AEM water electrolysis hydrogen production process monitoring method and system

Abstract

The invention relates to the technical field of detection systems, and discloses an AEM water electrolysis hydrogen production process monitoring method and system, a comprehensive, efficient and accurate monitoring and control platform is formed by integrating a plurality of modules, and compared with a current traditional monitoring system, the AEM water electrolysis hydrogen production process monitoring system not only can collect and process multi-source data, but also can monitor and control the water electrolysis hydrogen production process. Compared with the traditional mode depending on a single data source, the system can comprehensively consider the data of the electrolytic bath, hydrogen, electrolyte and the like, feeds back the running state in real time, and performs intelligent anomaly detection and adjustment, so that the system has the advantages of high reliability, high reliability, high reliability and the like. Through the improvement, the system can more accurately identify problems, early warn potential risks in advance, reduce the equipment failure rate, improve the yield and quality of hydrogen, and finally achieve the purposes of improving the hydrogen production efficiency, reducing the energy consumption and optimizing the production process.

Description

Technical Field

[0001] The present invention relates to the technical field of detection systems, and in particular to a process monitoring method and system for AEM water electrolysis hydrogen production. Background Art

[0002] There are many ways to produce hydrogen, among which water electrolysis is considered to be an environmentally friendly and efficient way to produce hydrogen, and has gradually been widely used and studied. Among the many hydrogen production technologies, water electrolysis based on anode exchange membrane (AEM) has received more and more attention in practical applications due to its relatively low cost and high stability.

[0003] Although AEM water electrolysis hydrogen production technology has made certain progress in industrial applications, the existing monitoring methods and technologies still face many shortcomings. The current monitoring system can often only collect limited single data, such as basic parameters such as current and voltage, but lacks the ability to collect and process multi-dimensional and multi-source data in real time. For example, the current, voltage, temperature of the electrolyzer, the flow rate and purity of hydrogen, the pH and concentration of the electrolyte and other parameters are key data in the hydrogen production process. They affect each other, and any abnormal fluctuation in any parameter may lead to a decrease in the efficiency of the entire reaction process or equipment failure. However, the existing system can often only analyze a certain type of data independently, and cannot perform comprehensive processing and correlation analysis on these multi-source data at the same time. This single and isolated data monitoring method leads to poor early warning capabilities for abnormal conditions in the electrolysis process, and it is difficult to detect potential problems or failures in a timely manner.

[0004] Therefore, we propose a process monitoring method and system for AEM water electrolysis to produce hydrogen in order to solve the above-mentioned problems. Summary of the invention

[0005] The purpose of the present invention is to provide a process monitoring method and system for AEM water electrolysis to produce hydrogen, so as to solve the problem proposed in the above background technology that the current monitoring system can often only collect limited single data, such as basic parameters such as current and voltage, but lacks the ability to collect and process multi-dimensional and multi-source data in real time.

[0006] To achieve the above object, the present invention provides the following technical solutions: a process monitoring system for AEM water electrolysis hydrogen production, comprising a first data acquisition module, a second data acquisition module, a third data acquisition module, a data processing module, a data calculation module, a data analysis module and a feedback module; The first data acquisition module is used to collect multi-source data of the electrolytic cell and form a first multi-source data set; The second data acquisition module is used to collect multi-source data of hydrogen and form a second multi-source data set; The third data acquisition module is used to collect multi-source data of the electrolyte and form a third multi-source data set; The data processing module is used to pre-process the first multi-source data set, the second multi-source data set and the third multi-source data set, and reorganize them into a first data set, a second data set, a third data set, a fourth data set, a fifth data set, a sixth data set, a seventh data set, an eighth data set and a ninth data set; The data calculation module is used to first perform step-by-step calculations on the first data set, the second data set, the third data set, the fourth data set, the fifth data set, the sixth data set, the seventh data set, the eighth data set and the ninth data set, so as to obtain the electrolytic cell characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2 and the electrolyte characteristic reference coefficient CS3, and integrate the electrolytic cell characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2 and the electrolyte characteristic reference coefficient CS3 to calculate the reasonable coefficient CLX of the processing process; The data analysis module is used to compare the processing process rationality coefficient CLX with a preset first threshold SY, thereby generating a first comparison result, so as to determine whether there is an abnormal point in the current processing process; When the first comparison result shows that there is no abnormal point, the electrolytic cell characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2 and the electrolyte characteristic reference coefficient CS3 are respectively compared with the preset threshold value, so as to generate a second comparison result, and the position of the abnormal point is determined according to the second comparison result; The feedback module is used to feed back the first comparison result and the second comparison result to the terminal.

[0007] Preferably, the first data acquisition module includes a current and voltage detection unit, a temperature and pressure detection unit, and a reaction efficiency detection unit; The current and voltage detection unit is used to collect the current value, voltage value and power value of the electrolytic cell; The temperature and pressure detection unit is used to collect the temperature value of the electrolytic cell, the pressure of the reaction chamber and the temperature difference; The reaction efficiency detection unit is used to collect reaction rate and capacity efficiency; The second data acquisition module includes a hydrogen flow detection unit, a hydrogen purity detection unit and a hydrogen pressure detection unit; The hydrogen flow detection unit is used to collect the hydrogen flow, gas velocity and gas density of hydrogen; The hydrogen purity detection unit is used to collect the hydrogen purity, oxygen concentration and impurity gas concentration of hydrogen; The hydrogen pressure detection unit is used to collect the hydrogen temperature and hydrogen pressure ratio of the hydrogen; The third data acquisition module includes an electrolyte pH detection unit, an electrolyte concentration detection unit and an electrolyte temperature detection unit; The electrolyte pH detection unit is used to collect the pH value, pH change rate and pH deviation of the electrolyte; The electrolyte concentration detection unit is used to collect the electrolyte concentration, electrolyte conductivity and concentration change rate of the electrolyte; The electrolyte temperature detection unit is used to collect the temperature change rate and temperature uniformity of the electrolyte; Wherein the current value, voltage value, power value, temperature value, reaction chamber pressure, temperature difference, reaction rate and capacity efficiency constitute the first multi-source data set; The hydrogen flow rate, gas velocity, gas density, hydrogen purity, oxygen concentration, impurity gas concentration, hydrogen temperature and hydrogen pressure constitute the second multi-source data set; Among them, pH value, pH change rate, pH deviation, electrolyte concentration, electrolyte conductivity, concentration change rate, temperature change rate and temperature uniformity constitute the third multi-source data set.

[0008] Preferably, the data processing module preprocesses and dimensionlessly transforms the first multi-source data set, the second multi-source data set and the third multi-source data set, and reorganizes the processed first multi-source data set, the second multi-source data set and the third multi-source data set into a first data set, a second data set, a third data set, a fourth data set, a fifth data set, a sixth data set, a seventh data set, an eighth data set and a ninth data set.

[0009] Preferably, the first data set includes current value, voltage value and power value; The current values ​​are recorded as A1, A2, A3, ..., An according to the timestamps; The voltage values ​​are recorded as B1, B2, B3, ..., Bn according to the timestamps; The power values ​​are recorded as C1, C2, C3, ..., Cn according to the timestamps; The second data set includes temperature values, reaction chamber pressure, and temperature difference; The temperature values ​​are recorded as D1, D2, D3, ..., Dn according to the timestamps; The reaction chamber pressures are recorded as E1, E2, E3, ..., En according to the timestamps; The temperature differences are recorded as F1, F2, F3, ..., Fn according to the timestamps; The third data set includes reaction rate and capacity efficiency; The reaction rates are recorded as G1, G2, G3, ..., Gn according to the timestamps; The capacity efficiency is recorded as H1, H2, H3, ..., Hn according to the timestamp; The fourth data set includes hydrogen flow rate, gas velocity and gas density; The hydrogen flow rates are recorded as I1, I2, I3, ..., In according to the timestamps; The gas velocities are recorded as J1, J2, J3, ..., Jn according to the timestamps; The gas density is recorded as K1, K2, K3, ..., Kn according to the timestamp; The fifth data set includes hydrogen purity, oxygen concentration, and impurity gas concentration; The purity of hydrogen is recorded as L1, L2, L3, ..., Ln according to the timestamp; The oxygen concentrations are recorded as M1, M2, M3, ..., Mn according to the timestamps; The impurity gas concentrations are recorded as N1, N2, N3, ..., Nn according to the timestamps; The sixth data set includes hydrogen temperature and hydrogen pressure ratio; The hydrogen temperatures are recorded as O1, O2, O3, ..., On according to the timestamps; The hydrogen pressure ratios are recorded as P1, P2, P3, ..., Pn according to the timestamps; The seventh data set includes pH value, pH change rate and pH deviation; The pH values ​​are recorded as Q1, Q2, Q3, ..., Qn according to the timestamp; The acid-base change rates are recorded as R1, R2, R3, ..., Rn according to the timestamp; The acid-base deviations are recorded as S1, S2, S3, ..., Sn according to the timestamps; The eighth data set includes electrolyte concentration, electrolyte conductivity, and concentration change rate; The electrolyte concentrations are recorded as T1, T2, T3, ..., Tn according to the timestamps; The electrolyte conductivity is recorded as U1, U2, U3, ..., Un according to the time stamp; The concentration change rates are recorded as V1, V2, V3, ..., Vn according to the timestamps; The ninth data set includes temperature change rate and temperature uniformity; The temperature change rates are recorded as W1, W2, W3, ..., Wn according to the timestamps; The temperature uniformity is recorded as X1, X2, X3, ..., Xn according to the timestamp.

[0010] Preferably, the data calculation module includes a first calculation unit, a second calculation unit and a third calculation unit; The first calculation unit is used to calculate the first data set, the second data set, the third data set, the fourth data set, the fifth data set, the sixth data set, the seventh data set, the eighth data set and the ninth data set respectively, so as to obtain the current value window average PA, the voltage value window average PB, the power value window average PC, the temperature value window average PD, the reaction chamber pressure window average PE, the temperature difference window average PF, the reaction rate window average PG, the capacity efficiency window average PH, the hydrogen flow window average PI, the gas velocity window average PJ, the gas density window average PK, the hydrogen purity window average PL, the oxygen concentration window average PM, the impurity gas concentration window average PN, the hydrogen temperature window average PO, the hydrogen pressure window average PP, the pH value window average PQ, the pH change rate window average PR, the pH deviation window average PS, the electrolyte concentration window average PT, the electrolyte conductivity window average PU, the concentration change rate window average PV, the temperature change rate window average PW and the temperature uniformity window average PX; The second calculation unit is used to calculate, so as to generate an electrolytic cell characteristic reference coefficient CS1, a hydrogen characteristic reference coefficient CS2 and an electrolyte characteristic reference coefficient CS3; The third calculation unit is used to calculate the electrolytic cell characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2 and the electrolyte characteristic reference coefficient CS3, so as to generate a treatment process rationality coefficient CLX.

[0011] Preferably, the first calculation unit calculates and obtains the current value window mean PA, the voltage value window mean PB, the power value window mean PC, the temperature value window mean PD, the reaction chamber pressure window mean PE, the temperature difference window mean PF, the reaction rate window mean PG, the capacity efficiency window mean PH, the hydrogen flow window mean PI, the gas velocity window mean PJ, the gas density window mean PK, the hydrogen purity window mean PL, the oxygen concentration window mean PM, the impurity gas concentration window mean PN, the hydrogen temperature window mean PO, the hydrogen pressure window mean PP, the pH value window mean PQ, the pH change rate window mean PR, the pH deviation window mean PS, the electrolyte concentration window mean PT, the electrolyte conductivity window mean PU, the concentration change rate window mean PV, the temperature change rate window mean PW and the temperature uniformity window mean PX through the following formulas respectively; Where: m is the size of the window, which means that there are m data points in each window, and m data points are taken continuously to calculate the mean, and n is the total number of timestamps; Ai, Bi, Ci, Di, Ei, Fi, Gi, Hi, Ii, Ji, Ki, Li, Mi, Ni, Oi, Pi, Qi, Ri, Si, Ti, Ui, Vi, Wi and Xi are respectively the current value, voltage value, power value, temperature value, reaction chamber pressure, temperature difference, reaction rate, capacity efficiency, hydrogen flow rate, gas velocity, gas density, hydrogen purity, oxygen concentration, impurity gas concentration, hydrogen temperature, hydrogen pressure, pH value, pH change rate, pH deviation, electrolyte concentration, electrolyte conductivity, concentration change rate, temperature change rate and temperature uniformity at timestamp i; An, Bn, Cn, Dn, En, Fn, Gn, Hn, In, Jn, Kn, Ln, Mn, Nn, On, Pn, Qn, Rn, Sn, Tn, Un, Vn, Wn and Xn are the current value, voltage value, power value, temperature value, reaction chamber pressure, temperature difference, reaction rate, capacity efficiency, hydrogen flow rate, gas velocity, gas density, hydrogen purity, oxygen concentration, impurity gas concentration, hydrogen temperature, hydrogen pressure, pH value, pH change rate, pH deviation, electrolyte concentration, electrolyte conductivity, concentration change rate, temperature change rate and temperature uniformity at timestamp n respectively.

[0012] Preferably, the second calculation unit calculates and obtains the electrolytic cell characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2 and the electrolyte characteristic reference coefficient CS3 by the following formulas respectively; Wherein: a1, a2, b1, b2, c1 and c2 are weight values ​​respectively, and the values ​​of a1, a2, b1, b2, c1 and c2 are adjusted and set by the user; An, Bn, Cn, Dn, En, Fn, In, Jn, Kn, Ln, Mn, Nn, Qn, Rn, Sn, Tn, Un and Vn are the current value, voltage value, power value, temperature value, reaction chamber pressure, temperature difference, hydrogen flow rate, gas velocity, gas density, hydrogen purity, oxygen concentration, impurity gas concentration, pH value, pH change rate, pH deviation, electrolyte concentration, electrolyte conductivity and concentration change rate at timestamp n respectively; PA is the current value window average, PB is the voltage value window average, PC is the power value window average, PD is the temperature value window average, PE is the reaction chamber pressure window average, PF is the temperature difference window average, PG is the reaction rate window average, PH is the capacity efficiency window average, PI is the hydrogen flow window average, PJ is the gas velocity window average, PK is the gas density window average, PL is the hydrogen purity window average, PM is the oxygen concentration window average, PN is the impurity gas concentration window average, PO is the hydrogen temperature window average, PP is the hydrogen pressure window average, PQ is the pH value window average, PR is the pH change rate window average, PS is the pH deviation window average, PT is the electrolyte concentration window average, PU is the electrolyte conductivity window average, PV is the concentration change rate window average, PW is the temperature change rate window average, and PX is the temperature uniformity window average.

[0013] Preferably, the third calculation unit calculates and obtains the reasonable coefficient CLX of the processing process by the following formula: Wherein: d1, d2 and d3 are weight values, and the values ​​of d1, d2 and d3 are adjusted and set by the user; CS1 is the characteristic reference coefficient of the electrolyzer, CS2 is the characteristic reference coefficient of hydrogen, and CS3 is the characteristic reference coefficient of the electrolyte.

[0014] Preferably, the data analysis module includes a first analysis unit and a second analysis unit; The first analysis unit is used to generate a first comparison result, which is specifically as follows: when When , it means that there is no abnormality in the current process. When , it means that the current process is abnormal; The second analysis unit is used to generate a second comparison result, which is as follows; when When , it means that there is an abnormality in the electrolytic cell during the current process; when When , it means that there is an abnormality in the hydrogen in the current process; when When , it means that there is an abnormality in the electrolyte during the current process; Among them, CY is the first sub-threshold, CR is the second sub-threshold, and CS is the third sub-threshold.

[0015] The present application also includes a process monitoring method for AEM water electrolysis hydrogen production, the specific steps are as follows: S1. Collect multi-source data of the electrolyzer through a first data acquisition module to form a first multi-source data set, collect multi-source data of hydrogen through a second data acquisition module to form a second multi-source data set, and collect multi-source data of the electrolyte through a third data acquisition module to form a third multi-source data set; S2, preprocessing the first multi-source data set, the second multi-source data set, and the third multi-source data set through a data processing module to eliminate noise, standardize data, and remove abnormal data, so that the data is more suitable for subsequent analysis, and reorganizing the processed data to form new data sets, including a first data set, a second data set, a third data set, a fourth data set, a fifth data set, a sixth data set, a seventh data set, an eighth data set, and a ninth data set; S3, through the data calculation module, calculate the above data sets to obtain the electrolytic cell characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2 and the electrolyte characteristic reference coefficient CS3, and integrate the electrolytic cell characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2 and the electrolyte characteristic reference coefficient CS3 to obtain the treatment process reasonable coefficient CLX; S4. By using the data analysis module, the reasonable coefficient CLX of the processing process is compared with the preset first threshold SY to generate a first comparison result. If the first comparison result shows that an abnormality exists, the characteristic reference coefficients of the electrolyzer, hydrogen and electrolyte are separately compared to generate a second comparison result. According to the second comparison result, the specific abnormal point position is determined; S5. Feedback the first comparison result and the second comparison result to the terminal through a feedback module for the operator to view.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. The AEM process monitoring system for hydrogen production by electrolysis of water integrates multiple modules to form a comprehensive, efficient and accurate monitoring and control platform. Compared with the current traditional monitoring system, it can not only collect and process multi-source data, but also perform complex data calculation and analysis to achieve all-round monitoring of the electrolysis process. Compared with the previous method of relying on a single data source, the system can comprehensively consider data from various aspects such as electrolyzers, hydrogen, electrolytes, etc., provide real-time feedback on operating status, and perform intelligent anomaly detection and adjustment. Such improvements enable the system to identify problems more accurately, warn of potential risks in advance, reduce equipment failure rates, and increase hydrogen production and quality, ultimately achieving the goals of improving hydrogen production efficiency, reducing energy consumption, and optimizing the production process.

[0017] 2. Compared with the traditional monitoring method of hydrogen production by water electrolysis, this system significantly improves the accuracy and processing efficiency of data analysis by introducing data preprocessing, dimensionless, time series and refined data set division. Whether from the perspective of standardization of data storage, intelligent processing, or the depth of subsequent analysis and optimization, this system has greatly enhanced the monitoring ability of the hydrogen production process by water electrolysis, ensuring the stability, optimization and safety of the production process. Through precise process monitoring and optimization, this system can more effectively improve the output efficiency and quality of hydrogen, while extending the service life of the equipment and reducing operating risks and maintenance costs.

[0018] 3. By introducing a comprehensive evaluation mechanism of window mean calculation, characteristic reference coefficients CS1, CS2, CS3 and reasonable processing coefficient CLX, this system provides a more accurate and stable monitoring and optimization method in the process of hydrogen production by water electrolysis. Compared with the traditional single data collection and analysis method, this system significantly improves the level of refined management of the entire hydrogen production process through multi-dimensional data analysis, dynamic calculation and personalized adjustment. The system can evaluate and adjust the status of the electrolyzer, hydrogen and electrolyte in real time and comprehensively, effectively reducing equipment failures, increasing hydrogen production and purity, while ensuring the efficiency and safety of system operation, which has brought significant improvements to the practical application of water electrolysis hydrogen production technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a system flow chart of the present invention.

[0020] Figure 2 It is a diagram of the method steps of the present invention.

[0021] In the figure: 1. First data acquisition module; 11. Current and voltage detection unit; 12. Temperature and pressure detection unit; 13. Reaction efficiency detection unit; 2. Second data acquisition module; 21. Hydrogen flow detection unit; 22. Hydrogen purity detection unit; 23. Hydrogen pressure detection unit; 3. Third data acquisition module; 31. Electrolyte pH detection unit; 32. Electrolyte concentration detection unit; 33. Electrolyte temperature detection unit; 4. Data processing module; 5. Data calculation module; 51. First calculation unit; 52. Second calculation unit; 53. Third calculation unit; 6. Data analysis module; 61. First analysis unit; 62. Second analysis unit; 7. Feedback module. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] Example 1: Please refer to Figure 1 , a process monitoring system for AEM water electrolysis hydrogen production, comprising a first data acquisition module 1, a second data acquisition module 2, a third data acquisition module 3, a data processing module 4, a data calculation module 5, a data analysis module 6 and a feedback module 7; The first data acquisition module 1 is used to collect multi-source data of the electrolytic cell and form a first multi-source data set; The second data acquisition module 2 is used to collect multi-source data of hydrogen and form a second multi-source data set; The third data collection module 3 is used to collect multi-source data of the electrolyte and form a third multi-source data set; The data processing module 4 is used to pre-process the first multi-source data set, the second multi-source data set and the third multi-source data set, and reorganize them into a first data set, a second data set, a third data set, a fourth data set, a fifth data set, a sixth data set, a seventh data set, an eighth data set and a ninth data set; The data calculation module 5 is used to first perform step-by-step calculations on the first data set, the second data set, the third data set, the fourth data set, the fifth data set, the sixth data set, the seventh data set, the eighth data set and the ninth data set, so as to obtain the electrolytic cell characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2 and the electrolyte characteristic reference coefficient CS3, and integrate the electrolytic cell characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2 and the electrolyte characteristic reference coefficient CS3 to calculate the reasonable coefficient CLX of the processing process; The data analysis module 6 is used to compare the processing process rationality coefficient CLX with the preset first threshold SY, thereby generating a first comparison result, so as to determine whether there is an abnormal point in the current processing process; When the first comparison result shows that there is no abnormal point, the electrolytic cell characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2 and the electrolyte characteristic reference coefficient CS3 are respectively compared with the preset threshold value, so as to generate a second comparison result, and the position of the abnormal point is determined according to the second comparison result; The feedback module 7 is used to feed back the first comparison result and the second comparison result to the terminal.

[0024] In this embodiment: the first data acquisition module 1 is responsible for collecting multi-source data from the electrolyzer, including multiple key parameters such as current, voltage, temperature, and pressure. These data are the basis for monitoring the reaction state during the electrolysis of water to produce hydrogen, and can provide the necessary original information for subsequent data processing and analysis. By comprehensively collecting the operating data of the electrolyzer, this module lays a data foundation for real-time monitoring of the electrolysis process. In this way, the operating status of the electrolyzer can be understood in real time, potential anomalies can be discovered in time, and problems can be avoided from being missed or delayed due to the inability of a single data source to fully reflect process changes.

[0025] The second data acquisition module 2 is mainly responsible for collecting relevant data of hydrogen, including multiple indicators such as hydrogen flow, purity, and pressure. These data can directly reflect the efficiency and quality of hydrogen production, and provide an important basis for the optimization and adjustment of the electrolysis process. By obtaining multi-dimensional data of hydrogen, the system can realize dynamic monitoring of hydrogen production and quality, and provide accurate reference for subsequent optimization operations and troubleshooting. The existence of this module makes the hydrogen production process no longer a black box operation, and can be accurately monitored and adjusted to ensure a stable supply of hydrogen and high-purity output.

[0026] The third data acquisition module 3 is responsible for collecting data such as the pH, concentration, and temperature of the electrolyte. These parameters have an important impact on the efficiency and stability of the water electrolysis reaction. By monitoring the state of the electrolyte in real time, the system can adjust the reaction conditions in time to ensure that the electrolyte is always in the best working state. The stability of the electrolyte is directly related to the long-term operating efficiency of the electrolysis process and the durability of the equipment. The real-time data acquisition of the third module ensures the precise control of the reaction conditions, thereby effectively improving the efficiency of the electrolysis reaction and the output of hydrogen.

[0027] The data processing module 4 preprocesses and organizes the multi-source data obtained from the first, second and third data acquisition modules. This processing includes operations such as data cleaning, denoising, and dimensionless conversion to ensure the consistency and comparability of the data. By organizing these data, the module can convert the multi-source data into different data sets for subsequent calculation and analysis. This processing step effectively improves the quality and availability of the data, allowing subsequent analysis work to be carried out on the basis of accurate data, thereby improving the reliability and accuracy of the entire monitoring system.

[0028] The data calculation module 5 calculates the data obtained from different data sets in steps to obtain the characteristic reference coefficients CS1, CS2, and CS3 of the electrolyzer, hydrogen, and electrolyte. These characteristic coefficients are quantitative descriptions of various aspects of the electrolysis process, which can comprehensively reflect the efficiency and state of the reaction process. By calculating and integrating these coefficients, the system can better understand and predict changes in the electrolysis process, and then make real-time adjustments. The operation of this module ensures the refined management of the monitoring system, so that each parameter can be quantified and participate in the evaluation of the overall operating status, thereby more accurately controlling the electrolysis process.

[0029] The data analysis module 6 compares the calculated reasonable coefficient CLX of the processing process with the preset threshold value to generate a comparison result, and then determines whether there is an abnormality in the electrolysis process. When an abnormality is detected, the system can promptly feedback to the operator or automatically adjust the system operating parameters. This module enhances the system's perception of abnormal conditions by introducing threshold comparison and data analysis, and improves the accuracy and response speed of fault warning. It provides a strong guarantee for the stability of the production process, ensuring that when an abnormality occurs, the problem can be identified and handled in the first time.

[0030] Feedback module 7 feeds back the first comparison result and the second comparison result to the terminal, so that the operator can promptly know the system operation status and whether there is any abnormality. This feedback mechanism realizes real-time monitoring and automatic adjustment, and enhances the adaptive ability of the system. Feedback module 7 not only provides a decision-making basis for manual operation, but also can be linked with other modules to improve the efficiency of the entire system at the automation level. Through the continuous feedback mechanism, the system can respond at the moment of discovering an abnormality, automatically adjust the reaction conditions, minimize human operation errors and delays, and ensure that the system always operates in the optimal state.

[0031] The AEM process monitoring system for hydrogen production by electrolysis of water integrates multiple modules to form a comprehensive, efficient and accurate monitoring and control platform. Compared with the current traditional monitoring system, it can not only collect and process multi-source data, but also perform complex data calculation and analysis to achieve all-round monitoring of the electrolysis process. Compared with the previous method of relying on a single data source, the system can comprehensively consider data from various aspects such as electrolyzers, hydrogen, electrolytes, etc., provide real-time feedback on operating status, and perform intelligent anomaly detection and adjustment. Such improvements enable the system to more accurately identify problems, warn of potential risks in advance, reduce equipment failure rates, and increase hydrogen production and quality, ultimately achieving the goals of improving hydrogen production efficiency, reducing energy consumption and optimizing the production process.

[0032] Example 2: Please refer to Figure 1, the first data acquisition module 1 includes a current and voltage detection unit 11, a temperature and pressure detection unit 12 and a reaction efficiency detection unit 13; The current and voltage detection unit 11 is used to collect the current value, voltage value and power value of the electrolytic cell; The temperature and pressure detection unit 12 is used to collect the temperature value of the electrolytic cell, the pressure of the reaction chamber and the temperature difference; The reaction efficiency detection unit 13 is used to collect reaction rate and capacity efficiency; The second data acquisition module 2 includes a hydrogen flow detection unit 21, a hydrogen purity detection unit 22 and a hydrogen pressure detection unit 23; The hydrogen flow detection unit 21 is used to collect the hydrogen flow, gas velocity and gas density of hydrogen; The hydrogen purity detection unit 22 is used to collect the hydrogen purity, oxygen concentration and impurity gas concentration of hydrogen; The hydrogen pressure detection unit 23 is used to collect the hydrogen temperature and hydrogen pressure ratio of the hydrogen; The third data acquisition module 3 includes an electrolyte pH detection unit 31, an electrolyte concentration detection unit 32 and an electrolyte temperature detection unit 33; The electrolyte pH detection unit 31 is used to collect the pH value, pH change rate and pH deviation of the electrolyte; The electrolyte concentration detection unit 32 is used to collect the electrolyte concentration, electrolyte conductivity and concentration change rate of the electrolyte; The electrolyte temperature detection unit 33 is used to collect the temperature change rate and temperature uniformity of the electrolyte; Wherein the current value, voltage value, power value, temperature value, reaction chamber pressure, temperature difference, reaction rate and capacity efficiency constitute the first multi-source data set; The hydrogen flow rate, gas velocity, gas density, hydrogen purity, oxygen concentration, impurity gas concentration, hydrogen temperature and hydrogen pressure constitute the second multi-source data set; Among them, pH value, pH change rate, pH deviation, electrolyte concentration, electrolyte conductivity, concentration change rate, temperature change rate and temperature uniformity constitute the third multi-source data set.

[0033] In this embodiment: First data acquisition module 1: through the current and voltage detection unit, temperature and pressure detection unit, reaction efficiency detection unit and multiple submodules, the system can obtain the real-time working status of the electrolytic cell and record key indicators such as current, voltage, power, temperature, pressure, reaction rate, etc. in detail. In this way, not only can the energy efficiency of the electrolytic cell be fully monitored, but also detailed data can be provided on the efficiency of the reaction and the stability of the process, so as to optimize the energy efficiency and avoid excessive consumption.

[0034] Second data acquisition module 2: Accurately collect parameters such as hydrogen flow, gas density, and hydrogen purity, and be able to grasp the production, quality, and purity of hydrogen in real time to ensure the stability of the final product and meet standard requirements. Through hydrogen temperature and pressure detection, the system's control over the hydrogen production process is further enhanced, which helps to improve production efficiency and the use value of hydrogen.

[0035] The third data acquisition module 3: Monitoring of the pH, concentration, conductivity and other indicators of the electrolyte helps to accurately adjust the optimal reaction environment of the electrolyte and ensure the high efficiency and long-term stability of the electrolysis reaction. The temperature change and uniformity detection of the electrolyte further optimizes the operating conditions of the electrolyzed water, reduces the phenomenon of uneven heating or excessive consumption, and prolongs the service life of the equipment.

[0036] The use of multi-source data sets and comprehensive detection units enables the system to more accurately capture every detail of the process of hydrogen production by water electrolysis, minimizing the efficiency loss, quality fluctuations and failure risks caused by incomplete information or inaccurate data in traditional monitoring methods. Through accurate data collection, the system can more scientifically perform real-time optimization and adjustments, thereby effectively improving the stability and efficiency of hydrogen production and ensuring the long-term safe operation of the equipment.

[0037] Example 3: Please refer to Figure 1 The data processing module 4 preprocesses and dimensionlessly transforms the first multi-source data set, the second multi-source data set, and the third multi-source data set, and reorganizes the processed first multi-source data set, the second multi-source data set, and the third multi-source data set into a first data set, a second data set, a third data set, a fourth data set, a fifth data set, a sixth data set, a seventh data set, an eighth data set, and a ninth data set.

[0038] The first data set includes current value, voltage value and power value; The current values ​​are recorded as A1, A2, A3, ..., An according to the timestamps; The voltage values ​​are recorded as B1, B2, B3, ..., Bn according to the timestamps; The power values ​​are recorded as C1, C2, C3, ..., Cn according to the timestamps; The second data set includes temperature values, reaction chamber pressure, and temperature difference; The temperature values ​​are recorded as D1, D2, D3, ..., Dn according to the timestamps; The reaction chamber pressures are recorded as E1, E2, E3, ..., En according to the timestamps; The temperature differences are recorded as F1, F2, F3, ..., Fn according to the timestamps; The third data set includes reaction rate and capacity efficiency; The reaction rates are recorded as G1, G2, G3, ..., Gn according to the timestamps; The capacity efficiency is recorded as H1, H2, H3, ..., Hn according to the timestamp; The fourth data set includes hydrogen flow rate, gas velocity, and gas density; The hydrogen flow rates are recorded as I1, I2, I3, ..., In according to the timestamps; The gas velocities are recorded as J1, J2, J3, ..., Jn according to the timestamps; The gas density is recorded as K1, K2, K3, ..., Kn according to the timestamp; The fifth data set includes hydrogen purity, oxygen concentration, and impurity gas concentration; The purity of hydrogen is recorded as L1, L2, L3, ..., Ln according to the timestamp; The oxygen concentrations are recorded as M1, M2, M3, ..., Mn according to the timestamps; The impurity gas concentrations are recorded as N1, N2, N3, ..., Nn according to the timestamps; The sixth data set includes hydrogen temperature and hydrogen pressure ratio; The hydrogen temperatures are recorded as O1, O2, O3, ..., On according to the timestamps; The hydrogen pressure ratios are recorded as P1, P2, P3, ..., Pn according to the timestamps; The seventh data set includes pH value, pH change rate, and pH deviation; The pH values ​​are recorded as Q1, Q2, Q3, ..., Qn according to the timestamp; The acid-base change rates are recorded as R1, R2, R3, ..., Rn according to the timestamp; The acid-base deviations are recorded as S1, S2, S3, ..., Sn according to the timestamps; The eighth data set includes electrolyte concentration, electrolyte conductivity, and concentration change rate; The electrolyte concentrations are recorded as T1, T2, T3, ..., Tn according to the timestamps; The electrolyte conductivity is recorded as U1, U2, U3, ..., Un according to the time stamp; The concentration change rates are recorded as V1, V2, V3, ..., Vn according to the timestamps; The ninth data set includes temperature change rate and temperature uniformity; The temperature change rates are recorded as W1, W2, W3, ..., Wn according to the timestamps; The temperature uniformity is recorded as X1, X2, X3, ..., Xn according to the timestamp.

[0039] In this embodiment: the data processing module 4 preprocesses and dimensionlessly converts the first multi-source data set, the second multi-source data set, and the third multi-source data set, effectively eliminating the dimension differences between different physical quantities, so that each data can be uniformly analyzed and compared under the same standard. This processing not only enhances the comparability of the data, but also improves the accuracy and reliability of subsequent data analysis. The dimensionlessly converted data can reduce the analysis errors caused by inconsistent dimensions, ensuring accurate data transmission and efficient calculation.

[0040] The system reorganizes different types of data and divides the data into multiple specific data sets such as current, voltage, temperature, gas flow, etc. according to timestamps, providing a clearer and more structured basis for subsequent data analysis and calculations. This orderly data storage method not only facilitates real-time monitoring, but also makes long-term trend analysis possible. By recording data accurately to timestamps, accurate tracking and monitoring of every moment of the process can be achieved, further improving the system's responsiveness and predictive capabilities.

[0041] The data is divided into nine independent data sets according to different characteristics and dimensions. Each data set corresponds to different physical quantities in the process of hydrogen production by water electrolysis, such as temperature, pressure, flow, gas density, pH, etc. This division method enables the system to accurately analyze and optimize each specific working condition dimension. This data refinement approach ensures that each parameter can be analyzed independently and in detail, avoiding the situation in which the traditional method causes insufficient or overly rough processing due to the large amount of data.

[0042] All data sets are marked with timestamps, forming a complete time series database that can reflect the dynamic changes in the process of hydrogen production by water electrolysis. Through time series analysis, the system can identify data change trends, potential anomalies and fluctuation patterns, thereby helping operators to adjust electrolysis conditions in real time to maintain stable operation of the equipment. This feature is crucial to improving process control accuracy and response speed.

[0043] By integrating and fine-tuning various data sets such as electrolyzers, hydrogen, hydrogen purity, and electrolyte, the system can comprehensively optimize the hydrogen production process in multiple dimensions. This all-round, multi-angle monitoring method can effectively identify possible bottlenecks or problem areas, thereby accurately guiding adjustment measures and improving overall production efficiency and safety.

[0044] Compared with the traditional monitoring method of hydrogen production by water electrolysis, this system significantly improves the accuracy and processing efficiency of data analysis by introducing data preprocessing, dimensionless, time series and refined data set division. Whether from the perspective of standardized data storage, intelligent processing, or the depth of subsequent analysis and optimization, this system has greatly enhanced the monitoring capability of the hydrogen production by water electrolysis process, ensuring the stability, optimization and safety of the production process. Through precise process monitoring and optimization, this system can more effectively improve the output efficiency and quality of hydrogen, while extending the service life of the equipment and reducing operating risks and maintenance costs.

[0045] Example 4: Please refer to Figure 1 , the data calculation module 5 includes a first calculation unit 51, a second calculation unit 52 and a third calculation unit 53; The first calculation unit 51 is used to calculate the first data set, the second data set, the third data set, the fourth data set, the fifth data set, the sixth data set, the seventh data set, the eighth data set and the ninth data set respectively, so as to obtain the current value window average PA, the voltage value window average PB, the power value window average PC, the temperature value window average PD, the reaction chamber pressure window average PE, the temperature difference window average PF, the reaction rate window average PG, the capacity efficiency window average PH, the hydrogen flow window average PI, the gas velocity window average PJ, the gas density window average PK, the hydrogen purity window average PL, the oxygen concentration window average PM, the impurity gas concentration window average PN, the hydrogen temperature window average PO, the hydrogen pressure window average PP, the pH value window average PQ, the pH change rate window average PR, the pH deviation window average PS, the electrolyte concentration window average PT, the electrolyte conductivity window average PU, the concentration change rate window average PV, the temperature change rate window average PW and the temperature uniformity window average PX; The second calculation unit 52 is used to calculate, so as to generate an electrolytic cell characteristic reference coefficient CS1, a hydrogen characteristic reference coefficient CS2 and an electrolyte characteristic reference coefficient CS3; The third calculation unit 53 is used to calculate the electrolytic cell characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2 and the electrolyte characteristic reference coefficient CS3, so as to generate a treatment process rationality coefficient CLX.

[0046] The first calculation unit 51 calculates and obtains the current value window mean PA, the voltage value window mean PB, the power value window mean PC, the temperature value window mean PD, the reaction chamber pressure window mean PE, the temperature difference window mean PF, the reaction rate window mean PG, the capacity efficiency window mean PH, the hydrogen flow window mean PI, the gas velocity window mean PJ, the gas density window mean PK, the hydrogen purity window mean PL, the oxygen concentration window mean PM, the impurity gas concentration window mean PN, the hydrogen temperature window mean PO, the hydrogen pressure window mean PP, the pH value window mean PQ, the pH change rate window mean PR, the pH deviation window mean PS, the electrolyte concentration window mean PT, the electrolyte conductivity window mean PU, the concentration change rate window mean PV, the temperature change rate window mean PW and the temperature uniformity window mean PX through the following formulas respectively; Where: m is the size of the window, which means that there are m data points in each window, and m data points are taken continuously to calculate the mean, and n is the total number of timestamps; Ai, Bi, Ci, Di, Ei, Fi, Gi, Hi, Ii, Ji, Ki, Li, Mi, Ni, Oi, Pi, Qi, Ri, Si, Ti, Ui, Vi, Wi and Xi are respectively the current value, voltage value, power value, temperature value, reaction chamber pressure, temperature difference, reaction rate, capacity efficiency, hydrogen flow rate, gas velocity, gas density, hydrogen purity, oxygen concentration, impurity gas concentration, hydrogen temperature, hydrogen pressure, pH value, pH change rate, pH deviation, electrolyte concentration, electrolyte conductivity, concentration change rate, temperature change rate and temperature uniformity at timestamp i; An, Bn, Cn, Dn, En, Fn, Gn, Hn, In, Jn, Kn, Ln, Mn, Nn, On, Pn, Qn, Rn, Sn, Tn, Un, Vn, Wn and Xn are the current value, voltage value, power value, temperature value, reaction chamber pressure, temperature difference, reaction rate, capacity efficiency, hydrogen flow rate, gas velocity, gas density, hydrogen purity, oxygen concentration, impurity gas concentration, hydrogen temperature, hydrogen pressure, pH value, pH change rate, pH deviation, electrolyte concentration, electrolyte conductivity, concentration change rate, temperature change rate and temperature uniformity at timestamp n respectively.

[0047] The second calculation unit 52 calculates and obtains the electrolytic cell characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2 and the electrolyte characteristic reference coefficient CS3 respectively through the following formulas; Wherein: a1, a2, b1, b2, c1 and c2 are weight values ​​respectively, and the values ​​of a1, a2, b1, b2, c1 and c2 are adjusted and set by the user; An, Bn, Cn, Dn, En, Fn, In, Jn, Kn, Ln, Mn, Nn, Qn, Rn, Sn, Tn, Un and Vn are the current value, voltage value, power value, temperature value, reaction chamber pressure, temperature difference, hydrogen flow rate, gas velocity, gas density, hydrogen purity, oxygen concentration, impurity gas concentration, pH value, pH change rate, pH deviation, electrolyte concentration, electrolyte conductivity and concentration change rate at timestamp n respectively; PA is the current value window average, PB is the voltage value window average, PC is the power value window average, PD is the temperature value window average, PE is the reaction chamber pressure window average, PF is the temperature difference window average, PG is the reaction rate window average, PH is the capacity efficiency window average, PI is the hydrogen flow window average, PJ is the gas velocity window average, PK is the gas density window average, PL is the hydrogen purity window average, PM is the oxygen concentration window average, PN is the impurity gas concentration window average, PO is the hydrogen temperature window average, PP is the hydrogen pressure window average, PQ is the pH value window average, PR is the pH change rate window average, PS is the pH deviation window average, PT is the electrolyte concentration window average, PU is the electrolyte conductivity window average, PV is the concentration change rate window average, PW is the temperature change rate window average, and PX is the temperature uniformity window average.

[0048] The third calculation unit 53 calculates the reasonable coefficient CLX of the acquisition process by the following formula: Wherein: d1, d2 and d3 are weight values, and the values ​​of d1, d2 and d3 are adjusted and set by the user; CS1 is the characteristic reference coefficient of the electrolyzer, CS2 is the characteristic reference coefficient of hydrogen, and CS3 is the characteristic reference coefficient of the electrolyte.

[0049] In this embodiment: the first calculation unit 51 can effectively smooth the fluctuations in each time window and eliminate the impact of short-term fluctuations on system analysis by performing window mean calculation on multi-dimensional data. This method can balance instantaneous measurement errors, making long-term trends and changes more prominent, thereby improving the reliability and stability of the data. The use of window mean enables the system to more accurately capture the regular changes in the process of hydrogen production by electrolysis of water, reducing misjudgments or misoperations caused by instantaneous abnormal fluctuations.

[0050] The second calculation unit 52 converts various data into characteristic indicators that can effectively represent the equipment operation status and hydrogen production performance by calculating the electrolyzer characteristic reference coefficient CS1, hydrogen characteristic reference coefficient CS2 and electrolyte characteristic reference coefficient CS3. These reference coefficients provide quantitative standards for the entire process monitoring, allowing the system to more accurately evaluate the performance of the electrolyzer, hydrogen and electrolyte, thereby helping to optimize operating parameters, reduce energy waste, and improve hydrogen production and quality.

[0051] The third calculation unit 53 calculates the reasonable coefficient CLX of the processing process according to different reference coefficients CS1, CS2, CS3 and the set weight values ​​d1, d2, d3. This coefficient, as a comprehensive evaluation index of the entire electrolysis of water hydrogen production process, can help the system to simultaneously analyze and optimize in multiple dimensions. By adjusting the weights of different coefficients, users can flexibly adjust the monitoring focus, adjust the equipment operation status in real time, and ensure the stability and efficiency of the entire hydrogen production process.

[0052] The system provides a custom adjustment function for weight values ​​such as a1, a2, b1, b2, c1, c2, d1, d2, and d3, allowing users to flexibly optimize the priority of each calculation according to actual production needs and operating conditions. This personalized setting method makes the system more adaptable and can adjust the focus of monitoring according to different production conditions to meet diverse application needs.

[0053] By calculating the window mean, characteristic reference coefficients and comprehensive evaluation of reasonable coefficients, the system can dynamically adjust the operating conditions of water electrolysis to produce hydrogen, and promptly detect potential equipment failures, gas purity not meeting standards and other abnormal conditions. This real-time monitoring and dynamic optimization capability enables the system to have higher flexibility and accuracy in long-term operation, and can better cope with complex factors such as environmental changes and load fluctuations.

[0054] By introducing a comprehensive evaluation mechanism of window mean calculation, characteristic reference coefficients CS1, CS2, CS3 and reasonable processing coefficient CLX, this system provides a more accurate and stable monitoring and optimization method in the process of hydrogen production by electrolysis of water. Compared with the traditional single data collection and analysis method, this system significantly improves the level of refined management of the entire hydrogen production process through multi-dimensional data analysis, dynamic calculation and personalized adjustment. The system can evaluate and adjust the status of the electrolyzer, hydrogen and electrolyte in real time and comprehensively, effectively reducing equipment failures, increasing hydrogen production and purity, while ensuring the efficiency and safety of system operation, which has brought significant improvements to the practical application of hydrogen production technology by electrolysis of water.

[0055] Example 5: Please refer to Figure 1 , the data analysis module 6 includes a first analysis unit 61 and a second analysis unit 62; The first analysis unit 61 is used to generate a first comparison result, which is as follows; when When , it means that there is no abnormality in the current process. When the current process is abnormal The second analysis unit 62 is used to generate a second comparison result, which is as follows; when When , it means that there is an abnormality in the electrolytic cell during the current process; when When , it means that there is an abnormality in the hydrogen in the current process; when When , it means that there is an abnormality in the electrolyte during the current process; Among them, CY is the first sub-threshold, CR is the second sub-threshold, and CS is the third sub-threshold.

[0056] In this embodiment: the data analysis module 6 introduces two analysis units, the first analysis unit 61 and the second analysis unit 62, so that the abnormality detection is not limited to the judgment of the overall process, but can also go deep into the electrolyzer, hydrogen, and electrolyte levels of each subsystem. This hierarchical abnormality analysis method helps to accurately identify the specific source of the problem, so as to take more effective targeted measures and reduce the possibility of false alarms and missed alarms.

[0057] The first analysis unit 61 can monitor in real time whether the entire water electrolysis hydrogen production process is within the normal range by comparing the reasonable coefficient CLX of the processing process with the preset threshold value SY. If CLX ≥ SY, it means that the entire process is abnormal, and the system can immediately trigger an alarm and start corresponding adjustment measures. This real-time abnormal alarm function ensures that any potential failure or performance degradation in the production process can be quickly detected and timely action can be taken, effectively reducing the risk of equipment damage or low production efficiency.

[0058] The second analysis unit 62 makes abnormal judgments on the electrolyzer, hydrogen, and electrolyte at the subsystem level, and can accurately locate which part has an abnormality by comparing the electrolyzer characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2, and the electrolyte characteristic reference coefficient CS3. For example, when CS1≥CY, it means that there is a problem with the electrolyzer; when CS2≥CR, it indicates that the hydrogen quality or flow is abnormal; when CS3≥CS, it indicates that there may be a problem with the pH or concentration of the electrolyte. This refined abnormality positioning can help operators quickly identify and deal with problems, avoiding excessive or incorrect adjustments.

[0059] The setting of CY, CR, CS and other thresholds enables the system to be flexibly adjusted. Under different production environments or operating conditions, users can customize these thresholds according to actual needs to ensure that the system can adapt to different working conditions and give the most accurate warning. Users can adjust the thresholds according to the actual operation of the equipment and experience, thereby improving the accuracy and reliability of monitoring and avoiding false alarms or missed alarms caused by overly strict or overly loose threshold settings.

[0060] The dual-level abnormal analysis mechanism of the data analysis module 6 provides a more accurate and intelligent monitoring capability for the AEM water electrolysis hydrogen production system. By comparing the reasonable coefficient CLX of the processing process with the threshold SY in real time, and comparing the characteristic reference coefficients CS1, CS2, CS3 of each subsystem with their respective sub-thresholds CY, CR, CS, the system can achieve more refined and flexible abnormal detection and positioning in the entire hydrogen production process. This multi-level and adjustable abnormal analysis method greatly improves the reliability, stability and adaptability of the system, ensures the smooth progress of the production process, and provides strong support for preventive maintenance and troubleshooting.

[0061] This application also includes a process monitoring method for AEM electrolysis of water to produce hydrogen, see Figure 2 , the specific steps are as follows: S1, through the first data acquisition module 1, collect multi-source data of the electrolyzer to form a first multi-source data set, through the second data acquisition module 2, collect multi-source data of hydrogen to form a second multi-source data set, through the third data acquisition module 3, collect multi-source data of the electrolyte to form a third multi-source data set; S2, preprocessing the first multi-source data set, the second multi-source data set, and the third multi-source data set through the data processing module 4, eliminating noise, standardizing data, and removing abnormal data, so that the data is more suitable for subsequent analysis, and reorganizing the processed data to form new data sets, including the first data set, the second data set, the third data set, the fourth data set, the fifth data set, the sixth data set, the seventh data set, the eighth data set, and the ninth data set; S3, through the data calculation module 5, the above data set is calculated to obtain the electrolytic cell characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2 and the electrolyte characteristic reference coefficient CS3, and the electrolytic cell characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2 and the electrolyte characteristic reference coefficient CS3 are integrated and calculated to obtain the reasonable coefficient CLX of the processing process; S4, through the data analysis module 6, the reasonable coefficient CLX of the processing process is compared with the preset first threshold SY to generate a first comparison result. If the first comparison result shows that there is an abnormality, the characteristic reference coefficients of the electrolyzer, hydrogen and electrolyte are separately compared to generate a second comparison result. According to the second comparison result, the specific abnormal point position is determined; S5. Feedback the first comparison result and the second comparison result to the terminal through the feedback module 7 for the operator to view.

[0062] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

[0063] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A process monitoring system for AEM water electrolysis hydrogen production, characterized in that: It comprises a first data acquisition module (1), a second data acquisition module (2), a third data acquisition module (3), a data processing module (4), a data calculation module (5), a data analysis module (6) and a feedback module (7); The first data acquisition module (1) is used to acquire multi-source data of the electrolytic cell and form a first multi-source data set; The second data acquisition module (2) is used to collect multi-source data of hydrogen and form a second multi-source data set; The third data acquisition module (3) is used to acquire multi-source data of the electrolyte and form a third multi-source data set; The data processing module (4) is used to pre-process the first multi-source data set, the second multi-source data set and the third multi-source data set, and reorganize them into a first data set, a second data set, a third data set, a fourth data set, a fifth data set, a sixth data set, a seventh data set, an eighth data set and a ninth data set; The data calculation module (5) is used to first perform step-by-step calculations on the first data set, the second data set, the third data set, the fourth data set, the fifth data set, the sixth data set, the seventh data set, the eighth data set, and the ninth data set, so as to obtain the electrolytic cell characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2, and the electrolyte characteristic reference coefficient CS3, and integrate the electrolytic cell characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2, and the electrolyte characteristic reference coefficient CS3 to calculate the reasonable coefficient CLX of the processing process; The data analysis module (6) is used to compare the processing process rationality coefficient CLX with a preset first threshold value SY, thereby generating a first comparison result, thereby determining whether there is an abnormal point in the current processing process; When the first comparison result shows that there is no abnormal point, the electrolytic cell characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2 and the electrolyte characteristic reference coefficient CS3 are respectively compared with the preset threshold value, so as to generate a second comparison result, and the position of the abnormal point is determined according to the second comparison result; The feedback module (7) is used to feed back the first comparison result and the second comparison result to the terminal.

2. A process monitoring system for producing hydrogen by electrolysis of water by AEM according to claim 1, characterized in that: The first data acquisition module (1) comprises a current and voltage detection unit (11), a temperature and pressure detection unit (12) and a reaction efficiency detection unit (13); The current and voltage detection unit (11) is used to collect the current value, voltage value and power value of the electrolytic cell; The temperature and pressure detection unit (12) is used to collect the temperature value of the electrolytic cell, the pressure of the reaction chamber and the temperature difference; The reaction efficiency detection unit (13) is used to collect reaction rate and capacity efficiency; The second data acquisition module (2) comprises a hydrogen flow detection unit (21), a hydrogen purity detection unit (22) and a hydrogen pressure detection unit (23); The hydrogen flow detection unit (21) is used to collect the hydrogen flow, gas velocity and gas density of hydrogen; The hydrogen purity detection unit (22) is used to collect the hydrogen purity, oxygen concentration and impurity gas concentration of the hydrogen; The hydrogen pressure detection unit (23) is used to collect the hydrogen temperature and hydrogen pressure ratio of the hydrogen; The third data acquisition module (3) comprises an electrolyte pH detection unit (31), an electrolyte concentration detection unit (32) and an electrolyte temperature detection unit (33); The electrolyte pH detection unit (31) is used to collect the pH value, pH change rate and pH deviation of the electrolyte; The electrolyte concentration detection unit (32) is used to collect the electrolyte concentration, electrolyte conductivity and concentration change rate of the electrolyte; The electrolyte temperature detection unit (33) is used to collect the temperature change rate and temperature uniformity of the electrolyte; Wherein the current value, voltage value, power value, temperature value, reaction chamber pressure, temperature difference, reaction rate and capacity efficiency constitute the first multi-source data set; The hydrogen flow rate, gas velocity, gas density, hydrogen purity, oxygen concentration, impurity gas concentration, hydrogen temperature and hydrogen pressure constitute the second multi-source data set; Among them, pH value, pH change rate, pH deviation, electrolyte concentration, electrolyte conductivity, concentration change rate, temperature change rate and temperature uniformity constitute the third multi-source data set.

3. A process monitoring system for producing hydrogen by electrolysis of water by AEM according to claim 2, characterized in that: The data processing module (4) preprocesses and dimensionlessly converts the first multi-source data set, the second multi-source data set, and the third multi-source data set, and reorganizes the processed first multi-source data set, the second multi-source data set, and the third multi-source data set into a first data set, a second data set, a third data set, a fourth data set, a fifth data set, a sixth data set, a seventh data set, an eighth data set, and a ninth data set.

4. A process monitoring system for producing hydrogen by electrolysis of water by AEM according to claim 3, characterized in that: The first data set includes current value, voltage value and power value; The current values ​​are recorded as A1, A2, A3, ..., An according to the timestamps; The voltage values ​​are recorded as B1, B2, B3, ..., Bn according to the timestamps; The power values ​​are recorded as C1, C2, C3, ..., Cn according to the timestamps; The second data set includes temperature values, reaction chamber pressure, and temperature difference; The temperature values ​​are recorded as D1, D2, D3, ..., Dn according to the timestamps; The reaction chamber pressures are recorded as E1, E2, E3, ..., En according to the timestamps; The temperature differences are recorded as F1, F2, F3, ..., Fn according to the timestamps; The third data set includes reaction rate and capacity efficiency; The reaction rates are recorded as G1, G2, G3, ..., Gn according to the timestamps; The capacity efficiency is recorded as H1, H2, H3, ..., Hn according to the timestamp; The fourth data set includes hydrogen flow rate, gas velocity and gas density; The hydrogen flow rates are recorded as I1, I2, I3, ..., In according to the timestamps; The gas velocities are recorded as J1, J2, J3, ..., Jn according to the timestamps; The gas density is recorded as K1, K2, K3, ..., Kn according to the timestamp; The fifth data set includes hydrogen purity, oxygen concentration, and impurity gas concentration; The purity of hydrogen is recorded as L1, L2, L3, ..., Ln according to the timestamp; The oxygen concentrations are recorded as M1, M2, M3, ..., Mn according to the timestamps; The impurity gas concentrations are recorded as N1, N2, N3, ..., Nn according to the timestamps; The sixth data set includes hydrogen temperature and hydrogen pressure ratio; The hydrogen temperatures are recorded as O1, O2, O3, ..., On according to the timestamps; The hydrogen pressure ratios are recorded as P1, P2, P3, ..., Pn according to the timestamps; The seventh data set includes pH value, pH change rate and pH deviation; The pH values ​​are recorded as Q1, Q2, Q3, ..., Qn according to the timestamp; The acid-base change rates are recorded as R1, R2, R3, ..., Rn according to the timestamp; The acid-base deviations are recorded as S1, S2, S3, ..., Sn according to the timestamps; The eighth data set includes electrolyte concentration, electrolyte conductivity, and concentration change rate; The electrolyte concentrations are recorded as T1, T2, T3, ..., Tn according to the timestamps; The electrolyte conductivity is recorded as U1, U2, U3, ..., Un according to the time stamp; The concentration change rates are recorded as V1, V2, V3, ..., Vn according to the timestamps; The ninth data set includes temperature change rate and temperature uniformity; The temperature change rates are recorded as W1, W2, W3, ..., Wn according to the timestamps; The temperature uniformity is recorded as X1, X2, X3, ..., Xn according to the timestamp.

5. A process monitoring system for producing hydrogen by electrolysis of water by AEM according to claim 4, characterized in that: The data calculation module (5) comprises a first calculation unit (51), a second calculation unit (52) and a third calculation unit (53); The first calculation unit (51) is used to calculate the first data set, the second data set, the third data set, the fourth data set, the fifth data set, the sixth data set, the seventh data set, the eighth data set and the ninth data set respectively, so as to obtain the current value window average PA, the voltage value window average PB, the power value window average PC, the temperature value window average PD, the reaction chamber pressure window average PE, the temperature difference window average PF, the reaction rate window average PG, the capacity efficiency window average PH, the hydrogen flow window average PI, the gas velocity window average PJ, the gas density window average PK, the hydrogen purity window average PL, the oxygen concentration window average PM, the impurity gas concentration window average PN, the hydrogen temperature window average PO, the hydrogen pressure window average PP, the pH value window average PQ, the pH change rate window average PR, the pH deviation window average PS, the electrolyte concentration window average PT, the electrolyte conductivity window average PU, the concentration change rate window average PV, the temperature change rate window average PW and the temperature uniformity window average PX; The second calculation unit (52) is used to calculate, thereby generating an electrolytic cell characteristic reference coefficient CS1, a hydrogen characteristic reference coefficient CS2, and an electrolyte characteristic reference coefficient CS3; The third calculation unit (53) is used to calculate the electrolytic cell characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2 and the electrolyte characteristic reference coefficient CS3, so as to generate a treatment process rationality coefficient CLX.

6. A process monitoring system for producing hydrogen by electrolysis of water by AEM according to claim 5, characterized in that: The first calculation unit (51) calculates and obtains the current value window average PA, the voltage value window average PB, the power value window average PC, the temperature value window average PD, the reaction chamber pressure window average PE, the temperature difference window average PF, the reaction rate window average PG, the capacity efficiency window average PH, the hydrogen flow window average PI, the gas velocity window average PJ, the gas density window average PK, the hydrogen purity window average PL, the oxygen concentration window average PM, the impurity gas concentration window average PN, the hydrogen temperature window average PO, the hydrogen pressure window average PP, the pH value window average PQ, the pH change rate window average PR, the pH deviation window average PS, the electrolyte concentration window average PT, the electrolyte conductivity window average PU, the concentration change rate window average PV, the temperature change rate window average PW and the temperature uniformity window average PX through the following formulas respectively; Where: m is the size of the window, which means that there are m data points in each window, and m data points are taken continuously to calculate the mean, and n is the total number of timestamps; Ai, Bi, Ci, Di, Ei, Fi, Gi, Hi, Ii, Ji, Ki, Li, Mi, Ni, Oi, Pi, Qi, Ri, Si, Ti, Ui, Vi, Wi and Xi are respectively the current value, voltage value, power value, temperature value, reaction chamber pressure, temperature difference, reaction rate, capacity efficiency, hydrogen flow rate, gas velocity, gas density, hydrogen purity, oxygen concentration, impurity gas concentration, hydrogen temperature, hydrogen pressure, pH value, pH change rate, pH deviation, electrolyte concentration, electrolyte conductivity, concentration change rate, temperature change rate and temperature uniformity at timestamp i; An, Bn, Cn, Dn, En, Fn, Gn, Hn, In, Jn, Kn, Ln, Mn, Nn, On, Pn, Qn, Rn, Sn, Tn, Un, Vn, Wn and Xn are the current value, voltage value, power value, temperature value, reaction chamber pressure, temperature difference, reaction rate, capacity efficiency, hydrogen flow rate, gas velocity, gas density, hydrogen purity, oxygen concentration, impurity gas concentration, hydrogen temperature, hydrogen pressure, pH value, pH change rate, pH deviation, electrolyte concentration, electrolyte conductivity, concentration change rate, temperature change rate and temperature uniformity at timestamp n respectively.

7. A process monitoring system for producing hydrogen by electrolysis of water by AEM according to claim 6, characterized in that: The second calculation unit (52) calculates and obtains the electrolytic cell characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2 and the electrolyte characteristic reference coefficient CS3 respectively through the following formulas; Wherein: a1, a2, b1, b2, c1 and c2 are weight values ​​respectively, and the values ​​of a1, a2, b1, b2, c1 and c2 are adjusted and set by the user; An, Bn, Cn, Dn, En, Fn, In, Jn, Kn, Ln, Mn, Nn, Qn, Rn, Sn, Tn, Un and Vn are the current value, voltage value, power value, temperature value, reaction chamber pressure, temperature difference, hydrogen flow rate, gas velocity, gas density, hydrogen purity, oxygen concentration, impurity gas concentration, pH value, pH change rate, pH deviation, electrolyte concentration, electrolyte conductivity and concentration change rate at timestamp n respectively; PA is the current value window average, PB is the voltage value window average, PC is the power value window average, PD is the temperature value window average, PE is the reaction chamber pressure window average, PF is the temperature difference window average, PG is the reaction rate window average, PH is the capacity efficiency window average, PI is the hydrogen flow window average, PJ is the gas velocity window average, PK is the gas density window average, PL is the hydrogen purity window average, PM is the oxygen concentration window average, PN is the impurity gas concentration window average, PO is the hydrogen temperature window average, PP is the hydrogen pressure window average, PQ is the pH value window average, PR is the pH change rate window average, PS is the pH deviation window average, PT is the electrolyte concentration window average, PU is the electrolyte conductivity window average, PV is the concentration change rate window average, PW is the temperature change rate window average, and PX is the temperature uniformity window average.

8. A process monitoring system for producing hydrogen by electrolysis of water by AEM according to claim 7, characterized in that: The third calculation unit (53) calculates and obtains the reasonable coefficient CLX of the processing process through the following formula; Wherein: d1, d2 and d3 are weight values, and the values ​​of d1, d2 and d3 are adjusted and set by the user; CS1 is the characteristic reference coefficient of the electrolyzer, CS2 is the characteristic reference coefficient of hydrogen, and CS3 is the characteristic reference coefficient of the electrolyte.

9. A process monitoring system for producing hydrogen by electrolysis of water by AEM according to claim 8, characterized in that: The data analysis module (6) comprises a first analysis unit (61) and a second analysis unit (62); The first analysis unit (61) is used to generate a first comparison result, specifically as follows; when When , it means that there is no abnormality in the current process. When , it means that the current process is abnormal; The second analysis unit (62) is used to generate a second comparison result, specifically as follows; when When , it means that there is an abnormality in the electrolytic cell during the current process; when When , it means that there is an abnormality in the hydrogen in the current process; when When , it means that there is an abnormality in the electrolyte during the current process; Among them, CY is the first sub-threshold, CR is the second sub-threshold, and CS is the third sub-threshold.

10. A process monitoring method for producing hydrogen by electrolysis of water by AEM, used to implement a process monitoring system for producing hydrogen by electrolysis of water by AEM as claimed in any one of claims 1 to 9, characterized in that: The specific steps are as follows: S1, collecting multi-source data of the electrolyzer through a first data collection module (1) to form a first multi-source data set, collecting multi-source data of hydrogen through a second data collection module (2) to form a second multi-source data set, and collecting multi-source data of the electrolyte through a third data collection module (3) to form a third multi-source data set; S2, preprocessing the first multi-source data set, the second multi-source data set and the third multi-source data set through a data processing module (4) to eliminate noise, standardize data and remove abnormal data, so that the data is more suitable for subsequent analysis, and reorganizing the processed data to form new data sets, including a first data set, a second data set, a third data set, a fourth data set, a fifth data set, a sixth data set, a seventh data set, an eighth data set and a ninth data set; S3, calculating the above data set through the data calculation module (5) to obtain the electrolytic cell characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2 and the electrolyte characteristic reference coefficient CS3, integrating the electrolytic cell characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2 and the electrolyte characteristic reference coefficient CS3 to obtain the treatment process reasonable coefficient CLX; S4, using the data analysis module (6), comparing the reasonable coefficient CLX of the processing process with a preset first threshold SY to generate a first comparison result. If the first comparison result shows that an abnormality exists, the characteristic reference coefficients of the electrolyzer, hydrogen and electrolyte are separately compared to generate a second comparison result. Based on the second comparison result, the specific location of the abnormal point is determined; S5. Feedback the first comparison result and the second comparison result to the terminal through the feedback module (7) for the operator to view.

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