Modularized water electrolysis hydrogen production system with multi-physics field cooperation and application method of modularized water electrolysis hydrogen production system

Abstract

The invention discloses a multi-physics-field collaborative modularized water electrolysis hydrogen production system and an application method, dynamic coupling regulation and control of an electrochemical field, a temperature field, a fluid field and a stress field are realized through collaborative work of a central intelligent regulation and control unit and a plurality of electrolytic cell modules which are connected in parallel and can be independently started and stopped, and the system adopts a modularized design and is simple in structure and convenient to operate. Dynamic switching and load balancing are supported; a multi-physics field digital twinborn model is built in, the system state is predicted in real time, and parameters are calibrated online; the self-adaptive control strategy comprises the following steps: the electrochemical field adopts a pulse width modulation technology to reduce the bubble shielding effect; the temperature of the temperature field is accurately controlled through the embedded heat exchanger; the fluid field dynamically adjusts the flow velocity to optimize gas-liquid distribution; the sealing performance of the stress field is ensured through a pre-tightening force mechanism. The energy consumption of unit hydrogen production is reduced to 4.3 kWh / Nm < 3 > or below, the energy efficiency is improved by 12%, the power adaptation range reaches 10%-100%, the service life of an electrolytic bath is prolonged by 30%, and mismatch accidents of multiple physical fields are completely avoided.

Description

Technical Field

[0001] This invention belongs to the field of water electrolysis hydrogen production technology, and specifically relates to a modular water electrolysis hydrogen production system and application method with multi-physics field synergy. Background Technology

[0002] Hydrogen production via water electrolysis is a key technology for achieving carbon neutrality, and its efficient operation relies on the precise analysis and coordinated control of the multi-physics fields within the electrolyzer. During actual operation, multiple physical quantities, such as current, temperature, flow rate, and gas-liquid two-phase flow, are distributed within the electrolyzer in a dual-coupled form of energy and matter, involving complex processes such as electrochemical reaction kinetics, heat and mass transfer, and fluid dynamics. Especially when the system is connected to fluctuating renewable energy sources, drastic power changes can lead to instability of the internal physical fields of the electrolyzer, such as the formation of local hot spots, increased bubble retention, and decay of electrode active area, significantly reducing energy efficiency and equipment lifespan. Existing hydrogen production systems mostly employ fixed electrolyzer clusters or simple start-stop control strategies, lacking the ability to dynamically perceive and coordinate the coupling relationships of multiple physical fields, making it difficult to adapt to the grid connection requirements of high-proportion renewable energy sources. Therefore, developing a hydrogen production system capable of real-time coordination of the interactions of electro-thermal-fluid-mechanical multi-physics fields and achieving adaptive power allocation through modular design is of great significance for improving the economy and reliability of green hydrogen production.

[0003] The publicly published patent CN117763881A proposes a modular framework for multiphysics simulation through parameter import, geometric modeling, and coupled field analysis. However, it focuses on offline simulation and model comparison, lacking real-time monitoring and dynamic control capabilities for the actual electrolyzer operating environment, and cannot directly support online optimization of the hydrogen production system. Another published document, CN104537193A, supports strong and weak coupling analysis of multiphysics fields such as structure-fluid-temperature-electromagnetism, but this system is only applicable to the simulation design stage and is not linked with the physical electrolyzer equipment. It cannot capture transient gas-liquid two-phase flow changes during actual operation, nor can it reverse adjust the physical field through actuators. Furthermore, although CN120671453A introduces digital twin technology, which can realize parallel computing and data interaction in multiple physical domains, its core still remains at the state mapping in virtual space. It does not involve flexible networking of electrolyzer modules, power distribution strategies, or multiphysics collaborative control algorithms, making it difficult to solve the problem of sudden energy efficiency drops in hydrogen production systems under variable load conditions. These existing technologies all focus on model building or offline analysis, failing to form a closed-loop control system integrating "perception-decision-execution," resulting in limited optimization effects on actual hydrogen production processes in engineering applications.

[0004] Faced with the urgent demands for system flexibility, efficiency, and lifespan in large-scale hydrogen production from renewable energy sources, existing technologies are insufficient to address the challenges of multi-physics mismatch in high-fluctuation power scenarios. This invention constructs a digital twin model coupling electro-thermal-fluid-mechanical multi-physics fields to invert the internal state of the electrolyzer in real time; it employs a modular electrolyzer design to support dynamic switching and load balancing based on power demand; and it introduces an adaptive control algorithm to collaboratively adjust electrolyte flow rate, temperature, and current density, thereby suppressing bubble shielding, optimizing thermal management, and reducing stress fatigue. This system-level solution not only improves hydrogen production efficiency and equipment durability but also provides key technological support for the large-scale supply of green hydrogen, driving the low-carbon transformation of the energy system. Summary of the Invention

[0005] This invention relates to a modular electrolysis water production system and application method with multi-physics field coordination. The system achieves dynamic coupling control of electrochemical field, temperature field, fluid field and stress field through the coordinated operation of a central intelligent control unit and multiple parallel electrolyzer modules that can be started and stopped independently, thereby achieving efficient and stable hydrogen production under the condition of renewable energy power fluctuation.

[0006] The system first uses a high-precision sensor network to collect the voltage, current, temperature, and pressure parameters of each electrolytic cell module in real time. The voltage measurement accuracy reaches ±0.1V, the current measurement accuracy is ±0.5A, the temperature measurement range is 20-100℃ with an accuracy of ±0.5℃, and the pressure measurement range is 0-1MPa with an accuracy of ±1kPa. The sampling frequency of all parameters is not less than 100Hz to ensure the real-time performance and integrity of the data acquisition.

[0007] The multiphysics digital twin model built into the central intelligent control unit is based on the laws of conservation of mass, charge, and energy. It coupled the solutions of the Nernst-Planck equation describing ion transport, Fourier's law describing heat transfer, and the Navier-Stokes equation describing fluid flow. The computational domain is discretized into no less than 100,000 grid elements using the finite element method, and the ion concentration field, temperature field, and velocity field distribution inside the system are dynamically updated at a rate of thousands of iterations per second.

[0008] The central control unit includes a temperature control unit, and the temperature control unit's circulation pipeline is connected to an embedded plate heat exchanger.

[0009] The embedded plate heat exchanger is made of titanium alloy and has a heat transfer area of ​​0.5m². 2 The module uses a PID algorithm to adjust the flow rate of the ethylene glycol solution, stabilizing the electrolyte inlet temperature within the optimal reaction range of 80±2℃.

[0010] The digital twin model integrates the Butler-Volmer equation of electrode reaction kinetics and Ohm's law of solution to accurately analyze the composition of activation overpotential and ohmic overpotential. It also introduces a correction factor for effective conductivity based on bubble shielding effect. This correction factor is dynamically adjusted according to the bubble coverage rate, with an adjustment range of 0.6-1.0, so that the model prediction error is controlled within 5%.

[0011] The electrolytic cell module adopts a bipolar filter press structure, and each module includes electrodes, diaphragms, and anode and cathode plates.

[0012] The electrode is made of a porous three-dimensional network nickel-based alloy material with a specific surface area of ​​500 m². 2 With a porosity of 70%-80%, the surface is supported by an oxygen evolution catalyst with a perovskite structure through electrodeposition, and the coating thickness is 10-20μm, which reduces the overpotential of the oxygen evolution reaction to below 300mV.

[0013] The diaphragm is a functionalized polyphenylene ether-based composite anion exchange membrane with a thickness of 150-200 μm, an ionic conductivity higher than 0.1 S / cm, and a hydrogen cross-permeability lower than 0.5 mA / cm. 2 The blasting strength shall not be less than 0.8 MPa.

[0014] The anode and cathode plates are designed with irregular flow channels for electrolyte flow. The electrolyte flow channels are optimized by computational fluid dynamics into a serpentine layout with a groove depth of 0.8 mm, a width of 1.2 mm, and a flow channel length of 1.5 m. This can generate sufficient shear force to strip away bubbles with a diameter greater than 50 μm at a flow rate of 0.5 m / s.

[0015] The electrolytic cell modules are connected quickly via standardized interfaces for mechanical, electrical and fluid connections. The pre-tightening mechanism applies 10-15kN of axial pressure to compensate for thermal expansion stress and ensure the integrity of the sealing interface during temperature cycling from -20℃ to 120℃.

[0016] The adaptive operation method of the system begins with continuous monitoring of the input power. A sliding time window algorithm is used to extract power fluctuation characteristics. The time window length is set to 30 seconds. When the power change rate is detected to exceed the threshold of 5% per second, the module switching strategy is immediately activated.

[0017] The central unit, based on a pre-calibrated efficiency spectrum, prioritizes operating the modules already in operation at 600-800 A / m. 2 Within the high-efficiency current density range, when the total power exceeds the carrying capacity of the operating module cluster, standby modules are sequentially activated in order of cumulative operating time from shortest to longest. New modules are started using a ramp-up method with a current density of 50 A / m². 2 The rate increases from zero to the target value to avoid thermal shock.

[0018] When power decreases, the system prioritizes reducing the current density of the operating modules to 400A / m. 2 If the minimum stable value is still excessive, the modules are shut down in descending order of running time. Before shutting down, a pressure reduction purging procedure is performed, and nitrogen is introduced to maintain system pressure balance. The purging time is no less than 2 minutes.

[0019] For a continuously operating module cluster, the central unit uses a digital twin model to invert the internal state of each module and dynamically adjusts the load distribution: if the temperature of a module rises above 85°C or the electrolyte concentration falls below 20%, its current density is temporarily reduced by 15%, and the load is transferred to a module in a better state to achieve a balance of health status within the cluster.

[0020] The electrochemical field coordination is achieved through pulse width modulation technology. The central unit applies a square wave current with a frequency of 1-10kHz and a duty cycle of 30%-70% to the electrolytic cell, and the instantaneous overpotential peak is controlled within 120% of the theoretical decomposition voltage.

[0021] The dielectric force generated by the pulsed current periodically acts on the bubbles on the electrode surface, reducing the bubble diameter from the millimeter level under conventional DC to below 100 μm, decreasing the bubble coverage from the conventional 40% to below 25%, and increasing the effective active area by about 25%.

[0022] The temperature field is achieved through an embedded plate heat exchanger network, with each heat exchanger conformally designed to fit the electrolyte flow channel, providing a heat transfer area of ​​0.5 m². 2 For each module, by adjusting the flow rate of the ethylene glycol solution within the range of 2-10 L / min, the temperature difference between modules is controlled within 3℃, and the hot spot temperature fluctuation does not exceed ±1℃.

[0023] The fluid field coordinates the dynamic adjustment of the feed pump speed according to the real-time gas production rate. For every 1 L / min increase in gas production rate, the electrolyte flow rate increases by 0.1 m / s accordingly, ensuring that the gas volume fraction in the gas-liquid two-phase flow is always below 30%. At the same time, the pressure between modules is monitored by a differential pressure sensor, and the pressure difference between the hydrogen and oxygen sides is maintained within the safe boundary threshold of 50 kPa by a proportional valve.

[0024] The online calibration process of the digital twin model employs a Kalman filter algorithm, comparing the measured voltage, temperature, and pressure data with the model's predicted values ​​every 5 seconds. This dynamically corrects key parameters such as thermal conductivity and exchange current density, with a correction range of ±15% for thermal conductivity and ±10% for exchange current density. This improves the model's prediction accuracy to over 98% over time.

[0025] The implicit physical quantities output by the model, such as the bubble coverage on the electrode surface and the local current density distribution, serve as inputs to the advanced control strategy.

[0026] When the predicted bubble coverage rate in a certain area exceeds 60%, the module's pulse current frequency is automatically increased to above 8kHz.

[0027] When the current density distribution non-uniformity is detected to be greater than 15%, adjust the electrolyte inlet flow distribution valve to compensate the flow deviation to within 5%.

[0028] The safety boundary protection mechanism establishes multi-level early warning thresholds: when the electrolyte temperature reaches 88℃, the concentration is lower than 18%, or the pressure difference between modules exceeds 45kPa, the system triggers a level one alarm and prompts optimization operation suggestions.

[0029] When the parameters further deteriorate to 90°C, 15% concentration, or 48 kPa pressure difference, the system automatically reduces the total current at a rate of 3% per second and starts the standby cooling pump.

[0030] If the parameters reach the final safety threshold of 92°C, 12% concentration, or 50 kPa pressure difference, the system will cut off the power supply within 100 milliseconds and simultaneously open the nitrogen purging valve to maintain the system pressure stable at a flow rate of 20 L / min for at least 10 minutes to ensure a safe shutdown.

[0031] The entire control process is visualized in real time through a human-machine interface, displaying cloud maps of various physical fields, efficiency curves, and safety status. Operators can intervene at any time to adjust control parameters.

[0032] The specific workflow of this system is as follows:

[0033] As photovoltaic power begins to rise at dawn, the system first activates a basic module at 400A / m. 2 The current density is low and the load is low for 30 minutes. After the temperature field and concentration field stabilize, 2-3 modules are added every hour as the power increases, until all modules are put into operation at noon.

[0034] When cloud cover occurred in the afternoon causing a sharp 30% drop in power, the system reduced the current density of the operating modules from 800 A / m within one minute. 2 Reduced to 600A / m 2 It also shuts down 20% of the modules, while the remaining modules continue to operate efficiently.

[0035] As power continued to decline in the evening, the system systematically shut down equipment based on module health scores, ultimately retaining one module at 400A / m. 2 Standby overnight under certain conditions.

[0036] After each day's operation, the system automatically generates a multi-physics field collaborative performance report, including unit hydrogen production energy consumption, module lifespan loss assessment, and optimization suggestions, providing data support for subsequent operation.

[0037] Throughout the process, the coordinated regulation of the four fields of electricity, heat, fluid and force ensures that the average energy efficiency of the system remains above 75%. Even under extreme conditions with power fluctuations of up to 80%, the system can still maintain safe and stable operation.

[0038] Beneficial effects

[0039] Compared with the prior art, the present invention has the following significant advantages and technical effects:

[0040] This invention achieves maximum energy efficiency and extended operational life of hydrogen production systems under fluctuating renewable energy power by constructing a multi-physics coupled digital twin model and a collaborative control architecture for a modular electrolyzer cluster.

[0041] The system employs a four-field coordinated control strategy, which reduces the average energy consumption per unit of hydrogen production to 4.3 kWh / Nm³. 3 The following methods improve energy efficiency by more than 12% compared to traditional single-field control methods.

[0042] The modular dynamic switching mechanism enables the system to operate efficiently within a power range of 10%-100% of its rated power, improving adaptability by more than three times compared to traditional fixed electrolytic cells.

[0043] Pulse width modulation technology combined with fluid dynamics optimization of the flow channel keeps the utilization rate of electrode active area above 85%, and reduces energy loss caused by bubble shielding effect by about 40%.

[0044] The embedded temperature control network stabilizes the temperature difference between modules within a range of 3°C, effectively eliminating the risk of degradation caused by local hot spots, and is expected to extend the life of the electrolyzer by 30%.

[0045] Digital twin models continuously optimize prediction accuracy through real-time data assimilation, providing a basis for preventative maintenance decisions and reducing unexpected downtime by 60%.

[0046] The standardized modular design significantly reduces maintenance complexity, with the replacement time for a single module controlled within 2 hours, and the system availability reaching 99.5%.

[0047] The multi-level protection mechanism of the safety boundary completely avoids catastrophic accidents caused by multi-physics mismatch through early warning and gradient intervention, increasing the safe operating time of the system by 5 times under extreme fluctuation scenarios.

[0048] This system architecture, which integrates intelligent prediction, multi-field collaboration, and modular flexibility, provides a highly reliable and economical technical path for the large-scale production of green hydrogen, and has significant practical value for promoting the low-carbon transformation of the energy structure.

[0049] This invention can be used in clean energy applications such as wind power and photovoltaics to convert surplus abandoned electricity into efficient clean energy, and has important engineering application value and scientific research significance. Attached Figure Description

[0050] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0051] Figure 1 This is an overall architecture diagram of the multi-physics field collaborative modular electrolysis water production system according to an embodiment of the present invention;

[0052] Figure 2 This is a flowchart illustrating the adaptive operation control of the system according to an embodiment of the present invention. Detailed Implementation

[0053] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0054] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0055] The following is in conjunction with the appendix Figure 1 To be continued Figure 2 The technical solution of the present invention will be further described below. For example... Figure 1 As shown, the modular water electrolysis hydrogen production system with multi-physics field collaboration provided by the present invention includes a central intelligent control unit, an electrolyzer module cluster, a multi-physics field digital twin model server, hydrogen and oxygen collection manifolds and connection components, a safety protection system, and related auxiliary systems.

[0056] The central intelligent control unit constitutes the intelligent decision-making core of the entire system. It integrates a multi-physics digital twin model server and a data storage unit, and communicates with each execution unit via high-speed industrial Ethernet.

[0057] The central intelligent control unit operates based on a multi-physics digital twin model of the laws of conservation of mass, charge, and energy, and solves the Nernst-Planck equation, Fourier's law of heat conduction, and the Navier-Stokes equation in a coupled manner.

[0058] The central intelligent control unit uses the Kalman filter algorithm to calibrate the digital twin model parameters in real time. Every 5 seconds, it compares the measured voltage, temperature and pressure data with the model prediction values ​​and dynamically corrects 12 key parameters such as thermal conductivity coefficient and exchange current density.

[0059] The multiphysics digital twin model server uses the finite element method to discretize and solve the multiphysics coupling equations, achieving a calculation accuracy of over 98% and an iteration speed of thousands of times per second.

[0060] The multiphysics digital twin model server includes an electrochemical field calculation unit, a temperature field calculation unit, a fluid field calculation unit, and a stress field calculation unit.

[0061] The electrochemical field calculation unit integrates the Butler-Volmer equation and Ohm's law for solutions to accurately analyze the composition of activation overpotential and ohmic overpotential, and introduces a correction factor for the effective conductivity due to the bubble shielding effect.

[0062] The temperature field calculation unit takes into account the reversible thermal effect caused by the reaction entropy change, the irreversible Joule heating effect caused by overpotential, and the heat exchange between the system and the external environment.

[0063] The fluid field calculation unit simulates the seepage process of electrolyte in porous electrodes and the generation, growth and detachment of bubbles, coupled with the influence of bubble presence on the effective conductivity of electrolyte.

[0064] The stress field calculation unit calculates the distribution of mechanical stress caused by temperature changes using a thermal expansion coefficient model, and predicts the integrity of the sealing interface during thermal cycling.

[0065] The electrolyzer module cluster consists of multiple parallel electrolyzer modules that can be started and stopped independently. Each module is quickly connected through a standard electrical connection interface and a standard fluid connection interface. The number of modules can be flexibly configured from 5 to 50 depending on the hydrogen production scale.

[0066] The electrolytic cell module adopts a bipolar filter press structure. Each module includes a porous three-dimensional mesh electrode, a composite anion exchange membrane, a serpentine electrolyte channel, and an embedded plate heat exchanger.

[0067] The porous three-dimensional mesh electrode is made of a nickel-based alloy material with a high specific surface area, reaching 500 μm². 2 / g or more, the surface is supported by perovskite structure oxygen evolution catalyst by electrodeposition, so that the overpotential of oxygen evolution reaction is reduced to below 300mV.

[0068] The composite anion exchange membrane is composed of functionalized polyphenylene ether material, with an intermediate layer providing mechanical strength to the reinforcing fabric. It exhibits an ionic conductivity higher than 0.1 S / cm and a hydrogen cross-permeability lower than 0.5 mA / cm.2 .

[0069] The serpentine electrolyte channel is designed using computational fluid dynamics optimization, with a groove depth of 0.8 mm and a width of 1.2 mm. At a flow rate of 0.5 m / s, it generates sufficient shear force to peel off bubbles with a diameter greater than 50 μm.

[0070] The embedded plate heat exchanger is made of titanium alloy and has a heat transfer area of ​​0.5m². 2 The module uses a PID algorithm to adjust the flow rate of the ethylene glycol solution, stabilizing the electrolyte inlet temperature within the optimal reaction range of 80±2℃.

[0071] The modules are provided with axial pressure of 10 to 15 kN through a pre-tightening force application mechanism to compensate for thermal expansion stress caused by temperature changes and ensure the integrity of the sealing interface in the temperature range of -20℃ to 100℃.

[0072] The water electrolysis hydrogen production system is equipped with a renewable energy power input interface at the front end, supporting direct access to photovoltaic and wind power fluctuating power sources, with the input power range covering 10% to 100% of the system's rated power.

[0073] The hydrogen and oxygen collection manifolds are made of 316L stainless steel and equipped with pressure sensors and flow meters to monitor the gas production and system pressure in real time.

[0074] The central control unit includes a temperature control unit, which is connected to an embedded plate heat exchanger via a circulation pipeline. It uses an ethylene glycol aqueous solution as the heat transfer medium and achieves precise flow rate adjustment from 2 to 10 L / min through a variable frequency pump.

[0075] The safety protection system integrates a nitrogen purging unit, an emergency stop button, and a multi-level alarm device to ensure the safe operation of the system under abnormal conditions.

[0076] The safety protection system is equipped with a safety boundary protection mechanism that sets up multiple warning thresholds, including three levels of warning for electrolyte temperature (88℃ / 90℃ / 92℃), three levels of warning for electrolyte concentration (18% / 15% / 12%), and three levels of warning for hydrogen-oxygen pressure difference (45kPa / 48kPa / 50kPa).

[0077] The central intelligent control unit is equipped with an adaptive prediction model that uses deep neural network technology. It takes power fluctuation characteristics, temperature distribution and bubble coverage as inputs and outputs the optimal module commissioning combination and load allocation scheme.

[0078] The central intelligent control unit is equipped with a decision-making system that evaluates the system's security status based on fuzzy logic rules and generates a three-level protection strategy, including a level one early warning optimization operation, a level two early warning automatic load reduction, and a level three early warning emergency shutdown.

[0079] The central intelligent control unit provides a human-machine interface to realize three-dimensional visualization monitoring, displaying cloud maps, efficiency curves and safety status of each physical field in real time, and supporting operators to set parameters and intervene in the process.

[0080] The system is equipped with a pulse width modulation power supply to generate a square wave current with a frequency of 1 to 10 kHz and a duty cycle of 30% to 70%, and the instantaneous overpotential peak is controlled within 120% of the theoretical decomposition voltage.

[0081] The electrolyte inlet of the safety protection system is equipped with an electric regulating valve with an accuracy class of 0.5 and a response time of less than 2 seconds, enabling precise distribution of flow to each module.

[0082] The central intelligent control unit is equipped with an adaptive prediction model that uses deep neural network technology. It takes power fluctuation characteristics, temperature distribution and bubble coverage as inputs and outputs the optimal module commissioning combination and load allocation scheme.

[0083] The central intelligent control unit is equipped with a decision-making system that evaluates the system's security status based on fuzzy logic rules and generates a three-level protection strategy, including a level one early warning optimization operation, a level two early warning automatic load reduction, and a level three early warning emergency shutdown.

[0084] The central intelligent control unit provides a human-machine interface to realize three-dimensional visualization monitoring, displaying cloud maps, efficiency curves and safety status of each physical field in real time, and supporting operators to set parameters and intervene in the process.

[0085] The operation procedure of the modular water electrolysis hydrogen production system with multi-physics field coordination is as follows:

[0086] S1: Start the modular water electrolysis hydrogen production system with multi-physics field coordination, perform system initialization and self-test, and enter standby mode after confirming that the status of each module is normal. The initialization process includes sensor calibration, actuator zeroing, safety interlock testing, and data communication verification to ensure that the system is in a safe and controllable state.

[0087] S2, renewable energy power monitoring and feature extraction, collects photovoltaic and wind power data in real time through smart meters at a sampling rate of 100Hz, applies a sliding time window algorithm to analyze the power sequence with a window size of 5 minutes and a step size of 1 second, calculates the power change rate dP / dt, identifies abrupt change points, sets the threshold to ±10% / s, and extracts statistical features such as mean, variance, kurtosis and fluctuation frequency.

[0088] S3, a multi-physics digital twin model for online prediction, predicts the distribution of electrochemical, temperature, fluid, and stress fields within the system based on current power fluctuation characteristics and environmental parameters. It outputs key parameters such as current density distribution, temperature gradient, bubble coverage, and thermal stress, providing a basis for decision-making in optimized control.

[0089] S4, Optimal module commissioning combination decision, queries the pre-calibrated three-dimensional efficiency map, determines the optimal operating point based on the current temperature of 75 to 85℃ and concentration of 20 to 30%, calculates the number of modules to be commissioned N = rounded up (actual power / optimal power), prioritizes healthy modules with a cumulative operating time of less than 5000 hours, and ensures that the system operates in the high-efficiency range.

[0090] S5, module switching and load balancing control, follows a gradual commissioning strategy, first deploying a basic module at 400A / m. 2 After operating at low current density for 30 minutes and stabilizing the temperature and concentration fields, the number of modules put into operation will be increased by 2 to 3 modules per hour, depending on power demand. When starting a new module, a ramp-up method will be used, with the current density increasing from zero to the target value at a rate of 50 A / m² per second.

[0091] S6 features multi-physics field coordinated parameter adjustment. The electrochemical field employs adaptive pulse width modulation technology, with a frequency of 1 to 10 kHz and a duty cycle of 30% to 70%, optimized in real time according to the gas production rate. The temperature field uses PID control to maintain the electrolyte temperature at 80±2℃ by controlling the ethylene glycol flow rate from 2 to 10 L / min. The fluid field adjusts the pump speed according to the gas production rate, with the electrolyte flow rate increasing by 0.1 m / s for every 1 L / min increase in gas production rate. The stress field uses servo control of the preload force from 10 to 15 kN to compensate for thermal expansion strain.

[0092] S7 features real-time monitoring and early warning of safety boundaries, continuously monitoring key parameters such as electrolyte temperature, concentration, and system differential pressure. When a parameter approaches the first-level early warning threshold, it issues an audible and visual alarm and provides optimization suggestions. When a parameter approaches the second-level early warning threshold, it automatically reduces the total current output at a rate of 3% per second and activates the backup cooling system. When a parameter reaches the third-level early warning threshold, it performs an emergency shutdown within 100 milliseconds and simultaneously opens the nitrogen purging valve to maintain stable system pressure at a flow rate of 20 L / min.

[0093] S8 is a digital twin model that performs online calibration and optimization. Every 5 seconds, it compares the measured voltage, temperature, and pressure data with the model's predicted values. It uses a Kalman filter algorithm to update parameters such as thermal conductivity and exchange current density, and cross-validation to ensure that the model's prediction error is less than 5%. It continuously optimizes the model structure and hyperparameters based on historical data.

[0094] S9 generates system performance evaluation and optimization suggestions. After daily operation, it automatically calculates key indicators such as unit hydrogen production energy consumption, average efficiency, and equipment availability. Based on the cumulative damage model, it predicts the remaining lifespan of modules, identifies efficiency bottlenecks, recommends control parameter optimization schemes, and establishes an operational knowledge base for continuous improvement through machine learning.

[0095] The entire system achieves coordinated operation and data sharing among its modules through a central intelligent control unit, forming a closed-loop management system covering the entire process from power monitoring and multi-field coordinated control to safety protection. This device not only deepens our understanding of the multi-physics coupling mechanism of electrolyzers but also significantly improves the adaptability and economy of hydrogen production systems under fluctuating power, possessing significant scientific research value and engineering application value.

[0096] This invention integrates multiphysics digital twin technology with modular electrolyzer design into the entire process of renewable energy hydrogen production to achieve dynamic synergistic optimization of electrochemical, temperature, fluid, and stress fields under fluctuating power. It includes a multiphysics coupled simulation analysis module that accurately predicts the system's internal state through real-time data assimilation; a synergistic control module integrating pulse width modulation and embedded heat exchangers to precisely manage electrode bubbles and temperature distribution; and a modular cluster employing intelligent switching and load balancing to flexibly adapt to power fluctuations ranging from 10% to 100%. The system achieves intelligent decision-making from power monitoring to safety protection through a central intelligent control unit, forming a complete adaptive optimization system. This invention can be applied not only to clean energy hydrogen production from wind and photovoltaic power but also to grid peak shaving and industrial by-product hydrogen utilization. This technology has the advantages of multi-field synergy, intelligent prediction, and modular flexibility, and has significant engineering value for the large-scale development of green hydrogen energy and energy structure transformation.

[0097] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A modular water electrolysis hydrogen production system with multi-physics field synergy, characterized in that, The system includes a central intelligent control unit, an electrolyzer module cluster, a multiphysics digital twin model server, hydrogen and oxygen collection and connection components, a safety protection system, and related auxiliary systems. The central intelligent control unit works in coordination with multiple parallel electrolyzer modules that can be started and stopped independently to achieve dynamic coupling control of electrochemical, temperature, fluid, and stress fields, enabling stable hydrogen production under conditions of renewable energy power fluctuations. The system front end is equipped with a renewable energy power input interface to support direct access to photovoltaic and wind power sources with fluctuating power, and the input power range covers 10% to 100% of the system's rated power. The central intelligent control unit constitutes the intelligent decision-making core of the entire system. It integrates a multi-physics digital twin model server and a data storage unit, and communicates with each execution unit through a high-speed industrial Ethernet. The central intelligent control unit is equipped with a temperature control unit, an adaptive prediction model, a decision system, a human-machine interface, and a pulse width modulation power supply. The temperature control unit's circulation pipeline is connected to an embedded plate heat exchanger. The electrolytic cell module cluster consists of multiple parallel electrolytic cell modules that can be started and stopped independently. Each module is quickly connected through a standard electrical connection interface and a standard fluid connection interface. The digital twin model integrates the Butler-Volmer equation for electrode reaction kinetics and Ohm's law for solution, accurately analyzes the composition of activation overpotential and ohmic overpotential, and introduces a correction factor for effective conductivity based on bubble shielding effect. The safety protection system integrates a nitrogen purging unit, an emergency stop button, and a multi-level alarm device to ensure the safe operation of the system under abnormal conditions.

2. The hydrogen production system as described in claim 1, characterized in that, The number of modules can be flexibly configured from 5 to 50 depending on the scale of hydrogen production.

3. The hydrogen production system as described in claim 1, characterized in that, The hydrogen and oxygen collection manifolds are made of 316L stainless steel and equipped with pressure sensors and flow meters to monitor the gas production and system pressure in real time.

4. The hydrogen production system as described in claim 1, characterized in that, The temperature control unit's circulation pipeline is connected to an embedded plate heat exchanger, using ethylene glycol aqueous solution as the heat transfer medium. A variable frequency pump precisely adjusts the flow rate from 2 to 10 L / min. The embedded plate heat exchanger is made of titanium alloy and has a heat transfer area of ​​0.5 m². 2 The module uses a PID algorithm to adjust the flow rate of the ethylene glycol solution, stabilizing the electrolyte inlet temperature within the optimal reaction range of 80±2℃.

5. The hydrogen production system as described in claim 1, characterized in that, The central intelligent control unit operates a multiphysics digital twin model based on the laws of mass conservation, charge conservation, and energy conservation, and solves the Nernst-Planck equation, Fourier's law of heat conduction, and the Navier-Stokes equation in a coupled manner. The Kalman filter algorithm is used to calibrate the parameters of the multiphysics digital twin model in real time. Every 5 seconds, the measured voltage, temperature, and pressure data are compared with the predicted values ​​of the multiphysics digital model to dynamically correct 12 key parameters, including the thermal conductivity coefficient and the exchange current density.

6. The hydrogen production system as described in claim 1, characterized in that, The multiphysics digital twin model server includes an electrochemical calculation unit, a temperature calculation unit, a fluid field calculation unit, and a stress field calculation unit. The electrochemical calculation unit integrates the Butler-Volmer equation and Ohm's law for electrode reaction kinetics, analyzes the composition of activation overpotential and Ohmic overpotential, and introduces a correction factor for the effective conductivity due to bubble shielding effect. This correction factor is dynamically adjusted according to the bubble coverage, with an adjustment range of 0.6-1.0, and the model prediction error is controlled within 5%. The temperature field calculation unit takes into account the reversible thermal effect caused by the reaction entropy change, the irreversible Joule heating effect caused by overpotential, and the heat exchange between the system and the external environment. The fluid field calculation unit simulates the seepage process of the electrolyte in the porous electrode and the generation, growth, and detachment behavior of bubbles, coupling the influence of bubble presence on the effective conductivity of the electrolyte. The stress field calculation unit calculates the distribution of mechanical stress caused by temperature changes using a thermal expansion coefficient model, and predicts the integrity of the sealing interface during thermal cycling.

7. The hydrogen production system as described in claim 1, characterized in that, The electrolytic cell module adopts a bipolar filter press structure. Each module includes electrodes, a diaphragm, and anode and cathode plates. Each module also includes a porous three-dimensional mesh electrode, a composite anion exchange membrane, a serpentine electrolyte channel, and an embedded plate heat exchanger. The diaphragm is a functionalized polyphenylene ether-based composite anion exchange membrane with a thickness of 150-200 μm, an ionic conductivity higher than 0.1 S / cm, and a hydrogen cross-permeability lower than 0.5 mA / cm. 2 The explosive strength is not less than 0.8MPa; the anode and cathode plates are designed with irregular flow channels for electrolyte flow. The electrolyte flow channels are optimized into a serpentine layout by computational fluid dynamics. The groove depth is 0.8mm, the width is 1.2mm, and the flow channel length is 1.5m. Sufficient shear force is generated at a flow velocity of 0.5m / s to peel off bubbles with a diameter greater than 50μm. The electrode is made of a porous three-dimensional network nickel-based alloy material with a specific surface area of ​​500 m². 2 With a porosity of 70%-80%, the surface is supported by an oxygen evolution catalyst with a perovskite structure through electrodeposition, and the coating thickness is 10-20μm, which reduces the overpotential of the oxygen evolution reaction to below 300mV.

8. The hydrogen production system as described in claim 1, characterized in that, The electrolytic cell modules are connected quickly via standardized interfaces for mechanical, electrical and fluid connections. The pre-tightening mechanism applies 10-15kN of axial pressure to compensate for thermal expansion stress and ensure the integrity of the sealing interface during temperature cycling from -20℃ to 120℃.

9. The hydrogen production system as described in claim 1, characterized in that, The safety protection system is equipped with a safety boundary protection mechanism that sets up multiple warning thresholds, including three levels of warning for electrolyte temperature (88℃ / 90℃ / 92℃), three levels of warning for electrolyte concentration (18% / 15% / 12%), and three levels of warning for hydrogen-oxygen pressure difference (45kPa / 48kPa / 50kPa).

10. The application method of the hydrogen production system as described in claim 1, characterized in that, Includes the following steps: S1, the hydrogen production system, performs system initialization and self-test. After confirming that the status of each module is normal, it enters standby mode. The initialization process includes sensor calibration, actuator zeroing, safety interlock test and data communication verification to ensure that the system is in a safe and controllable state. S2, renewable energy power monitoring and feature extraction, collects photovoltaic and wind power data in real time through smart meters at a sampling rate of 100Hz, applies a sliding time window algorithm to analyze the power sequence with a window size of 5 minutes and a step size of 1 second, calculates the power change rate dP / dt, identifies abrupt change points, sets the threshold to ±10% / s, and extracts statistical features such as mean, variance, kurtosis and fluctuation frequency. S3, a multi-physics digital twin model for online prediction, predicts the distribution of electrochemical, temperature, fluid and stress fields within the system based on current power fluctuation characteristics and environmental parameters. It outputs key parameters such as current density distribution, temperature gradient, bubble coverage and thermal stress, providing a basis for decision-making in optimized control. S4, Optimal module commissioning combination decision, query the pre-calibrated three-dimensional efficiency map, determine the optimal operating point based on the current temperature of 75 to 85℃ and concentration of 20 to 30%, calculate the number of modules commissioned N = rounded up, i.e. actual power / optimal power, prioritize healthy modules with a cumulative operating time of less than 5000 hours to ensure that the system operates in the high-efficiency range; S5, module switching and load balancing control, follows a gradual commissioning strategy. First, a basic module is put into operation and runs at a low load of 400A / m2 current density for 30 minutes. After the temperature field and concentration field stabilize, the number of modules put into operation is increased at a rate of 2 to 3 modules per hour according to power demand. When a new module is started, a ramp-up method is used, with the current density increasing from zero to the target value at a rate of 50A / m2 per second. S6 features multi-physics field coordinated parameter adjustment. The electrochemical field employs adaptive pulse width modulation technology, with a frequency of 1 to 10 kHz and a duty cycle of 30% to 70% optimized in real time according to the gas production rate. The temperature field is controlled by PID to maintain the electrolyte temperature at 80±2℃ by controlling the ethylene glycol flow rate from 2 to 10 L / min. The fluid field adjusts the pump speed according to the gas production rate. For every 1 L / min increase in gas production rate, the electrolyte flow rate increases by 0.1 m / s accordingly. The stress field is controlled by a preload servo to 10 to 15 kN to compensate for thermal expansion strain. S7 provides real-time monitoring and early warning of safety boundaries, continuously monitoring key parameters such as electrolyte temperature, concentration, and system differential pressure. When the parameters approach the first-level early warning threshold, it issues an audible and visual alarm and provides optimization suggestions. When the parameters approach the level 2 warning threshold, the total current output is automatically reduced at a rate of 3% per second, and the backup cooling system is activated; when the parameters reach the level 3 warning threshold, an emergency shutdown is performed within 100 milliseconds, and the nitrogen purging valve is opened to maintain the system pressure at a flow rate of 20L / min. S8, the digital twin model online calibration and optimization, compares the measured voltage, temperature and pressure data with the model predictions every 5 seconds, uses the Kalman filter algorithm to update the thermal conductivity coefficient and exchange current density parameters, and ensures that the model prediction error is less than 5% through cross-validation, and continuously optimizes the model structure and hyperparameters based on historical data; S9 generates system performance evaluation and optimization suggestions. After daily operation, it automatically calculates key indicators such as unit hydrogen production energy consumption, average efficiency, and equipment availability. Based on the cumulative damage model, it predicts the remaining lifespan of modules, identifies efficiency bottlenecks, recommends control parameter optimization schemes, and establishes an operational knowledge base for continuous improvement through machine learning.

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