CN118047351B - High-purity hydrogen production and preparation system and method - Google Patents
High-purity hydrogen production and preparation system and method Download PDFInfo
- Publication number
- CN118047351B CN118047351B CN202410451252.XA CN202410451252A CN118047351B CN 118047351 B CN118047351 B CN 118047351B CN 202410451252 A CN202410451252 A CN 202410451252A CN 118047351 B CN118047351 B CN 118047351B
- Authority
- CN
- China
- Prior art keywords
- purity
- hydrogen
- data
- module
- impurity
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000001257 hydrogen Substances 0.000 title claims abstract description 156
- 229910052739 hydrogen Inorganic materials 0.000 title claims abstract description 156
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 title claims abstract description 143
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 78
- 238000002360 preparation method Methods 0.000 title claims abstract description 19
- 238000000034 method Methods 0.000 title abstract description 15
- 239000012535 impurity Substances 0.000 claims abstract description 91
- 238000001228 spectrum Methods 0.000 claims abstract description 68
- 238000011156 evaluation Methods 0.000 claims abstract description 26
- 238000012544 monitoring process Methods 0.000 claims abstract description 20
- 230000007613 environmental effect Effects 0.000 claims abstract description 17
- 150000002431 hydrogen Chemical class 0.000 claims abstract description 12
- 238000005259 measurement Methods 0.000 claims abstract description 4
- 238000012360 testing method Methods 0.000 claims abstract description 4
- 230000003595 spectral effect Effects 0.000 claims description 41
- 238000004364 calculation method Methods 0.000 claims description 31
- 230000005540 biological transmission Effects 0.000 claims description 24
- 230000006870 function Effects 0.000 claims description 24
- 238000012545 processing Methods 0.000 claims description 21
- 230000009466 transformation Effects 0.000 claims description 19
- 238000005457 optimization Methods 0.000 claims description 11
- 230000000694 effects Effects 0.000 claims description 10
- 230000004044 response Effects 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 5
- 230000009022 nonlinear effect Effects 0.000 claims description 3
- 238000013519 translation Methods 0.000 claims description 3
- 239000002131 composite material Substances 0.000 claims description 2
- 230000008602 contraction Effects 0.000 claims description 2
- 238000012937 correction Methods 0.000 claims description 2
- 230000002708 enhancing effect Effects 0.000 claims description 2
- 238000004868 gas analysis Methods 0.000 claims description 2
- 230000010354 integration Effects 0.000 claims description 2
- 230000035945 sensitivity Effects 0.000 claims description 2
- 238000013139 quantization Methods 0.000 claims 1
- 230000008859 change Effects 0.000 abstract description 4
- 206010063385 Intellectualisation Diseases 0.000 abstract description 3
- 238000011002 quantification Methods 0.000 abstract description 3
- 238000001514 detection method Methods 0.000 description 9
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 7
- 239000001301 oxygen Substances 0.000 description 7
- 229910052760 oxygen Inorganic materials 0.000 description 7
- 238000001816 cooling Methods 0.000 description 6
- 239000007789 gas Substances 0.000 description 6
- 230000000737 periodic effect Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000000746 purification Methods 0.000 description 4
- 238000005070 sampling Methods 0.000 description 4
- 238000005868 electrolysis reaction Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000003993 interaction Effects 0.000 description 3
- 239000000243 solution Substances 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 238000007405 data analysis Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000007788 liquid Substances 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 238000003908 quality control method Methods 0.000 description 2
- 238000000926 separation method Methods 0.000 description 2
- 230000000087 stabilizing effect Effects 0.000 description 2
- 238000003860 storage Methods 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 239000003463 adsorbent Substances 0.000 description 1
- 239000003513 alkali Substances 0.000 description 1
- 239000007864 aqueous solution Substances 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 239000002585 base Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000002301 combined effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000018044 dehydration Effects 0.000 description 1
- 238000006297 dehydration reaction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 239000012847 fine chemical Substances 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- -1 hydrogen Chemical class 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000004091 panning Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000000275 quality assurance Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Landscapes
- Electrolytic Production Of Non-Metals, Compounds, Apparatuses Therefor (AREA)
Abstract
The invention relates to the technical field of hydrogen measurement and testing, in particular to a high-purity hydrogen production and preparation system and method. Comprising the following steps: collecting spectrum data, temperature data and pressure data of a hydrogen sample, converting the spectrum data from a time domain to a frequency domain, and dynamically adjusting the spectrum data according to the temperature and pressure data by using a multi-element nonlinear regression model; further calculating and correcting the content of impurities, and evaluating the total purity of the hydrogen; monitoring the real-time purity of the hydrogen, and judging whether purity deviation exists or not based on the evaluation result of the real-time purity of the hydrogen and the total purity of the hydrogen; setting a dynamic threshold of the purity deviation, and starting a purity deviation feedback adjustment program when the purity deviation exceeds the dynamic threshold; and dynamically optimizing production parameters based on the output of the purity deviation feedback adjustment function. The problem that the identification and quantification of impurities in a hydrogen sample are not accurate enough in the prior art is solved; the adaptability of the environmental change is insufficient; the technical problems of insufficient automation and intellectualization of production parameter adjustment.
Description
Technical Field
The invention relates to the technical field of hydrogen measurement and testing, in particular to a high-purity hydrogen production and preparation system and method.
Background
In the background of the increasingly strong global energy structure transformation and environmental protection requirements, high purity hydrogen is a carrier of clean energy, and the production and application of the hydrogen are receiving a great deal of attention. Hydrogen has important applications not only in chemical industry, fine chemical manufacturing, and electronics industry, but also as an important component in future energy systems, such as fuel cell vehicles and renewable energy storage, and has great potential and value. However, the problems of impurity control and quality assurance in the preparation process of high purity hydrogen become key technical bottlenecks restricting its wide application. The prior art has the defects in the aspects of hydrogen purity detection, environmental adaptability, automatic production parameter adjustment and the like, and cannot meet the requirements of high-efficiency, accurate and stable production of high-purity hydrogen. Therefore, a new technology capable of realizing accurate impurity detection and automatic quality control in high-purity hydrogen production is developed, and has important significance for promoting the application development of hydrogen energy and related technologies.
Chinese patent application number: CN202211449398.8, publication date: 2023.02.03 discloses a high-purity hydrogen production and purification process thereof, belonging to the technical field of hydrogen preparation, comprising the following steps of S1, electrolyzing an aqueous solution in an electrolytic tank, generating oxygen and hydrogen after electrolysis, and storing the oxygen in an oxygen storage device; s2, gas-liquid separation is carried out, so that hydrogen generated by electrolysis enters a hydrogen-alkali separator to carry out gas-liquid separation; s3, cooling the hydrogen, and cooling the hydrogen by using a hydrogen cooler to perform preliminary dehydration; s4, deeply cooling the hydrogen to remove oxygen and water, cooling the hydrogen to a buffer tank, and then entering a deep cooling oxygen removing and water removing device to achieve the purposes of removing oxygen and water and purifying the hydrogen; s5, storing hydrogen, purifying the hydrogen, entering a hydrogen purification process pipeline, and storing the purified hydrogen into a hydrogen storage tank. In the hydrogen purification process, the deep cooling is adopted to remove impurities, the residual concentration of oxygen can be reduced to below 1ppm, the purity of hydrogen reaches above 99.9999 percent, and the adopted hydrogen purification process does not need an adsorbent.
However, the above technology has at least the following technical problems: the identification and quantification of impurities in a hydrogen sample are not accurate enough in the prior art; the lack of adaptability to environmental changes may affect the accuracy of impurity detection under different environmental conditions due to the lack of the ability to dynamically adjust spectral data based on real-time temperature and pressure data; the automation and the intellectualization of the production parameter adjustment are insufficient, the adjustment of the production process depends on manual intervention, the efficiency is low, errors are easy to occur, the efficiency and the stability of the high-purity hydrogen production and preparation process are limited, and the quality standard of the hydrogen product is influenced.
Disclosure of Invention
The invention provides a high-purity hydrogen production and preparation system and a method, which solve the problem that the identification and quantification of impurities in a hydrogen sample are not accurate enough in the prior art; the lack of adaptability to environmental changes may affect the accuracy of impurity detection under different environmental conditions due to the lack of the ability to dynamically adjust spectral data based on real-time temperature and pressure data; the automation and the intellectualization of the production parameter adjustment are insufficient, the adjustment of the production process depends on manual intervention, the efficiency is low, errors are easy to occur, the efficiency and the stability of the high-purity hydrogen production and preparation process are limited, and the technical problem of influencing the quality standard of the hydrogen product is solved. Accurate impurity detection and automatic quality control in the high-purity hydrogen production process are realized, and the detection accuracy and production efficiency of the hydrogen purity are remarkably improved.
The invention provides a high-purity hydrogen production and preparation system and a method, which concretely comprise the following technical scheme:
the production and preparation method of the high-purity hydrogen comprises the following steps:
s1, collecting spectrum data, temperature data and pressure data of a hydrogen sample, converting the spectrum data from a time domain to a frequency domain, and dynamically adjusting the spectrum data according to the temperature data and the pressure data by using a multi-element nonlinear regression model; further calculating and correcting the content of the impurities, and evaluating the total purity of the hydrogen based on the corrected content of the impurities;
S2, monitoring the real-time purity of the hydrogen, and judging whether purity deviation exists or not based on an evaluation result of the real-time purity of the hydrogen and the total purity of the hydrogen; setting a dynamic threshold of the purity deviation, and starting a purity deviation feedback adjustment program when the purity deviation exceeds the dynamic threshold; and further, dynamically optimizing production parameters based on the output of the purity deviation feedback adjustment function.
Preferably, the S1 specifically includes:
Converting the spectral data from the time domain to the frequency domain using a fourier transform; performing wavelet transformation on the spectrum data after Fourier transformation processing, selecting a wavelet mother function matched with the spectrum characteristics of the impurity to be detected to analyze the intensity change of the spectrum data under each scale, and focusing on the spectrum data characteristics at the typical frequency of the impurity; based on the principle that the spectrum data reflects different impurity components at different frequencies, various impurity components in the hydrogen sample are distinguished and identified by extracting frequency characteristics.
Preferably, the S1 further includes:
Associating temperature data and pressure data with each of the spectral data; and synchronizing the temperature and pressure readings with the spectral data based on the temperature and pressure readings and the time stamp of the spectral data.
Preferably, the S1 further includes:
defining a multi-element nonlinear regression function, inputting temperature, pressure and spectrum data into a multi-element nonlinear regression model for fitting, and adjusting the spectrum data according to the output of the multi-element nonlinear regression model.
Preferably, the S1 further includes:
the content of each impurity is calculated and corrected based on the spectral data adjusted according to the output of the multiple nonlinear regression model, the frequency response of the impurity, and the influence of temperature and pressure.
Preferably, the S1 further includes:
Summarizing the corrected contents of all impurities, and weighting according to the influence degree of each impurity on the purity of the hydrogen; and based on the interaction between impurities, correcting the influence of the total purity of the hydrogen; and calculating and correcting the total purity of the hydrogen by using an evaluation formula, and adjusting parameters in the evaluation formula according to the variety of impurity types.
Preferably, the S2 specifically includes:
After the corrected total purity of the hydrogen is obtained, a model based on real-time data processing and feedback is introduced, and parameters of a high-purity hydrogen production and preparation system are automatically adjusted.
Preferably, the S2 further includes:
When the purity deviation between the real-time purity of the hydrogen and the corrected total purity of the hydrogen exceeds a dynamic threshold, a purity deviation feedback adjustment procedure is initiated and the purity deviation is corrected by adjusting the production parameters.
A high purity hydrogen production and production system comprising the following parts:
The device comprises a data acquisition module, a spectrum data processing module, a multi-element adjustment module, an impurity calculation module, a purity evaluation module, a real-time monitoring module, a threshold calculation and comparison module, a feedback adjustment module and a production optimization module;
the data acquisition module is used for collecting spectrum data, temperature data and pressure data of the hydrogen sample; the data acquisition module is connected with the spectrum data processing module and the multi-element adjustment module in a data transmission mode;
the spectrum data processing module is used for identifying and analyzing the spectrum characteristics of impurities in the hydrogen based on the spectrum data; the spectrum data processing module is connected with the multi-element adjusting module in a data transmission mode;
The multi-element adjustment module is used for associating the temperature data and the pressure data with the spectrum data and dynamically adjusting the spectrum data according to the environment variable; the multi-element adjusting module is connected with the impurity calculating module in a data transmission mode;
the impurity calculation module is used for identifying and correcting the content of various impurities in the hydrogen; the impurity calculation module is connected with the purity evaluation module in a data transmission mode;
the purity evaluation module is used for summarizing the corrected contents of all impurities and evaluating the total purity of the hydrogen; the purity evaluation module is connected with the threshold calculation and comparison module in a data transmission mode;
the real-time monitoring module is used for monitoring the purity of the hydrogen in real time to obtain the real-time purity of the hydrogen; the real-time monitoring module is connected with the threshold calculation and comparison module in a data transmission mode;
the threshold calculation and comparison module is used for automatically setting a dynamic threshold of the purity deviation, comparing the evaluation result of the total purity of the hydrogen with the real-time purity of the hydrogen, and determining whether the purity deviation exists or not; the threshold value calculation and comparison module is connected with the feedback adjustment module in a data transmission mode;
the feedback adjustment module is used for starting the feedback adjustment module when the purity deviation exceeds the dynamic threshold value, and adjusting the production parameters according to the purity deviation; the feedback adjustment module is connected with the production optimization module in a data transmission mode;
And the production optimization module is used for optimizing the production process.
The method is applied to the production and preparation method of the high-purity hydrogen.
The technical scheme of the invention has the beneficial effects that:
1. By confirming the state of the detecting instrument and stabilizing the hydrogen sample in the sampling container before data acquisition, the influence of temperature and pressure fluctuation is reduced, and the accuracy and the integrity of spectrum data, temperature data and pressure data are improved; repeated sampling and averaging further enhances the reliability of the data; the Fourier transform and wavelet transform are adopted, and particularly, a wavelet mother function matched with the spectral characteristics of the impurities to be detected is selected for analysis, so that the recognition capability of the spectral characteristics of the impurities in the hydrogen is greatly improved;
2. The introduced multiple nonlinear regression model allows the spectral data to be dynamically adjusted according to the real-time temperature and pressure data, so that the accuracy of impurity detection under different environmental conditions is improved; the dynamic threshold value setting and feedback adjustment mechanism further enhances the adaptability of the system to environmental changes; through the design of the purity deviation feedback adjustment module and the production optimization module, the system can automatically adjust production parameters, such as electrolysis voltage and gas flow rate, according to the purity information monitored in real time so as to correct the purity deviation in real time and keep the purity of the hydrogen within a preset standard.
Drawings
FIG. 1 is a block diagram of a high purity hydrogen production and production system according to the present invention;
FIG. 2 is a flow chart of a method for producing and preparing high purity hydrogen according to the present invention.
Detailed Description
In order to further illustrate the technical means and effects adopted by the present invention to achieve the preset purpose, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, 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.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of a high-purity hydrogen production and preparation system and method provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, there is shown a block diagram of a high purity hydrogen production and production system according to the present invention, comprising:
The device comprises a data acquisition module, a spectrum data processing module, a multi-element adjustment module, an impurity calculation module, a purity evaluation module, a real-time monitoring module, a threshold calculation and comparison module, a feedback adjustment module and a production optimization module;
the data acquisition module is used for collecting spectrum data, temperature data and pressure data of the hydrogen sample; the data acquisition module is connected with the spectrum data processing module and the multi-element adjustment module in a data transmission mode;
The spectrum data processing module is used for processing the collected hydrogen sample spectrum data by applying Fourier transformation and wavelet transformation, enhancing the identification and analysis of impurities, and selecting a proper wavelet mother function (such as Daubechies wavelet) to refine the extraction of the spectrum characteristics of the impurities in the hydrogen; the spectrum data processing module is connected with the multi-element adjusting module in a data transmission mode;
The multi-element adjustment module is used for correlating the temperature data and the pressure data of the hydrogen sample with the spectrum data to form a multi-dimensional data set; the spectrum data is dynamically adjusted according to the environment variable, the accuracy of impurity detection is enhanced, and the multi-element adjustment module is connected with the impurity calculation module in a data transmission mode;
the impurity calculation module is used for identifying and correcting the content of various impurities in the hydrogen, and is connected with the purity evaluation module in a data transmission mode;
The purity evaluation module is used for summarizing the content of all impurities and evaluating the total purity of the hydrogen by considering possible interactions among the impurities; the purity evaluation module is connected with the threshold calculation and comparison module in a data transmission mode;
the real-time monitoring module is used for monitoring the purity of the hydrogen in real time by utilizing a gas chromatograph and a mass spectrometer to obtain the real-time purity of the hydrogen; the real-time monitoring module is connected with the threshold calculation and comparison module in a data transmission mode;
The threshold value calculation and comparison module is used for automatically setting a dynamic threshold value of the purity deviation according to the historical data and the current environmental conditions, comparing the estimated total purity of the hydrogen with the real-time purity of the hydrogen obtained by the real-time monitoring module, and determining whether the purity deviation exists; the historical data comprises a purity historical record, production parameter historical data, environmental condition historical data and purity adjustment response data; current environmental conditions include temperature and pressure; the threshold value calculation and comparison module is connected with the feedback adjustment module in a data transmission mode;
The feedback adjustment module is started when the purity deviation exceeds the dynamic threshold value, automatically adjusts production parameters (such as the voltage of the electrolytic cell and the gas flow rate) according to the purity deviation so as to optimize the purity of the hydrogen, and is connected with the production optimization module in a data transmission mode;
And the production optimization module is used for optimizing the production process and ensuring that the purity of the hydrogen meets the preset standard.
Referring to fig. 2, a flow chart of a method for producing and preparing high purity hydrogen according to the present invention is shown, the method comprises the following steps:
S1, collecting spectrum data, temperature data and pressure data of a hydrogen sample, converting the spectrum data from a time domain to a frequency domain, and dynamically adjusting the spectrum data according to the temperature data and the pressure data by using a multi-element nonlinear regression model; further calculating and correcting the content of the impurities, and evaluating the total purity of the hydrogen based on the corrected content of the impurities;
The data acquisition module collects spectrum data, temperature data and pressure data of the hydrogen sample through the spectrometer, the temperature sensor and the pressure sensor, captures behavior characteristics of the hydrogen sample under different environmental conditions, provides comprehensive and accurate original data for subsequent data analysis, and ensures that the original data has high reliability and integrity. Before raw data are collected, confirming that a spectrometer, a temperature sensor and a pressure sensor are in good state and calibrated, stabilizing a hydrogen sample in a sampling container for at least 5 minutes, and reducing the influence of temperature and pressure fluctuation; and simultaneously, the temperature and the pressure of the hydrogen sample are recorded, so that the synchronism of data acquisition is ensured. Sampling is repeated at least three times, and an average value is taken to improve the reliability of the data.
The spectrum data processing module is used for processing the collected spectrum data of the hydrogen sample by Fourier transformation, and converting the spectrum data from a time domain to a frequency domain so as to better identify and analyze the spectrum characteristics of impurities in the hydrogen; performing wavelet transformation on the spectrum data after the Fourier transformation treatment, and selecting a wavelet mother function matched with the spectrum characteristics of the impurity to be detected for analysis, such as Daubechies wavelet (a plurality of Bei Xixiao waves); the spectral data intensity variations at each scale are analyzed and the spectral data characteristics at the typical frequencies of the impurities are of interest. Extracting and recording key spectral data features, such as feature peak positions, widths, areas and the like, as the basis of impurity identification; based on the principle that the spectrum data reflects different impurity components at different frequencies, various impurity components in the hydrogen sample can be effectively distinguished and identified by accurately extracting frequency characteristics. The specific implementation formula is as follows:
，
，
Wherein, Is spectral data in a frequency domain, and represents a result after Fourier transformation, and is used for identifying and analyzing the spectral characteristics of impurities in hydrogen; /(I)Is the time domain spectral data, is the raw spectral data collected from the spectrometer; /(I)Is a natural base number; /(I)Is an imaginary unit; /(I)Angular frequency, a variable representing different frequency components in the spectral data; /(I)Is a time variable representing the point in time of the original spectral data acquisition; /(I)Representing the frequency domain data after wavelet transformation; Is a complex conjugate form of an adjusted wavelet mother function for use at different scales/> And translation/>Performing fine analysis on the spectrum data; /(I)Is a scale parameter in wavelet transformation and is used for controlling the expansion and contraction of wavelet functions; /(I)Is a panning parameter in the wavelet transform for controlling the movement of the wavelet function on the time axis.
The multi-element adjustment module associates temperature and pressure data of the hydrogen sample with each spectrum data to form a multi-dimensional data set; synchronizing the temperature and pressure readings with the spectral data based on the temperature and pressure readings and the time stamps of the spectral data; introducing a multiple nonlinear regression model, adjusting spectrum data according to temperature and pressure data, and dynamically adjusting the spectrum data by using the multiple nonlinear regression model to improve the accuracy of impurity detection; defining a multi-element nonlinear regression function, inputting temperature, pressure and spectrum data into a multi-element nonlinear regression model, fitting, and adjusting the spectrum data according to the output of the multi-element nonlinear regression model; the multiple nonlinear regression function is:
，
，
Wherein, Representing the integration of temperature data/>And pressure data/>Initial spectral data of the effect;、/> an adjustment function representing the influence of temperature and pressure data on the spectral data, respectively,/> Representing the adjusted spectral data; /(I)Nonlinear adjustment parameters taking into account temperature data/>And pressure data/>The composite effect on the initial spectral data; /(I)Reflecting the nonlinear effects of environmental conditions (such as temperature and pressure) on the spectral data; by adjusting/>And/>The parameters can compensate nonlinear influence and ensure the accuracy and reliability of spectrum data analysis.
The impurity calculation module analyzes the data processed by wavelet transformation and calibrates the frequency characteristics corresponding to the known impurities; comparing the frequency characteristics of unknown impurities with those of the impurities in the database, and performing primary identification; comprehensively considering the spectral data adjusted by using a multi-element nonlinear regression model, the specific frequency response of impurities and the influence of temperature and pressure, and correcting the content of each impurity; the specific formula is as follows:
，
Wherein, Represents the/>Correction content of seed impurities; /(I)Representing the impurity at a specific frequency/>The spectral response below; /(I)And/>Is a parameter that enhances the spectral data recognition sensitivity and the accuracy of the quantitative estimation. Therefore, impurities in the hydrogen sample are accurately identified, the content of each impurity is calculated, and the accuracy of hydrogen purity assessment is improved.
The purity evaluation module summarizes the content of all impurities and weights the influence degree of each impurity on the purity of the hydrogen; correcting for the effect of total purity taking into account possible interactions between impurities; calculating and correcting total purity of hydrogen by using evaluation formula, and adjusting parameters in the evaluation formula according to variety of impurity types, such asTo ensure accuracy and rationality of the evaluation results. The total purity of hydrogen was evaluated as follows:
，
Wherein, Indicating the corrected total purity of hydrogen, taking into account the combined effect of all impurities; /(I)Represents the/>A weight coefficient of the effect of the seed impurity on the purity of the hydrogen; /(I)Is an adjustment coefficient considering the influence of impurity variety on the total purity,/>Is the number of impurity species; by introduction/>And/>The parameters flexibly adjust the calculation of the total purity according to the types and the quantity of the impurities, and ensure the high accuracy and the reliability of the evaluation result.
S2, monitoring the real-time purity of the hydrogen, and judging whether purity deviation exists or not based on an evaluation result of the real-time purity of the hydrogen and the total purity of the hydrogen; setting a dynamic threshold of the purity deviation, and starting a purity deviation feedback adjustment program when the purity deviation exceeds the dynamic threshold; and further, dynamically optimizing production parameters based on the output of the purity deviation feedback adjustment function.
After calculation, the corrected total purity of the hydrogenThe real-time monitoring module then uses gas analysis instruments, such as gas chromatographs and mass spectrometers, to monitor the real-time purity of the hydrogen. The purity of the hydrogen is determined through measurement and testing, and a model based on real-time data processing and feedback is introduced to automatically adjust parameters of a high-purity hydrogen production and preparation system, so that the purity of the hydrogen always meets preset standards.
Monitoring the purity of the hydrogen sample in real time by a gas chromatograph and a mass spectrometer, including the concentration of various impurities, so as to obtain the real-time purity of the hydrogen; the real-time purity of the hydrogen is compared with the corrected total purity of the hydrogen obtained by the pre-calculationA comparison is made to determine if there is a purity bias.
The threshold calculation and comparison module automatically sets a dynamic threshold of the purity deviation according to the historical data and the current environmental conditions (such as temperature and pressure) to optimize the hydrogen production efficiency and quality; the historical data includes purity history, production parameter history, environmental condition history, and purity adjustment response data. The setting of the dynamic threshold utilizes historical production parameter data and environment monitoring data, and the optimal deviation tolerance range is determined through statistical analysis, so that a reference is provided for subsequent deviation feedback adjustment. The calculation formula of the dynamic threshold is as follows:
，
Wherein, Is a dynamic threshold; /(I)Is a basic threshold, is a purity deviation threshold set under ideal conditions;、/>、/>、/> The adjustment coefficient is optimized according to experimental data and historical production performance so as to adapt to different production environments; /(I) Is a coefficient for adjusting the influence of the periodic change of the temperature on the threshold value; /(I)Is a periodically changing frequency parameter, and is set according to the periodic characteristics of the production environment. /(I)And/>Taking into account the periodic variation of temperature and the non-linear effect of pressure on threshold setting.
And when the deviation between the real-time purity of the hydrogen and the corrected total purity of the hydrogen exceeds a dynamic threshold value, the feedback adjustment module automatically starts a purity deviation feedback adjustment program, and corrects the purity deviation by adjusting production parameters, so that the quality of the hydrogen product is ensured. The adjustment formula is:
，
Wherein, An output representing a feedback adjustment function based on the purity deviation for determining an adjustment direction and magnitude of the production parameter; /(I)Is the purity deviation; /(I)Is a coefficient controlling the adjustment amplitude of the production parameter; /(I)Is a constant for avoiding the case where the denominator is zero; /(I)The attenuation coefficient of the influence of the purity deviation is adjusted and is used for smoothing the adjustment process; /(I)Is a sign function.Introduces an exponential decay factor/>Taking the influence of the purity deviation on adjustment into consideration; /(I)For determining the direction of adjustment of the production parameters.
The production optimization module dynamically optimizes production parameters, such as adjusting the voltage of the electrolytic tank, changing the gas flow rate and the like, based on the output of the purity deviation feedback adjustment function; correcting the purity deviation in real time and keeping the purity of the hydrogen within a preset standard. Calculating a specific adjustment value:
，
Wherein, Representing an optimized adjustment value of the production parameter; /(I)And/>Is an adjustment coefficient, and is optimized according to the production performance and the safety requirement; /(I)Is an adjustment frequency parameter for introducing a periodic variation of the adjustment process; /(I)AndArctangent and sine functions are introduced to increase the nonlinearity and dynamics of the tuning process; /(I)The direct influence of the size of the purity deviation on the adjustment value is taken into account, ensuring that the adjustment value is proportional to the severity of the purity deviation.
The method is characterized in that the effects of the change of the hydrogen purity and the adjustment of production parameters are continuously monitored, and the dynamic threshold setting and the purity deviation feedback adjustment are continuously updated by introducing the existing self-adaptive learning mechanism (such as a neural network algorithm) so as to optimize the production process and improve the purity control effect of the hydrogen.
In summary, a system and a method for producing and preparing high-purity hydrogen are completed.
The sequence of the embodiments of the invention is merely for description and does not represent the advantages or disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and the same or similar parts of each embodiment are referred to each other, and each embodiment mainly describes differences from other embodiments.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (2)
1. The production and preparation method of the high-purity hydrogen is characterized by comprising the following steps of:
S1, collecting spectrum data, temperature data and pressure data of a hydrogen sample, converting the spectrum data from a time domain to a frequency domain by a spectrum data processing module through Fourier transformation to identify and analyze spectrum characteristics of impurities in the hydrogen, carrying out wavelet transformation on the spectrum data after the Fourier transformation, selecting a wavelet mother function matched with the spectrum characteristics of the impurities to be detected for analysis, and distinguishing and identifying various impurity components in the hydrogen sample by extracting the frequency characteristics based on the principle that the spectrum data reflects different impurity components at different frequencies; the specific implementation formula is as follows:
,
,
Wherein, Spectral data in the frequency domain, representing the result after fourier transformation; /(I)Is the time domain spectral data, is the raw spectral data collected from the spectrometer; /(I)Is a natural base number; /(I)Is an imaginary unit; /(I)Angular frequency, a variable representing different frequency components in the spectral data; /(I)Is a time variable representing the point in time of the original spectral data acquisition; representing the frequency domain data after wavelet transformation; /(I) Is a complex conjugate form of an adjusted wavelet mother function for use at different scales/>And translation/>Analyzing the spectrum data; /(I)Is a scale parameter in wavelet transformation and is used for controlling the expansion and contraction of wavelet functions; /(I)Is a translation parameter in wavelet transformation for controlling the movement of the wavelet function on the time axis; dynamically adjusting spectral data according to the temperature data and the pressure data by using a multiple nonlinear regression model; the multiple nonlinear regression function is:
,
,
Wherein, Representing the integration of temperature data/>And pressure data/>Initial spectral data of the effect; /(I)、An adjustment function representing the influence of temperature and pressure data on the spectral data, respectively,/>Representing the adjusted spectral data; /(I)Nonlinear adjustment parameters taking into account temperature data/>And pressure data/>The composite effect on the initial spectral data; /(I)Reflecting the nonlinear effect of environmental conditions on the spectral data; further calculating and correcting the impurity content, wherein the specific formula is as follows:
,
Wherein, Represents the/>Correction content of seed impurities; /(I)Representing the impurity at a specific frequency/>The spectral response below; And/> Is a parameter for enhancing the identification sensitivity and the quantization estimation precision of the spectrum data;
Estimating the total purity of the hydrogen based on the corrected content of impurities; the purity evaluation module summarizes the content of all impurities and weights the influence degree of each impurity on the purity of the hydrogen; the total purity of hydrogen was evaluated as follows:
,
Wherein, Indicating the corrected total purity of hydrogen; /(I)Represents the/>A weight coefficient of the effect of the seed impurity on the purity of the hydrogen; The adjustment coefficient of the influence of impurity variety on the total purity is considered; /(I) Is the number of impurity species;
s2, obtaining the corrected total purity of the hydrogen through calculation Then, the real-time monitoring module uses a gas analysis instrument to monitor the real-time purity of the hydrogen; determining the purity of hydrogen through measurement and test, introducing a model based on real-time data processing and feedback, and automatically adjusting parameters of a high-purity hydrogen production and preparation system to ensure that the purity of hydrogen always meets preset standards; judging whether purity deviation exists or not based on the evaluation result of the real-time purity of the hydrogen and the total purity of the hydrogen; setting a dynamic threshold of the purity deviation, and starting a purity deviation feedback adjustment program when the purity deviation exceeds the dynamic threshold; and further, dynamically optimizing production parameters based on the output of the purity deviation feedback adjustment function.
2. A high purity hydrogen production and preparation system, which is applied to the high purity hydrogen production and preparation method as claimed in claim 1, and is characterized by comprising the following parts:
The device comprises a data acquisition module, a spectrum data processing module, a multi-element adjustment module, an impurity calculation module, a purity evaluation module, a real-time monitoring module, a threshold calculation and comparison module, a feedback adjustment module and a production optimization module;
the data acquisition module is used for collecting spectrum data, temperature data and pressure data of the hydrogen sample; the data acquisition module is connected with the spectrum data processing module and the multi-element adjustment module in a data transmission mode;
the spectrum data processing module is used for identifying and analyzing the spectrum characteristics of impurities in the hydrogen based on the spectrum data; the spectrum data processing module is connected with the multi-element adjusting module in a data transmission mode;
The multi-element adjustment module is used for associating the temperature data and the pressure data with the spectrum data and dynamically adjusting the spectrum data according to the environment variable; the multi-element adjusting module is connected with the impurity calculating module in a data transmission mode;
the impurity calculation module is used for identifying and correcting the content of various impurities in the hydrogen; the impurity calculation module is connected with the purity evaluation module in a data transmission mode;
the purity evaluation module is used for summarizing the corrected contents of all impurities and evaluating the total purity of the hydrogen; the purity evaluation module is connected with the threshold calculation and comparison module in a data transmission mode;
the real-time monitoring module is used for monitoring the purity of the hydrogen in real time to obtain the real-time purity of the hydrogen; the real-time monitoring module is connected with the threshold calculation and comparison module in a data transmission mode;
the threshold calculation and comparison module is used for automatically setting a dynamic threshold of the purity deviation, comparing the evaluation result of the total purity of the hydrogen with the real-time purity of the hydrogen, and determining whether the purity deviation exists or not; the threshold value calculation and comparison module is connected with the feedback adjustment module in a data transmission mode;
the feedback adjustment module is used for starting the feedback adjustment module when the purity deviation exceeds the dynamic threshold value, and adjusting the production parameters according to the purity deviation; the feedback adjustment module is connected with the production optimization module in a data transmission mode;
And the production optimization module is used for optimizing the production process.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410451252.XA CN118047351B (en) | 2024-04-16 | 2024-04-16 | High-purity hydrogen production and preparation system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410451252.XA CN118047351B (en) | 2024-04-16 | 2024-04-16 | High-purity hydrogen production and preparation system and method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN118047351A CN118047351A (en) | 2024-05-17 |
| CN118047351B true CN118047351B (en) | 2024-06-07 |
Family
ID=91046968
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410451252.XA Active CN118047351B (en) | 2024-04-16 | 2024-04-16 | High-purity hydrogen production and preparation system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118047351B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015182758A1 (en) * | 2014-05-30 | 2015-12-03 | Ｊｘ日鉱日石エネルギー株式会社 | Hydrogen supply system |
| CN112268864A (en) * | 2020-10-15 | 2021-01-26 | 四川省农业科学院农产品加工研究所 | Method for establishing color difference model for rapidly detecting lemon drying process and application |
| CN112378895A (en) * | 2020-09-24 | 2021-02-19 | 长江大学 | Method and system for measuring water content of crude oil, storage medium and equipment |
| CN116678839A (en) * | 2023-07-13 | 2023-09-01 | 季华实验室 | Luminescent material detection method, device, terminal equipment and storage medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2024519533A (en) * | 2021-05-06 | 2024-05-15 | ストロング フォース アイオーティ ポートフォリオ ２０１６，エルエルシー | Quantum, Biological, Computer Vision and Neural Network Systems for the Industrial Internet of Things |
-
2024
- 2024-04-16 CN CN202410451252.XA patent/CN118047351B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015182758A1 (en) * | 2014-05-30 | 2015-12-03 | Ｊｘ日鉱日石エネルギー株式会社 | Hydrogen supply system |
| CN112378895A (en) * | 2020-09-24 | 2021-02-19 | 长江大学 | Method and system for measuring water content of crude oil, storage medium and equipment |
| CN112268864A (en) * | 2020-10-15 | 2021-01-26 | 四川省农业科学院农产品加工研究所 | Method for establishing color difference model for rapidly detecting lemon drying process and application |
| CN116678839A (en) * | 2023-07-13 | 2023-09-01 | 季华实验室 | Luminescent material detection method, device, terminal equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN118047351A (en) | 2024-05-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111123188A (en) | Electric energy meter comprehensive verification method and system based on improved least square method | |
| CN108549908B (en) | Chemical process fault detection method based on multi-sampling probability kernel principal component model | |
| CN103018344A (en) | Calibrating method of color spectrum in oil on-line detecting device | |
| CN116985183B (en) | Quality monitoring and management method and system for near infrared spectrum analyzer | |
| CN110052050B (en) | Tower plate temperature-based internal thermally coupled rectifying tower global state observer and method | |
| CN116861313B (en) | Kalman filtering working condition identification method and system based on vibration energy trend | |
| CN116187861A (en) | Isotope-based water quality traceability monitoring method and related device | |
| CN114611582B (en) | Method and system for analyzing substance concentration based on near infrared spectrum technology | |
| CN118047351B (en) | High-purity hydrogen production and preparation system and method | |
| CN113703422B (en) | Gas turbine pneumatic actuator fault diagnosis method based on feature analysis processing | |
| CN113804861A (en) | Transformer on-line monitoring and early warning system and method | |
| CN112014344B (en) | Online sewage monitoring method | |
| CN114813614A (en) | Fourier infrared gas analysis method and system | |
| CN110763830B (en) | Method for predicting content of free calcium oxide in cement clinker | |
| CN115274001A (en) | Method for evaluating reliability of organic matter online detection result based on near infrared spectrum | |
| CN114371677A (en) | Industrial process state monitoring method based on spectral radius-interval principal component analysis | |
| CN118243737B (en) | Method, device, equipment and storage medium for detecting concentration of negative ions | |
| CN118016202B (en) | Chemical equipment operation analysis method and system based on steam-water quality | |
| CN116429161A (en) | Dynamic calibration method for online chemical instrument of thermal power plant | |
| CN118333326A (en) | Hierarchical early warning system of application sewage plant central control room | |
| CN117783885B (en) | Electrochemical mechanism-based energy storage battery thermal runaway prediction method and system | |
| CN117970978B (en) | Automatic regulation and control system of constant temperature and humidity experiment box suitable for experiments | |
| CN117761419B (en) | Operation state monitoring method for transformer | |
| CN118443886A (en) | Plasma equipment health monitoring method and system | |
| CN118225752A (en) | Coal quality detection method and device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |