Electrolytic hydrogen production system and dynamic operation method

Abstract

The application discloses an electrolytic hydrogen production system and a dynamic operation method, and relates to the technical field of electrolytic hydrogen production. The system comprises an electrolytic cell unit, a process pipeline unit, a gas-liquid separation unit and a detection control unit, and the structures of the units are adapted to the dynamic operation process of the system. The application predicts the system operation state by building a one-dimensional gas-liquid two-phase dynamic prediction model, takes the system energy consumption, safety risk and control variable change as the optimization target, combines the model prediction control algorithm to solve the control variable by rolling optimization and adjusts the system operation in real time. The application is adapted to the fluctuating power input scene, takes into account the system operation energy efficiency and safety, and solves the problems of low energy efficiency and difficult control of safety risks of the electrolytic hydrogen production system under fluctuating power.

Description

Technical Field

[0001] This invention relates to the field of hydrogen electrolysis technology, and in particular to an hydrogen electrolysis system and its dynamic operation method. Background Technology

[0002] With the large-scale development of the hydrogen energy industry and the increasing penetration rate of renewable energy power generation, the dynamic operation of electrolytic hydrogen production systems has become a core development trend for adapting to the volatility of new energy power and achieving cost reduction and efficiency improvement in the hydrogen production process. The dynamic operation of electrolytic hydrogen production systems involves complex heat and mass transfer and electrochemical coupling reactions within the electrolyzer. Its operating status is adjusted in real time according to changes in input power and environmental parameters, which places extremely high demands on the accuracy and adaptability of the system model.

[0003] Traditional electrolytic hydrogen production systems rely heavily on zero-dimensional models for design and operation optimization. These models treat the electrolyzer as a complete "black box," focusing only on the macroscopic parameter matching of system inputs and outputs, without quantitatively describing the microscopic dynamic characteristics within the electrolyzer, such as temperature distribution, fluid flow, and mass transfer. Limited by the simplification of these models, traditional zero-dimensional models cannot accurately capture the spatiotemporal evolution of heat and mass transfer during dynamic operation. This results in insufficient dynamic adaptability of process systems designed based on these models when faced with fluctuations in input conditions. Specifically, process parameter settings cannot match the dynamic changes in heat and mass transfer in real time, easily leading to problems such as excessively high or low local temperatures in the electrolyzer and uneven reactant distribution. This not only reduces the rationality of the hydrogen production process but also causes a series of problems such as high system energy consumption, fluctuations in hydrogen purity, and shortened equipment lifespan, severely restricting the economy and stability of electrolytic hydrogen production systems under dynamic scenarios. Summary of the Invention

[0004] The present invention aims to at least partially solve one of the technical problems in the related art.

[0005] This invention discloses an electrolytic hydrogen production system and its operation method, aiming to solve the technical problems of poor dynamic adaptability, unreasonable process, high energy consumption and insufficient stability caused by the reliance on zero-dimensional models in traditional electrolytic hydrogen production systems. By constructing a system temperature and energy consumption prediction model, the system can achieve precise optimization and selection of process pipelines and gas-liquid separators. Combined with dynamic operation parameter control, the system can improve hydrogen production efficiency and operational reliability under fluctuating new energy power conditions.

[0006] To achieve the above objectives, a first aspect of the present invention provides an electrolytic hydrogen production system, comprising: an electrolyzer unit, a process piping unit, a gas-liquid separation unit, and a detection and control unit; wherein:

[0007] The process piping unit is connected between the electrolytic cell unit and the gas-liquid separation unit, and is used to transport electrolyte and gas-liquid mixture flow; The gas-liquid separation unit is used to separate the gas-liquid mixture stream into gas and liquid components. The detection and control unit is used to collect system operating status data and output control commands; The process piping unit has a piping structure that matches the dynamic heat and mass transfer process of the electrolyzer, and the gas-liquid separation unit has a separation structure that matches the dynamic separation process of the gas-liquid mixture flow, so that the electrolytic hydrogen production system can meet the dynamic operation requirements of system energy efficiency and safety risks under fluctuating power input.

[0008] Optionally, the pipeline structure includes at least one of the following: pipeline inner diameter, pipeline length, pipeline slope, pipeline height difference, and resistance parameters.

[0009] Optionally, the separation structure includes at least one of separator volume, height-to-diameter ratio, surface area, and gas-liquid separation structure.

[0010] Optionally, the detection and control unit includes a sensing module, a control module, and an execution module; the sensing module is used to collect temperature, flow rate, and pressure parameters, the control module is used to generate control commands, and the execution module is used to adjust the system operating parameters according to the control commands.

[0011] To achieve the above objectives, a second aspect of the present invention provides a dynamic operation method for an electrolytic hydrogen production system, characterized in that it is applied to the electrolytic hydrogen production system described in any one of the first aspects, comprising: S1. Obtain real-time operating status data of the electrolysis hydrogen production system; S2. Based on the real-time operating status data and the one-dimensional gas-liquid two-phase dynamic prediction model, predict the system operating status in multiple future control cycles; S3. Based on the prediction results, with system energy consumption, safety risks and changes in control variables as optimization objectives, solve for the optimal control variables. S4. Adjust the operation of the electrolysis hydrogen production system according to the optimized control quantity, and repeat S1 to S3.

[0012] Optionally, the one-dimensional gas-liquid two-phase dynamic prediction model is used to characterize the coupled effects of the dynamic characteristics of the electrolyzer, the flow characteristics of the process pipeline, and the separation characteristics of the gas-liquid separation unit.

[0013] Optionally, the one-dimensional gas-liquid two-phase dynamic prediction model is used to predict at least one of the following: system temperature, system energy efficiency, reactant concentration, and product concentration.

[0014] Optionally, the control variables include at least one of the following: alkali circulation flow rate, electrolytic cell operating pressure, and electrolytic cell heat exchange rate.

[0015] Optionally, the safety risk is evaluated using the oxygen concentration in hydrogen as an indicator.

[0016] Optionally, step S3 includes: under the constraints of the range of control variables, the rate of change of control variables, and the system operating state, using a model predictive control algorithm to perform rolling optimization of the optimization objective, and executing the optimized control quantity corresponding to the current control cycle.

[0017] The embodiments of this invention have the following beneficial effects: By matching the unit structure design of the electrolytic pyrolysis mass transfer and gas-liquid dynamic separation process, and combining a one-dimensional gas-liquid two-phase dynamic prediction model and a model predictive control strategy, the changes in the system's operating state under fluctuating power conditions can be accurately predicted. Through multi-objective rolling optimization and real-time control of system operating parameters, the system effectively adapts to power fluctuation input scenarios, significantly improving the dynamic operating efficiency of the electrolytic hydrogen production system. Simultaneously, using the oxygen concentration in hydrogen as a safety evaluation index to constrain the operating state effectively suppresses system safety risks, solving the technical defects of traditional electrolytic hydrogen production systems such as poor energy efficiency, weak safety, and insufficient dynamic adaptability under fluctuating power conditions, thus improving the overall operational stability and economy of the system. Attached Figure Description

[0018] The above-described and additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which: Figure 1 This is a structural diagram of an electrolytic hydrogen production system provided in an embodiment of the present invention; Figure 2 A flowchart illustrating the operation method of an electrolytic hydrogen production system provided in an embodiment of the present invention. Detailed Implementation

[0019] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0021] An electrolytic hydrogen production system and its dynamic operation method according to an embodiment of the present invention are described below with reference to the accompanying drawings.

[0022] Example 1 This embodiment provides an electrolytic hydrogen production system. This system is used to achieve real-time monitoring and dynamic control of electrolyte flow, gas-liquid mixture transport, hydrogen-oxygen gas separation, and system operating status during the electrolysis reaction process, thereby improving system operational stability, energy utilization efficiency, and the synergistic optimization of gas-liquid mass transfer and separation effects.

[0023] like Figure 1 As shown, the electrolytic hydrogen production system 10 includes an electrolytic cell unit 100, a process piping unit 200, a gas-liquid separation unit 300, and a detection and control unit 400. The electrolytic cell unit performs water electrolysis under direct current to generate hydrogen and oxygen, forming an electrolyte output stream containing both gas and liquid phases. The process piping unit facilitates fluid communication and transport between the electrolytic cell and the gas-liquid separation unit, transporting the electrolyte and gas-liquid mixture, allowing the reaction products and circulating medium to flow continuously within the system. The gas-liquid separation unit separates the hydrogen-side gas-liquid mixture and the oxygen-side gas-liquid mixture from the electrolytic cell side, achieving effective separation and collection of hydrogen and oxygen. Simultaneously, it returns the separated electrolyte or incompletely reacted medium to the system circulation path to maintain stable operation of the electrolysis process. The detection and control unit monitors and provides feedback control of key parameters during system operation in real time, and adjusts the actuators according to the control strategy to achieve dynamic optimization of the system's operation.

[0024] The process piping unit serves as a transport channel connecting the electrolyzer and the gas-liquid separator, internally used for transporting electrolyte and gas-liquid mixtures. This unit includes not only electrolyte delivery pipelines but also flow channels for the hydrogen and oxygen-side gas-liquid mixtures. The overall piping system is zoned and optimized according to process requirements to accommodate the transport characteristics of different phase media. Specifically, the design parameters of the process piping include pipe routing, pipe diameter, inlet section length, outlet section length, pipe height difference, and friction resistance characteristics. The inlet section is located between the electrolyzer outlet and the main transport section of the system to stabilize the flow and reduce inlet disturbance; the outlet section is located near the front end of the gas-liquid separation unit to optimize flow distribution and improve the uniformity of gas-liquid entry into the separation unit. Through the comprehensive design of these structural parameters, the flow resistance, flow distribution, and phase distribution of the electrolyte and gas-liquid mixture within the system achieve the preset optimization targets, thereby improving overall mass transfer efficiency and separation efficiency.

[0025] The gas-liquid separation unit is used to separate the hydrogen and oxygen gas-liquid mixtures. Its specific implementation can be selected and configured according to system operating requirements. Preferably, the gas-liquid separation unit includes at least one gas-liquid separator for separating and collecting hydrogen and oxygen separately. The selection and configuration of the gas-liquid separator includes, but is not limited to, the number of separators, the type of separation method, the internal structure, geometric parameters, and heat dissipation structure design. The separation method can include different forms such as gravity settling, cyclone, or multi-stage separation structures to adapt to the gas-liquid separation efficiency requirements under different operating conditions. The internal structure can be equipped with flow guiding components, buffer structures, or secondary separation units to enhance separation efficiency, based on the gas-liquid flow characteristics, to improve the effect of bubble aggregation and droplet separation. Simultaneously, the gas-liquid separator can be equipped with corresponding heat dissipation structures according to the operating heat load to ensure that the system maintains a stable temperature distribution under high current density or high gas production rate conditions, thereby avoiding a decrease in separation efficiency or fluctuations in equipment performance.

[0026] The detection and control unit includes a sensing module, a control module, and an execution module, which together form a closed-loop control system to achieve real-time adjustment and optimization control of the operating status of the electrolytic hydrogen production system. The sensing module is used to collect key operating parameters of the system, specifically including various sensing elements such as temperature sensors, flow sensors, and pressure sensors. These sensors are deployed at key locations in the process piping unit and the gas-liquid separation unit to achieve comprehensive monitoring of the system's state parameters at different locations. Key nodes in the process piping include typical locations such as the pipe start point, pipe end point, and pipe bend points. The start point corresponds to the electrolyzer outlet or circulation inlet, and the end point corresponds to the gas-liquid separator inlet or outlet, thus comprehensively reflecting pressure loss, temperature changes, and flow distribution during the flow process. Simultaneously, sensors are installed at the inlet and outlet of the gas-liquid separator to monitor changes in the fluid state before and after separation, in order to evaluate separation efficiency and operational stability.

[0027] Temperature, flow rate, and pressure data collected by the sensing module are transmitted to the control module in real time. The control module calculates and analyzes the data based on a preset control algorithm and a system dynamic prediction model to obtain system operating status parameters and optimized control commands. The control module outputs control signals to the execution module based on the calculation results. The execution module then controls the regulating devices in the system according to the control commands, such as adjusting pump flow rate, valve opening, or circulation loop ratio, thereby achieving dynamic adjustment of the electrolyzer's operating conditions, pipeline flow status, and gas-liquid separation efficiency. Through this closed-loop control method, the system can maintain stable operation under different load conditions and achieve optimized operation based on real-time data feedback.

[0028] The overall structural design of the above-described electrolytic hydrogen production system is characterized by its optimized design of process piping distribution and gas-liquid separator selection based on a dynamic prediction model. This ensures that the system possesses excellent flow uniformity and gas-liquid separation adaptability at the structural level. By combining process parameter design with real-time control strategies, the system not only meets the requirements for efficient mass transfer and separation in its static structure but also adaptively adjusts according to changes in actual operating conditions during dynamic operation, thereby significantly improving the overall operating efficiency and stability of the system.

[0029] Example 2 This embodiment provides a dynamic operation method for an electrolytic hydrogen production system. This method is applied to the electrolytic hydrogen production system described in Embodiment 1 and is used to achieve predictive control and multi-objective optimization adjustment based on mechanism models during system operation, thereby improving the system's energy efficiency, safety, and operational stability under dynamic operating conditions.

[0030] Specifically, the dynamic operation method includes the following steps.

[0031] S1. Obtain real-time operating status data of the electrolysis hydrogen production system.

[0032] In this step, the operating status of the electrolytic hydrogen production system is collected in real time by the sensing module in the detection control unit. This real-time operating status data includes, but is not limited to, electrolyzer operating parameters, flow parameters within the process piping, and gas-liquid separation unit operating parameters. Electrolyzer operating parameters may include current, voltage, cell temperature, and reaction interface parameters; process piping operating parameters may include temperature, pressure, and flow rate data at key nodes in the piping; and gas-liquid separation unit operating parameters may include pressure difference between the separator inlet and outlet, gas-liquid ratio, and temperature variation data. Key nodes include the start, end, and turning points of the process piping, used to reflect the spatial distribution characteristics of the flow state within the system.

[0033] S2. Based on the real-time operating status data and the one-dimensional gas-liquid two-phase dynamic prediction model, predict the system operating status in multiple future control cycles.

[0034] In this step, a one-dimensional gas-liquid two-phase dynamic prediction model is used to predict and analyze the dynamic evolution of the system. The one-dimensional model is based on one-dimensional spatial coordinates along the flow direction and provides an equivalent description of the flow behavior of the gas and liquid phases in the process pipeline and gas-liquid separator, thereby ensuring both computational efficiency and the ability to express the system mechanism and prediction accuracy.

[0035] The predictive model outputs key operating parameters for multiple future control cycles. These parameters include temperature distribution, system energy efficiency indicators, and changes in reactant and product concentrations. Product concentrations primarily include the concentrations of hydrogen and oxygen produced and their spatial distribution characteristics. By incorporating system structural parameters, the model can characterize the influence of pipe diameter, length, and friction coefficient on flow conditions, as well as the influence of gas-liquid separator volume parameters, effective surface area, and separation structure on gas-liquid separation efficiency, thus achieving coupled prediction between structural parameters and operating conditions.

[0036] Furthermore, the model also satisfies several operational constraints during the prediction process, including the temperature difference constraint between the inlet and outlet of the electrolyzer, the flow distribution difference constraint of the hydrogen-oxygen gas-liquid mixture, and the flow behavior constraint of the gas-liquid mixture in the pipeline. The flow behavior includes changes in flow velocity, the degree of gas-liquid stratification, and changes in the purity of locally accumulated gas, to ensure that the prediction results conform to the actual physical operating laws.

[0037] S3. Based on the prediction results, with system energy consumption, safety risks and changes in control variables as optimization objectives, solve for the optimal control variables.

[0038] In this step, the predicted system states over multiple future control cycles are used as the basis for optimization decisions, constructing a multi-objective optimization problem. System energy consumption characterizes the overall energy consumption level of the electrolyzer and auxiliary systems; safety risk characterizes the potential risks of abnormal operating conditions during system operation, with HTO concentration specifically used as a safety risk indicator; and changes in control variables characterize the smoothness of the system's regulation process and the execution burden.

[0039] In the optimization process, multiple objectives are weighted or constrained to form a weighted comprehensive performance index, thereby achieving an overall optimization solution for the system's operational control variables. These control variables include electrolytic cell operating parameters, circulation flow rate, valve opening, and gas-liquid separation-related adjustment parameters. Through joint optimization of these variables, the system achieves a comprehensive goal of maximizing energy efficiency and minimizing risk while meeting safety constraints.

[0040] S4. Adjust the operation of the electrolysis hydrogen production system according to the optimized control quantity, and repeat S1 to S3.

[0041] In this step, the control variables obtained from the optimization solution are input to the execution module, which drives the corresponding actuators to adjust the system's operating state. This includes adjusting the flow distribution, pressure control, and operating parameters of the gas-liquid separation unit within the process pipeline, thereby achieving dynamic correction and optimized control of the system's operating state. Subsequently, the system enters the next control cycle, re-executing the real-time data acquisition, state prediction, and optimization calculation process, thus forming a continuously iterative closed-loop predictive control process.

[0042] Furthermore, the system dynamic prediction model in this embodiment adopts a one-dimensional gas-liquid two-phase dynamic modeling method, which can effectively characterize the key physical processes of the system while ensuring computational efficiency. By introducing the coupling relationship between structural parameters and operating parameters, it can accurately predict the dynamic behavior of the electrolysis hydrogen production system.

[0043] Furthermore, this model is not only used for operational status prediction but also for system structure optimization design. During the structural optimization process, based on a one-dimensional gas-liquid two-phase dynamic prediction model, the flow characteristics of the process pipeline and the separation characteristics of the gas-liquid separator are coupled and analyzed. The flow characteristics of the process pipeline include velocity distribution and gas-liquid stratification behavior, while the separation characteristics of the gas-liquid separator include separation efficiency and phase transformation capability. A unified evaluation system is established to optimize the system structural parameters.

[0044] In one embodiment of the present invention, the specific calculation of the objective function is based on the one-dimensional gas-liquid two-phase dynamic model. By uniformly describing the coupled dynamic processes of the electrolyzer, process pipelines, and gas-liquid separator, a quantitative assessment and optimization solution for system energy consumption, mass transfer processes, and safety risks can be achieved. The one-dimensional dynamic model includes a temperature and gas-liquid concentration distribution model for the electrolyzer, a two-phase mass balance model for the gas-liquid separator, a temperature dynamic model for the gas-liquid separator, and a gas-liquid flow behavior model within the process pipelines. The gas-liquid flow behavior includes velocity distribution, pressure drop changes, temperature distribution, and gas-liquid stratification characteristics. Through this multi-physics coupled modeling method, a unified expression of the conservation relationships of mass, momentum, and energy is achieved on a one-dimensional spatial scale.

[0045] The objective function is expressed as:

[0046] Where X is the vector of structural parameters to be optimized, including pipe inner diameter, pipe length, pipe slope i, separator volume, separator height-to-diameter ratio, separator surface area, etc. To maximize system energy efficiency, the objective is to take... ); The oxygen concentration in hydrogen (HTO) characterizes safety risks. This is a safety threshold; For the weighting coefficients, satisfying It can be adjusted according to the security level requirements.

[0047] For an electrolyzer, its energy balance is expressed as the difference between the input electrical power and the reaction exothermic energy and the chemical energy of the products. Specifically, the heat generated inside the electrolyzer equals the input electrical power minus the chemical energy of hydrogen (characterized by its higher calorific value). This heat is carried away by the circulating electrolyte or alkaline solution, causing dynamic changes in the electrolyzer temperature. It should be noted that while increasing the electrolyzer temperature within a certain range can improve the electrode reaction kinetics rate and thus reduce the equivalent electrolysis energy consumption, it also enhances the cross-permeability and dissolution-diffusion effects of gases in the electrolyte, potentially leading to a decrease in hydrogen and oxygen purity and an increase in safety risks. Therefore, the model considers both the energy efficiency and safety effects of temperature. The energy balance relationship of the electrolyzer is expressed as:

[0048] Regarding mass conservation, the electrolyzer, process piping, and gas-liquid separator as a whole satisfy the component mass conservation equation. In the one-dimensional model, the concentration changes of each component along the flow direction are controlled by the conservation equation, the basic form of which considers the combined effects of convection and source terms, where the source term characterizes the electrochemical reaction generation rate. As water is continuously consumed and hydrogen and oxygen are generated during the electrolysis reaction, the gas phase volume fraction gradually increases, i.e., the gas holdup or porosity gradually increases along the flow direction. During pipeline transport, pressure changes cause variations in gas density and volumetric flow rate, further affecting the local velocity and gas holdup distribution, resulting in a non-uniform concentration distribution along the flow path, which in turn dynamically affects the separator inlet conditions. The formula is expressed as:

[0049] Source Item This indicates the rate of electrochemical reaction formation.

[0050] For a gas-liquid separator, the internal properties satisfy the two-phase mass conservation and energy conservation relationships. The volume of the liquid phase inside the separator... The dissolved gas concentration is determined by both geometric parameters and the operating liquid level. This is used to characterize the dissolution state of residual hydrogen or oxygen in the liquid phase. During mass balance, the gas phase discharge, liquid phase reflux, and dissolution-precipitation equilibrium processes need to be considered simultaneously to achieve a dynamic description of the separation efficiency. Regarding energy balance, the dynamic temperature change of the gas-liquid separator is determined by the inlet sensible heat input, external heat dissipation, and potential latent heat of phase change. The effective heat exchange surface area of ​​the separator directly affects the heat exchange capacity; a larger surface area results in stronger heat dissipation per unit time, reduced system thermal inertia, and faster temperature response. This characteristic is significant for temperature control during cold starts and under low-load operating conditions, and can be used to improve system thermal management capabilities and operational stability.

[0051] The formula is expressed as:

[0052]

[0053]

[0054] In the process piping model, pressure loss is described using a two-phase flow frictional pressure drop model, the basic form of which includes the liquid phase friction coefficient. Single-phase assumed friction coefficient and the two-phase multiplication factor Among them, the coefficient of friction It is directly affected by the pipe diameter D, pipe length L, and inner wall roughness. The friction coefficient is derived based on the assumption of single-phase liquid flow and is related to the Reynolds number and relative roughness. Reducing the pipe diameter significantly increases the flow velocity and frictional pressure drop, and the pressure drop relationship exhibits a strongly nonlinear characteristic, which can be expressed as follows under engineering approximation conditions: At the same time, the two-phase multiplication factor Related to flow pattern, dryness fraction, and physical properties, it reflects the enhancing effect of gas-liquid two-phase flow on resistance loss. Regarding heat transfer, the temperature distribution within the pipe is determined by the heat exchange process between the pipe wall and the environment, where the overall heat transfer coefficient U characterizes the overall heat transfer capacity, and it is related to pipe diameter, insulation structure, and external environmental conditions. Latent heat of phase change is also considered. Impact on energy distribution. As the pipe diameter increases, the surface area per unit length decreases, thereby reducing the heat dissipation rate and slowing down temperature decay, which affects the system's thermal equilibrium response characteristics.

[0055] The formula is expressed as:

[0056]

[0057]

[0058] Furthermore, the pipe diameter D and pipe length L have a significant impact on the system's dynamic behavior. A smaller pipe diameter leads to increased flow velocity and a significant increase in pressure drop along the pipe, while also enhancing gas-liquid turbulence and mixing, potentially altering the uniformity of local concentration distribution. A larger pipe diameter helps buffer pressure fluctuations but may reduce flow shear. The pipe length L directly affects the total pressure drop and transport lag time, meaning the system exhibits a significant dynamic delay effect. This causes a time lag between the inlet state of the gas-liquid separator and the outlet state of the electrolyzer, thus affecting the prediction accuracy and regulation response speed of the control system.

[0059] In determining gas-liquid stratification, a dimensionless criterion F is used for characterization, and its expression is used to comprehensively reflect the apparent flow rate of the gas phase. The relationship between pipe diameter D and pipe inclination angle θ is discussed. When F < 0.5, the flow state tends towards stratified flow. Stratified flow causes the gas phase to accumulate in the upper part of the pipe, while the liquid phase remains in the lower part, thus forming a potentially hazardous area and increasing the risk of system operation. To suppress the formation of stratified flow, this invention preferably sets a slight inclination slope of 0.5% to 2% in the pipe structure design, with the slope direction facing the gas-liquid separator. This allows the liquid phase to be continuously transported forward under the action of gravity, thereby avoiding the accumulation of liquid at the bottom and the local stagnation of gas, reducing the risk of gas phase accumulation and improving the safety of system operation.

[0060] The formula for determining gas-liquid stratification in a pipeline is expressed as follows:

[0061] Based on the coupling relationships among the aforementioned sub-models, the optimization objective function is constructed by comprehensively considering maximizing system energy efficiency and minimizing safety risk. System energy efficiency is characterized by both electrolyzer efficiency and auxiliary system energy consumption, while safety risk is quantified using HTO concentration as a key evaluation indicator. During the optimization process, by jointly adjusting structural and operational parameters, the system achieves its optimal global performance configuration under constraints, thereby improving the overall operational performance and safety reliability of the electrolytic hydrogen production system under dynamic operating conditions. During the process of constructing the optimization objective function, the total energy consumption of the system is:

[0062] The energy consumption for electrolysis is:

[0063]

[0064] Flow loss energy consumption (pipeline + separator):

[0065] In the structural optimization process of this invention embodiment, the constraints include gas purity constraints, gas-liquid entrainment ratio constraints, flow velocity constraints, pressure drop constraints, temperature constraints, and geometric and physical boundary constraints, which can be expressed by the following constraints:

[0066]

[0067]

[0068]

[0069] In one embodiment of the present invention, the specific steps of the optimization process are as follows: First, step 1 is performed to initialize system parameters. In this step, based on the structural parameters and operating boundary conditions of the electrolytic hydrogen production system, the initial states required for the one-dimensional gas-liquid two-phase dynamic model are set, including the initial temperature of the electrolyzer, the initial current density, the initial pressure and flow distribution in the pipeline, the initial liquid level of the gas-liquid separator, and the initial gas phase content. At the same time, model parameters such as heat transfer coefficient, friction coefficient, and reaction rate constant are initialized to ensure that subsequent dynamic calculations have a consistent physical basis and numerical stability.

[0070] Then, proceed to step 2, inputting the fluctuating power curve P_renewable(t). In this step, the output power from the renewable energy side is used as the system input boundary condition. The power curve P_renewable(t) is used to characterize the intermittent and fluctuating power supply characteristics faced by the electrolysis hydrogen production system in actual operation. This power input directly affects the operating current of the electrolyzer, thereby further affecting the hydrogen production rate, system temperature changes, and the gas-liquid two-phase generation ratio. It is the core external excitation variable driving the dynamic response of the system.

[0071] Next, step 3 is performed to solve the one-dimensional dynamic model, obtaining the temperature response T(t), energy efficiency trajectory η(t), and HTO concentration trajectory C_HTO(t). In this step, based on the system state initialized in step 1 and the power curve input in step 2, the one-dimensional gas-liquid two-phase dynamic model is solved. The energy balance, mass conservation, and momentum conservation equations of the electrolyzer are solved simultaneously using numerical integration or discrete-time propagation methods to obtain the system's dynamic response process in the time dimension. Specifically, the temperature response T(t) characterizes the change in the system's thermal state over time, the energy efficiency trajectory η(t) characterizes the efficiency change in the conversion of electrical energy into hydrogen chemical energy, and the HTO concentration trajectory C_HTO(t) characterizes the dynamic evolution of the degree of hydrogen-oxygen cross-contamination and the system's safety risks.

[0072] Step 4 then proceeds to calculate the average energy efficiency and maximum HTO concentration. In this step, the time series results obtained in step 3 undergo post-processing analysis. The average energy efficiency is obtained by integrating η(t) over the entire scheduling cycle and then normalizing it, characterizing the overall energy utilization level of the system. The maximum HTO concentration is extracted from the peak value of the C_HTO(t) time series, characterizing the maximum potential safety risk level that the system may encounter during dynamic operation. These two indicators correspond to the economic indicators and safety constraint indicators in system performance evaluation, respectively.

[0073] Finally, step 5 is performed to return the objective function value F(X). In this step, the average energy efficiency index and the maximum HTO concentration index are weighted and combined according to preset weights to form a unified objective function F(X), where X represents the control variable or structural parameter vector to be optimized. Different control strategies or structural configuration schemes are evaluated through this objective function, thereby providing a target basis for subsequent optimization solutions.

[0074] Furthermore, under different load conditions, the energy efficiency weight and safety weight in the objective function can be dynamically adjusted according to the system operating status. Specifically, when the system is operating at high load, the hydrogen production rate of the electrolyzer is high, and the main challenge is energy efficiency optimization; therefore, the energy efficiency weight is relatively large. However, when the system is operating at low load, the HTO concentration rises more significantly due to decreased gas-liquid flow stability, increased risk of local stagnation and gas cross-diffusion, and increased system safety risks; therefore, the safety weight increases accordingly. Through this adaptive weight adjustment mechanism, the optimization objective can dynamically switch its focus according to changes in operating conditions, thereby achieving stable, safe, and efficient operation control of the electrolytic hydrogen production system across the entire operating range.

[0075] It is understood that the dynamic operation method of the electrolytic hydrogen production system proposed in this application actually adopts a multivariate model predictive control (MPC) strategy. With the alkaline solution circulation flow rate, electrolyzer working pressure, and electrolyzer heat exchange as the core control variables, and relying on the system's real-time monitoring data and the optimized structural parameters of the one-dimensional gas-liquid two-phase dynamic prediction model, the core operating parameters are dynamically adjusted through the rolling optimization logic of "initialization → state monitoring → multi-step prediction → rolling optimization → control execution → closed-loop feedback". This achieves the comprehensive effect of the electrolytic hydrogen production system with the lowest energy consumption, the lowest safety risk, and the best operational stability across the entire power fluctuation range.

[0076] Example 3 In one embodiment of the present invention, a dynamic control method for an electrolytic hydrogen production system is constructed with model predictive control (MPC) as its core. The control process is based on a one-dimensional gas-liquid two-phase dynamic prediction model, achieving rolling optimization and adjustment of the system's multi-variable coupled dynamic behavior. The specific implementation process is as follows: Step 1: Initialize MPC control parameters.

[0077] After system startup, load switching, or structural parameter adjustments, the MPC controller is first initialized. This step establishes a unified calculation benchmark for subsequent prediction and optimization, including the setting of the control period, prediction time domain, and control time domain, while clarifying the system operating constraints and initial control conditions.

[0078] Specifically, the initialization process includes setting initial values ​​for control variables, determining initial estimates for state variables, and configuring the weight matrix. The weight matrix is ​​used to balance the relative importance of energy consumption, safety risk, and control smoothness, thus providing a unified evaluation standard for multi-objective optimization. At this stage, the physical constraints on the control variables are also defined, such as the upper and lower limits of the electrolyzer current density, the range of circulating flow rate, and the opening range of the gas-liquid separation regulating valve, to ensure the feasibility and safety of subsequent optimization solutions.

[0079] Step 2: Real-time system status monitoring and status feedback (MPC input layer).

[0080] During system operation, with a set sampling period Ts=1 s as the time base, key operating parameters of the electrolytic hydrogen production system are collected in real time through online sensors, pressure transmitters, temperature sensors, and gas analyzers.

[0081] A system state vector x(k) is constructed based on the collected data. This state vector includes electrolyzer temperature, pressure, gas production rate, flow and pressure distribution at key pipeline nodes, and operating parameters of the gas-liquid separator. Simultaneously, a control variable measurement vector umeas(k) is constructed to characterize the currently executed control inputs, such as current input, circulating pump speed, and valve opening.

[0082] The aforementioned state vector and the measured values ​​of the control variables together serve as input information for the MPC controller, used to correct and estimate the current operating state of the system, thereby providing accurate initial conditions for subsequent predictions. In some implementations, a state observer or filtering algorithm can be used to correct x(k) to obtain the corrected state vector x. (k) is used to improve the accuracy of the initial conditions of the model.

[0083] Step 3: Multi-step prediction based on a one-dimensional model (MPC prediction layer).

[0084] In this step, the corrected state vector x (k) serves as the initial condition. The one-dimensional gas-liquid two-phase dynamic prediction model with optimized structural parameters is invoked to predict and calculate the dynamic behavior of the system over the next Np control cycles.

[0085] This one-dimensional model simultaneously considers the energy balance of the electrolyzer, the conservation of component mass, the two-phase flow characteristics of the process piping, and the dynamic response characteristics of the gas-liquid separator, thereby achieving a unified prediction of the multi-physics coupled behavior of the system. The prediction output includes a sequence of state variables in the future time domain. (k+i) and the sequence of system output variables (k+i), where i=1,2,…,Np.

[0086] This prediction process can provide a basis for predicting the future evolution of the system under different control strategies, including temperature change trends, pressure fluctuations, gas-liquid distribution changes, and HTO concentration evolution paths, thus providing a basis for optimization decisions.

[0087] Step 4: MPC rolling optimization (core decision-making layer).

[0088] At each current control time k, based on the predicted system output in the time domain Np, a rolling optimization problem is constructed, and the optimal control sequence u is obtained by solving it. .

[0089] Specifically, the weighted objective function J(k) for MPC control optimization is used to comprehensively describe system performance. Its optimization objectives include minimizing system energy consumption, minimizing safety risk, and minimizing the change in control variables within the prediction time domain. Among them, the energy consumption term reflects the overall power consumption level of the electrolyzer and auxiliary systems; the safety risk term characterizes the maximum or cumulative risk level of HTO concentration within the prediction time domain; and the control change term suppresses drastic fluctuations in control input to improve system operational stability and equipment lifespan.

[0090] Regarding constraints, the optimization problem considers multiple physical and engineering constraints, including upper and lower limits for control variables, rate of change constraints for control variables, and state variable and rate of change constraints. Among them, state variable constraints are used to ensure that key parameters such as temperature, pressure, and concentration of the electrolyzer are within safe operating ranges, avoiding abnormal operating conditions such as overheating, overpressure, or cross-contamination of gases.

[0091] After obtaining the optimal control sequence u, the MPC controller applies only the optimal control quantity u(k) of the first control cycle to the system execution layer, while the remaining control sequences are retained as prediction references. Subsequently, the system enters the next sampling cycle and repeats the state update, model prediction, and rolling optimization process, thus forming a closed-loop rolling optimization control mechanism.

[0092] By using the MPC control method described above, combined with the mechanism prediction capability of the one-dimensional gas-liquid two-phase dynamic model, the electrolytic hydrogen production system can achieve synergistic optimization control of energy efficiency and safety under the condition of renewable energy power fluctuations, thereby improving the overall robustness and dynamic adaptability of the system.

[0093] Example 4 As a possible embodiment, a method for structural optimization and dynamic operation of a 1300 Nm3 / h electrolytic hydrogen production system is provided: I. Dynamic structural optimization of the electrolysis hydrogen production system.

[0094] This embodiment provides a dynamic structural optimization method for an alkaline electrolysis hydrogen production system adapted to fluctuations in wind and solar loads. It can be directly applied to alkaline electrolysis hydrogen production equipment under rated hydrogen production conditions. Without changing the original pipeline layout and equipment installation foundation, it can accurately optimize the key structural parameters of the gas-liquid transport and separation units through one-dimensional gas-liquid two-phase dynamic modeling and multi-objective weighted optimization. This solves the technical problems of excessive pipeline flow velocity, insufficient gas-liquid separation, large fluctuations in hydrogen purity, high system energy consumption, and poor operational safety under variable load conditions, achieving high energy efficiency, high safety, and stable operation of the electrolysis hydrogen production system under all operating conditions.

[0095] 1. Implementation Example: Basic System and Initial Structural Parameters.

[0096] This embodiment employs a conventional fluctuating alkaline electrolysis hydrogen production system. The core components of the system include an electrolyzer unit, a process piping unit, a gas-liquid separation unit, and a detection and control unit. The system is capable of operating under rated hydrogen production conditions and with wide-range load fluctuations, and is configured with a 25wt% KOH alkaline electrolyte circulation system. The initial, unoptimized structural parameters of the system are as follows: the gas-liquid mixing and conveying pipeline is DN100 with a total length of 5m; the effective volume of the gas-liquid separator is 4.5m³. This initial structure exhibits significant performance defects under rated conditions and variable load operation, and cannot meet the stable operation requirements under wide-range load fluctuations.

[0097] 2. Determine system operating parameters.

[0098] First, the system's baseline operating conditions were determined, clarifying the system's rated operating state and load fluctuation characteristics. Basic fluid parameters were collected and calculated to provide precise boundary conditions for structural modeling and optimization. The system's rated operating conditions include rated hydrogen production, rated alkali circulation flow rate, and the normal load fluctuation range; the electrolyte operating temperature is constant at 80℃, and the system's baseline operating pressure is 1.1 bar. Through physical property calculations, the alkali density at 80℃ and 25wt% KOH is found to be 1270 kg / m³, and combined with the rated circulation flow rate, the alkali volumetric flow rate is calculated to be 78.74 m³ / h. Through temperature and pressure conversion between standard and operating conditions, the hydrogen volumetric flow rate under rated conditions is obtained as 1528 m³ / h.

[0099] 3. Construction of a one-dimensional dynamic prediction model for gas-liquid two-phase flow.

[0100] Based on the actual fluid flow characteristics of the system, a one-dimensional gas-liquid two-phase dynamic prediction model adapted to this electrolytic hydrogen production system was built. The model couples multi-dimensional dynamic characteristics such as two-phase flow in the pipeline, gas-liquid stratification determination, top gas accumulation, hydrogen purity degradation, gas-liquid entrainment, and system pressure drop response. It can accurately characterize the flow state, energy efficiency level, and safety performance of the system under different structural parameters and different load conditions. The model uses pipeline geometric parameters and separator geometric parameters as input variables, and pipeline mixing velocity, gas-liquid content, hydrogen purity, gas-liquid entrainment rate, total system pressure drop, and electrolysis energy efficiency as output state variables. It fully covers the influence of structural parameters on system operating performance and provides simulation support for subsequent multi-objective optimization.

[0101] 4. Construction of objective function and constraints for multi-objective optimization.

[0102] To balance system energy efficiency and safety stability, a multi-objective weighted optimization objective function that coordinates energy efficiency and safety is constructed. The comprehensive performance index formula is as follows: .in, To normalize the electrolysis energy efficiency index of the system, The system's normalized safety performance index is obtained by weighted fitting of hydrogen purity, gas-liquid entrainment rate, and system pressure drop. The weighting coefficients are matched according to the actual operation requirements of the project, prioritizing the safe operation of the system while maximizing electrolysis efficiency.

[0103] At the same time, hard constraints are set for the system to limit the boundary of the optimization solution and ensure that the optimization results are feasible in engineering: the mixing velocity in the pipeline does not exceed the safety limit, the purity of the outlet hydrogen is not less than 99.0 vol%, the gas-liquid entrainment rate is not higher than 1.5%, and the total pressure drop of the system does not exceed 50 kPa, so as to avoid safety hazards such as excessive flow velocity, separation failure, substandard purity, and excessive back pressure.

[0104] 5. Optimize variable selection and find the best solution.

[0105] Combining the system failure mechanism and structural influence weights, the structural parameters with the greatest impact on system performance and the lowest modification difficulty were selected as the core optimization variables, including the inner diameter of the process piping unit and the effective volume of the separator in the gas-liquid separation unit. To control modification costs and retain the original equipment layout, the pipe length remained unchanged at 5m. The Sequential Quadratic Programming (SQP) algorithm was adopted, using the constructed one-dimensional gas-liquid two-phase dynamic model as the simulation kernel. Under preset constraints, the comprehensive performance index J was iteratively optimized to obtain the optimal combination of structural parameters suitable for full load conditions.

[0106] 6. The optimal structural parameters are determined in this embodiment.

[0107] Through multi-objective iterative optimization, the optimal structural parameters for this embodiment were finally determined: the inner diameter of the gas-liquid mixing and conveying pipeline was optimized from the original DN100 to DN250, while the pipeline length remained unchanged at 5m; the effective volume of the gas-liquid separator was optimized from the original 4.5m³ to 6.0m³. This set of structural parameters does not require changes to the original pipeline layout or equipment installation foundation of the system; it can be achieved simply by replacing the pipeline with a larger diameter one and expanding the capacity of the separator, resulting in low modification costs and strong engineering feasibility.

[0108] 7. Verification of the optimized system's performance.

[0109] Substituting the optimal structural parameters into a one-dimensional gas-liquid two-phase dynamic model, simulation verification was performed on the system under full-load fluctuation conditions. After optimization, all performance indicators of the system met the design requirements and were significantly improved compared to the original structure: the mixing velocity in the pipeline under rated conditions was reduced to 9.1 m / s, which is within the safe flow range, completely solving the problems of excessive flow velocity, two-phase pulsation, and excessive flow resistance in the original structure; the medium residence time in the gas-liquid separator was increased to 4.6 s, resulting in more complete gas-liquid two-phase separation; the hydrogen purity at the system outlet was stable at ≥99.0 vol%, the gas-liquid entrainment rate was ≤1.5%, and the total system pressure drop was ≤30 kPa. At the same time, the average electrolysis efficiency of the system was improved by 8-10 percentage points, and the hydrogen purity fluctuation coefficient was reduced from more than 15% to less than 5%, resulting in a significant improvement in overall operating performance. It can stably adapt to wide-range load fluctuation conditions for a long period of time, balancing high energy efficiency and high safety.

[0110] II. Specific process of MPC optimization operation method.

[0111] Step 1: MPC control parameter initialization (control baseline setting).

[0112] After system startup, load switching, or structural parameter adjustment, the core parameters of MPC control are initialized, and the control boundaries, initial values, and weight matrices are defined to provide a benchmark for multi-step prediction and rolling optimization.

[0113] 1.1 Initialization of time domain and control parameters.

[0114]

[0115] 1.2 Control Variable Constraints and Initial Values.

[0116] Based on engineering safety and equipment protection requirements, hard constraints and initial values ​​are set for three major control variables (adapted to a rated operating condition of 1300 Nm³ / h):

[0117] 1.3 Initialization of weights and state parameters.

[0118] Optimization target weights: Energy efficiency weights Safety risk weights (Multivariate collaborative optimal weights); Smoothing weights for control variables: (Suppress drastic fluctuations in control parameters to protect equipment); Initial state vector values: Initialize the state vector based on real-time monitoring data. (Including core parameters such as flow rate, gas content, purity, energy consumption, and risk).

[0119] Step 2: Real-time system status monitoring and status feedback (MPC input layer).

[0120] Through online sensors, instruments, analyzers, and other equipment, To acquire the core operating parameters of the periodic acquisition system, a state vector is constructed. With control variable measurement vector This information is fed back to the MPC controller as the initial conditions for multi-step prediction.

[0121] 2.1 State vector construction (including core dynamic states).

[0122] Define the 8-dimensional state vector of MPC control ( (For the current control cycle), directly calculated from monitoring data or a one-dimensional model:

[0123] Quantization formulas for each state component: Real-time load factor (Power level quantification):

[0124] constraint: ; , This is the rated electrolysis power.

[0125] Real-time pipe mixing velocity (Core of Mobile Safety):

[0126] in: (Hydrogen operating condition volumetric flow rate); (Alkali solution volume flow rate); (Optimized pipe inner diameter); , ; (Formula for fitting the density of alkali solution) ).

[0127] Real-time local gas content (Gas-liquid stratification characterization):

[0128] Real-time hydrogen purity (Security Core):

[0129] in: (Gas content in the accumulation zone); Critical velocity (Taitel–Dukler criterion); , (Engineering fit coefficient).

[0130] Real-time gas-liquid entrainment rate (Corrosion / Explosion Risk Characterization):

[0131] Real-time total pressure drop (Electrolytic cell protection indicators):

[0132] in: (Pipe length); (Pipeline resistance coefficient); (Separator local resistance coefficient); For mixed density.

[0133] Real-time electrolysis energy efficiency (Core Energy Consumption):

[0134] in: (Total system energy consumption); Real-time slot pressure (polarization fitting formula); (Hydrogen has a low calorific value).

[0135] Real-time security risk comprehensive value (Safety Quantification):

[0136] in: , , (Risk weight); The higher the value, the higher the risk.

[0137] 2.2 Construction of the measurement vector for control variables.

[0138] Constructing a 3D control variable measurement vector Data is collected directly from sensors.

[0139] 2.3 Status feedback correction.

[0140] The state vector is corrected using the least squares method to eliminate monitoring and model errors, resulting in the corrected state vector. , as initial values ​​for multi-step prediction:

[0141] in: To monitor the state vector; Predict the state vector for the model; The state correction gain matrix (diagonal elements 0.1~0.3, engineering debugging values).

[0142] Step 3: Multi-step prediction based on a one-dimensional model (MPC prediction layer).

[0143] With the corrected state vector Using these as initial conditions, a one-dimensional gas-liquid two-phase dynamic prediction model with optimized structural parameters is invoked to predict future... The system state and output within each control cycle are predicted in multiple steps to obtain the prediction sequence. , ( ).

[0144] 3.1 Linearized state equations (multivariate prediction models).

[0145] The discrete linearized state equations for MPC control are constructed to accommodate the dynamic effects of the three major control variables. This patent can be directly cited.

[0146] Dimension Explanation: The predicted state vector is 8-dimensional; For a 3D predictive control variable vector ( ); A 1-dimensional disturbance vector (load fluctuation) ); Matrix definition: State matrix : 8×8, obtained by linearizing a one-dimensional model, describes the dynamic relationship between state variables; Control Matrix : 8×3, describing the weights of the three control variables on the state variable, obtained through engineering fitting; perturbation matrix : 8×1, describing the impact of load disturbance on state variables.

[0147] 3.2 Definition of Predicted Output Variables (Core of Optimization Objective).

[0148] Define a 2D output vector This provides core metrics (energy consumption, security risks) for rolling optimization:

[0149] Where: Output matrix Given a 2×8 vector, the output vector is:

[0150] Predicted electrolysis energy efficiency (energy consumption index, the higher the value, the better); Predicted safety performance indicators (the higher the value, the lower the risk).

[0151] Step 4: MPC rolling optimization (core decision-making layer).

[0152] In each control cycle To predict the time domain The output variable is the optimization objective. Under the constraints of control variables and state variables, the optimal control sequence is solved. Only the optimal control quantity of the first control cycle is executed.

[0153] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0154] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0155] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. An electrolytic hydrogen production system, characterized in that, include: Electrolytic cell unit, process piping unit, gas-liquid separation unit, and detection and control unit; wherein: The process piping unit is connected between the electrolytic cell unit and the gas-liquid separation unit, and is used to transport electrolyte and gas-liquid mixture flow; The gas-liquid separation unit is used to separate the gas-liquid mixture stream into gas and liquid components. The detection and control unit is used to collect system operating status data and output control commands; The process piping unit has a piping structure that matches the dynamic heat and mass transfer process of the electrolyzer, and the gas-liquid separation unit has a separation structure that matches the dynamic separation process of the gas-liquid mixture flow, so that the electrolytic hydrogen production system can meet the dynamic operation requirements of system energy efficiency and safety risks under fluctuating power input.

2. The electrolytic hydrogen production system according to claim 1, characterized in that, The pipeline structure includes at least one of the following: pipeline inner diameter, pipeline length, pipeline slope, pipeline height difference, and resistance parameters.

3. The electrolytic hydrogen production system according to claim 1, characterized in that, The separation structure includes at least one of the following: separator volume, height-to-diameter ratio, surface area, and gas-liquid separation structure.

4. The electrolytic hydrogen production system according to claim 1, characterized in that, The detection and control unit includes a sensing module, a control module, and an execution module; the sensing module is used to collect temperature, flow rate, and pressure parameters, the control module is used to generate control commands, and the execution module is used to adjust the system operating parameters according to the control commands.

5. A dynamic operation method for an electrolytic hydrogen production system, characterized in that, The system applied to the electrolytic hydrogen production system according to any one of claims 1 to 4 includes: S1. Obtain real-time operating status data of the electrolysis hydrogen production system; S2. Based on the real-time operating status data and the one-dimensional gas-liquid two-phase dynamic prediction model, predict the system operating status in multiple future control cycles; S3. Based on the prediction results, with system energy consumption, safety risks and changes in control variables as optimization objectives, solve for the optimal control variables. S4. Adjust the operation of the electrolysis hydrogen production system according to the optimized control quantity, and repeat S1 to S3.

6. The dynamic operation method according to claim 5, characterized in that, The one-dimensional gas-liquid two-phase dynamic prediction model is used to characterize the coupled effects of the dynamic characteristics of the electrolyzer, the flow characteristics of the process pipeline, and the separation characteristics of the gas-liquid separation unit.

7. The dynamic operation method according to claim 5, characterized in that, The one-dimensional gas-liquid two-phase dynamic prediction model is used to predict at least one of the following: system temperature, system energy efficiency, reactant concentration, and product concentration.

8. The dynamic operation method according to claim 5, characterized in that, The controlled variables include at least one of the following: alkaline solution circulation flow rate, electrolytic cell working pressure, and electrolytic cell heat exchange rate.

9. The dynamic operation method according to claim 5, characterized in that, The safety risk is evaluated using the oxygen concentration in hydrogen as an indicator.

10. The dynamic operation method according to claim 5, characterized in that, Step S3 includes: under the constraints of the range of control variables, the rate of change of control variables, and the system operating state, using a model predictive control algorithm to perform rolling optimization of the optimization objective, and executing the optimized control quantity corresponding to the current control cycle.

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