阅读说明：本技术 一种提高催化柴油加氢转化高附加值产品收率的方法 (Method for improving yield of high value-added product in catalytic diesel oil hydroconversion ) 是由 周晓龙 范宜俊 郑倩倩 李桂军 熊鹰 李健 江洪波 刘庆 周智 孙磊 于 2021-09-24 设计创作，主要内容包括：本发明公开了一种提高催化柴油加氢转化高附加值产品收率的方法,所述方法包括以下步骤：建立原料、产品简易分析方法,获取柴油加氢转化详细的原料组成、产品组成数据；将催化柴油加氢转化反应体系按照动力学特性相似的原则进行归并划分集总,建立机理模型,将获得的数据输入已建立的机理模型中；运用所建模型对不同反应条件下催化加氢转化反应结果进行预测；得到原料在相应工艺条件下的反应规律,获取模型输出的在不同反应条件下的目标产品的收率和性质,目标产品即为汽油；根据所述获取的预测结果,反馈优化原有柴油催化加氢转化工艺条件,提高目标产品的收率达3％以上,提高生产、经济效益。(The invention discloses a method for improving the yield of high value-added products in catalytic diesel oil hydroconversion, which comprises the following steps: establishing a simple analysis method for raw materials and products, and acquiring detailed raw material composition and product composition data of diesel oil hydroconversion; merging and dividing catalytic diesel oil hydro-conversion reaction systems into blocks according to the principle of similar dynamic characteristics, establishing a mechanism model, and inputting obtained data into the established mechanism model; predicting the catalytic hydroconversion reaction result under different reaction conditions by using the established model; obtaining the reaction rule of the raw materials under corresponding process conditions, and obtaining the yield and the property of a target product output by the model under different reaction conditions, wherein the target product is gasoline; according to the obtained prediction result, the original diesel oil catalytic hydrogenation conversion process conditions are fed back and optimized, the yield of the target product is improved by more than 3%, and the production and economic benefits are improved.)

1. A method for improving the yield of high value-added products in catalytic diesel oil hydroconversion is characterized by comprising the following steps:

(1) establishing a simple analysis method for raw materials and products, and acquiring detailed raw material composition and product composition data of diesel oil hydroconversion;

(2) merging and dividing catalytic diesel oil hydro-conversion reaction systems into aggregates according to the principle of similar dynamic characteristics, establishing a mechanism model, and inputting the data obtained in the step (1) into the established mechanism model;

(3) predicting the catalytic hydroconversion reaction result under different reaction conditions by using the established model;

(4) obtaining the reaction rule of the raw materials under corresponding process conditions, and obtaining the yield and the property of a target product output by the model under different reaction conditions, wherein the target product is gasoline;

(5) according to the obtained prediction result, the original diesel oil catalytic hydrogenation conversion process conditions are fed back and optimized, the yield of the target product is improved by more than 3%, and the production and economic benefits are improved.

2. The method for improving the yield of high value-added products of catalytic diesel oil hydroconversion according to claim 1, characterized in that: in the step (1), the raw material is 100% catalytic cracking diesel oil, the product comprises stable gasoline, C6 fraction and refined diesel oil, and the data comprises the content of normal paraffin, isoparaffin, cycloparaffin, olefin, aromatic hydrocarbon and benzene in the raw material and the product.

3. The method for improving the yield of high value-added products of catalytic diesel oil hydroconversion according to claim 1, characterized in that: in the step (2), the mechanism model comprises a diesel hydrofining kinetic model and a diesel hydrocracking kinetic model, wherein the diesel hydrofining kinetic model comprises 9 lumped elements of sulfur S, nitrogen N, normal paraffin P, isoparaffin I, cycloparaffin N, olefin O, monocyclic aromatic MA, bicyclic aromatic DA and tricyclic aromatic PA; the diesel hydrocracking kinetic model was divided into 21 lumped hydrocarbons in the longitudinal direction at intervals of 27.8 ℃ per boiling range into 7 layers, and in the transverse direction, i.e. within each layer, three lumped hydrocarbons, naphthenes and aromatics were further divided into 21 lumped hydrocarbons.

4. The method for improving the yield of high value-added products of catalytic diesel hydro-conversion according to claim 3, wherein the diesel hydro-refining kinetic model comprises:

HDS reaction rate equation for hydrodesulfurization:

the reaction rate equation of hydrodenitrogenation HDN is as follows:

aromatic saturated HDA reaction rate equation:

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

the above model equation can be written as:

HDS reaction rate equation:

HDN reaction rate equation:

HDA reaction rate equation:

wherein k is_i(i ═ S, N, PA, DA, MA) are the forward and reverse reaction rate constants for the hydrogenation saturation of HDS, HDN, tricyclic, bicyclic and monocyclic aromatics, respectively, h^-1；C_i(i ═ S, N, PA, DA, MA) are mass percentages of sulfide, nitride, tricyclic aromatic hydrocarbon, bicyclic aromatic hydrocarbon, monocyclic aromatic hydrocarbon in the raw materials of the reaction system, respectively; p_H2Hydrogen partial pressure, MPa; e_a,i(i ═ S, N, PA, DA, MA) for the corresponding respective values of the activation energy of the reaction, kJ/mol; r is a mole gas constant and takes the value of 8.3144J/(molk); t is the reaction temperature, DEG C; alpha is alpha_i(i ═ 1,2,3,4) is the reaction order; beta is a_i(i ═ 1,2,3,4) is hydrogen partial pressure index; k is a radical of_i,0(i ═ S, N, PA, DA, MA) for each respective reaction index, h^-1；；k_PA1,0,k_PA2,0,k_DA1,0,k_DA2,0In the formula, 1 is a forward reaction, 2 is a reverse reaction, and ki and 0 both represent pre-exponential factors; v (H)/V (oil) is the hydrogen-oil ratio; gamma is hydrogen-oil ratio index.

5. The method for improving yield of high added value products of catalytic diesel hydroconversion according to claim 3, wherein the diesel hydrocracking kinetic model comprises:

for the same class of hydrocarbons, the rate constant is exponential with its boiling point; the reaction mechanism is the same, and it is considered that their reaction activation energies are the same.

The above model equation can be written as:

wherein: f_aiThe mass fraction of aromatic hydrocarbons lumped in i layers, i.e. the number of carbon atoms in the lumped ring in i layers (i ═ 1,2,3,4,5,6, 7; the same applies hereinafter), F_niMass fraction of naphthenes lumped for i layers, F_piMass fraction of alkane lumped for i layers; f_ajMass fraction of aromatic hydrocarbons lumped for j layers, F_njMass fraction of naphthenes lumped for j layers, F_pjNumber of alkane carbon atoms lumped for j layers; k is a radical of_aniIs the i-layer aromatic hydrocarbon hydrogenation saturation reaction speed constant, h^-1，k_riIs the reaction rate constant of the cyclic hydrocarbon side chain removal in the i layer, h^-1，k_npiIs the i-layer naphthene ring-opening reaction rate constant, h^-1，k_piIs the i-layer alkane cracking reaction rate constant, h^-1，k_rjIs the reaction rate constant of j-layer cyclic hydrocarbon side chain removal, h^-1，k_pjIs the reaction rate constant of j layers of alkane cracking, h^-1；P_rijDistribution coefficient, P, for conversion of j layers of cyclic hydrocarbons to i layers_pijThe distribution coefficient of j layers of alkane to i layers; n is the total number of cut sets, the heaviest lumped number of the fraction is 1, the lightest lumped number is 1, the fractions are arranged in sequence, and t is the reaction time; k is a radical of_an0、k_r0、k_np0、k_p0Is a pre-exponential factor, h^-1；E_an、E_r、E_np、E_pRespectively is reaction activation energy, kJ/mol; PH is hydrogen partial pressure, MPa, P₀1MPa as reaction pressure constant; TBP_iIs the lumped solid boiling point, DEG C, TBP of the i layer_jJ layers of lumped solid boiling point, DEG C; r is a molar gas constant and takes the value of 8.3144J/(mol/k); t is the reaction temperature, DEG C; a. b, c and d are hydrogen partial pressure indexes, and alpha, beta, gamma and delta are real boiling point indexes.

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

distribution coefficient P_pijAnd P_rijIs a function of the average boiling point of the narrow cuts, taking into account that C4 cannot be formed from cyclic hydrocarbons^-And 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；

wherein: eta is the yield; b is₁、B₂Is the product distribution constant; c₁Is a parameter; rij is the reaction rate of the j-layer lump transfer to the i-layer lump; p_pnjPartition coefficient, P, for cracking of j layers of lumped paraffins_rnjPartition coefficient for removing side chains for j layers of lumped cyclic hydrocarbons, dF_aiThe i-layer lumped aromatic overall hydrocracking rate is represented by/dt; dF_niThe i-layer lumped naphthene hydrocracking rate is represented by/dt; dF_piThe i-layer lumped alkane hydrocracking rate is represented by/dt.

6. The method for improving the yield of high value-added products of catalytic diesel oil hydroconversion according to claim 1, characterized in that: in the step (2), the molecular composition determination method of the raw material is one or more of a gas chromatography method, a gas chromatography-mass spectrometer method, a simulated distillation chromatography method, a nuclear magnetic resonance spectroscopy method, a Raman spectroscopy method, a sulfur nitrogen fluorescence analyzer method and an element analyzer method.

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

8. The method for improving the yield of high value-added products of catalytic diesel oil hydroconversion according to claim 1, characterized in that: in the step (3), the prediction method comprises the following specific steps of inputting the molecular composition of the raw materials into an established mechanism model, primarily verifying the reliability of the model, then performing simulation calculation and prediction calculation on a series of different raw materials and different reaction conditions on the model, predicting the influence of diesel hydrorefining and hydrocracking reactions under different operation variables, and researching the change trend of the yield of main products and the dynamic relation of the operation variables by setting the change range of the operation variables, so that the optimal operation condition combination is selected, the product distribution is optimized, and the yield of the main products is improved by 3%, wherein the operation variables comprise reaction temperature, hydrogen partial pressure, hydrogen-oil ratio and catalyst volume airspeed.

9. The method for improving the yield of high value-added products in catalytic diesel hydroconversion according to claim 1, wherein in the step (3), the prediction result is fed back, adjusted and optimized under the following conditions in the original diesel catalytic hydroconversion process:

(1) a refining section: the average reaction temperature is 356-381 ℃, the hydrogen partial pressure is 7.0-9.0 MPa, the volume space velocity of the refining agent is 1.3-15 h < -1 >, and the hydrogen-oil ratio is 800: 1;

(2) a cracking section: the average reaction temperature is 389-409 ℃, the hydrogen partial pressure is 7.0-9.0 MPa, the volume space velocity of the cracking agent is 1.0-15 h < -1 >, and the hydrogen-oil ratio is 800: 1.

10. The method for improving the yield of high value-added products of catalytic diesel oil hydroconversion according to claim 3, characterized in that: the reaction rule obtained in the step (4) is as follows, along with the rise of the reaction temperature, the desulfurization rate and the denitrification rate are both obviously increased, the conversion rate of the PA component of the aromatic reaction is increased firstly and then decreased, and the conversion rates of the DA component and the MA component are gradually increased along with the rise of the reaction temperature and then basically kept unchanged; along with the reduction of the space velocity, the desulfurization rate and the denitrification rate are increased, the contents of the PA component and the DA component are gradually reduced, and the content of the MA component is gradually increased.

Technical Field

The invention relates to the technical field of diesel oil hydro-conversion reaction, in particular to a method for improving the yield of high value-added products in catalytic diesel oil hydro-conversion.

Background

The processing capacity of catalytic cracking (FCC) in China is about 40 percent of the primary processing capacity of crude oil, so that catalytic cracking diesel (catalytic diesel, LCO) accounts for a large amount in a diesel pool in China, reaches more than 30 percent, and becomes a main secondary processing diesel component. It features high content of arylhydrocarbon, sulfur and nitrogen impurities, high content of olefin, low cetane number and poor stability.

Furthermore, the national VI standard of the diesel oil for vehicles puts forward more strict requirements on the content of polycyclic aromatic hydrocarbon in the diesel oil for vehicles, and simultaneously, China unifies the standard of the diesel oil nationwide and cancels common diesel oil. In the face of the situation, for the oil refinery which adopts catalytic cracking as the main means for heavy oil conversion, the difficulty of comprehensively realizing the production of diesel oil for vehicles and reducing common diesel oil is great because the catalytic cracking diesel oil in the diesel oil pool has high occupation ratio. Therefore, how to reasonably and efficiently reduce low-value LCO, produce high-value products (yield-increasing gasoline and yield-increasing vehicle diesel) while realizing diesel quality upgrading, and reduce the diesel-gasoline ratio becomes a main research subject of various oil refining enterprises at present.

The main processes of catalytic hydrogenation can be divided into hydrofining and hydrocracking. The catalytic diesel oil has very bad properties, so the current treatment means is single, and in China, the means which can be relied on mainly comprises the combined processing of the catalytic diesel oil and hydrogenation technology, such as the hydrofining after mixing the catalytic diesel oil and the straight-run diesel oil, the hydrocracking after mixing the catalytic diesel oil and the straight-run wax oil and the conversion technology which is used for producing gasoline by independently cracking the catalytic diesel oil in recent years.

CN 104611050A discloses a catalytic cracking diesel oil conversion method. The method adopts a single-stage series process, the raw material directly flows into a hydrocracking reactor after reacting and flowing out through a hydrofining reactor, and the hydrogenation activity of a hydrocracking catalyst is in a decreasing trend. The method can ensure the hydrocracking effect of diesel oil to a certain extent, and can reduce the chemical hydrogen consumption, thereby improving the octane number and the liquid yield of naphtha. However, neglecting that the whole reaction period is long, the catalyst activity is high at the initial stage of the start-up of the device and is far higher than that at the later stage, and the device adjustment time cannot be shortened.

CN 104611029A discloses a catalytic cracking diesel oil hydro-conversion method, which is characterized in that a hydro-refining reactor and a hydro-cracking reactor are connected in series, and catalytic diesel oil and hydrogen gas are mixed and then enter the hydro-refining reactor and then enter the hydro-cracking reactor. Although the diesel oil component can be processed and catalyzed to produce high-octane gasoline through a certain catalyst grading action, the hydrogen consumption is higher, and the requirement on hydrogen resources of refineries is higher.

Therefore, in view of the deficiencies of the prior art, it is necessary to develop a method for adjusting process conditions based on kinetic model prediction to achieve the purpose of increasing the yield of gasoline components while reducing the consumption of reaction hydrogen, achieving a reduction in the adjustment time of the apparatus, and the like.

Disclosure of Invention

The invention aims to provide a method for improving the yield of high value-added products in catalytic diesel oil hydroconversion, which can be used for obtaining a dynamic simulation prediction result, systematically mastering the effects of operating conditions such as reaction temperature, hydrogen partial pressure and the like on an optimizing device, and not simply adjusting the width of product fractions by virtue of an empirical adjusting method to improve the required yield of a target product (gasoline).

In order to achieve the purpose, the invention provides the following technical scheme:

a method for improving the yield of high value-added products in catalytic diesel oil hydroconversion specifically comprises the following steps:

(1) establishing a simple analysis method for raw materials and products, and acquiring detailed raw material composition and product composition data of diesel oil hydroconversion; obtaining raw material product composition data, wherein the raw material product composition data is used for dividing a lump, and the model is established based on the divided lump;

(2) merging and dividing catalytic diesel oil hydroconversion reaction systems into aggregates according to the principle of similar dynamic characteristics, establishing a mechanism model, inputting the data obtained in the step (1) into the established mechanism model, wherein the aggregate model has the dynamic mechanism characteristics, so that the complex catalytic hydroconversion reaction system can be accurately simulated to a certain extent, and the reaction rule of the device raw materials under the corresponding process conditions is obtained.

(3) Predicting the catalytic hydro-conversion reaction result under different reaction conditions by using the established model;

(4) obtaining the reaction rule of the raw materials under corresponding process conditions, and obtaining the yield and the property of a target product output by the model under different reaction conditions, wherein the target product is gasoline;

(5) according to the obtained prediction result, the original diesel oil catalytic hydrogenation conversion process conditions are fed back and optimized, the yield of the target product is improved by more than 3%, and the production and economic benefits are improved.

Preferably, in the step (1), the raw material is 100% catalytic cracking diesel oil, the product comprises stable gasoline, C6 fraction and refined diesel oil, and the data comprises the normal paraffin, isoparaffin, cycloparaffin, olefin, aromatic hydrocarbon and benzene content in the raw material and the product.

Preferably, the catalytic diesel oil hydroconversion reaction system has complex reaction, the raw materials and the products have various compositions and are often complex mixtures, and meanwhile, each component oil can carry out more than one reaction, so that the catalytic diesel oil hydroconversion reaction system is merged into a plurality of virtual components according to the principle of similar dynamic characteristics, which is called as lump; in the kinetic study, each lump is considered as a virtual single component, a kinetic model established by taking each lump as an independent entity can approximately describe the reaction performance of the original system, and then a simplified lump component kinetic model is developed and established, and the molecular composition of the raw material in the step (1) is input into the established lump kinetic model.

Wherein the hydrocarbon composition of the diesel fraction is expressed in terms of group composition notation (PONA). The family composition expression method is to express the relative content of each family hydrocarbon in petroleum fraction by using the composition data, and adopts mass spectrum analysis to the catalytic diesel oil, and the family composition is expressed by alkane (normal alkane, isoalkane), cyclane (monocyclic, bicyclic and polycyclic alkane), aromatic hydrocarbon (monocyclic, bicyclic and polycyclic aromatic hydrocarbon) and non-hydrocarbon compound. The diesel oil hydrogenation conversion technology is actually formed by connecting two parts of hydrofining and hydrocracking in series, and is generally called as catalytic diesel oil hydrogenation: wherein, the hydrofining is to remove the heteroatoms such as sulfur, nitrogen and the like in the oil product by hydrogenation in the high-pressure hydrogen presence environment with a catalyst so as to improve the service performance of the oil product, and the main reactions are hydrodesulfurization, hydrodenitrogenation and aromatic saturation; hydrocracking is a conversion process in which hydrocarbon molecules and hydrogen react on the surface of a catalyst to produce smaller molecules under higher pressure, such as isomerization, cracking, hydrogenation and the like, can produce high-quality gasoline, diesel oil and the like, and has the characteristics of strong raw material applicability, flexible product scheme, high liquid product yield and good quality.

Preferably, in the step (2), the mechanism model comprises a diesel hydrofining kinetic model and a diesel hydrocracking kinetic model, and the diesel hydrofining kinetic model comprises 9 lumped elements of sulfur S, nitrogen N, normal paraffin P, isoparaffin I, cycloparaffin N, olefin O, monocyclic aromatic MA, bicyclic aromatic DA and tricyclic aromatic PA; the diesel hydrocracking kinetic model was divided into 21 lumped hydrocarbons in the longitudinal direction at intervals of 27.8 ℃ per boiling range into 7 layers, and in the transverse direction, i.e. within each layer, three lumped hydrocarbons, naphthenes and aromatics were further divided into 21 lumped hydrocarbons.

21 the lump is only a division method adopted in the cracking model, and the combination of two different dimensions in the transverse direction and the longitudinal direction is considered; cutting the raw oil into 7 layers at 27.8 ℃ according to the distillation range data of the raw oil in the longitudinal direction, namely, regarding the raw material and the product as a mixture consisting of a series of continuous compounds, cutting a narrow fraction from the raw material and the product according to the actual boiling point boiling range at every 27.8 ℃, and taking the average boiling point in each lump as the lump boiling point; the horizontal direction is divided into 3 lumped parts of alkane, cycloalkane and arene because the hydrocarbon composition in petroleum fraction is mainly the three. I.e. 3 lumped per layer, 21 lumped in 7 layers. Building a lumped model can simplify a complex reaction system, and describe the complex reaction system which can not be described into qualitative and quantitative research. The subsequent equation set-up is based on lumped.

Preferably, the diesel hydrofinishing kinetic model comprises:

HDS reaction rate equation for hydrodesulfurization:

the reaction rate equation of hydrodenitrogenation HDN is as follows:

aromatic saturated HDA reaction rate equation:

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

the above model equation can be written as:

HDS reaction rate equation:

HDN reaction rate equation:

HDA reaction rate equation:

wherein k is_i(i ═ S, N, PA, DA, MA) are the forward and reverse reaction rate constants for the hydrogenation saturation of HDS, HDN, tricyclic, bicyclic and monocyclic aromatics, respectively, h^-1；C_i(i＝S，N，PADA, MA) are respectively the mass percentage of sulfide, nitride, tricyclic aromatic hydrocarbon, bicyclic aromatic hydrocarbon and monocyclic aromatic hydrocarbon in the raw materials of the reaction system; p_H2Hydrogen partial pressure, MPa; ea, i (i ═ S, N, PA, DA, MA) are the corresponding values of the respective reactive energy, kJ/mol; r is a mole gas constant and takes the value of 8.3144J/(molk); t is the reaction temperature, DEG C; alpha is alpha_i(i ═ 1,2,3,4) is the reaction order; beta is a_i(i ═ 1,2,3,4) is hydrogen partial pressure index; k is a radical of_i,0(i ═ S, N, PA, DA, MA) for each respective reaction index, h^-1；k_PA1,0,k_PA2,0,k_DA1,0,k_DA2,0In the formula, 1 is a forward reaction, 2 is a reverse reaction, and ki and 0 both represent pre-exponential factors; v (H)/V (oil) is the hydrogen-oil ratio; gamma is hydrogen-oil ratio index.

Preferably, the diesel hydrocracking kinetics model comprises:

for the same class of hydrocarbons, the rate constant is exponential with its boiling point; the reaction mechanism is the same, and it is considered that their reaction activation energies are the same.

The above model equation can be written as:

wherein: f_aiThe mass fraction of i layers of lumped aromatic hydrocarbons, i.e. the number of i layers of lumped ring carbon atoms (i ═ 1,2,3,4,5,6, 7; the same applies below),_Fnimass fraction of naphthenes lumped for i layers, F_piMass fraction of alkane lumped for i layers; f_ajMass fraction of aromatic hydrocarbons lumped for j layers, F_njMass fraction of naphthenes lumped for j layers, F_pjNumber of alkane carbon atoms lumped for j layers; k is a radical of_aniIs the i-layer aromatic hydrocarbon hydrogenation saturation reaction speed constant, h^-1，k_riIs the reaction rate constant of the cyclic hydrocarbon side chain removal in the i layer, h^-1，k_npiIs the i-layer naphthene ring-opening reaction rate constant, h^-1，k_piIs the i-layer alkane cracking reaction rate constant, h^-1，k_rjIs the reaction rate constant of j-layer cyclic hydrocarbon side chain removal, h^-1，k_pjIs the reaction rate constant of j layers of alkane cracking, h^-1；P_rijDistribution coefficient, P, for conversion of j layers of cyclic hydrocarbons to i layers_pijThe distribution coefficient of j layers of alkane to i layers; n is the total number of cut sets, the heaviest lumped number of the fraction is 1, the lightest lumped number is 1, the fractions are arranged in sequence, and t is the reaction time; k is a radical of_an0、k_r0、k_np0、k_p0Is a pre-exponential factor, h^-1；E_an、E_r、E_np、E_pRespectively is reaction activation energy, kJ/mol; PH is hydrogen partial pressure, MPa, P₀1MPa as reaction pressure constant; TBP_iIs the lumped solid boiling point, DEG C, TBP of the i layer_jJ layers of lumped solid boiling point, DEG C; r is a molar gas constant and takes the value of 8.3144J/(mol/k); t is the reaction temperature, DEG C; a. b, c and d are hydrogen partial pressure indexes, and alpha, beta, gamma and delta are real boiling point indexes.

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

distribution coefficient P_pijAnd P_rijIs a function of the average boiling point of the narrow cuts, taking into account that C4 cannot be formed from cyclic hydrocarbons^-And 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；

wherein: eta is the yield; b is₁、B₂Is the product distribution constant; c₁Is a parameter; r is_ijThe response rate for transferring j layers of lumped to i layers of lumped; p_pnjPartition coefficient, P, for cracking of j layers of lumped paraffins_rnjThe partition coefficient for removing the side chains for the j layers of lumped cyclic hydrocarbons.

dF_aiThe/dt represents i-layer lumped aromatic hydrocarbon totalThe hydrocracking rate of (a); dF_niThe i-layer lumped naphthene hydrocracking rate is represented by/dt; dF_piThe i-layer lumped alkane hydrocracking rate is represented by/dt; p (paraffin, paraffin cracking reaction), n (naphthene), a (aromatic hydrocarbon), r (cyclic hydrocarbon including aromatic hydrocarbon and naphthene, etc.); an (aromatic saturation reaction), np (naphthenic ring opening reaction), r (cyclic hydrocarbon side chain removal reaction); and in the formula, the other r_ai，r_ni，r_piAnd the like indicates the corresponding hydrocarbon hydrocracking reaction rate, the hydrogenation saturation rate of the aromatic hydrocarbon in the i layer is equal to the hydrogenation saturation rate of the aromatic hydrocarbon in the i layer, the aromatic hydrocarbon in the i layer is removed from side chains, and the hydrocarbon in the j layer which is heavier than the i layer is transferred to the i layer.

The link between the above equations is as follows: the reaction of naphthenes and aromatics is composed of two main classes: heavier naphthenes and aromatics are converted to lighter naphthenes and aromatics due to side chain removal; aromatic saturation and naphthene ring opening; aromatic saturation is a reversible reaction, and aromatic hydrocarbons cannot be directly converted into alkanes; the aromatic saturation and naphthene ring opening reactions cannot change the lumped boiling range, i.e. the reactions only take place in the same layer; in the alkane cracking reaction, the change of the ith aggregate is the cracking of the ith aggregate, the ith aggregate ring opening of the cycloalkane in the same layer, and all the heavier alkane components are cracked to generate the result; for the side chain removing reaction of naphthene and aromatic hydrocarbon, the reaction rate constants of the side chain removing reaction of aromatic hydrocarbon and naphthene in the same layer are considered to be approximately the same because the reaction mechanisms are the same. Thus, the variation of naphthenes and aromatics due to side chain removal can be considered with total rings; the change of the total ring lumped sum is the comprehensive result of the transfer of the total ring lumped sum to the lighter ring lumped sum, the conversion of the ring open loop to the i-th layer alkane, and the transfer of all the heavier ring lumped sums to the i-th ring lumped sum; for the same class of hydrocarbons, the rate constant is exponential with its boiling point; for the same class of hydrocarbons, the reaction mechanism is the same and their activation energies are believed to be the same.

The pre-exponential factor k in the above equation₀The activation energy E, the reaction series alpha, the hydrogen partial pressure index beta and the like are named as kinetic parameters.

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

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

Preferably, in the step (3), the predicting method specifically includes inputting the molecular composition of the raw material into an established mechanism model, after the model is verified to be reliable primarily, performing a series of simulation calculation and prediction calculation of different raw materials and different reaction conditions on the model, predicting the influence of diesel hydrorefining and hydrocracking reactions under different operation variables, and researching the change trend of the main product yield and the dynamic relation of the operation variables by setting the change range of the operation variables, so that the optimal operation condition combination is selected, the product distribution is optimized, and the main product yield is improved by 3%, wherein the operation variables include reaction temperature, hydrogen partial pressure, hydrogen-oil ratio and catalyst volume space velocity.

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

(1) a refining section: the average reaction temperature is 356-381 ℃, the hydrogen partial pressure is 7.0-9.0 MPa, the volume space velocity of the refining agent is 1.3-15 h < -1 >, the hydrogen-oil ratio is 800:1, the initial reaction temperature is reduced, and the running period of the device is prolonged;

(2) a cracking section: the average reaction temperature is 389-409 ℃, the hydrogen partial pressure is 7.0-9.0 MPa, the volume space velocity of the cracking agent is 1.0-15 h < -1 >, and the hydrogen-oil ratio is 800:1, so that on the premise of meeting the product quality, the cracking reaction temperature is properly reduced, the cracking reaction pressure is increased, the catalyst inactivation rate is favorably slowed down, the running period of the device is favorably prolonged, and the theoretical guidance effect on the operation of the optimization device is realized.

Preferably, the reaction rule in step (4) is as follows, with the rise of the reaction temperature, the desulfurization rate and the denitrification rate both obviously rise, the conversion rate of the PA component of the aromatic hydrocarbon reaction first rises and then falls, and the conversion rates of the DA component and the MA component gradually increase with the rise of the reaction temperature and then basically keep unchanged; along with the reduction of the space velocity, the desulfurization rate and the denitrification rate are increased, the contents of the PA component and the DA component are gradually reduced, and the content of the MA component is gradually increased.

Compared with the prior art, the invention has the beneficial effects that:

(1) the yield of a target product (gasoline) for finally catalyzing the diesel oil hydrogenation conversion reaction can reach 48-53 percent, the gasoline yield is improved by 3 percent on the existing basis, and the production and economic benefits are improved;

(2) the lumped model has the mechanism characteristics on dynamics, so that a complex catalytic hydro-conversion reaction system can be accurately simulated to a certain extent, and the reaction rule of the device raw material under the corresponding process condition is obtained;

(3) according to the obtained prediction result, the original diesel oil catalytic hydrogenation conversion process conditions can be fed back and optimized, and the yield of the target product (gasoline) is improved.

Drawings

FIG. 1 is a flow chart of the steps of a method for increasing the yield of high value-added products from catalytic diesel hydroconversion according to an embodiment of the present invention;

FIG. 2 is a flow chart of information during model establishment and verification in solving of a method for improving yield of catalytic diesel hydro-conversion high value-added products according to an embodiment of the invention;

FIG. 3 is a block diagram of a parameter solution of a hydrofining reaction kinetics model of a method for improving yield of high value-added products of catalytic diesel hydroconversion according to an embodiment of the present invention;

FIG. 4 is a comparison of actual values of a diesel hydrodesulfurization reaction with model fitting values for a method of increasing yield of high value-added products from catalytic diesel hydroconversion according to an embodiment of the present invention;

FIG. 5 is a comparison of the actual values of the diesel hydrodenitrogenation reaction and the model fitting values in a method for improving the yield of high value-added products from catalytic diesel hydroconversion according to an embodiment of the present invention;

FIG. 6(a) is a comparison of an actual value of a tricyclic aromatic hydrocarbon hydrogenation saturation reaction and a model fitting value in a method for improving yield of high added-value products in catalytic diesel hydro-conversion according to an embodiment of the present invention;

fig. 6(b) is a comparison between an actual value and a model fitting value of a bicyclic aromatic hydrocarbon hydrogenation saturation reaction of a method for improving yield of high value-added products of catalytic diesel oil hydroconversion according to an embodiment of the present invention;

fig. 6(c) is a comparison between an actual value of a monocyclic aromatic hydrocarbon hydrogenation saturation reaction and a model fitting value in a method for improving yield of high value-added products in catalytic diesel oil hydroconversion according to an embodiment of the present invention;

FIG. 7 is a schematic diagram of a lumped kinetic reaction network for diesel hydrocracking according to a method for improving yield of high value-added products by catalytic diesel hydroconversion provided by an embodiment of the invention;

fig. 8 shows the change of the operating conditions of the diesel oil hydroconversion unit in a period of time according to the method for improving the yield of high value-added products by catalytic diesel oil hydroconversion provided by the embodiment of the invention.

FIG. 9 shows the product distribution results of diesel hydroconversion by the method for improving the yield of high value-added products from catalytic diesel hydroconversion according to the embodiment of the invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

As shown in fig. 1, a method for increasing the yield of high value-added products from catalytic diesel hydroconversion comprises the following steps:

step S1, the detailed molecular composition of the feedstock is determined. In this embodiment, the detailed molecular composition of the raw material may be determined by one or more methods including gas chromatography, gas chromatography-mass spectrometry, simulated distillation chromatography, nuclear magnetic resonance spectroscopy, raman spectroscopy, sulfur nitrogen fluorescence analyzer, and elemental analyzer.

And step S2, establishing a mechanism model. Inputting the raw material composition into an established dynamic mechanism model, wherein an information flow chart during model establishment and verification in solving is shown in figure 2.

And step S3, predicting the influence of different reaction conditions on the catalytic hydro-conversion reaction result by using the established model.

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

And step S5, flexibly adjusting process parameters according to the properties of different raw materials, product requirements and the like. According to the prediction result of the target product (gasoline) predicted by the model, the process condition is optimized, and the yield of the target product (gasoline) is improved by 3%.

The catalytic diesel oil hydro-conversion reaction system has complex reaction, the raw materials and the products have various compositions and are often complex mixtures, and meanwhile, each component oil can carry out more than one reaction, so the catalytic diesel oil hydro-conversion reaction system is merged into a plurality of virtual components according to the principle of similar dynamic characteristics, which is called as lump; in the kinetic study, each lump is considered as a virtual single component, a kinetic model established by taking each lump as an independent entity can approximately describe the reaction performance of the original system, and then a simplified lump component kinetic model is developed and established, and the molecular composition of the raw material in the step (1) is input into the established lump kinetic model.

In the step (1), the structures and detailed compositions of the raw materials and the products can be determined by gas chromatography, gas chromatography-mass spectrometer method and nuclear magnetic resonance spectroscopy; measuring the distillation range of the raw materials and the products by adopting a simulated distillation chromatograph method; and determining the sulfur content, the nitrogen content and the like of the raw materials and the products by adopting a sulfur-nitrogen fluorescence analyzer method and an element analyzer method.

FIG. 2 is a flow chart of information during model establishment and verification in solving of the method for improving yield of high value-added products in catalytic diesel oil hydroconversion provided by the embodiment of the invention. For a hydro-conversion reaction model, the modeling is mainly aimed at performing mathematical description of a hydro-conversion reaction process, after the model is established, actual operation data of a device is input into the model to obtain model parameters, the step is called parameter regression, after the parameters are obtained, the model is used for prediction, meanwhile, the prediction result of the model is compared with industrial actual data to verify the reliability of the model and correct the model parameters, and after a satisfactory effect is obtained, the model can be used in a reasonable confidence interval to predict and adjust the operation conditions, so that the yield of a steam target product can be improved by 3%.

The mechanism model of step S2 may include: a hydrofining model and a hydrocracking model. Further, the refining model is divided into 9 lumped parts including sulfur (S), nitrogen (N), normal alkane (P), isoparaffin (I), cycloparaffin (N), olefin (O), monocyclic aromatic hydrocarbon (MA), bicyclic aromatic hydrocarbon (DA) and tricyclic aromatic hydrocarbon (PA) according to the principle of similar kinetic properties; the cracking model was divided into 21 lumped blocks according to the cut-off of the real boiling point data of the petroleum fraction into finite numbers. Dividing the hydrocarbons into 7 layers at intervals of 27.8 ℃ per boiling range in the longitudinal direction; in the lateral direction, i.e. in each layer, the alkane (including the side chains on the ring), cycloalkane and arene (all minus the side chains) are subdivided into three lumped elements, 21 lumped elements.

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

The hydrofinishing kinetics model can be established based on the following assumptions:

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

(2) the rate expressions issued by all hydrogenation reactions in the LCO hydrogenation process all adopt power function forms;

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

(4) the hydrogenation saturation reaction of polycyclic aromatic hydrocarbon is carried out ring by ring, the hydrogenation difficulty is increased along with the reaction, and the naphthene dehydrogenation reaction rate is far less than the hydrogenation saturation rate, so that the hydrogenation saturation of polycyclic aromatic hydrocarbon and bicyclic aromatic hydrocarbon is assumed to be a reversible reaction, and the hydrogenation saturation of monocyclic aromatic hydrocarbon is assumed to be an irreversible reaction;

(5) the reactor is operated under adiabatic conditions, the internal flow being regarded as plug flow, without axial and radial diffusion;

the hydrocracking kinetics model can be established based on the following assumptions:

(1) the reaction of naphthenes and aromatics is composed of two main classes: heavier naphthenes and aromatics are converted to lighter naphthenes and aromatics due to side chain removal; aromatic saturation and naphthene ring opening;

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

(3) the aromatic saturation and naphthene ring opening reactions cannot change the lumped boiling range, i.e. the reactions only take place in the same layer;

(4) in the alkane cracking reaction, the change of the ith aggregate is the cracking of the ith aggregate, the ith aggregate ring opening of the cycloalkane in the same layer, and all the heavier alkane components are cracked to generate the result;

(5) for the side chain removing reaction of naphthene and aromatic hydrocarbon, the reaction rate constants of the side chain removing reaction of aromatic hydrocarbon and naphthene in the same layer are considered to be approximately the same because the reaction mechanisms are the same. Thus, the variation of naphthenes and aromatics due to side chain removal can be considered with total rings;

(6) the change of the total ring lumped sum is the comprehensive result of the transfer of the total ring lumped sum to the lighter ring lumped sum, the conversion of the ring open loop to the i-th layer alkane, and the transfer of all the heavier ring lumped sums to the i-th ring lumped sum;

(7) for the same class of hydrocarbons, the rate constant is exponential with its boiling point; the reaction mechanism is the same, and it is considered that their reaction activation energies are the same.

The hydrofining kinetic model can well predict actual industrial production based on power function type kinetics. Specifically, the hydrofinishing kinetic model includes:

the hydrofinishing kinetic model comprises:

hydrodesulfurization (HDS) reaction rate equation:

hydrodenitrogenation (HDS) reaction rate equation:

aromatic saturation (HDA) reaction rate equation:

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

the above model equation can be written as:

HDS reaction rate equation:

HDN reaction rate equation:

HDA reaction rate equation:

wherein k is_i(i ═ S, N, PA, DA, MA) are the forward and reverse reaction rate constants for the hydrogenation saturation of HDS, HDN, tricyclic, bicyclic and monocyclic aromatics, respectively, h^-1；C_i(i ═ S, N, PA, DA, MA) are mass percentages of sulfide, nitride, tricyclic aromatic hydrocarbon, bicyclic aromatic hydrocarbon, monocyclic aromatic hydrocarbon in the raw materials of the reaction system, respectively; p_H2Hydrogen partial pressure, MPa; e_a,i(i ═ S, N, PA, DA, MA) for the corresponding respective values of the activation energy of the reaction, kJ/mol; r is a mole gas constant and takes the value of 8.3144J/(molk); t is the reaction temperature, DEG C; alpha is alpha_i(i ═ 1,2,3,4) is the reaction order; beta is a_i(i ═ 1,2,3,4) is hydrogen partial pressure index; k is a radical of_i,0(i ═ S, N, PA, DA, MA) for each respective reaction index, h^-1；k_PA1,0,k_PA2,0,k_DA1,0,k_DA2,0In the formula, 1 is a forward reaction, 2 is a reverse reaction, and ki and 0 both represent pre-exponential factors; v (H)/V (oil) is the hydrogen-oil ratio; gamma is hydrogen-oil ratio index.

The composition of the raw material sampled and analyzed in step S1 is input into the model described above.

The calculation of the dynamic parameters in the model is based on a Python platform, a mathematical equation of the model is converted into a program language, a differential equation is solved by adopting a fourth-order Runge-Kutta method, a target function is optimized by adopting a variable scale method (BFGS), and the target function to be optimized is as follows:

wherein x is_icCalculated value of outlet concentration, x_irIs the actual value of the outlet concentration.

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

The composition of the feedstock analyzed by sampling is shown in table 1 and the kinetic parameters of the refining model section are shown in table 2.

TABLE 1 composition of blended raw materials PIONA

TABLE 2 fitting values of kinetic model parameters

And (6) carrying out model reliability analysis. The reliability of the model parameters can be analyzed from a comparison of the calculated values of the products found using the model parameters with actual values of the plant operation. The specific method comprises the following steps: after the actual operation data of several sets of devices are used, model parameters are obtained through parameter estimation, and the obtained model parameters are used for calculating the desulfurization rate, the denitrification rate and the PA, DA and MA aromatic hydrocarbon saturation results under corresponding conditions. The comparison results of the calculated value and the actual value are shown in fig. 4, fig. 5, fig. 6(a), fig. 6(b), and fig. 6(c), and the reliability of the model can be seen by analyzing the deviation.

Further, model verification calculations are performed.

Further, through discussion and analysis of the obtained model parameters, comparison and verification calculation of a calculated value and an actual value of the model, the result shows that the obtained model parameters conform to the rules, the average relative deviation of the calculated value and the actual value is mostly below 10%, the established hydrofining kinetic model is reliable, and 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: and establishing hydrocracking reaction kinetics, and determining parameters of the hydrocracking reaction kinetics.

The hydrocracking kinetics model comprises:

for the same class of hydrocarbons, the rate constant is exponential with its boiling point; the reaction mechanism is the same, and it is considered that their reaction activation energies are the same.

The above model equation can be written as:

wherein: f_aiThe mass fraction of i layers of lumped aromatic hydrocarbons, i.e. the number of i layers of lumped ring carbon atoms (i ═ 1,2,3,4,5,6, 7; the same applies below),_Fnimass fraction of naphthenes lumped for i layers, F_piMass fraction of alkane lumped for i layers; f_ajMass fraction of aromatic hydrocarbons lumped for j layers, F_njMass fraction of naphthenes lumped for j layers, F_pjNumber of alkane carbon atoms lumped for j layers; k is a radical of_aniIs the i-layer aromatic hydrocarbon hydrogenation saturation reaction speed constant, h^-1，k_riIs the reaction rate constant of the cyclic hydrocarbon side chain removal in the i layer, h^-1，k_npiIs the i-layer naphthene ring-opening reaction rate constant, h^-1，k_piIs the i-layer alkane cracking reaction rate constant, h^-1，k_rjIs the reaction rate constant of j-layer cyclic hydrocarbon side chain removal, h^-1，k_pjIs the reaction rate constant of j layers of alkane cracking, h^-1；P_rijDistribution coefficient, P, for conversion of j layers of cyclic hydrocarbons to i layers_pijThe distribution coefficient of j layers of alkane to i layers; n is the total number of cut sets, the heaviest lumped number of the fraction is 1, the lightest lumped number is 1, the fractions are arranged in sequence, and t is the reaction time; k is a radical of_an0、k_r0、k_np0、k_p0Is a pre-exponential factor, h^-1；E_an、E_r、E_np、E_pRespectively is reaction activation energy, kJ/mol; PH is hydrogen partial pressure, MPa, P₀1MPa as reaction pressure constant; TBP_iIs the lumped solid boiling point, DEG C, TBP of the i layer_jJ layers of lumped solid boiling point, DEG C; r is a molar gas constant and takes the value of 8.3144J/(mol/k); t is the reaction temperature, DEG C; a. b, c and d are hydrogen partial pressure indexes, and alpha, beta, gamma and delta are real boiling point indexes.

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

the partition coefficients Ppij and Prij are functions of the average boiling points of the respective narrow fractions, and considering that C4-cannot be formed by cyclic hydrocarbons:

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；

wherein: eta is the yield; b is₁、B₂Is the product distribution constant; c₁Is a parameter; rij is the reaction rate of the j-layer lump transfer to the i-layer lump; p_pnjPartition coefficient, P, for cracking of j layers of lumped paraffins_rnjThe partition coefficient for removing the side chains for the j layers of lumped cyclic hydrocarbons.

dF_aiThe i-layer lumped aromatic overall hydrocracking rate is represented by/dt; dF_niThe i-layer lumped naphthene hydrocracking rate is represented by/dt; dF_piThe i-layer lumped alkane hydrocracking rate is represented by/dt; p (paraffin, paraffin cracking reaction), n (naphthene), a (aromatic hydrocarbon), r (cyclic hydrocarbon including aromatic hydrocarbon and naphthene, etc.); an (aromatic saturation reaction), np (naphthenic ring opening reaction), r (cyclic hydrocarbon side chain removal reaction); and in the formula, the other r_ai，r_ni，r_piAnd the like indicate the corresponding hydrocarbon hydrocracking reaction rate.

Since the oil analysis Data of hydrocracking unit in a refinery is generally En-type distillation Data (ASTM D86 Data), and actually, the boiling point of the lightest component of the oil in the En-type distillation Data is 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, the oil analysis Data can only be used as an approximate standard. Therefore we need to convert the en-type distillation data of raw materials and products collected from the production site into real boiling point distillation data by the edmeister method in the simulation software Aspen Plus.

Prior to formal use of the kinetic model for calculations, it is necessary to convert the on-site collected feed and product data into usable data: dividing the reaction system into 21 lumped units; after completing a single calculation, the lumped data also needs to be reintegrated and restored into corresponding product data, and then compared with the field data.

Table 3 lists the results of the hydrocracking kinetics partial model parameter estimation.

TABLE 3 hydrocracking model parameter estimation results

The data in Table 3 show that the activation energy of naphthenic ring opening, naphthenic dehydrogenation and alkane cracking is higher, and the activation energy of aromatic hydrocarbon hydrogenation saturation is lowest, which indicates that increasing the reaction temperature is beneficial to naphthenic ring opening and alkane cracking but not beneficial to aromatic hydrocarbon hydrogenation saturation; from the influence of pressure, the partial pressure of the operating hydrogen is increased, which is beneficial to the hydrogenation saturation of the aromatic hydrocarbon and simultaneously inhibits the reverse reaction of the aromatic hydrocarbon, thereby being beneficial to achieving higher equilibrium conversion rate of the aromatic hydrocarbon.

And step S3, the influence of different reaction conditions on the catalytic hydroconversion reaction result is predicted by using the established model.

Furthermore, there are many factors affecting the product distribution of the hydrocracking reaction, but not all the reactions have a significant effect on the result, and in practice, some factors such as the aging of the apparatus itself, sudden changes of the external environment, etc. are uncontrollable factors, and the influence of the factors on the product distribution is temporarily ignored, and the invention mainly selects several key operating parameters such as: reaction temperature, hydrogen partial pressure, hydrogen-oil ratio and airspeed, the change ranges of the operation variables and the dynamic relation between the change trend of the gasoline yield and the operation variables are researched, so that the optimal operation condition combination is selected, and the gasoline yield is improved by 3%.

Taking the reaction temperature as an example, the reaction temperature is increased and the reaction rate is increased. Typically, the reaction rate can be increased by approximately 10% to 20% for every 10 ℃ increase in temperature. However, hydrocarbons are simultaneously hydrocracked and thermally cracked in the reactor, and thermal cracking reactions are generally more temperature sensitive than hydrocracking reactions. Based on the requirement on the product distribution, the reaction temperature needs to be strictly controlled, and is not suitable to be too high or too low. On the premise of meeting the product quality, the cracking reaction temperature is properly reduced, the cracking reaction pressure is improved, the catalyst deactivation rate is favorably slowed down, the running period of the device is favorably prolonged, and the theoretical guidance is played on the operation of the optimization device.

The hydrofining reaction: along with the rise of the reaction temperature, the desulfurization rate and the denitrification rate both obviously rise, the conversion rate of the PA component of the aromatic reaction first rises and then falls, and the conversion rates of the DA component and the MA component gradually increase along with the rise of the reaction temperature and then basically keep unchanged; along with the reduction of the space velocity, the desulfurization rate and the denitrification rate are increased, the contents of the PA component and the DA component are gradually reduced, and the content of the MA component is gradually increased.

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

And step S5, flexibly adjusting process parameters according to the properties of different raw materials, product requirements and the like. According to the prediction result of the target product (gasoline) predicted by the model, the process condition is optimized, and the yield of the target product (gasoline) is improved by 3%.

The specific process conditions are as follows: (1) a refining section: the average reaction temperature is 375 ℃, the hydrogen partial pressure is 8.0MPa, and the volume space velocity of the refining agent is 1.3h^-1The hydrogen-oil ratio is 800:1, the initial reaction temperature is reduced, and the running period of the device is prolonged;

(2) a cracking section: the average reaction temperature is 400 ℃, the hydrogen partial pressure is 8.5MPa, the volume space velocity of the cracking agent is 1.5h < -1 >, and the hydrogen-oil ratio is 800: 1.

Fig. 9 shows the product distribution of the diesel hydroconversion unit achieved after the above process conditions are adjusted according to the prediction results, which achieves an improvement in the yield of the target product gasoline by 3%.

The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the present invention as defined in the accompanying claims.