A dual riser catalytic cracking combined process modeling method

Abstract

The present application relates to the technical field of petrochemical production process modeling, and more particularly to a modeling method for a dual riser reactor catalytic cracking combined process.The present application comprises the following steps: S1, dividing reaction components according to process characteristics; S2, setting basic assumption conditions for reaction kinetics; S3, determining reaction relationships of each reaction component and constructing a reaction network; S4, establishing a reaction process mechanism model according to the reaction network and combining influencing factors; S5, calculating catalyst coking regeneration through a regenerator model to realize coupled circulation of the regeneration process and the catalytic cracking reaction process; S6, selecting a target function, optimizing kinetic parameters related to each reaction, and outputting an optimal kinetic parameter combination; and S7, predicting related product yield and properties based on the optimal kinetic parameter combination.The present application uses an improved whale optimization algorithm to optimize and solve kinetic parameters, and the established process mechanism model has good precision.

Description

Technical Field

[0001] This invention relates to the field of petrochemical production process modeling technology, and more specifically, to a modeling method for a dual-riseer reactor catalytic cracking combined process for heavy oil to produce more isoparaffins (MIP) and hydrocatalytic light cycle oil to produce more high-octane gasoline or light aromatics (LTAG). Background Technology

[0002] Catalytic cracking is a crucial process for the lightification of heavy oil, occupying a core position in the entire refinery production process. Catalytic cracking units are highly adaptable to different feedstocks. Generally, mixed heavy oil feedstocks are preheated before entering the unit, undergoing key processes such as reaction-regeneration, fractionation, and absorption stabilization. The main products produced include clean fuels such as gasoline and diesel, low-carbon olefin chemical feedstocks, and high-value-added products such as liquefied petroleum gas, thus improving refinery efficiency.

[0003] With the further standardization of clean fuel standards and increasingly stringent environmental indicators in my country, higher requirements are being placed on refining process technologies and product specifications. The MIP-LTAG (Maximizing Iso-Paraffins, MIP; LCO to Aromatics and Gasoline, LTAG) combined process utilizes a dual-riseer catalytic cracking process, employing separate reactors for the two feedstocks: heavy feedstock and hydrotreated catalytic light cycle oil (hydrotreated catalytic diesel), which have significantly different hydrocarbon molecular compositions, structures, and conversion ease. In the heavy feedstock reactor, the MIP process reduces the olefin content of the gasoline product; while in the hydrotreated catalytic diesel reactor, the LTAG process is used to process the hydrotreated, low-quality catalytic diesel fraction, increasing the production of high-octane gasoline rich in aromatics.

[0004] Hydrotreated catalytic diesel is rich in monocyclic aromatic hydrocarbons containing cycloalkane rings, such as tetrahydronaphthalenes, which act as hydrogen donors. During co-catalytic cracking with heavy oil feedstock, significant hydrogen transfer reactions occur between them, reducing the selectivity of the hydrotreated catalytic diesel cracking reaction. A dual-riseer reactor setup can achieve simultaneous, highly selective cracking of two different feedstocks.

[0005] The MIP process, which produces more isoalkanes, uses a series of variable-diameter riser reactors. In the first reaction zone, a higher reaction temperature and a shorter reaction time promote the cracking of heavy crude oil. In the second reaction zone (the wide-diameter reactor), a lower reaction temperature and a longer reaction time are controlled to promote isomerization and hydrogen transfer reactions. This allows olefins in the gasoline fraction to be converted into isoalkanes and aromatic components as much as possible, reducing the olefin content of gasoline and increasing the octane number of gasoline.

[0006] The LTAG process, which produces high-octane gasoline and aromatic feedstock, addresses the problem of low cetane number, high aromatic content, and significant discrepancies in compositional characteristics between the large amount of catalytic diesel fractions generated during catalytic cracking and automotive clean diesel standards. By selectively hydrogenating and saturating polycyclic aromatics in inferior catalytic diesel fractions to convert them into monocyclic aromatics or cycloalkylbenzenes, its cracking potential is significantly increased. Then, it undergoes another catalytic cracking reaction, maximizing ring-opening cracking to produce high-octane gasoline rich in aromatics, thus achieving high-value utilization of catalytic diesel fractions.

[0007] Because catalytic cracking is a typical multimodal, highly nonlinear, strongly coupled, continuous dynamic, long-cycle chemical process, the reaction system is complex, and factors such as feed properties, catalyst properties, and operating conditions all affect the product yield and properties of the unit.

[0008] Therefore, based on the feedstock properties and process mechanism characteristics of the MIP and LTAG processes in the dual riser reactor, reaction kinetic models were constructed respectively. A mechanism model of the dual riser catalytic converter for producing multiple isoalkanes and high-octane gasoline and aromatic feedstocks was constructed to predict the yield of the main products of the unit. This has important practical significance and application value for the stable operation and operation optimization of the unit. Summary of the Invention

[0009] The purpose of this invention is to provide a modeling method for a dual-riseer catalytic cracking combined process, which solves the problem of difficulty in accurately predicting the process mechanism, product yield, and properties of dual-riseer reactor catalytic cracking combined processes for heavy oil to produce more isoalkanes and hydrotreated light cycle oil to produce more high-octane gasoline or light aromatics.

[0010] To achieve the above objectives, the present invention provides a modeling method for a combined catalytic cracking process using a dual riser reactor, comprising the following steps:

[0011] Step S1: Divide the reaction components according to the process characteristics;

[0012] Step S2: Set basic assumptions about reaction kinetics for the combined process reaction;

[0013] Step S3: Based on the divided reaction components, determine the reaction relationships of each reaction component and construct the reaction network;

[0014] Step S4: Based on the reaction network and influencing factors, establish a reaction process mechanism model;

[0015] Step S5: Calculate catalyst coke regeneration using a regenerator model to achieve a coupled cycle between the regeneration process and the catalytic cracking reaction process;

[0016] Step S6: Select the objective function, optimize the kinetic parameters involved in each reaction, and output the optimal combination of kinetic parameters;

[0017] Step S7: Based on the optimal combination of kinetic parameters, predict the yield and properties of the relevant products.

[0018] In one embodiment, the process characteristics of step S1 are high production of isoalkanes and high production of high-octane gasoline and aromatics.

[0019] Step S1 further includes: for reaction systems that produce a large amount of isoalkanes and high-octane gasoline and aromatics, a method combining distillation range and hydrocarbon group composition is used to group and classify the reaction components according to the principle of similar kinetic characteristics.

[0020] In one embodiment, the basic assumptions of the reaction kinetics in step S2 further include:

[0021] All reactions in the reaction system are first-order irreversible reactions;

[0022] The reactions involved in the reaction system are considered homogeneous reactions.

[0023] The gas flow state involved in the reaction system is isothermal, gas phase, ideal plug flow, and diffusion within material particles is ignored;

[0024] The catalyst deactivation problem in the reaction system is characterized by time-varying catalyst deactivation, and its impact on the reaction process is only related to the catalyst residence time.

[0025] Catalyst wear in the reaction system is characterized by time-varying catalyst wear, and catalyst loss is only related to the relevant equipment and catalyst residence time.

[0026] The degree of influence of the adsorption of heavy aromatics and basic nitrogen compounds in the reaction system on the reaction results is corrected for the reaction rate by corresponding operators;

[0027] No coke is formed from the gases in the reaction system.

[0028] In one embodiment, the construction of the reaction network in step S3 further includes:

[0029] Reaction networks were constructed for processes that produce multiple isoalkanes and processes that produce multiple high-octane gasoline and aromatic feedstocks, respectively.

[0030] In one embodiment, step S4 further includes:

[0031] Based on the constructed reaction network, and considering the influencing factors of catalyst runoff and deactivation, alkali nitrogen adsorption, and heavy aromatics adsorption, a reaction mechanism model for producing more isoalkanes and producing more high-octane gasoline and aromatics is established.

[0032] In one embodiment, the reaction rate in the mechanism model for establishing the process of producing more isoalkane and more high-octane gasoline and aromatics in step S4 is expressed as follows:

[0033]

[0034] In the formula, a is the mass concentration of the reactant component;

[0035] K is the reaction rate constant matrix.

[0036] ρ is the gas density;

[0037] S WH This represents the actual space velocity over time.

[0038] For catalyst deactivation operator;

[0039] w(t c ) represents the catalyst loss operator;

[0040] f(A) is the heavy aromatics adsorption deactivation operator;

[0041] f(N) is the alkaline nitrogen adsorption deactivation operator.

[0042] In one embodiment, step S5 further includes: calculating catalyst coke regeneration through a regenerator model to achieve a coupled cycle of the regeneration process and the catalytic cracking reaction process that produces more isoalkanes and more high-octane gasoline and aromatics feedstock;

[0043] The differential equation for the mass transfer reaction in the bubble phase of the regenerator model is as follows:

[0044]

[0045] The reaction algebraic equations for the gas and solid phases in the dense emulsion phase of the regenerator model are as follows:

[0046]

[0047] A×L×(1-ε b )×(1-ε e )×r s ×M s =F cat ×(W s,in -W s,out );

[0048] ε e ε b These are the porosity of the emulsion phase and the bubble phase fraction, respectively.

[0049] K beMass transfer resistance at the bubble phase-emulsion phase interface;

[0050] L represents the bed height;

[0051] r s W s,in W s,out Represents the relevant variables of related materials in the solid phase;

[0052] Let i represent the relevant variables of the materials in the gas phase, i∈{O2, N2, CO2, CO, H2O}.

[0053] In one embodiment, step S6 further includes optimizing the kinetic parameters involved in each reaction using an improved whale optimization algorithm;

[0054] The improved whale optimization algorithm further includes the following steps:

[0055] Step S601: Initialize algorithm parameters;

[0056] Step S602: Initialize the population;

[0057] Step S603: Pre-select individuals based on niche technology;

[0058] Step S604: Based on the optimal individuals generated by the pre-selection, update the position of the offspring population according to the whale optimization algorithm and the sine and cosine algorithms respectively;

[0059] Step S605: Select the best individuals based on fitness ranking to generate the offspring population;

[0060] Step S606: Determine whether the termination condition of the algorithm optimization is met. If the termination condition is not met, return to step S603 for the next round of iteration calculation. If the termination condition is met, the optimization ends and the optimal solution is output.

[0061] In one embodiment, step S603 corresponds to the following expression:

[0062]

[0063] Among them, X i (t), X i (t+1) represents the i-th individual in the t-th and t+1-th generations, respectively;

[0064] The f function represents the function for calculating fitness values.

[0065] In one embodiment, the optimization termination condition set in step S606 includes: the error between all components and the target yield is less than a first percentage or the current iteration number reaches the maximum iteration number.

[0066] In one embodiment, step S6 further includes: using the minimum sum of squared errors between the model predictions and the actual values ​​as the objective function.

[0067] The present invention provides a modeling method for a combined catalytic cracking process using a dual riser reactor. The deviation between the model prediction and the actual product yield of this process is less than 3%, and the established process mechanism model has good accuracy. Attached Figure Description

[0068] The above and other features, properties and advantages of the present invention will become more apparent from the following description taken in conjunction with the accompanying drawings and embodiments, in which the same reference numerals always denote the same features, wherein:

[0069] Figure 1 A flowchart illustrating a modeling method for a combined catalytic cracking process using a dual riser reactor according to an embodiment of the present invention is disclosed.

[0070] Figure 2 A schematic diagram of a dual riser reactor catalytic device according to an embodiment of the present invention is shown;

[0071] Figure 3 A schematic diagram of the reaction network for a multi-yield isoparaffin catalytic cracking process according to an embodiment of the present invention is disclosed;

[0072] Figure 4 A schematic diagram of the reaction network for a catalytic cracking process that produces high-octane gasoline and aromatic feedstock according to an embodiment of the present invention is disclosed.

[0073] Figure 5 A schematic diagram illustrating the coupling process between the reaction model and the regeneration model according to an embodiment of the present invention is disclosed;

[0074] Figure 6 A flowchart of an improved whale optimization algorithm according to an embodiment of the present invention is disclosed.

[0075] The meanings of the labels in the figures are as follows:

[0076] 201 Main reactor;

[0077] 202 auxiliary reactors;

[0078] 203 Regenerator;

[0079] 204 Main Fractionating Column;

[0080] 205 auxiliary fractionating tower;

[0081] 206 Cyclone Separator. Detailed Implementation

[0082] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention.

[0083] Figure 1 A flowchart illustrating a modeling method for a combined catalytic cracking process using a dual-riseer reactor according to an embodiment of the present invention is disclosed, as follows: Figure 1 As shown, this invention provides a modeling method for a combined catalytic cracking process using a dual-riseer reactor, comprising the following steps:

[0084] Step S1: Divide the reaction components according to the process characteristics;

[0085] Step S2: Set basic assumptions about reaction kinetics for the combined process reaction;

[0086] Step S3: Based on the divided reaction components, determine the reaction relationships of each reaction component and construct the reaction network;

[0087] Step S4: Based on the reaction network and influencing factors, establish a reaction process mechanism model;

[0088] Step S5: Calculate catalyst coke regeneration using a regenerator model to achieve a coupled cycle between the regeneration process and the catalytic cracking reaction process;

[0089] Step S6: Select the objective function, optimize the kinetic parameters involved in each reaction, and output the optimal combination of kinetic parameters;

[0090] Step S7: Based on the optimal combination of kinetic parameters, predict the yield and properties of the relevant products.

[0091] Figure 2 A schematic diagram of a dual-riseer reactor catalytic device according to an embodiment of the present invention is shown, as follows: Figure 2 The dual-riseer catalytic converter shown is a combined process for producing high-octane gasoline and aromatics, where the main reactor 201 is a heavy oil reactor, which is a two-stage series variable-diameter riser reactor that produces high-octane gasoline and aromatics. The main feed is heavy oil, including atmospheric residue, vacuum residue, wax oil, etc.

[0092] Sub-reactor 202 is a hydrotreated diesel reactor, a riser reactor that produces high-octane gasoline and aromatics, and its main feedstock is hydrotreated catalytic diesel.

[0093] The catalysts in the main and auxiliary reactors enter the regenerator 203 for coke burning and regeneration, realizing a coupled cycle of reaction and regeneration processes that produce more isoalkanes and more high-octane gasoline and aromatics.

[0094] The oil and gas products from the main and auxiliary reactors enter the main fractionation tower 204 and the auxiliary fractionation tower 205, respectively, and exit the unit after passing through equipment units such as the absorption tower, re-absorption tower, and stabilization tower, thereby realizing the high-value utilization of high-octane gasoline or light aromatics and inferior catalytic diesel.

[0095] Understandably, this unit achieves highly selective cracking of two different feedstocks, heavy oil and hydrotreated catalytic diesel, through independent reactor setup, thereby improving unit efficiency.

[0096] The following text is incomplete and cannot be translated. Figure 2 The dual-riseer catalytic cracking shown is an example, and these steps of the modeling method for the dual-riseer reactor catalytic cracking combined process proposed in this invention are described in detail. It should be understood that, within the scope of this invention, the above-described technical features of this invention and the technical features specifically described below (such as in the examples) can be combined and correlated with each other to constitute preferred technical solutions.

[0097] The present invention proposes a modeling method for a dual-riseer catalytic cracking combined process, specifically a modeling method for a dual-riseer reactor catalytic cracking combined process that aims to produce more isoalkanes from heavy oil and more high-octane gasoline or light aromatics from hydrotreated light cycle oil.

[0098] Step S1: Divide the reaction components according to the process characteristics.

[0099] Based on the process characteristics of producing more isoalkanes (MIP) and more high-octane gasoline and aromatics feedstock (LTAG) in the dual riser, the reaction components are divided separately;

[0100] For reaction systems that produce a large amount of isoalkanes and high-octane gasoline and aromatics, a method combining distillation range and hydrocarbon group composition is adopted, and the reaction components are divided according to the principle of similar kinetic characteristics.

[0101] The feedstock for the polyisoalkane production process is heavy oil. According to the four-component composition (SARA), since the asphaltenes and gums are relatively low in content and mainly undergo coking reactions, they are combined into one component.

[0102] The process of producing high-octane gasoline and aromatic feedstocks through hydrocatalytic diesel fuel is further classified according to its hydrocarbon group composition. To clearly characterize the changes in the aromatic components of diesel fuel during the reaction process, it is further divided into diesel monocyclic aromatics and diesel polycyclic aromatics.

[0103] The liquid products of the unit include catalytic diesel (boiling range 200℃~380℃) and catalytic gasoline (boiling range 40℃~200℃), which are further divided according to the hydrocarbon group composition. Considering the process characteristics of producing more isoalkanes and more high-octane gasoline and aromatics, the diesel aromatic components are further divided into diesel monocyclic aromatics and diesel polycyclic aromatics to facilitate the observation of changes in diesel aromatics during the reaction process.

[0104] Among the gaseous products, low-carbon olefins (butene, propylene, ethylene) can be used as important chemical raw materials and are classified as separate components. The remaining products are classified into liquefied petroleum gas (C3-C4) and dry gas (C1-C2) according to their carbon content.

[0105] The feedstock for the polyisoalkane production process is heavy oil, which is divided according to its four-component composition (SARA). The contents of gum and asphaltenes are relatively small and mainly coking occurs during the reaction, so they are combined into one component. That is, the heavy oil feedstock is divided into three components: saturated component, aromatic component, and gum + asphaltenes.

[0106] The reaction components in the process of producing more isoalkanes include: saturated fractions of heavy oil (R). S Heavy oil aromatic fraction R A Heavy oil resin + asphalt resin R B Diesel fuel chain alkanes P l Diesel Olefins O l Diesel cycloalkanes N l Diesel monocyclic aromatic hydrocarbons A sl Diesel polycyclic aromatic hydrocarbons A ml Gasoline chain alkanes P e Gasoline olefins O e gasoline cycloalkanes N e Gasoline aromatics A e Butene C 4＝ Propylene C 3＝ Liquefied petroleum gas (LPG), ethylene (C) 2＝ Dry gas (DR), coke (C), a total of 18 reaction components;

[0107] The feedstock for the process of producing high-octane gasoline and aromatics is hydrotreated catalytic light cycle oil (hydrotreated catalytic diesel), which is classified according to the hydrocarbon group composition.

[0108] The reaction components in the process of producing high-octane gasoline and aromatics include: diesel fuel, alkanes, and P. l Diesel Olefins O l Diesel cycloalkanes N l Diesel monocyclic aromatic hydrocarbons A sl Diesel polycyclic aromatic hydrocarbons A ml Gasoline chain alkanes P e Gasoline olefins O e gasoline cycloalkanes N e Gasoline aromatics Ae Butene C 4＝ Propylene C 3＝ Liquefied petroleum gas (LPG), ethylene (C) 2＝ There are 15 reaction components: dry gas (DR), coke (C).

[0109] Step S2: Set basic assumptions about reaction kinetics for the combined process reaction.

[0110] For the complex gas-solid heterogeneous catalytic cracking process involving the combined production of isoalkanes and high-octane gasoline and aromatic feedstocks, basic kinetic assumptions are proposed, including:

[0111] ① All reactions in the reaction system are considered to be first-order irreversible reactions;

[0112] ② The reactions involved in the reaction system are considered as homogeneous reactions;

[0113] ③ The gas flow state involved in the reaction system is isothermal, gas phase, ideal plug flow, and diffusion within material particles is ignored;

[0114] ④ The catalyst deactivation problem in the reaction system is characterized by time-varying catalyst deactivation, and its impact on the reaction process is only related to the catalyst residence time;

[0115] ⑤ The catalyst wear problem in the reaction system was characterized by time-varying catalyst wear. It was concluded that catalyst loss was only related to the relevant equipment (such as cyclone separator 206) and the catalyst residence time.

[0116] ⑥ The degree of influence of the adsorption of heavy aromatics and basic nitrogen compounds on the reaction results in the reaction system is corrected for the reaction rate by the corresponding operators;

[0117] ⑦ No coke is formed from any of the gases in the reaction system.

[0118] Step S3: Based on the divided reaction components, determine the reaction relationships of each reaction component and construct the reaction network.

[0119] Based on the divided reaction components, the reaction relationships between the components are determined, and reaction networks for processes that produce more isoalkane and processes that produce more high-octane gasoline and aromatics are constructed respectively.

[0120] Figure 3 A schematic diagram of the reaction network for a multi-yield isoalkane catalytic cracking process according to an embodiment of the present invention is disclosed, such as... Figure 3 The reaction network shown contains 120 reaction pathways for the production of isoalkanes.

[0121] Figure 4 A schematic diagram of the reaction network for a catalytic cracking process producing high-octane gasoline and aromatic feedstock according to an embodiment of the present invention is disclosed, such as... Figure 4 The diagram shows a reaction network for producing high-octane gasoline and aromatic feedstocks, which contains 75 reaction pathways.

[0122] Specifically, it includes:

[0123] The saturated and aromatic components in heavy oil react with the asphaltenes and resins to produce diesel, gasoline and gas fractions. At the same time, a coking reaction occurs during the reaction to produce coke components.

[0124] Alkanes in diesel fractions can be cracked to produce alkanes and olefins, gases, and coke in gasoline fractions.

[0125] Diesel olefin components can undergo hydrogen transfer and alkylation reactions to produce diesel cycloalkanes and aromatics, and cracking to produce gasoline olefins, gaseous olefins and coke.

[0126] Diesel cycloalkanes can also be converted into diesel aromatics via hydrogen transfer reactions, and into gasoline cycloalkanes, olefins, gases, and coke via cracking reactions;

[0127] Monocyclic aromatics in diesel fuel can be dehydrogenated to form polycyclic aromatics, and cracking can produce alkanes, alkenes and aromatics in gasoline fractions. At the same time, cracking produces gases and coke.

[0128] Alkanes and aromatics in gasoline fractions can both undergo cracking to produce gaseous components and coke;

[0129] Gasoline olefins can be converted into gasoline cycloalkanes and aromatics, and cracked to produce gaseous olefins and coke.

[0130] Gasoline cycloalkanes can be converted into gasoline aromatics, gaseous olefins, and coke.

[0131] In the gaseous components, butene can react to produce propylene, ethylene, and dry gas;

[0132] Propylene can react to produce ethylene and dry gas;

[0133] Liquefied petroleum gas (LPG) can be converted into ethylene and dry gas, both of which are considered in the reaction network.

[0134] Step S4: Based on the reaction network and influencing factors, establish a reaction process mechanism model.

[0135] Based on the constructed reaction network, considering the influencing factors of catalyst runoff and deactivation, alkaline nitrogen adsorption, and heavy aromatic adsorption, a reaction mechanism model for producing more isoalkane and more high-octane gasoline and aromatic feedstock is established.

[0136] When establishing mechanism models for processes that produce more isoalkanes and processes that produce more high-octane gasoline and aromatics, the influencing factors of catalyst loss were considered, and the catalyst loss operator was used for characterization.

[0137]

[0138] Where λ is a constant related to the equipment and α is the catalyst runoff function exponent.

[0139] Considering the effects of catalyst runoff and deactivation, alkali nitrogen adsorption, and heavy aromatics adsorption, the reaction rate expression in the mechanism model for the process of producing more isoalkanes and the process of producing more high-octane gasoline and aromatics is as follows:

[0140]

[0141] In the formula, a is the mass concentration vector of the reactant component, in kg / kg;

[0142] K is the reaction rate constant matrix.

[0143] ρ is the gas density, kg / m³ 3 ;

[0144] S WH For the true weight space velocity, h -1 ;

[0145] For catalyst deactivation operator, C C denoted as the coke content of the catalyst, β as the catalyst coking deactivation factor, and M as the exponential constant;

[0146] w(t c ) represents the catalyst loss operator, w(t) c )=(1-e λ ·t c ) -α λ is a constant, and α is an exponential constant;

[0147] f(A) is the heavy aromatic hydrocarbon adsorption-deactivation operator. C A k represents the residual carbon content of the feed. A It is a deactivation factor for heavy aromatic hydrocarbon adsorption;

[0148] f(N) is the base nitrogen adsorption-deactivation operator. C N The feed alkali-nitrogen content, t c φ represents the residence time of the catalyst. C / O This refers to the ratio of the reactant to the oil.

[0149] Specifically, based on the content of steps S1, S2, and S3, the reaction kinetic model for the process of producing multiple isomeric alkanes is constructed as follows:

[0150]

[0151] in,

[0152] a = [R] S R A R B P l O l N l A sl A ml P e O e N e A e C 4＝ C 3＝ LPG C 2＝ DR C],

[0153] The reaction kinetic model for the process of producing high-octane gasoline and aromatics feedstock is as follows:

[0154]

[0155] Where, a2=[P l O l N l A sl A ml P e O e N e A e C 4＝ C 3＝ LPG C 2＝ DR C],

[0156]

[0157] K1 and K2 are reaction rate constant matrices.

[0158] The reaction rate constants all conform to the Arrhenius equation, i.e. k0 and Ea are the pre-exponential factor and activation energy of the corresponding conversion reaction process, respectively.

[0159] T is the reaction temperature, and R is the molar gas constant, which is 8.3145 in SI system.

[0160] Based on the reaction kinetic models constructed in steps S3 and S4, the process of producing more isoalkanes contains 120 reaction pathways, i.e., 120 reaction rate constants, and a total of 240 kinetic parameters; the process of producing more high-octane gasoline and aromatics contains 75 reaction pathways, i.e. 75 reaction rate constants, and a total of 150 kinetic parameters.

[0161] Step S5: Calculate catalyst coke regeneration using a regenerator model to achieve a coupled cycle between the regeneration process and the catalytic cracking reaction process.

[0162] The catalyst coke regeneration is calculated using a regenerator model, thus achieving a coupled cycle between the regeneration process and the catalytic cracking reaction process that produces more isoalkanes and more high-octane gasoline and aromatic feedstock.

[0163] Figure 5 A schematic diagram illustrating the coupling process of the reaction model and the regeneration model according to an embodiment of the present invention is shown, such as... Figure 5 The reaction process and regeneration process for producing high-octane gasoline and aromatics, which yields high-isoalkane hydrocarbons, are shown below:

[0164] In the reaction process of producing more isoalkanes and more high-octane gasoline and aromatics, the catalyst coking and deactivation is used as a waiting catalyst (waiting agent) to enter the regeneration process. Air is introduced to achieve coking regeneration, and after the activity is restored, it is used as a regenerated catalyst (regenerating agent). After adding fresh catalyst (fresh agent), it returns to the reaction process.

[0165] In this embodiment, catalyst loss due to factors such as equipment and reaction oil and gas adhering to the device during the main and side reactions is taken into account.

[0166] The model of the regeneration process of the recycled agent in the regenerator includes:

[0167] Differential equation for mass transfer reaction in the bubble phase of the regenerator:

[0168]

[0169] The reaction algebraic equations for the gas and solid phases in the dense-phase emulsion of the regenerator are as follows:

[0170]

[0171] A×L×(1-ε b )×(1-ε e )×r s ×M s =F cat ×(W s,in -W s,out );

[0172] ε e ε b These are the porosity of the emulsion phase and the bubble phase fraction, respectively.

[0173] K be The mass transfer resistance at the bubble phase-emulsion phase interface is L; the bed height is L.

[0174] r s W s,in Ws,out Represents the relevant variables of related materials (elements such as C and H) in the solid phase. Represents the relevant variables of the materials in the gas phase, i∈{O2, N2, CO2, CO, H2O};

[0175] In the coupling process of the reaction and regeneration of high-octane gasoline and aromatics with high-isomerized alkanes, it is assumed that the carbon content of the regeneration catalyst is 0. Based on the calculation results of the main and side reaction models and the regeneration model, the information of the pre-regeneration agent (coke content, flow rate, temperature, etc.) and the information of the regenerator (coke content, flow rate, etc.) are initialized.

[0176] Combined with the fresh agent flow rate, the regenerator flow rate was adjusted according to the appropriate main and auxiliary reactant-oil ratio, and then entered into the main and auxiliary reaction model for cyclic calculation, taking into account the catalyst loss during the reaction process;

[0177] Output the result when the information on the regenerator and the pre-regenerating agent no longer changes in the two consecutive calculations.

[0178] Achieve a coupled cycle between the catalytic cracking reaction process and the regeneration process, which produces more isoalkanes, high-octane gasoline, and aromatic feedstock.

[0179] Step S6: Select the objective function, optimize the kinetic parameters involved in each reaction, and output the optimal combination of kinetic parameters.

[0180] The objective function is identified by determining the parameters, and the dynamic parameters are optimized and solved using an improved whale optimization algorithm.

[0181] In this embodiment, the objective function is to minimize the sum of squared errors between the model predictions and the actual values. The objective function value is represented by F, and the reaction kinetic parameters are determined according to the principle of optimal fitting.

[0182]

[0183] in, Represents the model prediction value of the i-th component, as a percentage yield; This represents the actual value of the i-th component, as a percentage yield.

[0184] Figure 6 A flowchart of an improved whale optimization algorithm according to an embodiment of the present invention is disclosed, such as... Figure 6 The improved whale optimization algorithm shown is formed by combining niche technology with sine and cosine optimization.

[0185] The improved whale optimization algorithm further includes the following steps:

[0186] Step S601: Initialize algorithm parameters. Algorithm parameters include population size (Np), maximum number of iterations (Gm), and parameter variable range (Xmin, Xmax).

[0187] In this embodiment, a serial parameter solving strategy is adopted. After repeated experiments, the algorithm parameters are set as shown in Table 1:

[0188] Table 1

[0189]

[0190] Step S602: Initialize the population.

[0191] A uniformly random initial population is generated, and a reverse population is generated based on the initial population combined with a reverse learning strategy. Then, the best individuals are selected in turn according to their fitness to form the first generation population.

[0192] The uniformly random initial population is calculated as follows:

[0193]

[0194] represents the d-th dimension parameter of the i-th individual in the initial population, and rand represents a random number;

[0195] The reverse population calculation method based on the initial population is as follows:

[0196]

[0197] The parameter representing the d-th dimension of the i-th individual in the reverse population;

[0198] The process of selecting superior individuals based on fitness to form the initial population can be represented as:

[0199]

[0200] X i Let f represent the i-th individual in the first generation population, and f function represents the fitness calculation.

[0201] Step S603: Pre-select individuals based on niche technology.

[0202] Specifically, this refers to selecting the best individuals from the parent and offspring generations based on fitness during each iteration of the population, and then proceeding to the next iteration.

[0203] The specific calculation method is as follows:

[0204]

[0205] Among them, X i (t), Xi (t+1) represents the i-th individual in the t-th and t+1-th generations, respectively, and the f function represents the fitness value calculation.

[0206] During the algorithm iteration process, the position and fitness value of individuals in the population are recorded through variables, and the selection of the best individuals in the parent and offspring populations is completed after each iteration.

[0207] Step S604: Based on the optimal individuals generated by the pre-selection, update the offspring population positions using the whale optimization algorithm and the sine and cosine algorithms respectively.

[0208] Understandably, the whale optimization algorithm is a novel biomimetic swarm intelligence algorithm that has certain advantages in terms of algorithm convergence speed and optimization accuracy.

[0209] During the optimization iteration process, this algorithm causes individuals in the population to continuously shrink towards the current optimal solution, and updates the position of the offspring population in a spiral manner based on the distance between the individual and the optimal solution. The specific calculation method is as follows:

[0210]

[0211] In the formula, X * (t) represents the position vector of the optimal solution in the t-th generation of the population; during the early global optimization phase of the algorithm (|A|>1), C = 2r, D = |C·X rand (t)-X(t)|;

[0212] r is a random number between [0, 1];

[0213] In the later local search phase of the algorithm, D′=|X * (t)-X(t)|, b is the spiral constant, and l is a random number between [-1,1].

[0214] Sine and cosine optimization utilizes the oscillatory characteristics of sine and cosine functions to find the optimal solution. The algorithm has strong convergence and good robustness.

[0215] The update method for the offspring population during algorithm iteration is as follows:

[0216]

[0217] in, c is a constant greater than 1, h2 is a random number that follows a uniform distribution from 0 to 2π, and h3 and h4 are random numbers that follow a uniform distribution from 0 to 2π.

[0218] Step S605: Select the best individuals based on fitness ranking to generate the offspring population.

[0219] Step S606: Determine if the iteration termination condition is met. If it is met, the iteration ends and the optimal solution is output; if it is not met, return to S603 and proceed to the next iteration.

[0220] In this embodiment, the optimization termination conditions include: the error between all components and the target yield is less than a first percentage or the current iteration number reaches the maximum iteration number.

[0221] Preferably, the first percentage is 1%.

[0222] The dynamic parameters can be optimized by improving the whale optimization algorithm, either in parallel or serial mode, and the model can be validated based on the fitted optimal dynamic parameters.

[0223] The improved whale optimization algorithm is used in step S6 to optimize the dynamic parameters of steps S3-S5.

[0224] The inputs to the improved whale optimization algorithm in step 6 include:

[0225] ① The relevant variables involved in the constructed kinetic model, including raw material properties, operating conditions, catalyst properties, and product distribution;

[0226] ② Algorithm parameters, including: population size (Np), maximum number of iterations (Gm), and parameter variable range (Xmin, Xmax);

[0227] The algorithm outputs the optimal solution that satisfies the termination condition of the iterative search, which is the current optimal combination of dynamic parameters.

[0228] Table 2 shows some operating conditions and feed properties data in this embodiment; Tables 3 and 4 are the reaction kinetic parameters of the optimally fitted process for producing isoparaffins and high-octane gasoline and aromatics, respectively.

[0229] Table 2

[0230]

[0231] Table 3

[0232]

[0233]

[0234]

[0235] Table 4

[0236]

[0237]

[0238]

[0239] Table 5

[0240]

[0241] Table 5 compares the product yields of the combined process for producing high-isoalkane, high-octane gasoline, and aromatic feedstock under three operating conditions. As can be seen from the data in Table 5, the constructed dual-riseer catalytic cracking process mechanism model for the combined process of producing high-isoalkane, high-octane gasoline, and aromatic feedstock exhibits good predictive performance.

[0242] Based on the optimal combination of kinetic parameters, the model-predicted yield and the actual yield for the 18 divided reaction components do not deviate by more than 2%.

[0243] The model predictions for the actual product yield of the catalytic cracking process deviate from the actual values ​​by no more than 3%.

[0244] The present invention provides a modeling method for a combined catalytic cracking process using a dual riser reactor. The deviation between the model prediction and the actual product yield of this process is less than 3%, and the established process mechanism model has good accuracy.

[0245] Although the methods described above are illustrated and depicted as a series of actions for the sake of simplicity, it should be understood and appreciated that these methods are not limited by the order of the actions, as some actions may occur in a different order and / or concurrently with other actions from the illustrations and descriptions herein or not illustrated and described herein but which may be understood by those skilled in the art, according to one or more embodiments.

[0246] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include plural forms. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0247] The above embodiments are provided for those skilled in the art to implement or use the present invention. Those skilled in the art can make various modifications or changes to the above embodiments without departing from the inventive concept of the present invention. Therefore, the protection scope of the present invention is not limited to the above embodiments, but should be the maximum scope that conforms to the innovative features mentioned in the claims.

Claims

1. A modeling method for a combined catalytic cracking process using a dual-riseer reactor, characterized in that, Includes the following steps: Step S1: Divide the reaction components according to the process characteristics; Step S2: Set basic assumptions about reaction kinetics for the combined process reaction; Step S3: Based on the divided reaction components, determine the reaction relationships of each reaction component and construct the reaction network; Step S4: Based on the reaction network and influencing factors, establish a reaction process mechanism model; Step S5: Calculate catalyst coke regeneration using a regenerator model to achieve a coupled cycle between the regeneration process and the catalytic cracking reaction process; Step S6: Select the objective function, optimize the kinetic parameters involved in each reaction, and output the optimal combination of kinetic parameters; Step S7: Based on the optimal combination of kinetic parameters, predict the yield and properties of the relevant products; The process characteristics of step S1 are that the process produces more isoalkanes and more high-octane gasoline and aromatics in the dual riser. Step S2 includes proposing basic assumptions about reaction kinetics for the complex gas-solid heterogeneous catalytic cracking reaction process of the combined process of producing more isoalkane and more high-octane gasoline and aromatic feedstock. The construction of the reaction network in step S3 includes: constructing reaction networks for processes that produce more isoalkane and processes that produce more high-octane gasoline and aromatics, respectively. Step S4 includes: based on the constructed reaction network, and combined with the influencing factors of catalyst runoff and deactivation, alkaline nitrogen adsorption, and heavy aromatic adsorption, establishing a reaction mechanism model for the production of more isoalkanes and more high-octane gasoline and aromatic feedstock; In step S5, catalyst coking regeneration is calculated using a regenerator model to achieve a coupled cycle between the regeneration process and the catalytic cracking reaction process that produces more isoalkanes and more high-octane gasoline and aromatics. This includes: catalyst coking and deactivation entering the regeneration process as a pre-regenerating agent; air is introduced to achieve coking regeneration; after regaining activity, it becomes a regenerating agent and returns to the reaction process after adding freshener; assuming the regenerating agent has a fixed carbon content of 0, the pre-regenerating agent information and regenerating agent information are initialized based on the calculation results of the main and side reaction models and the regeneration model; combined with the freshener flow rate, the regenerating agent flow rate is adjusted according to the main and side reaction agent-oil ratio, and the process is entered into the main and side reaction models for cyclic calculation, taking into account catalyst loss during the reaction process; when the pre-regenerating agent and regenerating agent information no longer change in the previous two calculation results, the result is output.

2. The modeling method for the combined catalytic cracking process with a dual riser reactor according to claim 1, characterized in that, Step S1 further includes: for reaction systems that produce a large amount of isoalkanes and high-octane gasoline and aromatics, a method combining distillation range and hydrocarbon group composition is used to group and classify the reaction components according to the principle of similar kinetic characteristics.

3. The modeling method for the combined catalytic cracking process with a dual riser reactor according to claim 2, characterized in that, The basic assumptions of the reaction kinetics in step S2 further include: All reactions in the reaction system are first-order irreversible reactions; The reactions involved in the reaction system are considered homogeneous reactions. The gas flow state involved in the reaction system is isothermal, gas phase, ideal plug flow, and diffusion within material particles is ignored; The catalyst deactivation problem in the reaction system is characterized by time-varying catalyst deactivation, and its impact on the reaction process is only related to the catalyst residence time. Catalyst wear in the reaction system is characterized by time-varying catalyst wear, and catalyst loss is only related to the relevant equipment and catalyst residence time. The degree of influence of the adsorption of heavy aromatics and basic nitrogen compounds in the reaction system on the reaction results is corrected for the reaction rate by corresponding operators; No coke is formed from the gases in the reaction system.

4. The modeling method for the combined catalytic cracking process with a dual riser reactor according to claim 1, characterized in that, The reaction rate in the mechanism model for establishing the process of producing more isoalkane and more high-octane gasoline and aromatics in step S4 is expressed as follows: In the formula, a This refers to the mass concentration of the reactant component. K This is the reaction rate constant matrix; The density of the gas; This represents the actual space velocity over time. For catalyst deactivation operator; For catalyst loss operator; It is a heavy aromatic hydrocarbon adsorption-deactivation operator; This is the base nitrogen adsorption deactivation operator.

5. The modeling method for the combined catalytic cracking process using a dual-riseer reactor according to claim 1, characterized in that, In step S5, the differential equation of the mass transfer reaction in the bubble phase of the regenerator model is as follows: ； The reaction algebraic equations for the gas and solid phases in the dense emulsion phase of the regenerator model are as follows: These are the porosity of the emulsion phase and the bubble phase fraction, respectively. Mass transfer resistance at the bubble phase-emulsion phase interface; L represents the bed height; Represents the relevant variables of related materials in the solid phase; This represents the relevant variables of related materials in the gas phase. .

6. The modeling method for the combined catalytic cracking process using a dual-riseer reactor according to claim 1, characterized in that, Step S6 further includes optimizing the kinetic parameters of each reaction using an improved whale optimization algorithm; The improved whale optimization algorithm further includes the following steps: Step S601: Initialize algorithm parameters; Step S602: Initialize the population; Step S603: Pre-select individuals based on niche technology; Step S604: Based on the optimal individuals generated by the pre-selection, update the position of the offspring population according to the whale optimization algorithm and the sine and cosine algorithms respectively; Step S605: Select the best individuals based on fitness ranking to generate the offspring population; Step S606: Determine whether the termination condition of the algorithm optimization is met. If the termination condition is not met, return to step S603 for the next round of iteration calculation. If the termination condition is met, the optimization ends and the optimal solution is output.

7. The modeling method for the combined catalytic cracking process using a dual-riseer reactor according to claim 6, characterized in that, The expression corresponding to step S603 is: in, They represent the first time. t generation and first t+ The first generation i Individual; f The function represents the function for calculating the fitness value.

8. The modeling method for the combined catalytic cracking process of a dual-riseer reactor according to claim 6, characterized in that, The optimization termination conditions set in step S606 include: the error between all components and the target yield is less than a first percentage or the current iteration number reaches the maximum iteration number.

9. The modeling method for the combined catalytic cracking process of a dual-riseer reactor according to claim 6, characterized in that, Step S6 further includes: using the minimum sum of squares of the errors between the model predictions and the actual values ​​as the objective function.

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