system so as to improve the efficiency of the hydrogen production system. The server 104 may be implemented as a stand-alone server or as a server cluster comprised of multiple servers.
In one embodiment, as shown in fig. 2, a method for optimizing the performance of a hydrogen production system is provided, which is illustrated by applying the method to the server in fig. 1, and includes the following steps:
step S202, responding to an efficiency optimization instruction triggered by a user, and acquiring an operation parameter data set of an electrolytic cell in the hydrogen production system.
The system comprises a hydrogen production system, a server, an input module, an electrolytic bath, a separation module, a circulation module, a purification module, a power supply module and the like, wherein a user can input an efficiency optimization instruction through the input module on the server, the efficiency optimization instruction is used for indicating the server to optimize the efficiency of the hydrogen production system, the hydrogen production system comprises the electrolytic bath, the separation module, the circulation module, the purification module, the power supply module and the like, and as the core module of the hydrogen production system is the electrolytic bath, and the electrolytic bath is connected with other modules, such as the modules, the purpose of optimizing the efficiency of the hydrogen production system can be achieved by optimizing various operation parameters of the electrolytic bath. The server needs to obtain data to be optimized after receiving an efficiency optimization instruction input by a user, for a hydrogen production system, the data to be optimized is operation parameter data of an electrolytic cell, and the server needs to obtain a data set of the data to be optimized, namely a large amount of data of each operation parameter of the electrolytic cell needs to be obtained to form the operation parameter data set of the electrolytic cell due to the fact that an optimization control problem needs a large amount of data. The operational parameter data set of the electrolytic cell may be obtained by the server from historical operational parameter data of the electrolytic cell stored in the memory; or randomly assigning values based on historical operation parameter data of the electrolytic cell; but may also be obtained directly from the hydrogen production system, which is not limited in this application.
Illustratively, a user inputs an instruction for instructing a server to optimize the efficiency of the hydrogen production system through a keyboard, and the server, upon receiving the efficiency optimization instruction, reads a large amount of operation parameter data related to the electrolytic cells from a memory, combines the operation parameter data of the electrolytic cells to obtain a plurality of sets of data sets containing the operation parameter data, and stores the data sets into the memory for subsequent optimization.
And S204, carrying out optimization solution on a preset target function set based on the operation parameter data set through a multi-objective optimization algorithm to obtain an optimal solution of the target function set, wherein the target function set comprises an electrolytic cell efficiency function, an electrolytic cell hydrogen production rate function and an electrolytic cell voltage rise rate network model.
The multi-objective optimization algorithm is used for performing optimization solution on a function containing multiple objectives to find the optimal solution of each objective, and the multi-objective optimization algorithm can be, for example, a weighting algorithm, a constraint algorithm, a linear programming algorithm evolutionary algorithm, a particle swarm algorithm and the like.
The electrolyzer efficiency function is used for calculating the efficiency of the electrolyzer according to the operation parameters of the electrolyzer, the electrolyzer efficiency function can be a relation expression which represents the ratio of the energy converted from hydrogen produced by the electrolyzer to the electric energy consumed by the electrolyzer, the electrolyzer efficiency function can be a relational expression which comprises current efficiency, voltage, the lower heat value of hydrogen and a Faraday constant, the relational expression can be a quotient operational relation which comprises the current efficiency, the voltage, the lower heat value of the hydrogen, the Faraday constant and each operation parameter, can also be an integral operational relation which comprises the current efficiency, the voltage, the lower heat value of the hydrogen, the Faraday constant and each operation parameter, and can also be a root operational relation which comprises the current efficiency, the voltage, the lower heat value of the hydrogen, the Faraday constant and each operation parameter, and the like; the cell efficiency function can also be a relationship comprising hydrogen concentration in the anode oxygen of the cell, current, voltage, temperature, circulating water flow, lower heating value of hydrogen, faraday constant, the relational expression can comprise the quotient operational relationship among the hydrogen concentration, the current, the voltage, the temperature, the circulating water flow, the lower heating value of the hydrogen, the Faraday constant and various operational parameters in the anode oxygen of the electrolytic cell, can comprise the integral operational relationship among the hydrogen concentration, the current, the voltage, the temperature, the circulating water flow, the lower heating value of the hydrogen, the Faraday constant and various operational parameters in the anode oxygen of the electrolytic cell, and can also comprise the root operational relationship among the hydrogen concentration, the current, the voltage, the temperature, the circulating water flow, the lower heating value of the hydrogen, the Faraday constant and various operational parameters in the anode oxygen of the electrolytic cell, and the like; this is not limited in this application.
And the hydrogen production rate function of the electrolytic cell is used for calculating the hydrogen production rate of the electrolytic cell according to the operation parameters of the electrolytic cell. The hydrogen production rate function of the electrolytic cell can be a relational expression containing pressure, temperature and current, and a quotient operation relation, a root equation operation relation and an integral operation relation or a multiplication, division and integral mixed operation relation among the pressure, the temperature, the current and various operation parameters; the hydrogen production rate function of the electrolytic cell may also be a relational expression including hydrogen concentration, current, voltage, temperature, and circulating water flow, and the relational expression may be an operational relation including a quotient operational relation, a root equation operational relation, an integral operational relation, or a multiplication, division, and integral hybrid operational relation among the hydrogen concentration, the current, the voltage, the temperature, the circulating water flow, and various operational parameters, and the like, which is not limited in the present application.
The cell voltage increase rate network model may be a network model obtained by inputting a predetermined sample into any one of a BP neural network, an RBF (radial basis function) neural network, a linear neural network, a self-organizing neural network, and the like, and training and learning the neural network. The predetermined sample can be a sample set containing multiple sets of current data, temperature data, circulating water flow data and voltage rise rate, the input of the electrolytic cell voltage rise rate network model is the current data, the temperature data, the circulating water flow data, and the output is the voltage rise rate. Therefore, when the voltage increase rate of the electrolytic cell needs to be calculated, the voltage increase rate of the electrolytic cell can be obtained only by inputting corresponding data into the voltage increase rate network model of the electrolytic cell.
The target function set is a function set comprising an electrolytic cell efficiency function, an electrolytic cell hydrogen production rate function and an electrolytic cell voltage rise rate network model, so that the optimal solution of the target function set indicates that the electrolytic cell efficiency function, the electrolytic cell hydrogen production rate function and the electrolytic cell voltage rise rate network model all reach the optimal solution.
And S206, determining an optimization strategy of the hydrogen production system according to the optimal solution of the target function set, wherein the optimization strategy is used for optimizing the efficiency of the hydrogen production system.
And obtaining the optimal solution of the target function set according to the steps, namely obtaining the common optimal solution of the electrolytic cell efficiency function, the electrolytic cell hydrogen production rate function and the electrolytic cell voltage rise rate network model. The method comprises the steps of obtaining an optimal solution which is common to an electrolytic cell efficiency function, an electrolytic cell hydrogen production rate function and an electrolytic cell voltage rise rate network model, and obtaining values of various operating parameters of the electrolytic cell, wherein the optimal solution comprises the electrolytic cell operating parameters, the electrolytic cell efficiency function, the electrolytic cell hydrogen production rate function and the electrolytic cell voltage rise rate network model.
According to the efficiency optimization method of the hydrogen production system, the server obtains an operation parameter data set of an electrolytic cell in the hydrogen production system according to a triggered efficiency optimization instruction of the hydrogen production system; performing optimization solution on a preset target function set based on an operation parameter data set through a multi-objective optimization algorithm to obtain an optimal solution of the target function set, wherein the target function set comprises an electrolytic cell efficiency function, an electrolytic cell hydrogen production rate function and an electrolytic cell voltage rise rate network model; and determining an optimization strategy of the hydrogen production system according to the optimal solution of the target function set, wherein the optimization strategy is used for optimizing the efficiency of the hydrogen production system. The hydrogen production system efficiency optimization method can perform multi-objective optimization solution according to multiple indexes of the electrolytic cell, so that various operation parameters of the electrolytic cell are optimized from different angles, and the efficiency of the hydrogen production system is improved.
In one embodiment, as shown in FIG. 3, this embodiment is an alternative method embodiment of determining an operating parameter dataset, the method steps being as follows:
step S302, determining an assignment strategy of the operation parameters according to the operation condition of the electrolytic cell; the operating parameters include current, voltage, and hydrogen concentration in the anode oxygen of the cell.
And step S304, assigning values to the current, the voltage and the hydrogen concentration in the anode oxygen of the electrolytic cell based on an assignment strategy of the operation parameters to obtain multiple groups of current data, voltage data and hydrogen concentration data.
Step S306, determining an operation parameter data set according to the multiple groups of current data, voltage data and hydrogen concentration data.
The operation condition of the electrolytic cell represents the actual values of various operation parameters of the electrolytic cell during actual operation, so that when the operation parameter data set is obtained, although data is obtained by random assignment of the server, the assignment basis is the value of the actual operation parameters, the obtained operation parameter data set is more real, and the optimized parameters obtained according to the operation parameter data set are more in line with the actual requirements. The operation parameters of the electrolytic cell are many, each operation parameter represents the relation between the electrolytic cell and other modules of the hydrogen production system, wherein the efficiency, the hydrogen production rate and the voltage increase rate of the electrolytic cell can be calculated through the current, the voltage and the hydrogen concentration in the anode oxygen of the electrolytic cell, and when the efficiency, the hydrogen production rate and the voltage increase rate of the electrolytic cell are optimal, the efficiency of the hydrogen production system is optimal, so the current, the voltage and the hydrogen concentration in the anode oxygen of the electrolytic cell are selected to optimize the efficiency of the hydrogen production system.
Then, the server may determine an assignment policy of each operating parameter according to the historical data of the operating parameters, so as to assign the operating parameters according to the assignment policy to obtain multiple sets of operating parameter data, and the multiple sets of operating parameter data are combined to generate the operating parameter data set, so that the target function set is subjected to multi-objective optimization solution subsequently according to the operating parameter data set.
Illustratively, under the normal operation condition of the electrolytic cell, the current value range is between 10A and 20A, the voltage value range is between 0.5V and 2V, and the hydrogen concentration value range in the anode oxygen of the electrolytic cell is between 60 percent and 95 percent, so the server can assign values to various parameters according to the value ranges of the various operation parameters to obtain an operation parameter data set.
According to the hydrogen production system efficiency optimization method, the operation parameter data set of the electrolytic cell is obtained in an assignment mode, the operation parameters of the electrolytic cell do not need to be collected in real time, the required operation parameter data set of the electrolytic cell can be rapidly obtained, so that the target optimal solution of the target function can be rapidly obtained in the subsequent process, and the optimization efficiency of the hydrogen production system can be improved.
In one embodiment, this embodiment is an alternative embodiment of a method for obtaining an efficiency function of an electrolytic cell, the method comprising:
the electrolyzer efficiency function is determined from a relationship comprising hydrogen concentration, current, voltage, a first constant, which is the lower heating value of hydrogen, and a second constant, which is the Faraday constant.
Wherein, the current efficiency of the electrolytic cell can be calculated by the following formula (1):
wherein, CH2Is the hydrogen concentration in the anode oxygen of the electrolytic cell.
Then, the electrolyzer efficiency function is determined according to the current efficiency, the current, the lower heating value of the hydrogen gas, the Faraday constant, the voltage and the operational relationship among the operational parameters calculated by the formula (1), such as the quotient operational relationship, the integral operational relationship, the root equation operational relationship or the multiplication, division, integral mixing operational relationship and the like. For example, the resulting cell efficiency function is:
wherein eta is the efficiency of the electrolytic cell,
is a low calorific value of hydrogen, about 33 kWh/kg; i is current; etaIIs the current efficiency of the cell; f is a Faraday constant; u is a voltage.
In one embodiment, this embodiment is an alternative embodiment of a method for obtaining a hydrogen production rate function for an electrolytic cell, the method comprising:
and determining the hydrogen production rate function of the electrolytic cell according to a relational expression containing the hydrogen concentration, the current, the voltage and the second constant.
Wherein, according to the above, the hydrogen concentration is also used to calculate the current efficiency of the electrolyzer according to the above formula (1). The hydrogen production rate to the cell may then be a function determined from the current efficiency, current, voltage and faraday constant of the cell, for example:
wherein h represents the hydrogen production rate of the electrolytic cell, and I is current; etaIIs the current efficiency of the cell; f is a Faraday constant; u is a voltage.
In one embodiment, as shown in fig. 4, this embodiment is an alternative method embodiment for obtaining a cell voltage rise rate network model, and the method embodiment includes the following steps:
step S402, a first training sample is obtained, where the first training sample includes first sample data and a first voltage increase rate of corresponding sample data, and the first sample data includes first current data and first voltage data.
The first training sample may be obtained by the server from a memory, may be collected from the hydrogen production system, or may be obtained from a memory of the hydrogen production system, which is not limited in this application. Because the current research index for optimizing the efficiency of the hydrogen production system mostly uses the voltage rise rate, but the current research on the voltage rise rate only stays on qualitative analysis, which is unfavorable for the durable optimization control of the hydrogen production system, and therefore a quantitative corresponding relation needs to be established for the durable optimization of the voltage rise rate. Then, it is necessary to establish a correspondence between the rate of voltage increase and various operating parameters of the electrolytic cell. Wherein each operating parameter may be any one or more of voltage, current, etc. Therefore, the incidence relation between the voltage rise rate and the electrolytic cell efficiency and the electrolytic cell hydrogen production rate can be established, and further, the electrolytic cell efficiency function, the electrolytic cell hydrogen production rate function and the electrolytic cell voltage rise rate network model can be optimized and solved simultaneously through the multi-objective optimization algorithm, so that the effect that the electrolytic cell efficiency, the electrolytic cell hydrogen production rate and the electrolytic cell voltage rise rate are optimal is achieved, and the efficiency of the hydrogen production system is optimized.
The method can be used for establishing the incidence relation between the voltage rise rate of the electrolytic bath and the voltage and the current through model training, and can be used for obtaining the corresponding relation between the voltage rise rate of the electrolytic bath and the voltage and the current through the model training because no data or research which can reflect the clear incidence relation between the voltage rise rate of the electrolytic bath and the voltage and the current exists at present. And parameters such as the number of middle layers of the network, the number of processing units of each layer, the learning coefficient of the network and the like can be set according to specific conditions, so that the flexibility is high, and the method has wide application prospects in many fields such as optimization, signal processing and pattern recognition, intelligent control, fault diagnosis and the like. Therefore, the current data, the voltage data and the corresponding cell voltage increase rate are input into the BP neural network, and the cell voltage increase rate network model required by the application can be obtained.
And S404, inputting the training sample into a preset neural network model for training to obtain an initial electrolytic bath voltage increase rate network model.
The server obtains a first training sample, inputs the first training sample into the BP neural network, inputs the first training sample into current data and voltage data, and outputs the first training sample into the cell voltage increase rate. Through training of a large amount of sample data in the first training sample, the required electrolytic bath voltage rise rate network model can be obtained.
Step S406, the initial cell voltage increase rate network model is learned through a second training sample to generate a cell voltage increase rate network model, the second training sample comprises second sample data and a second voltage increase rate corresponding to the sample data, and the second sample data comprises second current data and second voltage data.
The robustness of the obtained cell voltage rise rate network model is not good through the training, and the server needs to select a training sample to perform machine learning on the cell voltage rise rate network model so as to improve the robustness of the cell voltage rise rate network model. Therefore, the server can acquire current data, temperature data, circulating water flow data and the voltage increase rate of the electrolytic bath from the memory, can also acquire the current data, the temperature data, the circulating water flow data and the voltage increase rate of the electrolytic bath from the hydrogen production system, and can also acquire the current data, the temperature data, the circulating water flow data and the voltage increase rate of the electrolytic bath from the memory of the hydrogen production system, which is not limited in the application. So that the cell voltage rise rate network model is learned through different data from those in the first training sample to generate a more stable cell voltage rise rate network model for subsequent use.
The method for optimizing the efficiency of the hydrogen production system provided by the application trains the operation parameters of the electrolytic cell needing to establish the incidence relation through the neural network model, can quickly obtain the network model of the voltage rise rate of the electrolytic cell required by the application, so as to establish the incidence relation between the voltage rise rate of the electrolytic cell and the efficiency of the electrolytic cell and the hydrogen production rate of the electrolytic cell, and further optimize and solve through the function of the efficiency of the electrolytic cell, the function of the hydrogen production rate of the electrolytic cell and the network model of the voltage rise rate of the electrolytic cell, so that the purpose of optimizing the efficiency of the hydrogen production system is achieved, and the efficiency optimization efficiency of the hydrogen production system is improved.
In an embodiment, as shown in fig. 5, this embodiment is an alternative method embodiment for solving the optimal solution of the objective function, and the method embodiment includes the following steps:
step S502, respectively inputting each group of data in the operation parameter data set into an electrolytic cell efficiency function, an electrolytic cell hydrogen production rate function and an electrolytic cell voltage rise rate network model, and calculating through a multi-objective optimization algorithm to obtain an initial solution set; the initial solution set comprises a plurality of values of the set of objective functions;
step S504, determine the minimum value of the plurality of values as the optimal solution of the objective function set.
The process of solving the optimal solution can be to calculate current efficiency according to hydrogen concentration data in a first group of data in the operation parameter data set, and then substitute the current efficiency, current data and voltage data in the first group of data into an electrolyzer efficiency function; substituting the current efficiency, the current data and the voltage data into the hydrogen production rate function of the electrolytic cell; inputting the current data and the voltage data into the electrolytic bath voltage rise rate network model, and obtaining a value of the target function set through a non-dominated sorting genetic algorithm; calculating hydrogen concentration data in the second group of data to obtain current efficiency, and substituting the current efficiency, the current data and the voltage data in the second group of data into an electrolyzer efficiency function; substituting the current efficiency, the current data and the voltage data into the hydrogen production rate function of the electrolytic cell; inputting the current data and the voltage data into the electrolytic bath voltage rise rate network model, and obtaining a value of the target function set through a non-dominated sorting genetic algorithm; and by analogy, obtaining a plurality of values of the target function set. The values of the objective function may be presented in ascending order, descending order, ascending order, or ascending order. Since the smaller the value of the objective function, the better the performance of the hydrogen production system, the minimum value of the objective function is selected as the optimal solution of the objective function.
Then, the operation parameter data capable of calculating the optimal solution of the objective function can be determined as the optimization target, and the optimization strategy of the hydrogen production system is to adjust the operation parameters of the electrolytic cell according to the operation parameter data corresponding to the optimal solution, so as to achieve the purpose of optimizing the efficiency of the hydrogen production system.
In an embodiment, as shown in fig. 6, this embodiment is an optional embodiment of a method after the server determines the optimization policy, where the method includes:
and step S602, adjusting the operation parameters of the electrolytic cell according to the optimization strategy.
The method comprises the steps of obtaining an optimal solution of an objective function according to the multi-objective optimization algorithm, determining a group of target operation parameter data in an operation parameter data set according to the optimal solution of the objective function, then appointing an optimization strategy according to the group of target operation parameter data, and adjusting each operation parameter of the electrolytic cell based on the optimization strategy, namely adjusting each operation parameter of the electrolytic cell to the target operation parameter data so that the electrolytic cell operates under the target operation parameter data.
Step S604, after a preset time period, acquiring and outputting a control curve of the operating parameter.
After the operation parameters of the electrolytic cell are adjusted based on the optimization strategy, the operation parameters of the electrolytic cell can be collected after a preset time period, and the control curve of the operation parameters of the electrolytic cell is output, so that a user can determine the operation condition of the electrolytic cell through the control curve of the operation parameters, further the efficiency of the hydrogen production system can be obtained, the efficiency control condition of the hydrogen production system can be judged, and whether the optimization strategy obtained by the method provided by the application can realize the optimization control of the hydrogen production system can be verified. The optimization scheme is convenient for users to adjust in real time, and the efficiency of the hydrogen production system is better optimized.
It should be understood that although the various steps in the flow charts of fig. 2-6 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-6 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.
In one embodiment, as shown in fig. 7, there is provided a hydrogen production system efficiency optimizing device 700 comprising: an acquisition module 702, a calculation module 704, and a determination module 706, wherein:
an obtaining module 702 for obtaining an operating parameter dataset for an electrolyzer in a hydrogen production system in response to a user-triggered performance optimization instruction;
the calculation module 704 is used for carrying out optimization solution on a preset target function set based on the operation parameter data set through a multi-objective optimization algorithm to obtain an optimal solution of the target function set, wherein the target function set comprises an electrolytic cell efficiency function, an electrolytic cell hydrogen production rate function and an electrolytic cell voltage rise rate network model;
a determining module 706 for determining an optimization strategy of the hydrogen production system based on the optimal solution of the set of objective functions, the optimization strategy for optimizing the performance of the hydrogen production system.
In one embodiment, the determining module 706 is further configured to determine an assignment strategy of the operation parameter according to the operation condition of the electrolytic cell; the operation parameters comprise current, voltage and hydrogen concentration in the anode oxygen of the electrolytic cell; assigning values to current, voltage and hydrogen concentration in the anode oxygen of the electrolytic cell based on an assignment strategy of operating parameters to obtain multiple groups of current data, voltage data and hydrogen concentration data; an operating parameter dataset is determined from the plurality of sets of current data, voltage data, and hydrogen concentration data.
In one embodiment, the determination module 706 is further configured to determine the electrolyzer efficiency function based on a relationship comprising a hydrogen concentration, a current, a voltage, a first constant, the first constant being a lower heating value of the hydrogen, and a second constant, the second constant being a Faraday constant.
In one embodiment, the determining module 706 is further configured to determine the cell hydrogen production rate function according to a relationship comprising the hydrogen concentration, the current, the voltage, and a second constant.
In one embodiment, the apparatus further comprises a generation module, wherein,
the generating module is used for acquiring a first training sample, wherein the first training sample comprises first sample data and a first voltage rise rate corresponding to the sample data, and the first sample data comprises first current data and first voltage data; inputting the training sample into a preset neural network model for training to obtain an initial electrolytic bath voltage increase rate network model; and learning the initial cell voltage increase rate network model through a second training sample to generate the cell voltage increase rate network model, wherein the second training sample comprises second sample data and a second voltage increase rate corresponding to the sample data, and the second sample data comprises second current data and second voltage data.
In one embodiment, the calculating module 704 is specifically configured to input each set of data in the operating parameter data set into the cell efficiency function, the cell hydrogen production rate function, and the cell voltage increase rate network model, respectively, and calculate an initial solution set through a multi-objective optimization algorithm; the initial solution set comprises a plurality of values of the set of objective functions; the minimum of the plurality of values is determined as the optimal solution for the set of objective functions.
In one embodiment, the above apparatus further comprises: an adjustment module and an output module, wherein,
the adjusting module is used for adjusting the operation parameters of the electrolytic cell according to the optimization strategy;
and the output module is used for acquiring and outputting the control curve of the operation parameter after a preset time period.
For specific limitations of the hydrogen production system efficiency optimization device, reference may be made to the limitations of the hydrogen production system efficiency optimization method described above, and further description thereof is omitted here. The various modules in the hydrogen generation system efficiency optimization apparatus described above may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 8. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing the electrolytic bath operation parameter data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method for optimizing the performance of a hydrogen production system.
Those skilled in the art will appreciate that the architecture shown in fig. 8 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
responding to an efficiency optimization instruction triggered by a user, and acquiring an operation parameter data set of an electrolytic cell in the hydrogen production system;
performing optimization solution on a preset target function set based on an operation parameter data set through a multi-objective optimization algorithm to obtain an optimal solution of the target function set, wherein the target function set comprises an electrolytic cell efficiency function, an electrolytic cell hydrogen production rate function and an electrolytic cell voltage rise rate network model;
and determining an optimization strategy of the hydrogen production system according to the optimal solution of the target function set, wherein the optimization strategy is used for optimizing the efficiency of the hydrogen production system.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
determining an assignment strategy of the operation parameters according to the operation condition of the electrolytic cell; the operation parameters comprise current, voltage and hydrogen concentration in the anode oxygen of the electrolytic cell; assigning values to current, voltage and hydrogen concentration in the anode oxygen of the electrolytic cell based on an assignment strategy of operating parameters to obtain multiple groups of current data, voltage data and hydrogen concentration data; an operating parameter dataset is determined from the plurality of sets of current data, voltage data, and hydrogen concentration data.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
the electrolyzer efficiency function is determined from a relationship comprising hydrogen concentration, current, voltage, a first constant, which is the lower heating value of hydrogen, and a second constant, which is the Faraday constant.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and determining the hydrogen production rate function of the electrolytic cell according to a relational expression containing the hydrogen concentration, the current, the voltage and the second constant.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
acquiring a first training sample, wherein the first training sample comprises first sample data and a first voltage rise rate of corresponding sample data, and the first sample data comprises first current data and first voltage data; inputting the training sample into a preset neural network model for training to obtain an initial electrolytic bath voltage increase rate network model; and learning the initial cell voltage increase rate network model through a second training sample to generate the cell voltage increase rate network model, wherein the second training sample comprises second sample data and a second voltage increase rate corresponding to the sample data, and the second sample data comprises second current data and second voltage data.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
respectively inputting each group of data in the operation parameter data set into an electrolytic cell efficiency function, an electrolytic cell hydrogen production rate function and an electrolytic cell voltage rise rate network model, and calculating through a multi-objective optimization algorithm to obtain an initial solution set; the initial solution set comprises a plurality of values of the set of objective functions; the minimum of the plurality of values is determined as the optimal solution for the set of objective functions.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
adjusting the operation parameters of the electrolytic cell according to an optimization strategy;
and after a preset time period, acquiring and outputting a control curve of the operation parameter.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
responding to an efficiency optimization instruction triggered by a user, and acquiring an operation parameter data set of an electrolytic cell in the hydrogen production system;
performing optimization solution on a preset target function set based on an operation parameter data set through a multi-objective optimization algorithm to obtain an optimal solution of the target function set, wherein the target function set comprises an electrolytic cell efficiency function, an electrolytic cell hydrogen production rate function and an electrolytic cell voltage rise rate network model;
and determining an optimization strategy of the hydrogen production system according to the optimal solution of the target function set, wherein the optimization strategy is used for optimizing the efficiency of the hydrogen production system.
In one embodiment, the computer program when executed by the processor further performs the steps of:
determining an assignment strategy of the operation parameters according to the operation condition of the electrolytic cell; the operation parameters comprise current, voltage and hydrogen concentration in the anode oxygen of the electrolytic cell; assigning values to current, voltage and hydrogen concentration in the anode oxygen of the electrolytic cell based on an assignment strategy of operating parameters to obtain multiple groups of current data, voltage data and hydrogen concentration data; an operating parameter dataset is determined from the plurality of sets of current data, voltage data, and hydrogen concentration data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
the electrolyzer efficiency function is determined from a relationship comprising hydrogen concentration, current, voltage, a first constant, which is the lower heating value of hydrogen, and a second constant, which is the Faraday constant.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and determining the hydrogen production rate function of the electrolytic cell according to a relational expression containing the hydrogen concentration, the current, the voltage and the second constant.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring a first training sample, wherein the first training sample comprises first sample data and a first voltage rise rate of corresponding sample data, and the first sample data comprises first current data and first voltage data; inputting the training sample into a preset neural network model for training to obtain an initial electrolytic bath voltage increase rate network model; and learning the initial cell voltage increase rate network model through a second training sample to generate the cell voltage increase rate network model, wherein the second training sample comprises second sample data and a second voltage increase rate corresponding to the sample data, and the second sample data comprises second current data and second voltage data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
respectively inputting each group of data in the operation parameter data set into an electrolytic cell efficiency function, an electrolytic cell hydrogen production rate function and an electrolytic cell voltage rise rate network model, and calculating through a multi-objective optimization algorithm to obtain an initial solution set; the initial solution set comprises a plurality of values of the set of objective functions; the minimum of the plurality of values is determined as the optimal solution for the set of objective functions.
In one embodiment, the computer program when executed by the processor further performs the steps of:
adjusting the operation parameters of the electrolytic cell according to an optimization strategy;
and after a preset time period, acquiring and outputting a control curve of the operation parameter.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above examples only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method for optimizing the performance of a hydrogen production system, the method comprising:
in response to a user-triggered performance optimization instruction, obtaining a data set of operating parameters of an electrolysis cell in the hydrogen production system;
performing optimization solution on a preset target function set based on the operation parameter data set through a multi-objective optimization algorithm to obtain an optimal solution of the target function set, wherein the target function set comprises an electrolytic cell efficiency function, an electrolytic cell hydrogen production rate function and an electrolytic cell voltage rise rate network model;
determining an optimization strategy for the hydrogen production system based on the optimal solution for the set of objective functions, the optimization strategy for optimizing the effectiveness of the hydrogen production system.
2. The method of claim 1, further comprising:
determining an assignment strategy of the operation parameters according to the operation condition of the electrolytic cell; the operating parameters comprise current, voltage and hydrogen concentration in the anode oxygen of the electrolytic cell;
assigning values to the current, the voltage and the hydrogen concentration in the anode oxygen of the electrolytic cell based on the assignment strategy of the operating parameters to obtain multiple groups of current data, voltage data and hydrogen concentration data;
determining the operating parameter dataset from the plurality of sets of current data, voltage data, and hydrogen concentration data.
3. The method of claim 2, further comprising:
determining the electrolyzer efficiency function according to a relational expression comprising the hydrogen concentration, the current, the voltage, a first constant and a second constant, the first constant being the lower heating value of hydrogen and the second constant being the Faraday constant.
4. The method of claim 3, further comprising:
and determining the hydrogen production rate function of the electrolytic cell according to a relational expression comprising the hydrogen concentration, the current, the voltage and the second constant.
5. The method of claim 1, further comprising the step of generating the cell voltage rise rate network model by:
obtaining a first training sample, wherein the first training sample comprises first sample data and a first voltage rise rate of corresponding sample data, and the first sample data comprises first current data and first voltage data;
inputting the training sample into a preset neural network model for training to obtain the initial electrolytic bath voltage increase rate network model;
and learning the initial cell voltage increase rate network model through a second training sample to generate the cell voltage increase rate network model, wherein the second training sample comprises second sample data and a second voltage increase rate corresponding to the sample data, and the second sample data comprises second current data and second voltage data.
6. The method of claim 1, wherein the optimal solution of the set of objective functions is obtained by performing an optimization solution on a preset set of objective functions based on the set of operational parameter data through a multi-objective optimization algorithm, comprising:
respectively inputting each group of data in the operation parameter data set into the electrolytic cell efficiency function, the electrolytic cell hydrogen production rate function and the electrolytic cell voltage rise rate network model, and calculating by the multi-objective optimization algorithm to obtain an initial solution set; the initial solution set comprises a plurality of values of the set of target functions;
determining a minimum value of the plurality of values as an optimal solution for the set of objective functions.
7. The method of claim 1, further comprising:
adjusting the operating parameters of the electrolytic cell according to the optimization strategy;
and after a preset time period, acquiring and outputting a control curve of the operation parameter.
8. A hydrogen production system performance optimization device, comprising:
an acquisition module for acquiring a data set of operating parameters of an electrolyzer in the hydrogen production system in response to a user-triggered performance optimization instruction;
the calculation module is used for carrying out optimization solution on a preset target function set based on the operation parameter data set through a multi-objective optimization algorithm to obtain an optimal solution of the target function set, wherein the target function set comprises an electrolytic cell efficiency function, an electrolytic cell hydrogen production rate function and an electrolytic cell voltage increase rate network model;
a determination module for determining an optimization strategy for the hydrogen production system based on the optimal solution for the set of objective functions, the optimization strategy for optimizing the effectiveness of the hydrogen production system.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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| CN114705251A (en) * | 2022-04-27 | 2022-07-05 | 北京雷动智创科技有限公司 | Hydrogen production electrolytic tank state monitoring device and method |
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| CN114705251A (en) * | 2022-04-27 | 2022-07-05 | 北京雷动智创科技有限公司 | Hydrogen production electrolytic tank state monitoring device and method |
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