A method for increasing the yield of catalytic diesel hydroconversion products

Abstract

The application discloses a method for improving the yield of catalytic diesel oil hydrogenation conversion products, which comprises the following steps: establishing a raw material and product simple analysis method, obtaining detailed raw material composition and product composition data of diesel oil hydrogenation conversion, merging and dividing the catalytic diesel oil hydrogenation conversion reaction system according to the principle of similar kinetic characteristics, establishing a mechanism model, inputting the obtained data into the established mechanism model, using the established model to predict the catalytic hydrogenation conversion reaction results under different reaction conditions, obtaining the reaction rule of the raw material under corresponding process conditions, obtaining the yield and properties of the target product under different reaction conditions, and the target product is gasoline, according to the obtained prediction results, feeding back and optimizing the original diesel oil catalytic hydrogenation conversion process conditions, improving the yield of the target product by more than 3%, and improving the production and economic benefits.

Description

Technical Field

[0001] This invention relates to the field of diesel hydroconversion reaction technology, and specifically to a method for improving the yield of catalytic diesel hydroconversion products. Background Technology

[0002] Catalytic cracking (FCC) accounts for approximately 40% of crude oil primary processing capacity, resulting in a significant proportion of catalytic cracked diesel (LCO) in the diesel fuel pool, exceeding 30%, making it a major component of secondary processed diesel. Its characteristics include high aromatic content, high sulfur and nitrogen impurities, high olefin content, low cetane number, and poor oxidation stability.

[0003] Furthermore, stricter requirements have been imposed on the content of polycyclic aromatic hydrocarbons (PAHs) in automotive diesel fuel. Faced with this situation, refineries that use catalytic cracking as the primary means of lightening heavy oil production find it difficult to fully realize the production of automotive diesel fuel and reduce conventional diesel fuel production due to the high proportion of catalytic cracked diesel fuel in their diesel fuel pans. Therefore, how to rationally and efficiently reduce low-value LCOs, while simultaneously upgrading diesel quality and producing high-value products (increasing gasoline and automotive diesel production) and lowering the diesel-to-gasoline ratio, has become a major research topic for various refineries.

[0004] Catalytic hydrogenation can be mainly divided into hydrorefining and hydrocracking. Due to the extremely harsh properties of catalytic diesel, the current processing methods are relatively limited. The main approach is to combine catalytic diesel with hydrogenation technology, such as hydrorefining a mixture of catalytic diesel and straight-run diesel, hydrocracking a mixture of catalytic diesel and straight-run wax oil, and, in recent years, the conversion technology of cracking catalytic diesel alone to produce gasoline.

[0005] CN 104611050 A discloses a method for catalytic cracking diesel conversion. This method employs a single-stage series process, where the feedstock, after reacting in the hydrorefining reactor, flows directly into the hydrocracking reactor, resulting in a decreasing trend in the hydrocracking catalyst's hydrogenation activity. This method can, to some extent, ensure the effectiveness of diesel hydrocracking while reducing chemical hydrogen consumption, thereby improving the octane number and liquid yield of naphtha. However, it overlooks the relatively long reaction cycle and the high catalyst activity at the initial startup phase, which remains significantly higher than later, making it impossible to shorten the unit setup time.

[0006] CN 104611029 A discloses a method for hydroconversion of catalytic cracking diesel. This method involves connecting a hydrorefining reactor and a hydrocracking reactor in series. Catalytic diesel and hydrogen are mixed and first fed into the hydrorefining reactor, then into the hydrocracking reactor. While this method can process catalytic diesel components to produce high-octane gasoline through certain catalyst gradation, it consumes a large amount of hydrogen and places significant demands on the refinery's hydrogen resources.

[0007] Therefore, in view of the shortcomings of existing technologies, it is necessary to develop a method based on kinetic model prediction to adjust process conditions in order to improve the yield of gasoline components while reducing hydrogen consumption and shortening the unit set-up time. Summary of the Invention

[0008] The purpose of this invention is to provide a method for improving the yield of catalytic diesel hydroconversion products. By obtaining kinetic simulation prediction results, it systematically understands the effects of operating conditions such as reaction temperature and hydrogen partial pressure on the optimization of the device, rather than simply relying on empirical adjustment methods to adjust the product fraction width to increase the demand output of the target product (gasoline).

[0009] To achieve the above objectives, the present invention provides the following technical solution:

[0010] A method for improving the yield of catalytic diesel hydroconversion products specifically includes the following steps:

[0011] (1) Establish a simple analysis method for raw materials and products to obtain detailed data on the raw material composition and product composition of diesel hydroconversion; the purpose of obtaining raw material and product composition data is to divide the lumped data, and the model is based on the divided lumped data.

[0012] (2) The catalytic diesel hydroconversion reaction system is grouped and lumped according to the principle of similar kinetic characteristics, and a mechanism model is established. The data obtained in step (1) is input into the established mechanism model. The lumped model has kinetic mechanism characteristics and can accurately simulate complex catalytic hydroconversion reaction systems to a considerable extent, so as to obtain the reaction law of the raw materials of the device under the corresponding process conditions.

[0013] (3) The established model was used to predict the results of the catalytic hydrogenation conversion reaction under different reaction conditions;

[0014] (4) Obtain the reaction law of raw materials under the corresponding process conditions, and obtain the yield and properties of the target product under different reaction conditions output by the model. The target product is gasoline.

[0015] (5) Based on the obtained prediction results, the original diesel catalytic hydrogenation conversion process conditions are optimized to increase the yield of the target product by more than 3%, thereby improving production and economic benefits.

[0016] Preferably, in step (1), the raw material is 100% catalytic cracked diesel, and the products include stabilized gasoline, C6 fraction, refined diesel. The data includes the content of n-alkanes, isoalkanes, cycloalkanes, olefins, aromatics, and benzene in the raw materials and products.

[0017] Preferably, the catalytic diesel hydroconversion reaction system is complex, with a wide variety of raw materials and products, often forming a complex mixture. In addition, each component oil can undergo more than one reaction. Therefore, the catalytic diesel hydroconversion reaction system is grouped into several virtual components according to the principle of similar kinetic characteristics, which are called lumped components. In kinetic studies, each lumped component is considered as a virtual single component. The kinetic model established by treating each lumped component as an independent entity can approximately describe the reaction performance of the original system. Then, the simplified lumped component kinetic model is developed and established, and the molecular composition of the raw materials mentioned in (1) above is input into the established lumped kinetic model.

[0018] The hydrocarbon composition of diesel fractions is represented using the Portfolio Naming Method (PONA). PONA uses the relative content of various hydrocarbon groups in a petroleum fraction to represent the composition. For catalytic diesel, mass spectrometry analysis is used, and the PONA is represented by alkanes (n-alkanes, isoalkanes), cycloalkanes (monocyclic, bicyclic, and polycyclic alkanes), aromatics (monocyclic, bicyclic, and polycyclic aromatics), and non-hydrocarbon compounds. Diesel hydroconversion technology actually consists of two parts connected in series: hydrorefining and hydrocracking, collectively referred to as catalytic diesel hydrotreating. Hydrorefining, under high pressure and with a catalyst, removes sulfur, nitrogen, and other heteroatoms from the oil to improve its performance. The main reactions include hydrodesulfurization, hydrodenitrogenation, and aromatic saturation. Hydrocracking, under higher pressure, involves isomerization, cracking, and hydrogenation reactions of hydrocarbon molecules with hydrogen on the catalyst surface to produce smaller molecules. This process can produce high-quality gasoline and diesel, and features strong feedstock adaptability, flexible product formulations, and high-yield and high-quality liquid products.

[0019] Preferably, in step (2), the mechanistic model includes a diesel hydrorefining kinetic model and a diesel hydrocracking kinetic model. The diesel hydrorefining kinetic model includes nine lumps: sulfur (S), nitrogen (N), n-alkanes (P), isoalkanes (I), cycloalkanes (N), olefins (O), monocyclic aromatics (MA), bicyclic aromatics (DA), and tricyclic aromatics (PA). The diesel hydrocracking kinetic model is divided into 21 lumps. Vertically, hydrocarbons are divided into seven layers at intervals of 27.8°C per boiling range. Horizontally, each layer is further divided into three lumps: alkanes, cycloalkanes, and aromatics, for a total of 21 lumps.

[0020] The 21 lumped clusters are merely one method of partitioning used in cracking models, considering a combination of two different dimensions: vertical and horizontal. Vertically, the feedstock is divided into seven layers based on its boiling range at 27.8°C. This treats the feedstock and product as a continuous mixture of compounds, and each lumped cluster is divided into narrow fractions at 27.8°C based on its true boiling point. The median average boiling point of each cluster is used as the boiling point of the cluster. Horizontally, it is divided into three clusters: alkanes, cycloalkanes, and aromatics, because these are the main hydrocarbon categories in petroleum fractions. Each layer contains three clusters, totaling 21 clusters across seven layers. Establishing a lumped cluster model simplifies complex reaction systems, allowing for qualitative and quantitative analysis of complex and indescribable reaction systems. Subsequent equations are based on these lumped clusters.

[0021] Preferably, the diesel hydrorefining kinetic model includes:

[0022] Rate equation for hydrodesulfurization of HDS:

[0023]

[0024] Rate equation for the hydrogenation denitrification of HDN:

[0025]

[0026] Rate equation for saturated aromatic HDA reaction:

[0027]

[0028]

[0029]

[0030] The relationship between the reaction rate constant k and temperature satisfies the Arrhenius equation:

[0031]

[0032] The above model equation can then be written as:

[0033] HDS reaction rate equation:

[0034]

[0035] HDN reaction rate equation:

[0036]

[0037] HDA reaction rate equation:

[0038]

[0039]

[0040]

[0041] Where k i (i = S, N, PA, DA, MA) represent the forward and reverse reaction rate constants for the hydrogenation saturation reactions of HDS, HDN, tricyclic, bicyclic, and monocyclic aromatic hydrocarbons, respectively. -1 C i (i = S, N, PA, DA, MA) represent the mass percentages of sulfides, nitrides, tricyclic aromatics, bicyclic aromatics, and monocyclic aromatics in the raw materials of the reaction system, respectively; P H2 α is the partial pressure of hydrogen, MPa; Ea,i (i = S, N, PA, DA, MA) are the activation energies of the corresponding reactions, kJ / mol; R is the molar gas constant, taken as 8.3144 J / (molk); T is the reaction temperature, °C; α i (i = 1, 2, 3, 4) represents the reaction order; β i (i = 1, 2, 3, 4) represents the hydrogen partial pressure exponent; k i,0 (i = S, N, PA, DA, MA) are the pre-exponential factors for each corresponding reaction, h -1 ;k PA1,0 ,k PA2,0 ,k DA1,0 ,k DA2,0 In this context, 1 refers to the forward reaction, 2 to the reverse reaction, ki and 0 represent pre-exponential factors, V(H) / V(oil) is the hydrogen-to-oil ratio, and γ is the hydrogen-to-oil ratio index.

[0042] Preferably, the diesel hydrocracking kinetic model includes:

[0043]

[0044]

[0045]

[0046] For the same type of hydrocarbon, the rate constant follows an exponential relationship with its boiling point; since the reaction mechanisms are the same, their activation energies are considered to be the same.

[0047]

[0048]

[0049]

[0050]

[0051] The above model equation can then be written as:

[0052]

[0053]

[0054]

[0055] Wherein: F ai This represents the mass fraction of aromatic hydrocarbons in the i-th lumped layer, i.e., the number of carbon atoms in the i-th lumped ring (i = 1, 2, 3, 4, 5, 6, 7; the same applies below). Fni F represents the mass fraction of cycloalkanes in the i-th layer lumped together. pi F represents the mass fraction of alkane in the i-th layer lumped together; aj F represents the mass fraction of aromatics in the j-th layer lumped together. nj F represents the mass fraction of cycloalkanes in the j-th layer lumped together. pj The number of alkane carbon atoms in the j-th layer lumped together; k ani Let h be the rate constant for the hydrogenation saturation reaction of aromatics in layer i. -1 k ri h is the rate constant for the side-chain removal reaction of i-layer cyclic hydrocarbons. -1 k npi Let h be the rate constant for the ring-opening reaction of the i-layer cycloalkane. -1 k pi h is the rate constant for the cracking reaction of alkane layer i. -1 k rj h is the rate constant for the side-chain removal reaction of j-layered cyclic hydrocarbons. -1 k pj h is the rate constant for the cracking reaction of alkane layer j. -1 ;P rij P is the partition coefficient for the transformation of cyclic hydrocarbons from layer j to layer i. pij is the distribution coefficient for the conversion of alkanes from layer j to layer i; n is the total number of cleaved sets, with the heaviest set numbered 1, the lightest set numbered 1, and so on; t is the reaction time; k an0 k r0 k np0 k p0 h is the pre-exponential factor. -1 E an E r E np E p These represent the activation energy of the reaction, kJ / mol; pH is the partial pressure of hydrogen, MPa; P0 is the reaction pressure constant, 1 MPa; TBP i The lumped true boiling point of layer i, °C, TBP j denoted as j, the lumped true boiling point, in °C; R is the molar gas constant, with a value of 8.3144 J / (mol / K); T is the reaction temperature, in °C; a, b, c, and d are the hydrogen partial pressure indices, and α, β, γ, and δ are the true boiling point indices.

[0056] For the lightest episode 7 (equivalent to C4) - The yield distribution is as follows:

[0057]

[0058] Allocation coefficient P pij and P rij It is a function of the average boiling point of each narrow fraction, taking into account that cyclic hydrocarbons cannot form C4. - ,but:

[0059] P rnj =0;

[0060] For chain hydrocarbons:

[0061]

[0062] P pij =P rij -P r(i+1),j ;

[0063] For cyclic hydrocarbons:

[0064]

[0065] P rij =P' rij -P' r(i+1),j ;

[0066] Where: η is the yield; B1 and B2 are product distribution constants; C1 is the parameter; r ij P represents the reaction rate at which lumped-down layers j transition to lumped-down layers i; pnj P is the partition coefficient for lumped alkane cracking in layer j. rnj is the partition coefficient for the removal of side chains from the j-layer lumped cyclic hydrocarbons.

[0067] dF ai / dt represents the total hydrocracking rate of aromatics in the i-layer lumped array; dF ni / dt represents the hydrocracking rate of the i-layer lumped cycloalkane; dF pi / dt represents the hydrogenation and hydrocracking rate of alkane in layer i; p (alkanes, alkane cracking reaction), n (cycloalkanes), a (aromatics), r (cyclic hydrocarbons, including aromatics and cycloalkanes); an (aromatic saturation reaction), np (cycloalkanes ring-opening reaction), r (cyclic hydrocarbon side-chain removal reaction); and other values ​​of r in the formula... ai r ni r pi The values ​​represent the corresponding hydrocarbon hydrocracking reaction rates. The rate of hydrogenation saturation of aromatics in layer -i = hydrogenation saturation of aromatics in layer - side chain removal of aromatics in layer - transfer of hydrocarbons from layer j (which are heavier than layer i) to layer i.

[0068] The relationships between the above formulas are as follows: The reactions of cycloalkanes and aromatics consist of two main categories: due to side chain removal, heavier cycloalkanes and aromatics are transformed into lighter cycloalkanes and aromatics; aromatic saturation and cycloalkane ring opening; aromatic saturation is a reversible reaction, and aromatics cannot be directly converted into alkanes; the saturation of aromatics and the ring opening of cycloalkanes cannot change the boiling range of the ensemble, that is, the reaction only occurs in the same layer; in the alkane cracking reaction, the change in the total amount of the i-th ensemble is its own cracking, the ring opening of the i-th ensemble of cycloalkanes in the same layer, and the cracking of all alkane components heavier than it to generate it; for the side chain removal reactions of cycloalkanes and aromatics, since their reaction mechanisms are the same, it can be considered that the rate constants of the side chain removal reactions of aromatics and cycloalkanes in the same layer are approximately the same. Therefore, the changes in cycloalkanes and aromatics caused by the removal of side chains can be considered in terms of the total ring; the change in the i-th lumped ring of the total ring is the combined result of its own transfer to a lighter ring lumped ring, the conversion of cycloalkanes to the i-th layer of alkanes through ring opening, and the transfer of all heavier ring lumped rings to the i-th ring lumped ring; for the same class of hydrocarbons, the rate constant follows an exponential relationship with its boiling point; for the same class of hydrocarbons, the reaction mechanism is the same, and their reaction activation energies are considered to be the same.

[0069] The pre-exponential factor k0, activation energy E, reaction order α, and hydrogen partial pressure exponent β in the above equation are named kinetic parameters.

[0070] Preferably, in step (2), the method for determining the molecular composition of the raw materials is one or more of the following: gas chromatography, gas chromatography-mass spectrometry, simulated distillation chromatography, nuclear magnetic resonance spectroscopy, Raman spectroscopy, sulfur-nitrogen fluorescence analyzer, and elemental analyzer.

[0071] Preferably, in step (3), the reaction conditions include reaction temperature, reaction pressure, and space velocity.

[0072] Preferably, in step (3), the specific prediction method is as follows: the molecular composition of the raw material is input into the established mechanism model. After the model is initially verified to be reliable, a series of simulation calculations and prediction calculations are performed on the model under different raw materials and different reaction conditions. The influence of different operating variables on the hydrorefining and hydrocracking reaction of diesel is predicted. By setting the range of these operating variables, the 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. The operating variables include reaction temperature, hydrogen partial pressure, hydrogen-to-oil ratio, and catalyst volume hourly space velocity.

[0073] Preferably, in step (3), the prediction results are fed back to adjust and optimize the original diesel catalytic hydroconversion process conditions as follows:

[0074] (1) Refining section: average reaction temperature 356~381℃, hydrogen partial pressure 7.0~9.0MPa, volume hourly space velocity of refining agent 1.3~15h-1, hydrogen-to-oil ratio 800:1, lowering the initial reaction temperature is beneficial to extending the operating cycle of the unit;

[0075] (2) Cracking section: average reaction temperature 389~409℃, hydrogen partial pressure 7.0~9.0MPa, cracking agent volume hourly space velocity 1.0~15h-1, hydrogen-oil ratio 800:1. Under the premise of meeting product quality, moderately reducing the cracking reaction temperature and increasing the cracking reaction pressure is beneficial to slowing down the catalyst deactivation rate and extending the unit operation cycle, which plays a theoretical guiding role in optimizing unit operation.

[0076] Preferably, the reaction law in step (4) is as follows: as the reaction temperature increases, the desulfurization rate and denitrification rate both increase significantly. The conversion of PA component in the aromatic reaction first increases and then decreases. The conversion of DA component and MA component first increases gradually with the increase of reaction temperature and then remains basically unchanged. As the space velocity decreases, 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.

[0077] Compared with the prior art, the beneficial effects of the present invention are:

[0078] (1) The final yield of the target product (gasoline) of the catalytic diesel hydroconversion reaction can reach 48% to 53%, which is 3% higher than the existing yield, thus improving production and economic efficiency.

[0079] (2) Due to its kinetic mechanism characteristics, the lumped model can accurately simulate complex catalytic hydrogenation conversion reaction systems to a considerable extent and obtain the reaction law of the raw materials of the device under the corresponding process conditions.

[0080] (3) Based on the obtained prediction results, the original diesel catalytic hydroconversion process conditions can be optimized to improve the yield of the target product (gasoline). Attached Figure Description

[0081] Figure 1 This is a flowchart of the steps of a method for improving the yield of catalytic diesel hydroconversion products provided in the embodiments of the invention;

[0082] Figure 2 This is an information flow diagram during the model establishment and solution verification of a method for improving the yield of catalytic diesel hydroconversion products according to an embodiment of the present invention;

[0083] Figure 3 This is a block diagram for solving the hydrorefining reaction kinetics model parameters of a method for improving the yield of catalytic diesel hydroconversion products according to an embodiment of the present invention;

[0084] Figure 4 This is a comparison between the actual values ​​and model fitting values ​​of the diesel hydrodesulfurization reaction in a method for improving the yield of catalytic diesel hydroconversion products provided by an embodiment of the present invention.

[0085] Figure 5 This is a comparison between the actual values ​​and model fitting values ​​of the diesel hydrodenitrification reaction in a method for improving the yield of catalytic diesel hydroconversion products provided in an embodiment of the present invention.

[0086] Figure 6(a) is a comparison of the actual values ​​and model fitting values ​​of the tricyclic aromatic hydrocarbon hydrogenation saturation reaction of a method for improving the yield of catalytic diesel hydroconversion products according to an embodiment of the present invention.

[0087] Figure 6(b) is a comparison of the actual values ​​and model fitting values ​​of the bicyclic aromatic hydrocarbon hydrogenation saturation reaction of a method for improving the yield of catalytic diesel hydroconversion products according to an embodiment of the present invention.

[0088] Figure 6(c) is a comparison of the actual values ​​and model fitting values ​​of the monocyclic aromatic hydrocarbon hydrogenation saturation reaction of a method for improving the yield of catalytic diesel hydroconversion products according to an embodiment of the present invention.

[0089] Figure 7 This is a schematic diagram of the lumped kinetic reaction network of diesel hydrocracking, provided by an embodiment of the present invention, for a method to improve the yield of catalytic diesel hydrocracking products.

[0090] Figure 8 This describes the changes in operating conditions of a diesel hydroconversion unit over a period of time, according to an embodiment of the present invention, which is a method for improving the yield of catalytic diesel hydroconversion products.

[0091] Figure 9 The product distribution results of diesel hydroconversion are provided in an embodiment of the present invention as a method for improving the product yield of catalytic diesel hydroconversion. Detailed Implementation

[0092] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0093] like Figure 1 As shown, a method for improving the yield of catalytic diesel hydroconversion products includes the following steps:

[0094] Step S1: Determine 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 the following methods: gas chromatography, gas chromatography-mass spectrometry, simulated distillation chromatography, nuclear magnetic resonance spectroscopy, Raman spectroscopy, sulfur-nitrogen fluorescence analysis, and elemental analysis.

[0095] Step S2, establish the mechanism model. Input the raw material composition into the established kinetic mechanism model, where the information flow diagram during model establishment and verification is as follows: Figure 2 As shown.

[0096] Step S3: Use the established model to predict the impact of different reaction conditions on the catalytic hydrogenation conversion reaction results.

[0097] Step S4: Obtain the prediction results for the target product (gasoline).

[0098] Step S5: Adjust process parameters flexibly according to the properties of different raw materials and product requirements. Based on the predicted results of the target product (gasoline) from the model, optimize process conditions to increase the yield of the target product (gasoline) by 3%.

[0099] The catalytic diesel hydroconversion reaction system described above is complex, with a wide variety of raw materials and products, often forming a complex mixture. Furthermore, each component oil can undergo more than one reaction. Therefore, the catalytic diesel hydroconversion reaction system is grouped into several virtual components based on the principle of similar kinetic characteristics, which are called lumped components. In kinetic studies, each lumped component is considered as a virtual single component. The kinetic model established by treating each lumped component as an independent entity can approximately describe the reaction performance of the original system. Then, the simplified lumped component kinetic model is developed and established, and the molecular composition of the raw materials described in step (1) above is input into the established lumped kinetic model.

[0100] In step (1) above, the structure and detailed composition of raw materials and products can be determined by methods including gas chromatography, gas chromatography-mass spectrometry, and nuclear magnetic resonance spectroscopy; the distillation range of raw materials and products can be determined by simulated distillation chromatography; and the sulfur and nitrogen content of raw materials and products can be determined by sulfur and nitrogen fluorescence analyzer and elemental analyzer.

[0101] Figure 2This is an information flow diagram illustrating the model establishment and verification process for a method to improve the yield of catalytic diesel hydroconversion products according to 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, actual operating data of the unit is input into the model to obtain model parameters; this step is called parameter regression. Once the parameters are obtained, the model's task is to make predictions. Simultaneously, the model's prediction results need to be compared with actual industrial data to verify the model's reliability and correct the model parameters. After achieving satisfactory results, the model can be used within a reasonable confidence interval to predict and adjust operating conditions, thereby increasing the yield of the target product by 3%.

[0102] The mechanistic model described in step S2 may include a hydrorefining model and a hydrocracking model. Further, based on the principle of similar kinetic characteristics, the refining model is divided into nine lumps: sulfur (S), nitrogen (N), n-alkanes (P), isoalkanes (I), cycloalkanes (N), alkenes (O), monocyclic aromatics (MA), bicyclic aromatics (DA), and tricyclic aromatics (PA). The cracking model is divided into 21 lumps based on the actual boiling point data of petroleum fractions. Vertically, hydrocarbons are divided into seven layers at intervals of 27.8°C per boiling range. Horizontally, within each layer, three lumps are further defined: alkanes (including side chains on the rings), cycloalkanes, and aromatics (excluding side chains), resulting in a total of 21 lumps.

[0103] In one embodiment, S2 may include: establishing the hydrorefining reaction kinetics and determining the hydrorefining reaction kinetic parameters.

[0104] The kinetic model for hydrorefining can be established based on the following assumptions:

[0105] (1) Based on the above analysis, the hydrogen consumed in the LCO hydrogenation process is mainly used for the three main reactions: HDS, HDN and HDA.

[0106] (2) The rate expressions for each hydrogenation reaction in the LCO hydrogenation process are all in power function form;

[0107] (3) The HDS and HDN reactions in LCO are both irreversible reactions;

[0108] (4) Since the hydrogenation saturation reaction of polycyclic aromatic hydrocarbons proceeds ring by ring, and the difficulty of hydrogenation increases ring by ring as the reaction proceeds, 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, while the hydrogenation saturation of monocyclic aromatic hydrocarbons is an irreversible reaction.

[0109] (5) The reactor operates under adiabatic conditions, and the internal flow is considered as plug flow with no axial or radial diffusion.

[0110] The establishment of a hydrocracking kinetic model can be based on the following assumptions:

[0111] (1) The reaction of cycloalkanes and aromatics consists of two main categories: heavier cycloalkanes and aromatics are converted into lighter cycloalkanes and aromatics due to the removal of side chains; aromatic saturation and cycloalkane ring opening;

[0112] (2) Aromatic saturation is a reversible reaction; aromatics cannot be directly converted into alkanes.

[0113] (3) The saturation of aromatic hydrocarbons and the ring-opening reaction of cycloalkanes cannot change the boiling range of the lumped layer, that is, the reaction only takes place in the same layer.

[0114] (4) In the alkane cracking reaction, the change in the total amount of the i-th set is the result of its own cracking, the total ring opening of the i-th set of cycloalkanes in the same layer, and the cracking of all alkane components heavier than it to generate it.

[0115] (5) For the side-chain removal reactions of cycloalkanes and aromatics, since their reaction mechanisms are the same, the rate constants of the side-chain removal reactions of aromatics and cycloalkanes in the same layer can be considered to be approximately the same. Therefore, the changes in cycloalkanes and aromatics caused by side-chain removal can be considered in terms of the total ring structure.

[0116] (6) The change of the i-th ring ensemble is the combined result of its own transfer to a lighter ring ensemble, the ring opening of cycloalkanes to the i-th layer of alkanes, and the transfer of all heavier ring ensembles to the i-th ring ensemble.

[0117] (7) For the same type of hydrocarbon, the rate constant and its boiling point follow an exponential relationship; the reaction mechanism is the same, so their reaction activation energy is considered to be the same.

[0118] The hydrorefining kinetic model, based on power-law kinetics, can predict actual industrial production well. Specifically, the hydrorefining kinetic model includes:

[0119] The hydrorefining kinetic model includes:

[0120] Hydrodesulfurization (HDS) reaction rate equation:

[0121]

[0122] Rate equation for hydrodenitrification (HDS) reaction:

[0123]

[0124] Rate equation for saturated aromatic hydrocarbon (HDA) reaction:

[0125]

[0126]

[0127]

[0128] The relationship between the reaction rate constant k and temperature satisfies the Arrhenius equation:

[0129]

[0130] The above model equation can then be written as:

[0131] HDS reaction rate equation:

[0132]

[0133] HDN reaction rate equation:

[0134]

[0135] HDA reaction rate equation:

[0136]

[0137]

[0138]

[0139] Where k i (i = S, N, PA, DA, MA) represent the forward and reverse reaction rate constants for the hydrogenation saturation reactions of HDS, HDN, tricyclic, bicyclic, and monocyclic aromatic hydrocarbons, respectively. -1 C i (i = S, N, PA, DA, MA) represent the mass percentages of sulfides, nitrides, tricyclic aromatics, bicyclic aromatics, and monocyclic aromatics in the raw materials of the reaction system, respectively; P H2 E is the partial pressure of hydrogen, MPa; a,i( i = S, N, PA, DA, MA) represents the activation energy of each reaction, kJ / mol; R is the molar gas constant, taken as 8.3144 J / (molk); T is the reaction temperature, °C; α i (i = 1, 2, 3, 4) represents the reaction order; β i (i = 1, 2, 3, 4) represents the hydrogen partial pressure exponent; k i,0 (i = S, N, PA, DA, MA) are the pre-exponential factors for each corresponding reaction, h -1 ;k PA1,0 ,k PA2,0 ,k DA1,0 ,k DA2,0 In this context, 1 refers to the forward reaction, 2 to the reverse reaction, ki and 0 represent pre-exponential factors, V(H) / V(oil) is the hydrogen-to-oil ratio, and γ is the hydrogen-to-oil ratio index.

[0140] The raw material composition determined by the sampling analysis in step S1 will now be input into the model described above.

[0141] The dynamic parameters in the model were calculated using the Python platform. The mathematical equations of the model were transformed into a programming language, and the differential equations were solved using the fourth-order Runge-Kutta method. The objective function was optimized using the variable metric method (BFGS). The objective function to be optimized is as follows:

[0142]

[0143] Where, x ic Calculated value of export concentration, x ir This represents the actual value of the export concentration.

[0144] The flowchart for solving the model parameters is as follows: Figure 3 As shown.

[0145] Table 1 lists the raw material composition of the sampled analysis, and Table 2 lists the kinetic parameters of the purification model.

[0146] Table 1. Composition of the mixed raw material PIONA

[0147]

[0148]

[0149] Table 2 Fitted values ​​of dynamic model parameters

[0150]

[0151] A model reliability analysis is conducted. The reliability of the model parameters can be analyzed by comparing the calculated values ​​of the products obtained using the model parameters with the actual values ​​from plant operation. Specifically, after obtaining the model parameters through parameter estimation using several sets of actual plant operating data, the obtained model parameters are then used to calculate the desulfurization rate, denitrification rate, and PA, DA, and MA aromatic saturation results under the corresponding conditions. The comparison results between the calculated and actual values ​​are shown in [the table / document / etc.]. Figure 4 , Figure 5 As shown in Figures 6(a), 6(b), and 6(c), the reliability of the model can be seen by analyzing the magnitude of the deviation.

[0152] Further, model validation calculations are performed.

[0153]

[0154] Furthermore, through discussion and analysis of the obtained model parameters, comparison of the calculated and actual values, and verification calculations, the results show that the obtained model parameters conform to the rules, and the average relative deviation between the calculated and actual values ​​is mostly below 10%. The established hydrorefining kinetic model is reliable, and the specific kinetic equations are as follows:

[0155] HDS reaction rate equation:

[0156]

[0157] HDN reaction rate equation:

[0158]

[0159] HDA reaction rate equation:

[0160]

[0161]

[0162]

[0163] In one embodiment, S2 may include: establishing hydrocracking reaction kinetics and determining hydrocracking reaction kinetic parameters.

[0164] The hydrocracking kinetic model includes:

[0165]

[0166]

[0167]

[0168] For the same type of hydrocarbon, the rate constant follows an exponential relationship with its boiling point; since the reaction mechanisms are the same, their activation energies are considered to be the same.

[0169]

[0170]

[0171]

[0172]

[0173] The above model equation can then be written as:

[0174]

[0175]

[0176]

[0177] Wherein: F ai This represents the mass fraction of aromatic hydrocarbons in the i-th lumped layer, i.e., the number of carbon atoms in the i-th lumped ring (i = 1, 2, 3, 4, 5, 6, 7; the same applies below). Fni F represents the mass fraction of cycloalkanes in the i-th layer lumped together. pi F represents the mass fraction of alkane in the i-th layer lumped together; aj F represents the mass fraction of aromatics in the j-th layer lumped together. nj F represents the mass fraction of cycloalkanes in the j-th layer lumped together. pj The number of alkane carbon atoms in the j-th layer lumped together; k ani Let h be the rate constant for the hydrogenation saturation reaction of aromatics in layer i. -1 k ri h is the rate constant for the side-chain removal reaction of i-layer cyclic hydrocarbons. -1 k npi Let h be the rate constant for the ring-opening reaction of the i-layer cycloalkane. -1 k pi h is the rate constant for the cracking reaction of alkane layer i. -1 k rj h is the rate constant for the side-chain removal reaction of j-layered cyclic hydrocarbons. -1 k pj h is the rate constant for the cracking reaction of alkane layer j. -1 ;P rij P is the partition coefficient for the transformation of cyclic hydrocarbons from layer j to layer i. pij is the distribution coefficient for the conversion of alkanes from layer j to layer i; n is the total number of cleaved sets, with the heaviest set numbered 1, the lightest set numbered 1, and so on; t is the reaction time; k an0 k r0 k np0 k p0 h is the pre-exponential factor. -1 E an E r E np E p These represent the activation energy of the reaction, kJ / mol; pH is the partial pressure of hydrogen, MPa; P0 is the reaction pressure constant, 1 MPa; TBP i The lumped true boiling point of layer i, °C, TBP j denoted as j, the lumped true boiling point, in °C; R is the molar gas constant, with a value of 8.3144 J / (mol / K); T is the reaction temperature, in °C; a, b, c, and d are the hydrogen partial pressure indices, and α, β, γ, and δ are the true boiling point indices.

[0178] For the lightest 7th set (equivalent to C4-), its yield distribution is as follows:

[0179]

[0180] The partition coefficients Ppij and Prij are functions of the average boiling point of each narrow fraction. Considering that cyclic hydrocarbons cannot form C4-, then:

[0181] P rnj =0;

[0182] For chain hydrocarbons:

[0183]

[0184] P pij =P rij -P r(i+1),j ;

[0185] For cyclic hydrocarbons:

[0186]

[0187] P rij =P' rij -P' r(i+1),j ;

[0188] Where: η is the yield; B1 and B2 are product distribution constants; C1 is a parameter; rij is the reaction rate of the lumped-off reaction from layer j to layer i; P pnj P is the partition coefficient for lumped alkane cracking in layer j. rnj is the partition coefficient for the removal of side chains from the j-layer lumped cyclic hydrocarbons.

[0189] dF ai / dt represents the total hydrocracking rate of aromatics in the i-layer lumped array; dF ni / dt represents the hydrocracking rate of the i-layer lumped cycloalkane; dF pi / dt represents the hydrogenation and hydrocracking rate of alkane in layer i; p (alkanes, alkane cracking reaction), n (cycloalkanes), a (aromatics), r (cyclic hydrocarbons, including aromatics and cycloalkanes); an (aromatic saturation reaction), np (cycloalkanes ring-opening reaction), r (cyclic hydrocarbon side-chain removal reaction); and other values ​​of r in the formula... ai r ni r pi The values ​​represent the corresponding hydrocarbon hydrocracking reaction rates.

[0190] Because refineries typically use Edmister distillation data (ASTM D86 Data) for analyzing oil products from hydrocracking units, and in reality, the boiling point of the lightest component in Edmister distillation data is usually lower than the initial boiling point of the blend, while the boiling point of the heaviest component is higher than the dry point of the blend, it can only serve as an approximate standard. Therefore, we need to convert the Edmister distillation data of feedstocks and products collected from the production site into true boiling point distillation data using the Edmister method in the simulation software Aspen Plus.

[0191] Before 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 re-integrated and restored to the corresponding product data, and then compared with the on-site data.

[0192] Table 3 lists the parameter estimation results for the partial model of hydrocracking kinetics.

[0193] Table 3. Parameter estimation results for the hydrocracking model.

[0194]

[0195] The data in Table 3 show that the activation energies for cycloalkane ring opening, cycloalkane dehydrogenation, and alkane cracking are relatively high, while the activation energy for aromatic hydrocarbon hydrogenation saturation is the lowest. This indicates that increasing the reaction temperature is beneficial for cycloalkane ring opening and alkane cracking, but not for aromatic hydrocarbon hydrogenation saturation. From the perspective of the effect of pressure, increasing the operating hydrogen partial pressure is beneficial for aromatic hydrocarbon hydrogenation saturation, while also inhibiting its reverse reaction, which is conducive to achieving a higher aromatic hydrocarbon equilibrium conversion rate.

[0196] Step S3 uses the established model to predict the effects of different reaction conditions on the catalytic hydrogenation conversion reaction results.

[0197] Furthermore, many factors influence the product distribution in hydrocracking reactions, but not all reactions have a significant impact on the results. In practice, some factors, such as the aging of the equipment itself and sudden changes in the external environment, are uncontrollable. The impact of these factors on product distribution is temporarily ignored. This invention mainly selects several key operating parameters, such as reaction temperature, hydrogen partial pressure, hydrogen-to-oil ratio, and space 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. In order to select the optimal combination of operating conditions, the gasoline yield can be increased by 3%.

[0198] Taking reaction temperature as an example, increasing the reaction temperature accelerates the reaction rate. Generally, for every 10°C increase in temperature, the reaction rate can increase by approximately 10%-20%. However, in reactors where hydrocarbons undergo both hydrocracking and thermal cracking simultaneously, thermal cracking is typically more temperature-sensitive than hydrocracking. Due to requirements for product distribution, the reaction temperature must be strictly controlled, avoiding both excessively high and low temperatures. While ensuring product quality, appropriately lowering the cracking reaction temperature and increasing the cracking reaction pressure can help slow down catalyst deactivation and extend the unit's operating cycle, providing theoretical guidance for optimizing unit operation.

[0199] Regarding the hydrorefining reaction: as the reaction temperature increases, the desulfurization rate and denitrification rate both increase significantly. The conversion of PA components in the aromatics reaction first increases and then decreases, while the conversion of DA and MA components first gradually increases with the increase of reaction temperature and then remains basically unchanged. As the space velocity decreases, the desulfurization rate and denitrification rate both increase, the content of PA and DA components gradually decreases, and the content of MA component gradually increases.

[0200] Step S4: Obtain the prediction results for the target product (gasoline).

[0201] Step S5 involves flexibly adjusting process parameters based on the properties of different raw materials and product requirements. Based on the predicted results of the target product (gasoline) from the model, the process conditions are optimized to increase the yield of the target product (gasoline) by 3%.

[0202] The specific process conditions are as follows: (1) Refining section: average reaction temperature 375℃, hydrogen partial pressure 8.0MPa, and volume hourly space velocity of the refining agent 1.3h. -1 A hydrogen-to-oil ratio of 800:1 lowers the initial reaction temperature and helps extend the operating cycle of the unit.

[0203] (2) Cracking section: average reaction temperature 400℃, hydrogen partial pressure 8.5MPa, cracking agent volume hourly space velocity 1.5h-1, hydrogen-to-oil ratio 800:1.

[0204] Figure 9 To improve the product distribution of the diesel hydrotreating unit after adjusting the above process conditions based on the prediction results, the yield of the target product, gasoline, was increased by 3%.

[0205] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the present invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

Claims

1. A method for improving the yield of products from catalytic diesel hydroconversion, characterized in that, Specifically, the steps include the following: (1) Establish simple analysis methods for raw materials and products to obtain detailed data on the raw material composition and product composition of diesel hydroconversion; (2) The catalytic diesel hydroconversion reaction system is grouped and aggregated according to the principle of similar kinetic characteristics, and a mechanism model is established. The data obtained in step (1) is input into the established mechanism model. (3) The established model was used to predict the results of catalytic hydrogenation conversion under different reaction conditions; (4) Obtain the reaction law of the raw materials under the corresponding process conditions, and obtain the yield and properties of the target product under different reaction conditions output by the model. The target product is gasoline. (5) Based on the obtained prediction results, the original diesel catalytic hydroconversion process conditions are optimized. In step (2), the mechanism model includes a diesel hydrorefining kinetic model and a diesel hydrocracking kinetic model. The diesel hydrorefining kinetic model includes nine lumps: sulfur (S), nitrogen (N), n-alkanes (P), isoalkanes (I), cycloalkanes (N), olefins (O), monocyclic aromatics (MA), bicyclic aromatics (DA), and tricyclic aromatics (PA). The diesel hydrocracking kinetic model is divided into 21 lumps. Vertically, hydrocarbons are divided into seven layers at intervals of 27.8°C per boiling range. Horizontally, each layer is further divided into three lumps: alkanes, cycloalkanes, and aromatics, for a total of 21 lumps. The diesel hydrorefining kinetic model includes: Rate equation for hydrodesulfurization of HDS: ； Rate equation for the hydrogenation denitrification of HDN: ； Rate equation for saturated aromatic HDA reaction: ； ； ； The relationship between the reaction rate constant k and temperature satisfies the Arrhenius equation: ； The above model equation can then be written as: HDS reaction rate equation: ； HDN reaction rate equation: ； HDA reaction rate equation: ； ； ； Where, k i (i = S, N, PA, DA, MA) represent the forward and reverse reaction rate constants for the hydrogenation saturation reactions of HDS, HDN, tricyclic, bicyclic, and monocyclic aromatic hydrocarbons, respectively, in h⁻¹. -1 C i (i = S, N, PA, DA, MA) represent the mass percentages of sulfides, nitrides, tricyclic aromatics, bicyclic aromatics, and monocyclic aromatics in the raw materials of the reaction system, respectively; P H2 E represents the partial pressure of hydrogen, measured in MPa. a,i (i = S, N, PA, DA, MA) represent the activation energies of the corresponding reactions, in kJ / mol; R is the molar gas constant, taken as 8.3144 J / (mol·K); T is the reaction temperature, in °C; α i (i=1, 2, 3, 4) represents the reaction order; β i (i=1, 2, 3, 4) represents the hydrogen partial pressure exponent; k i,0 (i = S, N, PA, DA, MA) represents the pre-exponential factor for each reaction, in h. -1 ;k PA1,0 ,k PA2,0 ,k DA1,0 ,k DA2,0 In this context, 1 refers to the forward reaction, 2 to the reverse reaction, ki and 0 represent pre-exponential factors; V(H) / V(oil) is the hydrogen-to-oil ratio; γ is the hydrogen-to-oil ratio exponent; r PA r DA r MA These represent the hydrogenation saturation reaction rates of tricyclic, bicyclic, and monocyclic aromatic hydrocarbons, respectively. In step (1), the raw material is 100% catalytic cracked diesel, and the products include stabilized gasoline, C6 fraction, and refined diesel. The data includes the content of n-alkanes, isoalkanes, cycloalkanes, olefins, aromatics, and benzene in the raw materials and products. In step (1), the method for determining the molecular composition of the raw materials is one or more of the following: gas chromatography, gas chromatography-mass spectrometry, simulated distillation chromatography, nuclear magnetic resonance spectroscopy, Raman spectroscopy, sulfur and nitrogen fluorescence analyzer, and elemental analyzer.

2. The method for improving the yield of catalytic diesel hydroconversion products according to claim 1, characterized in that, The diesel hydrocracking kinetic model includes: ； ； ； For the same type of hydrocarbon, the rate constant follows an exponential relationship with its boiling point; since the reaction mechanisms are the same, their activation energies are considered to be the same. ； ； ； ； The above model equation can then be written as: ； ； ； Wherein: F ai F represents the mass fraction of aromatic hydrocarbons in the i-th lumped layer, i.e., the number of carbon atoms in the i-th lumped ring (i=1, 2, 3, 4, 5, 6, 7). ni F represents the mass fraction of cycloalkanes in the i-th layer lumped together. pi F represents the mass fraction of alkane in the i-th layer lumped together; aj F represents the mass fraction of aromatics in the j-th layer lumped together. nj F represents the mass fraction of cycloalkanes in the j-th layer lumped together. pj The number of alkane carbon atoms in the j-th layer lumped together; k ani Let be the rate constant for the hydrogenation saturation reaction of aromatics in layer i, in h⁻¹. -1 k ri Let be the rate constant for the side-chain removal reaction of the i-layer cyclic hydrocarbon, in h. -1 k npi Let be the rate constant for the ring-opening reaction of the i-layer cycloalkane, in h⁻¹. -1 k pi Here is the rate constant for the cracking reaction of alkane layer i, in h. -1 k rj Let be the rate constant for the side-chain removal reaction of a j-layer cyclic hydrocarbon, in h. -1 k pj Let be the rate constant for the cracking reaction of layer j alkane, in h. -1 ;P rij P is the partition coefficient for the transformation of cyclic hydrocarbons from layer j to layer i. pij is the distribution coefficient for the conversion of alkanes from layer j to layer i; n is the total number of cleaved sets, with the heaviest set numbered 1, the lightest set numbered 1, and so on; t is the reaction time; k an0 k r0 k np0 k p0 Pre-exponential factor, unit is h -1 E an E r E np E p These represent the activation energy of the reaction, in kJ / mol; pH is the partial pressure of hydrogen, in MPa; P0 is the reaction pressure constant, 1 MPa; TBP i The lumped true boiling point of layer i is given in °C (TBP). j denoted as j, the lumped true boiling point is in °C; R is the molar gas constant, with a value of 8.3144 J / (mol·K); T is the reaction temperature in °C; a, b, c, and d are the hydrogen partial pressure exponents, and α, β, γ, and δ are the true boiling point exponents. For the lightest episode 7, it's equivalent to C4. - The yield distribution is as follows: ； Allocation coefficient P pij and P rij It is a function of the average boiling point of each narrow fraction, taking into account that cyclic hydrocarbons cannot form C4. - ,but: ； For chain hydrocarbons: ； ； For cyclic hydrocarbons: ； ； Where: η is the yield; B1 and B2 are product distribution constants; C1 is a parameter; r ij P represents the reaction rate at which lumped-down layers j transition to lumped-down layers i; pnj P is the partition coefficient for lumped alkane cracking in layer j. Pni P is the partition coefficient for lumped alkane cracking in layer j. rnj dF is the partition coefficient for the removal of side chains from the j-layer lumped cyclic hydrocarbons. ai / dt represents the total hydrocracking rate of aromatics in the i-layer lumped array; dF ni / dt represents the i-layer lumped cycloalkane hydrocracking rate; dF pi / dt represents the hydrocracking rate of lumped alkane in layer i, P ’ rij is the distribution coefficient for the transformation of cyclic hydrocarbons from layer j to layer i.

3. The method for improving the yield of catalytic diesel hydroconversion products according to claim 1, characterized in that: In step (3), the reaction conditions include reaction temperature, reaction pressure and space velocity.

4. The method for improving the yield of catalytic diesel hydroconversion products according to claim 1, characterized in that: In step (3), the specific prediction method is as follows: input the molecular composition of the raw material into the established mechanism model. After the model is initially verified to be reliable, perform a series of simulation calculations and prediction calculations on the model under different raw materials and different reaction conditions. Predict the influence of diesel hydrorefining and hydrocracking reaction under different operating variables. By setting the range of these operating variables, study the trend of the yield of the main product and the dynamic relationship between these operating variables, so as to select the best combination of operating conditions and optimize the product distribution. Among them, the operating variables include reaction temperature, hydrogen partial pressure, hydrogen-oil ratio and catalyst volume hourly space velocity.

5. The method for improving the yield of catalytic diesel hydroconversion products according to claim 1, characterized in that, In step (5), the prediction results are fed back to adjust and optimize the original diesel catalytic hydroconversion process conditions as follows: (1) Refining section: average reaction temperature 356-381 °C, hydrogen partial pressure 7.0-9.0 MPa, volume hourly space velocity of the refined product 1.3-15 h⁻¹ -1 The hydrogen-to-oil ratio is 800:

1. (2) Cracking section: average reaction temperature 389-409 °C, hydrogen partial pressure 7.0-9.0 MPa, cracking agent volume hourly space velocity 1.0-15 h⁻¹ -1 The hydrogen-to-oil ratio is 800:

1.

6. The method for improving the yield of catalytic diesel hydroconversion products according to claim 1, characterized in that: The reaction pattern in step (4) is as follows: as the reaction temperature increases, the desulfurization rate and denitrification rate both increase significantly. The conversion of the tricyclic aromatic hydrocarbon PA component in the aromatic hydrocarbon reaction first increases and then decreases. The conversion of the bicyclic aromatic hydrocarbon DA component and the monocyclic aromatic hydrocarbon MA component first increases gradually with the increase of the reaction temperature and then remains basically unchanged. As the space velocity decreases, the desulfurization rate and denitrification rate both increase. The content of the tricyclic aromatic hydrocarbon PA component and the bicyclic aromatic hydrocarbon DA component gradually decreases, while the content of the monocyclic aromatic hydrocarbon MA component gradually increases.

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