Quantitative measurement method and device for impurity concentration in gas and medium

Abstract

The invention discloses a quantitative measurement method and device for impurity concentration in gas and a medium, and relates to the field of gas detection.The method comprises the steps that B impurities in A gas and response signals of the A impurities in B gas are obtained; respectively diluting the gas A and the gas B by adopting a mass dilution method and taking the gas D as diluent gas to obtain diluted mixed gas A with a first set concentration value and diluted mixed gas B with a second set concentration value; acquiring response signals of a component A in the diluted mixed gas A and a component B in the diluted mixed gas B; constructing a measurement model of a bidirectional iteration intelligent algorithm; a measurement model of a bidirectional iteration intelligent algorithm is adopted, and according to the four response signals and gas quality data and molecular weight data in the dilution process, the concentration of the B impurities in the gas A and the measurement result of the concentration of the A impurities in the gas B are obtained. According to the invention, the problem of quantitative measurement of corresponding impurities of two gases which are mutually impurities can be solved.

Description

Technical Field

[0001] The present application relates to the field of gas detection, and in particular to a method, device and medium for quantitatively measuring the concentration of impurities in gas. Background Art

[0002] Gases of a certain purity are widely used in the environmental, energy, and industrial fields. For example, they serve as process gases, shielding gases, and purge gases. They can also be used to prepare standard gas mixtures (referred to as standard gases) for gas concentration measurement. During use, the purity and impurity concentration of a gas are important factors affecting its use. Gas purity testing typically requires subtracting impurities, necessitating quantitative measurement of the impurity concentration in the gas. With the continuous emergence of new gas types and new measurement requirements, especially when measuring gases containing isotopic or isomeric impurities, the measurement of gases that are mutually impure often presents challenges. This means quantitatively measuring impurity B in gas A, or impurity A in gas B.

[0003] For example, in the field of environmental monitoring, monitoring the concentration of carbon dioxide isotopes in the atmosphere can be used to explore the source of carbon dioxide emissions and distinguish whether carbon dioxide emissions are caused by human activities. 13 CO2 and 12 CO2 standard gas. Theoretically, the first-level mixed gas is prepared according to the weighing method disclosed in GB / T 5274.1-2018 Gas Analysis - Preparation of Mixed Gases for Calibration - Part 1. This type of standard gas can be prepared by 13 CO2 gas and 12 CO2 gas is mixed and prepared. During the preparation process, it is necessary to know 13 CO2 gas and 12 The purity of CO2 gas and the concentration of impurities it contains.

[0004] Existing 13 CO2 and 12 CO2 gas usually contains isotopic impurities. If you want to know the exact 13 The purity of CO2 gas requires the 12 To accurately measure CO2 impurities, it is necessary to use the conventional calibration measurement method. 12 CO2 gas is prepared with a certain concentration 12 CO2 standard gas is used to calibrate the instrument used for measurement. According to the requirements of the weighing method for preparing the first-level mixed gas in GB / T 5274.1-2018 Gas Analysis - Preparation of Mixed Gases for Calibration - Part 1, prepare 12 When using CO2 standard gas, you need to know exactly12 The purity of CO2 gas needs to be accurately measured 12 CO2 gas 13 The impurity content of CO2, which in turn requires the use of 13 CO2 gas is prepared with a certain concentration 13 CO2 standard gas is used to calibrate the instruments used for measurement. 13 When using CO2 standard gas, you need to know exactly 13 The purity of CO2 gas, which requires precise measurement 13 CO2 gas 12 The CO2 impurity content. This creates a vicious cycle of "chicken and egg".

[0005] It is not yet possible to accurately prepare 13 CO2 and 12 CO2 standard gas, there are two reasons: First, the conventional sales 13 CO2 gas contains 12 CO2 impurities 12 CO2 gas contains 13 CO2 impurities, that is, the situation of "mutual impurities", lead to 13 CO2 gas and 12 The purity of CO2 gas cannot be accurately measured and cannot be used 13 CO2 and 12 The accurate preparation of CO2 standard gas; secondly, it does not contain 13 High purity of CO2 impurities 12 CO2 gas and does not contain 12 High purity of CO2 impurities 13 CO2 gas is very difficult to obtain and the production cost is very high, so it cannot be used in large quantities. 13 CO2 and 12 The production and preparation of CO2 standard gas. Therefore, for the above reasons, accurate measurement of conventional 13 CO2 gas contains 12 The concentration of CO2 impurities and 12 CO2 gas contains 13 The concentration of CO2 impurities has become one of the research difficulties in the current field of gas measurement.

[0006] Another example is the new refrigerant gases trans-1,3,3,3-tetrafluoropropene and cis-1,3,3,3-tetrafluoropropene, which have strong application prospects due to their low warming potential and low greenhouse effect. However, trans-1,3,3,3-tetrafluoropropene gas contains cis-1,3,3,3-tetrafluoropropene as an impurity, and cis-1,3,3,3-tetrafluoropropene gas contains trans-1,3,3,3-tetrafluoropropene as an impurity. Only by accurately measuring the concentration of impurities can the purity and quality of these gases be scientifically evaluated. Currently, high-purity trans-1,3,3,3-tetrafluoropropene and cis-1,3,3,3-tetrafluoropropene gases are difficult to obtain.

[0007] Therefore, how to solve the problem of quantitative measurement of impurities in two gases that are impurities of each other has become a key technical issue that restricts technological progress and industrial development in this field. Summary of the Invention

[0008] In order to solve the above technical problems, the present application provides a method, equipment and medium for quantitatively measuring the impurity concentration in gas.

[0009] To achieve the above objectives, this application provides the following solutions: In a first aspect, the present application provides a method for quantitatively measuring impurity concentration in gas, comprising: Obtaining the response signal of impurity B in gas A and the response signal of impurity A in gas B; Using the mass dilution method, gas D is used as the diluent gas to dilute gas A and gas B respectively to obtain a diluted mixed gas of A with a first set concentration value and a diluted mixed gas of B with a second set concentration value; Obtaining a response signal of component A in the diluted mixed gas A and a response signal of component B in the diluted mixed gas B; Construct a measurement model for a bidirectional iterative intelligent algorithm; The input concentration of impurity B in gas A and the input concentration of impurity A in gas B are set, and a measurement model of a bidirectional iterative intelligent algorithm is adopted. The iterative solution is performed according to the response signal of impurity B in gas A, the response signal of impurity A in gas B, the response signal of component A in the diluted mixed gas A, the response signal of component B in the diluted mixed gas B, and the gas mass data and molecular weight data during the dilution process. When the output concentration of impurity B in gas A and the output concentration of impurity A in gas B tend to converge, the measurement of the concentration of impurity B in gas A and the measurement of the concentration of impurity A in gas B are completed.

[0010] Optionally, a measurement model using a bidirectional iterative intelligent algorithm is used to determine the concentration of impurity B in gas A and the concentration of impurity A in gas B based on the response signal of impurity B in gas A, the response signal of impurity A in gas B, the response signal of component A in the diluted mixed gas A, the response signal of component B in the diluted mixed gas B, and gas mass data and molecular weight data during the dilution process, including: Set the input concentration of impurity B in gas A and the input concentration of impurity A in gas B; The input concentration of impurity B in gas A, the input concentration of impurity A in gas B, the response signal of impurity B in gas A, the response signal of impurity A in gas B, the response signal of component A in diluted mixed gas A, the response signal of component B in diluted mixed gas B, and the gas mass data and molecular weight data during the dilution process are brought into the measurement model of the bidirectional iterative intelligent algorithm to obtain the output concentration of impurity B in gas A and the output concentration of impurity A in gas B; The output concentration of impurity B in gas A is used as the new input concentration of impurity B in gas A, and the output concentration of impurity A in gas B is used as the new input concentration of impurity A in gas B. The measurement model of the bidirectional iterative intelligent algorithm is iteratively solved until the output concentration of impurity B in gas A and the output concentration of impurity A in gas B tend to converge. The final output concentration of impurity B in gas A is used as the measurement result of the concentration of impurity B in gas A, and the output concentration of impurity A in gas B is used as the measurement result of the concentration of impurity A in gas B.

[0011] Optionally, the measurement model of the bidirectional iterative intelligent algorithm is expressed as: ; ; Where, is the input concentration of impurity B in gas A, is the input concentration of impurity A in gas B, is the output concentration of impurity B in gas A, is the output concentration of impurity A in gas B, is the response signal of impurity B in gas A, is the response signal of impurity A in gas B, is the response signal of component B in the B diluted mixture, is the response signal of component A in the diluted mixture. is the mass of gas B added during the dilution process of gas B, is the mass of gas D added during the dilution of gas B, is the mass of gas A added during the dilution process, is the mass of gas D added during the dilution of gas A, is the molecular weight of component A, is the molecular weight of component B, is the average molecular weight of gas D.

[0012] Optionally, the input concentration of the B impurity in the A gas and the input concentration of the A impurity in the B gas are both set to arbitrary numbers less than 1.

[0013] Optionally, the output concentration of impurity B in gas A and the output concentration of impurity A in gas B tend to converge means that the relative deviation between the output concentrations of impurity B in gas A for two consecutive times is less than a set value, and the relative deviation between the output concentrations of impurity A in gas B for two consecutive times is also less than the set value.

[0014] Optionally, the D gas is an inert gas and does not contain the A component and the B component.

[0015] Optionally, gas chromatography or spectroscopy is used to respectively obtain the response signal of impurity B in gas A, the response signal of impurity A in gas B, the response signal of component A in the diluted mixed gas A, and the response signal of component B in the diluted mixed gas B.

[0016] Optionally, when gas A contains other impurities in addition to component B, and gas B contains other impurities in addition to component A, the input concentration of other impurities in gas A, the input concentration of other impurities in gas B, the molecular weight of other impurities in gas A, and the molecular weight of other impurities in gas B are added as input quantities in the measurement model of the bidirectional iterative intelligent algorithm, and the following are obtained: ; ; Where, It is the second component in gas A except component B. The input concentration of the impurity, is the number of impurities in gas A except component B, It is the second component in gas B besides component A. The input concentration of the impurity, is the number of impurities in gas B other than component A, It is the first The molecular weight of the impurity, It is the first gas in B The molecular weight of the impurity.

[0017] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-mentioned method for quantitatively measuring the concentration of impurities in gas.

[0018] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned method for quantitatively measuring the concentration of impurities in a gas.

[0019] According to the specific embodiments provided in this application, this application has the following technical effects: The present application provides a method, device and medium for quantitatively measuring the concentration of impurities in gas. By measuring the impurities in the gas to obtain the corresponding response signal, and using a measurement model with a bidirectional iterative intelligent algorithm for calculation, it can solve the problem of quantitative measurement of the corresponding impurities of two gases that are impurities of each other. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 A schematic flow chart of a method for quantitatively measuring impurity concentration in gas provided in one embodiment of the present application; Figure 2 A quantitative measurement roadmap for corresponding impurities in gases that are impurities of each other provided in one embodiment of the present application; Figure 3 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

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

[0023] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0024] In an exemplary embodiment, the method for quantitatively measuring the concentration of impurities in gas provided by the present application is as follows: Figure 1 Shown, including: Step 100: Obtain a response signal for impurity B in gas A and a response signal for impurity A in gas B. The concentrations of impurities other than impurity B in gas A are negligible and do not require measurement. The concentrations of impurities other than impurity A in gas B are negligible and do not require measurement. Impurity B in gas A refers to a small amount of gas B present in gas A. In this case, gas B is referred to as impurity B. Similarly, a small amount of gas A in gas B is referred to as impurity A.

[0025] Step 101: Using a mass dilution method, gas D is used as a diluent to dilute gas A and gas B, respectively, to obtain a diluted mixed gas of gas A at a first predetermined concentration value and a diluted mixed gas of gas B at a second predetermined concentration value. Gas D is selected to be an inert gas that contains neither gas component A nor component B, or contains component A or component B at a very low concentration, so low that it can be ignored.

[0026] For example, the mass of Gas B and Gas D added during the dilution process, and the mass of Gas A and Gas D added during the dilution process of Gas A, can be accurately measured using a high-precision balance. When weighing, first select a suitable gas cylinder that has been evacuated to eliminate residual gas. In addition, the inner wall of the cylinder does not chemically react with either component A or component B, nor does it undergo physical adsorption. Based on this, the dilution process for Gas A is as follows: weigh a evacuated gas cylinder, then fill it with Gas A. Then, weigh the cylinder again. The added mass of Gas A is calculated based on the difference in mass before and after filling. Then, fill the cylinder with Gas D, weigh the cylinder again, and calculate the added mass of Gas D based on the difference in mass before and after filling. Finally, roll the cylinder to evenly mix Gas A and Gas D, resulting in a diluted mixed gas A for use. The dilution process of gas B is as follows: weigh the mass of another gas cylinder with a vacuum inside, then fill it with gas B, and then weigh the mass of the gas cylinder again. The added mass of gas B is calculated based on the mass difference before and after filling. Then fill this gas cylinder with gas D, weigh the gas cylinder again, and calculate the added mass of gas D based on the mass difference before and after filling. Finally, roll the gas cylinder to evenly mix gas B and gas D in the gas cylinder to obtain diluted gas B for use.

[0027] Among them, the mass of gas B added during the dilution of gas B, the mass of gas D added during the dilution of gas B, the mass of gas A added during the dilution of gas A, and the mass of gas D added during the dilution of gas A are all accurately measured using a high-precision balance.

[0028] In actual applications, the mass of gas B added during the dilution process of gas B and the mass of gas A added during the dilution process of gas A do not necessarily have to be the same. The mass of gas D added during the dilution process of gas B and the mass of gas D added during the dilution process of gas A also do not necessarily have to be the same.

[0029] Step 102: Acquire a response signal of component A in the A-diluted mixed gas and a response signal of component B in the B-diluted mixed gas.

[0030] Step 103: Construct a measurement model of a bidirectional iterative intelligent algorithm. The measurement model of the bidirectional iterative intelligent algorithm is expressed as: (1) (2) Where, is the input concentration of impurity B in gas A, is the input concentration of impurity A in gas B, is the output concentration of impurity B in gas A, is the output concentration of impurity A in gas B, is the response signal of impurity B in gas A, is the response signal of impurity A in gas B, is the response signal of component B in the B diluted mixture, is the response signal of component A in the diluted mixture. is the mass of gas B added during the dilution process of gas B, is the mass of gas D added during the dilution of gas B, is the mass of gas A added during the dilution process, is the mass of gas D added during the dilution of gas A, is the molecular weight of component A, is the molecular weight of component B, is the average molecular weight of gas D. It represents the amount of substance fraction (i.e. molar concentration) of component B in gas B excluding impurity A. It represents the average molecular weight of gas B; Indicates the quality of When , the amount of substance of gas B; Indicates the quality of When , the amount of substance of gas D; It represents the sum of the amount of substance of gas B and the amount of substance of gas D, that is, the sum of the amount of substance of the low-concentration mixed gas (i.e., diluted mixed gas B) obtained by diluting gas B with gas D; Indicates the quality When , the amount of substance of component B in gas B; It represents the amount of substance fraction of component B in the diluted gas B obtained by diluting gas B with gas D (i.e. the molar concentration of component B). It represents the amount of substance fraction (i.e. molar concentration) of component A in gas A excluding impurity B. It represents the average molecular weight of gas A; Indicates the quality of When , the amount of substance of gas A; Indicates the quality of When , the amount of substance of gas D; It represents the sum of the amount of substance of gas A and the amount of substance of gas D, that is, the sum of the amount of substance of the low-concentration mixed gas (i.e., diluted mixed gas A) obtained by diluting gas A with gas D; Indicates the quality When , the amount of substance of component A in gas A; It represents the amount of substance fraction of component A in the diluted mixed gas A obtained by diluting gas A with gas D (i.e. the molar concentration of component A).

[0031] Step 104: Using a bidirectional iterative intelligent algorithm measurement model, based on the response signal of the B impurity in gas A, the response signal of the A impurity in gas B, the response signal of the A component in the diluted mixed gas A, the response signal of the B component in the diluted mixed gas B, and the gas mass data and molecular weight data during the dilution process, obtain the measurement results of the B impurity concentration in gas A and the A impurity concentration in gas B. The iterative algorithm is implemented as follows: First, set the input concentration of impurity B in gas A (any number less than 1) and the input concentration of impurity A in gas B (any number less than 1). The input concentration of impurity B in gas A, the input concentration of impurity A in gas B, the response signal of impurity B in gas A, the response signal of impurity A in gas B, the response signal of component A in diluted mixed gas A, the response signal of component B in diluted mixed gas B, and the gas mass data and molecular weight data during the dilution process are brought into the measurement model of the bidirectional iterative intelligent algorithm to obtain the output concentration of impurity B in gas A and the output concentration of impurity A in gas B; Then, the output concentration of impurity B in gas A is used as the new input concentration of impurity B in gas A, and the output concentration of impurity A in gas B is used as the new input concentration of impurity A in gas B. The measurement model of the bidirectional iterative intelligent algorithm is iteratively solved until the output concentration of impurity B in gas A and the output concentration of impurity A in gas B tend to converge. The final output concentration of impurity B in gas A is used as the measurement result of the concentration of impurity B in gas A, and the output concentration of impurity A in gas B is used as the measurement result of the concentration of impurity A in gas B.

[0032] The above-mentioned output concentration tends to converge means that the relative deviation between the output concentrations of impurity B in gas A for two consecutive times is less than the set value, and the relative deviation between the output concentrations of impurity A in gas B for two consecutive times is also less than the set value.

[0033] The conditions for using formula (1) and formula (2) in the measurement model of the bidirectional iterative intelligent algorithm are that gas A does not contain impurities other than impurity B, or the concentration of impurities other than impurity B is low enough to be ignored; and gas B does not contain impurities other than impurity A, or the concentration of impurities other than impurity A is low enough to be ignored.

[0034] By implementing the above steps 100 to 104, it is achieved that when the two gases are impurities of each other and other impurities in the gas can be ignored, in the absence of a standard gas corresponding to the impurity component, the concentration of the impurity component of the two gases is accurately measured, providing a new solution to the problem of quantitative measurement of impurities in the gas. Among them, when measuring the concentration of the impurity component of the two gases, there is no need to use the high-purity gas corresponding to the impurity or the standard gas with accurate value. When high-purity gas or standard gas is not available, the target impurity can be measured, which can meet daily measurement needs. The method is simple and easy to use and saves costs. In addition, the present application constructs a measurement model of a bidirectional iterative intelligent algorithm, uses easily available data as input, and the calculation results are reliable and accurate.

[0035] In another exemplary embodiment of the present application, there are three technical routes for gas addition mass, gas dilution and weighing, namely, one-step dilution method, multi-step dilution method and transfer dilution method.

[0036] The basic steps of the one-step dilution method are as follows: First, select a suitable gas cylinder that has been evacuated and has no residual gas. The dilution process for gas A or B is as follows: weigh the evacuated gas cylinder, then fill it with gas A or B. Then, weigh the cylinder again. The added mass of gas A or B is calculated based on the difference in mass before and after filling. Then, fill the cylinder with gas D, weigh the cylinder again, and calculate the added mass of gas D based on the difference in mass before and after filling. Finally, roll the cylinder to evenly mix gas A or B and gas D, creating a mixed gas for use.

[0037] The basic steps of the multi-step dilution method are: first select a suitable gas cylinder, which has been evacuated and has no residual gas. The first step of the dilution process is: weigh the mass of a gas cylinder with a vacuum inside, then fill it with gas A or B, and then weigh the mass of the gas cylinder again, and calculate the added mass of gas A or B based on the mass difference before and after filling, and then fill the gas cylinder with gas D, and weigh the mass of the gas cylinder again, and calculate the added mass of gas D based on the mass difference before and after filling, and finally roll the gas cylinder to mix the gas A or B and gas D in the gas cylinder evenly to obtain a mixed gas of A and D or B and D for use; the second step of the dilution process is: take another gas cylinder with a vacuum inside, weigh the mass of the gas cylinder, and then fill it with gas A and D or B and D The cylinder is then weighed again, and the added mass of the primary mixture of A and D or B and D is calculated based on the mass difference before and after filling. The cylinder is then refilled with D gas, and the cylinder is weighed again. The added mass of D gas is calculated based on the mass difference before and after filling. Finally, the cylinder is rolled to evenly mix the primary mixture of A and D or B and D with D gas, resulting in a secondary mixture of A and D or B and D for later use. Similarly, multiple mixtures of A and D or B and D, such as tertiary and quaternary mixtures, can be obtained, up to the final target concentration of diluted A or diluted B mixture. Based on the multiple dilution process and the mass of gas added during each dilution, the added mass of A gas and the added mass of D gas in the diluted A mixture of the final target concentration can be calculated, as can the added mass of B gas and the added mass of D gas in the diluted B mixture of the final target concentration. Finally, the cylinder is rolled to evenly mix the gases in the cylinder for later use.

[0038] The basic steps of the transfer dilution method are as follows: First, select a suitable metal tube or small gas cylinder with a deadbolt joint at one end and a shutoff valve at the other end. The small gas cylinder should also have a shutoff valve at the gas outlet. Evacuate the metal tube or small gas cylinder to remove any residual gas. Then, select a suitable gas cylinder that has already been evacuated and is free of residual gas. The transfer dilution process involves filling the metal tube or small gas cylinder with a predetermined amount of gas A or B and weighing it. Transfer the gas A or B from the metal tube or small gas cylinder to the cylinder using specialized equipment. The metal tube or small gas cylinder should be weighed again, and the difference between the two weighings can be used to calculate the mass of gas A or B added to the cylinder. Before the transfer, the mass of the evacuated gas cylinder should also be weighed. After filling with gas A or B, gas D should be added to the cylinder. The mass of gas D should be weighed again. The difference in mass before and after filling with gas D should be used to subtract the mass of gas A or B added from the mass before and after filling to calculate the mass of gas D added. Finally, the gas cylinder should be rolled to mix the gas evenly and set aside for use.

[0039] Among them, the mass of B gas added during the dilution process ( ) and the mass of gas A added during the dilution process ( ) are the same or different. The mass of gas D added during the dilution process of gas B ( ) and the mass of gas D added during the dilution process of gas A ( ) are the same or different. The mass of gas B added during the dilution process ( ) and the mass of D gas added during the dilution process of B gas ( ) the same or different, usually Much smaller than The mass of gas A added during the dilution process ( ) and the mass of gas D added during the dilution process of gas A ( ) the same or different, usually Much smaller than .

[0040] The technical route for dilution and weighing of gas A may be the same as or different from the technical route for dilution and weighing of gas B, that is, the dilution of gas A may select any one of the one-step dilution method, the multi-step dilution method or the transfer dilution method, or the multi-step dilution method may be used in combination with the transfer dilution method; the dilution of gas B may select any one of the one-step dilution method, the multi-step dilution method or the transfer dilution method, or the multi-step dilution method may be used in combination with the transfer dilution method.

[0041] In another exemplary embodiment of the present application, in order to simplify the complexity of data acquisition, conventional testing means or measuring instruments can be selected to obtain response signals, among which balances for gas mass weighing, gas chromatographs and spectrometers for measuring gas are very popular. Based on this, gas chromatography or spectroscopy can be used to obtain the response signal of impurity B in gas A, the response signal of impurity A in gas B, the response signal of component A in the diluted mixed gas A, and the response signal of component B in the diluted mixed gas B. The molecular weight of component A and component B can be calculated based on the atomic weight of the substance in the periodic table of chemical elements and the molecular formula. The average molecular weight of gas D can be calculated based on the concentration of each component contained in gas D and the molecular weight of each component.

[0042] The measuring instruments for obtaining the response signal of impurity A in gas B and the response signal of impurity B in gas A may be the same instrument or different instruments; they may be instruments based on the same principle or different principles. ) and the response signal of component B in the diluted B mixture ( ) comes from the same analytical measuring instrument. This measuring instrument does not generate a response signal for component A and diluent D, or the response signal generated has no effect on the response signal of component B. The response signal of impurity A in gas B ( ) and the response signal of component A in the diluted mixture ( ) originates from the same analytical measuring instrument, which does not generate a response signal for component B and diluent gas D, or the response signal generated by it has no effect on the response signal for component A. The measuring instrument may be a gas chromatograph, spectrometer, or mass spectrometer.

[0043] For example, in practical applications, a suitable measuring instrument can be selected to measure the response signal of impurity B in gas A and the response signal of component B in the diluted gas mixture. The instrument can operate based on either gas chromatography or spectroscopy, but the requirement is that when measuring component B, components A and D do not affect its signal. This requirement is relatively easy to achieve in both gas chromatography and spectroscopy. For example, in gas chromatography, a suitable chromatographic column can be selected to separate component B from components A and D. Alternatively, a suitable chromatographic detector can be selected to respond only to component B and not to components A or D. Alternatively, a suitable chromatographic column can be combined with a suitable detector to separate component B from component A and not to gas D. For another example, in spectroscopy, a suitable light absorption band can be selected to ensure that component B responds in this band while components A and D do not.

[0044] Similarly, a suitable measuring instrument can be selected to measure the response signal of impurity A in gas B and the response signal of component A in the diluted mixture of gas A. The instrument can operate based on either gas chromatography or spectroscopy, but the requirement is that when measuring component A, components B and gas D do not affect its signal. This requirement is relatively easy to achieve in both gas chromatography and spectroscopy. For example, in gas chromatography, a suitable chromatographic column can be selected to separate component A from components B and gas D. Alternatively, a suitable chromatographic detector can be selected to respond only to component A and not to components B or gas D. Alternatively, a suitable chromatographic column can be combined with a suitable detector to separate components A from components B and not to gas D. For another example, in spectroscopy, a suitable light absorption band can be selected to ensure that component A responds in this band while components B and gas D do not respond.

[0045] Furthermore, in order to reduce the nonlinear effect of the measuring instrument on the response of samples of different concentrations, the mass of gas B and the mass of gas D added during the dilution process of gas B should be reasonably adjusted so that the response signal of the B component in the diluted B mixture measured by the measuring instrument is close in size to the response signal of the B impurity in gas A, so as to reduce the effect of the nonlinearity of the instrument response on the accuracy of the measurement results; the mass of gas A and the mass of gas D added during the dilution process of gas A should be reasonably adjusted so that the response signal of the A component in the diluted A mixture measured by the measuring instrument is close in size to the response signal of the A impurity in gas B, so as to reduce the effect of the nonlinearity of the instrument response on the accuracy of the measurement results.

[0046] As an optional embodiment, the D gas can be relatively inert high-purity nitrogen or high-purity helium, both of which are commonly used as carrier gases in gas chromatography. These gases will not affect the response signals of components A and B during gas chromatography measurements. Furthermore, these gases have low spectral absorption characteristics and therefore will not affect the response signals of components A and B during spectroscopic measurements.

[0047] In another exemplary embodiment of the present application, in order to further improve the reliability and accuracy of the calculation results, it is necessary to set the estimated values ​​of the impurity concentration of B in gas A and the impurity concentration of A in gas B during the process of step 104. This estimated value is given to the impurity concentrations in formulas (1) and (2) during the iterative calculation process. and The initial value of is also the concentration value input in the first round of iteration. These two values ​​can be any number less than 1. Based on this, the implementation process of the above step 104 can be described as: Step 1: Set the input concentration of impurity B in gas A (any number less than 1) and the input concentration of impurity A in gas B (any number less than 1).

[0048] Step 2: Substitute the input concentration of impurity B in gas A, the input concentration of impurity A in gas B, the response signal of impurity B in gas A, the response signal of impurity A in gas B, the response signal of component A in the diluted mixed gas A, the response signal of component B in the diluted mixed gas B, and the gas mass data during the dilution process (the mass of gas B and gas D added during the dilution of gas B, and the mass of gas A and gas D added during the dilution of gas A) and molecular weight data (the average molecular weight of components A, B, and gas D) into the measurement model of the bidirectional iterative intelligent algorithm to obtain the output concentration of impurity B in gas A and the output concentration of impurity A in gas B. The input concentration and output concentration are the molar concentrations (also known as molar concentrations) in mol / mol, and the unit of added mass is g.

[0049] Step 3: Use the output concentration of impurity B in gas A as the input concentration of impurity B in the new gas A (i.e. ), the output concentration of impurity A in gas B is used as the input concentration of impurity A in the new gas B (i.e. ), the measurement model of the bidirectional iterative intelligent algorithm is iteratively solved until the output concentration of the B impurity in gas A and the output concentration of the A impurity in gas B tend to converge. The final output concentration of the B impurity in gas A is used as the measurement result of the concentration of the B impurity in gas A, and the output concentration of the A impurity in gas B is used as the measurement result of the concentration of the A impurity in gas B. The output concentration of the B impurity in gas A and the output concentration of the A impurity in gas B tend to converge means that the relative deviation between the output concentrations of the B impurity in gas A for two consecutive times is less than a set value, and the relative deviation between the output concentrations of the A impurity in gas B for two consecutive times is also less than a set value. The set value can be any number less than 1, preferably any number not greater than 0.01%.

[0050] Based on the above description, the calculation method of the bidirectional iterative intelligent algorithm is: first input the value of each input quantity into formula (1) and formula (2), and calculate the value after the first round of iteration. Value and value, and then the first round of iteration Value and The value is assigned again and In the second iteration, Value and Input the value into formula (1) and formula (2) to get the value after the second round of iteration. and value; perform multiple rounds of iterative calculations until the calculation is Value and The value tends to converge to no significant change, and the converged Value and convergence The value is the measurement result of the molar concentration of impurity B in gas A and the molar concentration of impurity A in gas B. Value and convergence Value refers to the last two values ​​obtained after multiple loop iterations. The relative deviation of the values ​​is less than a certain set value, and the last two The relative deviation of the value is also less than a certain set value, which can be adjusted according to different requirements for measurement accuracy, such as 0.01%.

[0051] Bidirectional iteration refers to setting any The initial value is input into formula (2) to obtain An iteration value of The initial value is input into formula (1) to obtain The first iteration value of ; then the formula (1) is obtained Assign value to , and the formula (2) obtained Assign value to ; The new Enter the value into formula (2) to get The second iteration value of Enter the value into formula (1) to get The second iteration value of , and so on, to complete multiple rounds of bidirectional iterative calculations.

[0052] In actual application, the bidirectional iterative intelligent algorithm can be implemented manually, using a spreadsheet, or by writing program software. There is no restriction on the programming language used when writing the program software. As long as the input requirements of the bidirectional iterative intelligent algorithm are met, iterative calculations can be performed according to the above process.

[0053] In another exemplary embodiment of the present application, when the main impurity in gas A is component B and the main impurity in gas B is component A, when gas A and gas B also obviously contain other impurities in addition to component B and the combination of component A, in order to make the calculation results more accurate, the measurement model of the bidirectional iterative intelligent algorithm can add the input concentration of other impurities in gas A, the input concentration of other impurities in gas B, the molecular weight of other impurity components in gas A, and the molecular weight of other impurity components in gas B as input quantities, that is: .

[0054] .

[0055] Where, It is the second component in gas A except component B. The input concentration of the impurity, is the number of impurities in gas A except component B, It is the second component in gas B besides component A. The input concentration of the impurity, is the number of impurities in gas B other than component A, It is the first The molecular weight of the impurity, It is the first gas in B The molecular weight of the impurity.

[0056] The input concentrations of other impurities in gas A and the input concentrations of other impurities in gas B can be measured using conventional methods.

[0057] Based on the above description, the method provided by the present application is based on the response signal obtained by conventional gas chromatography or spectroscopy for measuring impurities in gas, and adopts a bidirectional iterative intelligent algorithm to solve the problem of quantitative measurement of the corresponding impurities of two gases that are impurities of each other. In the case where the two gases are impurities of each other and there is no standard gas corresponding to the impurity components of the two gases, the concentrations of the impurity components of the two gases are accurately measured. Among them, the quantitative measurement route is as follows Figure 2 shown.

[0058] Furthermore, compared with the prior art, this application also has the following beneficial effects: First: When measuring the concentrations of impurity components of two gases according to the method of the present application, there is no need to use high-purity gas corresponding to the impurity or standard gas with accurate measurement value. The target impurity can be measured when high-purity gas or standard gas is not available, which can meet daily measurement needs, and the method is simple and easy to use, saving costs.

[0059] Second: This application proposes a bidirectional iterative intelligent algorithm whose input is easy to obtain and can be completed by selecting conventional testing methods or measuring instruments. Balances for gas mass weighing, gas chromatographs and spectrometers for measuring gas are very popular, and the calculation results are reliable and accurate.

[0060] Third: The bidirectional iterative intelligent algorithm can implement calculations by writing program software. The program writing is not complicated and is not limited to the programming language used. The measurement results of impurities in the gas can be quickly obtained by running the program.

[0061] In another exemplary embodiment of the present application, the feasibility and accuracy of the quantitative measurement method for impurity concentration in the gas provided above in the present application are verified based on actual measurement requirements. In this embodiment, the gases used and their data are for illustration purposes only and are not specifically limited to the present application. The method provided in the present application can also be used for other measurement requirements that are impurities of each other but cannot be compared and verified. In addition to illustrating the feasibility of the present application scheme, the following implementation case also proves the accuracy of the present application scheme. Therefore, in the implementation process, nitrous oxide and carbon dioxide were selected for the selection of gas A and gas B because these two substances are currently available on the market as high-purity gases that do not contain obvious impurities. Based on this, the verification process in this embodiment includes: First, 0.0004013 mol / mol of carbon dioxide was added to high-purity nitrous oxide gas to produce a simulated nitrous oxide gas containing a carbon dioxide impurity. Then, 0.0006036 mol / mol of nitrous oxide was added to high-purity carbon dioxide gas to produce a simulated carbon dioxide gas containing a nitrous oxide impurity. Using these two simulated gases as the subjects of measurement research, the methods provided in this application were used to measure the components in each of the two simulated gases that were impurities of each other, obtaining impurity concentration measurements in mol / mol.

[0062] At the same time, in order to verify the accuracy of the method provided by this application, a comparative verification was conducted, comparing the simulated added concentration with the measurement results of the method provided by this application.

[0063] To measure the nitrous oxide impurity in carbon dioxide simulating gas and the carbon dioxide impurity in nitrous oxide simulating gas, high-purity nitrogen can be used as the diluent gas. The carbon dioxide simulating gas containing nitrous oxide impurities is diluted with high-purity nitrogen using the mass dilution method to a lower concentration, thereby obtaining a diluted carbon dioxide gas mixture. Similarly, the nitrous oxide simulating gas containing carbon dioxide impurities is diluted with high-purity nitrogen using the mass dilution method to a lower concentration, thereby obtaining a diluted nitrous oxide gas mixture. The nitrous oxide simulating gas containing carbon dioxide impurities and the diluted carbon dioxide gas mixture are then measured using a gas chromatograph (e.g., an Agilent 7890 gas chromatograph) equipped with high-purity nitrogen as carrier gas, a nickel converter, and a hydrogen flame ionization detector (FID). Chromatographic peak areas are obtained. Since nitrous oxide does not respond to signals under these chromatographic conditions, there is no mutual interference between nitrous oxide and carbon dioxide. Furthermore, high-purity nitrogen also does not respond to signals in the FID. Therefore, the obtained chromatographic peak areas can be directly used for subsequent calculations using the bidirectional iterative intelligent algorithm. Then, a Fourier transform infrared spectrometer (e.g., MKS MG2030 model Fourier transform infrared spectrometer) equipped with high-purity nitrogen as purge gas, a 5-meter optical path gas cell, and a semiconductor refrigeration detector is used to measure the carbon dioxide simulation gas containing nitrous oxide impurities and the diluted mixture of nitrous oxide to obtain the peak area of ​​the infrared spectrum. Since nitrous oxide has a peak area of ​​2514.51 cm -1 ~2613.83cm -1 There is a characteristic absorption peak in the wavenumber band, but carbon dioxide has no signal response in this wavenumber band, and high-purity nitrogen has no signal response in the Fourier transform infrared spectrometer, so the obtained spectral peak area can be directly used for the subsequent calculation of the bidirectional iterative intelligent algorithm.

[0064] The mass of carbon dioxide simulation gas and the mass of diluent nitrogen added during the dilution of carbon dioxide simulation gas, and the mass of nitrous oxide simulation gas and the mass of diluent nitrogen added during the dilution of nitrous oxide simulation gas, can be accurately measured using a high-precision balance (such as Mettler XPR26003LC model balance), which has a range of 26 kg and a sensitivity of 1 mg.

[0065] The weighing process is as follows: (1) First, select 4 aluminum alloy gas cylinders (with an internal volume of 4L) and evacuate the inside of the cylinders to remove any residual gas.

[0066] (2) Dilute the carbon dioxide simulation gas. First, extract one of the gas cylinders processed in step (1) and weigh it using an XPR26003LC balance to find it weighs 6850.522 g. Then, fill the gas cylinder with carbon dioxide simulation gas and weigh it again to find it weighs 6865.029 g. Based on the difference in mass between the two weighings, the added mass of carbon dioxide simulation gas is calculated to be 14.507 g. Then, fill the gas cylinder with high-purity nitrogen and weigh it again to find it weighs 7317.755 g. Based on the difference in mass between the two weighings, the added mass of nitrogen is calculated to be 452.726 g. Roll the gas cylinder for 4 hours to mix the mixed gas evenly. This mixture is recorded as low-concentration mixed gas I and is ready for use.

[0067] Take one of the gas cylinders processed in step (1) and weigh it using an XPR26003LC balance to find it weighs 7185.671 g. Then, fill the cylinder with low-concentration mixed gas I and weigh it again to find it weighs 7195.101 g. Based on the difference between the two weighings, calculate the added mass of low-concentration mixed gas I to be 9.430 g. Then, fill the cylinder with high-purity nitrogen and weigh it again to find it weighs 7650.769 g. Based on the difference between the two weighings, calculate the added mass of nitrogen to be 455.668 g. Roll the cylinder for 4 hours to mix the mixed gas uniformly. This is recorded as low-concentration mixed gas II and set aside.

[0068] According to the dilution process of low concentration mixed gas I and low concentration mixed gas II, the mass of carbon dioxide simulation gas actually contained in low concentration mixed gas II can be calculated. The actual nitrogen mass contained in the low-concentration mixed gas II is 9.430×14.507 / (14.507+452.726) = 0.2927897g. 9.430 × 452.726 / (14.507 + 452.726) + 455.668 = 464.8052103 g. Low-concentration mixed gas II is a diluted mixture of carbon dioxide.

[0069] (3) Dilute the nitrous oxide simulation gas. First, extract one of the gas cylinders treated in step (1) and weigh it using an XPR26003LC balance to find it weighs 7100.261 g. Then, fill the cylinder with nitrous oxide simulation gas and weigh it again to find it weighs 7114.473 g. Based on the difference between the two weighings, calculate the added mass of nitrous oxide simulation gas to be 14.212 g. Then, fill the cylinder with high-purity nitrogen and weigh it again to find it weighs 7559.304 g. Based on the difference between the two weighings, calculate the added mass of nitrogen to be 444.831 g. Roll the cylinder for 4 hours to mix the mixed gas evenly. Record it as low-concentration mixed gas III and set aside.

[0070] Another gas cylinder treated in step (1) was extracted and weighed using an XPR26003LC balance to obtain a mass of 7087.933 g. The low-concentration mixed gas III was then added to the cylinder and weighed again to obtain a mass of 7102.010 g. The mass of the low-concentration mixed gas III added was calculated to be 14.077 g based on the difference in mass between the two weighings. High-purity nitrogen was then added to the cylinder and weighed again to obtain a mass of 7551.337 g. The mass of nitrogen added was calculated to be 449.327 g based on the difference in mass between the two weighings. The mixed gas was mixed evenly by rolling the cylinder for 4 hours and recorded as low-concentration mixed gas IV for later use.

[0071] According to the dilution process of low concentration mixed gas III and low concentration mixed gas IV, the mass of nitrous oxide simulation gas actually contained in low concentration mixed gas IV can be calculated. The actual nitrogen mass contained in the low-concentration mixed gas IV is 14.077×14.212 / (14.212+444.831)=0.435824801g. =14.077×444.831 / (14.212+444.831)+449.327= 462.9681752g. Low-concentration mixed gas IV is the diluted mixed gas of nitrous oxide.

[0072] (4) Consult the IUPAC periodic table and calculate the molecular weight of nitrous oxide The molecular weight of carbon dioxide is 44.013. Mb The molecular weight of nitrogen is 28.014. High-purity nitrogen has no other obvious impurities, and its average molecular weight is Equal to the molecular weight of nitrogen 28.014.

[0073] According to the gas chromatographic peak area is 7965.080254, is 8044.213074, according to the infrared spectrum peak area is 397.2429375, It is 395.1368989.

[0074] set up and The initial value is 0.01 mol / mol.

[0075] Setting Measurements and The convergence limit is that the relative deviation of the results of two consecutive iterations is less than 0.01%.

[0076] Input the above parameter values ​​into formula (1) and formula (2) to obtain the first round and The values ​​of are 0.000392903mol / mol and 0.00059599mol / mol respectively. and , calculate formula (1) and formula (2) again, and get the second round and The values ​​of are 0.000396636mol / mol and 0.000601773mol / mol respectively. and , calculate formula (1) and formula (2) again, and get the third round and The values ​​are 0.000396634mol / mol and 0.000601771mol / mol respectively. The relative deviation between the values ​​is -0.0006%, and the two values ​​obtained in the third and second iterations are The relative deviation between the values ​​is -0.0004%, which is less than the set convergence limit of 0.01%. Therefore, the third round of iterations and The values ​​are the concentration values ​​of carbon dioxide impurities in nitrous oxide simulation gas and the concentration values ​​of nitrous oxide impurities in carbon dioxide simulation gas, respectively, that is, the concentration of carbon dioxide impurities in nitrous oxide simulation gas is 0.000396634 mol / mol, and the concentration of nitrous oxide impurities in carbon dioxide simulation gas is 0.000601771 mol / mol.

[0077] Furthermore, the simulated addition concentrations and the measurement results of the method provided by this application are shown in Table 1. Measurement results in the gas field are often subject to random effects, and generally, data are considered accurate and comparable when the relative deviation reaches approximately 1%. As can be seen from the data summarized in Table 1, the measurement results of the method provided by this application are consistent with the simulated addition concentrations at approximately 1%, demonstrating that the method provided by this application has good accuracy.

[0078] Table 1 Simulated added concentrations and measurement results of the method provided in this application

[0079] In summary, the quantitative measurement method of impurity concentration in gas provided by the present application is based on conventional gas chromatography or spectroscopy to measure the impurity response signal in the gas, and adopts a bidirectional iterative intelligent algorithm to solve the problem of quantitative measurement of corresponding impurities in two gases that are impurities of each other. This method does not need to use the high-purity gas corresponding to the impurity or the standard gas with accurate value when measuring the impurity concentration. The bidirectional iterative intelligent algorithm used is easy to implement and easy to write into a software program. In the absence of a standard gas corresponding to the impurity, the impurity concentration in the mutually impure gas can be accurately measured, which solves the problem that the standard gas corresponding to the impurity cannot be obtained by conventional calibration measurement method, the raw gas used to prepare the standard gas is too expensive, or the raw gas used to prepare the standard gas is difficult to obtain and the accurate measurement cannot be implemented, thereby providing a new solution to the problem of quantitative measurement of impurities in mutually impure gases.

[0080] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store quantitative measurement data of impurity concentration in gas. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for quantitatively measuring the impurity concentration in gas is implemented.

[0081] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0082] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0083] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0084] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0085] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0086] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (RRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0087] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0088] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0089] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of this application. In summary, the content of this specification should not be construed as limiting this application.

Claims

1. A method for quantitatively measuring impurity concentration in gas, characterized in that: include: Obtaining the response signal of impurity B in gas A and the response signal of impurity A in gas B; Using the mass dilution method, gas D is used as the diluent gas to dilute gas A and gas B respectively to obtain a diluted mixed gas of A with a first set concentration value and a diluted mixed gas of B with a second set concentration value; Obtaining a response signal of component A in the diluted mixed gas A and a response signal of component B in the diluted mixed gas B; Construct a measurement model for a bidirectional iterative intelligent algorithm; A measurement model using a bidirectional iterative intelligent algorithm is used to obtain the measurement results of the concentration of impurity B in gas A and the concentration of impurity A in gas B based on the response signal of impurity B in gas A, the response signal of impurity A in gas B, the response signal of component A in the diluted mixed gas A, the response signal of component B in the diluted mixed gas B, and the gas mass data and molecular weight data during the dilution process.

2. The method for quantitatively measuring impurity concentration in gas according to claim 1, characterized in that: A measurement model using a bidirectional iterative intelligent algorithm determines the concentration of impurity B in gas A and the concentration of impurity A in gas B based on the response signal of impurity B in gas A, the response signal of impurity A in gas B, the response signal of component A in diluted mixed gas A, the response signal of component B in diluted mixed gas B, and the gas mass data and molecular weight data during the dilution process, including: Set the input concentration of impurity B in gas A and the input concentration of impurity A in gas B; The input concentration of impurity B in gas A, the input concentration of impurity A in gas B, the response signal of impurity B in gas A, the response signal of impurity A in gas B, the response signal of component A in diluted mixed gas A, the response signal of component B in diluted mixed gas B, and the gas mass data and molecular weight data during the dilution process are brought into the measurement model of the bidirectional iterative intelligent algorithm to obtain the output concentration of impurity B in gas A and the output concentration of impurity A in gas B; The output concentration of impurity B in gas A is used as the new input concentration of impurity B in gas A, and the output concentration of impurity A in gas B is used as the new input concentration of impurity A in gas B. The measurement model of the bidirectional iterative intelligent algorithm is iteratively solved until the output concentration of impurity B in gas A and the output concentration of impurity A in gas B tend to converge. The final output concentration of impurity B in gas A is used as the measurement result of the concentration of impurity B in gas A, and the output concentration of impurity A in gas B is used as the measurement result of the concentration of impurity A in gas B.

3. The method for quantitatively measuring impurity concentration in gas according to claim 2, characterized in that: The measurement model of the bidirectional iterative intelligent algorithm is expressed as: ； ； Where, is the input concentration of impurity B in gas A, is the input concentration of impurity A in gas B, is the output concentration of impurity B in gas A, is the output concentration of impurity A in gas B, is the response signal of impurity B in gas A, is the response signal of impurity A in gas B, is the response signal of component B in the B diluted mixture, is the response signal of component A in the diluted mixture. is the mass of gas B added during the dilution process of gas B, is the mass of gas D added during the dilution of gas B, is the mass of gas A added during the dilution process, is the mass of gas D added during the dilution of gas A, is the molecular weight of component A, is the molecular weight of component B, is the average molecular weight of gas D.

4. The method for quantitatively measuring impurity concentration in gas according to claim 2, characterized in that: The input concentration of impurity B in gas A and the input concentration of impurity A in gas B are both set to arbitrary numbers less than 1.

5. The method for quantitatively measuring impurity concentration in gas according to claim 2, characterized in that: The output concentration of impurity B in gas A and the output concentration of impurity A in gas B tend to converge, which means that the relative deviation between the output concentrations of impurity B in gas A for two consecutive times is less than the set value, and the relative deviation between the output concentrations of impurity A in gas B for two consecutive times is also less than the set value.

6. The method for quantitatively measuring impurity concentration in gas according to claim 1, characterized in that: Gas D is an inert gas and does not contain component A and component B.

7. The method for quantitatively measuring impurity concentration in gas according to claim 1, characterized in that: Gas chromatography or spectroscopy is used to obtain the response signal of impurity B in gas A, the response signal of impurity A in gas B, the response signal of component A in the diluted mixed gas A, and the response signal of component B in the diluted mixed gas B respectively.

8. The method for quantitatively measuring impurity concentration in gas according to claim 3, characterized in that: When gas A contains other impurities in addition to component B, and gas B contains other impurities in addition to component A, the input concentration of other impurities in gas A, the input concentration of other impurities in gas B, the molecular weight of other impurities in gas A, and the molecular weight of other impurities in gas B are added as inputs in the measurement model of the bidirectional iterative intelligent algorithm, and the results are: ； ； Where, It is the second component in gas A except component B. The input concentration of the impurity, is the number of impurities in gas A except component B, It is the second component in gas B besides component A. The input concentration of the impurity, is the number of impurities in gas B other than component A, It is the first The molecular weight of the impurity, It is the first gas in B The molecular weight of the impurity.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for quantitatively measuring the concentration of impurities in a gas according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for quantitatively measuring the impurity concentration in gas according to any one of claims 1 to 8 is implemented.

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