CN118098403A - A method for improving the yield of catalytic diesel hydrogenation conversion products - Google Patents

Abstract

The invention discloses a method for improving the yield of catalytic diesel hydro-conversion products, which comprises the following steps: establishing a simple analysis method of raw materials and products, and acquiring detailed raw material composition and product composition data of diesel oil hydro-conversion; merging, dividing and lumped the catalytic diesel hydro-conversion reaction system according to the principle of similar dynamics characteristics, establishing a mechanism model, and inputting the obtained data into the established mechanism model; predicting catalytic hydrogenation conversion reaction results under different reaction conditions by using the built model; obtaining a reaction rule of raw materials under corresponding process conditions, and obtaining yield and properties of target products under different reaction conditions output by a model, wherein the target products are gasoline; and according to the obtained prediction result, the original diesel catalytic hydrogenation conversion process condition is feedback optimized, the yield of the target product is improved by more than 3%, and the production and economic benefits are improved.

Description

A method for improving the yield of catalytic diesel hydrogenation conversion products

Technical Field

The invention relates to the technical field of diesel fuel hydroconversion reaction, and in particular to a method for improving the yield of catalytic diesel fuel hydroconversion products.

Background Art

The catalytic cracking (FCC) processing capacity accounts for about 40% of the primary processing capacity of crude oil, resulting in a large proportion of catalytic cracking diesel (catalytic diesel, LCO) in the diesel pool, reaching more than 30%, becoming the main secondary processed diesel component. Its characteristics are high aromatic content, high sulfur and nitrogen impurity content, high olefin content, low cetane number, poor oxidation stability, etc.

Moreover, more stringent requirements are put forward for the content of polycyclic aromatic hydrocarbons in automotive diesel. Faced with this situation, for refineries that use catalytic cracking as the main means of lightening heavy oil, it is difficult to fully realize the production of automotive diesel and reduce ordinary diesel due to the high proportion of catalytic cracking diesel in their diesel pool. Therefore, how to reasonably and efficiently reduce low-value LCO, produce high-value products (increase gasoline and increase automotive diesel production) while achieving diesel quality upgrades, and reduce the diesel-to-gasoline ratio has become a major research topic for current refineries.

The main process of catalytic hydrogenation can be divided into hydrofining and hydrocracking. Due to the extremely bad properties of catalytic diesel, the current means of treatment are relatively simple, and the means that can be relied on are mainly to combine catalytic diesel with hydrogenation technology for processing, such as mixing catalytic diesel with straight-run diesel for hydrofining, mixing catalytic diesel with straight-run wax oil for hydrocracking, and the conversion technology that has emerged in recent years to crack catalytic diesel alone to produce gasoline.

CN 104611050 A discloses a catalytic cracking diesel conversion method. The method adopts a single-stage series process, and the raw material flows directly into the hydrocracking reactor after the reaction of the hydrofining reactor, and the hydrogenation activity of the hydrocracking catalyst tends to decrease. The inventive method can ensure the effect of diesel hydrocracking to a certain extent, and at the same time can also reduce the chemical hydrogen consumption, thereby improving the octane number and liquid yield of naphtha. However, it is ignored that the entire reaction cycle is relatively long, and the catalyst activity is relatively high at the initial stage of the device startup, which will be much higher than later, and the device adjustment time cannot be shortened.

CN 104611029 A discloses a catalytic cracking diesel hydrogenation conversion method, which comprises connecting a hydrofining reactor and a hydrocracking reactor in series, and mixing the catalytic diesel with hydrogen and first entering the hydrofining reactor, and then entering the hydrocracking reactor. Although the catalytic diesel component can be processed to produce high-octane gasoline through a certain catalyst grading effect, the hydrogen consumption is high, and the hydrogen resource requirements of the refinery are relatively large.

Therefore, in view of the shortcomings of the existing technology, it is necessary to develop a method based on kinetic model prediction to adjust the process conditions so as to increase the yield of gasoline components while reducing the reaction hydrogen consumption and shorten the device adjustment time.

Summary of the invention

The purpose of the present invention is to provide a method for improving the yield of catalytic diesel hydroconversion products. By obtaining kinetic simulation prediction results, the method systematically grasps the effects of operating conditions such as reaction temperature and hydrogen partial pressure on the optimization device, instead of simply relying on empirical adjustment methods to adjust the product fraction width to increase the required output of the target product (gasoline).

To achieve the above object, the present invention provides the following technical solutions:

A method for improving the yield of catalytic diesel hydrogenation conversion products, specifically comprising the following steps:

(1) Establish a simple analysis method for raw materials and products to obtain detailed raw material composition and product composition data for diesel hydroconversion; the purpose of obtaining raw material and product composition data is to divide the aggregate, and the establishment of the model is based on the divided aggregate;

(2) The catalytic diesel hydroconversion reaction system is merged and lumped according to the principle of similar kinetic characteristics to establish a mechanism model, and the data obtained in step (1) is input into the established mechanism model. Among them, the lumped model can accurately simulate the complex catalytic hydroconversion reaction system to a considerable extent due to its kinetic mechanism characteristics, and obtain the reaction law of the device raw materials under the corresponding process conditions.

(3) Use the established model to predict the effects of different reaction conditions on the catalytic hydrogenation reaction results;

(4) Obtaining the reaction law of the raw materials under the corresponding process conditions, and obtaining the yield and properties of the target product under different reaction conditions output by the model, where the target product is gasoline;

(5) Based on the prediction results obtained, feedback is provided to optimize the original diesel catalytic hydrogenation conversion process conditions, thereby increasing the yield of the target product by more than 3%, thereby improving production and economic benefits.

Preferably, in step (1), the raw material is 100% catalytic cracking diesel, the products include stabilized gasoline, C6 fraction, refined diesel, and the data include the content of normal alkanes, isoalkanes, cycloalkanes, olefins, aromatics and benzene in the raw materials and products.

Preferably, the catalytic diesel hydroconversion reaction system has complex reactions, and the compositions of the raw materials and products are very diverse, often a relatively complex mixture. At the same time, each component oil can undergo more than one reaction. Therefore, the catalytic diesel hydroconversion reaction system is merged into several virtual components, called lumps, according to the principle of similar kinetic characteristics. In the kinetic study, each lump is considered as a virtual single component, and the kinetic model established by taking each lump as an independent entity can approximately describe the reaction performance of the original system. Then, this simplified lumped component kinetic model is developed and established, and the molecular composition of the raw materials described in (1) above is input into the established lumped kinetic model.

The hydrocarbon composition of the diesel fraction is expressed by the group composition notation (PONA). The group composition notation is expressed by the composition data of the relative content of each group of hydrocarbons in the petroleum fraction. The catalytic diesel is analyzed by mass spectrometry, and the group composition is expressed by alkanes (normal alkanes, isoalkanes), cycloalkanes (monocyclic, dicyclic and polycyclic alkanes), aromatic hydrocarbons (monocyclic, dicyclic and polycyclic aromatic hydrocarbons) and non-hydrocarbon compounds. The diesel hydroconversion technology is actually composed of two parts in series: hydrofining and hydrocracking, collectively known as catalytic diesel hydrogenation: hydrofining is to remove sulfur, nitrogen and other heteroatoms in oil products under high pressure and hydrogen in the presence of catalysts to improve the performance of oil products. The main reactions are hydrodesulfurization, hydrodenitrogenation and aromatic saturation; hydrocracking is a conversion process in which hydrocarbon molecules and hydrogen react on the catalyst surface under high pressure to produce smaller molecules through isomerization, cracking and hydrogenation. It can produce high-quality gasoline, diesel, etc. It has the characteristics of strong raw material applicability, flexible product solutions, high yield and good quality of liquid products.

Preferably, in the step (2), the mechanism model includes a diesel hydrorefining kinetic model and a diesel hydrocracking kinetic model, and the diesel hydrorefining kinetic model includes 9 lumps, namely sulfur S, nitrogen N, normal alkanes P, isoalkanes I, cycloalkanes N, olefins O, monocyclic aromatic hydrocarbons MA, dicyclic aromatic hydrocarbons DA, and tricyclic aromatic hydrocarbons PA; the diesel hydrocracking kinetic model is divided into 21 lumps, and the hydrocarbons are divided into 7 layers vertically at intervals of 27.8°C per boiling range, and in the horizontal direction, that is, within each layer, three lumps of alkanes, cycloalkanes and aromatics are further divided, for a total of 21 lumps.

21 lumps are just one of the division methods used in the cracking model, considering the combination of two different dimensions, horizontal and vertical. Vertically, 7 layers are obtained by cutting the distillation range data of the crude oil at 27.8℃, that is, the raw materials and products are regarded as a mixture of a series of continuous compounds, and a narrow fraction is cut from the raw materials and products at every 27.8℃ according to the actual boiling point boiling range, and the average boiling point of each lumped is used as the boiling point of the lumped; horizontally, it is divided into 3 lumps of alkanes, cycloalkanes and aromatics, because the hydrocarbon composition in the petroleum fraction is mainly these three categories. That is, each layer has 3 lumps, and there are 21 lumps in 7 layers. Establishing a lumped model can simplify complex reaction systems and describe complex and indescribable reaction systems as qualitative and quantitative studies. The subsequent equation establishment is based on lumping.

Preferably, the diesel hydrofining kinetic model includes:

Hydrodesulfurization HDS reaction rate equation:

Hydrodenitrogenation HDN reaction rate equation:

Aromatic saturation HDA reaction rate equation:

The relationship between the reaction rate constant k and temperature satisfies the Arrhenius equation:

Then the above model equation can be written as:

HDS reaction rate equation:

HDN reaction rate equation:

HDA reaction rate equation:

Wherein, k _i (i＝S, N, PA, DA, MA) are the forward and reverse reaction rate constants of the hydrogenation saturation reaction of HDS, HDN, tricyclic, bicyclic and monocyclic aromatic hydrocarbons, h ^-1 ; C _i (i＝S, N, PA, DA, MA) are the mass percentages of sulfide, nitride, tricyclic aromatic hydrocarbon, bicyclic aromatic hydrocarbon and monocyclic aromatic hydrocarbon in the raw materials of the reaction system, h -1 ; P _H2 is the hydrogen partial pressure, MPa; Ea,i (i＝S, N, PA, DA, MA) is the activation energy value of each corresponding reaction, kJ/mol; R is the molar gas constant, which is 8.3144 J/(molk); T is the reaction temperature, °C; α _i (i＝1,2,3,4) is the reaction order; β _i (i＝1,2,3,4) is the hydrogen partial pressure index; k _i,0 (i＝S, N, PA, DA, MA) is the pre-exponential factor of each corresponding reaction, h ^-1 ; k _PA1,0 ,k _PA2,0 In ,k _DA1,0 ,k _DA2,0 , 1 refers to the forward reaction, 2 refers to the reverse reaction, ki,0 represents the pre-exponential factor; V(H)/V(oil) is the hydrogen-to-oil ratio; γ is the hydrogen-to-oil ratio index.

Preferably, the diesel hydrocracking kinetic model comprises:

For the same type of hydrocarbons, the rate constant and its boiling point conform to an exponential relationship; the reaction mechanism is the same, and their reaction activation energy is considered to be the same.

Then the above model equation can be written as:

Wherein: _Fai is the mass fraction of aromatic hydrocarbons lumped in layer i, that is, the number of cyclic carbon atoms lumped in layer i (i=1, 2, 3, 4, 5, 6, 7; the same below), _Fni is the mass fraction of cycloalkanes lumped in layer i, _Fpi is the mass fraction of alkanes lumped in layer i; _Faj is the mass fraction of aromatic hydrocarbons lumped in layer j, _Fnj is the mass fraction of cycloalkanes lumped in layer j, _Fpj is the number of carbon atoms of alkanes lumped in layer j; _kani is the rate constant for hydrogenation saturation reaction of aromatic hydrocarbons in layer i, _kri is the rate constant for side chain removal reaction of cyclic hydrocarbons in layer i, h ^{- 1} , _knpi is the rate constant for ring-opening reaction of cycloalkanes in layer i, h ^-1 , _kpi is the rate constant for cracking reaction of alkanes in layer i, h ^-1 , _krj is the rate constant for side chain removal reaction of cyclic hydrocarbons in layer j, h ^-1 , _kpj is the rate constant for cracking reaction of alkanes in layer j, h ^-1 ; P _rij is the distribution coefficient of the transformation of cyclic hydrocarbons in layer j to layer i, P _pij is the distribution coefficient of the transformation of alkanes in layer j to layer i; n is the total number of cuts, the heaviest fraction is numbered 1, the lightest is 1, and so on, t is the reaction time; k _an0 , k _r0 , k _np0 , k _p0 are pre-exponential factors, h ^-1 ; E _an , _Er , _Enp , E _p are the reaction activation energies, kJ/mol; PH is the hydrogen partial pressure, MPa, P ₀ is the reaction pressure constant, 1MPa; TBP _i is the i-layer lumped real boiling point, ℃, TBP _j is the j-layer lumped real boiling point, ℃; R is the molar gas constant, 8.3144J/(mol/k); T is the reaction temperature, ℃; a, b, c, d are hydrogen partial pressure indices, α, β, γ, δ are real boiling point indices.

For the lightest 7th lump (equivalent to C4 ^- ), the yield distribution is as follows:

The distribution coefficients _Ppij and _Prij are functions of the average boiling points of each narrow fraction. Considering that cyclic hydrocarbons cannot generate ^C4- , then:

P _rnj = 0;

For chain hydrocarbons:

P _pij =P _rij -P _r(i+1),j ;

For cyclic hydrocarbons:

P _rij =P' _rij -P'_r(i+1),j;

Where: η is the yield; B ₁ and B ₂ are product distribution constants; C ₁ is a parameter; r _ij is the reaction rate of the j-layer lumped to the i-layer lumped; P _pnj is the distribution coefficient of the j-layer lumped alkane cracking, and P _rnj is the distribution coefficient of the j-layer lumped cyclic hydrocarbons for the removal of side chains.

dF _ai /dt represents the total hydrocracking rate of the i-layer lumped aromatics; dF _ni /dt represents the i-layer lumped cycloalkane hydrocracking rate; dF _pi /dt represents the i-layer lumped alkane hydrogenation and hydrocracking rate; p (chain alkanes, chain alkane cracking reaction), n (cycloalkanes), a (aromatics), r (cyclic hydrocarbons, including aromatics and cycloalkanes, etc.); an (aromatic saturation reaction), np (cycloalkanes ring-opening reaction), r (cyclic hydrocarbon side chain removal reaction); and the other r _ai , r _ni , r _pi, etc. in the formula represent the corresponding hydrocarbon hydrocracking reaction rates, -i-layer aromatic hydrosaturation rate = -i-layer aromatic hydrosaturation -i-layer aromatic side chain removal + j-layer hydrocarbons heavier than i-layer transferred to i-layer.

The relationship between the above formulas is as follows: the reaction of cycloalkanes and aromatics is composed of two major categories: due to the removal of side chains, heavier cycloalkanes and aromatics are converted into lighter cycloalkanes and aromatics; aromatic saturation and cycloalkanes ring opening; aromatic saturation is a reversible reaction, and aromatics cannot be directly converted into alkanes; aromatic saturation and cycloalkanes ring opening reactions cannot change the boiling range of the aggregate, that is, the reaction only occurs in the same layer; in the alkane cracking reaction, the change in the i-th aggregate is the result of its own cracking, the i-th aggregate ring opening of the cycloalkane in the same layer, and the cracking of all alkane components heavier than it to generate it; for the de-side chain reaction of cycloalkanes and aromatics, since their reaction mechanisms are the same, it can be considered that the rate constants of the de-side chain reaction of aromatics and cycloalkanes in the same layer are approximately the same. Therefore, the changes of cycloalkanes and aromatics caused by the removal of side chains can be considered in terms of the total ring; the change of the i-th aggregate of the total ring is the combined result of its own transfer to the lighter ring aggregate, the conversion of cycloalkanes to the i-th layer alkanes, and the transfer of all ring aggregates heavier than it to the i-th ring aggregate; for the same type of hydrocarbons, the rate constant and its boiling point conform to an exponential relationship; for the same type of hydrocarbons, the reaction mechanism is the same, and their reaction activation energy is considered to be the same.

The pre-exponential factor k ₀ , activation energy E, reaction order α, hydrogen partial pressure index β, etc. in the above equation are named as kinetic parameters.

Preferably, in step (2), the method for determining the molecular composition of the raw material is one or more of gas chromatography, gas chromatography-mass spectrometry, simulated distillation chromatography, nuclear magnetic resonance spectroscopy, Raman spectroscopy, sulfur and nitrogen fluorescence analyzer, and elemental analyzer.

Preferably, in step (3), the reaction conditions include reaction temperature, reaction pressure and space velocity.

Preferably, in the step (3), the specific prediction method is as follows: the molecular composition of the raw material is input into the established mechanism model, and after the model is initially verified to be reliable, a series of simulation calculations and prediction calculations are performed on the model for different raw materials and different reaction conditions, and the influence of different operating variables on diesel hydrorefining and hydrocracking reactions is predicted. By setting the variation range of these operating variables, the variation trend of the main product yield and the dynamic relationship between these operating variables are studied, so as to select the best combination of operating conditions, optimize the product distribution, and achieve a 3% increase in the main product yield, wherein the operating variables include reaction temperature, hydrogen partial pressure, hydrogen-to-oil ratio, and catalyst volume space velocity.

Preferably, in step (3), the prediction result is fed back to adjust and optimize the original diesel catalytic hydrogenation conversion process conditions as follows:

(1) Refining stage: average reaction temperature 356-381°C, hydrogen partial pressure 7.0-9.0 MPa, refining agent volume space velocity 1.3-15 h-1, hydrogen-oil ratio 800:1, lowering the initial reaction temperature, which is conducive to extending the operation cycle of the device;

(2) Cracking stage: average reaction temperature 389-409°C, hydrogen partial pressure 7.0-9.0 MPa, cracking agent volume space velocity 1.0-15 h-1, hydrogen-to-oil ratio 800:1. On the premise of meeting product quality, moderately lowering the cracking reaction temperature and increasing the cracking reaction pressure will help slow down the catalyst deactivation rate and extend the unit operation cycle, which will play a theoretical guiding role in optimizing unit operation.

Preferably, the reaction law in step (4) is as follows: with the increase of reaction temperature, the desulfurization rate and the denitrification rate both increase significantly, the conversion of the PA component of the aromatics reaction first increases and then decreases, and the conversion of the DA component and the MA component first gradually increases with the increase of reaction temperature and then remains basically unchanged; with the decrease of space velocity, the desulfurization rate and the denitrification rate both increase, the contents of the PA component and the DA component gradually decrease, and the content of the MA component gradually increases.

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

(1) The final target product (gasoline) yield of the catalytic diesel hydroconversion reaction can reach 48% to 53%, which is 3% higher than the existing gasoline yield, thus improving production and economic benefits;

(2) Due to its kinetic mechanism characteristics, the lumped model can accurately simulate the complex catalytic hydrogenation reaction system to a considerable extent and obtain the reaction law of the raw materials under the corresponding process conditions;

(3) Based on the prediction results obtained, the original diesel catalytic hydrogenation conversion process conditions can be optimized to improve the yield of the target product (gasoline).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of the steps of a method for improving the yield of catalytic diesel hydroconversion products provided by an embodiment of the invention;

2 is an information flow diagram of model establishment and solution verification of a method for improving the yield of catalytic diesel hydroconversion products provided in accordance with an embodiment of the present invention;

3 is a block diagram of the parameters of the hydrofining reaction kinetic model according to a method for improving the yield of catalytic diesel hydroconversion products provided in an embodiment of the present invention;

4 is a comparison between the actual value and the model fitting value of the diesel hydrodesulfurization reaction according to a method for improving the yield of catalytic diesel hydroconversion products provided by an embodiment of the present invention;

5 is a comparison between the actual value and the model fitting value of the diesel hydrodenitrogenation reaction according to a method for improving the yield of catalytic diesel hydroconversion products provided by an embodiment of the present invention;

FIG6( a ) is a comparison between the actual value and the model fitting value of the tricyclic aromatic hydrocarbon hydrogenation saturation reaction of a method for improving the yield of catalytic diesel hydrogenation conversion products provided in accordance with an embodiment of the present invention;

FIG6( b ) is a comparison between the actual value and the model fitting value of the bicyclic aromatic hydrocarbon hydrogenation saturation reaction of a method for improving the yield of catalytic diesel hydrogenation conversion products provided in accordance with an embodiment of the present invention;

FIG6( c ) is a comparison between the actual value and the model fitting value of the monocyclic aromatic hydrocarbon hydrogenation saturation reaction of a method for improving the yield of catalytic diesel hydrogenation conversion products provided in accordance with an embodiment of the present invention;

7 is a schematic diagram of a diesel hydrocracking lumped kinetic reaction network according to a method for improving the yield of catalytic diesel hydroconversion products provided in an embodiment of the present invention;

FIG. 8 is a diagram showing the changes in operating conditions of a diesel hydroconversion unit over a period of time according to a method for improving the yield of catalytic diesel hydroconversion products provided in an embodiment of the present invention.

FIG. 9 is a product distribution result of diesel hydroconversion according to a method for improving the yield of catalytic diesel hydroconversion products provided in an embodiment of the present invention.

DETAILED DESCRIPTION

The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

As shown in FIG1 , a method for improving the yield of catalytic diesel hydroconversion products comprises the following steps:

Step S1, determining the detailed molecular composition of the raw material. In this embodiment, the detailed molecular composition of the raw material can be determined by one or more of gas chromatography, gas chromatography-mass spectrometry, simulated distillation chromatography, nuclear magnetic resonance spectroscopy, Raman spectroscopy, sulfur and nitrogen fluorescence analyzer, and elemental analyzer.

Step S2, establishing a mechanism model. The raw material composition is input into the established kinetic mechanism model, wherein the information flow diagram during model establishment and verification during solution is shown in FIG2 .

Step S3, using the established model to predict the effects of different reaction conditions on the results of the catalytic hydrogenation conversion reaction.

Step S4, obtaining the prediction result of the target product (gasoline).

Step S5, flexibly adjusting the process parameters according to the properties of different raw materials, product requirements, etc. According to the prediction results of the target product (gasoline) predicted by the model, the process conditions are optimized to increase the yield of the target product (gasoline) by 3%.

The above-mentioned catalytic diesel hydroconversion reaction system has complex reactions, and the compositions of raw materials and products are very diverse, often a relatively complex mixture. At the same time, each component oil can undergo more than one reaction. Therefore, the catalytic diesel hydroconversion reaction system is merged into several virtual components, called lumps, according to the principle of similar kinetic characteristics. In the kinetic study, each lump is considered as a virtual single component, and the kinetic model established by taking each lump as an independent entity can approximately describe the reaction performance of the original system. Then, this simplified lumped component kinetic model is developed and established, and the molecular composition of the raw materials described in the above step (1) is input into the established lumped kinetic model.

In the above step (1), the structure and detailed composition of the raw materials and products can be determined by gas chromatography, gas chromatography-mass spectrometry, and nuclear magnetic resonance spectroscopy; the distillation range of the raw materials and products can be determined by simulated distillation chromatography; the sulfur and nitrogen content of the raw materials and products can be determined by sulfur and nitrogen fluorescence analyzer and elemental analyzer.

Figure 2 is an information flow diagram for model establishment and verification of a method for improving the yield of catalytic diesel hydroconversion products provided by an embodiment of the present invention. For the hydroconversion reaction model, the main purpose of modeling is to mathematically describe the hydroconversion reaction process. After the model is established, the actual operation data of the device is input into the model to obtain the model parameters. This step is called parameter regression. After the parameters are obtained, the task of the model is to make predictions. At the same time, the results of the model prediction need to be compared with the actual industrial data to verify the reliability of the model and correct the model parameters. After obtaining satisfactory results, it can be used within a reasonable confidence interval to predict and adjust the operating conditions so that the yield of the target product of steam can be increased by 3%.

The mechanism model described in step S2 may include: a hydrorefining model and a hydrocracking model. Further, according to the principle of similar kinetic characteristics, the refining model is divided into 9 lumps including sulfur (S), nitrogen (N), normal alkanes (P), isoalkanes (I), cycloalkanes (N), olefins (O), monocyclic aromatics (MA), dicyclic aromatics (DA), and tricyclic aromatics (PA); according to the actual boiling point data of the petroleum fraction, the cracking model is divided into 21 lumps. Vertically, the hydrocarbons are divided into 7 layers at intervals of 27.8°C per boiling range; horizontally, that is, within each layer, three lumps are divided into alkanes (including side chains on the ring), cycloalkanes and aromatics (all excluding side chains), for a total of 21 lumps.

In one embodiment, S2 may include: establishing hydrofining reaction kinetics and determining hydrofining reaction kinetic parameters.

The hydrotreating kinetic model can be established based on the following assumptions:

(1) According to the above analysis, during the LCO hydrofining process, the hydrogen consumed is mainly used for three main reactions: HDS, HDN and HDA;

(2) The release rate expressions of each hydrogenation reaction in the LCO hydrogenation process are all in the form of power functions;

(3) Both HDS and HDN reactions in LCO are irreversible reactions;

(4) Since the hydrogenation saturation reaction of polycyclic aromatic hydrocarbons is carried out ring by ring, and the difficulty of hydrogenation increases with the progress of the reaction, and since the dehydrogenation reaction rate of cycloalkanes is much smaller than the hydrogenation saturation rate, it is assumed that the hydrogenation saturation of polycyclic aromatic hydrocarbons and bicyclic aromatic hydrocarbons is a reversible reaction, and the hydrogenation saturation of monocyclic aromatic hydrocarbons is an irreversible reaction;

(5) The reactor is operated under adiabatic conditions, and the internal flow is considered as plug flow, with no axial and radial diffusion;

The hydrocracking kinetic model can be established based on the following assumptions:

(1) The reactions of cycloalkanes and aromatics are divided into two categories: conversion of heavier cycloalkanes and aromatics to lighter cycloalkanes and aromatics due to side chain removal; saturation of aromatics and ring opening of cycloalkanes;

(2) Aromatic saturation is a reversible reaction, and aromatics cannot be directly converted into alkanes;

(3) Aromatic saturation and cycloalkane ring opening reactions cannot change the lumped boiling range, i.e., the reactions only occur in the same layer;

(4) In the alkane cracking reaction, the change in the total amount of the i-th group is the result of its own cracking, the ring opening of the i-th group of cycloalkanes in the same layer, and the cracking of all alkane components heavier than it to form it;

(5) For the de-side chain reaction of cycloalkanes and aromatics, since their reaction mechanisms are the same, it can be assumed that the de-side chain reaction rate constants of aromatics and cycloalkanes in the same layer are approximately the same. Therefore, the changes in cycloalkanes and aromatics caused by de-side chain can be considered in terms of the total ring;

(6) The change of the total ring aggregate i is the combined result of its own transfer to lighter ring aggregates, the conversion of cycloalkanes to i-th layer alkanes, and the transfer of all ring aggregates heavier than it to the i-th ring aggregate;

(7) For the same type of hydrocarbons, the rate constant and its boiling point have an exponential relationship; the reaction mechanism is the same, and their reaction activation energy is considered to be the same.

The hydrotreating kinetic model based on power function kinetics can better predict actual industrial production. Specifically, the hydrotreating kinetic model includes:

The hydrotreating kinetic model includes:

Hydrodesulfurization (HDS) reaction rate equation:

Hydrodenitrogenation (HDS) reaction rate equation:

Aromatic saturation (HDA) reaction rate equation:

The relationship between the reaction rate constant k and temperature satisfies the Arrhenius equation:

Then the above model equation can be written as:

HDS reaction rate equation:

HDN reaction rate equation:

HDA reaction rate equation:

Wherein, k _i (i＝S, N, PA, DA, MA) are the forward and reverse reaction rate constants of the hydrogenation saturation reaction of HDS, HDN, tricyclic, bicyclic and monocyclic aromatic hydrocarbons, h ^-1 ; C _i (i＝S, N, PA, DA, MA) are the mass percentages of sulfide, nitride, tricyclic aromatic hydrocarbon, bicyclic aromatic hydrocarbon and monocyclic aromatic hydrocarbon in the raw materials of the reaction system, h -1 ; P _H2 is the hydrogen partial pressure, MPa; E _{a,i (} i＝S, N, PA, DA, MA) is the activation energy value of each corresponding reaction, kJ/mol; R is the molar gas constant, which is 8.3144 J/(molk); T is the reaction temperature, °C; α _i (i＝1,2,3,4) is the reaction order; β _i (i＝1,2,3,4) is the hydrogen partial pressure index; k _i,0 (i＝S, N, PA, DA, MA) is the pre-exponential factor of each corresponding reaction, h ^-1 ; k _PA1,0 ,k _PA2,0 In ,k _DA1,0 ,k _DA2,0 , 1 refers to the forward reaction, 2 refers to the reverse reaction, ki,0 represents the pre-exponential factor; V(H)/V(oil) is the hydrogen-to-oil ratio; γ is the hydrogen-to-oil ratio index.

Next, the raw material composition determined by sampling and analysis in step S1 is input into the above-mentioned model.

The calculation of the dynamic parameters in the model is based on the Python platform. The mathematical equations of the model are converted into programming language. The fourth-order Runge-Kutta method is used to solve the differential equations. The variable scaling method (BFGS) is used to optimize the objective function. The objective function to be optimized is as follows:

Among them, xic _is the calculated value of the outlet concentration, and _xir is the actual value of the outlet concentration.

The model parameter solution block diagram is shown in Figure 3.

Table 1 lists the raw material composition of the sample analysis, and Table 2 lists some kinetic parameters of the refined model.

Table 1 Composition of mixed raw material PIONA

Table 2 Fitting values of kinetic model parameters

Conduct model reliability analysis. The reliability of the model parameters can be analyzed by comparing the calculated values of the products obtained by using the model parameters with the actual values of the plant operation. The specific approach is: after obtaining the model parameters through parameter estimation using several sets of actual device operation data, the obtained model parameters are used in turn to calculate the desulfurization rate, denitrification rate and PA, DA, MA aromatic saturation results under the corresponding conditions. The comparison results of the calculated values and the actual values are shown in Figures 4, 5, 6(a), 6(b), and 6(c). The reliability of the model can be seen by analyzing the deviation size.

Furthermore, model validation calculations are performed.

Furthermore, through the discussion and analysis of the obtained model parameters, the comparison of the model calculated values with the actual values and the verification calculation, the results show that the obtained model parameters are in line with the law, the average relative deviation between the calculated values and the actual values is mostly below 10%, and the established hydrotreating kinetic model is reliable. The specific kinetic equation is as follows:

HDS reaction rate equation:

HDN reaction rate equation:

HDA reaction rate equation:

In one embodiment, S2 may include: establishing hydrocracking reaction kinetics and determining hydrocracking reaction kinetic parameters.

The hydrocracking kinetic model includes:

For the same type of hydrocarbons, the rate constant and its boiling point conform to an exponential relationship; the reaction mechanism is the same, and their reaction activation energy is considered to be the same.

Then the above model equation can be written as:

Wherein: _Fai is the mass fraction of aromatic hydrocarbons lumped in layer i, that is, the number of cyclic carbon atoms lumped in layer i (i=1, 2, 3, 4, 5, 6, 7; the same below), _Fni is the mass fraction of cycloalkanes lumped in layer i, _Fpi is the mass fraction of alkanes lumped in layer i; _Faj is the mass fraction of aromatic hydrocarbons lumped in layer j, _Fnj is the mass fraction of cycloalkanes lumped in layer j, _Fpj is the number of carbon atoms of alkanes lumped in layer j; _kani is the rate constant for hydrogenation saturation reaction of aromatic hydrocarbons in layer i, _kri is the rate constant for side chain removal reaction of cyclic hydrocarbons in layer i, h ^{- 1} , _knpi is the rate constant for ring-opening reaction of cycloalkanes in layer i, h ^-1 , _kpi is the rate constant for cracking reaction of alkanes in layer i, h ^-1 , _krj is the rate constant for side chain removal reaction of cyclic hydrocarbons in layer j, h ^-1 , _kpj is the rate constant for cracking reaction of alkanes in layer j, h ^-1 ; P _rij is the distribution coefficient of the transformation of cyclic hydrocarbons in layer j to layer i, P _pij is the distribution coefficient of the transformation of alkanes in layer j to layer i; n is the total number of cuts, the heaviest fraction is numbered 1, the lightest is 1, and so on, t is the reaction time; k _an0 , k _r0 , k _np0 , k _p0 are pre-exponential factors, h ^-1 ; E _an , _Er , _Enp , E _p are the reaction activation energies, kJ/mol; PH is the hydrogen partial pressure, MPa, P ₀ is the reaction pressure constant, 1MPa; TBP _i is the i-layer lumped real boiling point, ℃, TBP _j is the j-layer lumped real boiling point, ℃; R is the molar gas constant, 8.3144J/(mol/k); T is the reaction temperature, ℃; a, b, c, d are hydrogen partial pressure indices, α, β, γ, δ are real boiling point indices.

For the lightest 7th lump (equivalent to C4-), the yield distribution is as follows:

The distribution coefficients Ppij and Prij are functions of the average boiling points of each narrow fraction. Considering that cyclic hydrocarbons cannot generate C4-, then:

P _rnj = 0;

For chain hydrocarbons:

P _pij =P _rij -P _r(i+1),j ;

For cyclic hydrocarbons:

P _rij =P' _rij -P'_r(i+1),j;

Where: η is the yield; B ₁ and B ₂ are product distribution constants; C ₁ is a parameter; rij is the reaction rate of the j-layer lumped to the i-layer lumped; P _pnj is the distribution coefficient of the j-layer lumped alkane cracking, and P _rnj is the distribution coefficient of the j-layer lumped cyclic hydrocarbons for the removal of side chains.

dF _ai /dt represents the total hydrocracking rate of aromatic hydrocarbons in layer i; dF _ni /dt represents the total hydrocracking rate of cycloalkanes in layer i; dF _pi /dt represents the hydrocracking rate of alkanes in layer i; p (chain alkanes, chain alkane cracking reaction), n (cycloalkanes), a (aromatics), r (cyclic hydrocarbons, including aromatics and cycloalkanes, etc.); an (aromatic saturation reaction), np (cycloalkanes ring-opening reaction), r (cyclic hydrocarbon desideration reaction); and the other r _ai , r _ni , r _pi, etc. in the formula represent the corresponding hydrocarbon hydrocracking reaction rates.

Since the oil product analysis data of the refinery for the hydrocracking unit is generally the Enn distillation data (ASTM D86 Data), and in fact, the boiling point of the lightest component of the oil product in the Enn distillation data is usually lower than the initial boiling point of the mixed oil, and the boiling point of the heaviest component is higher than the dry point of the mixed oil, so it can only be used as an approximate standard. Therefore, we need to convert the Enn distillation data of raw materials and products collected from the production site into real boiling point distillation data through the Edmister method in the simulation software Aspen Plus.

Before formally using the kinetic model for calculations, the feed and product data collected on site need to be converted into usable data: the reaction system is divided into 21 lumps; after completing a single calculation, the lumped data needs to be reintegrated and restored to the corresponding product data, and then compared with the field data.

Table 3 lists the estimation results of some model parameters of hydrocracking kinetics.

Table 3 Hydrocracking model parameter estimation results

The data in Table 3 show that the activation energies of cycloalkane ring opening, cycloalkane dehydrogenation and alkane cracking are relatively high, and the activation energy of aromatic hydrogenation saturation is the lowest, indicating that increasing the reaction temperature is beneficial to cycloalkane ring opening and alkane cracking, but not to aromatic hydrogenation saturation; from the perspective of the influence of pressure, increasing the operating hydrogen partial pressure is beneficial to aromatic hydrogenation saturation, while inhibiting its reverse reaction and helping to achieve a higher aromatic equilibrium conversion rate.

Step S3 uses the established model to predict the effects of different reaction conditions on the results of the catalytic hydrogenation conversion reaction.

Furthermore, there are many factors that affect the product distribution of the hydrocracking reaction, but not all reactions have a significant impact on the results, and in practice there are some factors such as aging of the device itself, sudden changes in the external environment, etc. that are uncontrollable factors. The impact of these factors on the product distribution is temporarily ignored. The present invention mainly selects several key operating parameters such as reaction temperature, hydrogen partial pressure, hydrogen-to-oil ratio, and air velocity to study the range of variation of these operating variables, and the dynamic relationship between the changing trend of gasoline yield and these operating variables, so as to select the best combination of operating conditions to achieve a 3% increase in gasoline yield.

Taking the reaction temperature as an example, the reaction rate increases with the increase of reaction temperature. Under normal circumstances, the reaction rate can be increased by about 10%-20% for every 10°C increase in temperature. However, in the reactor, hydrocarbons undergo hydrocracking and thermal cracking at the same time, and the thermal cracking reaction is usually more sensitive to temperature than the hydrocracking reaction. Based on the requirements for product distribution, the reaction temperature needs to be strictly controlled and should not be too high or too low. On the premise of meeting product quality, moderately lowering the cracking reaction temperature and increasing the cracking reaction pressure will help slow down the catalyst deactivation rate, extend the unit operation cycle, and play a theoretical guiding role in optimizing unit operation.

As for the hydrotreating reaction: with the increase of reaction temperature, the desulfurization rate and denitrification rate both increase significantly, the conversion of PA component of aromatic reaction first increases and then decreases, the conversion of DA component and MA component first gradually increases with the increase of reaction temperature and then remains basically unchanged; with the decrease of space velocity, the desulfurization rate and denitrification rate both increase, the content of PA component and DA component gradually decreases, and the content of MA component gradually increases.

Step S4 obtains the prediction result of the target product (gasoline).

Step S5 flexibly adjusts the process parameters according to the properties of different raw materials, product requirements, etc. According to the prediction results of the target product (gasoline) predicted by the model, the process conditions are optimized to increase the yield of the target product (gasoline) by 3%.

The specific process conditions are as follows: (1) Refining stage: average reaction temperature 375°C, hydrogen partial pressure 8.0 MPa, refining agent volume space velocity 1.3h ^-1 , hydrogen-to-oil ratio 800:1, lowering the initial reaction temperature, which is beneficial to extending the device operation cycle;

(2) Cracking stage: average reaction temperature 400°C, hydrogen partial pressure 8.5 MPa, cracking agent volume space velocity 1.5 h-1, hydrogen-to-oil ratio 800:1.

FIG9 shows the product distribution of the diesel hydroconversion unit after adjusting the above process conditions according to the prediction results, achieving an increase in the yield of the target product gasoline by 3%.

The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the present invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims (8)

1. A method for improving the yield of catalytic diesel hydrogenation conversion products, characterized in that it specifically comprises the following steps: (1) Establish simple analysis methods for raw materials and products to obtain detailed raw material composition and product composition data for diesel hydroprocessing; (2) merging and lumping the catalytic diesel hydroconversion reaction systems according to the principle of similar kinetic characteristics, establishing a mechanism model, and inputting the data obtained in step (1) into the established mechanism model; (3) Use the established model to predict the results of catalytic hydrogenation conversion reactions under different reaction conditions; (4) Obtaining the reaction law of the raw materials under the corresponding process conditions, and obtaining the yield and properties of the target product under different reaction conditions output by the model, where the target product is gasoline; (5) Based on the obtained prediction results, feedback is provided to optimize the original diesel catalytic hydroconversion process conditions, thereby increasing the yield of the target product by more than 3%, thereby improving production and economic benefits; Wherein, in the step (2), the mechanism model includes a diesel hydrorefining kinetic model and a diesel hydrocracking kinetic model, and the diesel hydrorefining kinetic model includes 9 lumps, namely, sulfur S, nitrogen N, normal alkanes P, isoalkanes I, cycloalkanes N, olefins O, monocyclic aromatic hydrocarbons MA, dicyclic aromatic hydrocarbons DA, and tricyclic aromatic hydrocarbons PA; the diesel hydrocracking kinetic model is divided into 21 lumps, and the hydrocarbons are divided into 7 layers at intervals of 27.8°C in the vertical direction, and three lumps, namely, alkanes, cycloalkanes and aromatics, are further divided in the horizontal direction, i.e., in each layer, for a total of 21 lumps; The diesel hydrofining kinetic model includes: Hydrodesulfurization HDS reaction rate equation: Hydrodenitrogenation HDN reaction rate equation: Aromatic saturation HDA reaction rate equation: The relationship between the reaction rate constant k and temperature satisfies the Arrhenius equation: Then the above model equation can be written as: HDS reaction rate equation: HDN reaction rate equation: HDA reaction rate equation: Wherein, k _i (i＝S, N, PA, DA, MA) are the forward and reverse reaction rate constants of the hydrogenation saturation reaction of HDS, HDN, tricyclic, bicyclic and monocyclic aromatic hydrocarbons, h ^-1 ; C _i (i＝S, N, PA, DA, MA) are the mass percentages of sulfide, nitride, tricyclic aromatic hydrocarbon, bicyclic aromatic hydrocarbon and monocyclic aromatic hydrocarbon in the raw materials of the reaction system, h -1 ; P _H2 is the hydrogen partial pressure, MPa; E _a,i (i＝S, N, PA, DA, MA) is the activation energy value of each corresponding reaction, kJ/mol; R is the molar gas constant, which is 8.3144 J/(molk); T is the reaction temperature, °C; α _i (i＝1,2,3,4) is the reaction order; β _i (i＝1,2,3,4) is the hydrogen partial pressure index; k _i,0 (i＝S, N, PA, DA, MA) is the pre-exponential factor of each corresponding reaction, h ^-1 ; k _PA1,0 ,k _PA2,0 ,k In _DA1,0 , _kDA2,0 , 1 refers to the forward reaction, 2 refers to the reverse reaction, ki,0 both represent the pre-exponential factor; V(H)/V(oil) is the hydrogen-to-oil ratio; γ is the hydrogen-to-oil ratio index; _rPA , _rDA and _rMA represent the hydrogenation saturation reaction rates of tricyclic, bicyclic and monocyclic aromatic hydrocarbons, respectively. 2. A method for improving the yield of catalytic diesel hydrogenation conversion products according to claim 1, characterized in that: in the step (1), the raw material is 100% catalytic cracking diesel, the products include stabilized gasoline, C6 fraction, refined diesel, and the data includes the content of normal alkanes, isoalkanes, cycloalkanes, olefins, aromatics and benzene in the raw materials and products. 3. A method for improving the yield of catalytic diesel hydrocracking products according to claim 1, characterized in that the diesel hydrocracking kinetic model comprises: For the same type of hydrocarbons, the rate constant and its boiling point conform to an exponential relationship; the reaction mechanism is the same, and their reaction activation energy is considered to be the same. Then the above model equation can be written as: Wherein: _Fai is the mass fraction of aromatic hydrocarbons lumped in layer i, that is, the number of cyclic carbon atoms lumped in layer i (i=1, 2, 3, 4, 5, 6, 7; the same below), _Fni is the mass fraction of cycloalkanes lumped in layer i, _Fpi is the mass fraction of alkanes lumped in layer i; _Faj is the mass fraction of aromatic hydrocarbons lumped in layer j, _Fnj is the mass fraction of cycloalkanes lumped in layer j, _Fpj is the number of carbon atoms of alkanes lumped in layer j; _kani is the rate constant for hydrogenation saturation of aromatic hydrocarbons in layer i, _kri is the rate constant for side chain removal of cyclic hydrocarbons in layer i, h ^{-1 ,} _knpi is the rate constant for ring-opening reaction of cycloalkanes in layer i, h ^-1 , _kpi is the rate constant for cracking reaction of alkanes in layer i, h ^-1 , _krj is the rate constant for side chain removal of cyclic hydrocarbons in layer j, h ^-1 , _kpj is the rate constant for cracking reaction of alkanes in layer j, h ^-1 ; P _rij is the distribution coefficient of the transformation of cyclic hydrocarbons in layer j to layer i, P _pij is the distribution coefficient of the transformation of alkanes in layer j to layer i; n is the total number of cuts, the heaviest fraction is numbered 1, the lightest is 1, and so on, t is the reaction time; k _an0 , k _r0 , k _np0 , k _p0 are pre-exponential factors, h ^-1 ; E _an , _Er , _Enp , E _p are the reaction activation energies, kJ/mol; PH is the hydrogen partial pressure, MPa, P ₀ is the reaction pressure constant, 1MPa; TBP _i is the i-layer lumped real boiling point, ℃, TBP _j is the j-layer lumped real boiling point, ℃; R is the molar gas constant, 8.3144J/(mol/k); T is the reaction temperature, ℃; a, b, c, d are hydrogen partial pressure indices, α, β, γ, δ are real boiling point indices. For the lightest 7th lump (equivalent to C4 ^- ), the yield distribution is as follows: The distribution coefficients _Ppij and _Prij are functions of the average boiling points of each narrow fraction. Considering that cyclic hydrocarbons cannot generate ^C4- , then: _Prnj = 0; For chain hydrocarbons: _Ppij = _{Prij -Pr(i+1),j} ; For cyclic hydrocarbons: P _rij =P' _rij -P'_r(i+1),j; Wherein: η is the yield; _B1 and _B2 are product distribution constants; _C1 is a parameter; rij is the reaction rate of the j-layer lumped to the i-layer lumped; _Ppnj is the distribution coefficient of the j-layer lumped alkane cracking, _Ppni is the distribution coefficient of the j-layer lumped alkane cracking, _Prnj is the distribution coefficient of the j-layer lumped cyclic hydrocarbons removing side chains, _dFai /dt represents the total hydrocracking rate of the i-layer lumped aromatics; _dFni /dt represents the i-layer lumped cycloalkane hydrocracking rate; _dFpi /dt represents the i-layer lumped alkane hydrocracking rate, and _P'rij is the distribution coefficient of the j-layer cyclic hydrocarbons converted to the i-layer. 4. A method for improving the yield of catalytic diesel hydroconversion products according to claim 1, characterized in that: in the step (1), the molecular composition of the raw material is determined by one or more of gas chromatography, gas chromatography-mass spectrometry, simulated distillation chromatography, nuclear magnetic resonance spectroscopy, Raman spectroscopy, sulfur and nitrogen fluorescence analyzer, and elemental analyzer. 5. A method for improving the yield of catalytic diesel hydroconversion products according to claim 1, characterized in that: in the step (3), the reaction conditions include reaction temperature, reaction pressure and space velocity. 6. A method for improving the yield of catalytic diesel hydroconversion products according to claim 1, characterized in that: in the step (3), the specific prediction method is as follows: the molecular composition of the raw material is input into the established mechanism model, and after preliminary verification of the reliability of the model, a series of simulation calculations and prediction calculations are performed on the model for different raw materials and different reaction conditions, and the influence of different operating variables on diesel hydrorefining and hydrocracking reactions is predicted. By setting the range of variation of these operating variables, the changing trend of the yield of the main product and the dynamic relationship between these operating variables are studied, so as to select the best combination of operating conditions, optimize the product distribution, and achieve a 3% increase in the yield of the main product, wherein the operating variables include reaction temperature, hydrogen partial pressure, hydrogen-to-oil ratio, and catalyst volume space velocity. 7. A method for improving the yield of catalytic diesel hydroconversion products according to claim 1, characterized in that in the step (5), the prediction result feedback is used to adjust and optimize the original diesel catalytic hydroconversion process conditions as follows: (1) Refining stage: average reaction temperature 356-381°C, hydrogen partial pressure 7.0-9.0 MPa, refining agent volume space velocity 1.3-15 h-1, hydrogen-to-oil ratio 800:1; (2) Cracking stage: average reaction temperature 389-409°C, hydrogen partial pressure 7.0-9.0 MPa, cracking agent volume space velocity 1.0-15 h-1, hydrogen-to-oil ratio 800:1. 8. A method for improving the yield of catalytic diesel hydroconversion products according to claim 1, characterized in that: the reaction law obtained in the step (4) is as follows: with the increase of reaction temperature, the desulfurization rate and the denitrification rate both increase significantly, the conversion of the tricyclic aromatic hydrocarbon PA component of the aromatic reaction first increases and then decreases, and the conversion of the dicyclic aromatic hydrocarbon DA component and the monocyclic aromatic hydrocarbon MA component first gradually increases with the increase of reaction temperature and then remains basically unchanged; with the decrease of space velocity, the desulfurization rate and the denitrification rate both increase, the content of the tricyclic aromatic hydrocarbon PA component and the dicyclic aromatic hydrocarbon DA gradually decreases, and the content of the monocyclic aromatic hydrocarbon MA component gradually increases.