Electrolytic cell monitoring control system and method
By connecting multiple electrolysis chambers to the monitoring and control system in the electrolytic cell, multi-dimensional data acquisition and intelligent control are achieved, solving the problem of identifying hidden dangers caused by multi-parameter coupling in the electrolytic cell and improving the safety and operating efficiency of the electrolytic cell.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-16
- Publication Date
- 2026-04-03
AI Technical Summary
Existing technologies are unable to comprehensively, timely and accurately capture the progressive or hidden safety hazards caused by the coupling of multiple parameters in electrolytic cells, resulting in low safety and efficiency in the operation of electrolytic cells.
Multiple electrolysis chambers are connected to a monitoring and control system, including a parameter monitoring module, a data processing and control module, and an action execution module. By integrating monitoring parameters through a state detection model, multi-dimensional data acquisition and intelligent control are achieved.
It improves the comprehensiveness and accuracy of electrolytic cell monitoring, enabling early detection of potential risks, reducing false alarm and missed alarm rates, ensuring efficient and safe operation of electrolytic cells, and reducing maintenance costs and accident rates.
Smart Images
Figure CN121781223A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of data monitoring technology, and in particular to an electrolytic cell monitoring and control system and method. Background Technology
[0002] In the process of hydrogen production through water electrolysis, the electrolyzer is the core equipment, and its safe operation is of paramount importance. The operating status of the electrolyzer is affected by a combination of factors, including voltage, temperature, electrolyte characteristics, and the state of gaseous products, leading to numerous safety hazards during operation. Current safety monitoring methods for electrolyzers are insufficient to comprehensively, timely, and accurately detect progressive or latent safety hazards caused by the coupling of multiple parameters, thus failing to meet the requirements for efficient and safe operation of electrolyzers. Summary of the Invention
[0003] To address the aforementioned technical problems, this disclosure provides an electrolytic cell monitoring and control system and method.
[0004] According to one aspect of this disclosure, an electrolytic cell monitoring and control system is provided. The electrolytic cell includes multiple sets of electrolysis chambers, and the multiple sets of electrolysis chambers are connected one-to-one with multiple monitoring and control systems. The monitoring and control system includes: a parameter monitoring module, a data processing and control module, and an action execution module. The parameter monitoring module includes: a voltage monitoring submodule, a temperature monitoring submodule, an electrolyte monitoring submodule, and a gas monitoring submodule. The parameter monitoring module is used to collect various monitoring parameters of the electrolysis chamber according to a preset sampling frequency; wherein, the monitoring parameters include at least: voltage, temperature, key physicochemical parameters of the electrolyte and gas parameters of the outlet of the electrolysis chamber; The data processing and control module is used to determine the current operating status of the electrolysis chamber based on various monitoring parameters using a status detection model; and to determine the control command corresponding to the current operating status. The action execution module is used to control the electrolysis chamber according to the control command.
[0005] According to another aspect of this disclosure, an electrolytic cell monitoring and control method is also provided, the method comprising: Multiple monitoring parameters of the electrolysis chamber are collected according to a preset sampling frequency; wherein, the monitoring parameters include at least: voltage, temperature, key physicochemical parameters of the electrolyte and gas parameters at the outlet of the electrolysis chamber; the electrolysis chamber is any one of the multiple sets of electrolysis chambers included in the electrolytic cell; The current operating status of the electrolysis chamber is determined by a state detection model based on various monitoring parameters. Determine the control command corresponding to the current operating state; The electrolysis chamber is controlled according to the control command.
[0006] According to another aspect of this disclosure, an electronic device is also provided, the electronic device comprising: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the above-described electrolytic cell monitoring and control method.
[0007] According to another aspect of this disclosure, a computer-readable storage medium is also provided, the storage medium storing a computer program for executing the above-described electrolytic cell monitoring and control method.
[0008] The technical solution provided in this disclosure has the following advantages compared with the prior art: This disclosure provides an electrolytic cell monitoring and control system and method, wherein the electrolytic cell includes multiple sets of electrolysis chambers, and each set of electrolysis chambers is connected to multiple monitoring and control systems. The monitoring and control systems include: a parameter monitoring module, a data processing and control module, and an action execution module. The parameter monitoring module includes: a voltage monitoring submodule, a temperature monitoring submodule, an electrolyte monitoring submodule, and a gas monitoring submodule. The parameter monitoring module is used to collect multiple monitoring parameters of the electrolysis chambers according to a preset sampling frequency. The monitoring parameters include at least: voltage, temperature, key physicochemical parameters of the electrolyte, and gas parameters at the outlet of the electrolysis chamber. The data processing and control module is used to determine the current operating state of the electrolysis chamber based on the multiple monitoring parameters through a state detection model, and to determine the control command corresponding to the current operating state. The action execution module is used to control the electrolysis chambers according to the control command. This disclosure can improve the comprehensiveness of electrolytic cell monitoring, more accurately capture gradual or hidden safety hazards caused by the coupling of multiple monitoring parameters, and better meet the needs of efficient and safe operation of electrolytic cells. Attached Figure Description
[0009] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.
[0010] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0011] Figure 1This is a schematic diagram of the electrolytic cell monitoring and control system described in an embodiment of this disclosure; Figure 2 This is a schematic diagram of the monitoring and control system described in an embodiment of the present disclosure; Figure 3 This is a flowchart of the electrolytic cell monitoring and control method described in the embodiments of this disclosure; Figure 4 This is a schematic diagram of the structure of the electronic device described in an embodiment of this disclosure. Detailed Implementation
[0012] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.
[0013] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.
[0014] Currently, some solutions for safety monitoring of electrolytic cells use independent voltage or temperature sensors to monitor a single parameter, either voltage or temperature, and set fixed safety thresholds. When the monitored voltage or temperature value exceeds its corresponding fixed threshold, it is considered abnormal and triggers an alarm or protection action. However, this monitoring method based on a single or a few isolated parameters and using fixed thresholds has significant limitations. These include the following issues: (1) The inside of an electrolytic cell is a complex multi-physics coupled system. Its safety hazards are often not caused by the sudden change of a single parameter. If only one or a few parameters are monitored, without considering the coupling relationship between parameters (such as the change of electrolyte concentration will simultaneously affect the voltage and temperature response), the identification of hazards will be one-sided, and false alarms or omissions are likely to occur.
[0015] (2) Using a fixed threshold, it cannot adapt to dynamic working conditions such as electrolytic cell startup, load change, and equipment aging, and has poor adaptability.
[0016] (3) Lack of accurate fault location capability, only able to identify abnormalities but unable to trace faulty components such as electrodes, electrolyte pipelines, membrane modules, etc., increasing maintenance difficulty.
[0017] (4) The control strategy is a post-event response mode, which lacks predictive regulation capabilities and makes it difficult to avoid gradual risks in advance, such as slow electrode polarization and gradual damage to membrane components.
[0018] (5)The safety protection is only limited to the adjustment of the electrolyzer body parameters and is not linked with the factory fire protection, ventilation, emergency shutdown and other systems. The protection is incomplete in extreme scenarios.
[0019] Therefore, the relevant solutions for safety monitoring of electrolyzers are difficult to comprehensively, timely and accurately capture the progressive or hidden safety hazards caused by multi-parameter coupling, and it is difficult to meet the requirements of efficient and safe operation of electrolyzers. There is an urgent need for a safety protection solution with multi-parameter fusion, adaptive learning, precise traceability and hierarchical linkage.
[0020] In this case, the present disclosure provides an electrolyzer monitoring and control system and method. For ease of understanding, the electrolyzer monitoring and control system will be described first below.
[0021] Figure 1 FIG. is a schematic structural diagram of an electrolyzer monitoring and control system provided by an embodiment of the present disclosure. The electrolyzer monitoring and control system provided by this embodiment may include an electrolyzer and a monitoring and control system 200; wherein, the electrolyzer includes multiple groups of electrolysis cells 100, and multiple groups of electrolysis cells 100 are connected to multiple monitoring and control systems 200 one by one. Refer to Figure 2 , the monitoring and control system 200 may include, but is not limited to: a parameter monitoring module 210, a data processing and control module 220, and an action execution module 230.
[0022] The parameter monitoring module 210 is configured to collect various monitoring parameters of the electrolysis cell 100 according to a preset sampling frequency; wherein, the monitoring parameters at least include: voltage, temperature, key physical and chemical parameters of the electrolyte, and gas parameters at the gas outlet of the electrolysis cell 100.
[0023] The data processing and control module 220 is configured to determine the current operating state of the electrolysis cell 100 based on various monitoring parameters through a state detection model; and determine a control instruction corresponding to the current operating state.
[0024] The action execution module 230 is configured to control the electrolysis cell 100 according to the control instruction.
[0025] Combined with Figure 1 and Figure 2 , in this embodiment, the electrolyzer is composed of multiple groups of electrolysis cells 100 connected in series or in parallel. Each group of electrolysis cells 100 is provided with an independent liquid inlet and gas outlet, which is the core place for hydrogen production by water electrolysis; each group of electrolysis cells 100 is also provided with an independent parameter sampling interface and a linkage control interface; wherein, the parameter sampling interface can be adapted to a variety of sensors, and the linkage control interface can be adapted to emergency linkage devices such as the factory fire protection system, ventilation system and main power cut-off device.
[0026] The implementation scheme for monitoring and controlling each electrolysis chamber 100 through the monitoring and control system 200 can be referred to in the following embodiment.
[0027] The parameter monitoring module 210 collects various monitoring parameters of the electrolysis chamber 100 at a preset sampling frequency (e.g., 100Hz). In this embodiment, the parameter monitoring module 210 may include, for example, a voltage monitoring submodule, a temperature monitoring submodule, an electrolyte monitoring submodule, and a gas monitoring submodule.
[0028] The voltage monitoring submodule is connected to the electrodes of the electrolysis chamber 100 and is used to collect the voltage of the electrolysis chamber 100. Specifically, the voltage monitoring submodule is connected to the electrodes of the electrolysis chamber 100 and can use a high-precision voltage sensor to collect the voltage of the electrolysis chamber 100 in real time and transmit the voltage to the data processing and control module 220 for subsequent processing.
[0029] A temperature monitoring submodule is installed at multiple key locations within the electrolysis chamber 100 to collect temperatures at these key locations. These key locations include the electrode positions and the electrolyte flow channels. Specifically, to achieve more accurate and comprehensive temperature monitoring, this embodiment can set multiple temperature monitoring points within the electrolyte flow channels. Based on this, temperature sensors are installed at key locations within the electrolysis chamber 100 (such as near the electrodes and in the electrolyte area) as the temperature monitoring submodule to monitor the temperature at each key location within the electrolysis chamber 100 in real time. The collected temperatures are transmitted to the data processing and control module 220 for subsequent processing.
[0030] Based on the monitoring of the above basic parameters, the parameter monitoring module 210 also includes the following electrolyte monitoring submodule and gas monitoring submodule to collect data from more dimensions.
[0031] The electrolyte monitoring submodule is used to collect key physicochemical parameters of the electrolyte within the electrolysis chamber 100. These key physicochemical parameters include: electrolyte conductivity, pH value of the alkaline electrolyzer, and humidity of the PEM electrolyzer. Specifically, the electrolyte monitoring submodule may include: a conductivity sensor, a pH sensor, and a humidity sensor installed within the electrolysis chamber 100. The conductivity sensor is used to collect the electrolyte conductivity, which indirectly reflects the electrolyte concentration; the pH sensor is used to collect the pH value for the alkaline electrolyzer; and the humidity sensor is used to collect the humidity for the PEM electrolyzer. The key physicochemical parameters of the electrolyte collected in real time are transmitted to the data processing and control module 220 for subsequent processing.
[0032] A gas monitoring submodule is installed at the outlet of the electrolysis chamber 100 to collect gas parameters at the outlet, including gas flow rate and the purity index between hydrogen and oxygen. Specifically, the gas monitoring submodule includes a gas flow sensor and a hydrogen / oxygen purity sensor installed at the outlet of the electrolysis chamber 100. The gas flow sensor collects the gas flow rate at the outlet of the electrolysis chamber 100; the hydrogen / oxygen purity sensor monitors the gas product flow ratio (e.g., hydrogen:oxygen = 2:1) and purity index, using this purity index to detect potential problems such as membrane module damage and seal leakage. The real-time collected gas parameters are transmitted to the data processing and control module 220 for subsequent processing.
[0033] For the voltage monitoring submodule, temperature monitoring submodule, electrolyte monitoring submodule and gas monitoring submodule mentioned above, they have the same sampling frequency to achieve synchronous acquisition of multiple monitoring parameters; and the sampling frequency can be a high value, such as 100Hz, to ensure the capture of transient anomalies.
[0034] According to the above embodiments, after the voltage, temperature, key physicochemical parameters of the electrolyte and gas parameters at the outlet of the electrolysis chamber 100 collected by the parameter monitoring module 210 are transmitted to the data processing and control module 220, the data processing and control module 220 first determines the current operating status of the electrolysis chamber 100 based on the multiple monitoring parameters through the state detection model.
[0035] In this embodiment, the data processing and control module 220 may include a computing node. Each electrolysis chamber 100 is configured with a local edge computing node, specifically a device with data processing and control capabilities, such as a tablet computer, desktop computer, laptop computer, or smartphone.
[0036] In this embodiment, the computing node performs data preprocessing on the monitoring parameters, including filtering, noise reduction, and outlier removal. The computing node can respond quickly to the preprocessed monitoring parameters locally (processing transient anomalies in milliseconds), and it can also synchronously transmit the preprocessed monitoring parameters to the cloud server.
[0037] In one embodiment, the computing node can be used to implement the following basic judgment scheme for each individual monitoring parameter: Determine whether each monitoring parameter exceeds its preset dynamic safety threshold; identify monitoring parameters that exceed the corresponding dynamic safety threshold as target abnormal parameters. For example: Determine if the voltage exceeds its preset voltage safety threshold; if it does, determine that the voltage is a target abnormal parameter. Determine if the temperature exceeds its preset temperature safety threshold; if it does, determine that the temperature is a target abnormal parameter. Determine whether the electrolyte conductivity exceeds its preset conductivity safety threshold; if it does, determine that the electrolyte conductivity is the target abnormal parameter. Determine whether the pH value of the alkaline electrolyzer exceeds its preset pH safety threshold; if it does, determine that the pH value of the alkaline electrolyzer is the target abnormal parameter. Determine whether the humidity of the PEM electrolyzer exceeds its preset humidity safety threshold; if it does, determine the humidity of the PEM electrolyzer as the target abnormal parameter. Determine whether the gas flow rate exceeds its preset flow safety threshold; if it does, determine that the gas flow rate is a target abnormal parameter. Determine whether the purity index between hydrogen and oxygen exceeds its preset purity safety threshold; if it does, then determine the purity index between hydrogen and oxygen as the target abnormal parameter.
[0038] While the examples above focus on judging individual monitoring parameters, a method for judging the coupling of multiple monitoring parameters can be provided. That is, if a single monitoring parameter is within and close to its safety threshold, and simultaneously, other related monitoring parameters exceed their respective safety thresholds—in other words, the related monitoring parameters are abnormal—then a potential coupling hazard is identified. For example, if the voltage is normal, but the hydrogen purity decreases and the temperature increases, this is considered a potential coupling hazard.
[0039] In another embodiment, the computing node can also be used to implement the following fusion judgment scheme that includes at least two monitoring dimensions: The state detection model determines whether at least one monitoring parameter meets a preset first abnormal condition; wherein the first abnormal condition includes: the monitoring parameter exceeds the corresponding safety parameter range, and / or the fluctuation value of the monitoring parameter within a preset time period exceeds the preset safety fluctuation range; if so, the current operating state of the electrolysis chamber 100 is determined to be an operating risk; and the monitoring parameter that meets the first abnormal condition is determined as the target abnormal parameter.
[0040] The state detection model in this embodiment is a model for detecting the state of the electrolysis chamber, which is constructed based on parameters of at least two monitoring dimensions. This state detection model can adopt a deep learning model (LSTM + attention mechanism).
[0041] For example, the state detection model is a model built based on parameters of two monitoring dimensions: voltage and temperature, used to detect whether at least one of the voltage and temperature of the electrolysis chamber 100 meets a preset first abnormal condition. Alternatively, the state detection model is a model built based on parameters of three monitoring dimensions: voltage, temperature, and physicochemical parameters, used to detect whether at least one of the voltage, temperature, electrolyte conductivity, pH value of the alkaline electrolyzer, and humidity of the PEM electrolyzer of the electrolysis chamber 100 meets a preset first abnormal condition. Or, the state detection model is a model built based on parameters of four monitoring dimensions: voltage, temperature, physicochemical parameters, and gas parameters, used to detect whether at least one of the voltage, temperature, electrolyte conductivity, pH value of the alkaline electrolyzer, humidity of the PEM electrolyzer, gas flow rate, and purity index between hydrogen and oxygen of the electrolysis chamber 100 meets a preset first abnormal condition.
[0042] This example illustrates the process using a state detection model based on voltage and temperature. In implementation, real-time collected voltage and temperature data are input into the state detection model. Based on a first anomaly condition, the model determines whether the voltage and temperature satisfy at least one of the following: a. The voltage exceeds the corresponding voltage safety parameter range; b. The voltage fluctuation value exceeds the preset voltage safety fluctuation range within a preset time period (e.g., 24 hours); c. The temperature exceeds the corresponding safe temperature parameter range; d. Temperature fluctuations within a preset time period (e.g., 24 hours) exceed the preset safe temperature fluctuation range.
[0043] In scenario a, the voltage has exceeded its safe voltage parameter range. If the voltage in electrolysis chamber 100 is higher than the upper limit of the safe voltage parameter range, there may be problems such as electrode polarization or short circuit; if the voltage is lower than the lower limit of the safe voltage parameter range, there may be electrode connection failure or electrolyte performance degradation. In this case, the current operating status of electrolysis chamber 100 is determined to be at risk, and the voltage is identified as the target abnormal parameter.
[0044] In case c, the temperature has exceeded its safe operating temperature range. When the temperature exceeds the upper limit of the safe operating temperature range, it indicates that heat is accumulating inside the electrolysis chamber, posing a safety risk. In this situation, the current operating status of electrolysis chamber 100 is determined to be one of operational risk, and the temperature is identified as the target abnormal parameter.
[0045] For scenarios b and d, although voltage and temperature do not exceed their respective safe parameter ranges, if their trends show abnormally rapid fluctuations or continuously deviate from the normal range, it also indicates a safety risk. In this case, the current operating status of the electrolysis chamber 100 is determined to be at risk, and voltage and temperature are identified as target abnormal parameters. By predicting the changing trends of the monitored parameters at a future target time (e.g., 5-10 minutes) using time-series data within a preset time period, an early warning can be triggered if the monitored parameter is predicted to exceed its safe parameter range.
[0046] If the voltage is abnormal, and this is identified as a target abnormal parameter, the following control commands can be generated: adjust the power supply output voltage, check electrode connections, etc. If the temperature is abnormal, and this is identified as a target abnormal parameter, the following control commands can be generated: start or increase the cooling system power, adjust the liquid inlet flow rate to improve heat dissipation, etc. After receiving the control commands, the action execution module 230 quickly executes the corresponding operations and feeds back the execution results to the data processing and control module 220. Based on the feedback information, the data processing and control module 220 continuously monitors the voltage and temperature changes of the electrolysis chamber 100, further optimizes the control strategy, forms a closed-loop safety protection mechanism, and ensures the safe operation of the electrolytic cell.
[0047] Based on the above embodiments, the monitoring parameters that meet the first abnormal condition are determined as target abnormal parameters.
[0048] In another embodiment, the computing node can also be used to implement the following fusion judgment scheme that includes at least two monitoring dimensions: The state detection model determines whether at least two monitoring parameters simultaneously meet a preset second abnormal condition; wherein the second abnormal condition includes: the monitoring parameter does not exceed the corresponding safety parameter range and the parameter difference between it and the extreme value of the safety parameter range is less than a preset difference, and / or, the fluctuation value of the monitoring parameter within a preset time period exceeds the preset safety fluctuation range; if so, the current operating state of the electrolysis chamber 100 is determined to be an operating risk; and the monitoring parameter that meets the second abnormal condition is determined as the target abnormal parameter.
[0049] Based on the above embodiments, after determining that the current operating status of the electrolysis chamber 100 is at risk and after identifying the monitoring parameters that meet the second abnormal condition as the target abnormal parameters, it is necessary to trace the source of the fault, locate the faulty component (such as the electrode or membrane assembly), and determine the risk level (minor / moderate / severe).
[0050] In this case, the data processing and control module 220 in this embodiment may further include: a fault tracing unit, which is used for: If the current operating status of the electrolysis chamber 100 is determined to be risky, the target risk level and the target faulty component in the electrolysis chamber 100 are determined based on the target abnormal parameters and the preset fault feature library; wherein, the fault feature library is used to describe the abnormal parameters, risk level and faulty component corresponding to various faults.
[0051] In this embodiment, a fault feature library is established by integrating the temporal variation characteristics of key physicochemical parameters of voltage, temperature, electrolyte, and gas parameters. This fault feature library covers many typical faults such as electrode polarization, membrane damage, electrolyte leakage, and cooling system failure.
[0052] By matching the target abnormal parameters with the fault feature database using a similarity matching algorithm, the target faulty component corresponding to the target abnormal parameters and the target risk level indicating the severity of the fault can be accurately located.
[0053] In the fault characteristic database, safety risk levels are typically divided into three levels: Level 1, Level 2, and Level 3; Level 1 risk is lower than Level 2 risk, which is lower than Level 3 risk. Specifically, Level 1 risk can be termed minor risk, Level 2 risk as moderate risk, and Level 3 risk as severe risk. Different risk levels correspond to different control commands to avoid production interruptions caused by over-protection.
[0054] After determining the target risk level and the target faulty component in the electrolysis chamber 100, corresponding control commands are generated and sent to the action execution module 230. The data processing and control module 220 employs a high-performance processor and professional data analysis software to ensure the accuracy of data processing and the timeliness of command generation.
[0055] In this embodiment, the data processing and control module 220 may further include an instruction generation unit, which generates control instructions based on the target risk level and target faulty component corresponding to the current operating state.
[0056] Furthermore, in this embodiment, the action execution module 230 includes: a body control submodule and an emergency linkage submodule; wherein: The main body control submodule is used to control the electrolysis chamber that poses an operational risk based on local control commands, combined control commands, or fault cut-off commands. The emergency linkage submodule is connected to the preset emergency system and is used to control the emergency system to perform emergency actions according to emergency response instructions. The emergency system includes: fire protection system, ventilation system and main power management device.
[0057] The aforementioned action execution module 230 is connected to relevant components of the electrolytic cell, such as the liquid inlet valve, cooling system, and power control system, and receives control commands from the data processing and control module 220. Based on the control commands, it performs corresponding operations, such as adjusting the liquid inlet flow rate, starting or enhancing the cooling system, and adjusting the power output, to eliminate safety hazards and ensure the safe operation of the electrolytic cell.
[0058] In a specific embodiment, the instruction generation unit is used to: when it is determined that the current operating state of the electrolysis chamber 100 is at risk, obtain the target risk level and the target faulty component. Then, it processes the following cases separately.
[0059] When the target risk level is the first risk level, generate local control instructions for each individual target faulty component.
[0060] Specifically, for the first risk level (i.e., minor risk), the instruction generation unit can generate local control instructions targeting a single faulty component. The main control submodule then controls the electrolysis chamber with operational risk based on these local control instructions. For example, if the first risk level is a slight deviation in electrolyte concentration, the target faulty component could be a valve or metering pump, which controls the electrolyte inflow and outflow. Thus, a local control instruction is generated for the aforementioned target faulty component, such as an inflow adjustment instruction or an electrolyte replenishment instruction. This local control instruction can be executed by the main control submodule to adjust the inflow or replenish the electrolyte in the electrolysis chamber. Subsequently, monitoring parameters are continuously collected to determine the recovery status of the operational condition.
[0061] When the target risk level is the second risk level, generate combined control commands for at least two target faulty components and generate alert information.
[0062] Specifically, for the second risk level (i.e., medium risk), the instruction generation unit can generate combined control instructions targeting at least two target faulty components and generate alert messages. The main control submodule then controls the electrolysis chamber with operational risks based on these combined control instructions. For example, the second risk level is characterized by electrode polarization and continuous temperature rise; correspondingly, the target faulty components could be the transformer controlling the voltage and the cooling system and stirring device controlling the temperature. Thus, combined control instructions are generated for these multiple target faulty components. These combined control instructions specifically include: reducing the supply voltage of the electrolysis chamber, increasing the power of the cooling system, and starting the local stirring device. These combined control instructions can be executed by the main control submodule controlling the transformer, cooling system, and stirring device. Simultaneously, alert messages are generated and pushed to the staff.
[0063] When the target risk level is the third risk level, a shutdown fault command, an emergency response command, and an alarm message are generated. The shutdown fault command is used to instruct the shutdown of the electrolysis chamber with operational risks, and the emergency response command is used to instruct the preset emergency system to execute preset emergency actions.
[0064] Specifically, for the third risk level (severe risk), the instruction generation unit can generate a fault cutoff instruction, an emergency response instruction, and alarm information. The main control submodule then controls the electrolysis cell with operational risk based on the fault cutoff instruction. For example, the third risk level is membrane module damage and / or a sudden drop in gas purity. On the one hand, the main control submodule cuts off the operation of the faulty electrolysis cell based on the fault cutoff instruction, thus not affecting the overall operation of the electrolyzer.
[0065] On the other hand, the emergency linkage submodule has communication interfaces with the factory's fire protection system, ventilation system, and main power cut-off device. Based on this, when it receives a cut-off fault command, emergency response command, and alarm information, it simultaneously triggers emergency measures of the external emergency system, such as: starting the ventilation system for ventilation, controlling the main power management device to cut off the main power, and sending alarm information to the fire protection system to trigger a fire warning.
[0066] In one embodiment, the action execution module 230 further includes a self-calibration submodule, which includes: According to the preset correction cycle, the various sensors included in the parameter monitoring module 210 are corrected for errors based on the reference data under the preset standard working conditions.
[0067] Specifically, the self-calibration submodule can initiate the sensor's self-calibration process according to a preset cycle. By comparing the data with the standard reference sensor, it corrects the drift errors of various sensors such as voltage, temperature, and conductivity, ensuring monitoring accuracy.
[0068] In one embodiment, the data processing and control module 220 may further include an adaptive machine learning unit, which is used to optimize and update the state detection model based on historical monitoring data within a historical time period.
[0069] Among them, the condition detection model can learn autonomously through historical operating data (including: normal operating conditions, fault operating conditions and aging process data, etc.), optimize the safety threshold and risk judgment logic in real time, and adapt to the changes in the entire life cycle of the electrolytic cell.
[0070] The computing nodes communicate bidirectionally with the cloud server, and the cloud stores massive amounts of historical data, supporting iterative training of the state detection model and collaborative management of multi-electrolytic cell clusters.
[0071] The cloud server stores fault data and processing results, forming equipment health records, and pushes predictive maintenance suggestions based on historical fault patterns (such as replacing aging electrodes in advance and replenishing electrolyte).
[0072] In summary, the electrolytic cell monitoring and control system provided in this disclosure has the following beneficial effects: (1) Multi-parameter fusion ensures comprehensive hazard identification. Compared to traditional single or dual-parameter monitoring, this disclosure provides comprehensive monitoring and accurate early warning. By integrating multi-dimensional data such as voltage, temperature, electrolyte characteristics, and gas parameters, it can comprehensively capture safety hazards during the operation of the electrolytic cell, effectively solving the problem of hazard omission caused by parameter coupling. Through coupling analysis and trend prediction, the early warning lead time is increased from the traditional second level to the minute level, reducing the false alarm rate by more than 90% and controlling the false alarm rate to within 5%. This disclosure can detect potential risks in advance, achieve accurate early warning, and buy more time for safe handling.
[0073] (2) Intelligent control and efficient protection. Based on the established state detection model, the system can intelligently analyze the safety risk level and automatically generate precise control commands. The action execution module responds quickly and adjusts the electrolytic cell operating parameters in a timely manner, effectively reducing the probability of accidents, ensuring stable operation of the electrolytic cell, and improving the service life of the equipment.
[0074] (3) Strong adaptability and wide application: The system can adapt to electrolytic cells of different specifications and different operating conditions. It can effectively play a safety protection role in various situations such as electrolytic cell startup, normal operation, load change, or equipment aging, and has a wide range of application prospects.
[0075] (4) Reduce maintenance costs: By timely detection and elimination of safety hazards, the frequency of equipment failures and damages is reduced, the cost of equipment repair and replacement is reduced, and production interruptions caused by accidents are avoided, thus improving production efficiency and bringing significant economic benefits.
[0076] (5) Adaptive learning, accurate adaptation to all scenarios. The state detection model can dynamically update safety thresholds and control strategies, adapting to all lifecycle scenarios such as electrolytic cell startup, load changes, and equipment aging without manual intervention. For the different characteristics of different types of electrolytic cells (alkaline, PEM), the model can autonomously optimize monitoring priorities, improving adaptability by 80% compared to traditional fixed threshold solutions.
[0077] (6) Fault tracing and graded protection double maintenance efficiency. The fault tracing algorithm can accurately locate faulty components, avoid blind repairs, and shorten maintenance time by 60%; the graded protection strategy distinguishes different risk levels, which can not only avoid excessive downtime caused by minor abnormalities (improving production continuity), but also achieve all-round linkage protection in case of serious risks, reducing the accident rate by more than 95%.
[0078] (7) Predictive maintenance and self-calibration optimize the entire life cycle cost. Through predictive maintenance recommendations, progressive failures can be avoided in advance, extending the service life of the equipment by 30%; the sensor self-calibration function ensures long-term stable monitoring accuracy and avoids misjudgments caused by sensor drift, reducing maintenance costs by 40%; the local control capability reduces the number of overall electrolytic cell shutdowns, increasing production efficiency by more than 20%.
[0079] (8) Emergency linkage and safety redundancy upgrade for extreme scenarios. The emergency linkage submodule achieves seamless connection between the electrolytic cell protection and the factory emergency system, solving the limitations of traditional methods that only focus on the protection of the cell itself. In severe fault scenarios, it can quickly cut off the risk source and activate external protection to prevent the accident from escalating (such as explosion or gas leak), significantly improving production safety.
[0080] (9) Cloud-edge collaboration for more efficient cluster management. The cloud-edge collaboration architecture supports local rapid response of a single electrolyzer and collaborative management of multiple electrolyzer clusters. It can be applied to the large-scale deployment of large hydrogen production plants. Managers can monitor the status of all equipment in real time through the cloud platform to achieve centralized and intelligent operation and maintenance.
[0081] Reference Figure 3 This embodiment provides a method for monitoring and controlling an electrolytic cell, the method comprising: S302, collects various monitoring parameters of the electrolysis chamber according to a preset sampling frequency; among which, the monitoring parameters include at least: voltage, temperature, key physicochemical parameters of the electrolyte and gas parameters of the outlet of the electrolysis chamber; the electrolysis chamber is any one of the multiple sets of electrolysis chambers included in the electrolytic cell; S304 determines the current operating status of the electrolysis chamber based on various monitoring parameters using a state detection model; S306, determine the control command corresponding to the current operating state; S308 controls the electrolysis chamber according to control commands.
[0082] The method provided in this embodiment has the same implementation principle and technical effect as the aforementioned system embodiment. For the sake of brevity, any parts not mentioned in the method embodiment can be referred to the corresponding content in the aforementioned system embodiment.
[0083] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Figure 4 As shown, the electronic device 400 includes one or more processors 401 and memory 402.
[0084] The processor 401 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 400 to perform desired functions.
[0085] The memory 402 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 401 may execute the program instructions to implement the electrolytic cell monitoring and control method of the embodiments of this disclosure described above, and / or other desired functions. Various contents such as input signals, signal components, and noise components may also be stored in the computer-readable storage medium.
[0086] In one example, the electronic device 400 may also include an input device 403 and an output device 404, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).
[0087] In addition, the input device 403 may also include, for example, a keyboard, a mouse, etc.
[0088] The output device 404 can output various information to the outside, including determined distance information, direction information, etc. The output device 404 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.
[0089] Of course, for the sake of simplicity, Figure 4 Only some of the components of the electronic device 400 relevant to this disclosure are shown, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device 400 may include any other suitable components depending on the specific application.
[0090] Furthermore, this embodiment also provides a computer-readable storage medium storing a computer program for executing the above-described electrolytic cell monitoring and control method.
[0091] The present disclosure provides a computer program product for an electrolytic cell monitoring and control method, apparatus, electronic device, and medium, including a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementation details, please refer to the method embodiments, which will not be repeated here.
[0092] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0093] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A monitoring and control system for an electrolytic cell, characterized in that, The electrolytic cell includes multiple sets of electrolysis chambers, and each set of electrolysis chambers is connected to multiple monitoring and control systems. The monitoring and control systems include: a parameter monitoring module, a data processing and control module, and an action execution module. The parameter monitoring module includes: a voltage monitoring submodule, a temperature monitoring submodule, an electrolyte monitoring submodule, and a gas monitoring submodule. The parameter monitoring module is used to collect various monitoring parameters of the electrolysis chamber according to a preset sampling frequency; wherein, the monitoring parameters include at least: voltage, temperature, key physicochemical parameters of the electrolyte and gas parameters of the outlet of the electrolysis chamber; The data processing and control module is used to determine the current operating status of the electrolysis chamber based on various monitoring parameters using a status detection model; and to determine the control command corresponding to the current operating status. The action execution module is used to control the electrolysis chamber according to the control command.
2. The system according to claim 1, characterized in that, The parameter monitoring module includes: The voltage monitoring submodule is connected to the electrodes of the electrolysis chamber and is used to collect the voltage of the electrolysis chamber; The temperature monitoring submodule is installed at multiple key locations within the electrolysis chamber to collect the temperature at these key locations; wherein, the key locations include: electrode locations and electrolyte flow channels; The electrolyte monitoring submodule is used to collect key physicochemical parameters of the electrolyte in the electrolysis chamber; wherein, the key physicochemical parameters include: electrolyte conductivity, pH value of alkaline electrolyzer and humidity of PEM electrolyzer; The gas monitoring submodule is installed at the gas outlet of the electrolysis chamber and is used to collect gas parameters at the gas outlet of the electrolysis chamber. The gas parameters include: gas flow rate and purity index between hydrogen and oxygen.
3. The system according to claim 1, characterized in that, The data processing and control module includes: a computing node, which is used for: The state detection model determines whether at least one monitoring parameter meets a preset first abnormal condition; wherein, the first abnormal condition includes: the monitoring parameter exceeds the corresponding safety parameter range, and / or, the fluctuation value of the monitoring parameter exceeds the preset safety fluctuation range within a preset time period; If so, then the current operating status of the electrolysis chamber is determined to be one of operational risk; and, The monitoring parameters that meet the first abnormal condition are determined as target abnormal parameters.
4. The system according to claim 1, characterized in that, The computing nodes included in the data processing and control module are used for: The state detection model determines whether at least two of the monitoring parameters simultaneously meet a preset second abnormal condition; wherein the second abnormal condition includes: the monitoring parameter does not exceed the corresponding safety parameter range and the parameter difference between it and the extreme value of the safety parameter range is less than a preset difference, and / or, the fluctuation value of the monitoring parameter within a preset time period exceeds a preset safety fluctuation range; In this case, the current operating status of the electrolysis chamber is determined to be one where there is an operational risk; and, The monitoring parameters that meet the second abnormal condition are determined as target abnormal parameters.
5. The system according to claim 3 or 4, characterized in that, The data processing and control module includes a fault tracing unit, which is used for: If the current operating status of the electrolysis chamber is determined to be risky, the target risk level and the target faulty component in the electrolysis chamber are determined based on the target abnormal parameters and the preset fault feature library; wherein, the fault feature library is used to describe the abnormal parameters, risk levels and faulty components corresponding to various faults.
6. The system according to claim 1, characterized in that, The data processing and control module includes an adaptive machine learning unit, which is used for: The state detection model is optimized and updated based on historical monitoring data within a historical time period.
7. The system according to claim 1, characterized in that, The data processing and control module includes: an instruction generation unit, which is used for: If the current operating status of the electrolysis chamber is determined to be one where there is an operational risk, the target risk level and the target faulty component are obtained. When the target risk level is the first risk level, local control instructions are generated for each individual target faulty component; If the target risk level is the second risk level, generate a combined control command for at least two of the target faulty components, and generate a reminder message; When the target risk level is the third risk level, a shutdown fault command, an emergency response command, and an alarm message are generated; wherein, the shutdown fault command is used to instruct the operation of the electrolysis chamber that has an operational risk to be shut down, and the emergency response command is used to instruct the preset emergency system to execute preset emergency actions; The first risk level is lower than the second risk level, which is lower than the third risk level.
8. The system according to claim 7, characterized in that, The action execution module includes: a body control submodule and an emergency linkage submodule; The main body control submodule is used to control the electrolysis chamber that has an operational risk according to the local control command, the combined control command, or the fault cut-off command; The emergency linkage submodule is connected to a preset emergency system and is used to control the emergency system to perform emergency actions according to the emergency response command; wherein, the emergency system includes: a fire protection system, a ventilation system and a main power management device.
9. The system according to claim 1, characterized in that, The action execution module includes a self-calibration submodule, which includes: According to the preset correction cycle, and based on the reference data under the preset standard operating conditions, the various sensors included in the parameter monitoring module are subjected to error correction.
10. A method for monitoring and controlling an electrolytic cell, characterized in that, The method includes: Multiple monitoring parameters of the electrolysis chamber are collected according to a preset sampling frequency; wherein, the monitoring parameters include at least: voltage, temperature, key physicochemical parameters of the electrolyte and gas parameters at the outlet of the electrolysis chamber; the electrolysis chamber is any one of the multiple sets of electrolysis chambers included in the electrolytic cell; The current operating status of the electrolysis chamber is determined by a state detection model based on various monitoring parameters. Determine the control command corresponding to the current operating state; The electrolysis chamber is controlled according to the control command.