Method for constructing and optimizing full-fraction oil catalytic cracking reaction kinetic model

Abstract

The invention belongs to the technical field of petrochemical process simulation and intelligent optimization control, and particularly relates to a method for constructing and optimizing a full-fraction oil catalytic cracking reaction kinetic model. The construction method comprises the steps of raw material matrix construction, reaction network construction, product property calculation, model parameter optimization and process prediction calculation. The optimization method comprises the following steps: constructing a process optimization index system comprehensively reflecting environmental and economic targets; systematic optimization of reaction conditions and product distribution is realized; intelligent regulation and efficient prediction of the catalytic cracking process are realized; and optimal reaction operation conditions meeting multiple requirements are obtained. The method is high in adaptability, process parameters can be adjusted according to the properties of the raw materials, and efficient conversion is achieved. By optimizing the catalytic cracking process, the yield of aromatic hydrocarbon can be remarkably improved. By predicting and optimizing reaction conditions through the kinetic model, the energy consumption is reduced, the reaction efficiency is improved, meanwhile, the equipment investment and operation cost can be reduced, and the production efficiency and economic benefits are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of petrochemical process simulation and intelligent optimization control, and specifically relates to a kinetic model construction and optimization method for catalytic cracking reactions of full-fraction oil products. Background Art

[0002] The current refining and chemical industry is facing a profound adjustment. The peak in the market demand for refined oil products has exacerbated the problem of structural overcapacity in refining capacity. "Reducing oil production and increasing chemical production" has gradually become the core goal of the transformation and upgrading of refining and chemical companies. Light olefins are the most important basic products of the petrochemical industry. Steam cracking has long been the mainstream technology for the production of light olefins, but its future development is limited by problems such as single product distribution, high energy consumption and high CO2 emissions. In contrast, catalytic cracking technology has the advantages of mild reaction conditions, strong adaptability to raw materials and flexible product structure. It has become an important supporting technology for the transformation of refining and chemical companies. Among them, the emergence of crude oil catalytic cracking technology not only realizes the efficient utilization of excess crude oil, but also effectively reduces equipment investment and device energy consumption. It is a booster for the transformation and upgrading of refineries in the future.

[0003] To break free from the traditional step-by-step scale-up model and achieve breakthroughs in cross-scale modeling and rapid scale-up of process flows, it is imperative to develop modeling methods that integrate artificial intelligence with reaction kinetics. Traditional reaction kinetic models are primarily based on lumped kinetic models. This approach, based on the kinetic differences between different fraction levels within a reaction system, divides reactants and products into a certain number of virtual components and then constructs a kinetic model based on a reaction network. However, this classification process is relatively crude, making it difficult to simulate reaction processes at the molecular level and accurately predict feedstock conversion and product distribution. Molecular management technology has become a research hotspot in the petroleum processing industry. Its core framework is the molecular-level reaction kinetic model. This model, combined with advanced analytical techniques and computational languages, has significantly improved prediction accuracy and model adaptability. Molecular-level reaction kinetic models developed based on structure-guided lumping have been applied to refining processes such as catalytic cracking, catalytic reforming, delayed coking, and hydrotreating. In particular, as target products shift from "fraction products" to "characteristic molecules," molecular-level reaction kinetic models can rapidly respond to the impact of changes in feedstock and process conditions on product distribution, supporting the development of "smart refineries."

[0004] However, the molecular-level reaction kinetic models that have been published so far only analyze the reaction process of a single distillate oil product, and its reaction rules are mainly based on random cracking mechanisms, so the repeatability of the model prediction calculation is poor. When the raw material is upgraded from a single distillate oil product to a complex distillate oil product, the multi-dimensional hydrocarbon composition exacerbates the complexity of the catalytic cracking reaction network, making it difficult to ensure the rationality and accuracy of the model prediction. At the same time, the rapid changes in market demand have put higher demands on process production. The adjustment of actual operating conditions must not only meet the target product yield requirements, but also need to comprehensively consider the product economy, etc. Therefore, it is urgent to develop a multi-objective optimization strategy suitable for kinetic models to improve the model's broad adaptability and rapid response capabilities, and to facilitate the rapid scale-up and large-scale application of catalytic cracking processes. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method for constructing and optimizing a kinetic model for catalytic cracking reactions of full-fraction oil products. The technical solution adopted is:

[0006] A method for constructing a kinetic model of catalytic cracking reactions of full-fraction oil products comprises the following steps:

[0007] S1. Raw material matrix construction: Based on a comprehensive characterization system and testing instruments, the physical properties of each distillate oil product are determined. Core molecular structures are selected from these, and the raw material molecular matrix is expanded in the form of structure vectors. The matrix properties are optimized through optimization algorithms.

[0008] S2. Reaction Network Construction: A reaction rule module is established based on the laws of catalytic cracking reactions. Molecular dynamics simulations are used to calculate the differences in kinetic parameters for hydrocarbon molecules with different structures. The results are combined to improve the reaction rule algorithm. A set of kinetic equations corresponding to the reaction rules is developed and substituted into the reaction conditions to implement the reaction network calculations.

[0009] S3. Product Property Calculation: Calculate the yield, boiling point, density, and other parameters of related products based on the product molecular composition matrix, and calculate the physical properties of catalytic cracking gasoline, diesel, and other distillate products.

[0010] S4. Model parameter optimization: Using the difference between the predicted model product property value and the experimental value as the optimization target, the kinetic parameters in the reaction rule are adjusted to ultimately obtain kinetic parameters such as the reaction energy barrier and pre-exponential factor that meet the optimization target;

[0011] S5. Process prediction calculation: Based on the input reaction conditions such as reaction temperature, residence time and catalyst-oil ratio, the distribution of hydrocarbon composition during the reaction process is calculated and the distribution prediction under the reaction conditions is completed.

[0012] Preferably, step S1 specifically comprises: obtaining a raw material molecular matrix comprising a full fraction composition, and determining the molecular composition of a primary or secondary processed product obtained by distilling and cutting the oil product, wherein the molecular composition of the processed product is determined by one or more of gas chromatography-mass spectrometry, comprehensive two-dimensional gas chromatography, quadrupole gas chromatography-mass spectrometry, gas chromatography or field ionization-time of flight mass spectrometry, 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;

[0013] Based on the molecular composition data of full-fraction oil products, representative core molecular structures are screened. A structure-guided aggregation method is used to construct the digital composition of various raw material molecules based on the core structures. The structural vector of each raw material molecule and its content are combined into a complete vector structure, which is then aggregated to form a raw material molecule matrix.

[0014] The molecular matrix includes the structural vector representation of each raw material molecule and its content; identifies the structural differences of the raw material molecules, and based on the multi-stage catalytic cracking reaction rules, obtains the product molecular structure vectors of each level of raw material molecules in the corresponding reaction path.

[0015] Preferably, the structural differences of the raw material molecules are identified, and based on the multi-stage catalytic cracking reaction rules, the product molecular structure vectors of the raw material molecules at each stage in the corresponding reaction path are obtained;

[0016] Among them, the multi-stage catalytic cracking reaction rules define the structural vector change rules of different structural raw material molecules in their reaction paths, including:

[0017] (1) Read and identify the structure vector of each molecule in the raw material molecule matrix;

[0018] (2) Based on the preset multi-stage catalytic cracking reaction rules, distinguish the structural types of the raw material molecules and determine their corresponding reaction pathways;

[0019] (3) Calculate the product molecular structure vector of each raw material molecule along its reaction path;

[0020] (4) retaining the obtained product molecular structure vector and its reaction path;

[0021] (5) Add the product molecules to the raw material molecule matrix for subsequent iterative calculations.

[0022] Preferably, the multi-stage catalytic cracking reaction rules include raw material molecular structure identification, reaction path directional division, and product molecular structure vector calculation, including:

[0023] a. Calculate the differences in reaction kinetic parameters of hydrocarbon molecules with different structures through molecular dynamics simulation, and establish multi-stage catalytic cracking reaction rules based on the correlation between kinetic parameters and molecular structure;

[0024] b. limiting the proportions of different reaction paths within a single reaction rule based on the differences in kinetic parameters of the reaction paths corresponding to different product molecules;

[0025] c. Read and identify the raw material molecular structure, and calculate the product molecular structure vector corresponding to the reaction path according to the preset ratio and the raw material molecular structure vector;

[0026] The preset multi-stage catalytic cracking reaction rules include alkane cleavage reaction rules, alkane dehydrogenation reaction rules, alkane isomerization reaction rules, olefin cleavage reaction rules, olefin hydrogen transfer reaction rules, olefin isomerization reaction rules, olefin cyclization reaction rules, olefin polymerization reaction rules, cycloalkane ring-opening reaction rules, cycloalkane side chain cleavage reaction rules, cycloalkane dehydrogenation reaction rules, aromatic side chain cleavage reaction rules, aromatic dehydrogenation condensation reaction rules, aromatic dehydrogenation reaction rules, aromatic hydrogenation saturation reaction rules, aromatic alkylation reaction rules, oxygen-containing compound carbon monoxide removal reaction rules, oxygen-containing compound carbon dioxide removal reaction rules and sulfur-containing compound desulfurization reaction rules.

[0027] Preferably, in step S3, the attribute parameters include physical property parameters, and the method of predicting the attribute parameters of the product based on the attribute parameters of each product molecule includes:

[0028] Determine the product molecules contained in each product;

[0029] According to the content and physical property parameters of the product molecules, the yield and physical property parameters of each product are obtained.

[0030] The attribute parameter of the product is at least one of a physical property parameter and a yield

[0031] Preferably, the physical property parameters include at least one of gas composition, gasoline density, viscosity, group composition, octane number, diesel density, diesel viscosity, diesel cetane index, wax oil density, wax oil viscosity, and wax oil metal content.

[0032] Preferably, in step S4, the overall property characteristics of the product are predicted based on the property parameters of the product molecules, and the difference between the predicted value and the actual value is used as the optimization target to adjust the kinetic parameters in the reaction rule, and finally obtain the catalytic cracking kinetic model parameters that meet the optimization target, including:

[0033] When the difference between the predicted value and the actual value is greater than a preset threshold, optimizing the kinetic parameters by one or more methods selected from a simulated annealing algorithm, a neural network algorithm, and a genetic algorithm, calculating a reaction rate constant based on the optimized kinetic parameters, and resolving the kinetic equation according to the reaction rate constant and the molecular content of the raw materials to obtain the content of each product molecule;

[0034] The preset threshold refers to the maximum error that can be accepted according to the actual application scenario.

[0035] Preferably, the reaction rate constant (k) corresponding to the catalytic cracking reaction rule is calculated:

[0036]

[0037] Among them, k B is the Boltzmann constant; h is the Planck constant; T is the reaction temperature; R is the ideal gas constant; ΔS is the entropy change during the reaction; and ΔE is the activation energy of the reaction. ΔS and ΔE are directly derived from molecular dynamics simulation results.

[0038] Preferably, in step S5, the reaction conditions are the reactor temperature and the residence time of the raw material in the reactor;

[0039] Based on the reaction kinetic equations and reaction conditions corresponding to each effective reaction path, the product molecular composition matrix after the reaction is calculated according to the content of each raw material molecule, including:

[0040] For each effective reaction path, determine the raw material molecules and product molecules of the current effective reaction path;

[0041] Substituting the reaction condition parameters and the content of the raw material molecules of the current effective reaction path into the reaction kinetic equations to obtain the content of the raw material molecules and product molecules of the current effective reaction path;

[0042] Summarizing the contents of raw material molecules and product molecules of all valid reaction paths, and determining the contents of all summarized product molecules of all valid reaction paths;

[0043] The structure-oriented lumped representation and content of each summarized product molecule are taken as a complete vector;

[0044] Combine the complete vectors of all summarized product molecules of the catalytic cracking reaction into a product molecule composition matrix.

[0045] A method for optimizing a kinetic model of a catalytic cracking reaction of a full-fraction oil product, comprising:

[0046] Based on the market demand for catalytic cracking products, carbon emission levels and overall economic benefits, a process optimization indicator system that comprehensively reflects environmental and economic goals is established;

[0047] Construct reaction process optimization strategies based on multi-objective optimization algorithms, coordinate the conflicting relationships between different indicators, and achieve systematic optimization of reaction conditions and product distribution;

[0048] Integrate reaction process optimization strategies and molecular-level reaction kinetic models to form an integrated model with both reaction path evolution capabilities and goal-oriented regulation capabilities, enabling intelligent regulation and efficient prediction of catalytic cracking processes;

[0049] Input the initial process parameters and set the process optimization indicators, call the integrated model to carry out evolutionary iteration, and obtain the optimal reaction operating conditions that meet multiple requirements.

[0050] Preferably, the input initial process parameters include one or more of reaction temperature, residence time, and agent-oil ratio;

[0051] The set process optimization indicators include one or more of product yield, product selectivity, product carbon emission contribution, and product comprehensive economic efficiency;

[0052] The integrated module outputs the optimal reaction condition parameters for guiding the operation of the catalytic cracking process.

[0053] The integrated model has an adaptive adjustment mechanism that can dynamically adjust the weights of various optimization objective functions based on real-time input market demand data.

[0054] The market demand data includes one or more of target output or ratio, market price or economic value index, and carbon emission factor.

[0055] The weight adjustment mechanism is based on the normalized priority mapping function to achieve dynamic balance between optimization objectives and reconstruction of the trade-off strategy.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] The present invention's full-fraction catalytic cracking technology is capable of processing a wide range of crude oils and distillates, offering strong adaptability and enabling efficient conversion by adjusting process parameters based on feedstock properties. By optimizing the catalytic cracking process, the yields of light olefins such as ethylene, propylene, and butenes, as well as light aromatics such as benzene, toluene, and xylene (BTX), can be significantly increased.

[0058] The present invention realizes the accurate prediction of hydrocarbon distribution and reaction mechanism ratio change during the reaction process through the molecular level reaction kinetic model, and provides a theoretical basis for the complex internal reaction mechanism of the catalytic cracking system.

[0059] The present invention reduces production energy consumption and improves reaction efficiency through kinetic model prediction and multi-objective reaction condition optimization, while also reducing equipment investment and operating costs, and improving production efficiency and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1The following schematically shows a flow chart of a method for constructing a catalytic cracking reaction model according to an embodiment of the present disclosure;

[0061] Figure 2 Schematic diagram showing the calculation results of multi-stage hydrocarbon molecular reaction kinetic parameters according to an embodiment of the present disclosure; wherein, Figure a shows the kinetic parameters of catalytic cracking reactions of alkanes, Figure b shows the kinetic parameters of catalytic cracking reactions of olefins, Figure c shows the kinetic parameters of cycloalkane ring-opening reactions, and Figure d shows the kinetic parameters of dealkylation reactions of cycloalkanes and aromatics;

[0062] Figure 3 The schematic diagram shows a catalytic cracking reaction network of a full-fraction oil product according to an embodiment of the present disclosure;

[0063] Figure 4 The figure schematically shows a flow chart of a multi-objective optimization strategy for process conditions according to an embodiment of the present disclosure.

[0064] 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 present disclosure.

[0065] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work. DETAILED DESCRIPTION

[0066] The following will clearly and completely describe the technical solutions in the embodiments of the present disclosure in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present disclosure without making any creative efforts shall fall within the scope of protection of the present disclosure.

[0067] like Figure 1 As shown, an embodiment of the present disclosure provides a method for constructing a kinetic model of catalytic cracking reaction of a full-fraction oil product, that is, a method for constructing a molecular-level kinetic model of catalytic cracking reaction of a full-fraction oil product, the method comprising:

[0068] S1. Based on a comprehensive characterization system and testing instruments, the physical properties of each distillate oil product are determined, and the core molecular structure is selected. The structure vector is expanded into a raw material molecule matrix, and the matrix properties are optimized through an optimization algorithm. The raw material molecule matrix includes the structure vector and mass content of each raw material molecule.

[0069] The hydrocarbon molecular composition of full-fraction oil products is obtained through the following steps:

[0070] The specific molecular composition of light crude oil products such as gasoline and diesel is analyzed by gas chromatography and comprehensive two-dimensional gas chromatography / time-of-flight mass spectrometry.

[0071] Heavy raw oil products such as heavy oil and crude oil, after secondary processing, the molecular composition of each fraction is determined by one or more of gas chromatography-mass spectrometry, comprehensive two-dimensional gas chromatography, gas chromatography or field ionization-time of flight mass spectrometry, nuclear magnetic resonance spectroscopy, and Raman spectroscopy.

[0072] Traverse the raw material molecular composition data, screen out the core molecular structure that can represent most homologue molecules, and establish the raw material molecular matrix based on this.

[0073] In step S1, obtaining a matrix of raw material molecules for catalytic cracking reaction includes:

[0074] A vector reconstruction of each raw material molecule is performed based on the structure-guided lumping method. The structure-guided lumping method uses 24 structural increments to characterize the basic structure of hydrocarbon molecules, ensuring that any petroleum molecule can be described by a specific set of structural increment fragments, as shown in Table 1 below.

[0075] Table 1 Structural increment fragments of petroleum molecules

[0076]

[0077] The above 24 structural increments represent the molecular composition of the raw materials according to the corresponding rules in the structure-oriented lumped representation method. Each molecule is converted into a one-dimensional structure vector composed of 24 structural increments. The molecular vector is recorded as A = [a1, a2, ... a 24 ], each component in A represents the number of structural increments in the molecule corresponding to Table 1. The raw material molecule matrix is represented by a molecule matrix with a dimension of n×24 and a vector of n×1 molecular content. The molecular vector in the raw material molecule matrix is recorded as B=[b1,b2,……b 24 ,b wt ], the raw material molecular matrix is recorded as Where n is the molecular species in the raw material, b wt / C wt is the mass content of the molecular vector, and each row of the molecular matrix C is a molecular vector.

[0078] The 24 structural increments are described as follows:

[0079] A6: A six-carbon aromatic ring that appears in all aromatic molecules. In the absence of any structural increments, A6 represents benzene.

[0080] A4: A four-carbon aromatic ring attached to A6 (or another A4 ring). This is a structural increment used to construct a polymeric polycyclic structure and cannot exist alone.

[0081] A2: Two-carbon aromatic structure increment. A2 is used to attach to the "bay area" of polycyclic aromatic hydrocarbons to form new polycyclic aromatic hydrocarbons and cannot exist alone.

[0082] N6 and N5: six-carbon and five-carbon cycloalkanes, both of which are structural units that can exist alone, such as cyclohexane and cyclopentane.

[0083] N4, N3, N2, N1: Additional aliphatic ring structure increments containing four, three, two and one carbon atoms. They must be attached to other aliphatic or aromatic ring structures and cannot exist alone.

[0084] R: The number of carbon atoms contained in all alkyl structures attached to the ring structure, or the number of carbon atoms in the aliphatic molecule when no ring structure exists. When the alkyl structure is attached to the ring structure, the R value represents the number of -CH2- and -CH3- groups.

[0085] IH: A structural increment involving hydrogen atoms to describe the degree of saturation of a molecule (excluding aromatic rings). IH represents the stoichiometric addition of two hydrogen atoms to a structural vector (molecule). If there are no rings, IH = 1 for alkanes, IH = 0 for monoolefins, and IH = -1 for diolefins. If a ring is present, IH = -1 for cycloolefins.

[0086] br: represents the number of branching nodes on a side chain alkyl, linear alkyl, or olefin. It does not distinguish between methyl, ethyl, and propyl branches, so it is assumed that only methyl branches are present. The specific location of branching nodes on a side chain or main chain is also not described. Furthermore, br does not affect the stoichiometric formula of the molecule. In actual refining processes, considering the effect of branch type on the reaction is of little significance. The above assumptions can reflect the influence of branch number while ignoring the influence of branch type, such as methyl, ethyl, and propyl branches, and meet practical needs.

[0087] me: Determines the number of methyl groups directly attached to carbon atoms in an aromatic or aliphatic ring in an alkyl structure. Specifically, 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 no longer used to indicate the number of methyl groups.

[0088] AA: A biphenyl bridge structure between any two non-structurally extended rings (A6, N6, or N5). This structure does not increase the carbon number but consumes two hydrogen atoms to establish a bridge bond.

[0089] NS, NN, and NO: Sulfur, nitrogen, and oxygen atoms attached to two carbon atoms in an aliphatic ring or chain. NS, NN, and NO refer to the replacement of a -CH2- group with a sulfur atom, an -NH- group, and an oxygen atom, respectively.

[0090] RS, RN and RO: A S atom, a N-containing -NH- group or an O atom is inserted between a carbon atom and a hydrogen atom to form a thiol, amine or alcohol group, respectively.

[0091] AN: A nitrogen group replaces a carbon in an aromatic ring, as in pyridine and quinoline. The AN group replaces the =CH- with =N-.

[0092] KO: -C=O replaces -CH2- or -CH3- to form a ketone or aldehyde group.

[0093] Ni, V: represent metallic nickel and vanadium, which appear in porphyrin molecules.

[0094] In some embodiments, the optimization of properties of the raw material molecular matrix is achieved by a simulated annealing algorithm.

[0095] S2. Establish a reaction rule module based on the catalytic cracking reaction rules, calculate the differences in kinetic parameters of hydrocarbon molecules with different structures through molecular dynamics simulation, improve the reaction rule algorithm based on the calculation results, and develop a set of kinetic equations corresponding to the reaction rules. Substitute the reaction conditions to solve the reaction network calculation, wherein the reaction rules include the changes in the structure-guided lumped representation of each raw material molecule in its corresponding reaction path;

[0096] In some embodiments, the method of obtaining the product molecular structure vectors of the raw material molecules at each stage in the corresponding reaction path based on the multi-stage catalytic cracking reaction rules, and constructing a catalytic cracking reaction network according to the catalytic cracking reaction path of each raw material molecule and its product molecules, includes:

[0097] The system reads and identifies the structure vectors of each molecule in the raw material molecule matrix. Based on the pre-set multi-stage catalytic cracking reaction rules, it distinguishes the structural types of the raw material molecules and determines their corresponding reaction pathways. It then calculates the structure vectors of the product molecules along their reaction pathways. The resulting product molecular structure vectors and their reaction pathways are retained and added to the raw material molecule matrix for subsequent iterative calculations. The catalytic cracking reaction network is constructed using the raw material and product molecules as network base points, and the corresponding reaction pathways as the connecting lines between the base points.

[0098] In some embodiments, the multi-stage catalytic cracking reaction rules include raw material molecular structure identification, reaction path directional division, and product molecular structure vector calculation, and the steps are as follows:

[0099] Through molecular dynamics simulation, the differences in reaction kinetic parameters of hydrocarbon molecules with different structures are calculated, and multi-stage catalytic cracking reaction rules are established based on the correlation between kinetic parameters and molecular structure. According to the differences in reaction path kinetic parameters corresponding to different product molecules, the different reaction paths within a single reaction rule are proportionally limited. The product molecular structure vectors under different reaction paths are calculated.

[0100] The preset catalytic cracking reaction rules include alkane cleavage reaction rules, alkane dehydrogenation reaction rules, alkane isomerization reaction rules, olefin cleavage reaction rules, olefin hydrogen transfer reaction rules, olefin isomerization reaction rules, olefin cyclization reaction rules, olefin polymerization reaction rules, cycloalkane ring-opening reaction rules, cycloalkane side chain cleavage reaction rules, cycloalkane dehydrogenation reaction rules, aromatic side chain cleavage reaction rules, aromatic dehydrogenation condensation reaction rules, aromatic dehydrogenation reaction rules, aromatic hydrogenation saturation reaction rules, aromatic alkylation reaction rules, oxygen-containing compound carbon monoxide removal reaction rules, oxygen-containing compound carbon dioxide removal reaction rules and sulfur-containing compound desulfurization reaction rules.

[0101] Dividing the current reaction into multiple microelement reaction segments, and constructing a kinetic equation system for each microelement reaction segment according to the reaction network, based on the reaction kinetic equation system corresponding to each effective reaction path and the reaction conditions, predicting the product molecular composition matrix of the catalytic cracking reaction according to the content of each raw material molecule, solving the kinetic equation system according to a preset reaction rate constant and the content of each raw material molecule to obtain the content of each product molecule;

[0102] The reaction kinetic equations and reaction time corresponding to the effective reaction path are determined by the following steps:

[0103] For each effective reaction path, determine the raw material molecules and product molecules of the current effective reaction path;

[0104] Substituting the reaction condition parameters and the content of the raw material molecules of the current effective reaction path into the reaction kinetic equations to obtain the content of the raw material molecules and product molecules of the current effective reaction path;

[0105] Summarizing the contents of raw material molecules and product molecules of all valid reaction paths, and determining the contents of all summarized product molecules of all valid reaction paths;

[0106] The structure-oriented lumped representation and content of each summarized product molecule are taken as a complete vector;

[0107] Combine the complete vectors of all summarized product molecules of the catalytic cracking reaction into a product molecule composition matrix.

[0108] The reaction activation energy and reaction rate constant corresponding to the catalytic cracking reaction rule are calculated using the following expressions:

[0109] The rate constant of the corresponding reaction rule under the corresponding reaction conditions is obtained through the transition state theory calculation formula, and the expression is as follows:

[0110]

[0111] Among them, k B is the Boltzmann constant; h is the Planck constant; T is the reaction temperature; R is the ideal gas constant; ΔS is the entropy change during the reaction; and ΔE is the activation energy of the reaction. ΔS and ΔE are directly derived from molecular dynamics simulation results.

[0112] The catalytic cracking reaction is mainly a first-order reaction. The reaction conversion rate is calculated based on the reaction rate constant as follows:

[0113]

[0114] Where k is the reaction rate constant and Δt is the average duration of each microelement reaction segment.

[0115] Combined with the above reaction rate constant and reaction conversion rate calculation formula, according to the characteristics of each reaction rule, a group of reaction kinetic equations corresponding to the reaction rules are established.

[0116] S3, calculates the property parameters of related products based on the product molecular composition matrix to realize the calculation of the physical property parameters of the main products of catalytic cracking.

[0117] In step S3, the attribute parameters include physical property parameters, and the attribute parameters of the product are predicted based on the attribute parameters of each product molecule, including:

[0118] Determine the product molecules contained in each product; and obtain the yield and physical property parameters of each product based on the content and physical property parameters of the product molecules.

[0119] The property parameters of the product are at least one of physical parameters and yield, and the physical parameters include at least one of gas composition, gasoline density, viscosity, group composition, octane number, diesel density, diesel viscosity, diesel cetane index, wax oil density, wax oil viscosity, and wax oil metal content.

[0120] S4. Taking the difference between the predicted value of the model product property and the experimental value as the optimization target, the kinetic parameters in the reaction rule are adjusted to finally obtain the catalytic cracking kinetic model parameters that meet the optimization target.

[0121] The method predicts the overall property characteristics of the product based on the property parameters of the product molecules, and uses the difference between the predicted value and the actual value as the optimization target to adjust the kinetic parameters in the reaction rules, and finally obtains the catalytic cracking kinetic model parameters that meet the optimization target, including:

[0122] When the difference between the predicted value and the actual value exceeds a preset threshold, the kinetic parameters are optimized using one or more methods selected from the group consisting of a simulated annealing algorithm, a neural network algorithm, and a genetic algorithm. The reaction rate constant is calculated based on the optimized kinetic parameters, and the kinetic equation is re-solved based on the reaction rate constant and the molecular weight of the raw materials to obtain the molecular weight of each product. The preset threshold refers to the maximum acceptable error based on the actual application scenario.

[0123] S5. Based on the input reaction conditions, the distribution calculation of hydrocarbon composition during the reaction process is realized, and the product distribution prediction under the reaction conditions is completed.

[0124] In step S5, the reaction conditions are one or more of reaction temperature, residence time, and agent-oil ratio.

[0125] The distribution calculation of the hydrocarbon composition includes the variation patterns of single hydrocarbon molecules and the overall hydrocarbon composition along the relative height of the reactor.

[0126] Based on this model, product distribution prediction under different reaction conditions can be achieved, providing data support for the optimization of reaction conditions.

[0127] like Figure 2 As shown, the embodiment of the present disclosure provides a data support for a multi-stage catalytic cracking reaction rule, including:

[0128] Calculate the correlation between hydrocarbon molecular structure and reaction kinetic parameters based on molecular dynamics simulation software, wherein the hydrocarbon molecular structure includes hydrocarbon group composition, molecular carbon number, etc., the reaction includes but is not limited to catalytic cracking reaction, dehydrogenation reaction, side chain cleavage reaction, ring opening reaction, etc., and the kinetic parameters include reaction activation energy, reaction entropy, etc.;

[0129] Optimize the reaction paths of hydrocarbon molecules based on the differences in kinetic parameters, define reaction rules based on the carbon number differences of molecules of the same type, and define the path ratio based on the product distribution under different reaction paths of a single molecule in combination with kinetic parameters;

[0130] Read and identify the molecular vectors in the raw material molecular matrix, calculate the product molecular structure vector under the corresponding reaction path according to the multi-stage reaction rules, and complete the calculation of the product molecular content by combining the raw material molecular content, reaction kinetic equations and kinetic parameters.

[0131] The application process of the above embodiment is detailed in the implementation process of the corresponding steps in the above method, which will not be repeated here.

[0132] The above-described embodiments are merely illustrative, wherein the calculation of the basic data can be implemented using a variety of software or algorithms. By exploring the differences in kinetic parameters of the desired reaction pathway, the corresponding reaction rules can be improved. Therefore, the target parameters can be determined according to actual needs to achieve the purpose of the present invention. Those skilled in the art can understand and implement the present invention without inventive effort.

[0133] like Figure 3 As shown, an embodiment of the present disclosure provides a visualization image of a catalytic cracking reaction network for a full-fraction oil product.

[0134] The hydrocarbon molecular structure is screened to determine the reaction network base point, and the intermolecular reaction path is established in combination with the reaction mechanism anchoring function, ultimately forming a catalytic cracking visual reaction network, in which the dot and line colors represent the hydrocarbon molecules and reaction mechanism types, respectively, and the carbon number of the hydrocarbon molecules is displayed in real time on the base point.

[0135] The visualized reaction network is calculated by a molecular-level reaction kinetic model of catalytic cracking of paraffin-based crude oil.

[0136] The reaction mechanism is based on Figure 2 The multi-stage catalytic cracking reaction rules obtained after optimization in the embodiment.

[0137] like Figure 4 As shown, the embodiment of the present disclosure provides a flow chart of a multi-objective optimization strategy for process conditions, including:

[0138] Input the optimization target demand and reaction condition interval, randomly generate an initial population Pt of size N, and after non-dominated sorting, selection, crossover and mutation, generate a child population Qt, and combine the two populations to form a population Rt of size 2N;

[0139] Perform fast non-dominated sorting and calculate the crowding degree of individuals in each non-dominated layer. Select appropriate individuals to form a new parent population Pt+1 based on the non-dominated relationship and the crowding degree of the individuals.

[0140] Through the basic operations of the genetic algorithm, a new offspring population Qt+1 is generated, and Pt+1 and Qt+1 are merged to form a new population Rt. The above operations are repeated until the calculation results meet the optimization requirements and the corresponding reaction conditions are output.

[0141] Based on the market demand for catalytic cracking products, carbon emission levels and overall economic benefits, a process optimization indicator system that comprehensively reflects environmental and economic goals is established;

[0142] Construct reaction process optimization strategies based on multi-objective optimization algorithms, coordinate the conflicting relationships between different indicators, and achieve systematic optimization of reaction conditions and product distribution;

[0143] Integrate reaction process optimization strategies and molecular-level reaction kinetic models to form an integrated model with both reaction path evolution capabilities and goal-oriented regulation capabilities, enabling intelligent regulation and efficient prediction of catalytic cracking processes;

[0144] Input the initial process parameters and set the process optimization indicators, call the integrated model to carry out evolutionary iteration, and obtain the optimal reaction operating conditions that meet multiple requirements.

[0145] The market demand for the product includes product yield, oil density, oil boiling point, gasoline octane number, diesel cetane number, etc. The carbon emission level includes carbon emission contribution, global warming potential, etc. The overall economic benefits include the weight of low-carbon olefin products, equipment energy consumption, etc.

[0146] The optimal reaction operating conditions include one or more of reaction temperature, residence time, and agent-oil ratio.

[0147] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by technicians in this technical field within the essential scope of the present invention should also fall within the scope of protection of the present invention.

Claims

1. A method for constructing a kinetic model of catalytic cracking reaction of full-fraction oil products, characterized in that: The steps include: S1. Raw material matrix construction: Based on a comprehensive characterization system and testing instruments, the physical properties of each distillate oil product are determined. Core molecular structures are selected from these, and the raw material molecular matrix is expanded in the form of structure vectors. The matrix properties are optimized through optimization algorithms. S2. Reaction Network Construction: A reaction rule module is established based on the laws of catalytic cracking reactions. Molecular dynamics simulations are used to calculate the differences in kinetic parameters for hydrocarbon molecules with different structures. The results are combined to improve the reaction rule algorithm. A set of kinetic equations corresponding to the reaction rules is developed and substituted into the reaction conditions to implement the reaction network calculations. S3. Product property calculation: Calculate the property parameters of the relevant products based on the product molecular composition matrix to achieve the calculation of the physical property parameters of the catalytic cracking products; S4. Model parameter optimization: Using the difference between the predicted model product property and the experimental value as the optimization target, the kinetic parameters in the reaction rules are adjusted to ultimately obtain the catalytic cracking kinetic model parameters that meet the optimization target. S5. Process prediction calculation: Based on the input reaction conditions, the distribution calculation of hydrocarbon composition during the reaction process is realized, and the distribution prediction under the reaction conditions is completed.

2. The method for constructing a kinetic model of catalytic cracking reaction of a full-fraction oil product according to claim 1, characterized in that: The step S1 specifically comprises: obtaining a raw material molecular matrix comprising a full fraction composition, and determining the molecular composition of a primary or secondary processed product obtained by distilling and cutting the oil product, wherein the molecular composition of the processed product is determined by one or more of gas chromatography-mass spectrometry, comprehensive two-dimensional gas chromatography, quadrupole gas chromatography-mass spectrometry, gas chromatography or field ionization-time of flight mass spectrometry, 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; Based on the molecular composition data of full-fraction oil products, representative core molecular structures are screened. A structure-guided aggregation method is used to construct the digital composition of various raw material molecules based on the core structures. The structural vector of each raw material molecule and its content are combined into a complete vector structure, which is then aggregated to form a raw material molecule matrix. The molecular matrix includes the structural vector representation of each raw material molecule and its content; identifies the structural differences of the raw material molecules, and based on the multi-stage catalytic cracking reaction rules, obtains the product molecular structure vectors of each level of raw material molecules in the corresponding reaction path.

3. The method for constructing a kinetic model of catalytic cracking reaction of a full-fraction oil product according to claim 2, characterized in that: Identify the structural differences of raw material molecules and, based on the multi-stage catalytic cracking reaction rules, obtain the product molecular structure vectors of each stage of raw material molecules in the corresponding reaction path; Among them, the multi-stage catalytic cracking reaction rules define the structural vector change rules of different structural raw material molecules in their reaction paths, including: (1) Read and identify the structure vector of each molecule in the raw material molecule matrix; (2) Based on the preset multi-stage catalytic cracking reaction rules, distinguish the structural types of the raw material molecules and determine their corresponding reaction pathways; (3) Calculate the product molecular structure vector of each raw material molecule along its reaction path; (4) retaining the obtained product molecular structure vector and its reaction path; (5) Add the product molecules to the raw material molecule matrix for subsequent iterative calculations.

4. The method for constructing a kinetic model of catalytic cracking reaction of a full-fraction oil product according to claim 3, characterized in that: The multi-stage catalytic cracking reaction rules include raw material molecular structure identification, reaction path directional division, and product molecular structure vector calculation, including: a. Calculate the differences in reaction kinetic parameters of hydrocarbon molecules with different structures through molecular dynamics simulation, and establish multi-stage catalytic cracking reaction rules based on the correlation between kinetic parameters and molecular structure; b. limiting the proportions of different reaction paths within a single reaction rule based on the differences in kinetic parameters of the reaction paths corresponding to different product molecules; c. Read and identify the raw material molecular structure, and calculate the product molecular structure vector corresponding to the reaction path according to the preset ratio and the raw material molecular structure vector; The preset multi-stage catalytic cracking reaction rules include alkane cleavage reaction rules, alkane dehydrogenation reaction rules, alkane isomerization reaction rules, olefin cleavage reaction rules, olefin hydrogen transfer reaction rules, olefin isomerization reaction rules, olefin cyclization reaction rules, olefin polymerization reaction rules, cycloalkane ring-opening reaction rules, cycloalkane side chain cleavage reaction rules, cycloalkane dehydrogenation reaction rules, aromatic side chain cleavage reaction rules, aromatic dehydrogenation condensation reaction rules, aromatic dehydrogenation reaction rules, aromatic hydrogenation saturation reaction rules, aromatic alkylation reaction rules, oxygen-containing compound carbon monoxide removal reaction rules, oxygen-containing compound carbon dioxide removal reaction rules and sulfur-containing compound desulfurization reaction rules.

5. The method for constructing a kinetic model of catalytic cracking reaction of a full-fraction oil product according to claim 1, characterized in that: In step S3, the attribute parameters include physical property parameters. The attribute parameters of the product are predicted based on the attribute parameters of each product molecule, including: Determine the product molecules contained in each product; According to the content and physical property parameters of the product molecules, the yield and physical property parameters of each product are obtained. The attribute parameter of the product is at least one of a physical property parameter and a yield.

6. The method for constructing a kinetic model of catalytic cracking reaction of a full-fraction oil product according to claim 5, characterized in that: The physical property parameters include at least one of gas composition, gasoline density, viscosity, group composition, octane number, diesel density, diesel viscosity, diesel cetane index, wax oil density, wax oil viscosity, and wax oil metal content.

7. The method for constructing a kinetic model of catalytic cracking reaction of a full-fraction oil product according to claim 1, characterized in that: In step S4, the overall property characteristics of the product are predicted based on the property parameters of the product molecules, and the difference between the predicted value and the actual value is used as the optimization target to adjust the kinetic parameters in the reaction rule, and finally obtain the catalytic cracking kinetic model parameters that meet the optimization target, including: When the difference between the predicted value and the actual value is greater than a preset threshold, optimizing the kinetic parameters by one or more methods selected from a simulated annealing algorithm, a neural network algorithm, and a genetic algorithm, calculating a reaction rate constant based on the optimized kinetic parameters, and resolving the kinetic equation according to the reaction rate constant and the molecular content of the raw materials to obtain the content of each product molecule; The preset threshold refers to the maximum error that can be accepted according to the actual application scenario.

8. The method for constructing a kinetic model of catalytic cracking reaction of a full-fraction oil product according to claim 7, characterized in that: Calculate the reaction rate constant (k) corresponding to the catalytic cracking reaction rule: Among them, k B is the Boltzmann constant; h is the Planck constant; T is the reaction temperature; R is the ideal gas constant; ΔS is the entropy change during the reaction; and ΔE is the activation energy of the reaction. ΔS and ΔE are directly derived from molecular dynamics simulation results.

9. The method for constructing a kinetic model of catalytic cracking reaction of a full-fraction oil product according to claim 1, characterized in that: In step S5, the reaction conditions are the reactor temperature and the residence time of the raw materials in the reactor; Based on the reaction kinetic equations and reaction conditions corresponding to each effective reaction path, the product molecular composition matrix after the reaction is calculated according to the content of each raw material molecule, including: For each effective reaction path, determine the raw material molecules and product molecules of the current effective reaction path; Substituting the reaction condition parameters and the content of the raw material molecules of the current effective reaction path into the reaction kinetic equations to obtain the content of the raw material molecules and product molecules of the current effective reaction path; Summarizing the contents of raw material molecules and product molecules of all valid reaction paths, and determining the contents of all summarized product molecules of all valid reaction paths; The structure-oriented lumped representation and content of each summarized product molecule are taken as a complete vector; Combine the complete vectors of all summarized product molecules of the catalytic cracking reaction into a product molecule composition matrix.

10. A method for optimizing a kinetic model of catalytic cracking reaction of a full-fraction oil product according to any one of claims 1 to 9, characterized in that: include: Based on the market demand for catalytic cracking products, carbon emission levels and overall economic benefits, a process optimization indicator system that comprehensively reflects environmental and economic goals is established; Construct reaction process optimization strategies based on multi-objective optimization algorithms, coordinate the conflicting relationships between different indicators, and achieve systematic optimization of reaction conditions and product distribution; Integrate reaction process optimization strategies and molecular-level reaction kinetic models to form an integrated model with both reaction path evolution capabilities and goal-oriented regulation capabilities, enabling intelligent regulation and efficient prediction of catalytic cracking processes; Input the initial process parameters and set the process optimization indicators, call the integrated model to carry out evolutionary iteration, and obtain the optimal reaction operating conditions that meet multiple requirements.

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