CN108956814B - Method for directly constructing gasoline molecular composition model and property prediction method - Google Patents
Method for directly constructing gasoline molecular composition model and property prediction method Download PDFInfo
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Abstract
The invention provides a method for directly constructing a gasoline molecular composition model and a property prediction method, wherein the construction method comprises the following steps: (1) performing monomer hydrocarbon analysis on the gas chromatography detection result of the gasoline sample to identify molecules possibly contained in each peak in the gas chromatogram, and calculating the relative fraction of each peak; (2) classifying chromatographic peaks according to the analysis result of the monomer hydrocarbon and the types of the chromatographic peaks; (3) analyzing the classified chromatographic peaks according to the chromatographic peak types and the molecular types to obtain a complete monomer hydrocarbon molecule composition result; (4) the results are composed of the complete monomeric hydrocarbon molecules to directly generate a compositional model of the gasoline molecules. The method provided by the invention is based on a gas chromatography detection result, the molecular composition is reconstructed by using a peak regulation algorithm based on statistical distribution, then a gasoline molecular composition model is established, and the macroscopic property of gasoline is predicted.
Description
Technical Field
The invention relates to a method for directly constructing a gasoline molecular composition model and a property prediction method, in particular to a method for directly constructing a gasoline molecular composition model from a gas chromatography result and a property prediction method, and belongs to the technical field of oil composition analysis.
Background
The requirements of gasoline processing and blending on quality and composition are very strict, and the traditional lumped model can not meet the requirement of a modern refinery more and more. In order to improve the efficiency of refineries and improve the quality standards of products, molecular level models are developed, and it is becoming more and more important to implement molecular level management on the processing and blending processes of light oils. The problem to be solved for establishing the molecular model is to obtain the molecular composition of the oil product.
The existing methods for obtaining the molecular composition of gasoline can be mainly divided into an experimental method and a method for reconstructing the molecular composition by using a computer. The analysis result of the traditional analytical instrument, such as a gas chromatography-hydrogen flame ion detector (GC-FID), contains more co-escaping peaks and unidentified peaks. The existing method for reconstructing the molecular composition of gasoline by computer assistance usually obtains the molecular composition of gasoline samples by reversely deducing from the macroscopic properties of the gasoline samples, and the calculation result does not have the problems of co-escape and result loss. However, the method has the problem of uncertainty, and influences on subsequent processing and harmonic simulation processes. In addition, the predicted gasoline property value obtained by the method is greatly influenced by the input experimental value, and part of the methods in the method also heavily depend on the quality of a molecular composition database and a method of training data.
Therefore, providing a method for directly constructing a gasoline molecular composition model and a property prediction method has become an urgent technical problem to be solved in the art.
Disclosure of Invention
In order to solve the above disadvantages and shortcomings, an object of the present invention is to provide a method for directly constructing a gasoline molecular composition model.
The invention also aims to provide a system for directly constructing the gasoline molecular composition model.
The invention also aims to provide a method and a system for predicting the macroscopic properties of gasoline. The technical scheme provided by the invention adopts a peak regulation algorithm based on statistical distribution to reconstruct a molecular result obtained by gas chromatography, so as to obtain a complete molecular composition, and the molecular composition is used for establishing a gasoline molecular composition model and predicting the property of the gasoline molecular composition model. The method combines an experimental method and a computer reconstruction method, splits or speculates the co-escape peak and the unidentified peak in the gas chromatography result according to certain statistical distribution, provides an accurate and stable molecular composition result, does not need to establish a molecular composition database and perform associated training, and overcomes the defects of the existing method. The predicted macroscopic property of the molecular composition obtained based on the method is not influenced by experimental values and has more reference value.
In order to achieve the above object, in one aspect, the present invention provides a method for directly constructing a gasoline molecular composition model, wherein the method comprises the following steps:
(1) performing monomer hydrocarbon analysis on the gas chromatography detection result of the gasoline sample to identify molecules possibly contained in each peak in the gas chromatogram, and calculating the relative fraction of each peak;
(2) classifying chromatographic peaks according to the analysis result of the monomer hydrocarbon and the types of the chromatographic peaks;
(3) analyzing the classified chromatographic peaks according to the chromatographic peak types and the molecular types to obtain a complete monomer hydrocarbon molecule composition result;
(4) the results are composed of the complete monomeric hydrocarbon molecules to directly generate a compositional model of the gasoline molecules.
According to the method of the present invention, preferably, the gasoline sample comprises catalytically cracked gasoline, catalytically reformed gasoline, straight run gasoline, catalytically cracked gasoline, hydrogenated gasoline or coker gasoline.
According to the method of the present invention, preferably, the chromatographic peak types include known peaks, co-escaping peaks and unidentified peaks.
According to the method of the present invention, preferably, the analyzing in step (3) is performed by analyzing the classified chromatographic peaks according to chromatographic peak types and molecular types by using a peak adjusting algorithm based on statistical distribution.
According to the method of the present invention, preferably, the peak adjustment algorithm based on statistical distribution specifically includes the following steps:
fitting the distribution of known peaks: classifying known peaks according to molecular types and carbon numbers, and fitting classified data of each series according to statistical distribution;
resolution of the cosescape peak: splitting the co-escaping peak, assuming the relative content of each component in the co-escaping peak, sequentially checking all hypotheses, and selecting a proper hypothesis to accept; continuously repeating the processes until all the common escape peaks are separated;
extrapolation of unidentified peaks: supposing the molecular type and carbon number of components contained in unidentified peaks, sequentially checking all hypotheses, and selecting proper hypotheses for acceptance; the above process is repeated until all unidentified peaks are concluded to be complete.
The method according to the invention, wherein fitting the distribution of known peaks comprises in particular the following steps: classifying known peaks according to molecular types and carbon numbers, and summing the content fractions of all molecules with the same molecular types and carbon numbers to obtain a data matrix; and fitting the data of each molecular series in the data matrix according to the statistical distribution relative to the carbon number.
According to the method of the present invention, preferably, the statistical distribution comprises a gamma distribution.
According to the method of the present invention, preferably, the molecular species in step (3) include Normal Paraffins (NP), Isoparaffins (IP), olefins (O), Naphthenes (NC) and aromatics (a). Wherein said isoparaffins further comprise mono-branched isoparaffins (MP), di-branched isoparaffins (DP), tri-branched isoparaffins (TP); the olefins also include linear olefins (NO), isoolefins (BO).
According to the method of the present invention, preferably, the directly generating the gasoline molecular composition model in the step (4) includes: after obtaining the complete monomer hydrocarbon molecule composition result, respectively establishing a molecule object for each molecule, wherein the molecule object is used for executing the operation of inquiring the molecule property and the molecule content;
a gasoline object is re-established, the gasoline object including the molecular object, the gasoline object being used to perform a calculation gasoline macro-property operation.
In another aspect, the present invention further provides a system for directly constructing a gasoline molecular composition model, wherein the system includes:
a first unit for performing monomer hydrocarbon analysis on a gas chromatography detection result of a gasoline sample to identify molecules possibly contained in each peak in a gas chromatogram, and calculating a relative fraction of each peak;
a second unit for classifying chromatographic peaks according to the results of the analysis of the monomeric hydrocarbons and according to their types;
the third unit is used for analyzing the classified chromatographic peaks according to the types of the chromatographic peaks and the types of molecules to obtain a complete monomer hydrocarbon molecule composition result;
a fourth unit for directly generating a compositional model of a gasoline molecule from the complete monomeric hydrocarbon molecule compositional result.
According to the system of the present invention, preferably, in the third unit, the analysis is performed by using a peak adjustment module based on statistical distribution to analyze the classified chromatographic peaks according to chromatographic peak types and molecular types.
According to the system of the present invention, preferably, the peak adjusting module based on statistical distribution specifically includes:
a first module for fitting a distribution of known peaks: classifying known peaks according to molecular types and carbon numbers, and fitting classified data of each series according to statistical distribution;
a second module for splitting of co-escape peaks: splitting the co-escaping peak, assuming the relative content of each component in the co-escaping peak, sequentially checking all hypotheses, and selecting a proper hypothesis to accept; continuously repeating the processes until all the common escape peaks are separated;
a third module for inferring that unidentified peaks: supposing the molecular type and carbon number of components contained in unidentified peaks, sequentially checking all hypotheses, and selecting proper hypotheses for acceptance; the above process is repeated until all unidentified peaks are concluded to be complete.
In yet another aspect, the present invention also provides a method for predicting macroscopic properties of gasoline, wherein the method comprises the following steps:
constructing a gasoline molecular composition model according to the method for directly constructing the gasoline molecular composition model so as to obtain the molecular composition of gasoline and the properties of each molecule;
and predicting the macroscopic properties of the gasoline according to the molecular composition of the gasoline and the properties of each molecule through a corresponding macroscopic property mixing rule.
According to the method for predicting the macroscopic properties of the gasoline, the macroscopic properties preferably comprise density, distillation range, octane number, refractive index, molecular weight and Reid vapor pressure.
In yet another aspect, the present invention further provides a system for predicting macroscopic properties of gasoline, wherein the system comprises:
a first unit, which is used for constructing a composition model of gasoline molecules according to the method for directly constructing the gasoline molecule composition model so as to obtain the molecular composition of gasoline and the properties of each molecule;
and the second unit is used for predicting the macroscopic properties of the gasoline according to the molecular composition of the gasoline and the properties of each molecule through a corresponding macroscopic property mixing rule.
The method provided by the invention is based on a gas chromatography detection result, the molecular composition is reconstructed by using a peak modulation algorithm (SPT algorithm) based on statistical distribution, then a gasoline molecular composition model is established, and the macroscopic property of gasoline is predicted.
Compared with the prior art, the method provided by the invention has the following advantages:
1. the method provided by the invention combines the advantages of an experimental method and a computer reconstruction method, and provides a complete and stable molecular composition result;
2. the method does not need to measure the wave spectrum and the physical properties of a large number of samples for association training, has small workload and low cost, and saves manpower and material resources;
3. the method can directly predict the property of the gasoline from the molecular composition, is not influenced by macroscopic property experimental values, and has more reference value;
4. the spectrogram fine tuning algorithm provided by the method is directly based on statistical distribution, macroscopic properties are not needed to participate in correction, and the method can be realized only by using gas chromatography results.
Drawings
FIG. 1 is a schematic flow chart of a method for directly constructing a gasoline molecular composition model and a property prediction method provided in example 1 of the present invention;
FIG. 2 is a gas chromatogram of gasoline in example 1 of the present invention and an example of three types of peaks (known peak, co-escaping peak, and unidentified peak);
FIG. 3 is a flow chart of step one of the SPT algorithm;
FIG. 4 is a flowchart of step two of the SPT algorithm;
FIG. 5 is a flowchart of step three of the SPT algorithm;
FIG. 6 is an exemplary graph of the processing results of the SPT algorithm for the n-alkane (NP) series molecular content in example 1 of the present invention;
FIG. 7 is an exemplary graph of the SPT algorithm processing results of the single branched alkane (MP) series molecular content in example 1 of the present invention;
FIG. 8 is an exemplary graph of SPT algorithm processing results of the double branched alkane (DP) series of molecules in example 1 of the present invention;
FIG. 9 is an exemplary graph of the processing results of the SPT algorithm for the three-branched alkane series (TP) molecular content in example 1 of the present invention;
FIG. 10 is a graph showing an example of the SPT algorithm processing results of the straight chain olefin (NO) series molecular content in example 1 of the present invention;
FIG. 11 is a graph showing an example of the processing result of the branched olefin series (BO) molecular content SPT algorithm in example 1 of the present invention;
FIG. 12 is an exemplary graph of the processing results of the cyclic alkane series (NC) molecular content SPT algorithm in example 1 of the present invention;
FIG. 13 is an exemplary graph of the SPT algorithm processing results of the aromatic hydrocarbon series (A) molecular content in example 1 of the present invention;
fig. 14 is a graph comparing the predicted values of the properties of the gasoline samples obtained in example 1 of the present invention with the experimental values of the properties of the gasoline samples.
Detailed Description
In order to clearly understand the technical features, objects and advantages of the present invention, the following detailed description of the technical solutions of the present invention will be made with reference to the following specific examples, which should not be construed as limiting the implementable scope of the present invention.
Example 1
The embodiment provides a method for directly constructing a gasoline molecular composition model from a gas chromatography result and a method for predicting a macroscopic property of gasoline, wherein a flow chart of the method is shown in fig. 1, and as can be seen from fig. 1, the method comprises the following steps:
performing gas chromatography detection on a catalytic cracking gasoline sample, and performing monomer hydrocarbon analysis (national petrochemical industry standard SH/T0714-;
in the present example, the gas chromatography detection was performed by using an Agilent 7890B gas chromatograph equipped with a hydrogen Flame Ionization Detector (FID), manufactured by Agilent corporation, usa. Wherein the chromatographic column is an elastic quartz capillary column special for PONA analysis, the stationary phase is 100% methyl silicone, the column length is 50m, the inner diameter is 0.2mm, and the liquid film thickness is 0.2 μm; the column front pressure is 86 KPa; the initial temperature of the chromatographic temperature raising program is 35 ℃, the temperature is kept for 5 minutes, the temperature raising rate is 2 ℃/min, the final temperature is 200 ℃, and the final temperature retention time is 10 minutes; the temperature of the sample injector is 250 ℃, the split ratio is 150:1, and the sample injection amount is 0.5 mu L; the temperature of the detector is 250 ℃, the gas is hydrogen, the flow rate is 35mL/min, the combustion-supporting gas is air, the flow rate is 350mL/min, the compensation gas is nitrogen, and the flow rate is 35 mL/min; the carrier gas is nitrogen, the average linear velocity is 12cm/s, and the gas chromatogram of the catalytic cracking gasoline sample is shown in figure 2.
(2) Classifying chromatographic peaks according to the analysis result of the monomer hydrocarbon and the types of the chromatographic peaks, and dividing the chromatographic peaks into known peaks, common escape peaks and unidentified peaks; examples of each type of chromatographic peak are shown in FIG. 2.
(3) Analyzing the classified chromatographic peaks by adopting a gamma distribution-based peak adjusting algorithm (SPT) according to the types of the chromatographic peaks and the types of molecules to obtain a complete monomer hydrocarbon molecule composition result, wherein a specific flow chart of the gamma distribution-based peak adjusting algorithm is shown in figures 3-5, and the complete monomer hydrocarbon molecule composition result is shown in figures 6-13;
in the step (3), the molecular types comprise Normal Paraffin (NP), isoparaffin, olefin, cycloparaffin (NC) and aromatic hydrocarbon (A); wherein the isoparaffin comprises mono-branched isoparaffin (MP), di-branched isoparaffin (DP), tri-branched isoparaffin (TP); the olefins also include linear olefins (NO), isoolefins (branched olefins, BO).
The sorted monomeric hydrocarbons (known peaks) were processed according to step 1 of fig. 3 to obtain a data matrix. The number of carbons in each series in the data matrix and the content fraction at the corresponding number of carbons are data used for fitting. In this example, the data of each series are fitted according to the gamma distribution, so that the parameters and the root mean square error of the molecular content distribution of each series can be obtained.
The cosumps were resolved as per step 2 of fig. 4: first assume the relative content of molecules contained by the coeffusion peaks. For example, a co-escape peak containing both the components of molecule a and molecule B, it is not assumed that the relative content of molecule a is 1 and the relative content of molecule B is 0 in the co-escape peak. There may be many such hypotheses, and we may build a list of hypotheses and examine the hypotheses separately.
After all the hypotheses are checked, selecting a proper hypothesis, receiving the relative content of the common-escape components in the hypothesis, classifying and updating the data matrix. And carrying out gamma fitting by using the updated data to obtain new parameters and root mean square errors.
The unidentified peaks were deduced according to figure 5, step 3: it was first assumed that the unidentified peaks contained only one component. The possible carbon number of the component is then assumed, as well as the possible molecular type. For example, it is not assumed that a molecule included in a certain unidentified peak is aromatic in type and has a carbon number of 10. Similarly, we bring together various hypotheses, build a list of hypotheses, and examine the hypotheses separately. After all the hypotheses are checked, selecting a proper hypothesis, receiving the molecular type and the carbon number in the hypothesis, classifying and updating the data matrix. And carrying out gamma fitting by using the updated data to obtain new parameters and root mean square errors.
Fig. 6-13 show the variation of the content distribution data of each molecular series after processing by the peak adjustment algorithm from step 1 to step 3, respectively. In this example, the molecular types will be divided into 8 categories, NP, MP, DP, TP, NO, BO, NC, A. The 8 types of processing results correspond to fig. 6 to 13, respectively. Each graph comprises three sub-graph steps 1-3, corresponding to the three steps of the peak adjustment algorithm. The abscissa of each of these plots is the carbon number, and the ordinate is the mass fraction.
The results of the three steps of the peak adjustment algorithm are now presented using fig. 8, which is an example of data corresponding to a double branched alkane. For convenience of presentation, the three sub-diagrams of FIG. 8 are labeled herein as FIG. 8-1, FIG. 8-2, and FIG. 8-3, respectively. Scatter in FIG. 8-1 is the molecular content of the double branched alkane series in the data matrix obtained after completion of step 1 of the peak adjustment algorithm; the dashed lines are Parameters Parameters obtained by the fitting of step 11,DPRepresentative gamma distribution. Similar to FIG. 8-1, the scatter points in FIGS. 8-2 and 8-3 are the molecular contents of the unbranched alkane series in the data matrix obtained after completion of step 2 and step 3, respectively, in the peak adjustment algorithm, and the blue dotted lines are the Parameters, respectively2,DPAnd Parameters3,DPThe gamma distribution represented. It can be seen that the divergence point in fig. 8-1 deviates to a greater extent from the dashed line, especially the divergence point at C7 is significantly lower than the dashed line. This is because some of the double-branched alkane molecules are present as co-escaping peaks or unidentified peaks, and therefore the content of this portion of molecules does not statistically enter the data matrix of step 1. It can be seen from fig. 8-2 that when step 2 of the peak adjustment algorithm, i.e. the cosecant peak splitting, is completed, the distribution of the scatter has been much closer to the dashed line.
Fig. 8-3 does not provide a significant improvement over fig. 8-2 for two possible reasons. Firstly, the content fraction of unidentified peaks is too low, so that the change is not obvious; another aspect is that there are other molecular types whose content distributions deviate further from the gamma distribution, thus making the peak adjustment algorithm more inclined to infer unidentified peaks as molecules of that type during the calculation of step 3. This can be seen in fig. 9. Fig. 9-2 demonstrates that the content distribution of the tri-branched alkane is still far from the ideal gamma distribution even though the resolution of the coeffset peak is completed. Thus, the algorithm would prefer to infer the unidentified peaks as molecules of the type of tri-branched alkane at step 3. The scatter distribution in fig. 9-3 is thus significantly improved.
Again, figure 13 is observed for aromatics data. FIG. 13-1 shows that the aromatic content at C7 is 0. This is because in the GC-FID experimental environment, toluene is always co-flowed with 2,3, 3-trimethylpentane. After the processing of a peak adjusting algorithm, a relatively reasonable toluene content can be obtained. In addition, it is worth mentioning that all 11 n-alkanes in the library are usually identified by GC-FID and there is no co-efflux. Thus, all images in fig. 6 corresponding to the n-alkane data are identical.
Therefore, the distribution curve of the gasoline molecular composition reconstructed by the peak regulation algorithm is more reasonable.
(4) The composition model of the gasoline molecules is directly generated from the complete monomer hydrocarbon molecule composition results by using software (a property prediction module, more specifically, a molecular composition model), and specifically comprises: reading molecular composition information, instantiating a gasoline object, each molecule in the molecular composition being instantiated as a molecular object contained in the gasoline object;
(5) obtaining the molecular composition of the gasoline and the properties of each molecule according to the composition model of the gasoline molecules, and predicting the macroscopic properties of the gasoline according to the molecular composition of the obtained gasoline and the properties of each molecule by a mixing rule; the method specifically comprises the following steps: the gasoline molecular composition model is that a gasoline object is established through molecular composition, the object comprises molecular objects established by various molecules, and the molecular objects can perform a series of operations of inquiring molecular properties, relative contents of the molecules in gasoline and the like. The properties of each molecule were derived from the NIST database. The gasoline object may perform a series of operations such as calculating a macroscopic property of gasoline. The macroscopic properties are calculated by the molecular properties of each molecule, the relative amounts, and the corresponding macroscopic property mixing rules.
The predicted values are shown in Table 1. A graph comparing the predicted values of the properties of the gasoline samples with the experimental values of the properties of the gasoline samples (measured using methods conventional in the art) is shown in fig. 14.
TABLE 1
Note: the experimental values for the macroscopic properties of the catalytically cracked gasoline samples in Table 1 were determined using methods conventional in the art, specifically, wherein the specific gravity was determined according to GBT 1884-2000, the distillation range was determined according to GBT 6536-2010, the research octane number was determined according to GBT 5487-1995, the hydrocarbon content was determined according to GBT 11132-2008, and the Reid vapor pressure (KPa) was determined according to GBT 8017-2012.
At present, the existing gasoline molecular level composition model in the field generally adjusts the composition according to the experimental value of the macroscopic property, and then the obtained composition is used for predicting the macroscopic property of the gasoline. Therefore, the model is greatly influenced by the input macroscopic property experiment value, and if the experiment error of the macroscopic property of the gasoline is large, the predicted value of the model has large error. Different from the model, the model provided by the application only depends on the results obtained after the GC-FID analysis and the peak regulation algorithm processing when the gasoline property is predicted, so that the model is not influenced by the experimental value of the gasoline property and has more reference value.
The prediction of the initial and final boiling points of the current ASTM D86 distillation curve is generally difficult, and as can be seen from Table 1, it also gives good results in the models provided herein; the experimental values of the volume fractions of the existing olefin, naphthenic hydrocarbon and aromatic hydrocarbon are measured by a fluorescence method, but the method has larger error and poor reproducibility, and the volume fraction predicted by the model is obtained based on the mass fraction of the monomer hydrocarbon obtained by GC-FID analysis, so that the result is more accurate and the reproducibility is good. Therefore, the volume fraction predicted by the model is more reliable; in addition, it can be seen from table 1 that the octane number and reid vapor pressure obtained by the method of the present invention have good prediction effect.
Claims (9)
1. A method for directly constructing a gasoline molecular composition model, the method comprising the steps of:
(1) performing monomer hydrocarbon analysis on the gas chromatography detection result of the gasoline sample to identify molecules possibly contained in each peak in the gas chromatogram, and calculating the relative fraction of each peak;
(2) classifying chromatographic peaks according to the analysis result of the monomer hydrocarbon and the types of the chromatographic peaks; the chromatographic peak types include known peaks, co-escaping peaks, and unidentified peaks;
(3) analyzing the classified chromatographic peaks according to the chromatographic peak types and the molecular types to obtain a complete monomer hydrocarbon molecule composition result;
in the analysis in the step (3), the classified chromatographic peaks are analyzed by adopting a peak adjusting algorithm based on statistical distribution according to the types of the chromatographic peaks and the types of molecules;
the peak adjustment algorithm based on statistical distribution specifically comprises the following steps:
fitting the distribution of known peaks: classifying known peaks according to molecular types and carbon numbers, and fitting classified data of each series according to statistical distribution;
resolution of the cosescape peak: splitting the co-escaping peak, assuming the relative content of each component in the co-escaping peak, sequentially checking all hypotheses, and selecting a proper hypothesis to accept; continuously repeating the processes until all the common escape peaks are separated;
extrapolation of unidentified peaks: supposing the molecular type and carbon number of components contained in unidentified peaks, sequentially checking all hypotheses, and selecting proper hypotheses for acceptance; continuously repeating the above processes until all unidentified peaks are concluded;
(4) directly generating a composition model of gasoline molecules from the complete monomer hydrocarbon molecule composition result; the step (4) of directly generating the gasoline molecular composition model comprises the following steps: after obtaining the complete monomer hydrocarbon molecule composition result, respectively establishing a molecule object for each molecule, wherein the molecule object is used for executing the operation of inquiring the molecule property and the molecule content;
a gasoline object is re-established, the gasoline object including the molecular object, the gasoline object being used to perform a calculation gasoline macro-property operation.
2. The method of claim 1, wherein the gasoline sample comprises catalytically cracked gasoline, catalytically reformed gasoline, straight run gasoline, catalytically cracked gasoline, hydrogenated gasoline, or coker gasoline.
3. The method of claim 1 or 2, wherein the statistical distribution comprises a gamma distribution.
4. The method of claim 1 or 2, wherein the molecular species in step (3) comprise normal paraffins, isoparaffins, olefins, naphthenes, and aromatics.
5. The method of claim 3, wherein the molecular species in step (3) include normal paraffins, isoparaffins, olefins, naphthenes, and aromatics.
6. A system for directly modeling the composition of gasoline molecules, the system comprising:
a first unit for performing monomer hydrocarbon analysis on a gas chromatography detection result of a gasoline sample to identify molecules possibly contained in each peak in a gas chromatogram, and calculating a relative fraction of each peak;
a second unit for classifying chromatographic peaks according to the results of the analysis of the monomeric hydrocarbons and according to their types; the chromatographic peak types include known peaks, co-escaping peaks, and unidentified peaks;
the third unit is used for analyzing the classified chromatographic peaks according to the types of the chromatographic peaks and the types of molecules to obtain a complete monomer hydrocarbon molecule composition result; in the third unit, the analysis is to analyze the classified chromatographic peaks by adopting a peak adjusting module based on statistical distribution according to the types of the chromatographic peaks and the types of molecules;
the peak adjustment module based on statistical distribution specifically includes:
a first module for fitting a distribution of known peaks: classifying known peaks according to molecular types and carbon numbers, and fitting classified data of each series according to statistical distribution;
a second module for splitting of co-escape peaks: splitting the co-escaping peak, assuming the relative content of each component in the co-escaping peak, sequentially checking all hypotheses, and selecting a proper hypothesis to accept; continuously repeating the processes until all the common escape peaks are separated;
a third module for inferring that unidentified peaks: supposing the molecular type and carbon number of components contained in unidentified peaks, sequentially checking all hypotheses, and selecting proper hypotheses for acceptance; continuously repeating the above processes until all unidentified peaks are concluded;
a fourth unit for directly generating a compositional model of gasoline molecules from the results of the complete monomeric hydrocarbon molecule composition; the fourth unit is specifically configured to: after obtaining the complete monomer hydrocarbon molecule composition result, respectively establishing a molecule object for each molecule, wherein the molecule object is used for executing the operation of inquiring the molecule property and the molecule content;
a gasoline object is re-established, the gasoline object including the molecular object, the gasoline object being used to perform a calculation gasoline macro-property operation.
7. A method for predicting macroscopic properties of gasoline, the method comprising the steps of:
constructing a gasoline molecular composition model according to the method for directly constructing a gasoline molecular composition model of any one of claims 1 to 5, so as to obtain the molecular composition of gasoline and the properties of each molecule;
and predicting the macroscopic properties of the gasoline according to the molecular composition of the gasoline and the properties of each molecule through a corresponding macroscopic property mixing rule.
8. The method of claim 7, wherein the macroscopic properties include density, distillation range, octane number, refractive index, molecular weight, and Reid vapor pressure.
9. A system for predicting macroscopic properties of gasoline, the system comprising:
a first unit for modeling the composition of gasoline molecules according to the method for directly modeling the composition of gasoline molecules as claimed in any one of claims 1 to 5, to obtain the molecular composition of gasoline and the properties of each molecule;
and the second unit is used for predicting the macroscopic properties of the gasoline according to the molecular composition of the gasoline and the properties of each molecule through a corresponding macroscopic property mixing rule.
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