A method and apparatus for kinetic modeling of light hydrocarbon catalytic cracking

Abstract

A kinetic modeling method and apparatus for catalytic cracking of light hydrocarbons, belonging to the field of catalytic cracking of light hydrocarbons, is disclosed. First, the light hydrocarbons are lumped together according to the principle of lumped kinetics. Then, a catalytic cracking reaction network is established based on the reaction mechanism. Next, a kinetic model of the catalytic cracking reaction is established, an objective function is proposed, and the kinetic model parameters are solved using an optimization algorithm based on experimental data. This invention, based on the catalytic cracking reaction mechanism and the reaction characteristics of the reactants, lumps together the gasoline fraction of the products and the gasoline fraction of the feedstock according to their POA composition, establishing a kinetic reaction network to better reflect the reaction laws, describe the reaction mechanism, and accurately predict the distribution of reaction products.

Description

Technical Field

[0001] This invention relates to the field of catalytic cracking of light hydrocarbons, specifically a kinetic modeling method and apparatus for catalytic cracking of light hydrocarbons. Background Technology

[0002] Ethylene, propylene, butene, and other low-carbon olefins are important raw materials for the petrochemical industry. Currently, the production of low-carbon olefins mainly employs steam cracking. After decades of development, steam cracking has become a highly mature technology; however, the large energy consumption required for olefin production remains a major problem that needs to be addressed. Other low-carbon olefin production technologies include catalytic cracking, propane dehydrogenation, methanol-to-olefins (MTO), olefin disproportionation, and light hydrocarbon catalytic cracking. Among these, the reaction temperature of light hydrocarbon catalytic cracking is 50–200°C lower than that of steam cracking, which not only significantly reduces energy consumption and emissions and extends reactor life, but also allows for control of product distribution and increases the yield of low-carbon olefins, especially propylene. Therefore, light hydrocarbon catalytic cracking technology has developed rapidly in recent years, and fluidized bed industrial plants have emerged.

[0003] To further improve the yield of low-carbon olefins in catalytic cracking units, it is necessary to optimize the unit's operating mode, change operating conditions, or even modify the unit. The most mature and effective method to achieve this is to conduct lumped kinetic modeling studies and establish a model that can reflect the reaction laws and describe the reaction process.

[0004] Many scholars have conducted research in this area:

[0005] Liu Yibin et al. lumped gasoline separately and established a six-lumped kinetic model for FCC gasoline catalytic cracking; Han Zhongxiang et al. divided gasoline into alkanes, olefins, cycloalkanes, and aromatics and established an eight-lumped kinetic model for FCC gasoline catalytic cracking; Wu Feiyue et al. divided gasoline into saturated hydrocarbons, olefins, and aromatics and established a nine-lumped model for gasoline reforming; Liu Fu'an et al. divided gasoline according to its PONA composition into n-alkanes, isoalkanes, cycloalkanes, olefins, and aromatics and established a ten-lumped kinetic model for FCC gasoline reforming; Wu Qing et al. divided gasoline according to its PONA composition into n-alkanes, isoalkanes, olefins, cycloalkanes, and aromatics and established an eight-lumped kinetic model for FCC gasoline reforming.

[0006] These kinetic models can predict product yields, but their accuracy is often low. This is because the composition of components such as n-alkanes, isoalkanes, cycloalkanes, alkenes, and aromatics in gasoline undergoes significant changes after cracking. For easily reactive components such as isoalkanes and alkenes, the content of each molecule is significantly reduced. For alkanes and aromatics with poor reactivity, the content of C9 and above macromolecules is significantly reduced, while the content of C6-C8 molecules is significantly increased.

[0007] Since the components in gasoline undergo significant changes after the cracking reaction, it is inappropriate to consider them as a single lumped aggregate when modeling them and to analyze them from the perspective of the reaction mechanism.

[0008] Based on the eight-lumped kinetic model, Wu Qing et al. further subdivided the isoalkanes and olefins in gasoline composition into seven lumped groups according to the number of carbon atoms: C3+C4, C5, C6, C7, C8, C9, and C10+7, establishing a twenty-lumped kinetic model for FCC gasoline reforming. He Jinlong et al. divided gasoline fractions into alkanes, olefins, cycloalkanes, and aromatics according to the PONA composition, and further divided gasoline olefins into six lumped groups: C5, C6, C7, C8, and C9+6, establishing a sixteen-lumped kinetic model for gasoline catalytic cracking. This method of lumped group division avoids the above problems to some extent, but the alkanes and aromatics in gasoline products are still considered the same components as the alkanes and aromatics in the feedstock. At the same time, due to the significant increase in the number of lumped groups and reactions, the difficulty of solving the model parameters increases significantly, and the prediction accuracy of the model is not significantly improved, and may even be slightly reduced. Summary of the Invention

[0009] To address the problem that existing modeling methods treat the cracked light hydrocarbons and the feedstock light hydrocarbons as the same component, resulting in inaccurate model predictions and significantly increased solution difficulty, this invention provides a kinetic modeling method and apparatus for the catalytic cracking of light hydrocarbons. This modeling method divides the feedstock light hydrocarbons and the cracked light hydrocarbons into different lumps, which can better reflect the reaction rules, describe the reaction mechanism, and accurately predict the distribution of reaction products.

[0010] The technical solution adopted by this invention to solve the above-mentioned technical problems is: a kinetic modeling method for catalytic cracking of light hydrocarbons, comprising the following steps:

[0011] S1. Based on the principle of lumped dynamics, light hydrocarbons are lumped into categories;

[0012] S2. Based on the catalytic cracking reaction mechanism, establish a light hydrocarbon catalytic cracking reaction network;

[0013] S3. Based on the light hydrocarbon catalytic cracking reaction network, establish a kinetic model for the light hydrocarbon catalytic cracking reaction;

[0014] S4. Propose the objective function and, based on the experimental data, apply the optimization algorithm to solve the kinetic model parameters, thus establishing the kinetic model for the catalytic cracking of light hydrocarbons.

[0015] When performing the lumped division in S1, the light hydrocarbon fraction after catalytic cracking reaction is treated as gasoline product and lumped according to the POA component.

[0016] As an optimized scheme for the kinetic modeling method of the above-mentioned light hydrocarbon catalytic cracking, the light hydrocarbon fraction after the catalytic cracking reaction includes cracked gasoline saturated hydrocarbons, cracked gasoline olefins, and cracked gasoline aromatics.

[0017] As another optimization scheme for the kinetic modeling method of the above-mentioned light hydrocarbon catalytic cracking, the clustering includes three feedstock clusters and ten product clusters. The feedstock clusters are saturated hydrocarbon clusters, olefin clusters, and aromatic hydrocarbon clusters, while the product clusters are ethylene clusters, propylene clusters, butene clusters, dry gas clusters, liquefied petroleum gas clusters, cracked gasoline saturated hydrocarbon clusters, cracked gasoline olefin clusters, cracked gasoline aromatic hydrocarbon clusters, diesel clusters, and coke clusters.

[0018] As another optimization scheme for the kinetic modeling method of the above-mentioned light hydrocarbon catalytic cracking, the light hydrocarbon catalytic cracking reaction network is established as follows based on the lumped partition:

[0019] The gasoline saturated hydrocarbon aggregate was reacted with the ethylene aggregate, propylene aggregate, butene aggregate, dry gas aggregate, liquefied petroleum gas aggregate, cracked gasoline saturated hydrocarbon aggregate, cracked gasoline olefin aggregate, and cracked gasoline aromatic hydrocarbon aggregate, respectively.

[0020] Gasoline olefin aggregates were reacted with ethylene aggregates, propylene aggregates, butene aggregates, dry gas aggregates, liquefied petroleum gas aggregates, cracked gasoline saturated hydrocarbon aggregates, cracked gasoline olefin aggregates, cracked gasoline aromatic hydrocarbon aggregates, diesel aggregates, and coke aggregates, respectively.

[0021] Gasoline aromatic hydrocarbon aggregates were reacted with ethylene aggregates, dry gas aggregates, cracked gasoline aromatic hydrocarbon aggregates, diesel aggregates, and coke aggregates, respectively.

[0022] The diesel fuel aggregate was reacted with the ethylene aggregate, dry gas aggregate, and coke aggregate, respectively.

[0023] As another optimization scheme for the kinetic modeling method of the above-mentioned light hydrocarbon catalytic cracking, the basic equation for establishing the kinetic model of the light hydrocarbon catalytic cracking reaction in S3 is as follows:

[0024]

[0025] In the formula, a i The concentration of the i-th set is expressed in molesi / g, P represents the system pressure, R is the gas constant, T represents the system temperature, X = x / H represents the dimensionless relative distance at section x in the bed, H represents the total length of the catalyst bed, and S... WH Represents the actual weight-time space velocity, K is the reaction rate constant matrix, and a = [a1, ..., a2]. ni ] T This represents the lumped component concentration vector; This indicates the effect of catalyst coking on activity, and assumes that the catalyst coking rate is only a function of the catalyst residence time, t. c This refers to the residence time of the catalyst.

[0026] As another optimized scheme of the above-mentioned kinetic modeling method for light hydrocarbon catalytic cracking, the basic equations of the light hydrocarbon catalytic cracking reaction kinetic model are derived from the continuity equation and the reaction rate equation:

[0027] The continuity equation is:

[0028]

[0029] The reaction rate equation is:

[0030]

[0031] In the formula, subscript i represents the lumped component, subscript j represents the j-th reaction, and ρ represents the density of the oil-gas mixture in g / cm³. 3 a i G represents the total concentration of the i-th set, in molesi / g, t represents the reaction time, and G v This represents the mass flow rate of oil and gas across a cross section, in g / (cm). 2 ·h), where x represents the distance from the riser inlet into the reactor, R i Let n represent the reaction rate of the i-th ensemble. r v represents the number of reactions. i,j R represents the stoichiometric coefficient of i lumped in reaction j. j k represents the reaction rate of reaction j. j ρ represents the rate constant of reaction j. c Catalyst density relative to reactor volume, in g / cm³ 3 ε represents the porosity, P represents the system pressure, R is the gas constant, and T represents the system temperature. This indicates the effect of catalyst coking on activity, and assumes that the catalyst coking rate is only a function of the catalyst residence time, t. c η is the catalyst residence time, and β is the catalyst deactivation constant.

[0032] As another optimization scheme for the kinetic modeling method of the above-mentioned light hydrocarbon catalytic cracking, the objective function proposed in S4 is:

[0033]

[0034] In the formula, Φ is the sum of squared errors between the experimental and fitted values, and n exp n is the number of trials; c It is a grouping fraction, a ijLet i be the experimental concentration lumped together in reaction j. Let be the fitted concentration of i lumped in reaction j.

[0035] A modeling apparatus for a catalytic cracking reaction process, the modeling apparatus comprising a modeling module for modeling using the above-described catalytic cracking reaction process modeling method.

[0036] An electronic device includes a processor and a memory, the memory storing computer-readable instructions that, when executed by the processor, enable the modeling method described above to be run.

[0037] A readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps in the modeling method described above.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The kinetic modeling method for light hydrocarbon catalytic cracking proposed in this invention divides the gasoline fraction of the product and the gasoline fraction of the feedstock according to the POA composition based on the catalytic cracking reaction mechanism and the reaction characteristics of the reactants, and establishes a kinetic reaction network to better reflect the reaction law, describe the reaction mechanism, and accurately predict the distribution of reaction products. Attached Figure Description

[0040] Figure 1 This is a lumped reaction network diagram of the catalytic cracking reaction kinetic model of this invention;

[0041] Figure 2 This is a lumped reaction network diagram of an existing catalytic cracking reaction kinetic model;

[0042] Figure 3 This is a comparison of the experimental values ​​and calculated values ​​for both the example and comparative examples. Detailed Implementation

[0043] The technical solution of the present invention will be further described in detail below with reference to specific embodiments. Any parts not explicitly described in the following embodiments should be understood as prior art known or should be known by those skilled in the art.

[0044] Example 1

[0045] A kinetic modeling method for catalytic cracking of light hydrocarbons includes the following steps:

[0046] S1. Based on the principle of lumped dynamics, light hydrocarbons are lumped into categories;

[0047] In this step, when performing the clustering and classification, the light hydrocarbon fraction after the catalytic cracking reaction is treated as gasoline product and clustered and classified according to the POA composition.

[0048] The light hydrocarbon fraction following the catalytic cracking reaction includes cracked gasoline saturated hydrocarbons, cracked gasoline olefins, and cracked gasoline aromatics.

[0049] The clusters are divided into three raw material clusters and ten product clusters. The raw material clusters are saturated hydrocarbon clusters, olefin clusters, and aromatic hydrocarbon clusters. The product clusters are ethylene clusters, propylene clusters, butene clusters, dry gas clusters, liquefied petroleum gas clusters, cracked gasoline saturated hydrocarbon clusters, cracked gasoline olefin clusters, cracked gasoline aromatic hydrocarbon clusters, diesel clusters, and coke clusters.

[0050] S2. Based on the catalytic cracking reaction mechanism, establish a light hydrocarbon catalytic cracking reaction network;

[0051] In this step, the light hydrocarbon catalytic cracking reaction network is established as follows, such as... Figure 1 As shown:

[0052] The reaction of gasoline saturated hydrocarbon aggregates with ethylene aggregates, propylene aggregates, butene aggregates, dry gas aggregates, liquefied petroleum gas aggregates, cracked gasoline saturated hydrocarbon aggregates, cracked gasoline olefin aggregates, and cracked gasoline aromatics aggregates is established. The basis for this relationship is that the cracking reaction of gasoline saturated hydrocarbons produces ethylene, propylene, butene, dry gas, liquefied petroleum gas, and cracked gasoline saturated hydrocarbons, which are then converted into cracked gasoline olefins through dehydrogenation and into cracked gasoline aromatics through aromatization.

[0053] The gasoline olefin aggregate is reacted with ethylene aggregate, propylene aggregate, butene aggregate, dry gas aggregate, liquefied petroleum gas aggregate, cracked gasoline saturated hydrocarbon aggregate, cracked gasoline olefin aggregate, cracked gasoline aromatic hydrocarbon aggregate, diesel aggregate, and coke aggregate, respectively. The basis for this relationship is that the cracking reaction of gasoline olefins produces ethylene, propylene, butene, dry gas, liquefied petroleum gas, and cracked gasoline olefins; the cyclization reaction produces cracked gasoline saturated hydrocarbons; the aromatization reaction produces cracked gasoline aromatic hydrocarbons; and the condensation reaction produces diesel and coke.

[0054] The gasoline aromatics aggregate is reacted with the ethylene aggregate, dry gas aggregate, cracked gasoline aromatics aggregate, diesel aggregate, and coke aggregate, respectively. The basis for this relationship is that gasoline aromatics generate ethylene, dry gas, and cracked gasoline aromatics through cracking, and generate diesel and coke through condensation.

[0055] The diesel fuel aggregate was reacted with ethylene fuel aggregate, dry gas fuel aggregate, and coke fuel aggregate, respectively. This relationship was established based on the fact that diesel fuel cracking produces ethylene and dry gas, which then undergoes a condensation reaction to produce coke.

[0056] S3. Based on the light hydrocarbon catalytic cracking reaction network, establish a kinetic model for the light hydrocarbon catalytic cracking reaction;

[0057] The basic equation of the kinetic model for the catalytic cracking reaction of light hydrocarbons is:

[0058]

[0059] In the formula, a i The concentration of the i-th set is expressed in molesi / g, P represents the system pressure, R is the gas constant, T represents the system temperature, X = x / H represents the dimensionless relative distance at section x in the bed, H represents the total length of the catalyst bed, and S... WH Represents the actual weight-time space velocity, K is the reaction rate constant matrix, and a = [a1, ..., a2]. ni ] T This represents the lumped component concentration vector; This indicates the effect of catalyst coking on activity, and assumes that the catalyst coking rate is only a function of the catalyst residence time, t. c This refers to the residence time of the catalyst.

[0060] The kinetic model for the catalytic cracking reaction of light hydrocarbons is derived from the continuity equation and the reaction rate equation:

[0061] The continuity equation is:

[0062]

[0063] The reaction rate equation is:

[0064]

[0065] In the formula, subscript i represents the lumped component, subscript j represents the j-th reaction, and ρ represents the density of the oil-gas mixture in g / cm³. 3 a i G represents the total concentration of the i-th set, in molesi / g, t represents the reaction time, and G v This represents the mass flow rate of oil and gas across a cross section, in g / (cm). 2 ·h), where x represents the distance from the riser inlet into the reactor, R i Let n represent the reaction rate of the i-th ensemble. r v represents the number of reactions. i,j R represents the stoichiometric coefficient of i lumped in reaction j. j k represents the reaction rate of reaction j. j ρ represents the rate constant of reaction j. c Catalyst density relative to reactor volume, in g / cm³ 3 ε represents the porosity, P represents the system pressure, R is the gas constant, and T represents the system temperature. This indicates the effect of catalyst coking on activity, and assumes that the catalyst coking rate is only a function of the catalyst residence time, t. cη is the catalyst residence time, and β is the catalyst deactivation constant.

[0066] S4. Propose the objective function and, based on the experimental data, apply the optimization algorithm to solve the kinetic model parameters, thus establishing the kinetic model for the catalytic cracking of light hydrocarbons.

[0067] The objective function proposed in this step is:

[0068]

[0069] In the formula, Φ is the sum of squared errors between the experimental and fitted values, and n exp n is the number of trials; c It is a grouping fraction, a ij Let i be the experimental concentration lumped together in reaction j. Let i be the fitted concentration of ensembled i in reaction j;

[0070] The dynamic parameters are estimated using the particle swarm optimization algorithm. After solving for the dynamic model parameters, the dynamic model can be used to predict product distribution and compare it with experimental data to calculate the average relative error between the experimental and predicted values.

[0071] Example 2

[0072] A modeling apparatus for a catalytic cracking reaction process, the modeling apparatus comprising a modeling module for modeling the catalytic cracking reaction process as described above.

[0073] Example 3

[0074] An electronic device includes a processor and a memory, the memory storing computer-readable instructions that, when executed by the processor, enable the modeling method described above to be run.

[0075] In this embodiment, the electronic device includes a modeling device, a processor, a memory, a storage controller, a peripheral interface, an input / output unit, an audio unit, and a display unit, etc.

[0076] Specifically, the memory, storage controller, processor, peripheral interface, input / output unit, audio unit, and display unit are electrically connected directly or indirectly to each other to achieve data transmission or interaction. The modeling device includes at least one software functional module that can be stored in the memory or embedded in the operating system (OS) of the modeling device in the form of software or firmware. The processor is used to execute the executable module stored in the memory, including the software functional module or computer program.

[0077] The memory can be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable read-only memory (EPROM), electrically erasable read-only memory (EEPROM), etc. The memory stores programs, and after receiving execution instructions, the processor executes the corresponding program. The method executed by the server defined in the flow process of this application can be applied to the processor or implemented by the processor.

[0078] A processor can be an integrated circuit chip with signal processing capabilities. The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0079] Peripheral interfaces couple various input / output devices to the processor and memory. Peripheral interfaces, processors, and memory controllers can be implemented in a single chip, or they can be implemented separately by independent chips.

[0080] The input / output unit, audio unit, and display unit are all existing technologies. For example, the input / output unit is used to provide users with input data to enable interaction between the user and the server (or local terminal), and can be a mouse, keyboard, etc.; the audio unit provides users with an audio interface, which may include one or more microphones, one or more speakers, and audio circuitry; the display unit provides an interactive interface (e.g., a user operation interface) between the electronic device and the user or is used to display image data for the user's reference.

[0081] Example 4

[0082] A readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps in the modeling method described above.

[0083] To verify the effectiveness of the present invention, the following comparative experiments were conducted:

[0084] Experimental Example

[0085] according to Figure 1A kinetic model for the catalytic cracking of light hydrocarbons was established. This model categorizes light hydrocarbons into three clusters: saturated hydrocarbons, olefins, and aromatics, and the products into ten clusters: ethylene, propylene, butene, dry gas, liquefied petroleum gas (LPG), gasoline saturated hydrocarbons, gasoline olefins, gasoline aromatics, diesel, and coke. The model comprises 13 clusters and 26 reactions. Pilot-scale experiments were conducted to investigate the catalytic cracking performance of light hydrocarbons under different feedstocks and operating conditions. Based on the pilot-scale experimental data, parameter estimates were performed on the kinetic model, and the product distribution was then predicted using the kinetic model. A comparison between calculated and experimental values ​​is shown below. Figure 3 .

[0086] Comparative Example

[0087] according to Figure 2 A kinetic model for the catalytic cracking of light hydrocarbons was established. This model categorizes light hydrocarbons into three clusters: saturated hydrocarbons, olefins, and aromatics, and products into seven clusters: ethylene, propylene, butene, dry gas, liquefied petroleum gas (LPG), diesel, and coke. The model comprises 10 clusters and 23 reactions. A comparison between predicted and experimental values ​​calculated using the kinetic model is shown below. Figure 3 .

[0088] from Figure 3 The data comparison shows that the experimental example has a smaller relative error in predicting the product and better prediction accuracy. This indicates that the kinetic model established based on the catalytic cracking reaction mechanism and reactant reaction characteristics can better reflect the reaction law, describe the reaction process, and accurately predict the distribution of reaction products. In other words, the modeling method proposed in this invention is more suitable for light hydrocarbon catalytic cracking reaction systems.

[0089] While specific embodiments of the present invention have been described above, those skilled in the art should understand that these are merely illustrative examples, and various changes and modifications can be made to these embodiments without departing from the principles and essence of the invention. The scope of protection of the present invention is defined only by the appended claims.

Claims

1. A kinetic modeling method for catalytic cracking of light hydrocarbons, comprising the following steps: S1. Based on the principle of lumped dynamics, light hydrocarbons are lumped into categories; S2. Based on the catalytic cracking reaction mechanism, establish a light hydrocarbon catalytic cracking reaction network; S3. Based on the light hydrocarbon catalytic cracking reaction network, establish a kinetic model for the light hydrocarbon catalytic cracking reaction; S4. Propose the objective function and, based on the experimental data, apply the optimization algorithm to solve the kinetic model parameters, thus establishing the kinetic model for the catalytic cracking of light hydrocarbons. The feature is that: when performing the clustering in S1, the light hydrocarbon fraction after catalytic cracking reaction is treated as gasoline product and clustered according to the POA composition; The light hydrocarbon fraction following the catalytic cracking reaction includes cracked gasoline saturated hydrocarbons, cracked gasoline olefins, and cracked gasoline aromatics. The clusters are divided into three raw material clusters and ten product clusters. The raw material clusters are saturated hydrocarbon clusters, olefin clusters, and aromatic hydrocarbon clusters. The product clusters are ethylene clusters, propylene clusters, butene clusters, dry gas clusters, liquefied petroleum gas clusters, cracked gasoline saturated hydrocarbon clusters, cracked gasoline olefin clusters, cracked gasoline aromatic hydrocarbon clusters, diesel clusters, and coke clusters. Based on the aforementioned lumped partitioning, the light hydrocarbon catalytic cracking reaction network is established as follows: The gasoline saturated hydrocarbon aggregate was reacted with the ethylene aggregate, propylene aggregate, butene aggregate, dry gas aggregate, liquefied petroleum gas aggregate, cracked gasoline saturated hydrocarbon aggregate, cracked gasoline olefin aggregate, and cracked gasoline aromatic hydrocarbon aggregate, respectively. Gasoline olefin aggregates were reacted with ethylene aggregates, propylene aggregates, butene aggregates, dry gas aggregates, liquefied petroleum gas aggregates, cracked gasoline saturated hydrocarbon aggregates, cracked gasoline olefin aggregates, cracked gasoline aromatic hydrocarbon aggregates, diesel aggregates, and coke aggregates, respectively. Gasoline aromatic hydrocarbon aggregates were reacted with ethylene aggregates, dry gas aggregates, cracked gasoline aromatic hydrocarbon aggregates, diesel aggregates, and coke aggregates, respectively. The diesel fuel aggregate was reacted with the ethylene aggregate, dry gas aggregate, and coke aggregate, respectively.

2. The kinetic modeling method for catalytic cracking of light hydrocarbons according to claim 1, characterized in that, The basic equations for establishing the kinetic model of light hydrocarbon catalytic cracking reaction in S3 are as follows: （1） In the formula, Indicates the first Lumped concentration, in units of , Indicates system pressure, The gas constant is Indicates the system temperature. , representing the dimensionless relative distance at section x in the bed. Indicates the total length of the catalyst bed. This represents the actual weight-time airspeed. This is the reaction rate constant matrix. This represents the lumped component concentration vector; This indicates the effect of catalyst coking on activity, and assumes that the catalyst coking rate is only a function of catalyst residence time. This refers to the residence time of the catalyst.

3. The kinetic modeling method for catalytic cracking of light hydrocarbons according to claim 2, characterized in that, The basic equations of the light hydrocarbon catalytic cracking reaction kinetic model are derived from the continuity equation and the reaction rate equation: The continuity equation is: （2） The reaction rate equation is: （3） In the formula, the subscript Indicates lumped components, subscript Indicates the first One reaction, This indicates the density of an oil-gas mixture, expressed in units of... , Indicates the first Lumped concentration, unit , Indicates reaction time. The mass flow rate of oil and gas across a cross section, in units of... , This indicates the distance from the riser inlet into the reactor. Indicates the first Lumped reaction rate Indicates the number of reactions. express Centralized reaction The stoichiometric coefficients in express The reaction rate, Indicates reaction The reaction rate constant, Catalyst density expressed as relative to reactor volume, in units of , Indicates porosity. Indicates system pressure, The gas constant is Indicates the system temperature. This indicates the effect of catalyst coking on activity, and assumes that the catalyst coking rate is only a function of catalyst residence time. For catalyst residence time, is the catalyst deactivation constant.

4. The kinetic modeling method for catalytic cracking of light hydrocarbons according to claim 1, characterized in that, The objective function proposed in S4 is: （4） In the formula, The sum of squared errors between the experimental and fitted values. It refers to the number of trials; It is a grouping number. for Centralized reaction The test concentration in for Centralized reaction The fitted concentration in the data.

5. A modeling apparatus for a catalytic cracking reaction process, characterized in that: The model building apparatus includes a modeling module that performs modeling using the catalytic cracking reaction process modeling method according to any one of claims 1-4.

6. An electronic device, characterized in that, It includes a processor and a memory, the memory storing computer-readable instructions that, when executed by the processor, enable the modeling method of any one of claims 1-4 to be run.

7. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it performs the steps of the modeling method according to any one of claims 1-4.

Images