Method, device and medium for quantitatively measuring concentration of impurities in gas

Abstract

The application discloses a kind of gas impurity concentration quantitative measurement method, equipment and medium, it is related to gas detection field, the method comprises: obtaining the response signal of B impurity in A gas, A impurity in B gas;Using mass dilution method, with D gas as diluent gas, dilute A gas and B gas respectively, obtain the A dilution mixed gas of first set concentration value and the B dilution mixed gas of second set concentration value;Obtain the response signal of A component in A dilution mixed gas and B component in B dilution mixed gas;The measurement model of bidirectional iterative intelligent algorithm is constructed;Using the measurement model of bidirectional iterative intelligent algorithm, according to four response signals, gas mass data and molecular weight data in dilution process, obtain the measurement result of B impurity concentration in A gas and A impurity concentration in B gas.The application can solve the quantitative measurement problem of corresponding impurity of two gases being impurity each other.

Description

Technical Field

[0001] This 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 a gas. Background Technology

[0002] Gases with a certain purity have wide applications in environmental, energy, and industrial fields. For example, they are used as reactants, protective gases, and purge gases in processes, and also for preparing standard gas mixtures (standard gases) in gas concentration measurement. When using these gases, their purity and the concentration of impurities are crucial factors affecting their application. Gas purity testing typically involves subtracting impurities, thus requiring quantitative measurement of impurity concentrations. With the continuous emergence of new gas types and measurement demands, especially in the measurement of gases containing isotopic impurities or isomer impurities, the problem of measuring gases that are impurities to each other often arises. That is, gas A requires quantitative measurement of impurity B, while gas B requires quantitative measurement of impurity A.

[0003] For example, in the field of environmental monitoring, monitoring the concentration of carbon dioxide isotopes in the atmosphere can be used to investigate the sources of carbon dioxide emissions and distinguish whether they are caused by human activities. To calibrate the analytical instruments used for monitoring, it is necessary to use instruments containing... 13 CO2 and 12 CO2 is a carbon dioxide standard gas. Theoretically, a primary 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 It is prepared by mixing CO2 gases. 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 typically contains isotopic impurities. To know precisely... 13 The purity of CO2 gas requires the determination of the components it contains. 12 For accurate measurement of CO2 impurities, conventional calibration methods require the use of... 12 CO2 gas is prepared to a certain concentration 12 CO2 standard gas was used to calibrate the instruments used in the measurements. The preparation of a primary gas mixture was carried out according to the requirements for the weighing method disclosed in GB / T 5274.1-2018, Gas Analysis—Preparation of Mixed Gases for Calibration—Part 1. 12 When using CO2 standard gas, precise knowledge is required.12 The purity of CO2 gas needs to be measured precisely. 12 CO2 gas 13 The impurity content of CO2 needs to be assessed, which requires the use of... 13 CO2 gas is prepared to a certain concentration 13 CO2 standard gas is used to calibrate the instruments used in the measurements. Preparation 13 When using CO2 standard gas, precise knowledge is required. 13 The purity of CO2 gas requires precise measurement. 13 CO2 gas 12 The CO2 impurity content. This creates a vicious cycle of "chicken and egg."

[0005] Currently, it is not possible to accurately prepare... 13 CO2 and 12 CO2 is the standard gas for carbon dioxide, for two reasons: firstly, it is a commonly sold... 13 CO2 gas contains 12 CO2 impurities 12 CO2 gas contains 13 CO2 impurities, i.e., situations where "two impurities are each other", lead to 13 CO2 gas and 12 The purity of CO2 gas could not be accurately measured, making it unusable. 13 CO2 and 12 The accurate preparation of CO2 standard gas; secondly, it does not contain... 13 High purity CO2 impurities 12 CO2 gas and does not contain 12 High purity CO2 impurities 13 CO2 gas is very difficult to obtain and has very high production costs, making it unsuitable for large-scale use. 13 CO2 and 12 The production and preparation of CO2 standard gas. Therefore, for the reasons mentioned above, 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 challenges in the field of gas measurement.

[0006] For example, the new refrigerant gas trans-1,3,3,3-tetrafluoropropene and cis-1,3,3,3-tetrafluoropropene have strong application prospects because of their low global warming potential and low greenhouse effect. However, the trans-1,3,3,3-tetrafluoropropene gas contains cis-1,3,3,3-tetrafluoropropene impurities, and the cis-1,3,3,3-tetrafluoropropene gas contains trans-1,3,3,3-tetrafluoropropene impurities. Only the concentration of impurities is accurately measured, the purity quality of these gases can be scientifically evaluated. However, it is difficult to obtain high-purity trans-1,3,3,3-tetrafluoropropene and cis-1,3,3,3-tetrafluoropropene gases at present.

[0007] Therefore, how to solve the problem of quantitative measurement of impurities of two gases which are mutual impurities becomes a key technical problem restricting the technical progress and industrial development in this field. SUMMARY

[0008] To solve the above technical problems, the present application provides a method for quantitatively measuring the concentration of impurities in a gas, a device and a medium.

[0009] To achieve the above object, the present application provides the following solutions:

[0010] In a first aspect, the present application provides a method for quantitatively measuring the concentration of impurities in a gas, comprising:

[0011] obtaining a response signal of B impurities in A gas and a response signal of A impurities in B gas;

[0012] using mass dilution method, taking D gas as dilution gas, diluting A gas and B gas respectively to obtain A dilution mixed gas with a first set concentration value and B dilution mixed gas with a second set concentration value;

[0013] obtaining a response signal of A component in A dilution mixed gas and a response signal of B component in B dilution mixed gas;

[0014] constructing a measurement model of bidirectional iterative intelligent algorithm;

[0015] setting the input concentration of B impurities in A gas and the input concentration of A impurities in B gas, using the measurement model of bidirectional iterative intelligent algorithm, and according to the response signal of B impurities in A gas, the response signal of A impurities in B gas, the response signal of A component in A dilution mixed gas, the response signal of B component in B dilution mixed gas, and the gas mass data and molecular weight data in the dilution process, iteratively solving until the output concentration of B impurities in A gas and the output concentration of A impurities in B gas tend to converge, completing the measurement of the concentration of B impurities in A gas and the measurement of the concentration of A impurities in B gas.

[0016] Optionally, a bidirectional iterative intelligent algorithm measurement model is adopted to determine the concentration of the B impurity in the A gas and the concentration of the A impurity in the B gas according to the response signal of the B impurity in the A gas, the response signal of the A impurity in the B gas, the response signal of the A component in the A dilution mixture, the response signal of the B component in the B dilution mixture, and the gas mass data and molecular weight data in the dilution process, including:

[0017] The input concentration of the B impurity in the A gas and the input concentration of the A impurity in the B gas are set;

[0018] The input concentration of the B impurity in the A gas, the input concentration of the A impurity in the B gas, the response signal of the B impurity in the A gas, the response signal of the A impurity in the B gas, the response signal of the A component in the A dilution mixture, the response signal of the B component in the B dilution mixture, and the gas mass data and molecular weight data in the dilution process are brought into the bidirectional iterative intelligent algorithm measurement model to obtain the output concentration of the B impurity in the A gas and the output concentration of the A impurity in the B gas;

[0019] The output concentration of the B impurity in the A gas is taken as the new input concentration of the B impurity in the A gas, the output concentration of the A impurity in the B gas is taken as the new input concentration of the A impurity in the B gas, and the bidirectional iterative intelligent algorithm measurement model is iteratively solved until the output concentration of the B impurity in the A gas and the output concentration of the A impurity in the B gas tend to converge, and the finally obtained output concentration of the B impurity in the A gas is taken as the measurement result of the concentration of the B impurity in the A gas, and the output concentration of the A impurity in the B gas is taken as the measurement result of the concentration of the A impurity in the B gas.

[0020] Optionally, the bidirectional iterative intelligent algorithm measurement model is represented as:

[0021] ;

[0022] ;

[0023] In the formula, is the input concentration of the B impurity in the A gas, is the input concentration of the A impurity in the B gas, is the output concentration of the B impurity in the A gas, is the output concentration of the A impurity in the B gas, is the response signal of the B impurity in the A gas, is the response signal of the A impurity in the B gas, is the response signal of the B component in the B dilution mixture, is the response signal of the A component in the A dilution mixture, is the added mass of the B gas in the process of diluting the B gas, the input concentration of the D gas during dilution of the A gas, the input concentration of the D gas during dilution of the A gas, the input concentration of the D gas during dilution of the A gas, the molecular weight of the A component, the molecular weight of the B component, the average molecular weight of the D gas.

[0024] 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 be any number less than 1.

[0025] Optionally, the output concentration of the B impurity in the A gas and the output concentration of the A impurity in the B gas tend to converge, which means that the relative deviation between the output concentration of the B impurity in the A gas in two consecutive times is less than a set value, and the relative deviation between the output concentration of the A impurity in the B gas in two consecutive times is also less than a set value.

[0026] Optionally, the D gas is an inert gas and does not contain the A component and the B component.

[0027] Optionally, the response signals of the B impurity in the A gas, the A impurity in the B gas, the A component in the A dilution mixed gas, and the B component in the B dilution mixed gas are obtained by gas chromatography or spectroscopy.

[0028] Optionally, when the A gas contains other impurities in addition to the B component, and the B gas contains other impurities in addition to the A component, the input concentration of the other impurities in the A gas, the input concentration of the other impurities in the B gas, the molecular weight of the other impurities in the A gas, and the molecular weight of the other impurities in the B gas are added as input quantities in the measurement model of the bidirectional iterative intelligent algorithm, and there are:

[0029] ;

[0030] ;

[0031] wherein, is the input concentration of the first impurity in the A gas in addition to the B component, is the number of types of impurities in the A gas in addition to the B component, is the input concentration of the first impurity in the B gas in addition to the A component, is the number of types of impurities in the B gas in addition to the A component, is the molecular weight of the first impurity in the A gas, is the molecular weight of the first impurity in the B gas.

[0032] 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 method for quantitatively measuring impurity concentration in a gas.

[0033] In a third aspect, the present application provides a computer readable storage medium, which stores a computer program, wherein the computer program is executable on a processor to implement the steps of the method for quantitatively measuring impurity concentration in a gas.

[0034] According to the embodiments of the present application, the following technical effects are achieved.

[0035] The present application provides a method for quantitatively measuring impurity concentration in a gas, a device and a medium, which obtains a corresponding response signal by measuring the impurity in the gas, and calculates by using a measurement model of a bidirectional iterative intelligent algorithm, thereby solving the problem of quantitatively measuring the corresponding impurities of two gases which are impurities of each other. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0037] Figure 1 A flowchart of a method for quantitatively measuring impurity concentration in a gas according to an embodiment of the present application is shown in the figure.

[0038] Figure 2 A route map for quantitatively measuring corresponding impurities of gases which are impurities of each other according to an embodiment of the present application is shown in the figure.

[0039] Figure 3 A structure diagram of a computer device according to an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0040] The technical solutions of the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0041] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0042] In one exemplary embodiment, the application provides a quantitative measurement method of impurity concentration in a gas, as shown in the accompanying drawings, comprising: Figure 1

[0043] Step 100, obtaining the response signal of B impurity in A gas and the response signal of A impurity in B gas. Wherein, the concentration of other impurities in A gas except B impurity is small enough to be ignored and does not need to be measured; the concentration of other impurities in B gas except A impurity is small enough to be ignored and does not need to be measured. Wherein, B impurity in A gas refers to a small amount of B gas contained in A gas, at this time B gas is called B impurity, and for the same reason, a small amount of A gas in B gas is called A impurity.

[0044] Step 101, using mass dilution method, taking D gas as dilution gas to dilute A gas and B gas respectively to obtain A dilution mixed gas with a first set concentration value and B dilution mixed gas with a second set concentration value. Wherein, the selected D gas is an inert gas, which does not contain A gas component or B component, or contains A component or B component with very low concentration, which is low enough to be ignored.

[0045] For example, the mass of B gas added in the dilution process of B gas and the mass of D gas, and the mass of A gas added in the dilution process of A gas and the mass of D gas can be accurately measured by using a high-precision balance. When weighing, a suitable gas cylinder is first selected, which has been vacuumed inside and has no residual gas, and the inner wall of the gas cylinder does not react with A component and B component chemically or physically. Based on this, the dilution process of A gas is as follows: weigh the mass of a gas cylinder which has been vacuumed inside, then fill in A gas, then weigh the mass of the gas cylinder again, calculate the added mass of A gas according to the mass difference before and after filling, then fill D gas in the gas cylinder again, weigh the mass of the gas cylinder again, calculate the added mass of D gas according to the mass difference before and after filling, and finally mix A gas and D gas in the gas cylinder uniformly by rolling the gas cylinder to obtain A dilution mixed gas for use. The dilution process of B gas is as follows: weigh the mass of another gas cylinder which has been vacuumed inside, then fill in B gas, then weigh the mass of the gas cylinder again, calculate the added mass of B gas according to the mass difference before and after filling, then fill D gas in the gas cylinder again, weigh the mass of the gas cylinder again, calculate the added mass of D gas according to the mass difference before and after filling, and finally mix B gas and D gas in the gas cylinder uniformly by rolling the gas cylinder to obtain B dilution mixed gas for use.

[0046] ​The added mass of B gas in the dilution process of B gas, the added mass of D gas in the dilution process of B gas, the added mass of A gas in the dilution process of A gas, and the added mass of D gas in the dilution process of A gas are accurately measured by high-precision scales.

[0047] In actual application, the mass of B gas added in the dilution process of B gas and the mass of A gas added in the dilution process of A gas do not have to be the same. The mass of D gas added in the dilution process of B gas and the mass of D gas added in the dilution process of A gas do not have to be the same.

[0048] Step 102, obtaining the response signal of A component in A dilution mixed gas and the response signal of B component in B dilution mixed gas.

[0049] Step 103, constructing a measurement model of bidirectional iterative intelligent algorithm. The measurement model of bidirectional iterative intelligent algorithm is represented as:

[0050] (1)

[0051] (2)

[0052] In the formula, is the input concentration of B impurities in A gas, is the input concentration of A impurities in B gas, is the output concentration of B impurities in A gas, is the output concentration of A impurities in B gas, is the response signal of B impurities in A gas, is the response signal of A impurities in B gas, is the response signal of B component in B dilution mixed gas, is the response signal of A component in A dilution mixed gas, is the added mass of B gas in the dilution process of B gas, is the added mass of D gas in the dilution process of B gas, is the added mass of A gas in the dilution process of A gas, is the added mass of D gas in the dilution process of A gas, is the molecular weight of A component, is the molecular weight of B component, is the average molecular weight of D gas. represents the amount-of-substance fraction (i.e. molar concentration) of B component in B gas except A impurities; represents the average molecular weight of B gas; represents the amount of substance of B gas when the mass is ; represents the amount of substance of the D gas when the mass is represents the sum of the amount of substance of the B gas and the amount of substance of the D gas, i.e. the total amount of substance of the low-concentration mixed gas obtained by diluting the B gas with the D gas (i.e. the B-diluted mixed gas); represents the amount of substance of the B gas when the mass is represents the fraction of the amount of substance of the B component in the B-diluted mixed gas obtained by diluting the B gas with the D gas (i.e. the molar concentration of the B component). represents the fraction of the amount of substance of the A component in the A gas (i.e. the molar concentration) except for the B impurity; represents the average molecular weight of the A gas; represents the amount of substance of the A gas when the mass is represents the amount of substance of the D gas when the mass is represents the sum of the amount of substance of the A gas and the amount of substance of the D gas, i.e. the total amount of substance of the low-concentration mixed gas obtained by diluting the A gas with the D gas (i.e. the A-diluted mixed gas); represents the amount of substance of the A gas when the mass is represents the fraction of the amount of substance of the A component in the A-diluted mixed gas obtained by diluting the A gas with the D gas (i.e. the molar concentration of the A component).

[0053] Step 104, using the measurement model of the bidirectional iterative intelligent algorithm, according to the response signal of the B impurity in the A gas, the response signal of the A impurity in the B gas, the response signal of the A component in the A-diluted mixed gas, the response signal of the B component in the B-diluted mixed gas, and the gas mass data and molecular weight data in the dilution process, the measurement result of the B impurity concentration in the A gas and the measurement of the A impurity concentration in the B gas are obtained. The iterative algorithm is implemented as follows:

[0054] First, set the input concentration of the B impurity in the A gas (any number less than 1) and the input concentration of the A impurity in the B gas (any number less than 1);

[0055] The input concentration of the B impurity in the A gas, the input concentration of the A impurity in the B gas, the response signal of the B impurity in the A gas, the response signal of the A impurity in the B gas, the response signal of the A component in the A-diluted mixed gas, the response signal of the B component in the B-diluted mixed gas, and the gas mass data and molecular weight data in the dilution process are brought into the measurement model of the bidirectional iterative intelligent algorithm to obtain the output concentration of the B impurity in the A gas and the output concentration of the A impurity in the B gas;

[0056] ​​​​​The output concentration of the B impurity in the A gas is taken as the new input concentration of the B impurity in the A gas, and the output concentration of the A impurity in the B gas is taken as the new input concentration of the A impurity in the B gas, and the measurement model of the bidirectional iterative intelligent algorithm is iteratively solved until the output concentration of the B impurity in the A gas and the output concentration of the A impurity in the B gas tend to converge, and the finally obtained output concentration of the B impurity in the A gas is taken as the measurement result of the concentration of the B impurity in the A gas, and the output concentration of the A impurity in the B gas is taken as the measurement result of the concentration of the A impurity in the B gas.

[0057] The above output concentration tends to converge means that the relative deviation between the output concentrations of the B impurity in the A gas for two consecutive times is less than a set value, and the relative deviation between the output concentrations of the A impurity in the B gas for two consecutive times is also less than a set value.

[0058] The use conditions of formula (1) and formula (2) in the measurement model of the bidirectional iterative intelligent algorithm are that the A gas does not contain other impurities except the B impurity, or the concentration of other impurities except the B impurity is low and can be ignored; and the B gas does not contain other impurities except the A impurity, or the concentration of other impurities except the A impurity is low and can be ignored.

[0059] By implementing the above steps 100 to 104, the concentration of the mutual impurity components in the two gases is accurately measured when the two gases are mutual impurities and other impurities in the gas can be ignored, and there is no standard gas corresponding to the mutual impurity components, which provides a new solution for solving the quantitative measurement problem of impurities in the gas. When measuring the concentration of the mutual impurity components in the two gases, the high-purity gas or the standard gas corresponding to the impurity does not need to be used, the target impurity can be measured when the high-purity gas or the standard gas cannot be obtained, the daily measurement demand can be met, the method is simple and easy to implement, and the cost is saved. Moreover, the bidirectional iterative intelligent algorithm measurement model is constructed, the data obtained is used as the input, and the calculation result is reliable and accurate.

[0060] In another exemplary embodiment of the present application, there are three technical routes for gas addition, gas dilution and weighing, which are one-step dilution method, multi-step dilution method and transfer dilution method.

[0061] The basic steps of one-step dilution method are as follows: first, select a proper gas cylinder, which has been vacuumized and has no residual gas. The dilution process of A or B gas is as follows: weigh the mass of the gas cylinder which has been vacuumized, then fill A or B gas into the cylinder, weigh the mass of the cylinder again, calculate the mass of A or B gas added according to the mass difference before and after filling, then fill D gas into the cylinder again, weigh the mass of the cylinder again, calculate the mass of D gas added according to the mass difference before and after filling, and finally mix A or B gas and D gas in the cylinder uniformly by rolling the cylinder to obtain mixed gas, which is ready for use.

[0062] The basic steps of multi-step dilution method are as follows: first, select a proper gas cylinder, which has been vacuumized and has no residual gas. The first-step dilution process is as follows: weigh the mass of the gas cylinder which has been vacuumized, then fill A or B gas into the cylinder, weigh the mass of the cylinder again, calculate the mass of A or B gas added according to the mass difference before and after filling, then fill D gas into the cylinder again, weigh the mass of the cylinder again, calculate the mass of D gas added according to the mass difference before and after filling, and finally mix A or B gas and D gas in the cylinder uniformly by rolling the cylinder to obtain A and D or B and D primary mixed gas, which is ready for use; the second-step dilution process is as follows: take another gas cylinder which has been vacuumized, weigh the mass of the cylinder, then fill A and D or B and D primary mixed gas into the cylinder, weigh the mass of the cylinder again, calculate the mass of A and D or B and D primary mixed gas added according to the mass difference before and after filling, then fill D gas into the cylinder again, weigh the mass of the cylinder again, calculate the mass of D gas added according to the mass difference before and after filling, and finally mix A and D or B and D primary mixed gas and D gas in the cylinder uniformly by rolling the cylinder to obtain A and D or B and D secondary mixed gas, which is ready for use; in this way, A and D or B and D tertiary mixed gas, A and D or B and D quaternary mixed gas, and other multiple mixed gas, and even A dilution mixed gas or B dilution mixed gas of final target concentration can be obtained. According to the process of multiple dilution and the mass of gas added in each dilution, the mass of A gas and D gas added in A dilution mixed gas of final target concentration, and the mass of B gas and D gas added in B dilution mixed gas of final target concentration can be calculated; finally, mix the gas in the cylinder uniformly by rolling the cylinder, which is ready for use.

[0063] The basic steps of the transfer dilution method are: first, select a suitable metal pipe or small gas tank, the metal pipe has a dead plug joint at one end and a stop valve at the other end, the small gas tank has a stop valve at the gas tank opening, the inside of the metal pipe or small gas tank is vacuumed, there is no residual gas, select a suitable gas cylinder, the inside of the gas cylinder has been vacuumed, there is no residual gas; the transfer dilution process is: a certain amount of A or B gas is filled into the metal pipe or small gas tank, then the mass is weighed, the A or B gas in the metal pipe or small gas tank is transferred to the gas cylinder through a special device, the mass of the metal pipe or small gas tank is weighed again, the mass of the A or B gas added to the gas cylinder can be calculated according to the mass difference of the two weighings; before the transfer, the mass of the gas cylinder which has been vacuumed inside should also be weighed, after filling the A or B gas, the D gas is filled into the gas cylinder again, the mass of the gas cylinder is weighed again, the mass of the D gas added can be calculated according to the mass difference before and after the D gas is filled, and then subtracting the mass of the A or B gas added to the gas cylinder. Finally, the gas in the gas cylinder is mixed uniformly by rolling the gas cylinder, and is ready for use.

[0064] wherein the mass of the B gas added in the dilution process of the B gas ( ) is the same as or different from the mass of the A gas added in the dilution process of the A gas ( ). The mass of the D gas added in the dilution process of the B gas ( ) is the same as or different from the mass of the D gas added in the dilution process of the A gas ( ). The mass of the B gas added in the dilution process of the B gas ( ) is the same as or different from the mass of the D gas added in the dilution process of the B gas ( ), and is usually much smaller than . The mass of the A gas added in the dilution process of the A gas ( ) is the same as or different from the mass of the D gas added in the dilution process of the A gas ( ), and is usually much smaller than .

[0065] The technical route of dilution and weighing of the A gas is the same as or different from the technical route of dilution and weighing of the B gas, that is, the dilution of the A gas can 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 and the transfer dilution method are combined for use; the dilution of the B gas can 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 and the transfer dilution method are combined for use.

[0066] In another exemplary embodiment of the present application, in order to simplify the complexity of data acquisition, a conventional testing method or measuring instrument can be selected to acquire the response signals, wherein a balance for weighing gas mass, a gas chromatograph for measuring gas, and a spectrometer are popular. Based on this, the gas chromatography method or the spectroscopy method can be used to acquire the response signal of the B impurity in the A gas, the response signal of the A impurity in the B gas, the response signal of the A component in the A dilution mixed gas, and the response signal of the B component in the B dilution mixed gas, respectively. The molecular weight of the A component and the B component can be calculated according to the atomic weight of the substances in the periodic table of chemical elements and the molecular formula.

[0067] The measuring instrument for acquiring the response signal of the B impurity in the A gas and the response signal of the B impurity in the A gas can be the same instrument or different instruments, and can be the same principle instrument or different principle instruments. The response signal of the B impurity in the A gas ( ) and the response signal of the B component in the B dilution mixed gas ( ) are acquired by the same analysis measuring instrument, which does not generate a response signal for the A component and the dilution gas D or the generated response signal does not affect the response signal of the B component. The response signal of the A impurity in the B gas ( ) and the response signal of the A component in the A dilution mixed gas ( ) are acquired by the same analysis measuring instrument, which does not generate a response signal for the B component and the dilution gas D or the generated response signal does not affect the response signal of the A component. The measuring instrument can be a gas chromatograph, a spectrometer, or a mass spectrometer.

[0068] For example, in actual application, a suitable measuring instrument can be selected to measure the response signal of the B impurity in the A gas and the response signal of the B component in the B dilution mixed gas, and the working principle of the instrument can be gas chromatography or spectroscopy, but it is required that the A component and the D gas do not affect the signal when the B component is measured. This requirement is relatively easy to achieve in gas chromatography and spectroscopy. For example, in gas chromatography, a suitable chromatographic column can be selected to separate the B component from the A component and the D gas; a suitable chromatographic detector can be selected to make it only respond to the B component and not respond to the A component or the D gas; or a suitable detector can be selected based on the selected chromatographic column to separate the B component from the A component and not respond to the D gas. For example, in spectroscopy, a suitable light absorption spectrum range can be selected to make the B component respond in this spectrum range and the A component and the D gas not respond in this spectrum range.

[0069] 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 on the principle of gas chromatography or spectrometry, but it is required that component B and gas D do not affect the signal when measuring component A. This requirement is relatively easy to achieve in both gas chromatography and spectrometry. For example, in gas chromatography, a suitable chromatographic column can be selected to separate component A from component B and gas D; a suitable chromatographic detector can be selected that responds only to component A and not to component B or gas D; or a suitable detector can be selected in addition to a suitable chromatographic column to separate component A from component B while gas D does not respond. Similarly, in spectrometry, a suitable light absorption band can be selected so that component A responds in this band while component B and gas D do not respond.

[0070] Furthermore, to reduce the nonlinear influence of the measuring instrument's response to samples of different concentrations, the mass of gas B added during the dilution process and the mass of gas D should be reasonably adjusted so that the response signal of the measuring instrument to component B in the diluted gas B mixture is close in magnitude to the response signal of impurity B in gas A, thereby reducing the impact of instrument response nonlinearity on the accuracy of the measurement results; similarly, the mass of gas A added during the dilution process and the mass of gas D should be reasonably adjusted so that the response signal of the measuring instrument to component A in the diluted gas A mixture is close in magnitude to the response signal of impurity A in gas B, thereby reducing the impact of instrument response nonlinearity on the accuracy of the measurement results.

[0071] As an optional implementation, the gas D used can be relatively inert high-purity nitrogen or high-purity helium, both of which are commonly used as carrier gases in gas chromatography. When measured using gas chromatography, it will not affect the response signals of components A and B. Furthermore, these two gases have low spectral absorption characteristics, so when measured using spectroscopic methods, they will also not affect the response signals of components A and B.

[0072] In another exemplary embodiment of this application, in order to further improve the reliability and accuracy of the calculation results, when performing step 104, it is necessary to set the estimated values ​​of the concentration of impurity B in gas A and the concentration of impurity A in gas B. This estimated value is assigned to formulas (1) and (2) during the iterative calculation process. and The initial values ​​are also the concentration values ​​input in the first iteration; these two values ​​can be any numbers less than 1. Based on this, the implementation process of step 104 above can be described as follows:

[0073] 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).

[0074] Step 2: Input the 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 gas A, the response signal of component B in the diluted gas B, and the gas mass data (mass of gas B and gas D added during the dilution of gas B, and mass of gas A and gas D added during the dilution of gas A) and molecular weight data (average molecular weight of components A, B, and 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; where the input concentration and output concentration are molar concentrations (also known as molar concentrations), with units of mol / mol; the unit of added mass is g.

[0075] Step 3: Use the output concentration of impurity B in gas A as the new input concentration of impurity B in 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 concentrations of impurity B in gas A and impurity A in gas B converge. The final output concentration of impurity B in gas A is then taken as the measurement result of the impurity B concentration in gas A, and the output concentration of impurity A in gas B is taken as the measurement result of the impurity A concentration in gas B. Convergence of the output concentrations of impurity B in gas A and impurity A in gas B means that the relative deviation between two consecutive output concentrations of impurity B in gas A is less than a set value, and simultaneously, the relative deviation between two consecutive output concentrations of impurity A in gas B is also less than a set value. This set value can be any number less than 1, preferably any number not greater than 0.01%.

[0076] Based on the above description, the calculation method of the bidirectional iterative intelligent algorithm is as follows: first, input the values ​​of each input quantity into formula (1) and formula (2), and calculate the result after the first iteration. Value and The value, and then the result of the first iteration Value and The value is reassigned to and To conduct a second round of iterations, Value and Input the values ​​into formulas (1) and (2) to obtain the values ​​after the second iteration. and The value is then calculated iteratively multiple times until the result is obtained. Value and The value tends to converge to no significant change, thus obtaining the converged value. Value and convergence The value represents the measured molar concentration of impurity B in gas A and the molar concentration of impurity A in gas B. Among these, the converged value... Value and convergence The value refers to the last two values ​​obtained after multiple iterations. The relative deviation of the values ​​is less than a certain set value, and the last two values ​​obtained are... 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%.

[0077] Bidirectional iteration refers to arbitrarily set... The initial value is obtained by inputting formula (2). The value of one iteration, and at the same time, arbitrarily set The initial value is obtained by inputting formula (1). The first iteration value; then formula (1) is used to obtain Value Assignment At the same time, the formula (2) obtained Value Assignment ; will the new The value is entered into formula (2) to obtain The second iteration value, and at the same time the new The value is obtained by inputting formula (1). The second iteration value is obtained, and so on, to complete multiple rounds of bidirectional iterative calculation.

[0078] In practical applications, the bidirectional iterative intelligent algorithm can be performed manually, using spreadsheets, or by writing software programs. When writing software programs, there are no restrictions on the programming language used, as long as the input requirements of the bidirectional iterative intelligent algorithm are met, and iterative calculations can be performed according to the above process.

[0079] In another exemplary embodiment of this application, when the main impurity in gas A is component B and the main impurity in gas B is component A, and when gases A and B also contain other impurities besides component B and component A, in order to make the calculation results more accurate, the measurement model of the bidirectional iterative intelligent algorithm can be supplemented with the input concentrations of other impurities in gas A, other impurities in gas B, the molecular weights of other impurity components in gas A, and the molecular weights of other impurity components in gas B as input quantities, i.e.:

[0080] .

[0081] .

[0082] In the formula, It is the first component of gas A, excluding component B. The input concentration of the impurities, It represents the types and quantities of impurities in gas A, excluding component B. It is the first component of gas B besides component A. The input concentration of the impurities, It represents the types and quantities of impurities in gas B, excluding component A. It is the first in gas A The molecular weight of the impurities, It is the first in gas B The molecular weight of the impurities.

[0083] The input concentrations of other impurities in gas A and gas B can be measured using conventional methods.

[0084] Based on the above description, the method provided in this application utilizes the response signal obtained from conventional gas chromatography or spectroscopy to measure impurities in gases. It employs a bidirectional iterative intelligent algorithm to solve the problem of quantitative measurement of corresponding impurities in two gases that are mutual impurities. In cases where two gases are mutual impurities and no standard gas corresponding to the mutual impurity component is available, the concentration of the mutual impurity component in the two gases is accurately measured. The quantitative measurement route is as follows: Figure 2 As shown.

[0085] Furthermore, compared with the prior art, this application also has the following beneficial effects:

[0086] First, when measuring the concentration of impurity components in two gases according to the method of this application, it is not necessary to use the high-purity gas or the standard gas with accurate value corresponding to the impurity. The target impurity can be measured when high-purity gas or standard gas is not available, which can meet the daily measurement needs. Moreover, the method is simple and easy to implement and saves costs.

[0087] Second: The bidirectional iterative intelligent algorithm proposed in this application has easily obtainable input quantities, which can be completed by selecting conventional testing methods or measuring instruments. Balances for weighing gas mass and gas chromatographs and spectrometers for measuring gas are very common, and the calculation results are reliable and accurate.

[0088] Third: The bidirectional iterative intelligent algorithm can be implemented by writing software programs. The programming is not complicated, and there are no restrictions on the programming language used. Running the program can quickly obtain the measurement results of impurities in the gas.

[0089] In another exemplary embodiment of the present application, the feasibility and accuracy of the method for quantitatively measuring the impurity concentration in the gas provided in the present application are verified based on actual measurement requirements. In this embodiment, the gas used and its data are for illustration only and are not a specific limitation of the present application. The method provided in the present application can also be used for other measurement requirements in which the impurities are mutually impurities but cannot be compared and verified. The following implementation cases not only illustrate the feasibility of the scheme of the present application, but also demonstrate the accuracy of the scheme of the present application. Therefore, in the implementation process, the selection of the A gas and the B gas is nitrous oxide and carbon dioxide, because these two substances are currently available on the market without obvious impurities in high-purity gas. Based on this, the verification process in this embodiment includes:

[0090] First, 0.0004013 mol / mol of carbon dioxide is added to high-purity nitrous oxide gas to produce a nitrous oxide simulation gas containing carbon dioxide impurities. 0.0006036 mol / mol of nitrous oxide is added to high-purity carbon dioxide gas to produce a carbon dioxide simulation gas containing nitrous oxide impurities. These two simulation gases are used as measurement research objects, and the method provided in the present application is used to measure the mutually impurity components in these two simulation gases, respectively, to obtain the impurity concentration measurement results in mol / mol.

[0091] At the same time, in order to verify the accuracy of the method provided in the present application, a comparative verification is performed. The simulation added concentration is compared with the measurement results of the method provided in the present application.

[0092] The impurities of nitrous oxide in carbon dioxide simulation gas and the impurities of carbon dioxide in nitrous oxide simulation gas can be measured. High-purity nitrogen can be selected as the dilution gas. The carbon dioxide simulation gas containing nitrous oxide impurities is diluted to a lower concentration by high-purity nitrogen using the mass dilution method to obtain a diluted carbon dioxide mixture. The nitrous oxide simulation gas containing carbon dioxide impurities is diluted to a lower concentration by high-purity nitrogen using the mass dilution method to obtain a diluted nitrous oxide mixture. Then, a gas chromatograph (for example, Agilent 7890 type gas chromatograph) equipped with high-purity nitrogen as the carrier gas, a nickel conversion furnace and a hydrogen flame ionization detector is used to measure the diluted carbon dioxide mixture and the nitrous oxide simulation gas containing carbon dioxide impurities to obtain the chromatographic peak area. Since nitrous oxide has no signal response under this chromatographic condition, there is no mutual interference between nitrous oxide and carbon dioxide. In addition, high-purity nitrogen has no signal response in the hydrogen flame ionization detector, so the obtained chromatographic peak area can be directly used for subsequent bidirectional iterative intelligent algorithm calculation. A Fourier transform infrared spectrometer (for example, MKS MG2030 type Fourier transform infrared spectrometer) equipped with high-purity nitrogen as the purge gas, a 5m optical path gas cell and a semiconductor refrigeration detector is used to measure the diluted nitrous oxide mixture and the carbon dioxide simulation gas containing nitrous oxide impurities to obtain the infrared spectrum peak area. Since nitrous oxide has a characteristic absorption peak at the 2514.51 cm -1 ~2613.83cm -1 wavenumber segment, and carbon dioxide has no signal response in this wavenumber segment, and high-purity nitrogen has no signal response in the Fourier transform infrared spectrometer, so the obtained spectrum peak area can be directly used for subsequent bidirectional iterative intelligent algorithm calculation.

[0093] The mass of the carbon dioxide simulation gas added during the dilution of the carbon dioxide simulation gas and the mass of the dilution gas nitrogen, and the mass of the nitrous oxide simulation gas added during the dilution of the nitrous oxide simulation gas and the mass of the dilution gas nitrogen can be accurately measured by using a high-precision balance (for example, Mettler XPR26003LC balance). The balance can have a range of 26 kg and a sensitivity of 1 mg.

[0094] The weighing process is as follows:

[0095] (1) First, select four aluminum alloy gas cylinders (internal volume of 4L), and vacuumize the inside of the gas cylinder without residual gas.

[0096] (2) Dilute carbon dioxide simulation gas. First, extract one of the gas cylinders processed in step (1), and use the XPR26003LC scale to weigh its mass as 6850.522 g. Then, fill the gas cylinder with carbon dioxide simulation gas, and weigh its mass again as 6865.029 g. According to the mass difference between the two weighings, the added mass of carbon dioxide simulation gas is calculated as 14.507 g. Then, fill the gas cylinder with high-purity nitrogen gas, and weigh its mass again as 7317.755 g. According to the mass difference between the two weighings, the added mass of nitrogen gas is calculated as 452.726 g. Mix the mixed gas uniformly by rolling the gas cylinder for 4 hours, and mark it as low-concentration mixed gas I, ready for use.

[0097] Extract one of the gas cylinders processed in step (1), and use the XPR26003LC scale to weigh its mass as 7185.671 g. Then, fill the gas cylinder with low-concentration mixed gas I, and weigh its mass again as 7195.101 g. According to the mass difference between the two weighings, the added mass of low-concentration mixed gas I is calculated as 9.430 g. Then, fill the gas cylinder with high-purity nitrogen gas, and weigh its mass again as 7650.769 g. According to the mass difference between the two weighings, the added mass of nitrogen gas is calculated as 455.668 g. Mix the mixed gas uniformly by rolling the gas cylinder for 4 hours, and mark it as low-concentration mixed gas II, ready for use.

[0098] 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 as 9.430 x 14.507 / (14.507 + 452.726) = 0.2927897 g, and the mass of nitrogen gas actually contained in low-concentration mixed gas II is 9.430 x 452.726 / (14.507 + 452.726) + 455.668 = 464.8052103 g. Low-concentration mixed gas II is the dilution mixed gas of carbon dioxide.

[0099] (3) Dilute nitrous oxide simulation gas. First, extract one of the gas cylinders processed in step (1), and use the XPR26003LC scale to weigh its mass as 7100.261 g. Then, fill the gas cylinder with nitrous oxide simulation gas, and weigh its mass again as 7114.473 g. According to the mass difference between the two weighings, the added mass of nitrous oxide simulation gas is calculated as 14.212 g. Then, fill the gas cylinder with high-purity nitrogen gas, and weigh its mass again as 7559.304 g. According to the mass difference between the two weighings, the added mass of nitrogen gas is calculated as 444.831 g. Mix the mixed gas uniformly by rolling the gas cylinder for 4 hours, and mark it as low-concentration mixed gas III, ready for use.

[0100] Take out one gas cylinder 1 treated in step (1), and weigh its mass as 7087.933 g by using XPR26003 LC scale. Then, fill the gas cylinder with low-concentration mixed gas III, and weigh its mass as 7102.010 g again. According to the mass difference between the two weighings, the added mass of low-concentration mixed gas III is calculated as 14.077 g. Then, fill the gas cylinder with high-purity nitrogen, and weigh its mass as 7551.337 g again. According to the mass difference between the two weighings, the added mass of nitrogen is calculated as 449.327 g. Roll the gas cylinder for 4 hours to mix the mixed gas uniformly, and mark it as low-concentration mixed gas IV, which is ready for use.

[0101] 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 as 14.077 × 14.212 / (14.212 + 444.831) = 0.435824801 g, and the mass of nitrogen actually contained in low-concentration mixed gas IV is 14.077 × 444.831 / (14.212 + 444.831) + 449.327 = 462.9681752 g. Low-concentration mixed gas IV is the dilution mixed gas of nitrous oxide.

[0102] (4) Refer to the periodic table of elements of IUPAC to calculate the molecular weight of nitrous oxide 44.013, the molecular weight of carbon dioxide Mb 44.009, and the molecular weight of nitrogen is 28.014. The average molecular weight of high-purity nitrogen without other obvious impurities is equal to the molecular weight of nitrogen 28.014.

[0103] According to the peak area of gas chromatography 7965.080254, 8044.213074, according to the peak area of infrared spectrum 397.2429375, 395.1368989.

[0104] Set the initial values of and 0.01 mol / mol.

[0105] Set the limit value of measuring and convergence to be that the relative deviation of the results of two consecutive iterations is less than 0.01%.

[0106] Input the above parameter values into formula (1) and formula (2) to obtain the first round and The values ​​were 0.000392903 mol / mol and 0.00059599 mol / mol, respectively. These values ​​were then further assigned... and Then, calculate formulas (1) and (2) again to obtain the second round. and The values ​​were 0.000396636 mol / mol and 0.000601773 mol / mol, respectively. These values ​​were then further assigned... and Then, calculate formulas (1) and (2) again to obtain the third round. and The values ​​were 0.000396634 mol / mol and 0.000601771 mol / mol, respectively. At this point, the two values ​​obtained in the third iteration and the second iteration... The relative deviation between the values ​​is -0.0006%, obtained from the third iteration and the second iteration. The relative deviation between the values ​​is -0.0004%, which is less than the set convergence limit of 0.01%. Therefore, the values ​​obtained in the third iteration are... and The values ​​are the concentration values ​​of carbon dioxide impurities in the nitrous oxide simulated gas and the concentration values ​​of nitrous oxide impurities in the carbon dioxide simulated gas, respectively. That is, the concentration of carbon dioxide impurities in the nitrous oxide simulated gas is 0.000396634 mol / mol, and the concentration of nitrous oxide impurities in the carbon dioxide simulated gas is 0.000601771 mol / mol.

[0107] Furthermore, the simulated added concentration and the measurement results of the method provided in this application are shown in Table 1. Measurement results in the gas field typically exhibit random effects; generally, a relative deviation of around 1% is considered the data to be accurate and comparable. As can be seen from the data summarized in Table 1, the measurement results of the method provided in this application are consistent with the simulated added concentration at around 1%, indicating that the method provided in this application has good accuracy.

[0108] Table 1. Simulated Added Concentrations and Measurement Results from the Method Provided in this Application

[0109]

[0110] In summary, the method for quantitatively measuring the impurity concentration in the gas provided by the application measures the impurity response signal in the gas based on the conventional gas chromatography or spectroscopy, and uses a bidirectional iterative intelligent algorithm to solve the quantitative measurement problem of the corresponding impurities in two gases that are impurities of each other. The method does not need to use high-purity gas corresponding to the impurity or 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 standard gas corresponding to the impurity, the impurity concentration in the gas that is an impurity of each other can be accurately measured, solving the problem that accurate measurement cannot be implemented due to the fact that the standard gas corresponding to the impurity cannot be obtained by using the 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 further providing a brand-new solution to the problem of quantitative measurement of impurities in gases that are impurities of each other.

[0111] In an exemplary embodiment, a computer device is provided, which can be a server or a terminal, and an internal structure diagram thereof can be as shown in Figure 3 The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the 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 the 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 a gas. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through network connection. The computer program is executed by the processor to implement a method for quantitatively measuring impurity concentration in a gas.

[0112] Those skilled in the art can understand that Figure 3 The structure shown in the above

[0113] In an exemplary embodiment, a computer device is provided, which includes a memory and a processor, and the memory stores a computer program. The processor executes the computer program to implement the steps in each of the above method embodiments.

[0114] In an exemplary embodiment, a computer readable storage medium storing a computer program is provided, the computer program, when executed by a processor, implements the steps in the above method embodiments.

[0115] In an exemplary embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the steps in the above method embodiments.

[0116] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0117] A person of ordinary skill in the art can understand that all or part of the processes in the above method embodiments can be completed by a computer program instructing related hardware. 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 method embodiments. Any reference to a memory, database or other medium used in the embodiments provided by the present application can include at least one of a non-volatile and volatile memory. The non-volatile memory can include a read-only memory (ROM), a magnetic tape, a floppy disk, a flash memory, an optical storage, a high-density embedded non-volatile memory, a resistive random access memory (RRAM), a magnetoresistive random access memory (MRAM), a ferroelectric random access memory (FRAM), a phase change memory (PCM), a graphene memory, etc. The volatile memory can include a random access memory (RAM) or an external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0118] The database involved in each of the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, and the like, without being limited thereto. The processor involved in each of the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, and the like, without being limited thereto.

[0119] The technical features of the above embodiments can be combined in any manner. To make the description concise, all possible combinations of the technical features in the above embodiments are not described, but it should be considered that any combination of the technical features is within the scope of the present disclosure, as long as there is no contradiction.

[0120] The principles and implementation manners of the present application are described by using specific examples herein, and the above embodiments are only used to help understand the method of the present application and its core idea. Meanwhile, for those skilled in the art, the specific implementation manners and application ranges can be changed according to the idea of the present application. In summary, the content of the present specification should not be understood as a limitation of the present application.

Claims

1. A method of quantitatively measuring the concentration of an impurity in a gas, characterized by, The method comprises the following steps: obtaining a response signal of B impurities in A gas and a response signal of A impurities in B gas; using mass dilution method, taking D gas as dilution gas, diluting A gas and B gas respectively to obtain A dilution mixed gas with a first set concentration value and B dilution mixed gas with a second set concentration value; obtaining a response signal of A component in A dilution mixed gas and a response signal of B component in B dilution mixed gas; constructing a measurement model of bidirectional iterative intelligent algorithm, which is expressed as: ； ； wherein, is the input concentration of B impurity in A gas, is the input concentration of A impurity in B gas, is the output concentration of B impurity in A gas, is the output concentration of A impurity in B gas, is the response signal of B impurity in A gas, is the response signal of A impurity in B gas, is the response signal of B component in B dilution gas, is the response signal of A component in A dilution gas, is the added mass of B gas in the process of diluting B gas, is the added mass of D gas in the process of diluting B gas, is the added mass of A gas in the process of diluting A gas, is the added mass of D gas in the process of diluting A gas, is the molecular weight of A component, is the molecular weight of B component, is the average molecular weight of D gas; using the measurement model of bidirectional iterative intelligent algorithm, obtaining a measurement result of B impurity concentration in A gas and a measurement result of A impurity concentration in B gas according to the response signal of B impurities in A gas, the response signal of A impurities in B gas, the response signal of A component in A dilution mixed gas, the response signal of B component in B dilution mixed gas, and gas mass data and molecular weight data in the dilution process.

2. The method of quantitative measurement of the concentration of impurities in a gas according to claim 1, characterized in that, using the measurement model of bidirectional iterative intelligent algorithm, determining the concentration of B impurities in A gas and the concentration of A impurities in B gas according to the response signal of B impurities in A gas, the response signal of A impurities in B gas, the response signal of A component in A dilution mixed gas, the response signal of B component in B dilution mixed gas, and gas mass data and molecular weight data in the dilution process, which comprises: setting the input concentration of B impurities in A gas and the input concentration of A impurities in B gas; inputting the input concentration of B impurities in A gas, the input concentration of A impurities in B gas, the response signal of B impurities in A gas, the response signal of A impurities in B gas, the response signal of A component in A dilution mixed gas, the response signal of B component in B dilution mixed gas, and gas mass data and molecular weight data in the dilution process into the measurement model of bidirectional iterative intelligent algorithm to obtain the output concentration of B impurities in A gas and the output concentration of A impurities in B gas; iterating the measurement model of bidirectional iterative intelligent algorithm by taking the output concentration of B impurities in A gas as the new input concentration of B impurities in A gas and taking the output concentration of A impurities in B gas as the new input concentration of A impurities in B gas until the output concentration of B impurities in A gas and the output concentration of A impurities in B gas tend to converge, and finally obtaining the output concentration of B impurities in A gas as the measurement result of B impurity concentration in A gas and obtaining the output concentration of A impurities in B gas as the measurement result of A impurity concentration in B gas.

3. The method of quantitative measurement of the concentration of impurities in a gas according to claim 2, characterized in that, The input concentration of B impurities in A gas and the input concentration of A impurities in B gas are both set as any number less than 1.

4. The method of quantitative measurement of the concentration of impurities in a gas according to claim 2, characterized in that, The output concentration of B impurities in A gas and the output concentration of A impurities in B gas tend to converge, which means that the relative deviation between the output concentration of B impurities in A gas in two consecutive times is less than a set value, and the relative deviation between the output concentration of A impurities in B gas in two consecutive times is also less than a set value.

5. The method of quantitative measurement of the concentration of impurities in a gas according to claim 1, characterized in that, The D gas is inert gas and does not contain A component and B component.

6. The method of quantitative measurement of the concentration of impurities in a gas according to claim 1, characterized in that, The response signal of B impurities in A gas, the response signal of A impurities in B gas, the response signal of A component in A dilution mixed gas, and the response signal of B component in B dilution mixed gas are obtained by gas chromatography or spectroscopy respectively.

7. The method of quantitative measurement of the concentration of impurities in a gas according to claim 1, characterized by, When the A gas contains other impurities besides the B component and the B gas contains other impurities besides the A component, the input concentration of the other impurities in the A gas, the input concentration of the other impurities in the B gas, the molecular weight of the other impurities in the A gas and the molecular weight of the other impurities in the B gas are added as input quantities in the measurement model of the bidirectional iterative intelligent algorithm, and there are: ； ； In the formula, It is the first component of gas A, excluding component B. The input concentration of the impurities, It represents the types and quantities of impurities in gas A, excluding component B. It is the first component of gas B besides component A. The input concentration of the impurities, It represents the types and quantities of impurities in gas B, excluding component A. It is the first in gas A The molecular weight of the impurities, It is the first in gas B The molecular weight of the impurities.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that 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 1-7.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the method for quantitatively measuring the concentration of impurities in a gas according to any one of claims 1-7.

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