Alkaline water electrolysis hydrogen production system and control method

Abstract

The application discloses a hydrogen production system by alkaline electrolysis of water and a control method thereof, and belongs to the field of hydrogen production by alkaline electrolysis of water. The control method comprises the following steps: obtaining real-time working condition parameters; predicting a first flow boundary for ensuring the safety of gas purity and a second flow boundary for ensuring the safety of electrolytic cell temperature based on a preset mechanism model; correcting the boundaries in combination with the actual measured values of gas purity and temperature, wherein if the purity is out of standard, the first boundary is lowered, and if the temperature difference is out of standard, the second boundary is raised, and the safety flow interval of actual operation is jointly defined; within the safety flow interval, the bubble overpotential ratio calculated in real time based on the bubble overpotential mechanism model is used as the target for dynamic optimization and adjustment of the electrolyte circulation flow. The three-level closed-loop control of model prediction boundary, real-time feedback correction and efficiency dynamic optimization can ensure the safety of gas purity while reducing the voltage loss caused by bubble overpotential, and realize the collaborative optimization of safety and efficiency.

Description

Technical Field

[0001] This invention relates to the field of alkaline water electrolysis for hydrogen production, specifically an alkaline water electrolysis hydrogen production system and control method. Background Technology

[0002] With the continuous expansion of renewable energy power generation capacity, its volatility and intermittency place higher demands on grid stability and energy storage technologies. Hydrogen energy, due to its high energy density, long storage time, and ability to achieve cross-seasonal energy storage, is considered an ideal carrier for the deep integration of renewable energy. Among these technologies, water electrolysis for hydrogen production can directly convert the electrical energy generated from renewable energy into the chemical energy in hydrogen gas, achieving efficient and long-term energy storage. Among various water electrolysis technologies, alkaline water electrolysis technology, due to its long lifespan, low cost, and high technological maturity, is currently the mainstream water electrolysis technology for hydrogen production and is widely used in large-scale hydrogen production scenarios.

[0003] However, performance improvements in alkaline water electrolysis hydrogen production systems face dual constraints of safety and economy. During electrolysis, bubbles generated can cover electrode active sites and increase electrolyte resistivity, leading to increased voltage drop and decreased electrolysis efficiency. Simultaneously, under low-load conditions, excessively low gas purity can cause excessive hydrogen concentration on the anode side, posing a risk of combustion and explosion. Therefore, the system must limit the minimum current density to ensure safe operation, further compressing the load response range and operational flexibility. Adding to the complexity, there is a strong coupling relationship between operating parameters affecting voltage drop and gas purity, such as temperature, pressure, and electrolyte flow rate, exhibiting a trade-off characteristic. Pursuing efficiency improvements often comes at the cost of increased safety risks, while overly conservative safety strategies can lead to efficiency losses and reduced renewable energy absorption capacity. Existing control methods typically adjust a single parameter, either by limiting the minimum flow rate based on gas purity or by adjusting operating parameters based on electrolysis efficiency, failing to effectively address the dynamic coupling between the two. This makes it difficult to simultaneously ensure operational safety and electrolysis efficiency under actual fluctuating operating conditions. Summary of the Invention

[0004] The purpose of this invention is to provide an alkaline water electrolysis hydrogen production system and control method to solve the above-mentioned problems.

[0005] The technical solution of this invention is: A control method for an alkaline water electrolysis hydrogen production system includes: Obtain real-time operating parameters of the alkaline water electrolysis hydrogen production system; Based on the preset mechanism model, and according to the real-time operating parameters, the first flow boundary required to ensure gas purity safety and the second flow boundary required to ensure electrolyzer temperature safety are predicted under the current operating conditions. The mechanism model is a mathematical model pre-constructed based on the gas cross-mass transfer mechanism and the electrolyzer heat generation and electrolyte flow heat transfer mechanism in the alkaline water electrolysis hydrogen production process. The mechanism model includes: gas purity mechanism model, temperature mechanism model and bubble overpotential mechanism model. Based on the measured values ​​of gas purity and temperature in the real-time operating parameters, the first flow boundary and the second flow boundary are corrected: if the measured value of gas purity exceeds the safety threshold, the first flow boundary is lowered; if the measured value of electrolytic cell temperature difference exceeds the allowable threshold, the second flow boundary is raised; the corrected first flow boundary and the second flow boundary are used together to define the safe flow range for actual operation. Within the safe flow range, the proportion of bubble overpotential is calculated in real time based on the bubble overpotential mechanism model. With the goal of minimizing this proportion, the electrolyte circulation flow rate is dynamically optimized and adjusted to achieve coordinated control of system safety and efficiency.

[0006] Furthermore, the first flow boundary is determined based on a gas purity mechanism model, which is constructed based on the gas cross-mass transfer mechanism. The model is constrained by a preset gas purity safety threshold and is solved in reverse to obtain the predicted flow upper limit under the current operating conditions. The gas purity mechanism model at least considers the influence of the circulating mixing of dissolved hydrogen in the electrolyte and the permeation of hydrogen across the membrane on the hydrogen purity on the anode side.

[0007] Furthermore, the second flow boundary is determined based on a temperature mechanism model, which is constructed based on the heat generation of the electrolyzer and the heat transfer mechanism of the electrolyte flow. The predicted lower limit of the flow rate under the current operating conditions is obtained by inverse solution with a preset maximum allowable temperature difference threshold as a constraint. The temperature mechanism model at least considers the balance between the heat generated by the operation of the electrolyzer and the heat carried away by the electrolyte flow.

[0008] Furthermore, it also includes an interval validity verification step, which is as follows: if the corrected first flow boundary is less than or equal to the corrected second flow boundary, it is determined that the current operating condition exceeds the safe adjustment range, triggering an early warning mechanism, and using the predicted boundary before correction as a temporary safe interval.

[0009] Furthermore, the process of dynamically optimizing and adjusting the electrolyte circulation flow rate includes: Based on the bubble overpotential mechanism model, the proportion of overpotential loss caused by bubbles in the total electrolysis chamber voltage is calculated in real time under the current operating conditions. The loss percentage is compared with a preset optimization threshold; If the loss ratio is higher than the optimization threshold, the flow rate is gradually increased within the safe flow rate range until the loss ratio is reduced to within the optimization threshold, and the flow rate corresponding to this time is recorded as the optimal flow rate. If the loss percentage is lower than the optimization threshold, maintain the current flow rate or appropriately reduce the flow rate to reduce the energy consumption of the circulating pump.

[0010] Furthermore, during the process of dynamically optimizing and adjusting the electrolyte circulation flow rate, the flow rate adjustment step size is set to 5% to 8% of the current flow rate.

[0011] Furthermore, it also includes model iterative optimization, which includes the following steps: Continuously collect system operation data and flow regulation effect data, and update and calibrate the empirical parameters in the gas purity mechanism model, temperature mechanism model and bubble overpotential mechanism model to improve the accuracy of subsequent prediction and control.

[0012] An alkaline water electrolysis hydrogen production system, used to implement the above-mentioned control method, includes: An electrolytic cell is used for alkaline water electrolysis. The parameter acquisition module includes sensors for monitoring gas purity, temperature, pressure, current, voltage, and flow rate. The calculation module has built-in gas purity mechanism model, temperature mechanism model and bubble overpotential mechanism model, which are used to perform predictive calculations based on real-time operating parameters. The control module is connected to the parameter acquisition module and the calculation module respectively. It is used to perform flow boundary determination, feedback correction and dynamic optimization logic, and output adjustment commands. The execution module, which is signal-connected to the control module, includes a flow regulating valve or a frequency converter pump, and is used to precisely control the electrolyte circulation flow rate according to the adjustment command.

[0013] Furthermore, the control module includes: The flow upper and lower limit definition unit is communicatively connected to the calculation module and is used to determine the theoretical flow boundary based on the model prediction results. The interval optimization unit is connected to the parameter acquisition module and the flow upper and lower limit definition unit respectively, and is used to correct the theoretical flow boundary based on the real-time measured gas purity value and temperature value to determine the safe flow range for actual operation. The feedback adjustment unit is connected to the interval optimization unit and the execution module respectively. It is used to perform dynamic optimization within the safe flow range with the goal of minimizing bubble overpotential loss, and output flow adjustment commands to the execution module.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This invention uses gas purity mechanism models, temperature mechanism models, and bubble overpotential mechanism models to predict the first flow boundary ensuring gas purity safety and the second flow boundary ensuring electrolytic cell temperature safety under current operating conditions, based on real-time operating parameters. These boundaries are then corrected by combining measured gas purity and temperature values. When purity exceeds the limit, the upper limit is lowered; when temperature difference exceeds the limit, the lower limit is raised, thereby dynamically defining the actually operable safe flow range. Within this range, the flow rate is optimally adjusted to minimize the proportion of bubble overpotential, effectively reducing voltage loss caused by bubbles while ensuring safety. Compared to existing single-objective control methods, this invention integrates safety boundary prediction, real-time feedback correction, and dynamic efficiency optimization through a three-level closed-loop control logic that includes model-predicted boundaries, feedback-corrected boundaries, and dynamic efficiency optimization. This avoids the risk of excessive gas purity due to excessive flow rate, and prevents insufficient heat exchange and increased bubble overpotential due to insufficient flow rate. It achieves synergistic optimization of system safety and electrolysis efficiency, significantly improving the operational adaptability and overall performance of the alkaline water electrolysis hydrogen production system under fluctuating operating conditions. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the control method of the present invention.

[0016] Figure 2 This is a schematic diagram of the alkaline water electrolysis hydrogen production system of the present invention.

[0017] Figure 3 This is a schematic diagram showing the gas purity analysis results of the system of the present invention.

[0018] Figure 4 This is a schematic diagram showing the results of the polarization characteristic analysis of the system of the present invention. Detailed Implementation

[0019] The following is combined with Figures 1 to 4 The specific embodiments of the present invention will be described in detail below.

[0020] It should be noted that the circuit connections involved in this invention all adopt conventional circuit connection methods and do not involve any innovation. HTO This refers to the hydrogen content in the oxygen on the anode side, that is, the hydrogen concentration in the oxygen.

[0021] Example like Figure 1 As shown, a control method for an alkaline water electrolysis hydrogen production system includes: Real-time operating parameters of the alkaline water electrolysis hydrogen production system are obtained; two types of parameters are collected in real time through sensors, which are used for model calculation and feedback correction, respectively.

[0022] Model input parameters: current density, system operating pressure, electrolysis chamber voltage, and temperature.

[0023] Real-time monitoring and feedback parameters: HTO Real-time measured values: inlet temperature of the electrolytic cell, outlet temperature, and real-time measured value of circulating electrolyte flow rate.

[0024] Real-time acquisition of operating parameters during the operation of the electrolyzer, including current density, electrolyzer pressure, electrolyte concentration, inlet electrolyte temperature, outlet electrolyte temperature, and electrolyte flow rate. HTO And the voltage of the electrolytic cell.

[0025] Based on the pre-set mechanism model, and according to real-time operating parameters, the first flow boundary required to ensure gas purity safety and the second flow boundary required to ensure electrolyzer temperature safety are predicted under the current operating conditions. The mechanism model is a mathematical model pre-constructed based on the gas cross-mass transfer mechanism and the electrolyzer heat generation and electrolyte flow heat transfer mechanism in the alkaline water electrolysis hydrogen production process. The mechanism model includes: gas purity mechanism model, temperature mechanism model and bubble overpotential mechanism model. By combining the measured values ​​of gas purity and temperature in the real-time operating parameters, the first flow boundary and the second flow boundary are corrected: if the measured value of gas purity exceeds the safety threshold, the first flow boundary is lowered; if the measured value of electrolytic cell temperature difference exceeds the allowable threshold, the second flow boundary is raised; the corrected first flow boundary and the second flow boundary are used together to define the safe flow range for actual operation. Within the safe flow range, the proportion of bubble overpotential is calculated in real time based on the bubble overpotential mechanism model. With the goal of minimizing this proportion, the electrolyte circulation flow rate is dynamically optimized and adjusted to achieve coordinated control of system safety and efficiency.

[0026] The first flow boundary is determined based on the gas purity mechanism model, which is constructed according to the gas cross-mass transfer mechanism. The model is constrained by a preset gas purity safety threshold and is solved in reverse to obtain the predicted flow upper limit under the current operating conditions. The gas purity mechanism model at least considers the influence of the circulating mixing of dissolved hydrogen in the electrolyte and the permeation of hydrogen across the membrane on the hydrogen purity on the anode side.

[0027] Gas cross-contamination during alkaline water electrolysis originates from two sources: firstly, hydrogen dissolved in the electrolyte on the cathode side enters the anode during electrolyte circulation and mixing, causing gas cross-contamination. On the other hand, dissolved hydrogen gas permeates across the membrane to the anode side within the electrolyzer. The measured values ​​of gas purity are expressed as follows: .

[0028] In the formula, and These represent the hydrogen flux entering the anode side via dissolution circulation and membrane permeation, respectively. For oxygen generation flux.

[0029] The dissolved hydrogen cross-flux is expressed as: .

[0030] in, This refers to the volumetric flow rate of the circulating electrolyte. This is the partial pressure of hydrogen gas. is the Henry's law constant for hydrogen gas in potassium hydroxide solution.

[0031] The cross-flux of hydrogen permeation through the diaphragm is expressed as: .

[0032] in, This refers to the diaphragm thickness; The effective diffusion coefficient of the diaphragm is determined by the diaphragm porosity, tortuosity, and diaphragm condition. This is the oversaturation coefficient.

[0033] .

[0034] For current density, is Faraday's constant.

[0035] In summary, alkaline electrolyzed water HTO Computational model: .

[0036] The second flow boundary is determined based on a temperature mechanism model, which is constructed based on the heat generation of the electrolyzer and the heat transfer mechanism of the electrolyte flow. The predicted lower limit of the flow rate under the current operating conditions is obtained by inversely solving the problem with the preset maximum allowable temperature difference threshold as a constraint. The temperature mechanism model at least considers the balance between the heat generated by the operation of the electrolyzer and the heat carried away by the electrolyte flow.

[0037] Electrolyte flow heat transfer is the main cooling pathway for alkaline electrolyzers. A temperature model for the electrolyzer is established based on the electrolyzer's exothermic mechanism and electrolyte flow. .

[0038] In the formula The specific heat capacity of the electrolyte. This is the maximum allowable temperature difference of the electrolytic cell. The heat generated during the operation of the electrolytic cell is calculated based on the actual voltage and thermal neutral voltage. .

[0039] In the formula This is the voltage of the electrolysis chamber. For effective active area, It is the thermal neutral voltage, determined by temperature.

[0040] Construction of the bubble overpotential mechanism model: During electrolysis, bubbles are generated and bubble regions are formed, which affect the voltage of the electrolysis chamber. This is mainly reflected in two aspects: First, bubbles will cover the active sites on the electrode surface, reducing the effective reaction area and thus increasing the activation overpotential; second, bubbles diffused in the electrolyte will affect the electrolyte conductivity, thereby increasing the ohmic overpotential.

[0041] Bubble coverage is expressed as: .

[0042] In the formula, For bubble coverage based on flow rate correction; This represents the bubble coverage rate under static conditions. Electrolyte flow rate These are the reference temperature and reference pressure, respectively.

[0043] The voltage in the electrolysis chamber is expressed as: .

[0044] In the formula, This is the voltage of the electrolysis chamber. It is a reversible voltage; To activate the overpotential, This is an ohmic overpotential.

[0045] Reversible voltage is represented as: .

[0046] In the formula, Reversible voltage at standard temperature This represents the saturated vapor pressure of the KOH solution. To reduce system operating pressure, This represents the water activity of the KOH solution.

[0047] The activation overpotential is represented as: .

[0048] In the formula for Activation overvoltage, and The charge transfer coefficients for the anode and cathode are denoted as . and Given the reference exchange current density for the anode and cathode, the ohmic overpotential is expressed as: 。

[0049] In the formula This is an ohmic overpotential. Electrode resistance, For diaphragm resistance, This refers to the electrolyte resistance. The electrode resistance and diaphragm resistance are related to material properties, while the electrolyte resistance is affected by the bubble coverage rate.

[0050] Further, the proportion of overpotential loss caused by bubbles was obtained: .

[0051] If the corrected first flow boundary is less than or equal to the corrected second flow boundary, the current operating condition is determined to be outside the safe adjustment range, triggering an early warning mechanism, and the predicted boundary before correction is used as a temporary safe zone.

[0052] Input parameters such as real-time measured current density, pressure, and temperature. HTO Mechanism prediction model, calculated HTO Predicted values: Input real-time measured parameters such as electrolysis chamber voltage, current density, and inlet flow rate into the temperature mechanism prediction model to calculate the predicted value of electrolytic cell temperature difference; Input real-time measured parameters such as current density, pressure, temperature, and flow rate into the bubble overpotential mechanism model to calculate the predicted value of bubble overvoltage ratio. For the above three observation values, preset threshold ranges are as follows: HTO Safety thresholds, maximum allowable temperature difference thresholds for the electrolyzer, and optimization thresholds for the proportion of bubble overvoltage are all used. The predicted values ​​and real-time measured values ​​are compared with these thresholds to determine if there are any safety risks or room for efficiency optimization in the current flow rate.

[0053] The process of dynamically optimizing and adjusting the electrolyte circulation flow rate includes: Based on the bubble overpotential mechanism model, the proportion of overpotential loss caused by bubbles in the total electrolysis chamber voltage is calculated in real time under the current operating conditions. The percentage of loss is compared with a preset optimization threshold; If the loss percentage is higher than the optimization threshold, the flow rate is gradually increased within the safe flow rate range until the loss percentage is reduced to within the optimization threshold, and the flow rate corresponding to this point is recorded as the optimal flow rate. If the loss percentage is below the optimization threshold, maintain the current flow rate or appropriately reduce the flow rate to reduce the energy consumption of the circulating pump.

[0054] In this embodiment, based on the mechanism model and preset threshold, the predicted optimal flow boundary (predicted value) under the current operating condition is derived in reverse: Predicted traffic limit ( ) Calculation: HTO Safety threshold ( ) as a constraint, Substitution HTO The mechanistic prediction model, by inversely solving for the flow rate, yields the upper limit of the predicted flow rate: this value is theoretically... HTO The maximum flow rate that does not exceed the limit is the theoretical upper limit benchmark for flow regulation; Ideal lower limit of flow ( ) Calculation: Based on the maximum allowable temperature difference threshold of the electrolytic cell ( ) as a constraint, Substituting into the temperature mechanism prediction model and solving the flow rate value in reverse, we obtain the ideal lower limit of flow rate: This value is the minimum flow rate when the temperature difference of the electrolyzer does not exceed the standard, and it is the theoretical lower limit benchmark for flow rate regulation. Based on the ideal flow boundary, and combined with real-time monitoring values ​​for feedback correction, the actual operational safe flow range is obtained. , ]: Actual traffic limit ( ) Correction: like HTO ≤ The model's predictions are accurate, so the upper limit of the prediction is directly used. = ; like HTO > This indicates that the actual gas purity has exceeded the standard, and the upper limit of the prediction needs to be lowered, corrected according to the proportion of the excess, to ensure real-time accuracy. HTO Return to safe range: .

[0055] Actual traffic lower limit ( ) Correction: like ΔT ≤ This indicates that the actual temperature difference meets the requirements, so the ideal lower limit can be directly adopted. = .

[0056] like ΔT ≥ This indicates insufficient actual heat exchange. The lower limit of the forecast needs to be raised, and adjustments made according to the overheat ratio to ensure that the real-time temperature difference returns to a safe range. .

[0057] Safety range constraint: The lower limit of actual traffic must be less than the upper limit of actual traffic. If this occurs after correction... ≥ Therefore, the principle of "prioritizing safety" will be adopted. = , = This triggers a working condition warning.

[0058] Dynamic optimization based on bubble overpotential calculation: The overpotential ratio x is calculated synchronously and in real time as operating parameters (such as current density and pressure) change. Within the actual safe flow range, the flow rate is dynamically adjusted to minimize the bubble overpotential ratio, ensuring efficient operation.

[0059] If x > This indicates that the overpotential loss caused by the bubble is too large. Within the safe range, gradually increase the flow rate, recalculating x after each increase, until x ≤ Record the current traffic flow as the optimal flow rate. ; If x≤ This indicates that the current traffic is in a high-efficiency range and should be maintained. Unchanged; if x < Within a safe range, the flow rate can be appropriately reduced to decrease cycle energy consumption without increasing overpotential loss.

[0060] During the process of dynamically optimizing and adjusting the electrolyte circulation flow rate, the flow rate adjustment step size is set to 5% to 8% of the current flow rate.

[0061] It also includes model iterative optimization, which includes the following steps: Continuously collect system operation data and flow regulation effect data to update and calibrate the empirical parameters in the gas purity mechanism model, temperature mechanism model, and bubble overpotential mechanism model to improve the accuracy of subsequent prediction and control. Simultaneously, repeat the above steps to update the predicted flow boundary, correct the actual safe range, and optimize the optimal flow rate in real time, achieving closed-loop adaptive control under all operating conditions.

[0062] like Figure 2 As shown, the alkaline water electrolysis hydrogen production system includes an electrolyzer, an oxygen gas-liquid separator, a hydrogen gas-liquid separator, an alkaline solution tank, an alkaline solution circulation pump, and a calculation and control unit. During system operation, the electrolyte flows from the alkaline solution tank into the cathode and anode of the electrolyzer via the alkaline solution circulation pump. After the electrolysis reaction occurs in the electrolyzer, the generated gas flows into the gas-liquid separator, and the electrolyte after gas-liquid separation flows back to the alkaline solution tank.

[0063] This embodiment discloses an alkaline water electrolysis hydrogen production system for implementing the aforementioned control method. The system includes an electrolyzer, a parameter acquisition module, a calculation module, a control module, and an execution module. The electrolyzer performs the alkaline water electrolysis reaction. The parameter acquisition module includes sensors for monitoring gas purity, temperature, pressure, current, voltage, and flow rate. The module also includes an oxygen-hydrogen concentration sensor, a temperature sensor, a pressure sensor, a current / voltage detection device, and a flow sensor. The calculation module incorporates gas purity mechanism models, temperature mechanism models, and bubble overpotential mechanism models for predictive calculations based on real-time operating parameters. The control module is signal-connected to both the parameter acquisition module and the calculation module, and performs flow boundary determination, feedback correction, and dynamic optimization logic, outputting adjustment commands. The execution module is signal-connected to the control module and includes a flow regulating valve or a variable frequency pump for precisely controlling the electrolyte circulation flow rate according to the adjustment commands.

[0064] The control module includes: a flow rate upper and lower limit defining unit, a range optimization unit, and a feedback adjustment unit. The flow rate upper and lower limit defining unit is communicatively connected to the calculation module and is used to determine the theoretical flow rate boundary based on the model prediction results. The range optimization unit is signal-connected to both the parameter acquisition module and the flow rate upper and lower limit defining unit and is used to correct the theoretical flow rate boundary based on real-time measured gas purity and temperature values ​​to determine the safe flow rate range for actual operation. The feedback adjustment unit is signal-connected to both the range optimization unit and the execution module and is used to dynamically optimize within the safe flow rate range with the goal of minimizing bubble overpotential loss, and output flow rate adjustment commands to the execution module.

[0065] The system structure parameters and system operating parameters of this embodiment are shown in Table 1: Table 1 like Figure 3 and Figure 4 As shown, with the increase of electrolyte flow rate, the purity of the gas... HTO The proportion increases significantly, and the higher flow rate in the low current density range will lead to HTO The value is much higher than the safety threshold; on the other hand, in terms of voltage performance, a higher electrolyte flow rate can effectively alleviate the increase in overpotential caused by the bubble effect, thereby reducing the electrolysis chamber voltage and improving electrolysis efficiency. Moreover, the optimization effect of flow rate is more significant in the high current density range. The control method and system described in this embodiment effectively balance the synergistic control requirements of safety and efficiency by dynamically optimizing the flow rate, while ensuring the safety of gas purity.

[0066] The above-disclosed embodiments are merely preferred embodiments of the present invention. However, the embodiments of the present invention are not limited thereto, and any variations that can be conceived by those skilled in the art should fall within the protection scope of the present invention.

Claims

1. A control method for an alkaline water electrolysis hydrogen production system, characterized in that, include: Obtain real-time operating parameters of the alkaline water electrolysis hydrogen production system; Based on the preset mechanism model, and according to the real-time operating parameters, the first flow boundary required to ensure gas purity safety and the second flow boundary required to ensure electrolyzer temperature safety are predicted under the current operating conditions. The mechanism model is a mathematical model pre-constructed based on the gas cross-mass transfer mechanism and the electrolyzer heat generation and electrolyte flow heat transfer mechanism in the alkaline water electrolysis hydrogen production process. The mechanism model includes: gas purity mechanism model, temperature mechanism model and bubble overpotential mechanism model. Based on the measured values ​​of gas purity and temperature in the real-time operating parameters, the first flow boundary and the second flow boundary are corrected: if the measured value of gas purity exceeds the safety threshold, the first flow boundary is lowered; if the measured value of electrolytic cell temperature difference exceeds the allowable threshold, the second flow boundary is raised; the corrected first flow boundary and the second flow boundary are used together to define the safe flow range for actual operation. Within the safe flow range, the proportion of bubble overpotential is calculated in real time based on the bubble overpotential mechanism model. With the goal of minimizing this proportion, the electrolyte circulation flow rate is dynamically optimized and adjusted to achieve coordinated control of system safety and efficiency.

2. The control method for an alkaline water electrolysis hydrogen production system according to claim 1, characterized in that, The first flow boundary is determined based on a gas purity mechanism model, which is constructed based on the gas cross-mass transfer mechanism. The model is constrained by a preset gas purity safety threshold and is solved in reverse to obtain the predicted flow upper limit under the current operating conditions. The gas purity mechanism model at least considers the effects of the circulating mixing of dissolved hydrogen in the electrolyte and the permeation of hydrogen across the membrane on the hydrogen purity on the anode side.

3. The control method for an alkaline water electrolysis hydrogen production system according to claim 1, characterized in that, The second flow boundary is determined based on a temperature mechanism model, which is constructed based on the heat generation of the electrolyzer and the heat transfer mechanism of the electrolyte flow. The predicted lower limit of the flow rate under the current operating conditions is obtained by inverse solution with a preset maximum allowable temperature difference threshold as a constraint. The temperature mechanism model at least considers the balance between the heat generated by the operation of the electrolyzer and the heat carried away by the electrolyte flow.

4. The control method for an alkaline water electrolysis hydrogen production system according to claim 1, characterized in that, It also includes an interval validity verification step, which is as follows: if the corrected first flow boundary is less than or equal to the corrected second flow boundary, it is determined that the current operating condition exceeds the safe adjustment range, triggering an early warning mechanism, and using the predicted boundary before correction as a temporary safe interval.

5. The control method for an alkaline water electrolysis hydrogen production system according to claim 1, characterized in that, The process of dynamically optimizing and adjusting the electrolyte circulation flow rate includes: Based on the bubble overpotential mechanism model, the proportion of overpotential loss caused by bubbles in the total electrolysis chamber voltage is calculated in real time under the current operating conditions. The loss percentage is compared with a preset optimization threshold; If the loss ratio is higher than the optimization threshold, the flow rate is gradually increased within the safe flow rate range until the loss ratio is reduced to within the optimization threshold, and the flow rate corresponding to this time is recorded as the optimal flow rate. If the loss percentage is lower than the optimization threshold, maintain the current flow rate or appropriately reduce the flow rate to reduce the energy consumption of the circulating pump.

6. The control method for an alkaline water electrolysis hydrogen production system according to claim 5, characterized in that, During the process of dynamically optimizing and adjusting the electrolyte circulation flow rate, the flow rate adjustment step size is set to 5% to 8% of the current flow rate.

7. The control method for an alkaline water electrolysis hydrogen production system according to claim 1, characterized in that, It also includes model iterative optimization, which includes the following steps: Continuously collect system operation data and flow regulation effect data, and update and calibrate the empirical parameters in the gas purity mechanism model, temperature mechanism model and bubble overpotential mechanism model to improve the accuracy of subsequent prediction and control.

8. An alkaline water electrolysis hydrogen production system, used to implement the control method as described in any one of claims 1-7, characterized in that, include: An electrolytic cell is used for alkaline water electrolysis. The parameter acquisition module includes sensors for monitoring gas purity, temperature, pressure, current, voltage, and flow rate. The calculation module has built-in gas purity mechanism model, temperature mechanism model and bubble overpotential mechanism model, which are used to perform predictive calculations based on real-time operating parameters. The control module is connected to the parameter acquisition module and the calculation module respectively. It is used to perform flow boundary determination, feedback correction and dynamic optimization logic, and output adjustment commands. The execution module, which is signal-connected to the control module, includes a flow regulating valve or a frequency converter pump, and is used to precisely control the electrolyte circulation flow rate according to the adjustment command.

9. The alkaline water electrolysis hydrogen production system according to claim 8, characterized in that, The control module includes: The flow upper and lower limit definition unit is communicatively connected to the calculation module and is used to determine the theoretical flow boundary based on the model prediction results. The interval optimization unit is connected to the parameter acquisition module and the flow upper and lower limit definition unit respectively, and is used to correct the theoretical flow boundary based on the real-time measured gas purity value and temperature value to determine the safe flow range for actual operation. The feedback adjustment unit is connected to the interval optimization unit and the execution module respectively. It is used to perform dynamic optimization within the safe flow range with the goal of minimizing bubble overpotential loss, and output flow adjustment commands to the execution module.

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