A method for constructing a diesel fraction molecular library and predicting diesel molecular composition based on conventional diesel detection methods

Abstract

The present invention relates to a method for constructing a diesel fraction molecular library and predicting diesel molecular composition based on conventional diesel detection means. The present invention makes full use of the existing laboratory detection results to establish a model that can more truly reflect the objective diesel molecular distribution law; through the established diesel fraction molecular distribution model, a diesel fraction molecular composition with a high degree of consistency with the molecular composition detected by the instrument is obtained. The diesel fraction molecular distribution model established by the present invention can be well adapted to the subsequent optimization algorithm, solving the problem of difficult convergence and slow speed of optimization during the calculation process.

Description

Technical Field

[0001] The invention relates to the technical field of petroleum refining and petrochemical production, and in particular to a method for constructing a diesel fraction molecular library and predicting diesel molecular composition based on conventional diesel detection means. Background Art

[0002] In the process of molecular management of oil processing, the molecular composition of fractions and oil products is the basic information required. Reasonable and complete description of the molecular structure and content of each stream in the processing process is of great significance for refineries to manage the oil processing process at the molecular level. At present, for components with a boiling point higher than gasoline, the mainstream analysis method cannot provide qualitative and quantitative information on all molecular details, and the same is true for diesel fractions. Therefore, it is particularly important to predict the molecular composition of diesel fractions by constructing a model of the molecular composition of diesel fractions based on conventional detection methods.

[0003] The existing molecular composition models can be summarized into two types: maximum information entropy method and random reconstruction method. The basic principle of the maximum information entropy method is to try to ensure the inherent uncertainty in the processed information and reduce the loss of information without knowing the exact information. In the construction of diesel molecular composition, the maximum entropy method means erasing the difference in molecular concentration, that is, taking the average value. It can be seen from the literature and related experimental reports that the concentration of petroleum molecules at different carbon numbers meets a certain distribution law, rather than being uniform, so the assumption of maximum information entropy is biased. The random reconstruction method requires the construction of tens of thousands to hundreds of thousands of virtual molecules, and only this number can keep the parameters for various properties stable. It can be seen that this method has a large randomness, low computational efficiency, and limited optimization ability of the optimization algorithm, making it difficult to optimize to the global optimum.

[0004] In general, the existing molecular composition model still has major defects and does not yet have a practical basis. The reasons are: 1) The experimental data and the objective laws of petroleum molecule distribution are not fully utilized, resulting in a large deviation between the calculated molecular composition and the actual; 2) The scale is too small when the single molecule concentration is used as a variable for adjustment, and there are too many optimization variables, which will increase the burden of the optimization algorithm, making the optimization algorithm's optimization ability limited, slow, and convergence impossible to guarantee, and the result is not a global solution; 3) The adjustment method using the single molecule concentration as a variable makes the molecular composition model too extensive. As a result, in the final result, there are some molecules with too large concentrations that are uncontrollable, which affects practical applications. Summary of the invention

[0005] The present invention aims to overcome the above-mentioned shortcomings and provides a method for constructing a diesel fraction molecular library and predicting diesel molecular composition based on conventional diesel detection means. The present invention makes full use of the existing laboratory detection results to establish a model that can more realistically reflect the objective diesel molecular distribution law; through the established diesel fraction molecular distribution model, a diesel fraction molecular composition with a high degree of consistency with the molecular composition detected by the instrument is obtained. The diesel fraction molecular distribution model established by the present invention can be well adapted to the subsequent optimization algorithm, solving the problem of difficult convergence and slow speed of optimization during the calculation process.

[0006] The present invention achieves the above-mentioned purpose through the following technical scheme: a method for constructing a diesel fraction molecular library and predicting diesel molecular composition based on conventional diesel detection means, comprising the following steps:

[0007] (1) Construct a macroscopic property library and molecular information library of diesel fractions based on conventional diesel detection methods;

[0008] (2) construct diesel molecular composition based on definitions at different scales;

[0009] (3) Predict the molecular composition of any diesel fraction based on its macroscopic properties.

[0010] Preferably, the step (1) comprises the following sub-steps:

[0011] (1.1) Based on conventional diesel testing methods, the unsaturation range, carbon number range, and relative content of diesel molecules under various unsaturation and carbon number ranges are obtained. This data is also used as reference data for step (3);

[0012] (1.2) Detect different diesel fractions based on conventional diesel detection methods and build a macroscopic property library of diesel fractions;

[0013] (1.3) constructing corresponding diesel fraction molecules according to the unsaturation range and carbon number range of the molecules in step (1.1);

[0014] (1.4) Based on the diesel fraction molecules generated in step (1.3), the relevant physical properties of each molecule are calculated to construct a complete diesel fraction molecule information database.

[0015] Preferably, the conventional diesel detection means in step (1.1) include but are not limited to one or more of gas chromatography / field ionization-time of flight mass spectrometry (GC / FI-TOF MS), Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), and gas chromatography-mass spectrometry (GC-MS).

[0016] Preferably, the conventional diesel detection means in step (1.2) include but are not limited to one or more of simulated distillation, actual boiling point distillation, Enrichment distillation, density, elemental analysis, group composition analysis, and cetane number analysis, to obtain the macroscopic properties of the diesel fraction, including one or more of the simulated distillation temperature, actual boiling point distillation temperature, Enrichment distillation temperature, density, distillation range, element content, group composition, and cetane number index of the diesel fraction.

[0017] Preferably, the relevant physical properties of the molecules in step (1.4) include physical properties of the molecules, i.e. boiling point, melting point, density, specific heat; critical properties, i.e. critical temperature, critical pressure, critical volume; thermodynamic properties, i.e. vapor pressure, enthalpy, entropy, Gibbs free energy.

[0018] Preferably, the step (2) comprises the following sub-steps:

[0019] (2.1) The composition of diesel molecules is defined at different scales, from microscopic to macroscopic levels. The molecules are defined at the microscopic level, core level, sub-family level and family level, a total of four levels; each level has a corresponding composition;

[0020] (2.2) The microscopic level is the molecular level, and the corresponding composition is the molecular composition;

[0021] (2.3) Molecules belonging to the same homologue are grouped into core families, and the sum of the molecular compositions belonging to the same core family is the core family composition;

[0022] (2.4) Core families with the same degree of unsaturation are grouped into subfamilies, and the sum of the core family compositions belonging to the same subfamily is the subfamily composition;

[0023] (2.5) Different sub-groups are classified into different macro-groups according to the characteristics tested by SH / T 0606 method. The sum of the sub-group compositions belonging to the same macro-group is the macro-group composition, which is equivalent to the hydrocarbon group composition of the diesel fraction determined by SH / T 0606 method;

[0024] (2.6) The relative content of molecules in the same core group is represented by a distribution function; wherein the distribution function is selected from one of the probability distribution functions such as gamma distribution, beta distribution, histogram distribution, chi-square distribution, etc., or is the superposition of the above multiple distribution functions.

[0025] Preferably, the step (3) comprises the following sub-steps:

[0026] (3.1) The diesel macroscopic properties that need to be provided include one or more of distillation temperature, density, elemental analysis, group composition, and cetane index;

[0027] (3.2) according to the provided macroscopic properties, in the diesel fraction macroscopic property library generated in step (1), find a fraction with the closest macroscopic properties; extract the molecular information library of the fraction for calculation of the actual molecular composition, and extract the relative content data of the fraction under various unsaturation and carbon number ranges as reference distribution data;

[0028] (3.3) Iterating by using global optimization algorithm to optimize parameter variables related to molecular composition;

[0029] (3.4) The parameter variables related to molecular composition include the composition of each subfamily, the composition of each core family, and the parameters of the distribution function;

[0030] (3.5) The optimization algorithms used include but are not limited to genetic algorithm, sequential quadratic programming (SQP) algorithm, and simplex method;

[0031] (3.6) In each iteration, the macroscopic family composition is the input GC-MS detection data, and the composition of each subfamily, the composition of each core family, and the parameters of the distribution function are determined by the algorithm; after the composition at each level from macroscopic to microscopic is determined, the concentration of each molecule can be obtained;

[0032] (3.7) After obtaining the molecular concentration, the corresponding physical property calculation model is used to calculate the corresponding macroscopic properties, thereby calculating the absolute error sum between the actual value and the estimated value of the macroscopic properties;

[0033] (3.8) After obtaining the molecular concentration, compare it with the reference data extracted in step (3.2) and calculate the absolute deviation and of the molecular distribution;

[0034] (3.9) Add the absolute error sum calculated in step (3.7) and the absolute deviation sum calculated in step (3.8) as the optimization objective function;

[0035] (3.10) After the optimization algorithm is completed, the parameters obtained are the optimal parameters of the prediction model, and the molecular composition calculated based on the parameters is the final diesel molecular composition.

[0036] The beneficial effects of the present invention are: (1) The construction of the molecular library of the present invention is based on experimental data, and the obtained molecular library is more reliable; (2) The molecular composition construction method of the present invention is established on different scales, and the molecular composition is controlled by step-by-step control of each sub-family composition, core composition and distribution function. Based on this, the molecular composition can be regulated, and there will be no sudden change in the molecular content, which is inconsistent with the actual situation; (3) The present invention uses a distribution function to represent the relative content of molecules in the same core family, which is closer to the analysis result of the diesel fraction, making the results obtained by the model more practical; (4) The present invention uses a distribution function to represent the relative content of molecules in the same core family, which reduces the number of optimization variables and greatly improves the optimization ability of the optimization algorithm; (5) When calculating the actual composition of the molecule, the present invention adopts a global optimization algorithm and takes into account the deviation of the reference distribution law, so the optimization effect is better, the molecular composition obtained by optimization is closer to the actual composition, and the result is more reasonable. DETAILED DESCRIPTION

[0037] The present invention is further described below in conjunction with specific embodiments, but the protection scope of the present invention is not limited thereto:

[0038] Example 1: Taking the establishment of a macroscopic property library and a molecular information library of a certain atmospheric diesel fraction as an example, a method for constructing a diesel fraction molecular library and predicting diesel molecular composition based on conventional diesel detection means comprises the following steps:

[0039] (1) Construct a macroscopic property library and molecular information library of diesel fractions based on conventional diesel detection methods;

[0040] 1) A certain atmospheric diesel fraction was analyzed by gas chromatography / field ionization-time of flight mass spectrometry (GC / FI-TOF MS) to obtain the data of the unsaturation range, carbon number range, and relative content of the atmospheric diesel molecule under each unsaturation and carbon number range.

[0041] 2) Based on conventional diesel testing methods, the macroscopic properties of diesel fractions are obtained, including: Ennis distillation temperature, density, distillation range, element content, group composition, and cetane index of atmospheric diesel. A macroscopic property library of atmospheric diesel fractions is constructed.

[0042] 3) Based on the unsaturation range and carbon number range of the molecules in 1), the molecules of the atmospheric diesel fraction are constructed, totaling 2113.

[0043] 4) According to the atmospheric pressure diesel fraction molecules generated in 3), the relevant physical properties of each molecule are calculated, including the physical properties of the molecule (such as boiling point, melting point, density, specific heat, etc.), critical properties (such as critical temperature, critical pressure, critical volume, etc.), and thermodynamic properties (such as vapor pressure, enthalpy, entropy, Gibbs free energy, etc.), so as to construct an atmospheric pressure diesel fraction molecule information database.

[0044] (2) construct diesel molecular composition based on definitions at different scales;

[0045] 1) The composition of a diesel fraction molecule is defined at different scales, and the molecule is defined as microscopic level, core level, sub-family level and family level, a total of four levels. Each level has a corresponding composition.

[0046] 2) The microscopic level is the molecular level, and the corresponding composition is the molecular composition.

[0047] 3) Molecules belonging to the same homologues are grouped into core families, and the sum of the molecular compositions belonging to the same core family is the core family composition.

[0048] 4) Core families with the same degree of unsaturation are classified into subfamilies, and the sum of the core family compositions belonging to the same subfamily is the subfamily composition.

[0049] 5) Different sub-groups are divided into different macro-groups according to the characteristics tested by SH / T 0606 method. The sum of the sub-group compositions belonging to the same macro-group is the macro-group composition, which is equivalent to the hydrocarbon group composition of the diesel fraction determined by SH / T 0606 method.

[0050] 6) The relative content of molecules in the same core group is expressed by a distribution function. The distribution function is gamma distribution.

[0051] (3) Predict the molecular composition of a certain atmospheric pressure diesel fraction through its macroscopic properties.

[0052] 1) The macroscopic properties of atmospheric diesel provided include: Enn distillation temperature, density, sulfur element analysis, nitrogen element analysis, and group composition.

[0053] 2) According to the provided macroscopic properties, find a fraction with the closest macroscopic properties in the atmospheric diesel fraction macroscopic property library. Extract the molecular information library of the fraction for calculation of the actual molecular composition, and extract the relative content of the fraction under various unsaturation and carbon number ranges as reference distribution data.

[0054] 3) Genetic algorithm optimization is used for iteration to optimize parameter variables related to molecular composition, including: the composition of each subfamily, the composition of each core family, and the parameters of the distribution function.

[0055] 4) At each iteration, the macroscopic group composition is the input GC-MS test data, and the composition of each subgroup, the composition of each core group, and the parameters of the distribution function are determined by the algorithm. Once the composition at each level from macro to micro is determined, the concentration of each molecule can be obtained.

[0056] 5) After obtaining the molecular concentration, the corresponding physical property calculation model can be used to calculate the corresponding macroscopic properties, thereby calculating the absolute error and the absolute deviation of the actual value and the estimated value of the macroscopic property and comparing them with the reference data extracted in 2) to calculate the absolute deviation and of the molecular distribution.

[0057] 6) The absolute error sum calculated in 5) and the absolute deviation sum calculated in 6) are added together to serve as the objective function of optimization.

[0058] 7) After the optimization algorithm is completed, the parameters obtained are the optimal parameters of the prediction model, and the molecular composition calculated based on the parameters is the final molecular composition of atmospheric diesel.

[0059] 8) Using the optimized parameters, recalculate the macroscopic properties and compare them with the provided macroscopic properties, see Table 1 below.

[0060]

[0061]

[0062] Table 1

[0063] Example 2: A method for constructing a diesel fraction molecular library and predicting diesel molecular composition based on conventional diesel detection means, comprising the following steps:

[0064] (1) Construct a macroscopic property library and molecular information library of diesel fractions based on conventional diesel detection methods;

[0065] 1) A certain atmospheric diesel fraction was analyzed by gas chromatography / field ionization-time of flight mass spectrometry (GC / FI-TOF MS) to obtain the data of the unsaturation range, carbon number range, and relative content of the atmospheric diesel molecule under each unsaturation and carbon number range.

[0066] 2) Based on conventional diesel testing methods, the macroscopic properties of diesel fractions are obtained, including: Ennis distillation temperature, density, distillation range, element content, group composition, and cetane index of atmospheric diesel. A macroscopic property library of atmospheric diesel fractions is constructed.

[0067] 3) Based on the unsaturation range and carbon number range of the molecules in 1), the molecules of the atmospheric diesel fraction are constructed, totaling 2113.

[0068] 4) According to the atmospheric pressure diesel fraction molecules generated in 3), the relevant physical properties of each molecule are calculated, including the physical properties of the molecule (such as boiling point, melting point, density, specific heat, etc.), critical properties (such as critical temperature, critical pressure, critical volume, etc.), and thermodynamic properties (such as vapor pressure, enthalpy, entropy, Gibbs free energy, etc.), so as to construct an atmospheric pressure diesel fraction molecule information database.

[0069] (2) construct diesel molecular composition based on definitions at different scales;

[0070] 1) The composition of a diesel fraction molecule is defined at different scales, and the molecule is defined as microscopic level, core level, sub-family level and family level, a total of four levels. Each level has a corresponding composition.

[0071] 2) The microscopic level is the molecular level, and the corresponding composition is the molecular composition.

[0072] 3) Molecules belonging to the same homologues are grouped into core families, and the sum of the molecular compositions belonging to the same core family is the core family composition.

[0073] 4) Core families with the same degree of unsaturation are classified into subfamilies, and the sum of the core family compositions belonging to the same subfamily is the subfamily composition.

[0074] 5) Different sub-groups are divided into different macro-groups according to the characteristics tested by SH / T 0606 method. The sum of the sub-group compositions belonging to the same macro-group is the macro-group composition, which is equivalent to the hydrocarbon group composition of the diesel fraction determined by SH / T 0606 method.

[0075] 6) The relative content of molecules in the same core group is expressed by a distribution function. The distribution function is gamma distribution.

[0076] (3) Predict the molecular composition of a catalytic diesel fraction through its macroscopic properties.

[0077] 1) The macroscopic properties of catalytic diesel provided include: Enn distillation temperature, density, sulfur element analysis, nitrogen element analysis, and group composition.

[0078] 2) According to the provided macroscopic properties, find a fraction with the closest macroscopic properties in the catalytic diesel fraction macroscopic property library. Extract the molecular information library of the fraction for calculation of the actual molecular composition, and extract the relative content data of the fraction under various unsaturation and carbon number ranges as reference distribution data.

[0079] 3) A sequential quadratic programming algorithm is used for iteration to optimize parameter variables related to molecular composition, including the composition of each subfamily, the composition of each core family, and the parameters of the distribution function.

[0080] 4) At each iteration, the macroscopic group composition is the input GC-MS test data, and the composition of each subgroup, the composition of each core group, and the parameters of the distribution function are determined by the algorithm. Once the composition at each level from macro to micro is determined, the concentration of each molecule can be obtained.

[0081] 5) After obtaining the molecular concentration, the corresponding physical property calculation model can be used to calculate the corresponding macroscopic properties, thereby calculating the absolute error and the absolute deviation of the actual value and the estimated value of the macroscopic property and comparing them with the reference data extracted in 2) to calculate the absolute deviation and of the molecular distribution.

[0082] 6) The absolute error sum calculated in 5) and the absolute deviation sum calculated in 6) are added together to serve as the objective function of optimization.

[0083] 7) After the optimization algorithm is completed, the obtained parameters are the optimal parameters of the prediction model, and the molecular composition calculated based on the parameters is the final molecular composition of the catalytic diesel.

[0084] 8) Using the optimized parameters, recalculate the macroscopic properties and compare them with the provided macroscopic properties, see Table 2 below.

[0085]

[0086] Table 2

[0087] Example 3: A method for constructing a diesel fraction molecular library and predicting diesel molecular composition based on conventional diesel detection means, comprising the following steps:

[0088] (1) Construct a macroscopic property library and molecular information library of diesel fractions based on conventional diesel detection methods;

[0089] 1) A certain atmospheric diesel fraction was analyzed by gas chromatography / field ionization-time of flight mass spectrometry (GC / FI-TOF MS) to obtain the data of the unsaturation range, carbon number range, and relative content of the atmospheric diesel molecule under each unsaturation and carbon number range.

[0090] 2) Based on conventional diesel testing methods, the macroscopic properties of diesel fractions are obtained, including: Ennis distillation temperature, density, distillation range, element content, group composition, and cetane index of atmospheric diesel. A macroscopic property library of atmospheric diesel fractions is constructed.

[0091] 3) Based on the unsaturation range and carbon number range of the molecules in 1), the molecules of the atmospheric diesel fraction are constructed, totaling 2113.

[0092] 4) According to the atmospheric pressure diesel fraction molecules generated in 3), the relevant physical properties of each molecule are calculated, including the physical properties of the molecule (such as boiling point, melting point, density, specific heat, etc.), critical properties (such as critical temperature, critical pressure, critical volume, etc.), and thermodynamic properties (such as vapor pressure, enthalpy, entropy, Gibbs free energy, etc.), so as to construct an atmospheric pressure diesel fraction molecule information database.

[0093] (2) construct diesel molecular composition based on definitions at different scales;

[0094] 1) The composition of a diesel fraction molecule is defined at different scales, and the molecule is defined as microscopic level, core level, sub-family level and family level, a total of four levels. Each level has a corresponding composition.

[0095] 2) The microscopic level is the molecular level, and the corresponding composition is the molecular composition.

[0096] 3) Molecules belonging to the same homologue are grouped into core families, and the sum of the molecular compositions belonging to the same core family is the core family composition.

[0097] 4) Core families with the same degree of unsaturation are classified into subfamilies, and the sum of the core family compositions belonging to the same subfamily is the subfamily composition.

[0098] 5) Different sub-groups are divided into different macro-groups according to the characteristics tested by SH / T 0606 method. The sum of the sub-group compositions belonging to the same macro-group is the macro-group composition, which is equivalent to the hydrocarbon group composition of the diesel fraction determined by SH / T 0606 method.

[0099] 6) The relative content of molecules in the same core group is expressed by a distribution function. The distribution function is selected as gamma distribution.

[0100] (3) Predict the molecular composition of a cracked diesel fraction through its macroscopic properties.

[0101] 1) The macroscopic properties of cracked diesel provided include: Enrichment distillation temperature, density, sulfur element analysis, and group composition.

[0102] 2) According to the provided macroscopic properties, find a fraction with the closest macroscopic properties in the cracked diesel fraction macroscopic property library. Extract the molecular information library of the fraction for calculation of the actual molecular composition, and extract the relative content data of the fraction under various unsaturation and carbon number ranges as reference distribution data.

[0103] 3) The simplex optimization is iteratively performed to optimize the parameter variables related to the molecular composition, including: the composition of each subfamily, the composition of each core family, and the parameters of the distribution function.

[0104] 4) At each iteration, the macroscopic group composition is the input GC-MS test data, and the composition of each subgroup, the composition of each core group, and the parameters of the distribution function are determined by the algorithm. Once the composition at each level from macro to micro is determined, the concentration of each molecule can be obtained.

[0105] 5) After obtaining the molecular concentration, the corresponding physical property calculation model can be used to calculate the corresponding macroscopic properties, thereby calculating the absolute error and the absolute deviation of the actual value and the estimated value of the macroscopic property and comparing them with the reference data extracted in 2) to calculate the absolute deviation and of the molecular distribution.

[0106] 6) The absolute error sum calculated in 5) and the absolute deviation sum calculated in 6) are added together to serve as the objective function of optimization.

[0107] 7) After the optimization algorithm is completed, the obtained parameters are the optimal parameters of the prediction model, and the molecular composition calculated based on the parameters is the final molecular composition of the cracked diesel.

[0108] 8) Using the optimized parameters, recalculate the macroscopic properties and compare them with the provided macroscopic properties, see Table 3 below.

[0109]

[0110] Table 3

[0111] The above description is a specific embodiment of the present invention and the technical principles used. If the changes made according to the concept of the present invention do not exceed the spirit covered by the specification, they should still fall within the scope of protection of the present invention.

Claims

1. A method for constructing a diesel fraction molecular library and predicting diesel molecular composition based on conventional diesel detection methods, characterized in that: The steps include: (1) Construct a macroscopic property library and molecular information library of diesel fractions based on conventional diesel detection methods; (2) Constructing the molecular composition of diesel based on definitions at different scales; specifically, it includes the following sub-steps: (2.1) The composition of diesel molecules is defined at different scales, from microscopic to macroscopic levels. The molecules are defined at the microscopic level, core level, sub-family level and family level, a total of four levels; each level has a corresponding composition; (2.2) The microscopic level is the molecular level, and the corresponding composition is the molecular composition; (2.3) Molecules belonging to the same homologue are grouped into core families, and the sum of the molecular compositions belonging to the same core family is the core family composition; (2.4) Core families with the same degree of unsaturation are grouped into subfamilies, and the sum of the core family compositions belonging to the same subfamily is the subfamily composition; (2.5) Different sub-groups are classified into different macro-groups according to the characteristics tested by SH / T 0606 method. The sum of the sub-group compositions belonging to the same macro-group is the macro-group composition, which is equivalent to the hydrocarbon group composition of the diesel fraction determined by SH / T 0606 method; (2.6) The relative content of molecules in the same core group is represented by a distribution function; wherein the distribution function is a superposition of one or more distribution functions selected from the group consisting of gamma distribution, beta distribution, histogram distribution, and chi-square distribution probability distribution function; (3) Predicting the molecular composition of any diesel fraction by macroscopic properties; specifically including the following sub-steps: (3.1) The diesel macroscopic properties that need to be provided include one or more of distillation temperature, density, elemental analysis, group composition, and cetane index; (3.2) according to the provided macroscopic properties, in the diesel fraction macroscopic property library generated in step (1), find a fraction with the closest macroscopic properties; extract the molecular information library of the fraction for calculating the actual molecular composition, and extract the relative content data of the fraction under various unsaturation and carbon number ranges as reference distribution data; (3.3) Iterating by using global optimization algorithm to optimize parameter variables related to molecular composition; (3.4) The parameter variables related to molecular composition include the composition of each subfamily, the composition of each core family, and the parameters of the distribution function; (3.5) The optimization algorithms used include but are not limited to genetic algorithm, sequential quadratic programming (SQP) algorithm, and simplex method; (3.6) In each iteration, the macroscopic family composition is the input GC-MS detection data, and the composition of each subfamily, the composition of each core family, and the parameters of the distribution function are determined by the algorithm; after the composition at each level from macroscopic to microscopic is determined, the concentration of each molecule can be obtained; (3.7) After obtaining the molecular concentration, the corresponding macroscopic properties are calculated using the corresponding physical property calculation model, thereby calculating the absolute error sum between the actual value of the macroscopic property and the estimated value; (3.8) After obtaining the molecular concentration, compare it with the reference data extracted in step (3.2) and calculate the absolute deviation and of the molecular distribution; (3.9) The absolute error sum calculated in step (3.7) and the absolute deviation sum calculated in step (3.8) are added together as the objective function of optimization; (3.10) After the optimization algorithm is completed, the parameters obtained are the optimal parameters of the prediction model, and the molecular composition calculated based on the parameters is the final diesel molecular composition.

2. A method for constructing a diesel fraction molecular library and predicting diesel molecular composition based on conventional diesel detection means according to claim 1, characterized in that: The step (1) comprises the following steps: (1.1) Based on conventional diesel detection methods, the unsaturation range, carbon number range, and relative content data of diesel molecules under each unsaturation and carbon number range are obtained, and this data is also used as reference data for step (3); (1.2) Detect different diesel fractions based on conventional diesel detection methods and build a macroscopic property library of diesel fractions; (1.3) constructing corresponding diesel fraction molecules according to the unsaturation range and carbon number range of the molecules in step (1.1); (1.4) Based on the diesel fraction molecules generated in step (1.3), the relevant physical properties of each molecule are calculated to construct a complete diesel fraction molecule information database.

3. A method for constructing a diesel fraction molecular library and predicting diesel molecular composition based on conventional diesel detection means according to claim 2, characterized in that: The conventional diesel detection means in step (1.1) include, but are not limited to, one or more of gas chromatography / field ionization-time of flight mass spectrometry GC / FI-TOF MS, Fourier transform ion cyclotron resonance mass spectrometry FT-ICR MS, and gas chromatography-mass spectrometry GC-MS.

4. A method for constructing a diesel fraction molecular library and predicting diesel molecular composition based on conventional diesel detection means according to claim 2, characterized in that: The conventional diesel detection means in the step (1.2) include but are not limited to one or more of simulated distillation, actual boiling point distillation, Enn's distillation, density, elemental analysis, group composition analysis, and cetane number analysis, to obtain the macroscopic properties of the diesel fraction, including one or more of the simulated distillation temperature, actual boiling point distillation temperature, Enn's distillation temperature, density, distillation range, element content, group composition, and cetane number index of the diesel fraction.

5. The method for constructing a diesel fraction molecular library and predicting diesel molecular composition based on conventional diesel detection means according to claim 2, characterized in that: The relevant physical properties of the molecule in step (1.4) include the physical properties of the molecule, namely, boiling point, melting point, density, and specific heat; Critical properties, namely critical temperature, critical pressure, and critical volume; thermodynamic properties, namely vapor pressure, enthalpy, entropy, and Gibbs free energy.

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