CN111429973A - Real-time calculation method for molecular information in gasoline product - Google Patents
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- 238000004364 calculation method Methods 0.000 title claims abstract description 39
- TVMXDCGIABBOFY-UHFFFAOYSA-N octane Chemical compound CCCCCCCC TVMXDCGIABBOFY-UHFFFAOYSA-N 0.000 claims abstract description 72
- 150000001335 aliphatic alkanes Chemical class 0.000 claims description 24
- 239000000203 mixture Substances 0.000 claims description 23
- 125000003118 aryl group Chemical group 0.000 claims description 20
- UHOVQNZJYSORNB-UHFFFAOYSA-N Benzene Chemical compound C1=CC=CC=C1 UHOVQNZJYSORNB-UHFFFAOYSA-N 0.000 claims description 12
- CTQNGGLPUBDAKN-UHFFFAOYSA-N O-Xylene Chemical compound CC1=CC=CC=C1C CTQNGGLPUBDAKN-UHFFFAOYSA-N 0.000 claims description 12
- YXFVVABEGXRONW-UHFFFAOYSA-N Toluene Chemical compound CC1=CC=CC=C1 YXFVVABEGXRONW-UHFFFAOYSA-N 0.000 claims description 12
- 150000001924 cycloalkanes Chemical class 0.000 claims description 12
- 238000000034 method Methods 0.000 claims description 12
- 238000011160 research Methods 0.000 claims description 12
- 238000005457 optimization Methods 0.000 claims description 9
- 150000004945 aromatic hydrocarbons Chemical class 0.000 claims description 8
- FYGHSUNMUKGBRK-UHFFFAOYSA-N 1,2,3-trimethylbenzene Chemical compound CC1=CC=CC(C)=C1C FYGHSUNMUKGBRK-UHFFFAOYSA-N 0.000 claims description 6
- YNQLUTRBYVCPMQ-UHFFFAOYSA-N Ethylbenzene Chemical compound CCC1=CC=CC=C1 YNQLUTRBYVCPMQ-UHFFFAOYSA-N 0.000 claims description 6
- URLKBWYHVLBVBO-UHFFFAOYSA-N Para-Xylene Chemical group CC1=CC=C(C)C=C1 URLKBWYHVLBVBO-UHFFFAOYSA-N 0.000 claims description 6
- 125000000524 functional group Chemical group 0.000 claims description 6
- IVSZLXZYQVIEFR-UHFFFAOYSA-N m-xylene Chemical group CC1=CC=CC(C)=C1 IVSZLXZYQVIEFR-UHFFFAOYSA-N 0.000 claims description 6
- UOHMMEJUHBCKEE-UHFFFAOYSA-N prehnitene Chemical compound CC1=CC=C(C)C(C)=C1C UOHMMEJUHBCKEE-UHFFFAOYSA-N 0.000 claims description 6
- 239000008096 xylene Substances 0.000 claims description 6
- 238000010586 diagram Methods 0.000 claims description 4
- 238000009826 distribution Methods 0.000 claims description 4
- 238000009472 formulation Methods 0.000 claims description 3
- 238000002407 reforming Methods 0.000 abstract description 15
- 230000008859 change Effects 0.000 abstract description 8
- 239000002994 raw material Substances 0.000 description 14
- 239000010779 crude oil Substances 0.000 description 9
- 238000006243 chemical reaction Methods 0.000 description 5
- 238000006057 reforming reaction Methods 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 2
- 238000003556 assay Methods 0.000 description 2
- GDOPTJXRTPNYNR-UHFFFAOYSA-N methylcyclopentane Chemical compound CC1CCCC1 GDOPTJXRTPNYNR-UHFFFAOYSA-N 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000000717 retained effect Effects 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- XDTMQSROBMDMFD-UHFFFAOYSA-N Cyclohexane Chemical compound C1CCCCC1 XDTMQSROBMDMFD-UHFFFAOYSA-N 0.000 description 1
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 description 1
- 238000005481 NMR spectroscopy Methods 0.000 description 1
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000009835 boiling Methods 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 229910052739 hydrogen Inorganic materials 0.000 description 1
- 239000001257 hydrogen Substances 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000009533 lab test Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 229910052757 nitrogen Inorganic materials 0.000 description 1
- 238000012946 outsourcing Methods 0.000 description 1
- 229910052717 sulfur Inorganic materials 0.000 description 1
- 239000011593 sulfur Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
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Abstract
The embodiment provides a real-time calculation method for molecular information in a gasoline product, which comprises the steps of carrying out primary classification on molecular types contained in the gasoline product based on molecular type similarity, and carrying out secondary classification according to different molecular structure types on the basis of the primary classification; calculating the current component proportion of each component in the gasoline product according to the known secondary classification result; and (3) constructing an octane number prediction model, introducing known component proportions, and calculating an octane number prediction value meeting the constraint condition. As the octane number prediction model is added aiming at the continuous reforming device, the octane number contribution parameter is added to correct the octane number prediction model in consideration of the nonlinear mixing of the octane number, and the octane number is subjected to step change by combining the actual operation of the device, so that the octane number prediction model can cover the operation change range of the continuous reforming device.
Description
Technical Field
The invention belongs to the field of petrochemical products, and particularly relates to a real-time calculation method for molecular information in a gasoline product.
Background
The prior art mostly focuses on establishing the relation between the crude oil macroscopic properties and the crude oil detailed molecular compositions on the premise of taking the crude oil macroscopic properties as the premise, matching a plurality of groups of data which are the same as or similar to the crude oil macroscopic properties in a new database by establishing a huge crude oil database on the premise of knowing the composition and the macroscopic properties of historical crude oil in various regions of the world, and performing optimization calculation by establishing a mathematical model so as to calculate the new detailed molecular compositions of the crude oil with the known macroscopic properties.
Where the crude oil's macroscopic properties include, but are not limited to, boiling range, density, carbon content, hydrogen content, sulfur content, nitrogen content, metal content, viscosity, flash point, nuclear magnetic resonance properties, and the like. The method has the advantages that the constraint conditions are excessive, the convergence condition of the model is to be examined, the accuracy of the output result of the model hardly meets the requirement of engineering implementation, no pertinence is caused on the continuous reforming reaction, and the workload is huge.
Disclosure of Invention
In order to solve the defects and shortcomings in the prior art, the invention provides a real-time calculation method for molecular information in a gasoline product.
Specifically, the method for calculating the molecular information in the gasoline product in real time provided by this embodiment includes:
performing primary classification on the molecular types contained in the gasoline product based on the molecular type similarity, and performing secondary classification according to different molecular structure types on the basis of the primary classification;
calculating the current component proportion of each component in the gasoline product according to the known secondary classification result;
and (3) constructing an octane number prediction model, introducing known component proportions, and calculating an octane number prediction value meeting the constraint condition.
Optionally, the primary classification of the molecular types contained in the gasoline product based on the molecular type similarity and the secondary classification based on the primary classification according to different molecular structure types includes:
performing primary classification according to the molecular structure expression form;
secondary classification is performed according to the arrangement of the functional groups in each molecular structure.
Optionally, the primary classification according to molecular structure representation form comprises:
the alkane in the gasoline product is divided into chain alkane and cyclane;
wherein, the chain alkane is divided into straight chain alkane and branched chain alkane, and the cycloalkane is divided into five-membered cycloalkane and six-membered cycloalkane.
Optionally, the secondary classification according to the arrangement of the functional groups in each molecular structure includes:
the branched chain alkane is divided into single-branched chain alkane and multi-branched chain alkane;
the aromatic hydrocarbon is divided into benzene, toluene, xylene, trimethylbenzene and tetramethylbenzene,
the xylene includes ethylbenzene, ortho-xylene, meta-xylene and para-xylene.
Optionally, the calculating, according to the known secondary classification result, a current component ratio of each component in the gasoline product includes:
constructing a target optimization equation as shown in formula one
Objective＝(ρCALCULATE-ρREAL)2The formula I is shown in the specification,
in the formula, ρCALCULATEFor the density calculation, the calculation is in rhoCALCULATE＝∑iρi×xi，ρREALIs the actual value of the density;
formulation of constraint ∑ x to be satisfiedi＝1，βi,min＜＜βi＜＜βi,max；
In the formula, βiTo distribute the proportion βi,min、βi,maxRespectively the lower limit and the upper limit of the distribution proportion βiRepresenting the proportion of different lumped elements in different PONA values, and calculating the concentration x of each lumped element by obtaining the composition proportion of each lumped element on the premise that only the PONA value is knowni。
Optionally, the constructing an octane number prediction model, introducing known component proportions, and calculating an octane number prediction value meeting the constraint condition includes:
constructing an octane number prediction model shown in formula two
Establishing a target optimization equation shown as formula III
In the formula, αi,min＜＜αi＜＜αi,max，ai<aj,when RONi<RONjα where the octane contribution parameters of the components are bounded above and below and the octane contribution parameter of the higher octane component is greater than the lower octane componentiRepresenting the contribution of the i component to the overall research octane number, the different components and the different concentrations contributing to the overall research octane number, the RONiRepresents the research octane number of the i component.
Optionally, the real-time computing method further includes:
constructing an aromatic potential value calculation model based on the standard aromatic potential component composition ratio;
and (4) leading the calculated current component proportion into the aromatic potential value calculation model, and calculating in real time to obtain the aromatic potential value.
Optionally, the real-time computing method further includes:
and displaying the input value and the output value in the aromatic latent value calculation model in a tree structure diagram mode.
The technical scheme provided by the invention has the beneficial effects that:
as the octane number prediction model is added aiming at the continuous reforming device, the octane number contribution parameter is added to correct the octane number prediction model in consideration of the nonlinear mixing of the octane number, and the octane number is subjected to step change by combining the actual operation of the device, so that the octane number prediction model can cover the operation change range of the continuous reforming device.
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In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for calculating molecular information in a gasoline product in real time according to an embodiment of the present application.
Detailed Description
To make the structure and advantages of the present invention clearer, the structure of the present invention will be further described with reference to the accompanying drawings.
Example one
In order to solve the problem of determining the detailed components of crude oil in the prior art, the embodiment of the application provides a non-linear octane number prediction model for mixed components, as shown in fig. 1, and a real-time calculation method for molecular information in a gasoline product implemented based on the model comprises the following steps:
11. performing primary classification on the molecular types contained in the gasoline product based on the molecular type similarity, and performing secondary classification according to different molecular structure types on the basis of the primary classification;
12. calculating the current component proportion of each component in the gasoline product according to the known secondary classification result;
13. and (3) constructing an octane number prediction model, introducing known component proportions, and calculating an octane number prediction value meeting the constraint condition.
In implementation, the real-time calculation method for molecular information in gasoline products proposed in this embodiment mainly includes constructing a raw material and product detailed assay database of a continuous reforming device, subdividing each group of components based on a continuous reforming lumped reaction network, training to obtain a ratio between the components, using the ratio as a default ratio, and updating the ratio irregularly according to the fluctuation condition of the raw material; the raw material and product composition are properly removed, summarized and retained aiming at a reaction network in consideration of the limitation of dynamic data of the continuous reforming reaction, and sufficient and effective input values are provided for the continuous reforming reaction while main composition information of the raw material and the product is retained.
Wherein the molecular-type-based classification process for gasoline products proposed in step 11 comprises:
21. performing primary classification according to the molecular structure expression form;
22. secondary classification is performed according to the arrangement of the functional groups in each molecular structure.
In practice, the classification process is carried out in two steps, the first step is classification according to the molecular structure, and the second step is classification of the functional groups in the molecular structure obtained after the previous step.
The primary classification corresponding to the step 21 includes:
211. the alkane in the gasoline product is divided into chain alkane and cyclane;
wherein, the chain alkane is divided into straight chain alkane and branched chain alkane, and the cycloalkane is divided into five-membered cycloalkane and six-membered cycloalkane.
Step 22, the corresponding secondary classification includes:
221. the branched chain alkane is divided into single-branched chain alkane and multi-branched chain alkane;
222. the aromatic hydrocarbon is divided into benzene, toluene, xylene, trimethylbenzene and tetramethylbenzene,
the xylene includes ethylbenzene, ortho-xylene, meta-xylene and para-xylene.
The classification process performed in the aforementioned step 11 is intended to collate complex molecular composition information in the continuously reformed raw materialClassifying into molecular composition information available for continuously reforming lumped reaction networks, and finding out distribution ratio β between compositions by mass dataiAnd the optimal solution is obtained by satisfying the target equation. An octane prediction model is used in this calculation step.
Subdividing each group of compositions based on a continuous reforming lumped reaction network, training to obtain the proportion among the compositions, using the proportion as a default proportion, and updating the proportion irregularly according to the fluctuation condition of the raw materials; the method is characterized in that analysis data obtained by an online analytical instrument is sorted and classified based on a continuous reforming lumped reaction network, the proportions among the lumped data are sorted out, and raw material and product molecular information is obtained by matching an optimization algorithm model, so that the dependence degree of the device on the online analytical instrument is reduced, the purchasing cost of the device is reduced, and guidance is provided for production operation.
After step 11 is finished, step 12 needs to be executed to calculate the component ratio of each component in the gasoline product, which specifically includes:
121. constructing a target optimization equation as shown in formula one
Objective＝(ρCALCULATE-ρREAL)2The formula I is shown in the specification,
in the formula, ρCALCULATEFor the density calculation, the calculation is in rhoCALCULATE＝∑iρi×xi，ρREALIs the actual value of the density;
122. formulation of constraint ∑ x to be satisfiedi＝1，βi,min＜＜βi＜＜βi,max；
In the formula, βiTo distribute the proportion βi,min、βi,maxRespectively the lower limit and the upper limit of the distribution proportion βiRepresenting the proportion of different lumped elements in different PONA values, and calculating the concentration x of each lumped element by obtaining the composition proportion of each lumped element on the premise that only the PONA value is knowni。
Formula (III) βiRepresenting the ratio of different groups of PONA values before the lump, e.g. the division of aromatics into benzene, toluene,The concentration x of each lump can be calculated by obtaining the composition proportion of each lump on the premise that only the PONA value is knowni。αiRepresenting the contribution degree of the component i to the overall research octane number, and the contribution degree of different components and different concentrations to the overall research octane number is different; RONiRepresents the research octane number of the i component.
After the step 12 is finished, the step of calculating the predicted octane value shown in the step 13 needs to be executed, which specifically includes:
131. constructing an octane number prediction model shown in formula two
132. Establishing a target optimization equation shown as formula III
In the formula, αi,min＜＜αi＜＜αi,max，ai<aj,when RONi<RONjα where the octane contribution parameters of the components are bounded above and below and the octane contribution parameter of the higher octane component is greater than the lower octane componentiRepresenting the contribution of the i component to the overall research octane number, the different components and the different concentrations contributing to the overall research octane number, the RONiRepresents the research octane number of the i component.
The octane number prediction model established in step 131 is a non-linear optimization model based on octane number contribution parameters, wherein known assay data is used as a target equation; the device is manually intervened according to different time periods by combining engineering operation experience, so that the octane number can show step change, and a plurality of groups of experimental data are taken as model correction basic data, so that the model can cover the required range of the continuous reforming device on the octane number of the reformed gasoline.
After the octane number prediction model obtained in step 131 is executed, the continuous reforming reaction raw material and the near-infrared analysis data of the product need to be imported, where the data import mode includes real-time calling of the near-infrared analysis data as a model input value, real-time calculation of the molecular composition information of the raw material and the product on the premise of the continuous reforming reaction raw material and the molecular information base, and provision of input values for a subsequent continuous reforming gasoline octane number prediction model and an aromatic potential value calculation model.
The mixed component nonlinear octane number calculation model provided by the embodiment is added with an octane number contribution parameter model while calculating to obtain detailed molecular composition information of raw materials and products, parameter estimation is carried out on octane number contribution parameters by utilizing a plurality of groups of samples with known octane numbers obtained by laboratory tests, appropriate variables are selected as model objective equations to reduce calculation errors of the model, and meanwhile, the octane number is subjected to step modification by combining with actual operation of the device, so that the model can cover the operation change range of the device.
Adding an octane number prediction model aiming at the continuous reforming device, taking account of the nonlinear mixing of octane numbers, adding an octane number contribution parameter to correct the octane number prediction model, and carrying out step change on the octane number by combining the actual operation of the device so that the octane number prediction model can cover the operation change range of the continuous reforming device; and calculating the aromatic potential values of the raw materials and the products according to the PONA family composition information. Thereby reducing the cost of octane value outsourcing test, having direct available economic value and providing real-time guidance for the production operation of the device.
Optionally, the real-time computing method further includes:
constructing an aromatic potential value calculation model based on the standard aromatic potential component composition ratio;
and (4) leading the calculated current component proportion into the aromatic potential value calculation model, and calculating in real time to obtain the aromatic potential value.
In the implementation, the concrete content of the aromatic value calculation model is
0.93 × aromatic hydrocarbon (cyclohexane content + methylcyclopentane content) +0.94 × C7 cycloalkane content) +0.95 × C8 cycloalkane content) +0.96 × C9 cycloalkane content) + C6 arene + C7 arene + C8 arene + C9 arene.
In consideration of the high attention of the continuous reforming device to the aromatic value of the raw material, a calculation model of the aromatic value of the raw material is added to provide guidance for production operation.
Optionally, the real-time computing method further includes:
and displaying the input value and the output value in the aromatic latent value calculation model in a tree structure diagram mode.
In the implementation, the input value and the output value of the system are displayed in the form of a tree diagram, and the inheritance relationship of molecular information is embodied, so that the molecular information display of the continuous reforming raw material and the product is more visual.
The sequence numbers in the above embodiments are merely for description, and do not represent the sequence of the assembly or the use of the components.
The above description is only exemplary of the present invention and should not be taken as limiting the invention, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A real-time calculation method for molecular information in a gasoline product, the real-time calculation method comprising:
performing primary classification on the molecular types contained in the gasoline product based on the molecular type similarity, and performing secondary classification according to different molecular structure types on the basis of the primary classification;
calculating the current component proportion of each component in the gasoline product according to the known secondary classification result;
and (3) constructing an octane number prediction model, introducing known component proportions, and calculating an octane number prediction value meeting the constraint condition.
2. The method of claim 1, wherein the step of performing a primary classification of the molecular types contained in the gasoline product based on the molecular type similarity and performing a secondary classification based on the primary classification according to the molecular structure type comprises:
performing primary classification according to the molecular structure expression form;
secondary classification is performed according to the arrangement of the functional groups in each molecular structure.
3. The method of claim 2, wherein said primary classification based on molecular structure representation comprises:
the alkane in the gasoline product is divided into chain alkane and cyclane;
wherein, the chain alkane is divided into straight chain alkane and branched chain alkane, and the cycloalkane is divided into five-membered cycloalkane and six-membered cycloalkane.
4. The method of claim 2, wherein said performing a secondary classification based on the arrangement of functional groups in each molecular structure comprises:
the branched chain alkane is divided into single-branched chain alkane and multi-branched chain alkane;
the aromatic hydrocarbon is divided into benzene, toluene, xylene, trimethylbenzene and tetramethylbenzene,
the xylene includes ethylbenzene, ortho-xylene, meta-xylene and para-xylene.
5. The method of claim 1, wherein the calculating a current component ratio of each component in the gasoline product based on the known secondary classification result comprises:
constructing a target optimization equation as shown in formula one
Objective＝(ρCALCULATE-ρREAL)2The formula I is shown in the specification,
in the formula, ρCALCULATEFor the density calculation, the calculation is in rhoCALCULATE＝∑iρi×xi，ρREALIs the actual value of the density;
formulation of constraint ∑ x to be satisfiedi＝1，βi,min＜＜βi＜＜βi,max；
In the formula, βiTo distribute the proportion βi,min、βi,maxRespectively the lower limit and the upper limit of the distribution proportion βiRepresenting the proportion of different lumped elements in different PONA values, and calculating the concentration x of each lumped element by obtaining the composition proportion of each lumped element on the premise that only the PONA value is knowni。
6. The method of claim 1, wherein the step of constructing an octane number prediction model, introducing known component proportions, and calculating an octane number prediction value under a constraint comprises:
constructing an octane number prediction model shown in formula two
Establishing a target optimization equation shown as formula III
In the formula, αi,min＜＜αi＜＜αi,max，ai<aj,when RONi<RONjα where the octane contribution parameters of the components are bounded above and below and the octane contribution parameter of the higher octane component is greater than the lower octane componentiRepresenting the contribution of the i component to the overall research octane number, the different components and the different concentrations contributing to the overall research octane number, the RONiRepresents the research octane number of the i component.
7. The real-time calculation method for molecular information in a gasoline product according to claim 1, characterized in that the real-time calculation method further comprises:
constructing an aromatic potential value calculation model based on the standard aromatic potential component composition ratio;
and (4) leading the calculated current component proportion into the aromatic potential value calculation model, and calculating in real time to obtain the aromatic potential value.
8. The real-time calculation method for molecular information in a gasoline product of claim 7, further comprising:
and displaying the input value and the output value in the aromatic latent value calculation model in a tree structure diagram mode.
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| CN114724644A (en) * | 2022-03-25 | 2022-07-08 | 中国石油大学(北京) | Method and equipment for predicting gasoline octane number based on intermolecular interaction |
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