Electrolyte bubble distribution monitoring method, system, equipment and medium

Abstract

The invention discloses an electrolyte bubble distribution monitoring method, system, equipment and medium, and relates to the technical field of electrochemical equipment monitoring. The method comprises the following steps: acquiring multi-source physical data of a target electrolyte; key feature parameters in the multi-source physical data are extracted; inputting the key characteristic parameters into a pre-constructed multi-physical field coupling model to analyze a dynamic rule of bubble generation and distribution in the target electrolyte; the multi-physical field coupling model is coupled with an electric field, a flow field and a temperature field; based on the output of the multi-physics field coupling model, reconstructing the bubble three-dimensional distribution of the target electrolyte through a region growing algorithm and a cellular automaton model; and carrying out abnormal state analysis and early warning according to the three-dimensional distribution of the bubbles. According to the invention, real-time and high-precision monitoring and abnormity early warning of electrolyte bubble distribution can be realized through multi-source data fusion and multi-physical field coupling analysis.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electrochemical device monitoring, in particular to an electrolyte bubble distribution monitoring method, system, device and medium. BACKGROUND

[0002] With the rapid development of renewable energy water electrolysis hydrogen production technology, the generation, transport and distribution behavior of electrolyte bubbles in the process of oxygen evolution (OER) and hydrogen evolution (HER) has become a key factor affecting the electrolysis efficiency and safe operation of the equipment. In industrial-grade proton exchange membrane (PEM) or alkaline electrolytic cell, excessive accumulation of bubbles on the electrode surface can easily form a "bubble shielding layer", leading to a decrease in effective reaction area, an increase in ohmic resistance, and thus an increase in electrolysis voltage and additional energy consumption. At the same time, the non-uniform distribution of bubbles in the flow channel can cause local overheating or two-phase flow oscillation, accelerating the aging and degradation of the membrane electrode structure.

[0003] At present, the monitoring technology for electrolyte bubbles is mostly based on a single physical field principle, which has significant limitations. The optical imaging method relies on a transparent reaction chamber and is easily disturbed by the turbidity of the electrolyte, making it difficult to apply in an industrial closed environment; the differential pressure sensor can measure the pressure fluctuation of the gas-liquid two-phase flow to infer the bubble content, but it lacks sensitivity to micron-sized bubbles and cannot obtain their size and spatial distribution information; the conductivity probe array can measure the local bubble volume fraction, but it is easily affected by electrode polarization in a strong electric field environment, resulting in large measurement errors. In addition, the above methods can only obtain a single type of physical field parameter and cannot reflect the coupling mechanism between multiple physical fields in the process of bubble nucleation, growth and detachment.

[0004] In terms of real-time performance and system adaptability, existing laboratory solutions mostly use offline sampling and image processing, which is time-consuming and difficult to capture millisecond-level dynamic changes; while the online monitoring system based on single-point sensors needs to arrange a large number of measuring points when facing large electrolytic cells, resulting in high cost, complex installation and difficult maintenance. The existing data fusion methods are mostly based on simple weighting or threshold judgment, which is difficult to effectively extract the spatiotemporal evolution characteristics of bubbles from multi-source heterogeneous data, and has a high false alarm rate under complex working conditions (such as start-stop transient, load mutation). SUMMARY

[0005] To solve the problems in the prior art, the present application provides an electrolyte bubble distribution monitoring method, system, device and medium, which can realize real-time, high-precision monitoring and abnormal early warning of electrolyte bubble distribution through multi-source data fusion and multi-physical field coupling analysis.

[0006] To achieve the above purpose, the present application provides an electrolyte bubble distribution monitoring method, comprising:

[0007] acquiring multi-source physical data of a target electrolyte; the multi-source physical data comprises optical image data, electrochemical parameter data, pressure data and temperature data;

[0008] extracting key characteristic parameters in the multi-source physical data; the key characteristic parameters comprise a gas bubble phase content, a gas evolution rate, a gas bubble surface area concentration, a local pressure gradient and a temperature gradient;

[0009] inputting the key characteristic parameters into a pre-constructed multi-physical field coupling model to analyze dynamic rules of bubble generation and distribution in the target electrolyte; the multi-physical field coupling model couples an electric field, a flow field and a temperature field;

[0010] based on an output of the multi-physical field coupling model, reconstructing a three-dimensional bubble distribution of the target electrolyte through a region growing algorithm and a cellular automaton model;

[0011] performing abnormal state analysis and early warning according to the three-dimensional bubble distribution.

[0012] Optionally, extracting the key characteristic parameters in the multi-source physical data comprises:

[0013] extracting bubble contour data in the optical image data;

[0014] calculating the gas bubble phase content and the gas bubble surface area concentration based on the bubble contour data;

[0015] performing filtering processing on an electric conductivity signal in the electrochemical parameter data, and calculating the gas evolution rate in combination with Faraday's law;

[0016] calculating the local pressure gradient by using a central difference method on the pressure data;

[0017] calculating the temperature gradient by using a central difference method on the temperature data.

[0018] Optionally, the construction method of the multi-physical field coupling model comprises:

[0019] establishing an electric field control equation based on an electrolyte Ohm's law;

[0020] establishing a flow field control equation based on a Navier-Stokes equation taking into account bubble drag force and buoyancy;

[0021] establishing a temperature field control equation based on an energy conservation equation taking into account Joule heat and phase change heat;

[0022] coupling the electric field control equation, the flow field control equation and the temperature field control equation to obtain the multi-physical field coupling model.

[0023] Optionally, the multiphysics coupling model is solved based on Ohm's law for electrolytes, the Navier-Stokes equations, and the energy conservation equations to analyze the dynamic laws governing the generation and distribution of bubbles in the target electrolyte.

[0024] Optionally, based on the three-dimensional distribution of the bubbles, anomaly analysis and early warning are performed, including:

[0025] When the local bubble phase content in the target electrolyte is determined to be greater than or equal to a first threshold based on the three-dimensional distribution of the bubbles, an electrode coverage warning is triggered.

[0026] When the local temperature gradient in the target electrolyte is determined to be greater than or equal to the second threshold based on the three-dimensional distribution of the bubbles, a local overheating warning is triggered.

[0027] Based on the electrode coverage warning and / or local overheating warning, the corresponding warning action is executed; the warning action includes at least one of the following: displaying the warning coordinates on the interface, automatically increasing the electrolyte flow rate, and cutting off the power supply to the electrolytic cell.

[0028] The present invention also provides an electrolyte bubble distribution monitoring system, comprising:

[0029] The data acquisition unit is used to acquire multi-source physical data of the target electrolyte; the multi-source physical data includes optical image data, electrochemical parameter data, pressure data, and temperature data;

[0030] The feature extraction unit is used to extract key feature parameters from the multi-source physical data; the key feature parameters include bubble phase content, gas evolution rate, bubble surface area concentration, local pressure gradient, and temperature gradient.

[0031] The bubble distribution reconstruction unit is used for:

[0032] The key feature parameters are input into a pre-constructed multiphysics coupling model to analyze the dynamic laws of bubble generation and distribution in the target electrolyte; the multiphysics coupling model couples the electric field, flow field and temperature field.

[0033] Based on the output of the multiphysics coupling model, the three-dimensional distribution of bubbles in the target electrolyte is reconstructed using a region growing algorithm and a cellular automata model.

[0034] The early warning unit is used to analyze abnormal states and issue early warnings based on the three-dimensional distribution of the bubbles.

[0035] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the monitoring method described above.

[0036] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the monitoring method described above.

[0037] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0038] The electrolyte bubble distribution monitoring method provided by this invention overcomes the limitations of traditional single-sensor monitoring by simultaneously acquiring multi-source physical data such as optical images, electrochemical parameters, pressure, and temperature. It effectively overcomes the problems of optical methods being affected by electrolyte turbidity, electrochemical methods having large errors in strong electric fields, and single-point sensors failing to comprehensively reflect spatial distribution. By extracting key feature parameters from multiple dimensions such as bubble phase content, bubble surface area concentration, local pressure gradient, and temperature gradient, it achieves a comprehensive quantitative characterization of bubble behavior, providing high-precision input for multi-physics coupling analysis.

[0039] Furthermore, by inputting the aforementioned characteristic parameters into a multiphysics model that couples the electric field, flow field, and temperature field, the interaction mechanism between the bubble generation, movement, and distribution processes and the multiphysics field can be accurately revealed. This fundamentally solves the monitoring bias problem caused by the lack of multi-field collaborative sensing capabilities in existing technologies. Ultimately, based on the three-dimensional bubble distribution reconstruction results, abnormal state early warning is achieved, significantly improving the accuracy and timeliness of identifying risks such as electrode coverage and local overheating under complex operating conditions, providing a reliable guarantee for the safe and efficient operation of the electrolytic cell. Attached Figure Description

[0040] The above and other objects, features and advantages of the present invention will become more apparent from the more detailed description of exemplary embodiments of the invention in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same parts in the exemplary embodiments of the invention.

[0041] Figure 1 This is a schematic flowchart of the electrolyte bubble distribution monitoring method according to an embodiment of the present invention;

[0042] Figure 2 This is a schematic diagram of the system architecture shown in an embodiment of the present invention;

[0043] Figure 3 This is a flowchart of a multiphysics coupling model shown in an embodiment of the present invention;

[0044] Figure 4 This is a flowchart illustrating the bubble distribution reconstruction algorithm according to an embodiment of the present invention;

[0045] Figure 5 This is a schematic diagram of the module structure of the electrolyte bubble distribution monitoring system shown in an embodiment of the present invention;

[0046] Figure 6 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] Please see Figure 1 , Figure 1 This is a schematic diagram of the process for monitoring the distribution of bubbles in an electrolyte solution.

[0049] Methods for monitoring electrolyte bubble distribution include:

[0050] S101: Acquire multi-source physical data of the target electrolyte.

[0051] The multi-source physical data includes optical image data, electrochemical parameter data, pressure data, and temperature data.

[0052] See Figure 2 In applications, acquiring multi-source physical data of the target electrolyte can be achieved through simultaneous acquisition by multiple sensors deployed at key locations in the electrolytic cell. Specifically, optical image data can be captured by a high-speed camera installed outside the transparent observation window of the electrolytic cell, with a frame rate of no less than 1000 frames / second and a resolution of 0.1 mm / pixel, clearly recording the generation, movement, and coalescence of bubbles; electrochemical parameter data includes electrolyte conductivity, cell voltage, and current density measured by an in-situ conductivity sensor, combined with real-time analysis of the composition and concentration of the released gas using gas chromatography; pressure data is acquired by a miniature pressure sensor embedded in the electrode plate surface, with a range of 0-10 kPa and an accuracy of 0.1% FS, used to monitor local pressure fluctuations caused by bubble adhesion and detachment; temperature data is acquired by thermocouples deployed at the same location, with an accuracy of ±0.5℃, used to reflect the temperature distribution and trend of the electrolyte.

[0053] All sensors are synchronized in time through a high-precision hardware triggering module, ensuring that multi-source data are collected within the same clock cycle with a time synchronization accuracy better than ±1μs, thus providing a spatiotemporally consistent data foundation for subsequent multiphysics coupling analysis.

[0054] After collecting multi-source data, preprocessing and data fusion can be performed. Data preprocessing involves filtering, noise reduction, and feature extraction of the raw data, such as using time-averaging and projection normalization to obtain the phase content distribution. Multi-source data fusion utilizes weighted average methods, principal component analysis (PCA), or ensemble learning algorithms to fuse data from multiple sources, including optical, electrochemical, pressure, and temperature data.

[0055] S102: Extract key feature parameters from multi-source physical data.

[0056] Key characteristic parameters include bubble phase content, gas evolution rate, bubble surface area concentration, local pressure gradient, and temperature gradient.

[0057] Specifically, key feature parameters are extracted from multi-source physical data, including:

[0058] Extract bubble contour data from optical image data;

[0059] Based on bubble profile data, calculate bubble phase content and bubble surface area concentration;

[0060] The conductivity signal in the electrochemical parameter data is filtered and the gas evolution rate is calculated using Faraday's law.

[0061] The local pressure gradient is obtained by calculating the pressure data using the central difference method.

[0062] The temperature gradient is obtained by using the central difference method to calculate the temperature data.

[0063] In applications, extracting key feature parameters from multi-source physical data can be achieved through preprocessing and feature calculation of the raw sensor data. Specifically, for optical image data, Gaussian filtering can be used to remove noise, followed by Otsu thresholding to extract bubble contours. This allows for the calculation of the number of bubbles, equivalent diameter, and velocity in each frame, thereby deriving the bubble phase content and bubble surface area concentration. The conductivity signal in the electrochemical parameter data is processed using a moving average filter, and the real-time gas evolution rate is calculated using Faraday's law. Pressure data is processed using the central difference method to obtain the local pressure gradient within the flow channel, reflecting changes in flow resistance caused by bubble aggregation. Temperature data also uses the central difference method to calculate the spatial temperature gradient, used to identify overheated areas caused by bubble adhesion or uneven local reactions.

[0064] The aforementioned feature extraction process fully considers the multiphysics coupling effect. The obtained parameters not only have clear physical meanings but also accurately characterize the interaction between bubble behavior and the electric, flow, and temperature fields. For example, the bubble phase content directly reflects the degree of gas coverage on the electrode surface, the local pressure gradient can indicate the risk of flow channel blockage, and the temperature gradient is used to warn of thermal runaway tendencies. These key feature parameters provide high-precision, multi-dimensional inputs for the subsequent establishment of multiphysics coupling models and bubble distribution reconstruction, significantly improving the adaptability and early warning reliability of bubble monitoring under complex operating conditions.

[0065] S103: Input key feature parameters into a pre-constructed multiphysics coupling model to analyze the dynamic laws of bubble generation and distribution in the target electrolyte.

[0066] Among them, the multiphysics coupling model couples the electric field, flow field and temperature field.

[0067] In the application, the extracted key feature parameters are input into a pre-constructed multiphysics coupled model. This model comprehensively analyzes the dynamic laws of bubble generation, motion, and distribution by coupling the control equations of the electric field, flow field, and temperature field. The electric field control is based on Ohm's law for the electrolyte, describing the current density distribution and the modulation effect of bubble presence on local conductivity. The flow field control introduces the Navier-Stokes equations with bubble drag and buoyancy terms to characterize the motion characteristics and pressure distribution of the gas-liquid two-phase flow. The temperature field control is based on the energy conservation equation including Joule heating and the phase transition heat of hydrogen / oxygen evolution reaction, reflecting the heat generation and heat transfer behavior during the electrolysis process.

[0068] The solution to the multiphysics coupled model employs the finite volume method to discretize the computational domain. It combines multiphysics simulation platforms such as COMSOL Multiphysics with a custom MATLAB algorithm to achieve real-time coupled iteration of the electric, flow, and temperature fields. The model uses key characteristic parameters such as bubble fill ratio, local pressure gradient, and temperature gradient as dynamic boundary conditions and source term inputs. Numerical calculations invert the interaction mechanism between bubble behavior and the multiphysics field, such as current density redistribution caused by bubble aggregation, changes in flow channel pressure drop, and local overheating trends.

[0069] This coupled model can overcome the limitations of traditional single physical field monitoring, systematically reveal the intrinsic relationship between bubble evolution and electrolysis process, provide quantitative physical basis for bubble three-dimensional distribution reconstruction and abnormal state early warning, and significantly improve the accuracy and adaptability of monitoring results.

[0070] The methods for constructing the above-mentioned multiphysics coupling model include:

[0071] Establish the electric field control equation based on Ohm's law for electrolytes;

[0072] The flow field control equations are established based on the Navier-Stokes equations that take into account bubble drag and buoyancy.

[0073] The temperature field control equation is established based on the energy conservation equation that takes into account Joule heat and phase change heat.

[0074] By coupling the electric field control equation, the flow field control equation, and the temperature field control equation, a multiphysics coupling model is obtained.

[0075] See Figure 3 The multiphysics coupling model is constructed based on a mathematical description of the interaction mechanism between the electric field, flow field, and temperature field during electrolysis. The electric field governing equations are established based on Ohm's law for the electrolyte, characterizing the potential distribution and current conduction behavior. The electrolyte conductivity is considered a function of the local bubble phase content, thus reflecting the modulation effect of bubble aggregation on the electric field distribution. The flow field governing equations employ the Navier-Stokes equations, incorporating bubble drag and buoyancy terms, to describe the momentum conservation relationship of the electrolyte in a gas-liquid two-phase flow state. The bubble phase, as the dispersed phase, influences the flow characteristics of the continuous phase through interphase forces. The temperature field governing equations are established based on the energy conservation equations incorporating Joule heating and the phase transition heat of the gas evolution reaction. These equations characterize the influence of the Joule heating effect generated by the current flow and the latent heat exchange caused by bubble evolution on the temperature distribution during electrolysis.

[0076] The governing equations of the three fields mentioned above achieve overall coupling through variable coupling and source term association: the current density calculated by the electric field equation serves as the Joule heat source term input to the temperature field equation, simultaneously affecting the bubble formation rate; the velocity and pressure distribution obtained from the flow field equation determine the transport and aggregation behavior of bubbles, which in turn affects the conductivity distribution in the electric field; the temperature result obtained from the temperature field equation reacts to the electric and flow fields by changing the electrolyte physical properties (such as conductivity and viscosity). Finally, the computational domain is spatially discretized using the finite volume method, and the coupled equations are solved simultaneously using an implicit iterative algorithm, thereby constructing a multiphysics coupled model that can fully reflect the dynamic interaction between bubble behavior and multiphysics.

[0077] The multiphysics coupling model is solved based on Ohm's law for electrolytes, the Navier-Stokes equations, and the energy conservation equations to analyze the dynamic laws governing the generation and distribution of bubbles in the target electrolyte.

[0078] Specifically, the electric field equation (Ohm's law for electrolytes) is expressed as follows:

[0079]

[0080] Where σ is the electrolyte conductivity (S / m) and φ is the potential (V).

[0081] The flow field equations (Navier-Stokes equations, considering bubble buoyancy and drag) are expressed as follows:

[0082]

[0083] Where ρ is the electrolyte density (kg / m³) 3 μ is the dynamic viscosity (Pa·s), p is the pressure (Pa), and F is the pressure. drag Bubble drag force (N / m) 3 u represents velocity (m / s), describing the motion of the fluid in the flow field, and g represents gravitational acceleration (m / s²). 2 ).

[0084] The temperature field equation (energy conservation, considering Joule heating and phase transition heat) is expressed as follows:

[0085]

[0086] Among them, C p Q is the specific heat capacity (J / kg·℃), k is the thermal conductivity (W / m·℃), and Q is the thermal conductivity. Joule Joule heat (W / m 3 ), Q evap Heat of phase transition of gas evolution (W / m 3 T represents temperature (°C), and t represents time (s).

[0087] S104: Based on the output of the multiphysics coupling model, the three-dimensional distribution of bubbles in the target electrolyte is reconstructed through the region growing algorithm and the cellular automata model.

[0088] In this application, based on the electric field, flow field, and temperature field distribution data output by the multiphysics coupling model, a region growing algorithm and a cellular automata model are used in conjunction to reconstruct the three-dimensional distribution of bubbles within the target electrolyte. The region growing algorithm first processes the optical image sequence, using the bubble centroid identified in a single frame as a seed point. Based on a grayscale continuity threshold and spatial distance constraints, it matches and associates bubble trajectories between adjacent frames, thereby constructing a two-dimensional temporal trajectory of bubble motion. The cellular automata model defines a three-dimensional computational domain and discretizes it into uniform cubic cells, each cell storing the probability state of bubble existence. By mapping the bubble motion trajectory obtained by the region growing algorithm to the three-dimensional cellular space and driving the spatiotemporal evolution of the bubble probability state based on the flow field velocity data provided by the multiphysics coupling model, a dynamic three-dimensional bubble distribution cloud map is generated.

[0089] See Figure 4 In 3D reconstruction, the region growing algorithm uses the centroid of the bubble in a single frame image as the seed point, and merges bubbles in adjacent frames based on the continuity of gray values ​​(threshold ± 10%) and spatial distance (≤ 2 times the bubble diameter) to generate a two-dimensional bubble motion trajectory.

[0090] Cellular Automata (CA) Model: Define a three-dimensional computational domain (10mm×10mm×20mm), with each cell measuring 0.5mm×0.5mm×0.5mm. The state variable is the probability of bubble existence (0-1). Based on the bubble velocity and flow direction of neighboring cells, update the bubble position for the next time step to construct a dynamic bubble distribution cloud map.

[0091] In this reconstruction process, the region growing algorithm ensures the continuity and accuracy of individual bubble motion, while the cellular automata model efficiently predicts the bubble distribution in three-dimensional space through simplified state transition rules. The combination of the two effectively overcomes the limitation of a single optical measurement perspective. The final constructed three-dimensional bubble distribution results can intuitively present the aggregation location, distribution density, and motion trend of bubbles in the electrolyzer, providing high-precision spatial information support for subsequent abnormal state identification and early warning, and significantly improving the visualization capability and quantitative analysis reliability of the complex gas-liquid two-phase flow state inside the industrial electrolyzer.

[0092] S105: Analyze and issue early warnings for abnormal states based on the three-dimensional distribution of bubbles.

[0093] Specifically, based on the three-dimensional distribution of bubbles, abnormal state analysis and early warning are performed, including:

[0094] When the local bubble phase content in the target electrolyte is determined to be greater than or equal to the first threshold based on the three-dimensional distribution of bubbles, an electrode coverage warning is triggered.

[0095] When the local temperature gradient in the target electrolyte is determined to be greater than or equal to the second threshold based on the three-dimensional distribution of bubbles, a local overheating warning is triggered.

[0096] Based on the electrode coverage warning and / or local overheating warning, the corresponding warning action is executed; the warning action includes at least one of the following: displaying the warning coordinates on the interface, automatically increasing the electrolyte flow rate, and cutting off the power supply to the electrolytic cell.

[0097] In the application, the internal state of the target electrolyte is analyzed and anomaly detected in real time based on the reconstructed three-dimensional bubble distribution. By calculating the bubble content in each local area of ​​the three-dimensional distribution map, when the bubble content in the area near the electrode surface is found to exceed the first threshold, an electrode coverage risk is identified and an electrode coverage warning is triggered. At the same time, combined with the temperature field data output by the multiphysics coupling model, the spatial temperature gradient distribution is calculated. When a local temperature gradient is detected to exceed the second threshold, a local overheating risk is identified and a temperature anomaly warning is triggered.

[0098] In response to the above warnings, the following actions can be taken: mark the spatial coordinates of the abnormal area on the human-machine interface and prompt manual intervention for inspection; automatically adjust the flow control valve in the electrolyte circulation pipeline to increase the electrolyte flow rate to enhance the scouring effect on the electrode surface and suppress bubble adhesion; immediately cut off the power supply to the electrolytic cell and activate the safety valve to release pressure and prevent the accident from escalating.

[0099] Specifically, different warning actions can be executed according to the warning level; for example, Level 1 warning (yellow): the interface displays the coordinates of the abnormal area and prompts manual inspection; Level 2 warning (orange): automatically increases the electrolyte flow rate to 0.5m / s to reduce the probability of bubble adhesion; Level 3 warning (red): cuts off the power supply to the electrolytic cell and releases pressure through the safety valve.

[0100] This early warning mechanism, based on a comprehensive criterion of multi-dimensional physical parameters, enables early identification and graded intervention of abnormal states in the electrolysis process, significantly improving the operational safety and stability of the water electrolysis hydrogen production system. At the same time, it effectively reduces the additional energy consumption caused by bubble accumulation by optimizing operating conditions.

[0101] The implementation effect of the electrolyte bubble distribution monitoring method of the present invention is as follows:

[0102] Monitoring accuracy: bubble diameter measurement error ≤5%, phase content reconstruction error ≤8%, pressure / temperature response time ≤20ms.

[0103] Application value: at 1000Nm 3 Tested in an alkaline electrolyzer, when the system detected anode bubble accumulation, it automatically adjusted the flow rate, reducing the oxygen evolution overpotential by 12%, and lowering the hydrogen production energy consumption from 4.8 kWh / Nm³. 3 Reduced to 4.5 kWh / Nm 3 The continuous operating time of the equipment is extended by 30%.

[0104] Corresponding to the aforementioned application function implementation method embodiments, the present invention also provides an electrolyte bubble distribution monitoring system and corresponding embodiments.

[0105] Please see Figure 5 , Figure 5 This is a schematic diagram of the module structure of an electrolyte bubble distribution monitoring system.

[0106] An electrolyte bubble distribution monitoring system includes:

[0107] The data acquisition unit 51 is used to acquire multi-source physical data of the target electrolyte; the multi-source physical data includes optical image data, electrochemical parameter data, pressure data and temperature data;

[0108] The feature extraction unit 52 is used to extract key feature parameters from multi-source physical data; key feature parameters include bubble phase content, gas evolution rate, bubble surface area concentration, local pressure gradient, and temperature gradient.

[0109] Bubble distribution reconstruction unit 53 is used for:

[0110] Key feature parameters are input into a pre-constructed multiphysics coupling model to analyze the dynamic laws of bubble generation and distribution in the target electrolyte; the multiphysics coupling model couples the electric field, flow field and temperature field;

[0111] Based on the output of the multiphysics coupling model, the three-dimensional distribution of bubbles in the target electrolyte is reconstructed using a region growing algorithm and a cellular automata model.

[0112] The early warning unit 54 is used to perform abnormal state analysis and issue early warnings based on the three-dimensional distribution of bubbles.

[0113] In one embodiment, the feature extraction unit 52 is specifically used for extracting key feature parameters from multi-source physical data:

[0114] Extract bubble contour data from optical image data;

[0115] Based on bubble profile data, calculate bubble phase content and bubble surface area concentration;

[0116] The conductivity signal in the electrochemical parameter data is filtered and the gas evolution rate is calculated using Faraday's law.

[0117] The local pressure gradient is obtained by calculating the pressure data using the central difference method.

[0118] The temperature gradient is obtained by using the central difference method to calculate the temperature data.

[0119] In one embodiment, in terms of performing abnormal state analysis and issuing early warnings based on the three-dimensional distribution of bubbles, the aforementioned early warning unit 54 is specifically used for:

[0120] When the local bubble phase content in the target electrolyte is determined to be greater than or equal to the first threshold based on the three-dimensional distribution of bubbles, an electrode coverage warning is triggered.

[0121] When the local temperature gradient in the target electrolyte is determined to be greater than or equal to the second threshold based on the three-dimensional distribution of bubbles, a local overheating warning is triggered.

[0122] Based on the electrode coverage warning and / or local overheating warning, the corresponding warning action is executed; the warning action includes at least one of the following: displaying the warning coordinates on the interface, automatically increasing the electrolyte flow rate, and cutting off the power supply to the electrolytic cell.

[0123] Regarding the system in the above embodiments, the specific manner in which each unit module performs operations has been described in detail in the embodiments related to the method, and will not be elaborated further here.

[0124] Please see Figure 6 The electronic device 6000 includes a memory 6010 and a processor 6020.

[0125] The processor 6020 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0126] Memory 6010 may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. ROM may store static data or instructions required by processor 6020 or other modules of the computer. Permanent storage devices may be read-write storage devices. Permanent storage devices may be non-volatile storage devices that retain stored instructions and data even when the computer is powered off. In some embodiments, permanent storage devices use mass storage devices (e.g., magnetic or optical disks, flash memory) as permanent storage devices. In other embodiments, permanent storage devices may be removable storage devices (e.g., floppy disks, optical drives). System memory may be a read-write storage device or a volatile read-write storage device, such as dynamic random access memory. System memory may store some or all of the instructions and data required by the processor during operation. Furthermore, memory 6010 may include any combination of computer-readable storage media, including various types of semiconductor memory chips (e.g., DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and disks and / or optical disks may also be used. In some embodiments, the memory 6010 may include a removable storage device that is readable and / or writable, such as a laser disc (CD), a read-only digital multifunction optical disc (e.g., DVD-ROM, dual-layer DVD-ROM), a read-only Blu-ray disc, an ultra-high density optical disc, a flash memory card (e.g., SD card, mini SD card, Micro-SD card, etc.), a magnetic floppy disk, etc. Computer-readable storage media do not contain carrier waves or transient electronic signals transmitted wirelessly or via wired connections.

[0127] The memory 6010 stores executable code, which, when processed by the processor 6020, can cause the processor 6020 to execute part or all of the methods described above.

[0128] Furthermore, the method according to the present invention can also be implemented as a computer program or computer program product, which includes computer program code instructions for performing some or all of the steps in the above-described method of the present invention.

[0129] Alternatively, the present invention may also be implemented as a computer-readable storage medium (or a non-transitory machine-readable storage medium or a machine-readable storage medium) storing executable code (or computer program or computer instruction code) thereon, which, when executed by a processor of an electronic device (or server, etc.), causes the processor to perform part or all of the steps of the above-described method according to the present application.

[0130] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for monitoring the distribution of bubbles in an electrolyte, characterized in that, include: Acquire multi-source physical data of the target electrolyte; The multi-source physical data includes optical image data, electrochemical parameter data, pressure data, and temperature data; Key feature parameters are extracted from the multi-source physical data; the key feature parameters include bubble phase content, gas evolution rate, bubble surface area concentration, local pressure gradient, and temperature gradient. The key feature parameters are input into a pre-constructed multiphysics coupling model to analyze the dynamic laws of bubble generation and distribution in the target electrolyte. The multiphysics coupling model couples the electric field, flow field, and temperature field. Based on the output of the multiphysics coupling model, the three-dimensional distribution of bubbles in the target electrolyte is reconstructed using a region growing algorithm and a cellular automata model. Based on the three-dimensional distribution of the bubbles, anomaly analysis and early warning are performed.

2. The method for monitoring electrolyte bubble distribution according to claim 1, characterized in that, Extracting key feature parameters from the multi-source physical data, including: Extract bubble contour data from the optical image data; Based on the bubble profile data, the bubble phase content and bubble surface area concentration are calculated; The conductivity signal in the electrochemical parameter data is filtered, and the gas evolution rate is calculated in conjunction with Faraday's law; The local pressure gradient is obtained by calculating the pressure data using the central difference method. The temperature gradient is obtained by calculating the temperature data using the central difference method.

3. The method for monitoring electrolyte bubble distribution according to claim 1, characterized in that, The method for constructing the multiphysics coupling model includes: Establish the electric field control equation based on Ohm's law for electrolytes; The flow field control equations are established based on the Navier-Stokes equations that take into account bubble drag and buoyancy. The temperature field control equation is established based on the energy conservation equation that takes into account Joule heat and phase change heat. By coupling the electric field control equation, the flow field control equation, and the temperature field control equation, a multiphysics coupling model is obtained.

4. The method for monitoring electrolyte bubble distribution according to claim 1, characterized in that, The multiphysics coupling model is solved based on Ohm's law for electrolytes, the Navier-Stokes equations, and the energy conservation equations to analyze the dynamic laws governing the generation and distribution of bubbles in the target electrolyte.

5. The method for monitoring electrolyte bubble distribution according to claim 1, characterized in that, Based on the three-dimensional distribution of the bubbles, anomaly analysis and early warning are performed, including: When the local bubble phase content in the target electrolyte is determined to be greater than or equal to a first threshold based on the three-dimensional distribution of the bubbles, an electrode coverage warning is triggered. When the local temperature gradient in the target electrolyte is determined to be greater than or equal to the second threshold based on the three-dimensional distribution of the bubbles, a local overheating warning is triggered. Based on the electrode coverage warning and / or local overheating warning, the corresponding warning action is executed; the warning action includes at least one of the following: displaying the warning coordinates on the interface, automatically increasing the electrolyte flow rate, and cutting off the power supply to the electrolytic cell.

6. An electrolyte bubble distribution monitoring system, characterized in that, include: The data acquisition unit is used to acquire multi-source physical data of the target electrolyte; The multi-source physical data includes optical image data, electrochemical parameter data, pressure data, and temperature data; The feature extraction unit is used to extract key feature parameters from the multi-source physical data; the key feature parameters include bubble phase content, gas evolution rate, bubble surface area concentration, local pressure gradient, and temperature gradient. The bubble distribution reconstruction unit is used for: The key feature parameters are input into a pre-constructed multiphysics coupling model to analyze the dynamic laws of bubble generation and distribution in the target electrolyte; the multiphysics coupling model couples the electric field, flow field and temperature field. Based on the output of the multiphysics coupling model, the three-dimensional distribution of bubbles in the target electrolyte is reconstructed using a region growing algorithm and a cellular automata model. The early warning unit is used to analyze abnormal states and issue early warnings based on the three-dimensional distribution of the bubbles.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the monitoring method as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the monitoring method as described in any one of claims 1-5.

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