JP2007309702A - Method of estimating gasoline density - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To solve the problem, wherein the density value obtained by a conventionally used calculation method does not express the density of actual gasoline and has errors, so as to enhance the estimation accuracy for the density of the gasoline. <P>SOLUTION: A density of each component is preliminarily found experimentally, in a gasoline product containing the same component species, and the density of the analytical object gasoline as the whole is calculated using the found density. The density of the each component contained in the analytical object gasoline is corrected by using corrected density, and the sum of the corrected densities of the respective components is found to compensate the error, based on a volume reduction due to mixing, as to the density of the analytical object gasoline as a whole. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a gasoline density estimation method, and more particularly to a gasoline density estimation method performed based on analysis data obtained by PONA analysis.

  Gasoline contains 300 or more hydrocarbons having 4 to 12 coal beds, and these hydrocarbons are roughly classified into five types according to the number and structure of carbon, such as paraffin, olefin, naphthene, aromatic, and others. As a method of classifying and quantifying this gasoline according to hydrocarbon type, “Japanese Industrial Standard Gas Chromatographic Total Composition Analysis Test Method (JIS K-2536)” and “The Petroleum Institute Standard Gasoline Total Composition Analysis Method-Capillary Column Gas Chromatogram” Law (JPI-5S-52-99) "is known and generally called PONA analysis.

  This PONA analysis system calculates the octane number, hydrogen content, carbon content, average density, vapor pressure, and distillation properties by measuring the volume concentration, weight concentration, and mol ratio of each component in gasoline and gasoline base materials. Can do.

  In general, in order to guarantee uniform quality as an industrial product, gasoline supplied to the market has the characteristics of gasoline as a liquid in addition to the above-mentioned characteristics of the component types and components contained in gasoline. The density is required to be uniform with no variation for each gasoline.

Conventionally, the density of this gasoline is calculated using the known density of each component that is obtained in advance and described in a data table such as a table, and the concentration of each component that is obtained by PONA analysis.
Japanese Industrial Standard Gas Composition Test Method (JIS K-2536) Petroleum Society Standard Gasoline Total Composition Analysis Method-Capillary Column Gas Chromatogram Method (JPI-5S-52-99)

  There is a problem that the density value obtained by a conventionally used calculation method does not accurately represent the actual gasoline density but has an error.

  Generally, when each component which comprises gasoline is prepared and these each component is mixed, the volume of the gasoline after mixing will reduce rather than the sum of the volume of each component before mixing. On the other hand, the weight of gasoline after mixing is the same as the sum of the weight of each component before mixing. Therefore, the density of gasoline is larger than the value obtained by adding the density of each component by the component ratio.

  On the other hand, when the density of gasoline is obtained by dividing the sum of the weight of each component by the sum of the volume of each component before mixing, the density of the density obtained by calculation is reduced because of the volume reduction due to mixing of the components. The value becomes smaller than the actual density of gasoline, and there is a problem that an accurate gasoline density cannot be obtained.

  SUMMARY OF THE INVENTION Accordingly, an object of the present invention is to solve the above-described problems and increase the accuracy of estimation of gasoline density.

  The gasoline density estimation method of the present invention is a method of estimating the density of the entire gasoline for each gasoline to be analyzed in a gasoline product containing the same component type. The density of each component was tested in a gasoline product containing the same component type. The density of the whole gasoline to be analyzed is calculated using this density. In gasoline products containing the same component species, the density of each component obtained in advance by a plurality of experimental values is the actual density of each component in a mixed state, and the density of each component is the density of each component when not mixed. Is a density corrected to reflect the actual density increase due to volume reduction. The present invention corrects the density of each component contained in the gasoline to be analyzed using this corrected density, and obtains the sum of the density of each corrected component, thereby obtaining the total density of the gasoline to be analyzed. Errors based on volume reduction due to mixing can be compensated.

  The gasoline density estimating method of the present invention includes a step of identifying each component in the gasoline by a gas collatogram based on a peak obtained by PONA analysis of the gasoline to be analyzed, and an area% of each peak of each component. The process of calculating the weight% of the component, the process of calculating the volume% by dividing the calculated weight% by the density of each component, and the gasoline to be analyzed by multiplying the volume% of each component by the density of each component and adding The process of calculating the average density of is provided. In the calculation step using the component density described above, the density of each component used in the step of calculating at least the average density is based on experimental values of a plurality of densities for a gasoline product containing the same component type as the gasoline to be analyzed. The correction density of each component specific to the gasoline product obtained in advance is used.

  As for the density of each component used in the step of calculating the volume%, a correction density specific to the gasoline product obtained from a plurality of density experimental values for the gasoline product including the gasoline to be analyzed may be used.

  In the gasoline product containing the same component type as the gasoline to be analyzed, the gasoline of the same type of gasoline product has the same component type and almost the same gasoline density. For this gasoline, a large number of density data of each component is obtained in advance by experiments, and a standard value is obtained for each component in the same kind of gasoline product based on this large number of density data. This standard value is a density value that has changed due to the volume reduction caused by mixing each component, and this density value is a corrected density for obtaining the average density of the entire gasoline.

  In the present invention, the density of each component uses a corrected density of each component specific to a gasoline product that is obtained in advance based on experimental values of a plurality of densities for a gasoline product containing the same component type as the gasoline to be analyzed.

  Using this corrected density, the density of each component contained in the gasoline to be analyzed is corrected, and the sum of the density of each corrected component is calculated to reduce the volume of the analyzed gasoline by mixing. The error based on can be compensated.

  Further, in the weight% calculating step, the area% of each peak of each component is multiplied by the sensitivity correction coefficient, and the calculated multiplication value is divided by the sum of the multiplication values of all the components. Further, in the volume% calculation step, each weight% of each component is divided by the density of each component, and the calculated division value is divided by the sum of the division values of all components.

  According to the present invention, the accuracy of gasoline density estimation can be increased.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

  FIG. 1 is a flowchart for explaining the gasoline density estimation method of the present invention.

  First, the gasoline to be analyzed is identified by PONA analysis, and the contained components are obtained. Here, the gasoline to be analyzed can be a gasoline sampled and extracted from gasoline products containing the same component species. The correction density used in the following steps is obtained by experimenting with gasoline as a gasoline product and obtaining a plurality of data, and obtaining density data in advance based on this data. Therefore, this correction density is corrected for the density value that increases by mixing the components contained in gasoline (S1).

In the PONA analysis, a detection area C _iA is obtained for each component i obtained in the gas chromatogram (S2).

Using the detection area C _iA obtained in S2, the weight% (C _iw ) of each component i is calculated. Weight% (C _iw ) is calculated by the following formula (1).

In formula (1), C _iA is the detection area of component i in the gas chromatogram, and f _i is the sensitivity correction coefficient. The sensitivity correction coefficient can be calculated based on the FID detection principle for, for example, carbon hydrogen contained in gasoline, and is obtained by actual measurement for MTBE (tertiary butyl methyl ether) and alcohol. Since the method for calculating the sensitivity correction coefficient is known, the description thereof is omitted here (S3).

Next, the volume% (C _iv ) is calculated using the weight% (C _iw ) of each component. The volume% (C _iv ) is calculated by the following formula (2).

In formula (2), C _iv is the volume% of component i, and d _i is the density of component i. As the density d _i of the component i, known data obtained in advance for each component i can be used, and an existing data table can be used.

Next, the correction density d _i ^* of each component is read out. As described above, this correction density d _i ^* is obtained by experimenting with gasoline as a gasoline product and obtaining a plurality of data, and density data is obtained in advance based on this data (S5).

Next, the average density d is calculated using the volume% (C _iv ) of each component and the correction density d _i ^* of each component. The average density d is calculated by the following formula (3).

In Expression (3), C _jv is the capacity% of the component j, and d _j ^* is the correction density of the component j. As the correction density d _j ^* of the component j, the value read in (S5) is used (S6).

In the step of calculating the capacity% (C _iv ) of (S4) described above, an example is shown in which data values described in an existing data table are used as the density d _i of each component i. The above correction density d _i ^* may also be used.

The density d _i can be obtained by the following calculation. A known amount a of the internal standard substance (A) is previously added to the sample. A relative correction coefficient g (reciprocal of the detection area of the chromatogram per unit weight) is obtained from the detection area of the chromatogram obtained by analyzing the known amount of the component I and the internal standard substance A by the gas chromatogram.

In the chromatogram obtained by measurement by gas chromatogram of the sample, the detection area of the component I and the internal standard A, when S _i, and S _a, respectively, by weight i following the formula for component I (4) Desired.

i = a · (g _i · S _i / g _a · S _a ) (4)

Here, a is the amount of the internal standard substance A, g _i is the relative correction coefficient of component I, S _i is the detection area of component I, g _a is the relative correction coefficient of internal standard substance A, and S _a is the internal standard substance A Is the detection area. Thereby, the density d _i of the component I is obtained by d _i = actual value (i) / theoretical value.

  FIG. 2 shows a configuration when the above-described gasoline density estimation method of the present invention is realized by each functional block. Note that this configuration may be implemented by hardware in addition to being implemented by software.

  In FIG. 2, an estimation device 1 that estimates gasoline density includes an input unit 2 for inputting analysis data for performing identification and density estimation, and setting an analysis target, a storage unit 3 for storing various data, and an identification unit. An identification calculation unit 4 that performs an identification calculation based on identification data set in the table, a concentration calculation unit 5 that calculates a component concentration based on the identification result, a concentration (weight%, volume%) determined by the concentration calculation unit 5 and each Mean density calculating means 6 for calculating the average density of gasoline using the component correction density is provided. Moreover, you may provide the display part 7 which displays an identification result, a density | concentration, and an average density, and the output part 8 which outputs an identification result, a density | concentration, and an average density to other apparatuses (for example, a recording device, a transmission apparatus, etc.).

The storage unit 3 includes an identification data storage unit 3A for storing identification data, an analysis data storage unit 3B for storing analysis data input by the input unit 2, and an identification result for storing identification results obtained by the identification calculation unit 4 A storage unit 3C, a sensitivity correction count storage unit 3D for storing the sensitivity correction coefficient f, a density result storage unit 3E for storing the density calculated by the density calculation unit 5, and a correction density d ^{i *} used for calculation of the average density d are stored. A correction density calculation unit 3F is provided.

  The identification data storage unit 3A may store the identification data (holding time and bandwidth indicating the peak position of the component) and a priority value of the component for a plurality of analysis targets. Further, based on the analysis target set from the input unit 2, the identification data storage unit 3 </ b> A reads the identification data of the analysis target into the identification calculation unit 4.

  The identification calculation unit 4 identifies each component of the analysis data stored in the analysis data storage unit 3B using the identification data read from the identification data storage unit 3A, and identifies the analysis target. In this identification, the analysis target may be identified by identifying each component in the order of the component priority values. Identification of each component using this priority value will be described later.

The concentration calculation unit 5 calculates weight% and volume% using the above-described equations (1) and (2). The sensitivity correction coefficient f used for this calculation is read from the sensitivity correction coefficient storage unit 3D, and the correction density d ^{i *} is read from the correction density storage unit 3F.

  In the above configuration example, an example is shown in which analysis data acquired by a measurement device (not shown) (for example, a chromatogram) is analyzed. However, the measurement device is not limited to a chromatogram and may be applied to other measurement devices. It is also possible to adopt a configuration in which a measuring device is incorporated in the analyzer 1.

  3 and 4 are a flowchart and a schematic explanatory diagram for explaining an example of identification that can be applied to the PONA analysis of the estimation method of the present invention.

  First, analysis data 10 to be analyzed is input. Here, the case where the analysis data 10 includes the analysis object A and the analysis object B will be described as an example. In the right rectangle of FIG. 4, the analysis data 10 is indicated by a solid line, the analysis target A is indicated by a broken line, and the analysis target B is indicated by a one-dot chain line (S11).

  Assuming the analysis object included in the analysis data, the assumed analysis object is set from a plurality of analysis objects provided in one identification table (S12).

  Each component has identification data of holding time T and bandwidth W, and a priority value is set. The priority value determines a priority order that determines the selection order of peak positions used for identification when identifying an analysis target using an identification table. This priority value can be determined, for example, in order of the peak value of each analysis object, and can be indicated by a numerical value or symbol representing an order.

  For example, in the analysis target A, the components A1, A7, A5 and A6 have peaks, the priority value “1” is set for the component A1, the priority value “7” is set for the component A7, The priority value “5” is set for A5, and the priority value “6” is set for component A6. Therefore, when identifying the analysis target A, the component A1 for which the priority value “1” is set is identified based on the retention time and the bandwidth according to the set priority value, The component A5 for which the priority value “5” is set is identified based on the holding time and the bandwidth, and the component A6 for which the priority value “6” is set is determined based on the holding time and the bandwidth. The component is identified, and the component to be analyzed is identified by identifying the component based on the retention time and the bandwidth of the component A7 for which the priority value “7” is set.

  Therefore, priority values are set in order from the highest in the determined analysis target (S13), the identification data (holding time T, bandwidth W) of the components of the set priority values are read (S14), and the peak of the analysis data 10 is read. And the component is identified (S15).

  In FIG. 4, for example, for component A, priority values “1”, “7”, “5”, and “6” are set in the identification table for components A1, A7, A5, and A6, respectively. . Therefore, component identification is performed on the component A1 having the highest priority value “1” based on the holding time TA1 and the bandwidth WA1.

  When the component A1 having the priority value “1” is identified, the next priority value is processed in the same manner. Here, for the component A5 having the next highest priority value “5”, the component identification is performed based on the holding time TA5 and the bandwidth WA5, and the next highest priority value “6”. The component A6 is identified based on the holding time TA6 and the bandwidth WA6, and the component A7 having the next highest priority value “7” has the holding time TA7 and the bandwidth WA7. Based on the component identification.

  When the above steps (S13) to (S15) are completed for all the components set in the identification table or the number of components determined in all components, the analysis target set in the step (S11) Identification is complete. 4 (b), (c), and (d) show examples in which the analysis objects A, B, and C are identified, respectively, and the analysis data includes components A and B identified, but component C is not identified Is shown. The number of components to be identified may be set to the minimum number that can be identified in the identification of the analysis target by the identification table in addition to all the components set in the identification table (S16).

  Then, the concentration is determined for the component by the above-described procedure (S17). The steps (S13) to (S17) described above are repeated with the analysis target changed in the step (S12) (S18). Thereby, a plurality of components included in the analysis data can be identified.

  After identifying a plurality of components and determining their concentrations, the average density of the analysis target is determined by the above-described procedure (S19).

  FIG. 5 and FIG. 6 are diagrams for explaining the comparison between the result of the gasoline density estimation method of the present invention and the result of the method defined by JIS, FIG. 5 shows the correlation of the calculation results, and FIG. Indicates an error.

The correlation of the calculation results shown in FIG. 5 shows that there is a correlation between the gasoline density estimation method of the present invention and the JIS method, and the variation in average density of the gasoline density estimation method of the present invention is ± 0.0024 g / cm ^3. It is shown that the accuracy is double that of the JIS method.

Further, the error distribution of the calculation result shown in FIG. 6 indicates that the error is approximately within −0.006 to 0.006 g / cm ³ .

  According to the present invention, gasoline density estimation and gasoline PONA analysis can be performed simultaneously.

  The present invention can be applied to the analysis of petrochemical products.

It is a flowchart for demonstrating the estimation method of the gasoline density of this invention. It is a figure which shows each functional block structure at the time of implement | achieving the estimation method of the gasoline density of this invention. It is a flowchart for demonstrating an example of the identification which can be applied to the PONA analysis of the estimation method of this invention. It is a schematic explanatory drawing for demonstrating an example of the identification which can be applied to PONA analysis of the estimation method of this invention. It is a figure which shows the result by the estimation method of the gasoline density of this invention. It is a figure for demonstrating the comparison with the result by the method defined by JIS of this invention.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 1 ... Estimation apparatus, 2 ... Input part, 3 ... Storage part, 3A ... Identification table storage part, 3B ... Analysis data storage part, 3C ... Identification result storage part, 3D ... Sensitivity correction coefficient storage part, 3E ... Concentration result storage part 3F: Correction density storage unit, 4 ... Identification calculation unit, 5 ... Concentration calculation, 6 ... Average density calculation unit, 7 ... Display unit, 8 ... Output unit.

Claims (4)

A method for estimating the density of gasoline to be analyzed in gasoline products containing the same component species,
Identifying each component in the gasoline with a gas colomatogram based on a peak obtained by PONA analysis of the gasoline to be analyzed;
Calculating the weight percent of each component from the area% of each peak of each component;
Dividing the calculated weight percent by the density of each component to calculate volume percent;
A step of calculating the average density of the gasoline by multiplying the volume% of each component by the density of each component and adding the product,
At least the density of each component used in the process of calculating the average density is determined based on experimental values of a plurality of densities for gasoline products including the same component type as the gasoline to be analyzed. A method for estimating a gasoline density, which is a corrected density of a component.   The density of each component used in the step of calculating the volume% is a correction density specific to the gasoline product obtained from experimental values of a plurality of densities for the gasoline product including the gasoline to be analyzed. The gasoline density estimation method according to claim 1.   In the weight% calculation step, the area% of each peak of each component is multiplied by a sensitivity correction coefficient, and the multiplication value is calculated by dividing the multiplication value by the sum of all the component multiplication values. Item 3. The gasoline density estimation method according to Item 1 or 2.   The calculation step of calculating the volume%, wherein each weight% of each component is divided by the density of each component, and the division value is divided by the sum of the division values of all the components. The gasoline density estimation method according to 1 or 2.

Images