Adjusting method for improving hydrogen humidity of hydrogen production equipment product

Abstract

The invention relates to the technical field of hydrogen production, and discloses an adjusting method for improving hydrogen humidity of a hydrogen production equipment product, which comprises the following steps: initially setting hydrogen production equipment to obtain basic hydrogen humidity data; on the basis of the basic hydrogen humidity data, optimizing the hydrogen humidity by adjusting parameters of hydrogen production equipment so as to obtain the optimized hydrogen humidity; the hydrogen production equipment is readjusted through the optimized hydrogen humidity, so that the hydrogen humidity is further improved; and recording and analyzing the adjusted hydrogen humidity data to determine whether the required humidity standard is reached or not. The hydrogen humidity of the hydrogen production equipment can be accurately controlled, so that the hydrogen production efficiency and the product quality are improved, and meanwhile, the energy consumption and the cost are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrogen production, and in particular to a method for adjusting the humidity of hydrogen produced by hydrogen production equipment. Background Art

[0002] In existing hydrogen production technologies, the control of hydrogen humidity is one of the key factors in improving hydrogen production efficiency and product quality. Traditional hydrogen production equipment often relies on fixed operating parameters such as temperature, pressure and flow, and the setting of these parameters is usually based on experience rather than precise scientific calculations. Although this method can control the humidity of hydrogen to a certain extent, it is often difficult to achieve the optimal state, resulting in low hydrogen production efficiency and hydrogen purity, as well as high energy consumption. In addition, existing hydrogen production equipment lacks flexibility in parameter adjustment and is difficult to adapt to changing hydrogen production conditions, such as different raw materials, ambient temperature and pressure changes. These factors limit the performance improvement and expansion of the application range of hydrogen production equipment.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: the existing hydrogen production equipment often requires multiple manual trials and errors when adjusting the humidity of hydrogen, which is not only time-consuming and labor-intensive, but also difficult to ensure the accuracy and repeatability of the adjustment results. In addition, the existing technology lacks systematicity and continuity in parameter adjustment, and it is difficult to achieve coordinated optimization between parameters, resulting in insufficient precision in the control of hydrogen humidity in the hydrogen production process, which cannot meet the needs of high-precision hydrogen production. Summary of the invention

[0004] The embodiment of the present invention aims to provide a method for adjusting the humidity of hydrogen produced by a hydrogen production device to solve the technical problems raised in the prior art.

[0005] The embodiment of the present invention solves the technical problem by adopting the following technical solution:

[0006] In a first aspect, a method for adjusting the humidity of hydrogen produced by a hydrogen production device is provided, comprising:

[0007] Initially set up the hydrogen production equipment to obtain basic hydrogen humidity data;

[0008] Based on the basic hydrogen humidity data, the hydrogen humidity is optimized by adjusting the parameters of the hydrogen production equipment, thereby obtaining optimized hydrogen humidity;

[0009] Using the optimized hydrogen humidity to readjust the hydrogen production equipment to further increase the hydrogen humidity;

[0010] Record and analyze the adjusted hydrogen humidity data to determine whether the required humidity standard is achieved.

[0011] Furthermore, the humidity of hydrogen is adjusted by the following method:

[0012] The parameter adjustment range of the hydrogen production equipment is defined as an adjustment unit; the adjustment unit is a system composed of multiple parameters, and an associated operation is set between each parameter for coordinated adjustment between the parameters;

[0013] Converting the discrete parameter adjustment range of the hydrogen production equipment into a continuous adjustment space;

[0014] In the continuous adjustment space, the parameters of the adjustment unit are optimized, the hydrogen humidity is used as the objective function of the optimization process, and the parameters are adjusted multiple times through an iterative adjustment method to optimize the parameters.

[0015] Furthermore, in the process of defining the adjustment unit, the associated operations between the parameters include temperature adjustment, pressure adjustment, flow control and catalyst use;

[0016] The temperature adjustment operation is used to control the reaction temperature during the hydrogen production process to optimize the humidity of the hydrogen.

[0017] Furthermore, a feedback control algorithm is used to continuously relax the association operation between each parameter into a comprehensive adjustment operation, thereby obtaining multiple comprehensive adjustment operations, and the multiple comprehensive adjustment operations are converted into parameter vector representations, each parameter in the parameter vector is the weight in the corresponding comprehensive adjustment operation.

[0018] Furthermore, the optimization function of the parameters is:

[0019]

[0020] The optimization formula of structural parameters is:

[0021]

[0022] Among them, p is the parameter obtained by the previous iterative optimization, β is the parameter vector obtained by the previous iterative optimization, and p ′ represents the parameters obtained by the current iterative optimization, β ′ represents the parameter vector obtained by the current iterative optimization, H target is the target hydrogen humidity function, H actual is the actual hydrogen humidity function.

[0023] Furthermore, the parameters and parameter vectors are optimized in the following manner:

[0024] Based on minimizing the target hydrogen humidity and minimizing the actual hydrogen humidity, the parameters and parameter vectors are optimized multiple times through iterative adjustment methods, and the p that minimizes the target hydrogen humidity in the Mth iterative optimization process is obtained. ′ The value is the optimal parameter;

[0025] Furthermore, the gradient of the parameter vector is processed using a finite difference approximation method to reduce computational complexity.

[0026] Furthermore, the argmax function is used to replace the comprehensive adjustment operation between each parameter with the adjustment operation corresponding to the maximum weight in the corresponding comprehensive adjustment operation in the parameter vector;

[0027] The adjustment operation corresponding to the maximum weight is determined by the following formula:

[0028]

[0029] Among them, ′ represents the adjustment operation between point x(i) and node x(j), β o ′ (i,j) indicates the relationship between node x(i) and node x(j) and o ′ The parameters corresponding to the adjustment operation, O represents the set of multiple adjustment operations between node x(i) and node x(j), β o ′ (i, j) represents the parameter with the largest weight between node x(i) and node x(j), and o represents the adjustment operation corresponding to the parameter with the largest weight between point x(i) and node x(j).

[0030] Furthermore, the reaction temperature is controlled by temperature regulation operation to optimize the hydrogen humidity, obtain the humidity characteristics of the reaction under different conditions, and couple the humidity characteristics under different conditions into a humidity characteristic set to the adjusted hydrogen production equipment to obtain the optimized hydrogen production equipment.

[0031] Furthermore, the humidity features are stacked in sequence according to the reaction conditions to obtain a humidity feature set.

[0032] The above-mentioned embodiments of the present invention have at least the following beneficial effects: by implementing the hydrogen humidity adjustment method of the hydrogen production equipment of the present invention, the control accuracy and stability of the hydrogen humidity can be significantly improved. The method defines the parameter adjustment range of the hydrogen production equipment and converts it into a continuous adjustment space, so that the coordinated adjustment between parameters is more flexible and efficient. By using iterative optimization algorithms and parameter vector representations, the operating parameters of the hydrogen production equipment can be accurately adjusted to achieve the optimization of hydrogen humidity. In addition, through feedback control algorithms and finite difference approximation methods, the computational complexity can be reduced, and the real-time and response speed of the adjustment process can be improved. The application of these technologies can not only improve the efficiency of hydrogen production, but also reduce energy consumption and costs, thereby improving the overall economic benefits.

[0033] In addition, the embodiments of the present invention can also achieve refined management of the hydrogen production process by integrating humidity characteristics under different conditions into the hydrogen production equipment. By recording and analyzing the adjusted hydrogen humidity data, the operating characteristics of the hydrogen production equipment under different working conditions can be more accurately grasped, thereby providing a scientific basis for the optimization and improvement of the hydrogen production equipment. The application of this method can not only improve the performance and reliability of the hydrogen production equipment, but also enhance its ability to adapt to different hydrogen production conditions and broaden its scope of application. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] One or more embodiments are exemplarily described by the figures in the corresponding drawings, and these exemplifications do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0035] Figure 1 It is a flow chart of a method for adjusting the humidity of hydrogen produced by hydrogen production equipment provided by the present invention. DETAILED DESCRIPTION

[0036] In order to facilitate the understanding of the present invention, the present invention is described in more detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that when an element is described as "connecting" another element, it can be directly on another element, or there can be one or more centered elements therebetween. The orientation or positional relationship indicated by the terms "upper", "lower", "left", "right", "upper end", "lower end", "top" and "bottom" used in this specification is based on the orientation or positional relationship shown in the accompanying drawings, only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. In addition, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0037] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0038] Combine the following Figure 1 , the adjustment method 100 for increasing the humidity of hydrogen produced by a hydrogen production equipment product provided in an embodiment of the present application is described in detail through a specific example.

[0039] Figure 1 The figure is a flow chart of a method for adjusting the humidity of hydrogen produced by a hydrogen production equipment provided by the present invention. The method for adjusting the humidity of hydrogen produced by a hydrogen production equipment provided by one embodiment of the present invention comprises:

[0040] Step 101, performing initial settings on the hydrogen production equipment to obtain basic hydrogen humidity data;

[0041] Step 102, based on the basic hydrogen humidity data, optimizing the hydrogen humidity by adjusting the parameters of the hydrogen production equipment, so as to obtain optimized hydrogen humidity;

[0042] Step 103, readjusting the hydrogen production equipment using the optimized hydrogen humidity to further increase the hydrogen humidity;

[0043] Step 104 , recording and analyzing the adjusted hydrogen humidity data to determine whether the required humidity standard is reached.

[0044] It should be noted that the method for adjusting the humidity of hydrogen produced by the hydrogen production equipment optimizes the humidity of hydrogen in the hydrogen production process through a series of steps to improve the efficiency of hydrogen production and product quality. This process not only includes the initial setting of the hydrogen production equipment, but also involves parameter adjustment based on the initial data, as well as recording and analysis of the adjustment results.

[0045] Specifically, the initial setting may include setting the operating parameters of the hydrogen production equipment, such as temperature, pressure, flow rate, etc., as well as the type and amount of catalyst. The setting of these parameters will be based on the recommended values ​​of the equipment manufacturer or previous empirical data. After the initial setting, basic hydrogen humidity data will be collected, which may be obtained through real-time monitoring by humidity sensors, and the data will be used for subsequent parameter optimization.

[0046] Preferably, the initial setting may include pre-diagnosis of the hydrogen production equipment to determine whether its current state is suitable for humidity adjustment. In addition, the collection of basic hydrogen humidity data may be performed by an automated data acquisition system to ensure the accuracy and real-time nature of the data. During the parameter adjustment process, advanced algorithms, such as genetic algorithms or machine learning techniques, may be used to optimize parameter settings to achieve more precise humidity control.

[0047] In some embodiments, the hydrogen humidity is adjusted by:

[0048] The parameter adjustment range of the hydrogen production equipment is defined as an adjustment unit; the adjustment unit is a system composed of multiple parameters, and an associated operation is set between each parameter for coordinated adjustment between the parameters;

[0049] Converting the discrete parameter adjustment range of the hydrogen production equipment into a continuous adjustment space;

[0050] In the continuous adjustment space, the parameters of the adjustment unit are optimized, the hydrogen humidity is used as the objective function of the optimization process, and the parameters are adjusted multiple times through an iterative adjustment method to optimize the parameters.

[0051] It should be noted that the adjustment method achieves optimization of hydrogen humidity by defining the parameter adjustment range of the hydrogen production equipment and converting it into a continuous adjustment space. This method allows coordinated adjustment of multiple parameters in the hydrogen production equipment to achieve more precise humidity control.

[0052] Specifically, defining the parameter adjustment range may involve determining the minimum and maximum values ​​of each parameter (such as temperature, pressure, flow rate, and catalyst usage), as well as their interdependencies. The adjustment range of these parameters will constitute a multi-dimensional adjustment space, in which each dimension represents an adjustable parameter. The conversion of the discretized parameter adjustment range into a continuous adjustment space means that more detailed adjustments can be made within this space to find the optimal humidity control point.

[0053] Preferably, the correlation operation between parameters can be achieved by establishing a mathematical model that describes how different parameters affect the humidity of hydrogen. For example, temperature regulation may be associated with pressure regulation because changes in temperature may affect the pressure of the gas. In addition, computer simulation can be used to predict the humidity of hydrogen under different parameter combinations, so as to perform virtual tests before actual adjustments. This method can reduce the number of experiments and speed up the optimization process. In actual operation, PID controllers or other advanced control algorithms can be used to achieve precise adjustment of parameters.

[0054] In some embodiments, in the process of defining the adjustment unit, the associated operations between the parameters include temperature adjustment, pressure adjustment, flow control and catalyst use;

[0055] The temperature adjustment operation is used to control the reaction temperature during the hydrogen production process to optimize the humidity of the hydrogen.

[0056] It should be noted that in the process of defining the adjustment unit, the associated operations between the parameters include temperature adjustment, pressure adjustment, flow control and catalyst use. These operations are to control the reaction conditions in the hydrogen production process to optimize the humidity of the hydrogen.

[0057] Specifically, temperature regulation may involve the use of temperature sensors and controllers to monitor and adjust the temperature in the reactor to ensure that the reaction is carried out at the optimal temperature. Pressure regulation may require the use of pressure sensors and regulating valves to control the pressure in the reactor. Flow control may involve the use of flow meters and regulating valves to control the flow of raw gas entering the reactor. Catalyst use may include selecting the appropriate type and amount of catalyst, as well as adding or replacing the catalyst in a timely manner during the reaction.

[0058] Preferably, temperature regulation can be achieved by an accurate temperature control system that can automatically adjust the intensity of heating or cooling based on real-time data. Pressure regulation can be achieved by using a pressure transmitter and an automatic regulating valve to ensure that the pressure remains within a set range. Flow control can be achieved by a mass flow controller that can accurately control the flow of gas. The use of catalysts can be achieved by an automated catalyst addition system that can automatically adjust the amount of catalyst added according to the needs of the reaction. In addition, advanced process analysis technology (PAT) can be used to monitor the reaction conditions in real time, and dynamically adjust the above parameters based on the analysis results to achieve more accurate hydrogen humidity control.

[0059] In some embodiments, a feedback control algorithm is used to continuously relax the association operations between each parameter into a comprehensive adjustment operation, thereby obtaining multiple comprehensive adjustment operations, and the multiple comprehensive adjustment operations are converted into parameter vector representations, and each parameter in the parameter vector is the weight in the corresponding comprehensive adjustment operation.

[0060] It should be noted that the feedback control algorithm is used to continuously relax the association operation between each parameter into a comprehensive adjustment operation, thereby obtaining multiple comprehensive adjustment operations, and these operations are converted into parameter vector representation. This method allows the complex multi-parameter adjustment process to be simplified to the optimization of the parameter vector, where each parameter represents the weight of a comprehensive adjustment operation.

[0061] Specifically, the feedback control algorithm may involve using the data collected by the sensors to adjust the operating parameters of the hydrogen production equipment in real time. For example, the data from the humidity sensor can be used to adjust the temperature, pressure, and flow rate to maintain the required hydrogen humidity. The comprehensive adjustment operation may include a series of predefined adjustment strategies, such as "increase temperature and increase flow rate" or "reduce pressure and reduce catalyst dosage". These strategies are converted into parameter vectors, where each element represents the weight of a specific adjustment strategy.

[0062] Preferably, the optimization of the parameter vector can be achieved by a mathematical optimization algorithm, such as a gradient descent method or a genetic algorithm. These algorithms can adjust the weights in the parameter vector according to an objective function (such as minimizing the difference between the hydrogen humidity and the target humidity). In addition, machine learning techniques can be used to predict the effects of different parameter vectors on the hydrogen humidity, thereby guiding the optimization process of the parameter vector. In actual operation, an optimization loop can be set up in which the parameter vector is updated in each iteration according to the results of the feedback control algorithm until the desired hydrogen humidity is reached. This method can improve the automation level of the hydrogen production process, reduce manual intervention, and improve efficiency and accuracy.

[0063] In some embodiments, the optimization function of the parameters is:

[0064]

[0065] The optimization formula of structural parameters is:

[0066]

[0067] Among them, p is the parameter obtained by the previous iterative optimization, β is the parameter vector obtained by the previous iterative optimization, and p ′ represents the parameters obtained by the current iterative optimization, β ′ represents the parameter vector obtained by the current iterative optimization, H target is the target hydrogen humidity function, H actual is the actual hydrogen humidity function.

[0068] It should be noted that the parameter optimization function and the structural parameter optimization formula are used to calculate and update the parameters during the adjustment process to achieve the optimization of hydrogen humidity. Through these mathematical expressions, the direction and amplitude of parameter adjustment can be clearly guided.

[0069] Specifically, the parameter optimization function may involve complex mathematical models based on the operating data of the hydrogen production equipment and the target value of hydrogen humidity. For example, the target hydrogen humidity function may be defined as an objective function that describes the relationship between the hydrogen humidity under ideal conditions and the actual measured value. The actual hydrogen humidity function may predict the hydrogen humidity based on the current operating parameters of the hydrogen production equipment. The specific form of these functions may include linear or nonlinear equations, and may involve variables such as temperature, pressure, flow rate and catalyst dosage.

[0070] Preferably, the parameter optimization process can be implemented by numerical methods, such as Newton's method or quasi-Newton's method, which can effectively solve nonlinear optimization problems. In each iteration, the gradient of the current parameter can be calculated according to the optimization function, and then the parameter is updated to reduce the value of the objective function. In addition, constrained optimization techniques can be used to ensure that the operating constraints of the hydrogen production equipment are met during the parameter update process. For example, upper and lower limits of temperature, pressure, and flow rate can be set to prevent parameter updates from causing the equipment to operate under unsafe or inefficient conditions. It is also possible to consider using an adaptive algorithm to dynamically adjust the learning rate during the iteration process to speed up convergence and improve the stability of the optimization.

[0071] In some embodiments, the parameters and parameter vectors are optimized in the following manner:

[0072] Based on minimizing the target hydrogen humidity and minimizing the actual hydrogen humidity, the parameters and parameter vectors are optimized multiple times through iterative adjustment methods, and the p that minimizes the target hydrogen humidity in the Mth iterative optimization process is obtained. ′ The value is the optimal parameter;

[0073] It should be noted that the process of optimizing parameters and parameter vectors by iterative adjustment involves comparison of target hydrogen humidity and actual hydrogen humidity, and parameter adjustment based on the comparison.

[0074] Specifically, the iterative adjustment method may include setting an initial parameter vector, and then evaluating the performance of the current parameter vector by calculating the value of the target hydrogen humidity function and the actual hydrogen humidity function. In each iteration, the parameter vector is adjusted according to the difference between the two functions to reduce the gap between the target hydrogen humidity and the actual hydrogen humidity. Each parameter in the parameter vector may represent an operating parameter of the hydrogen production equipment, such as temperature, pressure or flow.

[0075] Preferably, the iterative adjustment process can use an automated optimization algorithm, such as a gradient descent method or an evolutionary algorithm, to automatically adjust the parameter vector. These algorithms can guide the update of parameters based on the gradient information of the objective function, thereby accelerating convergence to the optimal solution. In addition, a regularization term can be introduced to prevent overfitting, ensuring that the optimal parameters found not only perform well on the current data set, but also remain stable on new data. In actual operation, an upper limit on the number of iterations or a threshold of the objective function can be set to determine when to stop the iterative process. Multi-starting point optimization or global optimization techniques can also be used to avoid local optimal solutions and ensure that the globally optimal parameter configuration is found.

[0076] In some embodiments, the gradient of the parameter vector is processed using a finite difference approximation method to reduce computational complexity.

[0077] It should be noted that the method of using finite difference approximation to process the gradient of the parameter vector to reduce the complexity of cost calculation is to reduce the consumption of computing resources and improve the efficiency of the optimization process by approximate calculation rather than precise calculation of the gradient during the iterative optimization process.

[0078] Specifically, finite difference approximation is a numerical method that approximates the gradient by calculating the value of the function in a small range near the parameter vector. For example, for each parameter in the parameter vector, the gradient of the parameter can be approximated by increasing and decreasing a small amount, such as ε, and then observing the change in the value of the objective function. This method does not require complex derivative calculations and is therefore computationally simpler and more efficient.

[0079] Preferably, the implementation of the finite difference approximation can be done through automated numerical optimization libraries that provide ready-made functions to perform finite difference calculations. In practical applications, an appropriate value of ε can be selected, which needs to be small enough to ensure the accuracy of the approximation, but not too small to avoid the influence of numerical errors. In addition, the accuracy of the gradient approximation can be improved by using central differences instead of forward differences. Central differences approximate the gradient by calculating the difference in function values ​​when the parameter increases and decreases ε. It is also possible to consider using adaptive finite difference methods, in which the value of ε is dynamically adjusted according to the current value of the parameter to optimize computational efficiency and accuracy.

[0080] In some embodiments, an argmax function is used to replace the comprehensive adjustment operation between each parameter with the adjustment operation corresponding to the maximum weight in the corresponding comprehensive adjustment operation in the parameter vector;

[0081] The adjustment operation corresponding to the maximum weight is determined by the following formula:

[0082]

[0083] Among them, ′ represents the adjustment operation between point x(i) and node x(j), β o ′ (i,j) indicates the relationship between node x(i) and node x(j) and o ′ The parameters corresponding to the adjustment operation, O represents the set of multiple adjustment operations between node x(i) and node x(j), β o ′ (i, j) represents the parameter with the largest weight between node x(i) and node x(j), and o represents the adjustment operation corresponding to the parameter with the largest weight between point x(i) and node x(j).

[0084] It should be noted that the argmax function is used to select the adjustment operation corresponding to the maximum weight in the comprehensive adjustment operation in the parameter vector. This method allows the best one to be selected from multiple possible adjustment operations to achieve optimization of hydrogen humidity.

[0085] Specifically, the argmax function is a mathematical tool for selecting the element that maximizes a given function from a set of values. In the context of the present claim, the argmax function is used to select the parameter with the largest weight from a parameter vector, which represents different adjustment operations of the hydrogen production equipment. Each parameter in the parameter vector is associated with a specific adjustment operation, and the parameter with the largest weight indicates the operation that is most likely to improve the humidity of the hydrogen under the current conditions.

[0086] Preferably, the application of the argmax function can be implemented by programming, where the parameter vectors and weights can be stored in computer memory and processed by the algorithm. In actual operation, a threshold can be set to determine when the weight of a parameter is considered to be "maximum", and this threshold can be adjusted according to the specific requirements and operating conditions of the hydrogen production equipment. In addition, a multi-objective optimization method can be used to consider the weights of multiple parameters at the same time to achieve a more comprehensive optimization. In some cases, machine learning methods can also be used to predict the impact of different adjustment operations on the humidity of hydrogen, thereby assisting the selection process of the argmax function. This method can further improve the automation and optimization level of the hydrogen production process.

[0087] In some embodiments, the reaction temperature is controlled by temperature regulation operation to optimize the humidity of hydrogen, obtain the humidity characteristics of the reaction under different conditions, and combine the humidity characteristics under different conditions into a humidity characteristic set and couple it to the adjusted hydrogen production equipment to obtain an optimized hydrogen production equipment.

[0088] It should be noted that the temperature regulation operation is used to control the reaction temperature to optimize the hydrogen humidity and obtain the humidity characteristics under different conditions, and then these characteristics are integrated into the hydrogen production equipment to obtain optimized equipment performance. This method allows the equipment to automatically adjust according to different operating conditions to maintain the optimal hydrogen humidity.

[0089] Specifically, temperature regulation operations may involve the use of temperature sensors and controllers to accurately control the temperature within the reactor. These operations can be adjusted according to the actual needs of the hydrogen production process to ensure that the reaction is carried out under optimal temperature conditions. Humidity characteristics refer to the humidity characteristics of hydrogen produced by hydrogen production equipment under specific temperature, pressure and flow conditions. These characteristics can be recorded and analyzed for future parameter adjustments and equipment optimization.

[0090] Preferably, the temperature regulation operation can be achieved through advanced control systems, such as fuzzy logic controllers or adaptive control algorithms, which can automatically adjust the temperature set point based on real-time data. The acquisition of humidity characteristics can be carried out through high-precision humidity sensors that can provide continuous humidity readings. In addition, a data acquisition system can be used to record humidity data under different operating conditions and processed by data analysis software to identify the optimal operating parameters. When integrating these humidity characteristics into the hydrogen production equipment, model predictive control (MPC) technology can be used, which can dynamically adjust operating parameters based on the predicted humidity characteristics to achieve more accurate humidity control.

[0091] In some embodiments, the humidity features are stacked in sequence according to the reaction conditions to obtain a humidity feature set.

[0092] Specifically, the humidity feature set refers to a collection of hydrogen humidity data collected under different reaction conditions, such as different temperatures, pressures, flow rates, and catalyst dosages. These data can be used for analysis and comparison to determine which conditions are most conducive to achieving the desired hydrogen humidity. Stacking of reaction conditions refers to combining the data under these different conditions in a certain order or pattern to form a comprehensive data set to guide the adjustment and optimization of hydrogen production equipment.

[0093] Preferably, the creation of humidity feature sets can be achieved through automated data acquisition and analysis systems. These systems can monitor the operating conditions and hydrogen humidity of the hydrogen production equipment in real time and automatically record relevant data. The stacking of data can be completed through a data management system that can organize and store data under different conditions according to time series, condition changes or other relevant factors. In addition, statistical analysis or machine learning algorithms can be used to analyze these data to identify the optimal operating conditions and predict the changing trend of hydrogen humidity. In actual operation, the operating parameters of the hydrogen production equipment can be adjusted according to the analysis results, or new control strategies can be developed to achieve more accurate humidity control and higher hydrogen production efficiency.

[0094] The above-mentioned embodiments of the present invention have the following beneficial effects: The hydrogen humidity adjustment method of the hydrogen production equipment of the present invention can provide a systematic and automated hydrogen humidity control strategy, thereby optimizing the hydrogen production process. Through initial settings and subsequent iterative adjustments, the method can accurately control the hydrogen humidity to meet specific hydrogen production needs. This method can reduce reliance on experience, improve hydrogen production efficiency and product quality, while reducing energy consumption and costs. By recording and analyzing the adjusted hydrogen humidity data, the performance of the hydrogen production equipment can be further optimized to ensure that the hydrogen humidity is always in the best state.

[0095] In addition, the present invention can effectively handle the associated operations between parameters and realize the coordinated optimization of parameters by adopting parameter vectors and feedback control algorithms. This method can improve the flexibility and adaptability of hydrogen production equipment, enabling it to quickly respond to different hydrogen production conditions. By using finite difference approximation and argmax function, the calculation process can be simplified and the calculation complexity can be reduced, thereby improving the real-time and accuracy of the adjustment process. The combined use of these technologies can not only improve the efficiency and reliability of hydrogen production equipment, but also enhance its stability and durability in a changing environment, bringing significant technological progress and economic benefits to the hydrogen production industry.

[0096] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. Under the concept of the present invention, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other changes in different aspects of the present invention as above, which are not provided in detail for the sake of simplicity. Although the present invention is described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for increasing the humidity of hydrogen produced by a hydrogen production equipment, characterized in that: The following steps are involved: Initially set up the hydrogen production equipment to obtain basic hydrogen humidity data; Based on the basic hydrogen humidity data, the hydrogen humidity is optimized by adjusting the parameters of the hydrogen production equipment, thereby obtaining optimized hydrogen humidity; Using the optimized hydrogen humidity to readjust the hydrogen production equipment to further increase the hydrogen humidity; Record and analyze the adjusted hydrogen humidity data to determine whether the required humidity standard is achieved.

2. The method for adjusting the humidity of hydrogen produced by hydrogen production equipment according to claim 1, characterized in that: The hydrogen humidity is adjusted in the following way: The parameter adjustment range of the hydrogen production equipment is defined as an adjustment unit; the adjustment unit is a system composed of multiple parameters, and an associated operation is set between each parameter for coordinated adjustment between the parameters; Converting the discrete parameter adjustment range of the hydrogen production equipment into a continuous adjustment space; In the continuous adjustment space, the parameters of the adjustment unit are optimized, the hydrogen humidity is used as the objective function of the optimization process, and the parameters are adjusted multiple times through an iterative adjustment method to optimize the parameters.

3. The method for adjusting the humidity of hydrogen produced by hydrogen production equipment according to claim 2, characterized in that: In the process of defining the adjustment unit, the associated operations between parameters include temperature regulation, pressure regulation, flow control and catalyst use; The temperature adjustment operation is used to control the reaction temperature during the hydrogen production process to optimize the humidity of the hydrogen.

4. The method for adjusting the humidity of hydrogen produced by hydrogen production equipment according to claim 3, characterized in that: A feedback control algorithm is used to continuously relax the associated operation between each parameter into a comprehensive adjustment operation, thereby obtaining multiple comprehensive adjustment operations, and the multiple comprehensive adjustment operations are converted into parameter vector representations, each parameter in the parameter vector is the weight in the corresponding comprehensive adjustment operation.

5. The method for adjusting the humidity of hydrogen produced by hydrogen production equipment according to claim 4, characterized in that: The optimization function of the parameters is: The optimization formula of structural parameters is: Among them, p is the parameter obtained by the previous iterative optimization, β is the parameter vector obtained by the previous iterative optimization, and p ′ represents the parameters obtained by the current iterative optimization, β ′ represents the parameter vector obtained by the current iterative optimization, H target is the target hydrogen humidity function, H actual is the actual hydrogen humidity function.

6. The method for adjusting the humidity of hydrogen produced by hydrogen production equipment according to claim 5, characterized in that: The parameters and parameter vectors are optimized in the following manner: Based on minimizing the target hydrogen humidity and minimizing the actual hydrogen humidity, the parameters and parameter vectors are optimized multiple times through iterative adjustment methods, and the p that minimizes the target hydrogen humidity in the Mth iterative optimization process is obtained. ′ The value is the optimal parameter.

7. The method for increasing the humidity of hydrogen produced by hydrogen production equipment according to claim 6, characterized in that: The gradient of the parameter vector is processed using a finite difference approximation method to reduce computational complexity.

8. The method for adjusting the humidity of hydrogen produced by hydrogen production equipment according to claim 6, characterized in that: The argmax function is used to replace the comprehensive adjustment operation between each parameter with the adjustment operation corresponding to the maximum weight in the corresponding comprehensive adjustment operation in the parameter vector; The adjustment operation corresponding to the maximum weight is determined by the following formula: Among them, ′ represents the adjustment operation between point x(i) and node x(j), β o ′ (i,j) indicates the relationship between node x(i) and node x(j) and o ′ The parameters corresponding to the adjustment operation, O represents the set of multiple adjustment operations between node x(i) and node x(j), β o ′ (i, j) represents the parameter with the largest weight between node x(i) and node x(j), and o represents the adjustment operation corresponding to the parameter with the largest weight between point x(i) and node x(j).

9. The method for increasing the humidity of hydrogen produced by a hydrogen production equipment according to any one of claims 1 to 8, characterized in that: The reaction temperature is controlled by temperature regulation operation to optimize the hydrogen humidity, the humidity characteristics of the reaction under different conditions are obtained, and the humidity characteristics under different conditions are combined into a humidity characteristic set and coupled to the adjusted hydrogen production equipment to obtain an optimized hydrogen production equipment.

10. The method for adjusting the humidity of hydrogen produced by hydrogen production equipment according to claim 9, characterized in that: The humidity features are stacked in sequence according to the reaction conditions to obtain a humidity feature set.

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