A new type of heavy oil high-efficiency catalytic cracking reactor dynamic simulation system and method

Abstract

The present invention provides a novel heavy oil high-efficiency catalytic cracking reactor dynamic simulation system, which relates to the technical field of petrochemical industry. The system includes an input unit, an output unit, a dynamic simulation unit and a control calculation unit; the dynamic simulation unit includes a reactor model and a reaction mathematical model, and the reactor model includes a reactor simulation module and a reaction process simulation module; the reaction mathematical model includes ten lumped schemes, a raw oil lumped group composition module and a differential equation of the mass fraction vector of each lumped in oil and gas changing with the residence time of the reaction oil and gas; the raw oil lumped group composition module includes the PONA value of the raw oil and the hydrocarbon correlation factor; the present invention combines the lumped reaction kinetics with the calculation method of the raw oil group composition properties, and according to the current ratio of the raw oil, the lumped properties participating in the reaction are corrected in real time, thereby calculating the reaction results and state values ​​such as temperature and pressure that are close to the actual situation, and can accurately simulate the entire catalytic cracking process.

Description

Technical Field

[0001] The present invention relates to the technical field of petrochemical industry, and more specifically, to a novel heavy oil high-efficiency catalytic cracking reactor dynamic simulation system and method. Background Art

[0002] With the increasing depletion of crude oil resources, the efficient conversion of heavy raw materials has become an important issue in the petroleum refining industry. The high-efficiency catalytic cracking technology of heavy oil aims to convert heavy hydrocarbon raw materials into more valuable light hydrocarbon products, such as low-carbon olefins, liquefied gas, gasoline and diesel. Heavy oil catalytic cracking is a complex multi-component, multi-phase, multi-reaction process involving the conversion and generation of multiple hydrocarbon molecules. Therefore, it is necessary to establish an accurate mathematical model to describe this process in order to better understand and optimize the reaction process.

[0003] In order to simplify the complex multi-component reaction system, researchers usually use lumped kinetic models to describe the heavy oil catalytic cracking process. This model divides the reactants and products into several lumped components and assumes that the molecules in each lumped component have the same reactivity. This method can greatly reduce the complexity and amount of calculation of the mathematical model. The lumped kinetic model is relatively accurate in calculating the reaction results when the properties and proportions of the crude oil are fixed, but when the properties and proportions of the crude oil change, there is often a large deviation from the production results of the actual device. The feed fluctuations in the actual production process are restricted by the upstream device, which will often change the proportion of the crude oil, such as the proportion of residual oil / wax oil in the crude oil.

[0004] Therefore, the problems existing in the prior art need to be further improved and developed. Summary of the invention

[0005] (I) Purpose of the invention: In order to solve the problems existing in the above-mentioned prior art, the purpose of the present invention is to provide a new type of heavy oil high-efficiency catalytic cracking reactor dynamic simulation system for dynamically simulating the high-efficiency catalytic cracking reaction process of crude oil.

[0006] (II) Technical solution: In order to solve the above technical problems, the present technical solution provides a new type of heavy oil high-efficiency catalytic cracking reactor dynamic simulation system, including an input unit, an output unit, a dynamic simulation unit and a control calculation unit;

[0007] The dynamic simulation unit includes a reactor model and a reaction mathematical model. The reactor model includes a reactor simulation module and a reaction process simulation module. The reaction mathematical model simulates the catalytic cracking process of the reaction heavy oil. The simulation includes ten lumped schemes, a raw oil lumped group composition module, and a differential equation of the mass fraction vector of each lumped in the oil and gas changing with the residence time of the reaction oil and gas;

[0008] The feedstock oil aggregate group composition module is used to calculate the PONA value and hydrocarbon association factor of the feedstock oil;

[0009] The PONA value of the feedstock oil includes the mass percentage of each hydrocarbon in the feedstock oil; the hydrocarbon correlation factor is specifically the influence coefficient of each hydrocarbon in the feedstock oil on the aggregate of each reaction product, and the hydrocarbon correlation factor is:

[0010] ,

[0011] Wherein, k represents the raw oil composition, represents the 1st to 10th lumps, α is the transfer coefficient, is the transition state partition function, is the reactant partition function, β is the configuration factor, S is the molecular flexibility parameter, represents the intrinsic activation energy, R is the gas constant, and T is the reaction temperature;

[0012] The input unit inputs the real PONA value and hydrocarbon correlation factor of the feedstock oil;

[0013] The control calculation unit includes a calculation module and a model optimization module, wherein the calculation module calculates the equation in the reaction mathematical model of the dynamic simulation unit, and the model optimization module compares the actual PONA value of the feedstock oil with the PONA value of the feedstock oil in the reaction mathematical model, updates the PONA value of the feedstock oil in the reaction mathematical model, and optimizes the reaction mathematical model;

[0014] The output unit outputs the reaction pressure, reaction temperature and mass fraction vectors of each lumped mass in the oil and gas as they change with the reaction oil and gas residence time.

[0015] Preferably, the hydrocarbons in the feedstock oil include normal paraffin group nP, isoparaffin group isoP, cycloparaffin group cP, olefin group O, monocyclic aromatic group mA, dicyclic aromatic group dA, and polycyclic aromatic group pA;

[0016] The expression of the PONA value of the raw oil is F=nP:isoP:cP:O:mA:dA:pA, wherein nP+isoP+cP+O+mA+dA+pA=1.

[0017] Preferably, the ten-lump scheme component divides the heavy oil catalytic cracking reaction process into ten lumps according to the overall process conditions of the heavy oil efficient catalytic cracking reaction, the first lump being the feedstock oil lump, the second lump being the diesel lump, the third lump being the gasoline olefin lump, the fourth lump being the gasoline aromatics lump, the fifth lump being the gasoline saturated hydrocarbon lump, the sixth lump being the propane+butane lump, the seventh lump being the butene lump, the eighth lump being the propylene lump, the ninth lump being the dry gas lump, and the tenth lump being the coke lump.

[0018] Preferably, the control calculation unit includes a calculation module and a model optimization module;

[0019] The calculation module calculates the equations in the reaction mathematical model of the dynamic simulation unit, and the calculation module includes a pressure calculation component, a reaction distribution calculation component, a temperature calculation component and a balance component. The pressure calculation component calculates the pressure value of each pressure node in the simulation system; the reaction distribution calculation component calculates the reaction distribution of each lumped in the reaction process, including the consumption of reactants and the generation of products; the temperature calculation component calculates the temperature distribution in the reaction process, and the balance component is used to simulate the material balance and energy balance in the reactor;

[0020] The model optimization module compares the actual PONA value of the crude oil regularly fed back by the input unit with the PONA value of the crude oil in the reaction mathematical model to determine whether to perform model optimization.

[0021] Preferably, when the difference between the true PONA value of the feedstock oil and the true PONA value of the feedstock oil in the reaction mathematical model is greater than a preset threshold, the true PONA value of the feedstock oil is assigned to the PONA value of the feedstock oil in the reaction mathematical model to optimize the reaction mathematical model.

[0022] Preferably, the pressure calculation component of the calculation module of the control calculation unit calculates the pressure distribution in the reactor model of the reaction simulation unit according to the pressure-flow equation, comprising the following steps:

[0023] Step 11: Combine the capacity balance equation and the resistance equation to obtain the pressure-flow equation group;

[0024] Step 12: Write the pressure-flow equations in vector form and use the improved damped Newton iteration method to numerically solve them to obtain the pressure and flow at each pressure node;

[0025] Step 13: Calculate the reactor pressure difference and bottom pressure based on the oil and gas pressure at the top of the reactor and the catalyst bed height inside the reactor.

[0026] Preferably, the reactor pressure difference is linearly correlated with the catalyst bed height in the fluidized state inside the device;

[0027] ,

[0028] ,

[0029] is the reactor pressure difference, is the reactor bottom pressure, is the reactor top pressure, is the pressure drop coefficient, is the density of the catalyst particles, is the diameter of the catalyst particle, is the superficial gas velocity of the reactor feed oil and gas and the lift gas, is the sulfiding gas velocity required for the catalyst bed to reach the critical fluidization state. It is the critical height for the catalyst to reach fluidization state.

[0030] Preferably, the temperature calculation component of the control calculation unit performs pressure-enthalpy flash evaporation to calculate the phase fraction and temperature of the reaction product according to the total heat combined with the calculated reaction pressure, comprising the following steps:

[0031] Step 31: After solving the composition, the aggregate composition of the reaction gasoline is obtained;

[0032] Step 32: Calculate the reaction heat by taking the formation enthalpy of each component of the reactor feed and discharge;

[0033] Step 33: Calculate the total heat by calculating the reaction heat, external heating / heat extraction heat, and reactor heat dissipation heat;

[0034] Step 34: Calculate the phase fraction and temperature of the reaction products according to the reaction product composition, total heat and reaction pressure through the pressure-enthalpy balance equation.

[0035] Preferably, the step 34 comprises the following steps:

[0036] Step 341: Obtain reaction pressure P, heat load Q, reactant flow rate F, current material composition ;

[0037] Step 342: Calculate enthalpy H, bubble dew point TB, TD, pressure p and enthalpy value at bubble dew point temperature;

[0038] Step 343: Determine the phase state based on enthalpy. If it is a vapor phase or a liquid phase, set the initial temperature value T=T D Or T=T B , iterate the temperature and output the calculation result; when it is a vapor-liquid two-phase, go to step 344;

[0039] Step 344: Set the initial temperature value , set the initial value of vaporization fraction e, , ;

[0040] Step 345: Calculate the objective function F(e) and the objective derivative function F'(e);

[0041] Step 346: determine whether the objective function meets the accuracy requirement. If it meets the accuracy requirement, proceed to step 347; if it does not meet the accuracy requirement, iterate the vaporization fraction e;

[0042] Step 347: Calculate vapor and liquid phase composition , , calculate the phase equilibrium constant ;

[0043] Step 348: Determine the phase equilibrium constant Whether the accuracy is met, if it is met, go to step 349; if it is not met, update , return to step 345;

[0044] Step 349: Calculate vapor-liquid phase enthalpy and enthalpy balance equation;

[0045] Step 3410: Determine the enthalpy balance equation Whether the accuracy requirement is met, if the accuracy requirement is met, the calculation result is output; if the accuracy requirement is not met, the temperature T is adjusted and the process returns to step 345.

[0046] A new type of heavy oil high-efficiency catalytic cracking reactor dynamic simulation method is applicable to a new type of heavy oil high-efficiency catalytic cracking reactor dynamic simulation system, comprising the steps of:

[0047] Step 1: Input the real PONA value and hydrocarbon correlation factor of the feedstock oil into the input unit;

[0048] Step 2: The dynamic simulation unit simulates the catalytic cracking process of the reaction heavy oil and calculates the PONA value and hydrocarbon correlation factor of the feedstock oil; the PONA value of the feedstock oil includes the mass percentage of each hydrocarbon in the feedstock oil; the hydrocarbon correlation factor is specifically the influence coefficient of each hydrocarbon in the feedstock oil on the aggregation of each reaction product, and the hydrocarbon correlation factor is:

[0049] ,

[0050] Wherein, k represents the raw oil composition, represents the 1st to 10th lumps, α is the transfer coefficient, is the transition state partition function, is the reactant partition function, β is the configuration factor, S is the molecular flexibility parameter, represents the intrinsic activation energy, R is the gas constant, and T is the reaction temperature;

[0051] Step 3: The control calculation unit compares the actual PONA value of the feedstock oil with the PONA value of the feedstock oil in the reaction mathematical model, updates the PONA value of the feedstock oil in the reaction mathematical model, and optimizes the reaction mathematical model;

[0052] Step 4: The output unit outputs the reaction pressure, reaction temperature and the mass fraction vector of each lumped mass in the oil and gas as the reaction oil and gas residence time changes.

[0053] (III) Beneficial effects: The present invention provides a new type of heavy oil high-efficiency catalytic cracking reactor dynamic simulation system and method, which combines the lumped reaction kinetics with the calculation method of the raw oil group composition properties, and corrects the lumped properties participating in the reaction in real time according to the current ratio of the raw oil, so as to calculate the reaction results and state values ​​such as temperature and pressure that are close to the actual situation, thereby improving the accuracy and application scope of the mathematical model of the catalytic cracking reaction, so that the new type of heavy oil high-efficiency catalytic cracking reactor dynamic simulation system can accurately simulate and control the entire catalytic cracking process, thereby improving the heavy oil conversion efficiency and product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a schematic diagram of the lumped reaction routes of a dynamic simulation system of a novel heavy oil high-efficiency catalytic cracking reactor of the present invention;

[0055] Figure 2 It is a schematic diagram of a process in which a pressure calculation component calculates pressure distribution in a reactor model of a reaction simulation unit according to a pressure-flow equation;

[0056] Figure 3 It is a schematic diagram of the process in which the temperature calculation component calculates the phase fraction and temperature of the reaction product by pressure-enthalpy (PH) flash according to the total heat combined with the calculated reaction pressure;

[0057] Figure 4 It is a schematic diagram of the steps of a dynamic simulation method of a novel heavy oil high-efficiency catalytic cracking reactor of the present invention. DETAILED DESCRIPTION

[0058] The present invention is further described in detail below in conjunction with preferred embodiments. More details are elaborated in the following description to facilitate a full understanding of the present invention. However, the present invention can obviously be implemented in a variety of other ways different from the description. Those skilled in the art can make similar generalizations and deductions based on actual application situations without violating the connotation of the present invention. Therefore, the protection scope of the present invention should not be limited by the content of this specific embodiment.

[0059] The accompanying drawings are schematic diagrams of embodiments of the present invention. It should be noted that the drawings are only examples and are not drawn to scale, and should not be used to limit the actual protection scope of the present invention.

[0060] High-efficiency catalytic cracking refers to the process in which heavy crude oils such as residual oil, wax oil, and slurry oil come into contact with solid catalyst particles in a fluidized state in a fast bed reactor to undergo a high-temperature catalytic cracking reaction, with propylene as the main target product and light aromatics as a by-product.

[0061] The present invention provides a novel heavy oil high-efficiency catalytic cracking reactor dynamic simulation system, which is used for dynamically simulating the high-efficiency catalytic cracking reaction process of crude oil, and comprises an input unit, an output unit, a dynamic simulation unit and a control calculation unit;

[0062] The dynamic simulation unit includes a reactor model and a reaction mathematical model. The reactor model includes a reactor simulation module and a reaction process simulation module. The reaction mathematical model truly reacts to the reaction process in the heavy oil catalytic cracking process, including ten lumped schemes, a raw oil lumped group composition module, and a differential equation of the mass fraction vector of each lumped in the oil and gas changing with the residence time of the reaction oil and gas;

[0063] The feedstock oil aggregate group component module includes the feedstock oil PONA value and hydrocarbon correlation factor;

[0064] The PONA value of the feedstock oil is the mass percentage of each hydrocarbon of normal paraffin group (nP), isoparaffin group (isoP), cycloparaffin group (cP), olefin group (O), monocyclic aromatic group (mA), dicyclic aromatic group (dA), and polycyclic aromatic group (pA) in the feedstock oil; the expression of the PONA value of the feedstock oil is F=nP:isoP:cP:O:mA:dA:pA, wherein nP+isoP+cP+O+mA+dA+pA=1;

[0065] The hydrocarbon association factor is specifically the influence coefficient of each hydrocarbon of normal paraffin group (nP), isoparaffin group (isoP), cycloparaffin group (cP), olefin group (O), monocyclic aromatic group (mA), dicyclic aromatic group (dA), and polycyclic aromatic group (pA) in the feedstock oil on the aggregate of each reaction product. The hydrocarbon association factor is:

[0066] Represents episode 1 to episode 10;

[0067] The control calculation unit includes a calculation module and a model optimization module. The calculation module calculates the equation in the reaction mathematical model of the dynamic simulation unit. The model optimization module compares the actual PONA value of the feedstock oil regularly fed back by the input unit with the PONA value of the feedstock oil in the reaction mathematical model. If the difference between the two is greater than a preset threshold, the PONA value of the feedstock oil in the reaction mathematical model is updated to optimize the reaction mathematical model.

[0068] The output unit outputs the reaction pressure, reaction temperature and the mass fraction vector of each lumped in the oil and gas as the reaction oil and gas residence time changes according to the simulation process of the dynamic simulation unit.

[0069] Preferably, the reactor model of the present invention includes a reactor simulation module and a reaction process simulation module. The reactor simulation module truly simulates the physical structure of the reactor of the actual production device, such as the size, shape, catalyst bed distribution, etc. of the reactor. The reaction process simulation module includes an oil and gas distribution simulation component, a reaction pressure simulation component, and a reaction temperature simulation component. The oil and gas distribution simulation component represents different aggregates in the reactor by different colors, and dynamically simulates the process in which the mass fraction vector of each aggregate in the oil and gas changes with the residence time of the reaction oil and gas during the reaction process; the reaction pressure simulation component dynamically simulates the pressure distribution of each pressure node in the simulation system; and the temperature simulation component dynamically simulates the reaction temperature in the simulation system. The pressure nodes are points in the simulation system used for dynamically simulating the pressure distribution in the simulation system, including various parts of the reactor simulation module in the reactor model, which can dynamically simulate the pressure distribution of each pressure node in the simulation system.

[0070] Preferably, the control calculation unit includes a calculation module and a model optimization module, the calculation module calculates the equations in the reaction mathematical model of the dynamic simulation unit, the calculation module includes a pressure calculation component, a reaction distribution calculation component, a temperature calculation component and a balance component, the pressure calculation component calculates the pressure value of each pressure node in the simulation system; the reaction distribution calculation component calculates the reaction distribution of each lumped in the reaction process, including the consumption of reactants and the generation of products; the temperature calculation component calculates the temperature distribution in the reaction process, and the balance component is used to simulate the material balance and energy balance in the reactor.

[0071] The model optimization module compares the real PONA value of the feedstock oil regularly fed back by the input unit with the PONA value of the feedstock oil in the reaction mathematical model to determine whether to perform model optimization; if the difference between the real PONA value of the feedstock oil and the real PONA value of the feedstock oil in the reaction mathematical model is greater than a preset threshold, the real PONA value of the feedstock oil is updated to the reaction mathematical model, and the real PONA value of the feedstock oil is assigned to the PONA value of the feedstock oil in the reaction mathematical model, thereby optimizing the reaction mathematical model to improve the model's predictive ability and adaptability.

[0072] Preferably, the ten-lump scheme divides the heavy oil catalytic cracking reaction process into ten lumps according to the overall process of the heavy oil efficient catalytic cracking reaction, the first lump is the feedstock oil lump (HO), the second lump is the diesel lump (DO), the third lump is the gasoline olefin lump (GO), the fourth lump is the gasoline aromatics lump (GA), the fifth lump is the gasoline saturated hydrocarbon lump (GS), the sixth lump is the propane + butane lump (C 34 ), the seventh set is the butene set (C 4= ), the eighth lump is the propylene lump (C3= ), the 9th lump is the dry gas lump (DG), and the 10th lump is the coke lump (CK).

[0073] The raw oil aggregate includes various heavy raw oils such as residual oil, wax oil, and slurry oil, which are contacted with the catalyst in the reactor to undergo catalytic cracking reaction. The dry gas aggregate includes small molecule gases generated by the cracking reaction, such as hydrogen, methane, ethylene, ethane, etc. The propylene aggregate includes propylene generated in the catalytic cracking reaction. The butene aggregate includes butene, butadiene, etc. generated in the catalytic cracking reaction. The propane+butane aggregate includes propane and butane generated in the catalytic cracking reaction, which are the main components of liquefied petroleum gas (LPG). The gasoline saturated hydrocarbon aggregate includes saturated hydrocarbon compounds generated in the catalytic cracking reaction, which are one of the main components of gasoline. The gasoline olefin aggregate includes olefin compounds generated in the catalytic cracking reaction, which are also a component of gasoline. The gasoline aromatics aggregate includes aromatic compounds generated in the catalytic cracking reaction, which are another important component of gasoline. The diesel aggregate includes compounds suitable for diesel generated in the catalytic cracking reaction. The coke aggregate includes solid coke byproducts in the raw oil collected at the bottom of the reactor during the catalytic cracking process.

[0074] The ten aggregates cover all the major products that may be generated during the catalytic cracking reaction. By monitoring and controlling the generation and consumption of these aggregates, the reaction conditions can be optimized and the yield and quality of the target products can be improved.

[0075] The reaction path Q between the ten lumps i,j represents the relationship between the reactants and products corresponding to each lumped, where i represents the lumped number of the reactant, i=1-10, representing the 1st lumped to the 10th lumped, j represents the lumped number of the product, j=1-10, representing the 1st lumped to the 10th lumped, and the reaction paths between the lumped Figure 1 shown.

[0076] The matrix Q of the reaction path is expressed as

[0077]

[0078] Among them, 35 reaction pathways are included.

[0079] Each reaction path Q i,j The reaction rate constant is K i,j , the reaction rate constant matrix K is expressed as:

[0080]

[0081] in, Represents the reaction rate constant of the reaction route with the reactants as the i-th lumped and the products as the j-th lumped. Each element on the diagonal represents the negative sum of the reaction rate constants in the column where the element is located, indicating the reaction consumption based on the reactants.

[0082] Each reaction rate constant in the matrix follows the Arrhenius formula form, ,in represents the mass concentration of a single reactant in the reaction pathway, Represents the mass concentration of a single reaction standard substance in the reaction path, represents the reaction temperature, represents the Clapeyron constant, represents the reaction temperature index, represents the pre-exponential factor, Represents the activation energy of the reaction.

[0083] The ten-lump scheme component of the present invention includes 35 reaction paths, that is, 35 reaction rate constants, so it is necessary to obtain 35 pre-exponential factors ( value) and 35 reaction activation energies ( Values), a total of 70 kinetic parameters, are used as known input parameters of the reaction mathematical model to solve the oil and gas composition after the catalytic cracking reaction. The 70 kinetic parameters are derived from actual production data and have wide applicability. Users can fine-tune this group of kinetic parameters appropriately according to actual production equipment to achieve better simulation effects.

[0084] For hydrocarbon correlation factors The value of can be determined by combining theoretical calculation with experimental determination. Under limited experimental conditions, the initial estimated value of the hydrocarbon correlation factor is obtained by theoretical calculation, and then verified and corrected in combination with limited experimental data. Theoretical calculation is based on transition state theory and Marcus theory, fully considering the influence of molecular structure characteristics on reaction activity, and can provide a reference for experimental determination. Specifically, the initial value of the hydrocarbon correlation factor is first obtained by theoretical calculation method, and the calculation method is as follows:

[0085] Specifically, the hydrocarbon correlation factor The value of can be obtained by the following theoretical method. First, at the reaction temperature of 873.15K, the free energy change of each hydrocarbon molecule in the transition state of normal paraffin (nP), isoparaffin (isoP), cycloparaffin (cP), olefin (O), monocyclic aromatic (mA), dicyclic aromatic (dA), and polycyclic aromatic (pA) in the feedstock oil is calculated. For each type of hydrocarbon molecule, the transition state free energy change is Calculated by the following equation: , where R is the gas constant 8.314 J / (mol·K), T is the reaction temperature 873.15K, and kB is the Boltzmann constant 1.381× J / K, h is Planck's constant 6.626× J.S. is the transition state equilibrium constant.

[0086] Based on the calculated transition state free energy change, the intrinsic activation energy of each hydrocarbon converted into different lumped products is further calculated in combination with Marcus theory. For the reaction of hydrocarbon k converted into lumped product j, its intrinsic activation energy is Calculated by the following Marcus equation: , where λ is the reorganization energy, is the change in reaction free energy. The reorganization energy λ was obtained through molecular dynamics simulation and was taken as 23.6 kJ / mol for normal paraffins, 27.2 kJ / mol for isoparaffins, 31.8 kJ / mol for cycloalkanes, 19.4 kJ / mol for olefins, 35.7 kJ / mol for monocyclic aromatics, 42.3 kJ / mol for dicyclic aromatics, and 48.9 kJ / mol for polycyclic aromatics.

[0087] On this basis, the intrinsic activation energy is related to the change in reaction free energy through the Bell-Evans-Polanyi principle, and the correlation factor of hydrocarbons at different temperatures is calculated. For the reaction of hydrocarbon k into lumped j, its correlation factor is Calculated by the following formula: , where α is the transfer coefficient and is taken as 0.42, is the transition state partition function, is the reactant partition function, β is the configuration factor with a value of 0.156, and S is the molecular flexibility parameter. The partition function is calculated using the statistical thermodynamic method.

[0088] Preferably, the specific calculation of the partition function can be carried out by the following method: transition state partition function and the reactant partition function Determined by the relationship between reaction temperature and activation energy. For the catalytic cracking reaction at 873.15K: , where A is the pre-exponential factor, which depends on the type of hydrocarbon and is 2.83× for normal paraffins. , the isomeric paraffin family takes 3.14× , cycloalkanes take 3.52× , olefin family takes 2.65× , the monocyclic aromatic hydrocarbon family takes 3.86× , the bicyclic aromatic hydrocarbon family takes 4.12× , the polycyclic aromatic hydrocarbons are 4.45× . For C10-C20 hydrocarbons, take 167-208 kJ / mol. Reactant partition function Calculated by the carbon number b of hydrocarbons: , where B is the correction factor, which is 4.26× for normal paraffins. , the isomeric paraffin family takes 3.92× , cycloalkanes take 2.85× , olefin family takes 3.73× , monocyclic aromatic hydrocarbons take 2.14× , the bicyclic aromatic hydrocarbon family takes 1.86× , polycyclic aromatic hydrocarbons take 1.52× The molecular flexibility parameter S is determined by the structural characteristics of hydrocarbons, mainly considering the number of branches and rings of the molecule: S = 2.0 + 0.15Nb - 0.32Nr, where Nb is the number of branches and Nr is the number of rings (including aromatic rings).

[0089] To ensure the accuracy of the calculation results, the calculated hydrocarbon correlation factors are normalized. For each type of hydrocarbon k, its correlation factor converted into all lumped products j satisfies: . It is corrected by introducing a normalization factor γ: ,in The normalized correlation factor is directly used in the reaction mathematical model to construct the reaction differential equation group together with the PONA value of the feedstock oil and the reaction rate constant matrix.

[0090] The theoretically calculated hydrocarbon correlation factor fully considers the influence of molecular structural characteristics on reactivity. When calculating through the partition function, for hydrocarbons with cyclic structures such as cycloalkanes and aromatics, the rotational partition function needs to consider the configurational symmetry; for chain hydrocarbons such as normal chain alkanes and isochain alkanes, the contribution of conformational freedom needs to be considered. These structural features are quantified by the molecular flexibility parameter S, and the S value is obtained by calculating the eigenvalue of the moment of inertia tensor of the molecule. For the normal chain alkanes, S is 2.13, for the isochain alkanes, it is 2.46, for the cycloalkanes, it is 1.87, for the olefins, it is 2.28, for the monocyclic aromatics, it is 1.65, for the dicyclic aromatics, it is 1.42, and for the polycyclic aromatics, it is 1.23.

[0091] In practical applications, the theoretically calculated hydrocarbon correlation factors can be directly used to predict the distribution of catalytic cracking products of different feedstocks. By multiplying the PONA value of the feedstock with the corresponding hydrocarbon correlation factor, the generation tendency of each lumped product can be obtained. This theoretical calculation method avoids a lot of experimental measurement work while maintaining a high prediction accuracy. When the PONA value of the feedstock changes, there is no need to re-perform experimental measurement. Simply substitute the new PONA value into the calculation to obtain an updated reaction mathematical model.

[0092] Furthermore, to ensure that the hydrocarbon correlation factors obtained by theoretical calculation are It can be directly applied to the original reaction mathematical model, and the calculation results need to be verified and corrected. First, the theoretical calculation results The law of conservation of mass must be satisfied. For any hydrocarbon k, the sum of the masses of all aggregated products j generated by its conversion should be equal to the initial mass, that is: ,in is the mass of the lumped product j generated by the conversion of hydrocarbon k, is the initial mass of hydrocarbon k. This constraint can be adjusted by The normalization factor γ is implemented.

[0093] Secondly, the theoretical calculation After substituting into the target reaction differential equation group, it should be able to accurately describe the change law of the mass fraction vector of each lumped in oil and gas over time. For the ith lumped, the change of its mass fraction over time must satisfy the differential equation form of the target reaction. This requires the theoretical calculation to be Dimensional consistency with the reaction rate constant matrix K must be maintained.

[0094] Through theoretical calculation under standard conditions of reaction temperature 873.15K and pressure 0.2MPa, we get It can be directly used in the target reaction mathematical model without additional unit conversion. It has the same physical meaning as the experimentally measured value and can accurately reflect the tendency of various hydrocarbons to transform into different aggregates.

[0095] Theoretically calculated hydrocarbon correlation factors It can be used as an important supplement to experimental determination. When the catalytic cracking behavior of new crude oil needs to be quickly evaluated, preliminary prediction results can be obtained through theoretical calculations, and then verified and corrected through a small amount of experiments. For the case of existing experimental data, the experimental determination method is as follows:

[0096] In a specific embodiment of the present invention, the hydrocarbon correlation factor is determined by experiment The process of obtaining the value of includes:

[0097] Select representative crude oil samples of different sources and different properties, the crude oil samples cover common heavy crude oil types, such as residual oil, wax oil, slurry oil, etc. from different origins, test the PONA value of each crude oil sample, and determine the mass percentage of each hydrocarbon of normal paraffin family (nP), isoparaffin family (isoP), cycloparaffin family (cP), olefin family (O), monocyclic aromatic hydrocarbon family (mA), dicyclic aromatic hydrocarbon family (dA), and polycyclic aromatic hydrocarbon family (pA);

[0098] Building an experimental reactor that can accurately control key parameters such as reaction temperature, pressure, and feed rate of raw materials to simulate the conditions of actual catalytic cracking reactions;

[0099] Under the same experimental conditions, i.e., the same reaction temperature, pressure, and feed rate, a catalytic cracking reaction experiment was conducted on each of the crude oil samples. The initial mass m of each hydrocarbon in the crude oil sample was detected before the reaction. k After the reaction is completed, the mass m of each aggregate product from each hydrocarbon is detected. j，k , the conversion rate of each hydrocarbon in the reaction X k And the proportion of each hydrocarbon in the feedstock oil converted into each aggregate product of propylene to the total conversion product S j , the hydrocarbon correlation factor is calculated by the following formula:

[0100] .

[0101] By analyzing the experimental data of various crude oil samples and different reaction conditions, the numerical range and variation law of hydrocarbon correlation factors under different situations can be obtained, providing a reliable parameter basis for the simulation and optimization of catalytic cracking reactions.

[0102] The macroscopic reaction kinetics formula in the present invention is expressed in matrix form as follows:

[0103]

[0104] in, is the mass fraction vector of each aggregate in oil and gas, is the reaction oil and gas residence time, F is the PONA value of the feed oil in the reaction mathematical model, is the reaction rate constant matrix, is the influence coefficient of each hydrocarbon in the feed oil on the lumps of each reaction product. The mass fraction vector of each lumps is expressed as:

[0105]

[0106] Among them y1, y2, y3, y4, y5, y6, y7, y8, y9, y 10 They represent the mass fraction vectors of the 1st to 10th lumps in oil and gas respectively.

[0107] The lumped reaction differential equation corresponding to the reaction rate constant matrix can be used to obtain the differential equation of the mass fraction vector of each lumped in the oil and gas as the residence time of the reaction oil and gas changes.

[0108] The differential equation expression of the mass fraction vector of the first set in the oil and gas changes with the residence time of the reaction oil and gas is: .

[0109] The differential equation expression of the mass fraction vector of the second set in the oil and gas changing with the residence time of the reaction oil and gas is: .

[0110] The differential equation expression of the mass fraction vector of the third set in the oil and gas as the reaction oil and gas residence time changes is: .

[0111] The differential equation expression of the mass fraction vector of the fourth set in the oil and gas as the reaction oil and gas residence time changes is: .

[0112] The differential equation expression of the mass fraction vector of the fifth set in oil and gas changing with the residence time of the reaction oil and gas is: .

[0113] The differential equation expression of the mass fraction vector of the sixth set in oil and gas changing with the residence time of the reaction oil and gas is: .

[0114] The differential equation expression of the mass fraction vector of the seventh set in the oil and gas as the reaction oil and gas residence time changes is: .

[0115] The differential equation expression of the mass fraction vector of the eighth set in oil and gas changing with the residence time of the reaction oil and gas is: .

[0116] The differential equation expression of the mass fraction vector of the 9th lumped mass in the oil and gas as the reaction oil and gas residence time changes is:

[0117] The differential equation expression of the mass fraction vector of the 10th lumped mass in the oil and gas as the reaction oil and gas residence time changes is: .

[0118] Among them, y1, y2, y3, y4, y5, y6, y7, y8, y9, y 10 Respectively represent the mass fraction vector of the 1st to 10th lumps in oil and gas, represents the reaction rate constant of the reaction route with the reactant as the i-th lumped product and the product as the j-th lumped product. The summation formula represents the reaction consumption based on the reactant. F is the PONA value of the feedstock oil in the reaction mathematical model. It is the hydrocarbon association factor, which represents the influence coefficient of each hydrocarbon in the feed oil on the total of each reaction product.

[0119] Preferably, in the present invention, the balance components of the calculation module include a material balance equation and an energy balance equation.

[0120] The change of each component in the reactor = the feed amount of each component – ​​the discharge amount of each component – ​​the reaction consumption of each component + the reaction generation of each component.

[0121] The change in energy in the reactor = feed energy – discharge energy – reaction heat + external heating + heat dissipation.

[0122] Therefore, the material balance equation is:

[0123]

[0124] Where Z represents the lumped molar composition, represents the reaction volume, represents the reaction feed flow rate, Represents the reaction discharge flow rate, represents the countercurrent flow rate of the reaction discharge, represents the reaction feed countercurrent flow rate, Reaction consumption

[0125] The energy balance equation is:

[0126]

[0127] in, represents the molar enthalpy, It means the reaction is exothermic. represents the reaction feed flow rate, Represents the reaction discharge flow rate, represents the countercurrent flow rate of the reaction discharge, represents the reaction feed countercurrent flow rate, represents the reaction consumption, Indicates the external heating / heat extraction of the reactor, Represents the heat dissipation of the reactor.

[0128] Preferably, in the present invention, the input unit regularly feeds back the real PONA value of the crude oil to the control calculation unit. The detection of the real PONA value of the crude oil is periodic, and the detection period is usually one week or even one month. After each detection, the input unit feeds back the detected real PONA value of the crude oil to the control calculation unit.

[0129] Preferably, in the present invention, the pressure calculation component of the calculation module of the control calculation unit calculates the pressure distribution in the reactor model of the reaction simulation unit according to the pressure-flow equation; the reaction distribution calculation component of the calculation module of the control calculation unit solves the reaction mathematical model of the reaction simulation unit by the implicit Euler method to obtain the oil and gas composition that changes with the reaction residence time; the temperature calculation component of the calculation module of the control calculation unit performs pressure-enthalpy (PH) flash evaporation to calculate the phase fraction and temperature of the reaction products according to the total heat combined with the calculated reaction pressure; the output unit outputs the reaction pressure, reaction temperature and the mass fraction vectors of each lumped in the oil and gas as they change with the reaction oil and gas residence time according to the calculation results of the calculation module of the control calculation unit.

[0130] More preferably, the model optimization module of the control calculation unit compares the real PONA value of the feedstock oil regularly fed back by the input unit with the PONA value of the feedstock oil in the reaction mathematical model to determine whether to perform model optimization; if the difference between the real PONA value of the feedstock oil and the real PONA value of the feedstock oil in the reaction mathematical model is greater than a preset threshold, the real PONA value of the feedstock oil is updated to the reaction mathematical model, and the real PONA value of the feedstock oil is assigned to the PONA value of the feedstock oil in the reaction mathematical model, thereby optimizing the reaction mathematical model.

[0131] The input unit periodically feeds back the actual PONA value of the crude oil to the model optimization module of the control calculation unit. This feedback depends on the detection component of the actual production device. In actual production, the group composition detection cycle of the crude oil is usually one week or even one month. The cycle in which the input unit feeds back to the control calculation unit is the detection cycle of the detection device of the actual production device.

[0132] The initial value of the PONA value of the feedstock oil in the reaction mathematical model of the present invention is derived from the actual measurement of the feedstock oil.

[0133] Preferably, the pressure calculation component of the calculation module of the control calculation unit calculates the pressure distribution in the reactor model of the reaction simulation unit according to the pressure-flow equation, such as Figure 2 As shown, the specific steps include:

[0134] Step 11: Combine the capacity balance equation and the resistance equation to obtain the pressure-flow equation system ,

[0135] Among them, the capacity balance equation is ,Right now: , where V is the spatial volume of the pressure node, flow represents the flow rate in and out of the pressure node, P is the pressure of the pressure node, T is the temperature of the pressure node, h represents the volume change of the pressure node caused by other factors, and dV / dt=0 means that the change of the pressure node volume over time is 0 in the steady state;

[0136] The capacity balance equation indicates that: capacity change caused by pressure + capacity change caused by flow + capacity change caused by temperature + capacity change caused by other factors = 0.

[0137] The resistance equation is , where k is the flow coefficient between the pipeline and the valve, △P is the pressure drop of the flow through the valve, and ρ is the density of the flow. The resistance equation calculates the flow rate based on the pressure difference between the front and rear pressure nodes, flow rate = flow coefficient * pressure difference * fluid density.

[0138] There are a large number of pressure nodes in the simulated reaction process. There is a pressure difference between them, and fluid flows between the pressure nodes. In the platform, all pressures and flows are taken out to form a set of equations, and the flow between the pressure and pressure difference of each node is solved at the same time, that is, the nonlinear set of equations containing pressure and flow is solved.

[0139] Step 12: Write the pressure-flow equations in vector form , put the above formula in Taylor expansion at this point and taking the first two terms: , can be obtained , the improved damped Newton iteration method is used for numerical solution to obtain the pressure of each pressure node and the flow rate associated with the pressure node.

[0140] Preferably, the process of using the improved damped Newton iteration method to perform numerical solution includes:

[0141] Step 121: Select any initial point X0 (X0 is within the range of real numbers), k=0.

[0142] Step 122: Take partial derivatives of the system of equations to form the Jacobian matrix and calculate the function value at X0, while solving the inverse matrix of the Jacobian matrix.

[0143] Step 123: Calculate the Newton gradient to determine the search direction, and the downhill factor λ ensures monotonically decreasing during the iterative calculation process.

[0144] Step 124: Introduce damping factor.

[0145] Step 125: Determine the solution update iteration step size, increase the damping factor and perform line search in the Newton direction.

[0146] Step 126: When the difference between the calculation results in two adjacent steps meets the accuracy, the solution is completed and the result is output; if the accuracy is not met but the deviation is small, the damping factor is adjusted to shorten the iteration step to increase the accuracy; if the deviation is large, return to step 122 for the next round of calculation.

[0147] Step 13: Calculate the reactor pressure difference and bottom pressure based on the oil and gas pressure at the top of the reactor and the catalyst bed height inside the reactor.

[0148] The reactor pressure difference is linearly related to the catalyst bed height in the fluidized state inside the device;

[0149]

[0150] in, is the reactor pressure difference, is the reactor bottom pressure, is the pressure at the top of the reactor (obtained by the pressure-flow solver), is the pressure drop coefficient, is the density of the catalyst particles, is the diameter of the catalyst particle, is the superficial velocity of the reactor feed oil and gas and the lift gas, is the sulfiding gas velocity required for the catalyst bed to reach the critical fluidization state. It is the critical height for the catalyst to reach the fluidized state.

[0151] Preferably, the reaction distribution calculation component of the calculation module of the control calculation unit solves the reaction mathematical model of the reaction simulation unit by the implicit Euler method to obtain the oil and gas composition that changes with the reaction residence time. Specifically, the differential equation is expressed as a difference equation defined on discrete grid points, and the value on the unknown boundary is iteratively calculated by the difference relationship between the adjacent grid points according to the given boundary conditions (the differential equation is transformed into a difference equation and the space is divided into the average of the n and n+1 moments):

[0152] For example, the differential formula of a reaction product with reaction time is:

[0153] , where y represents the reaction product and t represents the reaction time

[0154] Initial conditions:

[0155]

[0156] Let h be the time step of each step. time , (n = 0, 1, 2, 3…), at time T(n+1):

[0157]

[0158] The left side of the above equation is the composition of the reaction product y at time n+1, and the right side of the equation is the composition of the reaction product at time n + the integral step and the correlation The product of Therefore, in the solution process, it is necessary to iterate and converge each step through the Newton iteration method until it converges to the specified deviation range. The general explicit algorithm has high requirements for the time step. To obtain accurate results, a very small time step value is required, resulting in too many calculations. The implicit method has low requirements for the time step and can take a larger time step while maintaining high accuracy, improving the algorithm's computational efficiency and stability.

[0159] Preferably, the temperature calculation component of the calculation module of the control calculation unit performs pressure-enthalpy (PH) flash evaporation calculation of the phase fraction and temperature of the reaction product according to the total heat combined with the calculated reaction pressure, specifically comprising the following steps:

[0160] Step: 31: After solving the composition, the aggregate composition of the reaction gasoline is obtained;

[0161] Step 32: Calculate the reaction heat by taking the formation enthalpy of each component of the reactor feed and discharge;

[0162] Step 33: Calculate the total heat by calculating the reaction heat, external heating / heat extraction heat, and reactor heat dissipation heat;

[0163] Step 34: Use the pressure-enthalpy balance equation based on the reaction product composition, total heat and reaction pressure , calculate the phase fraction and temperature of the reaction products, where Represents the liquid, vapor and overall mixed phase flow rates in the stream; , represents the mole fraction of each component in the liquid phase, vapor phase and overall miscible phase; represents the phase equilibrium constant of each composition; Represents enthalpy, pressure, and temperature; unit molar enthalpy .

[0164] Specifically, step 34 includes: treating the solid catalyst as a single solid phase and not performing flash calculation, only performing heat transfer calculation, treating the reaction oil, gas vapor, and reaction products as gas-liquid two phases, and performing flash phase equilibrium calculation, so for materials other than the catalyst:

[0165] Material balance equation:

[0166]

[0167] Phase equilibrium equation

[0168]

[0169] Normalized equation

[0170]

[0171]

[0172] Physical property correlation

[0173]

[0174] Enthalpy balance equation

[0175]

[0176] Define the unit molar enthalpy , rewrite the enthalpy balance equation as:

[0177]

[0178] In the formula Represents the liquid, vapor and overall mixed phase flow rates in the stream; represents the molar fraction of each component in the liquid phase, vapor phase and overall mixed phase; e represents the general term for molar fraction, that is, represents the phase equilibrium constant of each composition; Represents enthalpy, pressure, and temperature.

[0179] Preferably, the solution step of step 34 is as follows: Figure 3 As shown, the following steps are included:

[0180] Step 341: Obtain reaction pressure P, heat load Q, reactant flow rate F, current material composition and related parameters;

[0181] Step 342: Calculate enthalpy H, bubble dew point TB, TD, pressure p and enthalpy value at bubble dew point temperature;

[0182] Step 343: Determine the phase state according to the enthalpy value. If it is a vapor phase (or liquid phase), give an initial temperature value T=TD (or T=TB), iterate the temperature until the objective function meets the accuracy requirement, and output the calculation result; if it is a vapor-liquid two-phase, proceed to the next step of calculation;

[0183] Step 344: Set the initial temperature value. , set the vaporization fraction Initial value, The initial value is the ideal K, that is ;

[0184] Step 345: Calculate the objective function F(x) and the objective derivative function F'(x);

[0185] Step 346: Determine whether the objective function meets the accuracy requirement. If it does, proceed to the next step of calculation. If it does not, iterate the vaporization fraction according to the Newton-Raphson method. ;

[0186] Step 347: Calculate vapor and liquid phase composition , calculate the phase equilibrium constant according to the equation of state method (EOS) or the activity coefficient method (γi method) ;

[0187] Step 348: Determine the phase equilibrium constant Whether the accuracy is met, if it is met, proceed to the next step of calculation; if it is not met, update , return to step 345;

[0188] Step 349: Calculate vapor-liquid phase enthalpy and enthalpy balance equation;

[0189] Step 3410: Determine the enthalpy balance equation Whether the accuracy requirement is met, if the accuracy requirement is met, output the calculation result; if the accuracy requirement is not met, adjust the temperature T and return to step 345.

[0190] The present invention provides a novel heavy oil high-efficiency catalytic cracking reactor dynamic simulation system, which combines the lumped reaction kinetics with the calculation method of the raw oil group composition properties, and corrects the lumped properties participating in the reaction in real time according to the current ratio of the raw oil, so as to calculate the reaction results and state values ​​such as temperature and pressure that are close to the actual situation, thereby improving the accuracy and application scope of the mathematical model of the catalytic cracking reaction, so that the novel heavy oil high-efficiency catalytic cracking reactor dynamic simulation system can accurately simulate and control the entire catalytic cracking process, thereby improving the heavy oil conversion efficiency and product quality.

[0191] The present invention also provides a novel heavy oil high-efficiency catalytic cracking reactor dynamic simulation method, which is applicable to the novel heavy oil high-efficiency catalytic cracking reactor dynamic simulation system, such as Figure 4 As shown, the following steps are included:

[0192] Step 1: Input the real PONA value and hydrocarbon correlation factor of the feedstock oil into the input unit;

[0193] Step 2: The dynamic simulation unit simulates the catalytic cracking process of the reaction heavy oil and calculates the PONA value and hydrocarbon correlation factor of the feed oil;

[0194] Step 3: The control calculation unit compares the actual PONA value of the feedstock oil with the PONA value of the feedstock oil in the reaction mathematical model, updates the PONA value of the feedstock oil in the reaction mathematical model, and optimizes the reaction mathematical model;

[0195] Step 4: The output unit outputs the reaction pressure, reaction temperature and the mass fraction vector of each lumped mass in the oil and gas as the reaction oil and gas residence time changes.

[0196] The present invention provides a novel heavy oil high-efficiency catalytic cracking reactor dynamic simulation method, which combines the lumped reaction kinetics with the calculation method of the raw oil group composition properties, and corrects the lumped properties participating in the reaction in real time according to the current ratio of the raw oil, so as to calculate the reaction results and state values ​​such as temperature and pressure that are close to the actual situation, thereby improving the accuracy and application scope of the mathematical model of the catalytic cracking reaction, so that the novel heavy oil high-efficiency catalytic cracking reactor dynamic simulation system can accurately simulate and control the entire catalytic cracking process, thereby improving the heavy oil conversion efficiency and product quality.

[0197] The above content is an explanation of the preferred embodiments of the present invention, which can help those skilled in the art to more fully understand the technical solution of the present invention. However, these embodiments are merely illustrative, and it cannot be determined that the specific implementation methods of the present invention are limited to the description of these embodiments. For ordinary technicians in the technical field to which the present invention belongs, without departing from the concept of the present invention, several simple deductions and transformations can be made, which should be regarded as belonging to the protection scope of the present invention.

Claims

1. A new type of heavy oil high-efficiency catalytic cracking reactor dynamic simulation system, characterized in that: It includes an input unit, an output unit, a dynamic simulation unit and a control calculation unit; The dynamic simulation unit includes a reactor model and a reaction mathematical model. The reactor model includes a reactor simulation module and a reaction process simulation module. The reaction mathematical model simulates the catalytic cracking process of the reaction heavy oil. The simulation includes ten lumped schemes, a raw oil lumped group composition module, and a differential equation of the mass fraction vector of each lumped in the oil and gas changing with the residence time of the reaction oil and gas; The feedstock oil aggregate group composition module is used to calculate the PONA value and hydrocarbon correlation factor of the feedstock oil; The PONA value of the feedstock oil includes the mass percentage of each hydrocarbon in the feedstock oil; the hydrocarbon correlation factor is specifically the influence coefficient of each hydrocarbon in the feedstock oil on the aggregate of each reaction product, and the hydrocarbon correlation factor is: ， Wherein, k represents the raw oil composition, represents the 1st to 10th lumps, α is the transfer coefficient, is the transition state partition function, is the reactant partition function, β is the configuration factor, S is the molecular flexibility parameter, represents the intrinsic activation energy, R is the gas constant, and T is the reaction temperature; The input unit inputs the real PONA value and hydrocarbon correlation factor of the feedstock oil; The control calculation unit includes a calculation module and a model optimization module, wherein the calculation module calculates the equation in the reaction mathematical model of the dynamic simulation unit, and the model optimization module compares the actual PONA value of the feedstock oil with the PONA value of the feedstock oil in the reaction mathematical model, updates the PONA value of the feedstock oil in the reaction mathematical model, and optimizes the reaction mathematical model; The output unit outputs the reaction pressure, reaction temperature and mass fraction vectors of each lumped mass in the oil and gas as they change with the reaction oil and gas residence time.

2. According to claim 1, a new type of heavy oil high-efficiency catalytic cracking reactor dynamic simulation system is characterized in that: The hydrocarbons in the crude oil include normal paraffin group nP, isoparaffin group isoP, cycloparaffin group cP, olefin group O, monocyclic aromatic group mA, dicyclic aromatic group dA, and polycyclic aromatic group pA; The expression of the PONA value of the raw oil is F=nP:isoP:cP:O:mA:dA:pA, wherein nP+isoP+cP+O+mA+dA+pA=1.

3. According to claim 1, a new type of heavy oil high-efficiency catalytic cracking reactor dynamic simulation system is characterized in that: The ten-lump scheme divides the heavy oil catalytic cracking reaction process into ten lumps according to the overall process conditions of the heavy oil efficient catalytic cracking reaction, the first lump is the feedstock oil lump, the second lump is the diesel lump, the third lump is the gasoline olefin lump, the fourth lump is the gasoline aromatics lump, the fifth lump is the gasoline saturated hydrocarbon lump, the sixth lump is the propane+butane lump, the seventh lump is the butene lump, the eighth lump is the propylene lump, the ninth lump is the dry gas lump, and the tenth lump is the coke lump.

4. According to claim 1, a new type of heavy oil high-efficiency catalytic cracking reactor dynamic simulation system is characterized in that: The control calculation unit includes a calculation module and a model optimization module; The calculation module calculates the equations in the reaction mathematical model of the dynamic simulation unit, and the calculation module includes a pressure calculation component, a reaction distribution calculation component, a temperature calculation component and a balance component. The pressure calculation component calculates the pressure value of each pressure node in the simulation system; the reaction distribution calculation component calculates the reaction distribution of each lumped in the reaction process, including the consumption of reactants and the generation of products; the temperature calculation component calculates the temperature distribution in the reaction process, and the balance component is used to simulate the material balance and energy balance in the reactor; The model optimization module compares the actual PONA value of the crude oil regularly fed back by the input unit with the PONA value of the crude oil in the reaction mathematical model to determine whether to perform model optimization.

5. According to claim 4, a new type of heavy oil high-efficiency catalytic cracking reactor dynamic simulation system is characterized in that: When the difference between the actual PONA value of the feedstock oil and the actual PONA value of the feedstock oil in the reaction mathematical model is greater than a preset threshold, the actual PONA value of the feedstock oil is assigned to the PONA value of the feedstock oil in the reaction mathematical model to optimize the reaction mathematical model.

6. According to claim 4, a new type of heavy oil high-efficiency catalytic cracking reactor dynamic simulation system is characterized in that: The pressure calculation component of the calculation module of the control calculation unit calculates the pressure distribution in the reactor model of the reaction simulation unit according to the pressure-flow equation, including the steps of: Step 11: Combine the capacity balance equation and the resistance equation to obtain the pressure-flow equation group; Step 12: Write the pressure-flow equations in vector form and use the improved damped Newton iteration method to numerically solve them to obtain the pressure and flow at each pressure node; Step 13: Calculate the reactor pressure difference and bottom pressure based on the oil and gas pressure at the top of the reactor and the catalyst bed height inside the reactor.

7. According to claim 6, a new type of heavy oil high-efficiency catalytic cracking reactor dynamic simulation system is characterized in that: The reactor pressure difference is linearly related to the catalyst bed height in the fluidized state inside the device; ， ， is the reactor pressure difference, is the reactor bottom pressure, is the reactor top pressure, is the pressure drop coefficient, is the density of the catalyst particles, is the diameter of the catalyst particle, is the superficial gas velocity of the reactor feed oil and gas and the lift gas, is the sulfiding gas velocity required for the catalyst bed to reach the critical fluidization state. It is the critical height for the catalyst to reach fluidization state.

8. According to claim 4, a new type of heavy oil high-efficiency catalytic cracking reactor dynamic simulation system is characterized in that: The temperature calculation component of the control calculation unit performs pressure-enthalpy flash evaporation to calculate the phase fraction and temperature of the reaction product according to the total heat combined with the calculated reaction pressure, comprising the steps of: Step 31: After solving the composition, the aggregate composition of the reaction gasoline is obtained; Step 32: Calculate the reaction heat by taking the formation enthalpy of each component of the reactor feed and discharge; Step 33: Calculate the total heat by calculating the reaction heat, external heating / heat extraction heat, and reactor heat dissipation heat; Step 34: Calculate the phase fraction and temperature of the reaction products according to the reaction product composition, total heat and reaction pressure through the pressure-enthalpy balance equation.

9. The novel heavy oil high-efficiency catalytic cracking reactor dynamic simulation system according to claim 8 is characterized in that: The step 34 comprises the following steps: Step 341: Obtain reaction pressure P, heat load Q, reactant flow rate F, current material composition ; Step 342: Calculate enthalpy H, bubble dew point TB, TD, pressure p and enthalpy value at bubble dew point temperature; Step 343: Determine the phase state based on enthalpy. If it is a vapor phase or a liquid phase, set the initial temperature value T=T D Or T=T B , iterate the temperature and output the calculation result; when it is a vapor-liquid two-phase, go to step 344; Step 344: Set the initial temperature value , set the vaporization fraction Initial value, , ; Step 345: Calculate the objective function F(x) and the objective derivative function F'(x); Step 346: Determine whether the objective function meets the accuracy requirement. If it meets the accuracy requirement, proceed to step 347. If it does not meet the accuracy requirement, iterate the vaporization fraction. ; Step 347: Calculate vapor and liquid phase composition , , calculate the phase equilibrium constant ; Step 348: Determine the phase equilibrium constant Whether the accuracy is met, if it is met, go to step 349; if it is not met, update , return to step 345; Step 349: Calculate vapor-liquid phase enthalpy and enthalpy balance equation; Step 3410: Determine the enthalpy balance equation Whether the accuracy requirement is met, if the accuracy requirement is met, the calculation result is output; if the accuracy requirement is not met, the temperature T is adjusted and the process returns to step 345.

10. A new type of heavy oil high-efficiency catalytic cracking reactor dynamic simulation method, applicable to a new type of heavy oil high-efficiency catalytic cracking reactor dynamic simulation system as claimed in claim 1, characterized in that: Includes steps: Step 1: Input the real PONA value and hydrocarbon correlation factor of the feedstock oil into the input unit; Step 2: The dynamic simulation unit simulates the catalytic cracking process of the reaction heavy oil and calculates the PONA value and hydrocarbon correlation factor of the feedstock oil; the PONA value of the feedstock oil includes the mass percentage of each hydrocarbon in the feedstock oil; the hydrocarbon correlation factor is specifically the influence coefficient of each hydrocarbon in the feedstock oil on the aggregation of each reaction product, and the hydrocarbon correlation factor is: ， Where k represents the raw oil composition represents the 1st to 10th lumps, α is the transfer coefficient, is the transition state partition function, is the reactant partition function, β is the configuration factor, S is the molecular flexibility parameter, represents the intrinsic activation energy, R is the gas constant, and T is the reaction temperature; Step 3: The control calculation unit compares the actual PONA value of the feedstock oil with the PONA value of the feedstock oil in the reaction mathematical model, updates the PONA value of the feedstock oil in the reaction mathematical model, and optimizes the reaction mathematical model; Step 4: The output unit outputs the reaction pressure, reaction temperature and the mass fraction vector of each lumped mass in the oil and gas as the reaction oil and gas residence time changes.

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