Pulse current regulation and control method for water electrolysis hydrogen production

Abstract

The invention relates to the technical field of water electrolysis hydrogen production, and discloses a pulse current regulation and control method for water electrolysis hydrogen production, which comprises the following steps: S1, inputting parameter information acquired in real time and basic parameters acquired by initialization into a preset control algorithm model; s2, the control algorithm model calculates the frequency, the duty ratio and the peak current of the pulse current according to the input data; s3, the pulse power supply controller generates a corresponding pulse current signal according to the received parameters and outputs the pulse current signal to an electrode of the electrolytic bath; s4, the operation state of the water electrolysis hydrogen production process is evaluated every certain time interval, deviation data is fed back to the control algorithm model, and the calculation rule of pulse current parameters is optimized and adjusted; the hydrogen generation rate is remarkably increased, so that the hydrogen production efficiency is improved, the energy consumption in the water electrolysis hydrogen production process is effectively reduced, and the hydrogen production cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrogen production by electrolyzing water, and specifically relates to a method for regulating pulse current for hydrogen production by electrolyzing water. Background Art

[0002] With the continuous growth of the demand for clean energy, hydrogen production by electrolyzing water, as a sustainable hydrogen production method, has received extensive attention. In the traditional hydrogen production process by electrolyzing water, direct current is usually used for electrolysis. However, this method has problems such as high energy consumption, limited hydrogen production efficiency, easy polarization and corrosion of electrodes, etc. In order to improve the efficiency of hydrogen production by electrolyzing water and reduce energy consumption, researchers have begun to explore the use of pulse current for hydrogen production by electrolyzing water. Currently, most of the existing pulse current regulation methods simply set fixed pulse frequencies and duty cycles, without fully considering the influence of factors such as the characteristics of electrode materials, changes in electrolyte concentration, reaction temperature, and system operating conditions on the electrolysis effect during the electrolysis process. Therefore, it is difficult to achieve precise control of the hydrogen production process by electrolyzing water, and the advantages of pulse current in hydrogen production by electrolyzing water cannot be fully utilized, resulting in limited improvement in hydrogen production efficiency and quality. Summary of the Invention

[0003] The purpose of the present invention is to solve the above problems and design a method for regulating pulse current for hydrogen production by electrolyzing water.

[0004] The present invention provides a method for regulating pulse current for hydrogen production by electrolyzing water, which includes the following steps: S1. During the hydrogen production process by electrolyzing water, use sensors to collect parameter information in the electrolytic cell in real time, and input the real-time collected parameter information and the basic parameters obtained by initialization into a preset control algorithm model; S2. In the control algorithm model, the control algorithm model calculates the frequency, duty cycle, and peak current of the pulse current according to the input data; S3. Send the calculated parameters of the pulse current frequency, duty cycle, and peak current to the pulse power controller. The pulse power controller generates a corresponding pulse current signal according to the received parameters and outputs it to the electrodes of the electrolytic cell to achieve the regulation of the pulse current during the hydrogen production process by electrolyzing water; S4. At regular time intervals, evaluate the operating state of the hydrogen production process by electrolyzing water, compare the actual data with the preset target value. If it is found that there is a deviation between the actual data and the target value, feedback the deviation data to the control algorithm model, and the control algorithm model optimizes and adjusts the calculation rules of the pulse current parameters according to the feedback data.

[0005] Optionally, in the first implementation of the present invention, the basic parameters include at least the electrode material type, the initial concentration of the electrolyte, the specifications of the electrolytic cell and the ambient temperature, and the parameter information includes at least the electrolyte temperature, the change in electrolyte concentration, the electrode potential and the hydrogen generation rate.

[0006] Optionally, in a second implementation of the present invention, step S1 specifically includes the following process: Initialize temperature sensors, spectrum analysis sensors, potentiometer sensors, gas flow meters and other equipment; The acquisition period is set according to the parameter change characteristics, wherein the parameter change characteristics include hydrogen generation rate and electrode potential as fast-changing parameters, and electrolyte temperature and electrolyte concentration as slow-changing parameters; According to the set collection cycle and sequence, the sensor is triggered to collect data, and the sensor converts the analog signal into a digital signal; Preprocess the collected data, remove abnormal data, and use adjacent data interpolation method to supplement the missing data; The preprocessed parameter information is integrated with the basic parameters obtained by initialization to form complete input data, and the input data is input into the preset control algorithm model.

[0007] Optionally, in a third implementation of the present invention, the hydrogen generation rate collection period is set to 1-10 seconds, the electrode potential collection period is 1-5 seconds, the electrolyte temperature collection period is 30-120 seconds, and the electrolyte concentration change collection period is 300-1800 seconds.

[0008] Optionally, in a fourth implementation of the present invention, the control algorithm model is constructed using a fuzzy neural network control algorithm, and the control algorithm model includes an input layer, a fuzzification layer, a neural network layer and an output layer.

[0009] Optionally, in a fifth implementation of the present invention, step S2 specifically includes the following process: Receive input data, normalize the input data, map parameters of different dimensions to the interval [0, 1], and apply the sliding average filter algorithm to eliminate data noise; A Gaussian membership function is defined for each input data, and the membership value of each input data to each fuzzy level is calculated; The hidden layer neurons receive the membership values output by the fuzzification layer, each hidden layer neuron calculates the weighted sum, and the output layer neurons receive the hidden layer output and calculate the preliminary values of the pulse current parameters; Reasoning is performed based on the fuzzy rule base, the activation strength of each rule is calculated, and weighted adjustment is performed on the pulse current parameters calculated initially based on the activation strength; The centroid method is used to calculate the final parameter values. The defuzzified parameter values are de-normalized and converted into physical quantities. The frequency, duty cycle, and peak current of the final pulse current are output by applying parameter limit constraints.

[0010] Optionally, in the sixth implementation manner of the present invention, step S3 specifically includes the following processes: Transmit the parameters of the pulse current frequency, duty cycle, and peak current calculated by the control algorithm model to the pulse power controller; After receiving the parameters, the pulse power controller sets the period of the pulse signal according to the frequency, determines the proportion of the high-level duration in one period according to the duty cycle, and sets the maximum value of the output current according to the peak current to generate a pulse current signal; Output the generated pulse current signal to the electrodes of the electrolytic cell, so that the current acts on the electrolysis process according to the set parameters, realizing the pulse current regulation of the electrolytic water hydrogen production process.

[0011] Optionally, in the seventh implementation manner of the present invention, step S4 specifically includes the following processes: Set the target threshold range corresponding to each index. At the end of each evaluation cycle, collect the average value of relevant parameters, and calculate the evaluation index based on the collected average value of the parameters; Calculate the deviation between each evaluation index and the target threshold range, divide the deviation level, analyze the cause of the deviation, and output the abnormal link in the electrolytic water hydrogen production process; Based on the abnormal links in the electrolytic water hydrogen production process, determine the priority of pulse current parameter adjustment, and use a fuzzy logic controller to generate parameter adjustment suggestions; Adjust the weights in the control algorithm model by the gradient descent method, optimize the membership function parameters, send the optimized parameter adjustment scheme to the pulse power controller, and set the smooth transition time to detect the adjustment effect in the next evaluation cycle.

[0012] Optionally, in the eighth implementation manner of the present invention, the evaluation index at least includes the hydrogen production efficiency and the energy consumption ratio. The target threshold range of the hydrogen production efficiency is set to 80% - 95%, and the target threshold range of the energy consumption ratio is set to 3 - 5 kWh / NmA ^3 .

[0013] Optionally, in the ninth implementation manner of the present invention, the smooth transition time is 5 - 30 seconds.

[0014] In the technical solution provided by the present invention, during the process of electrolytic water hydrogen production, a sensor is used to collect the parameter information in the electrolytic cell in real time, and the parameter information collected in real time and the basic parameters obtained through initialization are input into a preset control algorithm model together; the control algorithm model calculates the frequency, duty cycle, and peak current of the pulsed current according to the input data; the calculated parameters of the pulsed current frequency, duty cycle, and peak current are sent to the pulsed power controller, and the pulsed power controller generates a corresponding pulsed current signal according to the received parameters and outputs it to the electrodes of the electrolytic cell to achieve the pulsed current regulation of the electrolytic water hydrogen production process; at regular time intervals, the operating state of the electrolytic water hydrogen production process is evaluated, and the actual data is compared with the preset target value. If it is found that there is a deviation between the actual data and the target value, the deviation data is fed back to the control algorithm model, and the control algorithm model optimizes and adjusts the calculation rules of the pulsed current parameters according to the feedback data; by comprehensively considering various factors affecting the electrolytic water hydrogen production process and using an advanced control algorithm model to dynamically adjust the pulsed current parameters, the present invention can keep the electrolytic reaction in the best state all the time, significantly improve the hydrogen generation rate, and thus improve the hydrogen production efficiency; the precise pulsed current regulation avoids energy waste caused by unreasonable current parameters, effectively reduces the energy consumption in the electrolytic water hydrogen production process while ensuring the hydrogen production efficiency, and reduces the hydrogen production cost; by adjusting the pulsed current parameters in a timely manner according to the electrode potential and polarization phenomenon, the polarization degree and corrosion rate of the electrode can be effectively reduced, the electrode loss can be reduced, the service life of the electrode can be extended, and the equipment maintenance cost can be reduced; this method can automatically adjust the pulsed current parameters according to different electrode materials, electrolyte characteristics, and system operating states, has strong adaptability, and is applicable to various types of electrolytic water hydrogen production systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention.

[0017] Figure 1 Schematic diagram of the first embodiment of the pulsed current regulation method for electrolytic water hydrogen production provided by an embodiment of the present invention; Figure 2 Schematic diagram of the second embodiment of the pulsed current regulation method for electrolytic water hydrogen production provided by an embodiment of the present invention; Figure 3 Schematic diagram of the third embodiment of the pulsed current regulation method for electrolytic water hydrogen production provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] In the description, claims and the above drawings of the present invention, the terms "first", "second", "third", "fourth", etc. (if any) are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order different from that illustrated or described herein. In addition, the term "comprising" or "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or equipment comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or equipment.

[0019] For ease of understanding, the specific process of the embodiments of the present invention will be described below. Please refer to Figure 1 The following is a schematic diagram of the first embodiment of the pulsed current regulation method for electrolytic water hydrogen production provided by the embodiments of the present invention. The method specifically includes the following steps: S1. During the process of electrolytic water hydrogen production, use a sensor to collect parameter information in the electrolytic cell in real time, and input the parameter information collected in real time and the basic parameters obtained by initialization into a preset control algorithm model; S2. In the control algorithm model, the control algorithm model calculates the frequency, duty cycle and peak current of the pulsed current according to the input data; S3. Send the calculated parameters of the pulsed current frequency, duty cycle and peak current to the pulsed power supply controller. The pulsed power supply controller generates a corresponding pulsed current signal according to the received parameters and outputs it to the electrodes of the electrolytic cell to achieve pulsed current regulation of the electrolytic water hydrogen production process; S4. At regular time intervals, evaluate the operating state of the electrolytic water hydrogen production process, compare the actual data with the preset target value. If it is found that there is a deviation between the actual data and the target value, feedback the deviation data to the control algorithm model, and the control algorithm model optimizes and adjusts the calculation rules of the pulsed current parameters according to the feedback data. In this embodiment, the basic parameters at least include the type of electrode material, the initial concentration of the electrolyte, the specifications of the electrolytic cell and the ambient temperature, and the parameter information at least includes the electrolyte temperature, the change in electrolyte concentration, the electrode potential and the hydrogen production rate.

[0020] In this embodiment, the acquisition period of the hydrogen production rate is set to 1 - 10 seconds, the acquisition period of the electrode potential is 1 - 5 seconds, the acquisition period of the electrolyte temperature is 30 - 120 seconds, and the acquisition period of the change in electrolyte concentration is 300 - 1800 seconds.

[0021] In this embodiment, the control algorithm model is constructed by using a fuzzy neural network control algorithm. The control algorithm model includes an input layer, a fuzzification layer, a neural network layer, and an output layer.

[0022] In this embodiment, step S3 specifically includes the following process: Transmit the parameters of the pulse current frequency, duty cycle, and peak current calculated by the control algorithm model to the pulse power controller; After receiving the parameters, the pulse power controller sets the period of the pulse signal according to the frequency, determines the proportion of the high-level duration in one cycle according to the duty cycle, sets the maximum value of the output current according to the peak current, and generates a pulse current signal; Output the generated pulse current signal to the electrodes of the electrolytic cell, so that the current acts on the electrolysis process according to the set parameters, and realizes the pulse current regulation of the electrolytic water hydrogen production process.

[0023] In this embodiment, step S4 specifically includes the following process: Set the target threshold range corresponding to each index. At the end of each evaluation cycle, collect the average value of relevant parameters, and calculate the evaluation index based on the collected average value of the parameters; Calculate the deviation between each evaluation index and the target threshold range, divide the deviation level, analyze the cause of the deviation, and output the abnormal link in the electrolytic water hydrogen production process; Based on the abnormal link in the electrolytic water hydrogen production process, determine the priority of the pulse current parameter adjustment, and use the fuzzy logic controller to generate parameter adjustment suggestions; Use the gradient descent method to adjust the weights in the control algorithm model, optimize the membership function parameters, send the optimized parameter adjustment scheme to the pulse power controller, and set the smooth transition time to detect the adjustment effect in the next evaluation cycle.

[0024] In this embodiment, the evaluation indexes at least include the hydrogen production efficiency and the energy consumption ratio. The target threshold range of the hydrogen production efficiency is set to 80% - 95%, and the target threshold range of the energy consumption ratio is set to 3 - 5 kWh / NmA ^3 .

[0025] In this embodiment, the smooth transition time is 5 - 30 seconds.

[0026] In this embodiment, when the electrolytic water hydrogen production system is started, basic parameters such as the type of electrode material, the initial concentration of the electrolyte, the specifications of the electrolytic cell, and the ambient temperature are obtained. According to the type of electrode material, characteristic data such as the polarization curve and the hydrogen evolution overpotential of the electrode material under different pulsed current parameters are retrieved from the pre-established electrode characteristic database; at the same time, according to the initial concentration of the electrolyte and the specifications of the electrolytic cell, combined with Faraday's electrolysis law, the theoretically maximum hydrogen production rate and the required minimum electrolysis voltage are calculated; during the electrolytic water hydrogen production process, sensors are used to collect data such as the electrolyte temperature, the change in electrolyte concentration, the electrode potential, and the hydrogen production rate in the electrolytic cell in real time. Among them, the electrolyte temperature is measured by a temperature sensor; the change in electrolyte concentration is monitored in real time by a spectroscopic analysis sensor; the electrode potential is detected by a potential sensor; and the hydrogen production rate is measured by a gas flow meter.

[0027] In this embodiment, when the complete input data set enters the preset control algorithm model, the key parameters of the pulsed current are determined through the complex and precise calculation logic inside the model. This model is constructed using a fuzzy neural network control algorithm. The input layer receives and preliminarily processes the input data, performs a normalization operation on it to eliminate the influence caused by the dimension difference of different parameters, and at the same time uses a filtering algorithm to remove data noise, making the data smoother and more stable. Then, the data enters the fuzzification layer. By defining appropriate membership functions, the precise input data is transformed into a fuzzy set and divided into different fuzzy levels. For example, the electrolyte temperature is divided into categories such as "low", "medium", and "high". Subsequently, the neural network layer exerts its powerful learning and mapping capabilities. The neurons in the hidden layer establish a complex non-linear mapping relationship between the input parameters and the pulsed current parameters (frequency, duty cycle, peak current) through learning and training of a large amount of historical data. The output layer then outputs the preliminary calculated values of the pulsed current parameters based on the calculation results of the hidden layer. The model also needs to perform reasoning based on the pre-established fuzzy rule base, combine the fuzzy level combinations of the input parameters, and adjust the preliminary calculated values according to the established fuzzy rules. Finally, after defuzzification processing, the fuzzy output is transformed into precise pulsed current frequency, duty cycle, and peak current values that can be used for actual control. The pulsed current frequency, duty cycle, and peak current parameters accurately calculated by the control algorithm model are transmitted to the pulsed power supply controller, which is the key link to convert the calculation results into actual physical signals. According to the received frequency parameter, the period of the pulsed signal is set, which determines the speed rhythm of the current change. According to the duty cycle parameter, the proportion of the high-level duration in a cycle is determined, controlling the time distribution of the current output. According to the peak current parameter, the maximum value of the output current is set, limiting the upper limit of the current intensity. The pulsed power supply controller converts these parameters into corresponding pulsed current signals. To ensure the stability and safety of signal transmission and action, before the signal is output to the electrolytic cell electrodes, a series of processing and protection measures are also taken, such as using isolation technology to prevent signal interference and electrical faults, and setting safety thresholds to prevent excessive current from damaging equipment or causing safety accidents. Finally, the generated pulsed current signal is accurately output to the electrodes of the electrolytic cell, driving the electrolytic water hydrogen production reaction to proceed according to the optimized parameters, realizing the precise control of the pulsed current in the hydrogen production process, which directly affects key indicators such as the efficiency, energy consumption, and quality of hydrogen production. During the continuous operation of the electrolytic water hydrogen production system, the target threshold ranges of key indicators such as hydrogen production efficiency, energy consumption ratio, and hydrogen purity are preset in advance as the reference standards for evaluating the system operation state. At regular time intervals, the system collects the average values of relevant parameters in the current operation cycle and calculates the actual values of each evaluation index accordingly. Then, the actual values are carefully compared with the preset target values, the deviation values are calculated, and different deviation levels are divided according to the deviation size to clearly judge the degree to which the system deviates from the expected state.When deviations are found, the fault tree algorithm and other analytical methods are used to deeply explore the causes of the deviations and accurately locate abnormal links in the process of hydrogen production by electrolysis of water, such as electrode polarization, abnormal electrolyte concentration or gas separation failure. Based on the determined abnormal links, combined with pre-established rules and strategies, the priority of pulse current parameter adjustment is determined, and the fuzzy logic controller is used to generate targeted parameter adjustment suggestions. Finally, these adjustment suggestions are fed back to the control algorithm model, and the model uses optimization algorithms such as gradient descent method to adjust the internal weights and membership function parameters and optimize the calculation rules of pulse current parameters. When the adjusted parameter scheme is sent to the pulse power supply controller, a smooth transition time will be set to avoid the impact of parameter mutations on the system. The system operation status will be detected again in the next evaluation cycle to form a closed-loop control, and the process of hydrogen production by electrolysis of water will be continuously optimized so that it gradually approaches the preset ideal operation state. ;

[0028] See also Figure 2 , a schematic diagram of a second embodiment of a pulse current control method for producing hydrogen by electrolysis of water provided in an embodiment of the present invention, the method comprising: S11, initializing the temperature sensor, spectrum analysis sensor, potentiometer sensor, gas flow meter and other equipment; S12, setting a collection period according to parameter change characteristics, wherein the parameter change characteristics include hydrogen generation rate and electrode potential as fast-changing parameters, and electrolyte temperature and electrolyte concentration as slow-changing parameters; S13, triggering the sensor to collect data according to the set collection cycle and sequence, and the sensor converts the analog signal into a digital signal; S14, pre-processing the collected data, eliminating abnormal data, and supplementing the missing data by adjacent data interpolation method; S15, integrating the preprocessed parameter information with the basic parameters obtained by initialization to form complete input data, and inputting the input data into a preset control algorithm model.

[0029] In this embodiment, through the analog-to-digital conversion circuit, the continuously changing analog signal is discretized into a digital signal, enabling it to be accurately recognized and processed by subsequent data processing links and control algorithm models, providing effective data support for the precise regulation of the hydrogen production process. The raw data collected may have various problems and cannot be directly used for the calculation of the control algorithm model. Therefore, strict data preprocessing is required. During the data collection process, due to factors such as the error of the sensor itself and environmental interference, some abnormal data may be generated. If these abnormal data are not processed, they will seriously affect the accuracy of subsequent parameter calculation and control decision-making. Therefore, through specific discrimination rules, the abnormal data that clearly does not conform to the actual situation is eliminated. In addition, data loss is also a common situation. For example, during a short-term sensor failure or signal transmission interruption, some data will be missing. For the missing data, the adjacent data interpolation method is used for supplementation, that is, based on the valid data before and after the missing data, through reasonable mathematical estimation, the missing data points are filled to keep the data sequence complete. After eliminating abnormal data and supplementing missing data, the quality of the collected data is significantly improved, laying a foundation for constructing accurate and reliable input data. After completing the data preprocessing, these parameter information is integrated with the basic parameters obtained during system initialization. The basic parameters include the type of electrode material, the initial concentration of the electrolyte, the specifications of the electrolytic cell, and the environmental temperature, etc. These parameters are the basic setting conditions for the electrolytic water hydrogen production process and have an important impact on the hydrogen production reaction. The preprocessed parameter information reflects the real-time operating state of the hydrogen production process. Integrating the two forms complete and comprehensive input data, covering the static conditions and dynamic changes of the hydrogen production process. Finally, this integrated input data is input into the preset control algorithm model, providing rich and accurate information for the control algorithm model, enabling the model to conduct in-depth analysis and calculation based on these data, and then obtaining reasonable pulse current parameters to achieve precise regulation of the electrolytic water hydrogen production process.

[0030] Please refer to Figure 3 , the schematic diagram of the third embodiment of the pulsed current regulation method for electrolytic water hydrogen production provided by the embodiment of the present invention. The method includes: S21. Receive the input data, perform normalization processing on the input data, map the parameters with different dimensions to the interval [0, 1], and apply the moving average filtering algorithm to eliminate data noise; S22. Define a Gaussian membership function for each input data, and calculate the membership degree value of each input data for each fuzzy level; S23. The hidden layer neurons receive the membership degree values output by the fuzzification layer. Each hidden layer neuron calculates the weighted sum. The output layer neuron receives the output of the hidden layer and calculates the initial value of the pulsed current parameter; S24. Perform reasoning based on the fuzzy rule base, calculate the activation strength of each rule, and perform weighted adjustment on the preliminarily calculated pulse current parameters based on the activation strength; S25. Calculate the final parameter values using the centroid method, perform anti-normalization processing on the defuzzified parameter values, convert them into physical quantities, and apply parameter limit constraints to output the frequency, duty cycle, and peak current of the final pulse current.

[0031] In this embodiment, the data input into the control algorithm model is preprocessed to ensure its quality and consistency; the data collected by different types of sensors have different physical dimensions. For example, the unit of temperature is degrees Celsius, the unit of electrode potential is volts, and the unit of hydrogen generation rate is liters per minute; these data with different dimensions cannot be directly compared and calculated in the model. Therefore, they need to be mapped to the [0,1] interval through normalization; normalization not only eliminates the influence of dimensional differences but also prevents certain features from dominating the model due to excessive numerical ranges; subsequently, in order to eliminate the possible random noise in the data, a moving average filtering algorithm is used to smooth the data; this algorithm effectively suppresses high-frequency noise and retains the true trend of the data by calculating the local average of the data points; the size of the moving window is dynamically adjusted according to the variation characteristics of the parameters. For rapidly changing parameters such as electrode potential, a smaller window is used to ensure the response speed; for slowly changing parameters such as electrolyte temperature, a larger window is used to improve the filtering effect; the preprocessed input data is converted into fuzzy sets for processing in the fuzzy logic system; Gaussian membership functions are defined for each input parameter and divided into multiple fuzzy levels, such as "low", "medium", "high", etc.; Gaussian membership functions are widely used due to their smoothness and good mathematical properties, and they can calculate the degree to which a value belongs to each fuzzy level, that is, the membership value; for example, for the input parameter of electrolyte temperature, when the temperature is 50°C, its membership degree to the "low" temperature level may be 0.1, the membership degree to the "medium" temperature may be 0.7, and the membership degree to the "high" temperature may be 0.2; The parameters of the membership function (center value and width) are determined through the analysis of a large amount of historical data and expert experience, and will be adjusted according to the actual operation data in the subsequent optimization process; The neurons in the hidden layer receive the membership values from the fuzzification layer as inputs, and each neuron performs a weighted sum of these inputs; The weights are parameters learned by the model during the training process, and they determine the influence degree of each input on the neuron output; By adjusting these weights, the model can learn the complex non-linear relationship between the input parameters and the output parameters; After calculating the weighted sum, each neuron in the hidden layer will perform a non-linear transformation through an activation function to convert the input signal into an output signal; This non-linear transformation enables the model to represent complex functional relationships and enhances the expressive ability of the model; The neurons in the output layer receive the output of the hidden layer and perform similar calculations, and finally output the initial values of the pulse current parameters; These initial values are obtained based on the neural network's learning of historical data and the comprehensive analysis of the current input parameters, but further optimization is required through the fuzzy rule base; Based on the pre-established fuzzy rule base, the system further infers and adjusts the pulse current parameters calculated initially; The fuzzy rule base contains a series of rules in the form of "IF-THEN", such as "IF the temperature is high AND the electrode potential is low THEN reduce the pulse frequency"; For each combination of input parameters, the system calculates the activation strength of each rule, that is, the degree to which the input combination satisfies the premise conditions of the rule; The calculation of the activation strength is based on the membership values of the input parameters and the logical combination of the rule premise conditions; For example, for the above rule, if the membership degree of the current temperature to "high" is 0.8, the membership degree of the electrode potential to "low" is 0.6, and the premise condition of the rule is an "AND" relationship, then the activation strength of this rule is min(0.8, 0.6) = 0.6; Then, the conclusion part of each rule is weighted according to the activation intensity to obtain the final parameter adjustment suggestion; This reasoning mechanism allows the model to comprehensively consider multiple factors and make flexible decisions according to different situations; The parameter adjustment suggestions obtained through fuzzy rule reasoning are still in the form of fuzzy sets and need to be converted into precise numerical values through defuzzification processing; Here, the centroid method is used for defuzzification calculation, which determines the final parameter value by calculating the centroid position of the fuzzy set; The centroid method can comprehensively consider all possible output values and their membership degrees and provide a precise value that best represents the fuzzy set; After obtaining the precise parameter value, it is necessary to perform anti-normalization processing to convert it from the [0,1] interval back to the actual physical dimension, such as converting the normalized frequency value to the actual frequency value in Hertz units; Finally, to ensure that the output parameters are within a safe and reasonable range, parameter limit constraints are applied to check and correct the results; For example, the pulse frequency is limited between 10Hz and 1000Hz, the duty cycle is limited between 10% and 90%, and the peak current is limited within the range allowed by the electrode material and the electrolytic cell specifications; Through these constraints, the safety and stability of the system are ensured, and equipment damage or hydrogen production efficiency decline caused by abnormal parameters is prevented.

[0032] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A pulsed current regulation method for hydrogen production by electrolyzing water, characterized in that, The method comprises the following steps: S1. In the process of producing hydrogen by electrolysis of water, the sensor is used to collect parameter information in the electrolytic cell in real time, and the parameter information collected in real time and the basic parameters obtained by initialization are input into the preset control algorithm model; S2. In the control algorithm model, the control algorithm model calculates the frequency, duty cycle and peak current of the pulse current according to the input data; S3, sending the calculated parameters of pulse current frequency, duty cycle and peak current to the pulse power controller, which generates a corresponding pulse current signal according to the received parameters and outputs it to the electrodes of the electrolyzer to achieve pulse current regulation of the hydrogen production process by electrolysis of water; S4. At regular time intervals, the operating status of the hydrogen production process by electrolysis of water is evaluated, and the actual data is compared with the preset target value. If a deviation is found between the actual data and the target value, the deviation data is fed back to the control algorithm model, and the control algorithm model optimizes and adjusts the calculation rules of the pulse current parameters according to the feedback data.

2. The pulsed current regulation method for hydrogen production by electrolyzing water according to claim 1, wherein The basic parameters include at least the electrode material type, the initial concentration of the electrolyte, the specifications of the electrolytic cell and the ambient temperature, and the parameter information includes at least the electrolyte temperature, the change in electrolyte concentration, the electrode potential and the hydrogen generation rate.

3. A pulse current regulation method for hydrogen production by electrolyzing water according to claim 1, characterized in that Step S1 specifically The process includes: Initialize temperature sensors, spectrum analysis sensors, potentiometer sensors, gas flow meters and other equipment; The acquisition period is set according to the parameter change characteristics, wherein the parameter change characteristics include hydrogen generation rate and electrode potential as fast-changing parameters, and electrolyte temperature and electrolyte concentration as slow-changing parameters; According to the set collection cycle and sequence, the sensor is triggered to collect data, and the sensor converts the analog signal into a digital signal; Preprocess the collected data, remove abnormal data, and use adjacent data interpolation method to supplement the missing data; The preprocessed parameter information is integrated with the basic parameters obtained by initialization to form complete input data, and the input data is input into the preset control algorithm model.

4. A pulse current regulation method for hydrogen production by electrolyzing water according to claim 1, characterized in that, The hydrogen generation rate acquisition period is set to 1-10 seconds, the electrode potential acquisition period is 1-5 seconds, the electrolyte temperature acquisition period is 30-120 seconds, and the electrolyte concentration change acquisition period is 300-1800 seconds.

5. A pulse current regulation method for hydrogen production by electrolyzing water according to claim 1, characterized in that, The control algorithm model is constructed by adopting a fuzzy neural network control algorithm, and the control algorithm model comprises an input layer, a fuzzification layer, a neural network layer and an output layer.

6. A pulse current regulation method for electrolytic water hydrogen production according to claim 1, characterized in that, Step S2 specifically The process includes: Receive input data, normalize the input data, map parameters of different dimensions to the interval [0, 1], and apply the sliding average filter algorithm to eliminate data noise; A Gaussian membership function is defined for each input data, and the membership value of each input data to each fuzzy level is calculated; The hidden layer neurons receive the membership values output by the fuzzification layer, each hidden layer neuron calculates the weighted sum, and the output layer neurons receive the hidden layer output and calculate the preliminary values of the pulse current parameters; Reasoning is performed based on the fuzzy rule base, the activation strength of each rule is calculated, and weighted adjustment is performed on the pulse current parameters calculated initially based on the activation strength; The centroid method is used to calculate the final parameter values. The defuzzified parameter values are denormalized and converted into physical quantities. The frequency, duty cycle, and peak current of the final pulse current are output by applying parameter limit constraints.

7. A pulse current regulation method for hydrogen production by electrolyzing water according to claim 1, characterized in that, Step S3 specifically includes the following processes: Transmit the parameters of the pulse current frequency, duty cycle, and peak current calculated by the control algorithm model to the pulse power controller; After receiving the parameters, the pulse power controller sets the period of the pulse signal according to the frequency, determines the proportion of the high-level duration in one period according to the duty cycle, and sets the maximum output current according to the peak current to generate a pulse current signal; Output the generated pulse current signal to the electrodes of the electrolytic cell, so that the current acts on the electrolysis process according to the set parameters, realizing the pulse current regulation of the electrolytic water hydrogen production process.

8. A pulse current regulation method for hydrogen production by electrolyzing water according to claim 1, characterized in that, Step S4 specifically includes the following processes: Set the target threshold range corresponding to each index. At the end of each evaluation cycle, collect the average values of relevant parameters and calculate the evaluation index based on the collected average parameter values; Calculate the deviation between each evaluation index and the target threshold range, divide the deviation level, analyze the cause of the deviation, and output the abnormal links in the electrolytic water hydrogen production process; Based on the abnormal links in the electrolytic water hydrogen production process, determine the priority of pulse current parameter adjustment, and use the fuzzy logic controller to generate parameter adjustment suggestions; Adjust the weights in the control algorithm model by the gradient descent method, optimize the membership function parameters, send the optimized parameter adjustment scheme to the pulse power controller, and set the smooth transition time to detect the adjustment effect in the next evaluation cycle.

9. A pulse current regulation method for hydrogen production by electrolyzing water according to claim 8, characterized in that, The evaluation metrics at least include hydrogen production efficiency and energy consumption ratio. The target threshold range of hydrogen production efficiency is set at 80% - 95%, and the target threshold range of energy consumption ratio is set at 3 - 5 kWh / NmA ^3 .

10. A pulse current regulation method for hydrogen production by electrolyzing water according to claim 8, characterized in that, The smooth transition time is 5 - 30 seconds.

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