CN115831256A - Molecular horizontal reaction kinetic model construction method and device and storage medium - Google Patents
Molecular horizontal reaction kinetic model construction method and device and storage medium Download PDFInfo
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Abstract
The present disclosure relates to a molecular horizontal reaction kinetics model construction method and apparatus, a storage medium and a device, wherein the method comprises: obtaining a raw material molecule composition matrix; based on a preset reaction rule, generating a plurality of reaction paths and reaction networks according to the structure-oriented lumped representation of each raw material molecule; dividing the current reaction into a plurality of infinitesimal reaction sections, constructing an ordinary differential equation for each infinitesimal reaction section according to a reaction network, and solving the ordinary differential equation according to a preset reaction rate constant and the content of each raw material molecule to obtain the content of each product molecule and the yield of each product; and calculating a relative difference value between the yield of each product and the yield of the actual reaction product, adjusting a preset reaction rate constant according to the relative difference value, and taking the reaction rate constant when the sum of the relative difference values meets a preset condition as a parameter of a molecular level reaction kinetic model to realize the conversion tracking of molecular composition in the reaction process and the prediction of product properties.
Description
Technical Field
The disclosure relates to the technical field of molecular oil refining, in particular to a molecular horizontal reaction kinetic model construction method and device, a storage medium and equipment.
Background
At present, a modeling method of an oil refining process device mainly adopts a lumped model method, similarity classification is carried out according to the dynamic properties such as macroscopic physical properties, structural characteristics and the like of each component in a material, and then construction of a reaction network among all lumped components and calculation of reaction parameters are carried out. However, each lumped component is actually composed of a large number of pure molecules, which are actually treated as virtual single components with uniform physical properties during modeling. Therefore, lumped models have significant drawbacks in the expanded adaptability of new feeds and new catalysts.
Disclosure of Invention
In order to solve the technical problem or at least partially solve the technical problem, embodiments of the present disclosure provide a molecular horizontal reaction kinetic model construction method and apparatus, a storage medium, and a device.
In a first aspect, an embodiment of the present disclosure provides a molecular horizontal reaction kinetics model building method, where the method includes:
obtaining a raw material molecule composition matrix, wherein the raw material molecule composition matrix comprises structure-oriented lumped representation and content of each raw material molecule;
generating a plurality of reaction paths and a reaction network consisting of the plurality of reaction paths according to the structure-oriented lumped representation of each raw material molecule based on a preset reaction rule, wherein the reaction rule comprises the change of the structure-oriented lumped representation of each raw material molecule in the corresponding reaction path;
dividing the current reaction into a plurality of infinitesimal reaction sections, constructing an ordinary differential equation for each infinitesimal reaction section according to a reaction network, solving the ordinary differential equation according to a preset reaction rate constant and the content of each raw material molecule to obtain the content of each product molecule, and calculating the yield of each product according to the content of the product molecule;
and calculating a relative difference value between the yield of each product and the yield of the actual reaction product, adjusting a preset reaction rate constant according to the relative difference value, and taking the reaction rate constant when the sum of the relative difference values meets a preset condition as a parameter of a molecular level reaction kinetic model, so that the molecular level reaction kinetic model predicts a molecular composition matrix of the reaction product according to the molecular composition matrix of the reaction raw material.
In one possible embodiment, the molecular level reaction kinetics model is one of an atmospheric and vacuum distillation model, a residue hydrogenation model, a catalytic cracking model, a delayed coking model, a hydrocracking model, a catalytic reforming model, an alkylation model, a gasoline hydrogenation model, a diesel hydrogenation model, a wax oil hydrogenation model, a gasoline and diesel hydrogenation model, a gas fractionation model, an aromatic extraction model, and a hydrogen production model.
In one possible embodiment, the generating, based on the preset reaction rule, a plurality of reaction paths according to the structure-oriented lumped representation of each raw material molecule and the reaction network composed of the plurality of reaction paths includes:
and traversing the structure-oriented lumped representation of each raw material molecule according to a preset reaction rule to obtain a reaction path corresponding to each raw material molecule and a reaction network consisting of a plurality of reaction paths.
In a possible embodiment, the dividing the current reaction into a plurality of infinitesimal reaction sections, constructing an ordinary differential equation for each infinitesimal reaction section according to a reaction network, and solving the ordinary differential equation according to a preset initial value of a reaction rate constant and the content of each raw material molecule to obtain the content of each product molecule includes:
averagely dividing the reaction duration of the current reaction into a plurality of infinitesimal reaction sections;
the reaction temperature at the inlet of the actual production unit of the current reactionTPressure, pressurepEnthalpy valueHSpecific heat of andCas the initial temperature, initial pressure, initial enthalpy and specific heat capacity of the inlet of the first infinitesimal reaction section;
for each infinitesimal reaction section, solving an ordinary differential equation set under a reaction rate constant corresponding to each reaction rule by using a Runge-Kutta algorithm to obtain the product concentration at the outlet of the infinitesimal reaction section
And the variation of the concentration with time t
；
According to
And initial enthalpy value of inlet of infinitesimal reaction sectionHCalculating enthalpy valueHVariation with time t
To obtain the enthalpy value of the outlet of the infinitesimal reaction section;
according to
Specific heat of andCcalculating the temperatureTOver timetVariations of (2)
To obtain the temperature of the outlet of the infinitesimal reaction section;
according to ideal gas constantRAnd temperatureTChange over time
Calculating pressurepOver timetChange of (2)
To obtain the pressure at the outlet of the infinitesimal reaction section;
and taking the temperature, the pressure, the enthalpy value and the concentration of each product at the outlet of the infinitesimal reaction section as the inlet parameters of the next infinitesimal reaction section, calculating the parameters of the outlet of the next infinitesimal reaction section until the last infinitesimal reaction section, and taking the content of the product molecules at the outlet of the last infinitesimal reaction section as the content of the product molecules of the production unit.
In one possible embodiment, the reaction time period is the time period during which the feed molecules are retained in the production unit.
In one possible embodiment, calculating the relative difference between the yield of each product and the yield of the actual reaction product and summing the relative differences by the following expression comprises:
err is the sum of the relative differences between the calculated and actual yields for each product,
is the calculated yield for the ith product,
is the actual yield of the ith product,
is the relative difference between the calculated yield and the actual yield for the ith product, and n is the total number of product species.
In one possible embodiment, the reaction rate constant is calculated by the following expression:
wherein ,kis the reaction rate constant of the reaction rule,k a 、k b 、k c respectively, reaction kinetic parameters related to the catalyst, the reaction temperature and the reaction pressure,Ein order to activate the energy of the reaction,Tas the reaction temperature, the reaction temperature is,pin order to obtain the reaction pressure, the reaction solution is,p k is a constant of the effect of reaction pressure on the reaction rate constant.
In a possible embodiment, adjusting a preset reaction rate constant according to the relative difference value, and using the reaction rate constant when the sum of the relative difference values meets a preset condition as a parameter of a molecular level reaction kinetic model includes:
when the relative difference value is larger than a preset threshold value, adjusting the kinetic parameters to obtain adjusted kinetic parameters, calculating a reaction rate constant based on the adjusted kinetic parameters, and re-solving the ordinary differential equation according to the reaction rate constant and the content of each raw material molecule to obtain the content of each product molecule;
the preset condition refers to the maximum error which can be accepted according to the actual application scenario.
In one possible embodiment, the molecular level reaction kinetics model predicts the molecular composition matrix of the reaction product from the molecular composition matrix of the reaction feedstock, comprising:
and based on the Runge Kutta algorithm, solving the ordinary differential equation according to the reaction rate constant when the sum of the relative differences meets the preset condition and the content of each raw material molecule to obtain the content of each product molecule.
In one possible embodiment, the obtaining a feedstock molecular composition matrix comprises:
performing vector characterization on each raw material molecule based on a structure-oriented lumped molecule characterization method to obtain a structure-oriented lumped representation of each raw material molecule;
guiding the structure of each raw material molecule to lumped representation and content as a complete vector;
and combining the complete vectors of all the raw material molecules into a raw material molecule composition matrix.
In a second aspect, an embodiment of the present disclosure provides a molecular horizontal reaction kinetics model building apparatus, including:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring a raw material molecule composition matrix, and the raw material molecule composition matrix comprises structure-oriented lumped representation and content of each raw material molecule;
the generating module is used for generating a plurality of reaction paths and a reaction network consisting of the plurality of reaction paths according to the structure-oriented lumped representation of each raw material molecule based on a preset reaction rule, wherein the reaction rule comprises the change of the structure-oriented lumped representation of each raw material molecule in the corresponding reaction path;
the solving module is used for dividing the current reaction into a plurality of infinitesimal reaction sections, constructing an ordinary differential equation for each infinitesimal reaction section according to a reaction network, and solving the ordinary differential equation according to a preset reaction rate constant and the content of each raw material molecule to obtain the content of each product molecule;
and the adjusting module is used for calculating a relative difference value between the yield of each product and the yield of an actual reaction product, adjusting a preset reaction rate constant according to the relative difference value, and taking the reaction rate constant when the sum of the relative difference values meets a preset condition as a parameter of the molecular level reaction kinetic model so that the molecular level reaction kinetic model predicts a molecular composition matrix of the reaction product according to the molecular composition matrix of the reaction raw material.
In a third aspect, an embodiment of the present disclosure provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory complete communication with each other through the communication bus;
a memory for storing a computer program;
and the processor is used for realizing the molecular level reaction kinetic model building method when executing the program stored in the memory.
In a fourth aspect, embodiments of the present disclosure provide a computer-readable storage medium on which a computer program is stored, the computer program, when executed by a processor, implementing the molecular-level reaction kinetic model building method described above.
Compared with the prior art, the technical scheme provided by the embodiment of the disclosure at least has part or all of the following advantages:
the molecular level reaction kinetic model construction method disclosed by the embodiment of the disclosure is based on raw material molecular level representation, realizes conversion tracking of molecular composition in a reaction process aiming at different process reaction mechanisms under the condition of not depending on raw material components, has clear explanation on the reaction process of each device in a refining process, and realizes product property prediction.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the related art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 schematically shows a flow diagram of a molecular level reaction kinetics model construction method according to an embodiment of the disclosure;
fig. 2 schematically shows a block diagram of a structure of a molecular level reaction kinetics model construction apparatus according to an embodiment of the present disclosure;
fig. 3 schematically shows a block diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are some embodiments of the present disclosure, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
Referring to fig. 1, an embodiment of the present disclosure provides a molecular horizontal reaction kinetic model building method, including:
s1, obtaining a raw material molecule composition matrix, wherein the raw material molecule composition matrix comprises structure-oriented lumped representation and content of each raw material molecule;
in some embodiments, the feedstock molecular composition matrix is obtained by: the method comprises a raw material molecule composition structure oriented lumped representation matrix and a raw material molecule composition content matrix, wherein the structure oriented lumped representation is shown in the following table 1.
TABLE 1
| A6 | A4 | A2 | N6 | N5 | N4 | N3 | N2 | N1 | R | br | me | IH | AA | NS | RS | AN | NN | RN | NO | RO | KO | Ni | V |
The composition of raw material molecules is expressed by the 24 characteristic structures according to corresponding rules in a molecular guide set total expression method, each molecule is converted into a one-dimensional structure vector consisting of the number of 24 fragment units, and the molecular directionThe amount is marked as A = [ a ] 11 , a 12 , ……a 1n ]. Each component in A represents the number of the characteristic structures in the molecule corresponding to the table 1, the raw material is represented by a molecular matrix with dimension of n × 24 and a molecular content vector with dimension of 1 × n, and the molecular matrix is marked as B =
The molecular content vector is marked as C = [ C = [ ] 1 ,c 2 , ……c n ]Wherein n is the molecular species in the raw material, each row of the molecular matrix B is a molecular vector, each component of the molecular content vector C corresponds to the content of the molecular vector in each row of the molecular matrix B, and the content represents the mole fraction of the corresponding molecules in the raw material;
a6: a six carbon aromatic ring, which is present in all aromatic molecules, may be present alone.
A4: a four carbon aromatic ring attached to the A6 (or another A4 ring) is a structural increment used to build polymeric polycyclic structures that cannot exist alone.
A2: the two-carbon aromatic structure is increased and A2 is used to attach to the "gulf region" of the polycyclic aromatic hydrocarbon to form a new polycyclic aromatic hydrocarbon.
N6 and N5: six-carbon and five-carbon cycloalkanes.
N4, N3, N2, N1: additional aliphatic ring structure extenders containing four, three, two and one carbons, which must be attached to other aliphatic or aromatic ring structures, cannot be present alone.
R: the number of carbon atoms contained in all alkyl structures attached to the ring structure, or in the aliphatic molecule in the absence of a ring structure.
IH: the molecular saturation is described by introducing the structural increment related to the hydrogen element. If there is no ring structure, IH =1 represents paraffin, IH =0 represents monoolefin, and IH = -1 represents diolefin; if a ring is present, IH = -1 represents a cyclic olefin.
br: the method represents the number of branch nodes on side chain alkyl, straight chain alkyl or olefin, and cannot distinguish methyl, ethyl and propyl branches, so that only methyl branches are assumed to exist uniformly, the influence of the branch type on the reaction is not significant in the actual oil refining process, the influence of the branch number can be represented by the assumption, and the influence of the branch type, such as methyl, ethyl and propyl branches, can be ignored, and the actual requirement can be met.
me: the number of methyl groups in the alkyl structure directly attached to a carbon atom in an aromatic or aliphatic ring is determined. In particular, when R =1 or me = R-1, other structural increments can determine the number of methyl groups on the ring structure, by convention me is not in the number used to represent methyl groups.
AA: biphenyl bridging between any two nonstructural incremental rings (A6, N6, or N5).
NS, NN and NO: sulfur, nitrogen, oxygen atoms located in an aliphatic ring or chain and linked to two carbon atoms. NS, NN and NO refer to the replacement of a-CH with an S atom, an-NH-group and an O atom, respectively 2 -。
RS, RN and RO: an S atom, a N-containing-NH-group or an O atom is inserted between a carbon atom and a hydrogen atom, constituting a thiol, amine or alcohol group, respectively.
AN: the carbon is replaced in the aromatic ring by a nitrogen group, such as pyridine and quinoline. The AN group is replaced with = N-by = CH-.
KO：
Substitute for-CH 2 -or-CH 3 Forming ketone or aldehyde groups.
Ni and V: is found in porphyrin molecules.
S2, generating a plurality of reaction paths and a reaction network consisting of the plurality of reaction paths according to the structure-oriented lumped representation of each raw material molecule based on a preset reaction rule, wherein the reaction rule comprises the change of the structure-oriented lumped representation of each raw material molecule in the corresponding reaction path;
s3, dividing the current reaction into a plurality of infinitesimal reaction sections, constructing an ordinary differential equation for each infinitesimal reaction section according to a reaction network, solving the ordinary differential equation according to a preset reaction rate constant and the content of each raw material molecule to obtain the content of each product molecule, and calculating the yield of each product according to the content of the product molecule;
in some embodiments, the current reaction is divided into a plurality of infinitesimal reaction sections, the infinitesimal reaction sections are divided according to reaction residence time, and in each infinitesimal reaction section, the change of the quantity concentration of each molecular substance in the reaction network with time is calculated according to the ordinary differential equation, wherein the ordinary differential equation is automatically generated according to the reaction network and describes the equation of the change of the concentration of all molecules in the reaction network with time, and in addition, the reaction network is automatically generated according to the raw material molecules and the reaction rule.
Calculating a relative difference value between the yield of each product and the yield of an actual reaction product, adjusting a preset reaction rate constant according to the relative difference value, and taking the reaction rate constant when the sum of the relative difference values meets a preset condition as a parameter of a molecular level reaction kinetic model, so that the molecular level reaction kinetic model predicts a molecular composition matrix of the reaction product according to the molecular composition matrix of a reaction raw material, wherein the preset reaction rate constant is adjusted by utilizing a Nelder-Mead nonlinear optimization algorithm.
In some embodiments, after generating a plurality of reaction paths and a reaction network composed of the plurality of reaction paths according to the structurally directed lumped representation of each feedstock molecule, the method further comprises:
comparing the structure-oriented lumped representation of the product molecules of each reaction path with a preset subset, only keeping the product molecules and the corresponding reaction paths in the preset molecular set as effective product molecules and effective reaction paths to calculate the content of each effective product molecule, wherein the preset subset is obtained by a structure-oriented lumped method according to actual reactions and molecular products of each device in the oil refining process, and is used for primarily judging the products generated by the model based on the reaction rules.
In one possible embodiment, the molecular level reaction kinetics model is one of a distillation model, a residue hydrogenation model, a catalytic cracking model, a delayed coking model, a hydrocracking model, a catalytic reforming model, an alkylation model, a gasoline hydrogenation model, a diesel hydrogenation model, a wax oil hydrogenation model, a gasoline and diesel hydrogenation model, a gas component model, an aromatics extraction model, and a hydrogen production model.
In some embodiments, for each model, a corresponding reaction rule is formulated according to the chemical reaction actually occurring in the model, and a reaction path corresponding to each single molecule in the composition of the corresponding raw material molecule in each model is obtained.
In some embodiments, when the raw material is input, first, the crude oil cut calculation is performed according to the real boiling point (TBP) distillation curve data of the raw material, i.e. naphtha, diesel oil, wax oil, residual oil and the like are separated from the crude oil by the distillation process by using the boiling range difference of the cut, and the naphtha, the diesel oil, the wax oil, the residual oil and the like respectively correspond to different model reaction rules. And each raw material molecule reacts according to the reaction rule in the corresponding model reaction rule set to obtain a reaction path corresponding to each molecule. After each molecule is subjected to a first reaction according to a reaction rule to generate an intermediate product, the intermediate product is continuously used as a reactant to judge whether the intermediate product meets another reaction rule and is continuously subjected to subsequent reactions according to the reaction rule until the intermediate product does not meet any reaction rule in the reaction rule set, the intermediate product is a final product, and the summary of the reactions, namely the reaction path of the molecule, is generated to generate a corresponding reaction network.
In some embodiments, the generating a plurality of reaction paths according to the structure-oriented lumped representation of each raw material molecule based on the preset reaction rule and the reaction network composed of the plurality of reaction paths includes:
and traversing the structure-oriented lumped representation of each raw material molecule according to a preset reaction rule to obtain a reaction path corresponding to each raw material molecule and a reaction network consisting of a plurality of reaction paths.
In some embodiments, the dividing the current reaction into a plurality of infinitesimal reaction sections, constructing an ordinary differential equation for each infinitesimal reaction section according to a reaction network, and solving the ordinary differential equation according to a preset reaction rate constant and the content of each raw material molecule to obtain the content of each product molecule includes:
the reaction duration of the current reaction is averagely divided into n infinitesimal reaction duration sections, and the reaction duration of each infinitesimal reaction section is
；
The reaction temperature at the inlet of the actual production unit of the current reactionTPressure, pressurepEnthalpy valueHSpecific heat of andCas initial temperature, initial pressure, initial enthalpy and specific heat capacity of the inlet of the first infinitesimal reaction section;
for each infinitesimal reaction section, solving the reaction rate constant corresponding to each reaction rule by using the Longge Kutta algorithmk i Obtaining the concentration and concentration of the reactant at the outlet of the infinitesimal reaction section by using the ordinary differential equation systemcChange over time
Wherein the system of ordinary differential equations comprises: the zero-order reaction is carried out,
(ii) a The first-order reaction is carried out,
(ii) a The secondary reaction is carried out,
；
according to
And the inlet enthalpy value of the infinitesimal reaction section, and calculating the change of the enthalpy value with time
；
According to
And specific heat capacity, calculating the change of temperature with time
To do so byObtaining the temperature of the outlet of the infinitesimal reaction section;
according to ideal gas constantRAnd temperature change with time
Calculating the change of pressure with time
To obtain the pressure at the outlet of the infinitesimal reaction section;
and taking the temperature, the pressure, the enthalpy value and the concentration of each product at the outlet of the infinitesimal reaction section as the inlet parameters of the next infinitesimal reaction section, calculating the parameters of the outlet of the next infinitesimal reaction section until the last infinitesimal reaction section, and taking the content of the product at the outlet of the last infinitesimal reaction section as the content of the product of the production unit.
In some embodiments, the relative difference between the yield of each product and the yield of the actual reaction product is calculated and summed by the following expression, including:
where Err is the sum of the relative differences between the calculated yield and the actual yield for each product,
calculated yield (mass fraction) for the ith product,
is the actual yield (mass fraction) of the ith product,
is the relative difference between the calculated yield and the actual yield for the ith product, and n is the total number of product species.
In some embodiments, the reaction rate constant is calculated by the expression:
wherein ,kis the reaction rate constant of the reaction rule,k a 、k b 、k c respectively, reaction kinetic parameters related to the catalyst, the reaction temperature and the reaction pressure,Ein order to activate the energy of the reaction,Tas the reaction temperature, the reaction temperature is,pin order to obtain the reaction pressure, the reaction solution is,p k is a constant of the effect of reaction pressure on the reaction rate constant.
In some embodiments, the adjusting the preset reaction rate constant according to the relative difference value includes:
and responding to the fact that the relative difference value is larger than a preset threshold value, adjusting kinetic parameters, and solving the ordinary differential equation again according to a reaction rate constant calculated by the adjusted kinetic parameters and the content of each raw material molecule to obtain the content of each product molecule.
In some embodiments, the molecular level reaction kinetics model predicts the molecular composition matrix of the reaction product from the molecular composition matrix of the reaction feedstock, comprising:
based on a Radao Radau IIA algorithm, solving the ordinary differential equation according to the reaction rate constant and the content of each raw material molecule when the sum of the relative differences meets a preset condition, and obtaining the content of each product molecule.
In some embodiments, the feedstock molecular composition is obtained by:
obtained from the molecular composition of crude oil by means of a cleavage process, or,
the molecular composition of a primary or secondary processed product obtained after distilling and cutting crude oil is determined by one or more of gas chromatography-mass spectrometry, full two-dimensional gas chromatography, quadrupole gas chromatography-mass spectrometry detection, gas chromatography or field ionization-time-of-flight mass spectrometry detection, near infrared spectroscopy, nuclear magnetic resonance spectroscopy, raman spectroscopy, fourier transform ion cyclotron resonance mass spectrometry, electrostatic field orbitrap mass spectrometry and ion mobility mass spectrometry.
In some embodiments, the obtaining a feedstock molecular composition matrix comprises:
performing vector characterization on each raw material molecule based on a structure-oriented lumped molecule characterization method to obtain a structure-oriented lumped representation of each raw material molecule;
guiding the structure of each raw material molecule to lumped representation and content as a complete vector;
and combining the complete vectors of all the raw material molecules into a raw material molecule composition matrix.
The molecular level reaction dynamics model construction method is based on a structure-oriented aggregation method, by formulating different device molecular scale reaction rules and establishing a molecular level refining device simulation model based on infinitesimal reaction segment division, realizes automatic generation of a complex reaction network in an oil refining processing process, realizes material balance and energy balance, verifies the effectiveness of product molecules according to the presetting of a subset, greatly reduces calculated amount, simultaneously enables the reaction to be more practical, realizes conversion tracking of molecular composition in a reaction process according to the molecular components and content of raw materials of an actual device, has clear explanation on the reaction process of each device in the refining process, and realizes product property prediction. In addition, the model can be any device in the refining process at the molecular level, certain universality is achieved, the calculation precision of the model is increased by the calculation at the molecular level, the product yield can be well predicted by the calculation result, a good guiding effect is achieved on the production in the refining process, and the production efficiency of each device in the refining process is improved.
Referring to fig. 2, an embodiment of the present disclosure provides a molecular horizontal reaction kinetics model construction apparatus, including:
an obtaining module 11, configured to obtain a feedstock molecule composition matrix, where the feedstock molecule composition matrix includes a structure-oriented lumped representation and content of each feedstock molecule;
the generating module 12 is configured to generate a plurality of reaction paths and a reaction network composed of the plurality of reaction paths according to the structure-oriented lumped representation of each raw material molecule based on a preset reaction rule, where the reaction rule includes a change of the structure-oriented lumped representation of each raw material molecule in its corresponding reaction path;
the solving module 13 is used for dividing the current reaction into a plurality of infinitesimal reaction sections, constructing an ordinary differential equation for each infinitesimal reaction section according to a reaction network, and solving the ordinary differential equation according to a preset reaction rate constant and the content of each raw material molecule to obtain the content of each product molecule;
and the adjusting module 14 is configured to calculate a relative difference between the yield of each product and the yield of the actual reaction product, adjust a preset reaction rate constant according to the relative difference, and use the reaction rate constant when the sum of the relative differences meets a preset condition as a parameter of the molecular level reaction kinetic model, so that the molecular level reaction kinetic model predicts a molecular composition matrix of the reaction product according to the molecular composition matrix of the reaction raw material.
The implementation process of the functions and actions of each unit in the above device is specifically described in the implementation process of the corresponding step in the above method, and is not described herein again.
For the device embodiments, since they substantially correspond to the method embodiments, reference may be made to the partial description of the method embodiments for relevant points. The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the invention. One of ordinary skill in the art can understand and implement it without inventive effort.
In the above embodiments, any plurality of the obtaining module 11, the generating module 12, the solving module 13, and the adjusting module 14 may be combined and implemented in one module, or any one of the modules may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. At least one of the obtaining module 11, the generating module 12, the solving module 13 and the adjusting module 14 may be implemented at least partially as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by any other reasonable manner of integrating or packaging a circuit, such as hardware or firmware, or implemented by any one of three implementations of software, hardware and firmware, or any suitable combination of any of them. Alternatively, at least one of the obtaining module 11, the generating module 12, the solving module 13 and the adjusting module 14 may be at least partially implemented as a computer program module, which when executed may perform a corresponding function.
Referring to fig. 3, an electronic device provided in an embodiment of the present disclosure includes a processor 1110, a communication interface 1120, a memory 1130, and a communication bus 1140, where the processor 1110, the communication interface 1120, and the memory 1130 complete communication with each other through the communication bus 1140;
a memory 1130 for storing computer programs;
the processor 1110, when executing the program stored in the memory 1130, implements a molecular-level reaction kinetics model construction method as follows:
obtaining a raw material molecule composition matrix, wherein the raw material molecule composition matrix comprises structure-oriented lumped representation and content of each raw material molecule;
generating a plurality of reaction paths and a reaction network consisting of the plurality of reaction paths according to the structure-oriented lumped representation of each raw material molecule based on a preset reaction rule, wherein the reaction rule comprises the change of the structure-oriented lumped representation of each raw material molecule in the corresponding reaction path;
dividing the current reaction into a plurality of infinitesimal reaction sections, constructing an ordinary differential equation for each infinitesimal reaction section according to a reaction network, and solving the ordinary differential equation according to a preset reaction rate constant and the content of each raw material molecule to obtain the content of each product molecule;
and calculating a relative difference value between the yield of each product and the yield of the actual reaction product, adjusting a preset reaction rate constant according to the relative difference value, and taking the reaction rate constant when the sum of the relative difference values meets a preset condition as a parameter of the molecular level reaction kinetic model, so that the molecular level reaction kinetic model predicts a molecular composition matrix of the reaction product according to the molecular composition matrix of the reaction raw material.
The communication bus 1140 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus 1140 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The communication interface 1120 is used for communication between the electronic device and other devices.
The memory 1130 may include a Random Access Memory (RAM) or a non-volatile memory (non-volatile memory), such as at least one disk memory. Optionally, the memory 1130 may also be at least one memory device located remotely from the processor 1110.
The processor 1110 may be a general-purpose processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, or discrete hardware components.
Embodiments of the present disclosure also provide a computer-readable storage medium. The computer readable storage medium has a computer program stored thereon, and the computer program, when executed by a processor, implements the molecular level reaction kinetic model construction method as described above.
The computer-readable storage medium may be contained in the apparatus/device described in the above embodiments; or may be separate and not incorporated into the device/apparatus. The computer readable storage medium carries one or more programs which, when executed, implement the molecular level reaction kinetics model construction method according to an embodiment of the present disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present disclosure, which enable those skilled in the art to understand or practice the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (13)
1. A molecular horizontal reaction kinetics model construction method is characterized by comprising the following steps:
obtaining a raw material molecule composition matrix, wherein the raw material molecule composition matrix comprises structure-oriented lumped representation and content of each raw material molecule;
generating a plurality of reaction paths and a reaction network consisting of the plurality of reaction paths according to the structure-oriented lumped representation of each raw material molecule based on a preset reaction rule, wherein the reaction rule comprises the change of the structure-oriented lumped representation of each raw material molecule in the corresponding reaction path;
dividing the current reaction into a plurality of infinitesimal reaction sections, constructing an ordinary differential equation for each infinitesimal reaction section according to a reaction network, solving the ordinary differential equation according to a preset reaction rate constant and the content of each raw material molecule to obtain the content of each product molecule, and calculating the yield of each product according to the content of the product molecule;
and calculating a relative difference value between the yield of each product and the yield of the actual reaction product, adjusting a preset reaction rate constant according to the relative difference value, and taking the reaction rate constant when the sum of the relative difference values meets a preset condition as a parameter of a molecular level reaction kinetic model, so that the molecular level reaction kinetic model predicts a molecular composition matrix of the reaction product according to the molecular composition matrix of the reaction raw material.
2. The method of claim 1, wherein the molecular level reaction kinetics model is one of an atmospheric distillation model, a residue hydrogenation model, a catalytic cracking model, a delayed coking model, a hydrocracking model, a catalytic reforming model, an alkylation model, a gasoline hydrogenation model, a diesel hydrogenation model, a wax oil hydrogenation model, a gasoline and diesel hydrogenation model, a gas fractionation model, an aromatics extraction model, and a hydrogen production model.
3. The method according to claim 1, wherein the generating a plurality of reaction paths and a reaction network composed of the plurality of reaction paths according to the structure-oriented lumped representation of each raw material molecule based on the preset reaction rule comprises:
and traversing the structure-oriented lumped representation of each raw material molecule according to a preset reaction rule to obtain a reaction path corresponding to each raw material molecule and a reaction network consisting of a plurality of reaction paths.
4. The method of claim 1, wherein the dividing the current reaction into a plurality of infinitesimal reaction sections, constructing an ordinary differential equation for each infinitesimal reaction section according to a reaction network, and solving the ordinary differential equation according to a preset initial value of a reaction rate constant and the content of each raw material molecule to obtain the content of each product molecule comprises:
averagely dividing the reaction duration of the current reaction into a plurality of infinitesimal reaction sections;
the reaction temperature at the inlet of the actual production unit of the current reactionTPressure, pressurepEnthalpy valueHSpecific heat of andCas initial temperature, initial pressure, initial enthalpy and specific heat capacity of the inlet of the first infinitesimal reaction section;
for each infinitesimal reaction section, solving an ordinary differential equation set under a reaction rate constant corresponding to each reaction rule by using a Longge Kutta algorithm to obtain the product concentration at the outlet of the infinitesimal reaction section
And the variation of the concentration with time t
；
According to
And initial enthalpy value of inlet of infinitesimal reaction sectionHCalculating enthalpy valueHVariation with time t
To obtain the enthalpy value of the outlet of the infinitesimal reaction section;
according to
Specific heat of andCcalculating the temperatureTOver timetVariations of (2)
To obtain the temperature of the outlet of the infinitesimal reaction section;
according to ideal gas constantRAnd temperatureTChange over time
Calculating pressurepOver timetVariations of (2)
To obtain the pressure at the outlet of the infinitesimal reaction section;
and taking the temperature, the pressure, the enthalpy value and the concentration of each product at the outlet of the infinitesimal reaction section as the inlet parameters of the next infinitesimal reaction section, calculating the parameters of the outlet of the next infinitesimal reaction section until the last infinitesimal reaction section, and taking the content of the product molecules at the outlet of the last infinitesimal reaction section as the content of the product molecules of the production unit.
5. The method according to claim 4, characterized in that the reaction time period is the time period during which the feed molecules are resident in the production unit.
6. The method of claim 1, wherein calculating the relative difference between the yield of each product and the yield of the actual reaction product and summing the relative differences comprises:
7. The method of claim 1, wherein the reaction rate constant is calculated by the following expression:
8. The method of claim 7, wherein adjusting a predetermined reaction rate constant according to the relative difference value, and using the reaction rate constant when the sum of the relative difference values satisfies a predetermined condition as a parameter of the molecular-level reaction kinetic model comprises:
when the relative difference value is larger than a preset threshold value, adjusting the kinetic parameters to obtain adjusted kinetic parameters, calculating a reaction rate constant based on the adjusted kinetic parameters, and re-solving the ordinary differential equation according to the reaction rate constant and the content of each raw material molecule to obtain the content of each product molecule;
the preset condition refers to the maximum error which can be accepted according to the actual application scene.
9. The method of claim 1, wherein the molecular level reaction kinetics model predicts a molecular composition matrix of the reaction product from a molecular composition matrix of the reaction feedstock, comprising:
and based on the Runge Kutta algorithm, solving the ordinary differential equation according to the reaction rate constant when the sum of the relative differences meets the preset condition and the content of each raw material molecule to obtain the content of each product molecule.
10. The method of claim 1, wherein the obtaining a feedstock molecular composition matrix comprises:
performing vector characterization on each raw material molecule based on a structure-oriented lumped molecule characterization method to obtain a structure-oriented lumped representation of each raw material molecule;
guiding the structure of each raw material molecule to lumped representation and content as a complete vector;
and combining the complete vectors of all the raw material molecules into a raw material molecule composition matrix.
11. A molecular horizontal reaction kinetics model construction device is characterized by comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring a raw material molecule composition matrix, and the raw material molecule composition matrix comprises structure-oriented lumped representation and content of each raw material molecule;
the generating module is used for generating a plurality of reaction paths and a reaction network consisting of the plurality of reaction paths according to the structure-oriented lumped representation of each raw material molecule based on a preset reaction rule, wherein the reaction rule comprises the change of the structure-oriented lumped representation of each raw material molecule in the corresponding reaction path;
the solving module is used for dividing the current reaction into a plurality of infinitesimal reaction sections, constructing an ordinary differential equation for each infinitesimal reaction section according to a reaction network, and solving the ordinary differential equation according to a preset initial value of a reaction rate constant and the content of each raw material molecule to obtain the content of each product molecule;
and the adjusting module is used for calculating a relative difference value between the yield of each product and the yield of an actual reaction product, adjusting a preset reaction rate constant according to the relative difference value, and taking the reaction rate constant when the sum of the relative difference values meets a preset condition as a parameter of the molecular level reaction kinetic model, so that the molecular level reaction kinetic model predicts a molecular composition matrix of the reaction product according to the molecular composition matrix of the reaction raw material.
12. An electronic device is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;
a memory for storing a computer program;
a processor for implementing the molecular level reaction kinetic model building method of any one of claims 1 to 10 when executing the program stored in the memory.
13. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the molecular-level reaction kinetic model building method according to any one of claims 1 to 10.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104789256A (en) * | 2015-03-26 | 2015-07-22 | 华东理工大学 | Real-time yield predicting method for catalytic cracking device |
| CN108279251A (en) * | 2018-01-26 | 2018-07-13 | 中国石油大学(北京) | A kind of method and its device of petroleum molecules level separation process simulation |
| WO2020016669A1 (en) * | 2018-07-18 | 2020-01-23 | Creoptix Ag | Method for calculating kinetic parameters of a reaction network |
| CN111899811A (en) * | 2020-06-12 | 2020-11-06 | 中国石油天然气股份有限公司 | Method, system, equipment and storage medium for establishing catalytic reforming device product prediction model |
| CN111892953A (en) * | 2020-06-12 | 2020-11-06 | 中国石油天然气股份有限公司 | Method, system, equipment and storage medium for determining crude oil molecular conversion path |
| CN111899813A (en) * | 2020-06-12 | 2020-11-06 | 中国石油天然气股份有限公司 | Product prediction model optimization method, system, equipment and storage medium of diesel hydrogenation device |
| CN111899801A (en) * | 2020-06-12 | 2020-11-06 | 中国石油天然气股份有限公司 | Reactant concentration calculation method, device and system and storage medium |
| CN113724800A (en) * | 2021-09-16 | 2021-11-30 | 广东辛孚科技有限公司 | Catalytic cracking unit simulation prediction method based on molecular level mechanism model and big data technology |
-
2023
- 2023-02-20 CN CN202310133375.4A patent/CN115831256B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104789256A (en) * | 2015-03-26 | 2015-07-22 | 华东理工大学 | Real-time yield predicting method for catalytic cracking device |
| CN108279251A (en) * | 2018-01-26 | 2018-07-13 | 中国石油大学(北京) | A kind of method and its device of petroleum molecules level separation process simulation |
| WO2020016669A1 (en) * | 2018-07-18 | 2020-01-23 | Creoptix Ag | Method for calculating kinetic parameters of a reaction network |
| CN111899811A (en) * | 2020-06-12 | 2020-11-06 | 中国石油天然气股份有限公司 | Method, system, equipment and storage medium for establishing catalytic reforming device product prediction model |
| CN111892953A (en) * | 2020-06-12 | 2020-11-06 | 中国石油天然气股份有限公司 | Method, system, equipment and storage medium for determining crude oil molecular conversion path |
| CN111899813A (en) * | 2020-06-12 | 2020-11-06 | 中国石油天然气股份有限公司 | Product prediction model optimization method, system, equipment and storage medium of diesel hydrogenation device |
| CN111899801A (en) * | 2020-06-12 | 2020-11-06 | 中国石油天然气股份有限公司 | Reactant concentration calculation method, device and system and storage medium |
| CN113724800A (en) * | 2021-09-16 | 2021-11-30 | 广东辛孚科技有限公司 | Catalytic cracking unit simulation prediction method based on molecular level mechanism model and big data technology |
Non-Patent Citations (1)
| Title |
|---|
| 仲从伟 等: "基于结构导向集总的柴油加氢精制分子水平反应动力学模型Ⅰ.模型的建立与验证" * |
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