CN119465296A - A process monitoring method and system for AEM water electrolysis hydrogen production - Google Patents
A process monitoring method and system for AEM water electrolysis hydrogen production Download PDFInfo
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- 239000001257 hydrogen Substances 0.000 title claims abstract description 271
- 229910052739 hydrogen Inorganic materials 0.000 title claims abstract description 271
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 title claims abstract description 258
- 238000000034 method Methods 0.000 title claims abstract description 97
- 230000008569 process Effects 0.000 title claims abstract description 79
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 68
- 238000012544 monitoring process Methods 0.000 title claims abstract description 63
- 238000005868 electrolysis reaction Methods 0.000 title claims abstract description 45
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 45
- 239000003792 electrolyte Substances 0.000 claims abstract description 149
- 238000001514 detection method Methods 0.000 claims abstract description 76
- 238000004364 calculation method Methods 0.000 claims abstract description 55
- 230000002159 abnormal effect Effects 0.000 claims abstract description 40
- 238000004458 analytical method Methods 0.000 claims abstract description 38
- 150000002431 hydrogen Chemical class 0.000 claims abstract description 18
- 239000007789 gas Substances 0.000 claims description 94
- 230000008859 change Effects 0.000 claims description 83
- 238000006243 chemical reaction Methods 0.000 claims description 77
- 238000012545 processing Methods 0.000 claims description 42
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 30
- 239000012535 impurity Substances 0.000 claims description 30
- 239000001301 oxygen Substances 0.000 claims description 30
- 229910052760 oxygen Inorganic materials 0.000 claims description 30
- 238000007405 data analysis Methods 0.000 claims description 23
- 230000005856 abnormality Effects 0.000 claims description 17
- 238000007781 pre-processing Methods 0.000 claims description 11
- 238000005265 energy consumption Methods 0.000 abstract description 3
- 238000005457 optimization Methods 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 6
- 230000007774 longterm Effects 0.000 description 6
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- 238000013500 data storage Methods 0.000 description 3
- 238000012423 maintenance Methods 0.000 description 3
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- 230000007246 mechanism Effects 0.000 description 3
- 206010063385 Intellectualisation Diseases 0.000 description 2
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- C—CHEMISTRY; METALLURGY
- C25—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES; APPARATUS THEREFOR
- C25B—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES FOR THE PRODUCTION OF COMPOUNDS OR NON-METALS; APPARATUS THEREFOR
- C25B15/00—Operating or servicing cells
- C25B15/02—Process control or regulation
- C25B15/023—Measuring, analysing or testing during electrolytic production
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- C—CHEMISTRY; METALLURGY
- C25—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES; APPARATUS THEREFOR
- C25B—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES FOR THE PRODUCTION OF COMPOUNDS OR NON-METALS; APPARATUS THEREFOR
- C25B1/00—Electrolytic production of inorganic compounds or non-metals
- C25B1/01—Products
- C25B1/02—Hydrogen or oxygen
- C25B1/04—Hydrogen or oxygen by electrolysis of water
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/30—Hydrogen technology
- Y02E60/36—Hydrogen production from non-carbon containing sources, e.g. by water electrolysis
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Abstract
The invention relates to the technical field of detection systems, and discloses a process monitoring method and a system for producing hydrogen by AEM electrolysis of water, which form a comprehensive, efficient and accurate monitoring and control platform by integrating a plurality of modules, compared with the current traditional monitoring system, the system not only can collect and process multi-source data, but also can perform complex data calculation and analysis, realize the omnibearing monitoring of the electrolysis process, and compared with the prior method relying on a single data source, the system can comprehensively consider data of various aspects such as an electrolytic tank, hydrogen, electrolyte and the like, feed back the running state in real time, and conduct intelligent abnormal detection and adjustment, so that the system can identify a problem more accurately, early warn potential risks in advance, reduce equipment failure rate, improve the yield and quality of hydrogen, and finally achieve the aims of improving hydrogen production efficiency, reducing energy consumption and optimizing production process.
Description
Technical Field
The invention relates to the technical field of detection systems, in particular to a process monitoring method and a system for AEM water electrolysis hydrogen production.
Background
The hydrogen production mode is various, wherein the water electrolysis hydrogen production technology is considered as an environment-friendly and efficient hydrogen production mode, and is gradually widely applied and researched. Among the hydrogen production technologies, the water electrolysis hydrogen production technology based on an Anode Exchange Membrane (AEM) has been attracting more and more attention in practical application due to relatively low cost and high stability.
Although AEM electrolyzed water hydrogen production technology has advanced to some extent in industrial applications, existing monitoring means and techniques still face a number of shortcomings. Current monitoring systems often collect only a limited single data, such as current and voltage, but lack real-time collection and processing capabilities for multi-dimensional, multi-source data. Parameters such as current, voltage and temperature of an electrolytic tank, flow rate and purity of hydrogen, pH value and concentration of electrolyte are key data in the hydrogen production process, and influence each other, so that abnormal fluctuation of any one parameter can cause efficiency reduction or equipment failure in the whole reaction process. However, the existing system can only independently analyze certain types of data, and cannot comprehensively process and correlate the multi-source data at the same time. The single and isolated data monitoring mode leads to poor early warning capability for abnormal conditions in the electrolysis process, and potential problems or faults are difficult to discover in time.
Therefore, we propose a process monitoring method and system for producing hydrogen by AEM water electrolysis, so as to solve the above-mentioned problems.
Disclosure of Invention
The invention aims to provide a process monitoring method and a system for AEM water electrolysis hydrogen production, which aim to solve the problems that the prior monitoring system is always only capable of collecting limited single data, such as current, voltage and other basic parameters, but lacks the capability of collecting and processing multidimensional and multi-source data in real time.
In order to achieve the aim, the invention provides the technical scheme that the AEM water electrolysis hydrogen production process monitoring system comprises 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 for acquiring multi-source data of the electrolytic cell and forming a first multi-source data set;
the second data acquisition module is used for acquiring multi-source data of the hydrogen and forming a second multi-source data set;
the third data acquisition module is used for acquiring multi-source data of the electrolyte and forming a third multi-source data set;
the data processing module is used for preprocessing a first multi-source data set, a second multi-source data set and a third multi-source data set and rearranging the first multi-source data set, the second multi-source data set, the third multi-source data set, the fourth multi-source data set, the fifth data set, the sixth data set, the seventh data set, the eighth data set and the ninth data set;
The data calculation module is used for performing step-by-step calculation 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 an electrolytic cell characteristic reference coefficient CS1, a hydrogen characteristic reference coefficient CS2 and an electrolyte characteristic reference coefficient CS3, and integrate and calculate the electrolytic cell characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2 and the electrolyte characteristic reference coefficient CS3 into a reasonable processing process coefficient CLX;
The data analysis module is used for comparing the reasonable coefficient CLX of the processing process with a preset first threshold SY so as to generate a first comparison result and judge whether an abnormal point exists in the current processing process;
when the first comparison result shows that no abnormal point exists, the method is used for comparing the characteristic reference coefficient CS1 of the electrolytic tank, the characteristic reference coefficient CS2 of the hydrogen and the characteristic reference coefficient CS3 of the electrolyte with preset thresholds respectively, so as to generate a second comparison result, and judging the position of the abnormal point according to the second comparison result;
The feedback module is used for feeding back the first comparison result and the second comparison result to the terminal.
Preferably, the first data acquisition module comprises 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 for collecting a current value, a voltage value and a power value of the electrolytic cell;
The temperature and pressure detection unit is used for collecting the temperature value of the electrolytic tank, the pressure of the reaction chamber and the temperature difference;
the reaction efficiency detection unit is used for collecting reaction rate and capacity efficiency;
The second data acquisition module comprises a hydrogen flow detection unit, a hydrogen purity detection unit and a hydrogen pressure detection unit;
the hydrogen flow detection unit is used for collecting the hydrogen flow, the gas speed and the gas density of the hydrogen;
the hydrogen purity detection unit is used for collecting the hydrogen purity, the oxygen concentration and the impurity gas concentration of the hydrogen;
the hydrogen pressure detection unit is used for collecting the hydrogen temperature and the hydrogen pressure ratio of the hydrogen;
the third data acquisition module comprises an electrolyte pH value detection unit, an electrolyte concentration detection unit and an electrolyte temperature detection unit;
the electrolyte pH value detection unit is used for collecting the acid-base number, the acid-base change rate and the acid-base deviation of the electrolyte;
the electrolyte concentration detection unit is used for collecting electrolyte concentration, electrolyte conductivity and concentration change rate of the electrolyte;
the electrolyte temperature detection unit is used for collecting the temperature change rate and the temperature uniformity of the electrolyte;
Wherein the current value, the voltage value, the power value, the temperature value, the reaction chamber pressure, the temperature difference, the reaction rate, and the capacity efficiency form a first multisource data set;
wherein the hydrogen flow rate, gas velocity, gas density, hydrogen purity, oxygen concentration, impurity gas concentration, hydrogen temperature, and hydrogen pressure comprise a second multisource data set;
Wherein the acid-base number, acid-base change rate, acid-base bias, electrolyte concentration, electrolyte conductivity, concentration change rate, temperature change rate, and temperature uniformity comprise a third multisource data set.
Preferably, the data processing module performs preprocessing and dimensionless treatment on the first multi-source data set, the second multi-source data set and the third multi-source data set, and rearranges 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.
Preferably, the first data set includes a current value, a voltage value, and a power value;
the current values are respectively marked as A1, A2, A3, a.and An according to the time stamps;
the voltage values are respectively marked as B1, B2, B3, & gt, bn according to the time stamps;
The power values are respectively marked as C1, C2, C3, & Cn according to the time stamps;
the second data set includes temperature values, reaction chamber pressures, and temperature differences;
The temperature values are respectively marked as D1, D2, D3, & gt, dn according to the time stamps;
reaction chamber pressures were noted as E1, E2, E3, & gt, en, respectively, according to time stamps;
The temperature differences are respectively marked as F1, F2, F3, and Fn according to the time stamps;
the third data set includes reaction rate and capacity efficiency;
the reaction rates are denoted G1, G2, G3, and Gn according to time stamps, respectively;
The capacity efficiencies are respectively marked as H1, H2, H3, & gt, hn according to time stamps;
the fourth data set includes hydrogen flow, gas velocity, and gas density;
The hydrogen flows are respectively marked as I1, I2, I3, I.C., in according to the time stamps;
the gas velocities are respectively noted as J1, J2, J3,..and Jn according to the time stamps;
The gas densities are respectively marked as K1, K2, K3, & gt, kn according to the time stamps;
The fifth data set includes hydrogen purity, oxygen concentration, and impurity gas concentration;
the purity of the hydrogen is respectively marked as L1, L2, L3, and Ln according to the time stamp;
The oxygen concentrations were recorded as M1, M2, M3, & gt, mn according to time stamps, respectively;
impurity gas concentrations were recorded as N1, N2, N3, and Nn according to time stamps, respectively;
the sixth data set includes a hydrogen temperature and a hydrogen pressure ratio;
the hydrogen temperatures are respectively marked as O1, O2, O3, and O according to the time stamps;
the hydrogen pressure ratio is denoted by time stamps P1, P2, P3, P.
The seventh data set includes an acid-base number, an acid-base rate of change, and an acid-base bias;
the acid-base values are respectively marked as Q1, Q2, Q3, qn according to the time stamps;
the acid-base change rates are respectively marked as R1, R2, R3, and Rn according to time stamps;
the acid-base deviation is respectively marked as S1, S2, S3, & gt, sn according to the time stamps;
The eighth data set includes electrolyte concentration, electrolyte conductivity, and concentration change rate;
the electrolyte concentrations were recorded as T1, T2, T3, & gt, tn, respectively, according to the time stamps;
The conductivity of the electrolyte was noted as U1, U2, U3, & gt, un, respectively, according to the time stamp;
the concentration change rates are respectively marked as V1, V2, V3, and Vn according to time stamps;
the ninth dataset includes a temperature rate of change and a temperature uniformity;
the temperature change rates are respectively marked as W1, W2, W3, & gt, wn according to the time stamps;
temperature uniformity was noted as X1, X2, X3,..and Xn, respectively, according to time stamps.
Preferably, the data calculation module comprises a first calculation unit, a second calculation unit and a third calculation unit;
The first calculation unit is configured to calculate 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, respectively, so as to obtain a current value window average PA, a voltage value window average PB, a power value window average PC, a temperature value window average PD, a reaction chamber pressure window average PE, a temperature difference window average PF, a reaction rate window average PG, a capacity efficiency window average PH, a hydrogen flow window average PI, a gas velocity window average PJ, a gas density window average PK, a hydrogen purity window average PL, an oxygen concentration window average PM, an impurity gas concentration window average PN, a hydrogen temperature window average PO, a hydrogen pressure window average PP, an acid-base value window average PQ, an acid-base variation rate window average PR, an acid-base variation window average PS, an electrolyte concentration window average PT, an electrolyte conductivity window average PU, a concentration variation rate window average PV, a temperature variation rate window average PW, and a temperature uniformity window average PX;
The second calculation unit is used for calculating so as to generate an electrolytic tank characteristic reference coefficient CS1, a hydrogen characteristic reference coefficient CS2 and an electrolyte characteristic reference coefficient CS3;
The third calculation unit is used for calculating the characteristic reference coefficient CS1 of the electrolytic tank, the characteristic reference coefficient CS2 of the hydrogen and the characteristic reference coefficient CS3 of the electrolyte, so as to generate a reasonable coefficient CLX of the treatment process.
Preferably, the first calculation unit calculates and obtains a current value window mean PA, a voltage value window mean PB, a power value window mean PC, a temperature value window mean PD, a reaction chamber pressure window mean PE, a temperature difference window mean PF, a reaction rate window mean PG, a capacity efficiency window mean PH, a hydrogen flow window mean PI, a gas speed window mean PJ, a gas density window mean PK, a hydrogen purity window mean PL, an oxygen concentration window mean PM, an impurity gas concentration window mean PN, a hydrogen temperature window mean PO, a hydrogen pressure window mean PP, an acid-base value window mean PQ, an acid-base change rate window mean PR, an acid-base deviation window mean PS, an electrolyte concentration window mean PT, an electrolyte conductivity window mean PU, a concentration change rate window mean PV, a temperature change rate window mean PW, and a temperature uniformity window mean PX through the following formulas;
wherein m is the size of a window, which means that m data points exist in each window, m data points are continuously taken to calculate the average value, and n is the total amount of the time stamp;
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 i current value, voltage value, power value, temperature value, reaction chamber pressure, temperature difference, reaction rate, capacity efficiency, hydrogen flow, gas speed, gas density, hydrogen purity, oxygen concentration, impurity gas concentration, hydrogen temperature, hydrogen pressure, acid-base number, acid-base change rate, acid-base deviation, electrolyte concentration, electrolyte conductivity, concentration change rate, temperature change rate and temperature uniformity under the time stamp;
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 respectively the current value, voltage value, power value, temperature value, reaction chamber pressure, temperature difference, reaction rate, capacity efficiency, hydrogen flow, gas velocity, gas density, hydrogen purity, oxygen concentration, impurity gas concentration, hydrogen temperature, hydrogen pressure, acid-base number, acid-base change rate, acid-base deviation, electrolyte concentration, electrolyte conductivity, concentration change rate, temperature change rate and temperature uniformity under n time stamp.
Preferably, the second calculating unit calculates and obtains the characteristic reference coefficient CS1 of the electrolytic tank, the characteristic reference coefficient CS2 of the hydrogen gas and the characteristic reference coefficient CS3 of the electrolytic solution 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 a 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, gas velocity, gas density, hydrogen purity, oxygen concentration, impurity gas concentration, acid-base number, acid-base change rate, acid-base deviation, electrolyte concentration, electrolyte conductivity and concentration change rate under n time stamp respectively;
PA is a current value window mean, PB is a voltage value window mean, PC is a power value window mean, PD is a temperature value window mean, PE is a reaction chamber pressure window mean, PF is a temperature difference window mean, PG is a reaction rate window mean, PH is a capacity efficiency window mean, PI is a hydrogen flow window mean, PJ is a gas velocity window mean, PK is a gas density window mean, PL is a hydrogen purity window mean, PM is an oxygen concentration window mean, PN is an impurity gas concentration window mean, PO is a hydrogen temperature window mean, PP is a hydrogen pressure window mean, PQ is an acid-base value window mean, PR is an acid-base change rate window mean, PS is an acid-base deviation window mean, PT is an electrolyte concentration window mean, PU is an electrolyte conductivity window mean, PV is a concentration change rate window mean, PW is a temperature change rate window mean, PX is a temperature uniformity window mean.
Preferably, the third calculation unit calculates and obtains the reasonable coefficient CLX of the processing procedure through the following formula;
wherein d1, d2 and d3 are weight values respectively, and the values of d1, d2 and d3 are adjusted and set by a user;
CS1 is the characteristic reference coefficient of the electrolytic tank, CS2 is the characteristic reference coefficient of hydrogen, and CS3 is the characteristic reference coefficient of the electrolyte.
Preferably, the data analysis module comprises a first analysis unit and a second analysis unit;
The first analysis unit is used for generating a first comparison result, and is specifically as follows;
When (when) When representing no abnormality in the current process, whenWhen the current process is abnormal;
The second analysis unit is used for generating a second comparison result, and is specifically as follows;
When (when) When the electrolytic tank is abnormal in the current process;
When (when) When the hydrogen is abnormal in the current process;
When (when) When the electrolyte is abnormal in the current process;
wherein CY is a first sub-threshold, CR is a second sub-threshold, and CS is a third sub-threshold.
The application also comprises a process monitoring method for producing hydrogen by AEM water electrolysis, which comprises the following specific steps:
S1, collecting multi-source data of an electrolytic tank through a first data collecting module to form a first multi-source data set, collecting multi-source data of hydrogen through a second data collecting module to form a second multi-source data set, and collecting multi-source data of electrolyte through a third data collecting module to form a third multi-source data set;
S2, preprocessing a first multi-source data set, a second multi-source data set and a third multi-source data set through a data processing module, eliminating noise, normalizing data and removing abnormal data, so that the data is more suitable for subsequent analysis, rearranging the processed data to form a new data set, wherein the new data set comprises 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 data set through a data calculation module to obtain an electrolytic cell characteristic reference coefficient CS1, a hydrogen characteristic reference coefficient CS2 and an electrolyte characteristic reference coefficient CS3, and integrating and calculating the electrolytic cell characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2 and the electrolyte characteristic reference coefficient CS3 to obtain a reasonable coefficient CLX of the treatment process;
S4, comparing the reasonable coefficient CLX of the processing process with a preset first threshold SY through a data analysis module to generate a first comparison result, and if the first comparison result shows that the first comparison result is abnormal, independently comparing characteristic reference coefficients of the electrolytic tank, hydrogen and electrolyte to generate a second comparison result, and judging a specific abnormal point position according to the second comparison result;
S5, feeding the first comparison result and the second comparison result back to the terminal through the feedback module so as to be checked by an operator.
Compared with the prior art, the invention has the beneficial effects that:
1. The AEM electrolyzed water hydrogen production process monitoring system forms a comprehensive, efficient and accurate monitoring and control platform by integrating a plurality of modules. Compared with the current traditional monitoring system, the system not only can collect and process multi-source data, but also can perform complex data calculation and analysis, thereby realizing omnibearing monitoring of the electrolysis process. Compared with the prior art relying on a single data source, the system can comprehensively consider data of various aspects such as an electrolytic tank, hydrogen, electrolyte and the like, feed back the running state in real time, and perform intelligent abnormality detection and adjustment. The improvement enables the system to identify the problem more accurately, pre-warn the potential risk in advance, reduce the equipment failure rate, improve the yield and quality of hydrogen, and finally achieve the aims of improving the hydrogen production efficiency, reducing the energy consumption and optimizing the production process.
2. Compared with the traditional monitoring mode of the hydrogen production process by water electrolysis, the system remarkably improves the accuracy and the processing efficiency of data analysis by introducing data preprocessing, dimensionless, time series and refined data set division. The system greatly enhances the monitoring capability of the hydrogen production process by electrolyzing water and ensures the stability, the optimality and the safety of the production process from the aspects of standardization of data storage, intellectualization of processing and the depth of subsequent analysis and optimization. Through accurate process monitoring and optimization, the system can more effectively improve the output efficiency and quality of hydrogen, prolong the service life of equipment and reduce the operation risk and maintenance cost.
3. By introducing a comprehensive evaluation mechanism of window mean value calculation, characteristic reference coefficients CS1, CS2 and CS3 and a reasonable coefficient CLX of the treatment process, the system provides a more accurate and stable monitoring and optimizing means in the process of hydrogen production by water electrolysis. Compared with the traditional single data acquisition and analysis method, the system remarkably improves the fine management level of the whole hydrogen production process through multi-dimensional data analysis, dynamic calculation and personalized adjustment. The system can evaluate and adjust the states of the electrolytic tank, the hydrogen and the electrolyte comprehensively in real time, effectively reduces equipment faults, improves the yield and purity of the hydrogen, ensures the high efficiency and safety of the system operation, and brings remarkable improvement to the practical application of the water electrolysis hydrogen production technology.
Drawings
FIG. 1 is a flow chart of the system of the present invention.
Fig. 2 is a process step diagram of the present invention.
In the figure, 1, a first data acquisition module, 11, a current and voltage detection unit, 12, a temperature and pressure detection unit, 13, a reaction efficiency detection unit, 2, a second data acquisition module, 21, a hydrogen flow detection unit, 22, a hydrogen purity detection unit, 23, a hydrogen pressure detection unit, 3, a third data acquisition module, 31, an electrolyte pH value detection unit, 32, an electrolyte concentration detection unit, 33, an electrolyte temperature detection unit, 4, a data processing module, 5, a data calculation module, 51, a first calculation unit, 52, a second calculation unit, 53, a third calculation unit, 6, a data analysis module, 61, a first analysis unit, 62, a second analysis unit and 7, a feedback module.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a system for monitoring a process of producing hydrogen by AEM electrolysis of water includes 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 for acquiring multi-source data of the electrolytic cell and forming a first multi-source data set;
the second data acquisition module 2 is used for acquiring multi-source data of hydrogen and forming a second multi-source data set;
the third data acquisition module 3 is used for acquiring multi-source data of the electrolyte and forming a third multi-source data set;
the data processing module 4 is configured to pre-process the first multi-source data set, the second multi-source data set, and the third multi-source data set, and rearrange the first multi-source data set, the second multi-source data set, the third multi-source data set, the fourth multi-source data set, the fifth multi-source data set, the sixth multi-source data set, the seventh multi-source data set, the eighth multi-source data set, and the ninth multi-source data set;
The data calculation module 5 is configured 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 step by step, so as to obtain an electrolyzer characteristic reference coefficient CS1, a hydrogen characteristic reference coefficient CS2, and an electrolyte characteristic reference coefficient CS3, and integrate and calculate the electrolyzer characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2, and the electrolyte characteristic reference coefficient CS3 as a reasonable processing coefficient CLX;
the data analysis module 6 is used for comparing the reasonable coefficient CLX of the processing process with a preset first threshold value SY so as to generate a first comparison result and judge whether an abnormal point exists in the current processing process;
when the first comparison result shows that no abnormal point exists, the method is used for comparing the characteristic reference coefficient CS1 of the electrolytic tank, the characteristic reference coefficient CS2 of the hydrogen and the characteristic reference coefficient CS3 of the electrolyte with preset thresholds respectively, so as to generate a second comparison result, and judging the position of the abnormal point according to the second comparison result;
the feedback module 7 is configured to feed back the first comparison result and the second comparison result to the terminal.
In the embodiment, the first data acquisition module 1 is responsible for acquiring multi-source data from the electrolytic cell, including a plurality of key parameters such as current, voltage, temperature, pressure and the like. The data are the basis for monitoring the reaction state in the process of producing hydrogen by electrolyzing water, and can provide necessary original information for subsequent data processing and analysis. By comprehensively collecting the operation data of the electrolytic tank, the module lays a data foundation for real-time monitoring of the electrolytic process. Thus, the running condition of the electrolytic tank can be known in real time, potential abnormality can be found in time, and the problem of missed detection or delayed treatment caused by the fact that a single data source cannot comprehensively reflect the process change is avoided.
The second data acquisition module 2 is mainly responsible for acquiring relevant data of hydrogen, including a plurality of indexes such as hydrogen flow, purity, pressure and the like. The data can directly reflect the efficiency and quality of hydrogen production, and provide important basis for optimization and adjustment of the electrolysis process. By acquiring the multidimensional data of the hydrogen, the system can realize dynamic monitoring of the yield and the quality of the hydrogen, and provides accurate reference for subsequent optimization operation and fault elimination. The existence of the module enables the production process of the hydrogen to be no longer black box operation, and accurate monitoring and adjustment can be performed, so that stable supply and high purity output of the hydrogen are ensured.
The third data acquisition module 3 is responsible for acquiring data of pH value, concentration, temperature and the like of the electrolyte, and the parameters have important influence on the efficiency and stability of the electrolytic water reaction. By monitoring the state of the electrolyte in real time, the system can adjust the reaction conditions in time, and ensure that the electrolyte is always in the optimal working state. The stability of the electrolyte is directly related to the long-term operation efficiency of the electrolysis process and the durability of the equipment, and the real-time data acquisition of the third module ensures the accurate control of the reaction conditions, thereby effectively improving the efficiency of the electrolysis reaction and the yield of hydrogen.
The data processing module 4 performs preprocessing and sorting on the multi-source data acquired from the first, second and third data acquisition modules. The processing comprises the operations of data cleaning, denoising, dimensionless treatment and the like, and the consistency and the comparability of the data are ensured. By sorting out these data, the module is able to convert the multi-source data into different data sets for subsequent computation and analysis. The processing step effectively improves the quality and usability of the data, so that the subsequent analysis work can be performed on the basis of the accurate data, and the reliability and the accuracy of the whole monitoring system are improved.
The data calculation module 5 performs step-by-step calculation on the data acquired from different data sets to obtain characteristic reference coefficients CS1, CS2 and CS3 of the electrolytic tank, the hydrogen and the electrolyte. The characteristic coefficients are quantitative descriptions of various aspects of the electrolytic process, and can comprehensively reflect the efficiency and the state of the reaction process. By calculating and integrating these coefficients, the system is able to better understand and predict changes in the electrolysis process, and thus make real-time adjustments. The operation of this module ensures a fine management of the monitoring system, so that each parameter can be quantified and involved in the evaluation of the overall operating conditions, thus controlling the electrolysis process more precisely.
The data analysis module 6 compares the calculated reasonable coefficient CLX of the processing process with a preset threshold value to generate a comparison result, and further judges whether the electrolytic process is abnormal or not. When an abnormality is detected, the system can timely feed back to an operator or automatically adjust system operation parameters. The module enhances the perception capability of the system to abnormal states by introducing threshold comparison and data analysis, and improves the accuracy and response speed of fault early warning. The method provides powerful guarantee for the stability of the production process, and ensures that the problem can be identified and processed at the first time when abnormality occurs.
The feedback module 7 feeds back the first comparison result and the second comparison result to the terminal, so that an operator can know the running state of the system in time and whether the system is abnormal or not. The feedback mechanism realizes real-time monitoring and automatic adjustment, and enhances the self-adaptive capacity of the system. The feedback module 7 not only provides decision basis for manual operation, but also can be linked with other modules, and the efficiency of the whole system is improved on an automation level. Through a continuous feedback mechanism, the system can react at the moment of finding abnormality, automatically adjust reaction conditions, furthest reduce human misoperation and delay, and ensure that the system always operates in an optimal state.
The AEM electrolyzed water hydrogen production process monitoring system forms a comprehensive, efficient and accurate monitoring and control platform by integrating a plurality of modules. Compared with the current traditional monitoring system, the system not only can collect and process multi-source data, but also can perform complex data calculation and analysis, thereby realizing omnibearing monitoring of the electrolysis process. Compared with the prior art relying on a single data source, the system can comprehensively consider data of various aspects such as an electrolytic tank, hydrogen, electrolyte and the like, feed back the running state in real time, and perform intelligent abnormality detection and adjustment. The improvement enables the system to identify the problem more accurately, pre-warn the potential risk in advance, reduce the equipment failure rate, improve the yield and quality of hydrogen, and finally achieve the aims of improving the hydrogen production efficiency, reducing the energy consumption and optimizing the production process.
Referring to fig. 1, a first data acquisition module1 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 for collecting a current value, a voltage value and a power value of the electrolytic cell;
the temperature and pressure detection unit 12 is used for collecting the temperature value, the reaction chamber pressure and the temperature difference of the electrolytic cell;
the reaction efficiency detection unit 13 is used for collecting the reaction rate and the capacity efficiency;
the second data acquisition module 2 includes a hydrogen flow rate detection unit 21, a hydrogen purity detection unit 22, and a hydrogen pressure detection unit 23;
the hydrogen flow detection unit 21 is used for collecting the hydrogen flow, the gas speed and the gas density of the hydrogen;
the hydrogen purity detection unit 22 is used for collecting the hydrogen purity, the oxygen concentration and the impurity gas concentration of the hydrogen;
the hydrogen pressure detecting unit 23 is used for collecting the hydrogen temperature and the hydrogen pressure ratio of the hydrogen;
The third data acquisition module 3 comprises an electrolyte pH value detection unit 31, an electrolyte concentration detection unit 32 and an electrolyte temperature detection unit 33;
The electrolyte pH value detection unit 31 is used for collecting the pH value, the pH change rate and the pH deviation of the electrolyte;
The electrolyte concentration detection unit 32 is used for collecting the electrolyte concentration, the electrolyte conductivity and the concentration change rate of the electrolyte;
The electrolyte temperature detection unit 33 is used for collecting the temperature change rate and the temperature uniformity of the electrolyte;
Wherein the current value, the voltage value, the power value, the temperature value, the reaction chamber pressure, the temperature difference, the reaction rate, and the capacity efficiency form a first multisource data set;
wherein the hydrogen flow rate, gas velocity, gas density, hydrogen purity, oxygen concentration, impurity gas concentration, hydrogen temperature, and hydrogen pressure comprise a second multisource data set;
Wherein the acid-base number, acid-base change rate, acid-base bias, electrolyte concentration, electrolyte conductivity, concentration change rate, temperature change rate, and temperature uniformity comprise a third multisource data set.
In the embodiment, the first data acquisition module 1 can acquire the real-time working state of the electrolytic tank through a plurality of sub-modules of the current and voltage detection unit, the temperature and pressure detection unit and the reaction efficiency detection unit, and the system can record key indexes such as current, voltage, power, temperature, pressure, reaction rate and the like in detail. Thus, not only can the energy efficiency of the electrolytic cell be comprehensively monitored, but also detailed data can be provided for the efficiency of the reaction and the stability of the process, the energy use efficiency is optimized, and excessive consumption is avoided.
The second data acquisition module 2 is used for accurately acquiring parameters such as hydrogen flow, gas density, hydrogen purity and the like, and can master the yield, quality and purity of the hydrogen in real time, so that the stability of a final product is ensured and the standard requirement is met. The control capability of the system to the hydrogen production process is further enhanced through the temperature and pressure detection of the hydrogen, and the production efficiency and the use value of the hydrogen are improved.
And the third data acquisition module 3 is used for monitoring indexes such as pH value, concentration and conductivity of the electrolyte, is beneficial to accurately adjusting the optimal reaction environment of the electrolyte, and ensures the high efficiency and long-term stability of the electrolytic reaction. The temperature change and uniformity detection of the electrolyte further optimize the operation condition of the electrolyzed water, reduce the phenomenon of uneven heating or excessive consumption, and prolong the service life of equipment.
By adopting the multi-source data set and the comprehensive detection unit, the system can more accurately capture each detail in the hydrogen production process by water electrolysis, and efficiency loss, quality fluctuation and fault risk caused by incomplete information or inaccurate data in the traditional monitoring means are reduced to the greatest extent. Through accurate data acquisition, the system can more scientifically optimize and adjust in real time to effectively promote the stability and the efficiency of hydrogen production, and ensure the long-term safe operation of equipment.
Referring to fig. 1, the data processing module 4 performs preprocessing and dimensionless treatment on the first multi-source data set, the second multi-source data set and the third multi-source data set, and rearranges the processed first multi-source data set, second multi-source data set and 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.
The first data set includes a current value, a voltage value, and a power value;
the current values are respectively marked as A1, A2, A3, a.and An according to the time stamps;
the voltage values are respectively marked as B1, B2, B3, & gt, bn according to the time stamps;
The power values are respectively marked as C1, C2, C3, & Cn according to the time stamps;
the second data set includes temperature values, reaction chamber pressures, and temperature differences;
The temperature values are respectively marked as D1, D2, D3, & gt, dn according to the time stamps;
reaction chamber pressures were noted as E1, E2, E3, & gt, en, respectively, according to time stamps;
The temperature differences are respectively marked as F1, F2, F3, and Fn according to the time stamps;
The third data set includes reaction rate and capacity efficiency;
the reaction rates are denoted G1, G2, G3, and Gn according to time stamps, respectively;
The capacity efficiencies are respectively marked as H1, H2, H3, & gt, hn according to time stamps;
The fourth data set includes hydrogen flow, gas velocity, and gas density;
The hydrogen flows are respectively marked as I1, I2, I3, I.C., in according to the time stamps;
the gas velocities are respectively noted as J1, J2, J3,..and Jn according to the time stamps;
The gas densities are respectively marked as K1, K2, K3, & gt, kn according to the time stamps;
the fifth data set includes hydrogen purity, oxygen concentration, and impurity gas concentration;
the purity of the hydrogen is respectively marked as L1, L2, L3, and Ln according to the time stamp;
The oxygen concentrations were recorded as M1, M2, M3, & gt, mn according to time stamps, respectively;
impurity gas concentrations were recorded as N1, N2, N3, and Nn according to time stamps, respectively;
The sixth data set includes a hydrogen temperature and a hydrogen pressure ratio;
the hydrogen temperatures are respectively marked as O1, O2, O3, and O according to the time stamps;
the hydrogen pressure ratio is denoted by time stamps P1, P2, P3, P.
The seventh data set includes acid-base number, acid-base rate of change, and acid-base bias;
the acid-base values are respectively marked as Q1, Q2, Q3, qn according to the time stamps;
the acid-base change rates are respectively marked as R1, R2, R3, and Rn according to time stamps;
the acid-base deviation is respectively marked as S1, S2, S3, & gt, sn according to the time stamps;
the eighth data set includes electrolyte concentration, electrolyte conductivity, and concentration change rate;
the electrolyte concentrations were recorded as T1, T2, T3, & gt, tn, respectively, according to the time stamps;
The conductivity of the electrolyte was noted as U1, U2, U3, & gt, un, respectively, according to the time stamp;
the concentration change rates are respectively marked as V1, V2, V3, and Vn according to time stamps;
the ninth data set includes a temperature rate of change and a temperature uniformity;
the temperature change rates are respectively marked as W1, W2, W3, & gt, wn according to the time stamps;
temperature uniformity was noted as X1, X2, X3,..and Xn, respectively, according to time stamps.
In the embodiment, the data processing module 4 effectively eliminates the dimension difference among different physical quantities by preprocessing and dimensionless the first multi-source data set, the second multi-source data set and the third multi-source data set, so that the 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 dimensionless data can reduce analysis errors caused by inconsistent dimensionality, and ensure accurate transmission and efficient calculation of the data.
The system rearranges different types of data, divides the data into a plurality of specific data sets such as current, voltage, temperature, gas flow and the like according to the time stamp, and provides a clearer and structured basis for subsequent data analysis and calculation. The ordered data storage mode is convenient for real-time monitoring and also enables long-term trend analysis to be possible. By means of the data record accurate to the time stamp, accurate tracking and monitoring of each moment in the process can be achieved, and the reaction capacity and the prediction capacity of the system are further improved.
The data are divided into nine independent data sets according to different characteristics and dimensions, and each data set corresponds to different physical quantities such as temperature, pressure, flow, gas density, pH value and the like in the process of producing hydrogen by electrolyzing water. The division mode enables the system to accurately analyze and optimize for each specific working condition dimension. The data refinement method ensures that each parameter can be independently and accurately analyzed, and avoids insufficient or too rough processing caused by huge data volume in the traditional method.
All data sets are marked according to the time stamp, so that a complete time sequence database is formed, and the dynamic change of the water electrolysis hydrogen production process can be reflected. Through time sequence analysis, the system can identify the data change trend, potential abnormality and fluctuation rule, thereby helping operators to adjust electrolysis conditions in real time and keeping the equipment stably running. This feature is critical to improving process control accuracy and response speed.
By integrating and carefully managing various data sets such as electrolyzer, hydrogen purity, electrolyte, etc., the system is able to optimize the hydrogen production process in a number of dimensions overall. The omnibearing and multi-angle monitoring mode can effectively identify possible bottleneck or problem areas, so that adjustment measures are accurately guided, and the overall production efficiency and safety are improved.
Compared with the traditional monitoring mode of the hydrogen production process by water electrolysis, the system remarkably improves the accuracy and the processing efficiency of data analysis by introducing data preprocessing, dimensionless, time series and refined data set division. The system greatly enhances the monitoring capability of the hydrogen production process by electrolyzing water and ensures the stability, the optimality and the safety of the production process from the aspects of standardization of data storage, intellectualization of processing and the depth of subsequent analysis and optimization. Through accurate process monitoring and optimization, the system can more effectively improve the output efficiency and quality of hydrogen, prolong the service life of equipment and reduce the operation risk and maintenance cost.
Referring to fig. 1, the data computing module 5 includes a first computing unit 51, a second computing unit 52, and a third computing unit 53;
The first calculation unit 51 is configured to calculate 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, respectively, so as to obtain a current value window average PA, a voltage value window average PB, a power value window average PC, a temperature value window average PD, a reaction chamber pressure window average PE, a temperature difference window average PF, a reaction rate window average PG, a capacity efficiency window average PH, a hydrogen flow window average PI, a gas velocity window average PJ, a gas density window average PK, a hydrogen purity window average PL, an oxygen concentration window average PM, an impurity gas concentration window average PN, a hydrogen temperature window average PO, a hydrogen pressure window average PP, an acid-base value window average PQ, an acid-base variation rate window average PR, an acid-base deviation window average PS, an electrolyte concentration window average PT, an electrolyte conductivity window average PU, a concentration variation rate window average PV, a temperature variation rate window average PW, and a temperature uniformity window average PW;
the second calculation unit 52 is configured to perform calculation to generate an electrolyzer characteristic reference coefficient CS1, a hydrogen characteristic reference coefficient CS2, and an electrolyte characteristic reference coefficient CS3;
The third calculation unit 53 is configured to calculate the cell characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2, and the electrolyte characteristic reference coefficient CS3, thereby generating a process rationality coefficient CLX.
The first calculation unit 51 calculates and obtains a current value window mean PA, a voltage value window mean PB, a power value window mean PC, a temperature value window mean PD, a reaction chamber pressure window mean PE, a temperature difference window mean PF, a reaction rate window mean PG, a capacity efficiency window mean PH, a hydrogen flow window mean PI, a gas velocity window mean PJ, a gas density window mean PK, a hydrogen purity window mean PL, an oxygen concentration window mean PM, an impurity gas concentration window mean PN, a hydrogen temperature window mean PO, a hydrogen pressure window mean PP, an acid-base value window mean PQ, an acid-base change rate window mean PR, an acid-base deviation window mean PS, an electrolyte concentration window mean PT, an electrolyte conductivity window mean PU, a concentration change rate window mean PV, a temperature change rate window mean PW, and a temperature uniformity window mean PX, respectively, by the following formulas;
wherein m is the size of a window, which means that m data points exist in each window, m data points are continuously taken to calculate the average value, and n is the total amount of the time stamp;
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 i current value, voltage value, power value, temperature value, reaction chamber pressure, temperature difference, reaction rate, capacity efficiency, hydrogen flow, gas speed, gas density, hydrogen purity, oxygen concentration, impurity gas concentration, hydrogen temperature, hydrogen pressure, acid-base number, acid-base change rate, acid-base deviation, electrolyte concentration, electrolyte conductivity, concentration change rate, temperature change rate and temperature uniformity under the time stamp;
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 respectively the current value, voltage value, power value, temperature value, reaction chamber pressure, temperature difference, reaction rate, capacity efficiency, hydrogen flow, gas velocity, gas density, hydrogen purity, oxygen concentration, impurity gas concentration, hydrogen temperature, hydrogen pressure, acid-base number, acid-base change rate, acid-base deviation, electrolyte concentration, electrolyte conductivity, concentration change rate, temperature change rate and temperature uniformity under n time stamp.
The second calculation unit 52 calculates and acquires the 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 a 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, gas velocity, gas density, hydrogen purity, oxygen concentration, impurity gas concentration, acid-base number, acid-base change rate, acid-base deviation, electrolyte concentration, electrolyte conductivity and concentration change rate under n time stamp respectively;
PA is a current value window mean, PB is a voltage value window mean, PC is a power value window mean, PD is a temperature value window mean, PE is a reaction chamber pressure window mean, PF is a temperature difference window mean, PG is a reaction rate window mean, PH is a capacity efficiency window mean, PI is a hydrogen flow window mean, PJ is a gas velocity window mean, PK is a gas density window mean, PL is a hydrogen purity window mean, PM is an oxygen concentration window mean, PN is an impurity gas concentration window mean, PO is a hydrogen temperature window mean, PP is a hydrogen pressure window mean, PQ is an acid-base value window mean, PR is an acid-base change rate window mean, PS is an acid-base deviation window mean, PT is an electrolyte concentration window mean, PU is an electrolyte conductivity window mean, PV is a concentration change rate window mean, PW is a temperature change rate window mean, PX is a temperature uniformity window mean.
The third calculation unit 53 calculates and acquires a process rational coefficient CLX by the following formula;
wherein d1, d2 and d3 are weight values respectively, and the values of d1, d2 and d3 are adjusted and set by a user;
CS1 is the characteristic reference coefficient of the electrolytic tank, CS2 is the characteristic reference coefficient of hydrogen, and CS3 is the characteristic reference coefficient of the electrolyte.
In this embodiment, the first calculation unit 51 can effectively smooth the fluctuation in each time window by performing window mean calculation on the multidimensional data, and eliminate the influence of short-term fluctuation on the system analysis. The method can balance instantaneous measurement errors, so that long-term trends and changes are more prominent, and the reliability and stability of data are improved. The use of the window mean value enables the system to more accurately capture the regular change in the hydrogen production process by water electrolysis, and reduces misjudgment or misoperation caused by instantaneous abnormal fluctuation.
The second calculation unit 52 converts various kinds of data into characteristic indexes capable of effectively representing the operation state of the apparatus and the hydrogen production performance by calculating the cell characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2, and the electrolyte characteristic reference coefficient CS 3. These reference coefficients provide a quantified criterion for overall process monitoring, enabling 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 yield and quality.
The third calculation unit 53 calculates the process reasonable coefficient CLX from the different reference coefficients CS1, CS2, CS3 and the set weight values d1, d2, d 3. The coefficient is used as a comprehensive evaluation index for the whole water electrolysis hydrogen production process, and can help the system to synchronously analyze and optimize in multiple dimensions. By adjusting the weights of different coefficients, a user can flexibly adjust the monitoring key points and adjust the running state of the equipment in real time, so that the stability and the high efficiency of the whole hydrogen production process are ensured.
The system provides a self-defined adjustment function for weight values such as a1, a2, b1, b2, c1, c2, d1, d2 and d3, so that a user can flexibly optimize the priority of each calculation according to actual production requirements and running conditions. The personalized setting mode enables the system to have stronger adaptability, the monitoring key points can be adjusted according to different production working conditions, and diversified application requirements are met.
By calculating the comprehensive evaluation of the window mean value, the characteristic reference coefficient and the reasonable coefficient, the system can dynamically adjust the operation condition of the electrolytic water hydrogen production and timely discover potential abnormal conditions such as equipment failure, unqualified gas purity and the like. The capability of real-time monitoring and dynamic optimization ensures that the system has higher flexibility and accuracy in long-term operation and can better cope with complex factors such as environmental changes, load fluctuation and the like.
By introducing a comprehensive evaluation mechanism of window mean value calculation, characteristic reference coefficients CS1, CS2 and CS3 and a reasonable coefficient CLX of the treatment process, the system provides a more accurate and stable monitoring and optimizing means in the process of hydrogen production by water electrolysis. Compared with the traditional single data acquisition and analysis method, the system remarkably improves the fine management level of the whole hydrogen production process through multi-dimensional data analysis, dynamic calculation and personalized adjustment. The system can evaluate and adjust the states of the electrolytic tank, the hydrogen and the electrolyte comprehensively in real time, effectively reduces equipment faults, improves the yield and purity of the hydrogen, ensures the high efficiency and safety of the system operation, and brings remarkable improvement to the practical application of the water electrolysis hydrogen production technology.
Fifth, referring to fig. 1, the data analysis module 6 includes a first analysis unit 61 and a second analysis unit 62;
The first analysis unit 61 is configured to generate a first comparison result, specifically as follows;
When (when) When representing no abnormality in the current process, whenWhen representing that the current process is abnormal
The second analysis unit 62 is configured to generate a second comparison result, specifically as follows;
When (when) When the electrolytic tank is abnormal in the current process;
When (when) When the hydrogen is abnormal in the current process;
When (when) When the electrolyte is abnormal in the current process;
wherein CY is a first sub-threshold, CR is a second sub-threshold, and CS is a third sub-threshold.
In the present embodiment, the data analysis module 6 introduces two analysis units, namely the first analysis unit 61 and the second analysis unit 62, so that the abnormality detection is not limited to the judgment of the whole process, but can also go deep into the levels of the electrolytic tank, the hydrogen and the electrolyte of each subsystem. The layering anomaly analysis mode is helpful for accurately identifying the specific source of the problem, so that more effective targeted measures are adopted, and the possibility of false alarm and missing alarm is reduced.
The first analysis unit 61 can monitor in real time whether the whole water electrolysis hydrogen production process is in a normal range by comparing the process rational coefficient CLX with a preset threshold SY. If CLX is more than or equal to SY, the system can immediately trigger an alarm and start corresponding regulating measures, wherein the condition is indicated that the whole process is abnormal. The real-time abnormal alarm function ensures that any potential faults or performance degradation in the production process can be detected rapidly and timely actions can be taken, so that the risk of equipment damage or low production efficiency is effectively reduced.
The second analysis unit 62 performs abnormality determination on the subsystem level electrolytic cell, hydrogen gas, and electrolyte, and can accurately locate which part is abnormal by comparing the feature reference coefficient CS1 of the electrolytic cell, the feature reference coefficient CS2 of the hydrogen gas, and the feature reference coefficient CS3 of the electrolyte. For example, when CS1 is equal to or greater than CY, it represents a problem in the electrolytic cell portion, when CS2 is equal to or greater than CR, it represents an abnormality in the hydrogen gas mass or flow rate, and when CS3 is equal to or greater than CS, it indicates that the pH or concentration of the electrolyte may be problematic. Through the refined abnormal positioning, operators can be helped to quickly identify and process the problems, and excessive or wrong adjustment is avoided.
The threshold values of CY, CR, CS and the like are set, so that the system has flexible adjustment capability. Under different production environments or operation conditions, users can customize the thresholds according to actual needs so as to ensure that the system can adapt to different working conditions and give the most accurate early warning. The user can adjust the threshold value according to the actual running condition and experience of the equipment, so that the monitoring precision and reliability are improved, and false alarm or missing report caused by too strict or too loose threshold value setting is avoided.
The double-layer anomaly analysis mechanism of the data analysis module 6 provides more accurate and intelligent monitoring capability for the AEM water electrolysis hydrogen production system. By comparing the reasonable coefficient CLX and the threshold SY in real time and comparing the characteristic reference coefficients CS1, CS2 and CS3 of each subsystem with the sub-thresholds CY, CR and CS, the system can realize more precise and flexible abnormality detection and positioning in the whole hydrogen production process. The multi-level and adjustable abnormality analysis method greatly improves the reliability, stability and self-adaptive capacity of the system, ensures the smooth production process and provides powerful support for preventive maintenance and fault elimination.
The application also comprises a process monitoring method for producing hydrogen by AEM water electrolysis, referring to FIG. 2, and the specific steps are as follows:
S1, collecting multi-source data of an electrolytic tank through a first data collecting module 1 to form a first multi-source data set, collecting multi-source data of hydrogen through a second data collecting module 2 to form a second multi-source data set, and collecting multi-source data of electrolyte through a third data collecting module 3 to form a third multi-source data set;
S2, preprocessing a first multi-source data set, a second multi-source data set and a third multi-source data set through a data processing module 4, eliminating noise, normalizing data and removing abnormal data, so that the data is more suitable for subsequent analysis, and rearranging the processed data to form a new data set, wherein the new data set comprises 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 data set through a data calculation module 5 to obtain an electrolytic cell characteristic reference coefficient CS1, a hydrogen characteristic reference coefficient CS2 and an electrolyte characteristic reference coefficient CS3, and integrating and calculating the electrolytic cell characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2 and the electrolyte characteristic reference coefficient CS3 to obtain a reasonable processing process coefficient CLX;
S4, comparing the reasonable coefficient CLX of the processing process with a preset first threshold SY through a data analysis module 6 to generate a first comparison result, and if the first comparison result shows that the abnormality exists, independently comparing characteristic reference coefficients of the electrolytic tank, the hydrogen and the electrolyte to generate a second comparison result, and judging a specific abnormal point position according to the second comparison result;
s5, feeding the first comparison result and the second comparison result back to the terminal through the feedback module 7 for the operator to check.
What is not described in detail in this specification is prior art known to those skilled in the art.
Although the present invention has been described with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described, or equivalents may be substituted for elements thereof, and any modifications, equivalents, improvements and changes may be made without departing from the spirit and principles of the present invention.
Claims (10)
1. The AEM water electrolysis hydrogen production process monitoring system is characterized by 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 for acquiring multi-source data of the electrolytic cell and forming a first multi-source data set;
The second data acquisition module (2) is used for acquiring multi-source data of hydrogen and forming a second multi-source data set;
The third data acquisition module (3) is used for acquiring multi-source data of the electrolyte and forming a third multi-source data set;
The data processing module (4) is used for preprocessing a first multi-source data set, a second multi-source data set and a third multi-source data set and rearranging the first multi-source data set, the second multi-source data set, the third multi-source data set, the fourth multi-source data set, the fifth multi-source data set, the sixth multi-source data set, the seventh multi-source data set, the eighth multi-source data set and the ninth multi-source data set;
The data calculation module (5) is configured to calculate 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 step by step, so as to obtain an electrolyzer characteristic reference coefficient CS1, a hydrogen characteristic reference coefficient CS2 and an electrolyte characteristic reference coefficient CS3, and integrate and calculate the electrolyzer characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2 and the electrolyte characteristic reference coefficient CS3 as a reasonable processing coefficient CLX;
The data analysis module (6) is used for comparing the reasonable coefficient CLX of the processing process with a preset first threshold SY so as to generate a first comparison result and judge whether an abnormal point exists in the current processing process;
when the first comparison result shows that no abnormal point exists, the method is used for comparing the characteristic reference coefficient CS1 of the electrolytic tank, the characteristic reference coefficient CS2 of the hydrogen and the characteristic reference coefficient CS3 of the electrolyte with preset thresholds respectively, so as to generate a second comparison result, and judging the position of the abnormal point according to the second comparison result;
The feedback module (7) is used for feeding back the first comparison result and the second comparison result to the terminal.
2. The AEM water electrolysis hydrogen production process monitoring system according to claim 1, wherein 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 for collecting a current value, a voltage value and a power value of the electrolytic cell;
the temperature and pressure detection unit (12) is used for collecting the temperature value, the reaction chamber pressure and the temperature difference of the electrolytic tank;
the reaction efficiency detection unit (13) is used for collecting the reaction rate and the 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 for collecting the hydrogen flow, the gas speed and the gas density of the hydrogen;
the hydrogen purity detection unit (22) is used for collecting the hydrogen purity, the oxygen concentration and the impurity gas concentration of the hydrogen;
the hydrogen pressure detection unit (23) is used for collecting the hydrogen temperature and the hydrogen pressure ratio of the hydrogen;
The third data acquisition module (3) comprises an electrolyte pH value detection unit (31), an electrolyte concentration detection unit (32) and an electrolyte temperature detection unit (33);
the electrolyte pH value detection unit (31) is used for collecting the acid-base number, the acid-base change rate and the acid-base deviation of the electrolyte;
The electrolyte concentration detection unit (32) is used for collecting electrolyte concentration, electrolyte conductivity and concentration change rate of the electrolyte;
The electrolyte temperature detection unit (33) is used for collecting the temperature change rate and the temperature uniformity of the electrolyte;
Wherein the current value, the voltage value, the power value, the temperature value, the reaction chamber pressure, the temperature difference, the reaction rate, and the capacity efficiency form a first multisource data set;
wherein the hydrogen flow rate, gas velocity, gas density, hydrogen purity, oxygen concentration, impurity gas concentration, hydrogen temperature, and hydrogen pressure comprise a second multisource data set;
Wherein the acid-base number, acid-base change rate, acid-base bias, electrolyte concentration, electrolyte conductivity, concentration change rate, temperature change rate, and temperature uniformity comprise a third multisource data set.
3. A process monitoring system for hydrogen production by AEM water electrolysis according to claim 2, wherein the data processing module (4) pre-processes and dimensionless numbers of the first, second and third multi-source data sets and rearranges the processed first, second and third multi-source data sets into first, second, third, fourth, fifth, sixth, seventh, eighth and ninth data sets.
4. A process monitoring system for AEM water electrolysis hydrogen production according to claim 3, wherein the first data set comprises current values, voltage values, and power values;
the current values are respectively marked as A1, A2, A3, a.and An according to the time stamps;
the voltage values are respectively marked as B1, B2, B3, & gt, bn according to the time stamps;
The power values are respectively marked as C1, C2, C3, & Cn according to the time stamps;
the second data set includes temperature values, reaction chamber pressures, and temperature differences;
The temperature values are respectively marked as D1, D2, D3, & gt, dn according to the time stamps;
reaction chamber pressures were noted as E1, E2, E3, & gt, en, respectively, according to time stamps;
The temperature differences are respectively marked as F1, F2, F3, and Fn according to the time stamps;
the third data set includes reaction rate and capacity efficiency;
the reaction rates are denoted G1, G2, G3, and Gn according to time stamps, respectively;
The capacity efficiencies are respectively marked as H1, H2, H3, & gt, hn according to time stamps;
the fourth data set includes hydrogen flow, gas velocity, and gas density;
The hydrogen flows are respectively marked as I1, I2, I3, I.C., in according to the time stamps;
the gas velocities are respectively noted as J1, J2, J3,..and Jn according to the time stamps;
The gas densities are respectively marked as K1, K2, K3, & gt, kn according to the time stamps;
The fifth data set includes hydrogen purity, oxygen concentration, and impurity gas concentration;
the purity of the hydrogen is respectively marked as L1, L2, L3, and Ln according to the time stamp;
The oxygen concentrations were recorded as M1, M2, M3, & gt, mn according to time stamps, respectively;
impurity gas concentrations were recorded as N1, N2, N3, and Nn according to time stamps, respectively;
the sixth data set includes a hydrogen temperature and a hydrogen pressure ratio;
the hydrogen temperatures are respectively marked as O1, O2, O3, and O according to the time stamps;
the hydrogen pressure ratio is denoted by time stamps P1, P2, P3, P.
The seventh data set includes an acid-base number, an acid-base rate of change, and an acid-base bias;
the acid-base values are respectively marked as Q1, Q2, Q3, qn according to the time stamps;
the acid-base change rates are respectively marked as R1, R2, R3, and Rn according to time stamps;
the acid-base deviation is respectively marked as S1, S2, S3, & gt, sn according to the time stamps;
The eighth data set includes electrolyte concentration, electrolyte conductivity, and concentration change rate;
the electrolyte concentrations were recorded as T1, T2, T3, & gt, tn, respectively, according to the time stamps;
The conductivity of the electrolyte was noted as U1, U2, U3, & gt, un, respectively, according to the time stamp;
the concentration change rates are respectively marked as V1, V2, V3, and Vn according to time stamps;
the ninth dataset includes a temperature rate of change and a temperature uniformity;
the temperature change rates are respectively marked as W1, W2, W3, & gt, wn according to the time stamps;
temperature uniformity was noted as X1, X2, X3,..and Xn, respectively, according to time stamps.
5. The AEM water electrolysis hydrogen production process monitoring system according to claim 4, wherein 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 configured to calculate 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, respectively, so as to obtain a current value window mean PA, a voltage value window mean PB, a power value window mean PC, a temperature value window mean PD, a reaction chamber pressure window mean PE, a temperature difference window mean PF, a reaction rate window mean PG, a capacity efficiency window mean PH, a hydrogen flow window mean PI, a gas velocity window mean PJ, a gas density window mean PK, a hydrogen purity window mean PL, an oxygen concentration window mean PM, an impurity gas concentration window mean PN, a hydrogen temperature window mean PO, a hydrogen pressure window mean PP, an acid-base value window mean PQ, an acid-base variation rate window mean PR, an acid-base variation window mean PS, an electrolyte concentration window mean PT, an electrolyte conductivity window mean PU, a concentration variation rate window mean PV, a temperature variation rate window mean PW, and a temperature uniformity window PX;
The second calculating unit (52) is used for calculating so as to generate an electrolytic tank characteristic reference coefficient CS1, a hydrogen characteristic reference coefficient CS2 and an electrolyte characteristic reference coefficient CS3;
The third calculation unit (53) is configured to calculate the characteristic reference coefficient CS1 of the electrolytic cell, the characteristic reference coefficient CS2 of the hydrogen gas, and the characteristic reference coefficient CS3 of the electrolyte, so as to generate a reasonable coefficient CLX of the processing procedure.
6. The AEM water electrolysis hydrogen production process monitoring system is characterized in that a first calculation unit (51) calculates and obtains a current value window mean PA, a voltage value window mean PB, a power value window mean PC and a temperature value window mean PD through the following formulas respectively, wherein the reaction chamber pressure window mean PE, a temperature difference window mean PF, a reaction rate window mean PG, a capacity efficiency window mean PH, a hydrogen flow window mean PI, a gas speed window mean PJ, a gas density window mean PK, a hydrogen purity window mean PL, an oxygen concentration window mean PM, an impurity gas concentration window mean PN, a hydrogen temperature window mean PO, a hydrogen pressure window mean PP, an acid-base value window mean PQ, an acid-base change rate window mean PR, an acid-base deviation window mean PS, an electrolyte concentration window mean PT, an electrolyte conductivity window mean PU, a concentration change rate window mean PV, a temperature change rate window mean PW and a temperature uniformity window mean PX;
wherein m is the size of a window, which means that m data points exist in each window, m data points are continuously taken to calculate the average value, and n is the total amount of the time stamp;
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 i current value, voltage value, power value, temperature value, reaction chamber pressure, temperature difference, reaction rate, capacity efficiency, hydrogen flow, gas speed, gas density, hydrogen purity, oxygen concentration, impurity gas concentration, hydrogen temperature, hydrogen pressure, acid-base number, acid-base change rate, acid-base deviation, electrolyte concentration, electrolyte conductivity, concentration change rate, temperature change rate and temperature uniformity under the time stamp;
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 respectively the current value, voltage value, power value, temperature value, reaction chamber pressure, temperature difference, reaction rate, capacity efficiency, hydrogen flow, gas velocity, gas density, hydrogen purity, oxygen concentration, impurity gas concentration, hydrogen temperature, hydrogen pressure, acid-base number, acid-base change rate, acid-base deviation, electrolyte concentration, electrolyte conductivity, concentration change rate, temperature change rate and temperature uniformity under n time stamp.
7. The AEM water electrolysis hydrogen production process monitoring system according to claim 6, wherein the second calculation unit (52) calculates and obtains the electrolyzer characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2 and the electrolyte characteristic reference coefficient CS3 through 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 a 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, gas velocity, gas density, hydrogen purity, oxygen concentration, impurity gas concentration, acid-base number, acid-base change rate, acid-base deviation, electrolyte concentration, electrolyte conductivity and concentration change rate under n time stamp respectively;
PA is a current value window mean, PB is a voltage value window mean, PC is a power value window mean, PD is a temperature value window mean, PE is a reaction chamber pressure window mean, PF is a temperature difference window mean, PG is a reaction rate window mean, PH is a capacity efficiency window mean, PI is a hydrogen flow window mean, PJ is a gas velocity window mean, PK is a gas density window mean, PL is a hydrogen purity window mean, PM is an oxygen concentration window mean, PN is an impurity gas concentration window mean, PO is a hydrogen temperature window mean, PP is a hydrogen pressure window mean, PQ is an acid-base value window mean, PR is an acid-base change rate window mean, PS is an acid-base deviation window mean, PT is an electrolyte concentration window mean, PU is an electrolyte conductivity window mean, PV is a concentration change rate window mean, PW is a temperature change rate window mean, PX is a temperature uniformity window mean.
8. The AEM water electrolysis hydrogen production process monitoring system according to claim 7, wherein the third calculation unit (53) calculates and obtains a process rationality coefficient CLX by the following formula;
wherein d1, d2 and d3 are weight values respectively, and the values of d1, d2 and d3 are adjusted and set by a user;
CS1 is the characteristic reference coefficient of the electrolytic tank, CS2 is the characteristic reference coefficient of hydrogen, and CS3 is the characteristic reference coefficient of the electrolyte.
9. The AEM water electrolysis hydrogen production process monitoring system according to claim 8, wherein the data analysis module (6) comprises a first analysis unit (61) and a second analysis unit (62);
the first analysis unit (61) is configured to generate a first comparison result, specifically as follows;
When (when) When representing no abnormality in the current process, whenWhen the current process is abnormal;
The second analysis unit (62) is configured to generate a second comparison result, specifically as follows;
When (when) When the electrolytic tank is abnormal in the current process;
When (when) When the hydrogen is abnormal in the current process;
When (when) When the electrolyte is abnormal in the current process;
wherein CY is a first sub-threshold, CR is a second sub-threshold, and CS is a third sub-threshold.
10. A process monitoring method for producing hydrogen by AEM electrolysis of water, which is used for executing the process monitoring system for producing hydrogen by AEM electrolysis of water according to any one of claims 1 to 9, and is characterized by comprising the following specific steps:
S1, acquiring multi-source data of an electrolytic tank through a first data acquisition module (1) to form a first multi-source data set, acquiring multi-source data of hydrogen through a second data acquisition module (2) to form a second multi-source data set, and acquiring multi-source data of electrolyte through a third data acquisition module (3) to form a third multi-source data set;
S2, preprocessing a first multi-source data set, a second multi-source data set and a third multi-source data set through a data processing module (4), eliminating noise, normalizing data and removing abnormal data, so that the data is more suitable for subsequent analysis, and rearranging the processed data to form a new data set, wherein the new data set comprises 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 data set through a data calculation module (5) to obtain an electrolytic tank characteristic reference coefficient CS1, a hydrogen characteristic reference coefficient CS2 and an electrolyte characteristic reference coefficient CS3, and integrating and calculating the electrolytic tank characteristic reference coefficient CS1, the hydrogen characteristic reference coefficient CS2 and the electrolyte characteristic reference coefficient CS3 to obtain a reasonable coefficient CLX of the treatment process;
s4, comparing the reasonable coefficient CLX of the processing process with a preset first threshold SY through a data analysis module (6) to generate a first comparison result, and if the first comparison result shows that the abnormality exists, independently comparing characteristic reference coefficients of the electrolytic tank, the hydrogen and the electrolyte to generate a second comparison result, and judging a specific abnormal point position according to the second comparison result;
S5, feeding back the first comparison result and the second comparison result to the terminal through a feedback module (7) so as to be checked by an operator.
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| CN117587457A (en) * | 2023-11-28 | 2024-02-23 | 深圳润世华研发科技有限公司 | Fault early warning system of PEM (PEM) water electrolysis hydrogen production equipment |
| CN117409878A (en) * | 2023-12-14 | 2024-01-16 | 深圳稳石氢能科技有限公司 | AEM technology-based high-efficiency energy management system |
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