Screening method and device for selective hydrogenation alloy catalyst of reformate

Abstract

The invention provides a method and a device for screening an alloy catalyst for selective hydrogenation of reformate. The method comprises the following steps: acquiring reactant information, small molecule substance information capable of reflecting structural characteristics of reactants, all alloy catalyst systems to be screened, and a relationship between transition metal and hydrogen atom adsorption capacity; an alloy catalyst surface model is generated for each alloy catalyst system, the surface hydrogen atom coverage degree is predicted, a kinetic model is built in combination with small molecule substance information, and intrinsic reaction kinetic information of each alloy catalyst surface model is obtained; constructing a prediction model by taking the predicted surface hydrogen atom coverage and hydrogen atom adsorption energy as input data and the catalytic performance as output data; according to the prediction model, obtaining a catalytic performance prediction value of each alloy catalyst surface model; and screening the alloy catalyst according to the predicted value of the catalytic performance to obtain the selective hydrogenation alloy catalyst for the reformate. The method can realize rapid screening of the catalyst.

Description

Technical Field

[0001] The invention relates to the technical field of catalyst materials, and in particular to a screening method and device for a reforming oil selective hydrogenation alloy catalyst. Background Art

[0002] Aromatics (benzene, toluene, xylene) are important organic chemical raw materials second only to ethylene and propylene in terms of output and scale. They are widely used in the production of synthetic fibers, synthetic resins, synthetic rubbers and various fine chemicals. Catalytic reforming / aromatic extraction is one of the main processes for producing aromatics. However, in addition to being rich in aromatics and solvent oil, the catalytic reforming oil also contains a small amount of olefins (1% to 3%). To produce qualified aromatics and solvent oil, the olefins must be removed, otherwise the bromine value, corrosiveness and pickling color of the aromatic products will be unqualified; at the same time, the small amount of olefins in the reforming oil will polymerize in the extraction solvent and contaminate the extraction solvent and cause corrosion of the extraction system equipment, which will have varying degrees of impact on the performance of downstream equipment, adsorbents and catalysts.

[0003] One of the methods to remove olefins from reforming oil is to perform hydrorefining under the action of a hydrogenation catalyst to selectively hydrogenate olefins in saturated reforming oil. Currently, the widely used precious metal catalysts (Pd, Pt, etc.) have the disadvantages of high price, small reserves, and low selectivity, while some non-precious metal catalysts (such as Ni) have shown catalytic performance comparable to that of precious metal catalysts in certain reduction reactions. In addition, by doping active metals with other metal elements to prepare alloy catalysts, not only can the selectivity of the olefin selective hydrogenation reaction be improved while ensuring its activity as much as possible, but also the stability of the catalytic material can be enhanced.

[0004] However, there are many types of metals, and different alloy materials have different mechanisms of action in the reaction, so the traditional trial-and-error method of selecting relatively ideal catalysts through a large number of experiments is time-consuming and uneconomical. The latest research shows that the catalytic reaction mechanism can be studied based on theoretical calculations, and the catalytic performance can be evaluated, so as to predict the catalytic performance of unknown alloy materials. This type of method that uses theoretical calculations to effectively screen alloy catalytic materials can effectively reduce the catalyst development cycle.

[0005] In recent years, although many researchers have achieved certain results in the field of first-principles calculation screening of olefin selective hydrogenation alloy catalyst design, most of them are small molecule (C2-C4) selective hydrogenation reactions, and the calculation research on the selective hydrogenation catalyst of reforming oil components (C6 macromolecules) is very limited. This is because the calculations related to the selective hydrogenation of macromolecules have problems such as large calculation system and long calculation time. Summary of the invention

[0006] In view of the deficiencies of the prior art, the present invention provides a method and device for screening alloy catalysts for selective hydrogenation of reforming oil, which can realize rapid screening of catalysts.

[0007] In order to achieve the above object, the present invention provides a method for screening a reforming oil selective hydrogenation alloy catalyst, comprising:

[0008] Obtain information on reactants, small molecules that can reflect the structural characteristics of reactants, all alloy catalyst systems to be screened, and the relationship between the adsorption capacity of transition metals and hydrogen atoms;

[0009] According to the relationship between the adsorption capacity of transition metals and hydrogen atoms, generating an alloy catalyst surface model satisfying a first preset condition for each of the alloy catalyst systems;

[0010] According to each of the alloy catalyst surface models, the surface hydrogen atom coverage is predicted, and a kinetic model is constructed in combination with the small molecule substance information to obtain the intrinsic reaction kinetic information of each of the alloy catalyst surface models, wherein the intrinsic reaction kinetic information includes catalytic performance;

[0011] The prediction model was constructed using the predicted surface hydrogen atom coverage and hydrogen atom adsorption energy as input data and the catalytic performance as output data;

[0012] According to the prediction model and the intrinsic reaction kinetics information of each alloy catalyst surface model, the macromolecular olefin hydrogenation rate and the aromatic hydrocarbon loss rate are predicted to obtain the catalytic performance prediction value of each alloy catalyst surface model;

[0013] According to the predicted value of catalytic performance, alloy catalysts are screened to obtain reforming oil selective hydrogenation alloy catalysts that meet the second preset condition, where the second preset condition is related to the olefin hydrogenation rate and the aromatic loss rate.

[0014] In one embodiment, the relationship between the adsorption capacity of transition metals and hydrogen atoms is obtained as follows:

[0015] For transition metal systems, a transition metal surface model is built to determine how the adsorption capacity of hydrogen atoms on the transition metal surface changes with the coverage of hydrogen atoms;

[0016] The transition metal is divided into a strongly adsorbed metal and a weakly adsorbed metal. The surface hydrogen atom coverage of the strongly adsorbed metal is set to a first coverage value, and the adsorption capacity of hydrogen atoms is set to 1; the surface hydrogen atom coverage of the weakly adsorbed metal is set to a second coverage value, there is no adsorption energy of hydrogen atoms, the adsorption capacity of hydrogen atoms is set to 0, and the first coverage value is greater than the second coverage value.

[0017] In one embodiment, according to each of the alloy catalyst surface models, a method for predicting the surface hydrogen atom coverage is:

[0018] According to the ratio of strongly adsorbed metal to weakly adsorbed metal in the alloy catalyst surface model, the surface hydrogen atom coverage of the strongly adsorbed metal and the weakly adsorbed metal is calculated respectively;

[0019] The surface hydrogen atom coverage of the strongly adsorbed metal and the surface hydrogen atom coverage of the weakly adsorbed metal are weighted averaged to obtain the predicted surface hydrogen atom coverage.

[0020] In one embodiment, the method further comprises:

[0021] A first-principles calculation verification is performed on the reforming oil selective hydrogenation alloy catalyst that meets the second preset condition;

[0022] If the verification does not satisfy the third preset condition, modifying the prediction model and the catalytic performance prediction value;

[0023] The third preset condition is determined according to the deviation between the calculated value of the catalytic performance and the predicted value of the catalytic performance.

[0024] In one embodiment, obtaining intrinsic reaction kinetic information of each of the alloy catalyst surface models comprises:

[0025] Based on the chemical reaction path network of the small molecule information, the catalytic reaction process is simulated by first-principles calculation to determine the reactants, intermediate products, final products, adsorption energy of hydrogen atoms, and rate-controlling steps on the surface model of each alloy catalyst;

[0026] Search for the transition state structure of the rate-controlling step and obtain the maximum activation energy barrier in the corresponding reaction process;

[0027] A microscopic kinetic model is established based on the reactants, intermediate products, final products, adsorption energy of hydrogen atoms, transition state structure, and maximum activation energy barrier. The intrinsic reaction kinetic information of each alloy catalyst surface model is obtained through microscopic reaction kinetic analysis.

[0028] In one embodiment, constructing a prediction model includes:

[0029] According to the correlation between the adsorption energy of reactants, the adsorption energy of products and the reaction heat, a first correlation model between adsorption energy and reaction heat is established;

[0030] According to the correlation between the adsorption energy of reactants, the adsorption energy of intermediates and the activation energy barrier, a second correlation model between adsorption energy and activation energy barrier is established;

[0031] According to the proportional relationship between the adsorption energies of reactants, intermediate products and final products, a third correlation model is established;

[0032] A fourth correlation model is established based on the proportional relationship between the surface hydrogen atom coverage, the adsorption energy of hydrogen atoms and the adsorption energy of reactants;

[0033] Based on the first correlation model, the second correlation model, the third correlation model, the fourth correlation model and the kinetic model, the catalytic performance information is determined to generate the prediction model regarding the surface hydrogen atom coverage, the adsorption energy of hydrogen atoms and the catalytic performance.

[0034] Optionally, the predicted surface hydrogen atom coverage and hydrogen atom adsorption energy of the alloy catalyst to be screened are input into the prediction model,

[0035] Correct the equilibrium constants of the adsorption and desorption processes, calculate the benzene hydrogenation energy barrier and the hydrogenation reaction energy barrier, predict the macromolecular olefin hydrogenation rate and aromatic hydrocarbon loss rate, and output the catalytic performance prediction value of each alloy catalyst surface model.

[0036] In one embodiment, obtaining reactant information and small molecule substance information that can reflect the structural characteristics of the reactants includes:

[0037] Based on the functional groups and active sites in the reactant information, small molecule substance information that can reflect the structural characteristics of each reactant is screened out.

[0038] In one embodiment, the first preset condition includes: reaction temperature, atmosphere pressure, and model stability;

[0039] Generating an alloy catalyst surface model satisfying a first preset reaction condition for each of the alloy catalyst systems, comprising:

[0040] For each of the alloy catalyst systems,

[0041] Considering the H pre-adsorption on the catalyst surface in the hydrogenation environment,

[0042] By calculating the surface Gibbs free energy at different H coverages, the surface model of the alloy catalyst that meets the first preset reaction condition is determined, wherein the lower the surface free energy, the higher the stability of the corresponding alloy catalyst surface model.

[0043] In one embodiment, the alloy catalyst comprises a non-precious metal catalyst,

[0044] Catalyst materials with the same doping metal and different alloy ratios are selected as the same alloy catalyst system.

[0045] In one embodiment, the second preset condition includes: olefin hydrogenation rate ≥ 95%, aromatic loss rate ≤ 0.3%.

[0046] Another aspect of the present invention provides a screening device for a reforming oil selective hydrogenation alloy catalyst, comprising at least:

[0047] An acquisition module, used to acquire reactant information and small molecule substance information that can reflect the structural characteristics of the reactants; and

[0048] Obtain all alloy catalyst systems to be screened;

[0049] Obtain the relationship between transition metals and hydrogen atom adsorption capacity;

[0050] A model building module, for generating an alloy catalyst surface model satisfying a first preset reaction condition for each of the alloy catalyst systems according to the relationship between the adsorption capacity of the transition metal and the hydrogen atom;

[0051] According to each of the alloy catalyst surface models, the surface hydrogen atom coverage is predicted, and a kinetic model is constructed in combination with the small molecule substance information to obtain the intrinsic reaction kinetic information of each of the alloy catalyst surface models, wherein the intrinsic reaction kinetic information includes catalytic performance; and,

[0052] The prediction model was constructed using the predicted surface hydrogen atom coverage and hydrogen atom adsorption energy as input data and the catalytic performance as output data;

[0053] A prediction module, used to predict the macromolecular olefin hydrogenation rate and the aromatic hydrocarbon loss rate according to the prediction model, and obtain the catalytic performance prediction value of each alloy catalyst surface model;

[0054] The screening module is used to screen the alloy catalyst according to the predicted value of catalytic performance to obtain the reforming oil selective hydrogenation alloy catalyst that meets the second preset condition, and the second preset condition is related to the olefin hydrogenation rate and the aromatic loss rate.

[0055] It can be seen from the above scheme that the advantages of the present invention are:

[0056] The screening method of the reforming oil selective hydrogenation alloy catalyst provided by the present invention obtains reactant information, small molecule substance information that can reflect the structural characteristics of the reactants, all alloy catalyst systems to be screened, and the relationship between the transition metal and the hydrogen atom adsorption capacity; then, according to the relationship between the transition metal and the hydrogen atom adsorption capacity, an alloy catalyst surface model that meets the first preset condition is generated for each alloy catalyst system; according to each alloy catalyst surface model, the surface hydrogen atom coverage is predicted, and a kinetic model is constructed in combination with the small molecule substance information to obtain the intrinsic reaction kinetic information of each alloy catalyst surface model; with the predicted surface hydrogen atom coverage and the adsorption energy of hydrogen atoms as input data and the catalytic performance as output data, a prediction model is constructed; according to the prediction model, the macromolecular olefin hydrogenation rate and the aromatic hydrocarbon loss rate are predicted to obtain the catalytic performance prediction value of each alloy catalyst surface model; according to the catalytic performance prediction value, the alloy catalyst is screened. The method has high accuracy and screening speed, and uses this prediction model to quickly screen out alloy catalysts with high target performance and low cost, which greatly accelerates the research and development speed of reforming oil selective hydrogenation alloy catalysts and effectively reduces the development cost and production cost of the catalyst. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic diagram of the overall process of the screening method of the reforming oil selective hydrogenation alloy catalyst of the present invention;

[0058] Figure 2 for Figure 1 The specific flow chart of step S5;

[0059] Figure 3 for Figure 1 Specific flow chart of step S6;

[0060] Figure 4 A schematic diagram of the structure of a screening device for selective hydrogenation alloy catalysts for reforming oil;

[0061] The reference numerals are as follows:

[0062] 400- Screening device for selective hydrogenation alloy catalyst of reforming oil;

[0063] 401-Get module;

[0064] 402-model building module;

[0065] 403-prediction module;

[0066] 404-Filter module;

[0067] S1-S8, S51-S53, S61-S64: steps. DETAILED DESCRIPTION

[0068] In order to make the above features and effects of the present invention more clearly understood, embodiments are given below and described in detail with reference to the accompanying drawings.

[0069] As mentioned above, since the alloy has the characteristics of low cost and good catalytic performance, the alloy catalyst is selected as the selective hydrogenation catalyst of the reformed oil, and a group of small molecules that can reflect the reaction characteristics of the macromolecular substances in the reformed oil components need to be provided, and the alloy catalyst can be quickly screened according to the catalytic site structure and intrinsic kinetic information of the alloy catalyst. Based on this, the present invention provides a screening method for the selective hydrogenation alloy catalyst of the reformed oil, which can complete the screening of the catalyst formula without carrying out a large number of experiments, and the screening method has high accuracy and screening speed. This screening method will be specifically described below.

[0070] A method for screening a reforming oil selective hydrogenation alloy catalyst, comprising:

[0071] S1. Obtain information about reactants and small molecule substances that can reflect the structural characteristics of reactants.

[0072] In this embodiment, based on the functional groups and active sites in the reactant information, small molecule substance information that can reflect the structural characteristics of each reactant can be screened out. Among them, the reactant information is the type of reformed oil olefins, such as n-hexene, cyclohexene, etc. The small molecule substance information that can reflect the structural characteristics of the reactant is the small molecule substance structure with similar functional groups or reactive sites as the reactant, such as ethylene, cyclopropene, etc.

[0073] S2. Obtain all alloy catalyst systems to be screened.

[0074] In this embodiment, the alloy catalyst includes a non-precious metal catalyst such as Ni, and catalyst materials with the same doping metal and different alloy ratios can be selected as the same alloy catalyst system.

[0075] Taking Ni-based alloy catalysts as an example, the Ni-based alloy catalyst system refers to the metals doped with different types and proportions in pure Ni. Ni-based catalyst materials with the same doped metals but different alloy ratios can be regarded as an alloy catalyst system. For example, the alloy ratio of Ni to Pd can be 1:3, 1:1, and 3:1. These three NiPd catalysts with different alloy ratios can be regarded as the same alloy catalyst system. Similarly, the structures of different alloy ratios of Ni and other metals (such as Rh, Pt, Cu, etc.) are obtained according to this standard. In addition, the crystal structures of different Ni-based alloy catalysts can be obtained using the Materials Project website.

[0076] S3. Obtain the relationship between transition metals and hydrogen atom adsorption capacity.

[0077] In this embodiment, a surface model is constructed for the transition metal system, and the variation of the adsorption capacity of surface hydrogen atoms with the hydrogen atom coverage is calculated. The transition metals are divided into strong adsorption metals and weak adsorption metals according to the surface hydrogen atom coverage of the transition metals, wherein the surface hydrogen atom coverage of the strongly adsorbed metal is set to a first coverage value (for example, 1.0), and the adsorption capacity of hydrogen atoms is set to 1; the surface hydrogen atom coverage of the weakly adsorbed metal is set to a second coverage value (for example, 0), there is no adsorption energy of hydrogen atoms, the adsorption capacity of hydrogen atoms is set to 0, and the first coverage value is greater than the second coverage value.

[0078] S4. Generate an alloy catalyst surface model that meets the first preset condition for each alloy catalyst system.

[0079] In this embodiment, after all alloy catalyst systems to be screened are obtained through step S2, an alloy catalyst surface model that satisfies the first preset condition is further generated for each alloy catalyst system in combination with the relationship between the transition metal and the hydrogen atom adsorption capacity obtained in step S3. Specifically, the setting of the first preset condition is related to the reaction temperature, the atmosphere pressure, and the stability of the model. For each alloy catalyst system, the H pre-adsorption on the catalyst surface in the hydrogenation environment is considered, and the surface Gibbs free energy under different H coverage is calculated to determine the alloy catalyst surface model that satisfies the first preset reaction condition, wherein the lower the surface free energy, the higher the stability of the corresponding alloy catalyst surface model.

[0080] In the specific implementation, for example, the first preset condition is set as: reducing atmosphere, reaction temperature 150-200°C, pressure 1-2Mpa, and the model stability is the highest. In order to obtain an alloy catalyst surface model that meets the first preset condition, first obtain the crystal structure of a common Ni-based alloy from the Materials Project website, cut along the most stable crystal plane index direction, and build a Ni-based alloy catalyst surface model. Then consider the H pre-adsorption on the catalyst surface in a hydrogenation environment, and by calculating the surface Gibbs free energy under different H coverages, determine that the alloy catalyst surface model corresponding to the lowest value of the surface free energy is the most stable alloy catalyst surface model, that is, the alloy catalyst surface model that meets the first preset condition.

[0081] S5. According to each alloy catalyst surface model, the surface hydrogen atom coverage is predicted, and a kinetic model is constructed in combination with the information of small molecule substances to obtain the intrinsic reaction kinetic information of each alloy catalyst surface model, wherein the intrinsic reaction kinetic information includes catalytic performance.

[0082] In this embodiment, after obtaining the small molecule substance information in step S1 and generating the alloy catalyst surface model through steps S2-S4, the surface hydrogen atom coverage is further predicted according to each alloy catalyst surface model to obtain the adsorption energy of hydrogen atoms. The surface hydrogen atom coverage, the adsorption energy of hydrogen atoms, the small molecule substance information and the alloy catalyst surface model structure are further combined to construct a kinetic model to obtain the intrinsic reaction kinetic information of each alloy surface model, that is, to obtain the relevant catalytic performance information.

[0083] In this embodiment, the surface hydrogen atom coverage of the strongly adsorbed metal and the weakly adsorbed metal are calculated separately according to the ratio of the strongly adsorbed metal to the weakly adsorbed metal in the surface model of the alloy catalyst; then the surface hydrogen atom coverage of the strongly adsorbed metal and the surface hydrogen atom coverage of the weakly adsorbed metal are weighted averaged to obtain the predicted surface hydrogen atom coverage.

[0084] In addition, specific Figure 2 As shown in Figure 2 The specific flow diagram of step S5 is shown. First, based on the chemical reaction path network of small molecule substance information, the catalytic reaction process is simulated by first-principles calculation to determine the adsorption energy of reactants, intermediate products, final products, hydrogen atoms, and rate-controlling steps on each alloy catalyst surface model (S51); then, the transition state structure of the rate-controlling step is searched to obtain the maximum activation energy barrier in the corresponding reaction process (S52); finally, based on the adsorption energy of reactants, intermediate products, final products, hydrogen atoms, transition state structure, and maximum activation energy barrier, a microscopic kinetic model is established, and the intrinsic reaction kinetic information of each alloy catalyst surface model is obtained through microscopic reaction kinetic analysis (S53).

[0085] In the specific implementation, firstly, the first principle calculation simulation of the catalytic reaction process can be performed using computational simulation software such as VASP (Vienna Ab Initio Simulation Package). According to the adsorbed species contained in the reforming olefin hydrogenation reaction network, the adsorption energy of reactants, intermediates, final products, and hydrogen atoms on different surface models is calculated. It is preliminarily considered that the CC double bond breakage is the rate-controlling step, and the transition state structure of the rate-controlling step is preferentially searched to obtain the maximum activation energy barrier in the corresponding reaction process. Specifically, the transition state calculation method of CI-NEB or Dimer can be used to search for the lowest energy consumption reaction path of the elementary reaction of the rate-controlling step, and obtain the energy of its saddle point state. After determining the transition state structure through frequency calculation, the activation energy barrier of the corresponding elementary reaction can be obtained, and then the maximum activation energy barrier can be obtained. Then, after determining the adsorption energy of reactants, intermediates, final products, hydrogen atoms, transition state structures, and maximum activation energy barriers, a microscopic kinetic model can be established using kinetic calculation software such as Matlab or Catmap. The obtained adsorption energy of reactants, intermediates, final products, hydrogen atoms, transition state structures, and maximum activation energy barriers are input into the microscopic kinetic model to obtain the coverage of each substance and the elementary reaction steps, thereby obtaining the net reaction rate, and finally obtaining intrinsic kinetic data such as apparent activation energy, that is, obtaining catalytic performance information.

[0086] S6. Construct a prediction model using the predicted surface hydrogen atom coverage and the adsorption energy of hydrogen atoms as input data and the catalytic performance as output data.

[0087] In this embodiment, it can be seen from the microscopic kinetic model that the adsorption energy of the reaction species contained in the chemical reaction path network and the energy barrier of each elementary reaction in the target reaction can be used to jointly determine the product generation rate of the chemical reaction based on the predicted surface hydrogen atom coverage and the adsorption energy of hydrogen atoms. It can be seen from the microscopic kinetic model that the chemical reaction rate can be determined by combining the adsorption energy of free species in the reaction network with the elementary reaction energy barrier. There are four intrinsic correlations between the above parameters: (1) There is a correlation between the adsorption energy of reactants, the adsorption energy of products, and the heat of reaction; (2) There is a correlation between the adsorption energy of reactants, the adsorption energy of intermediates, and the activation energy barrier; (3) There is a correlation between the adsorption energies of reactants, intermediates, and final products; (4) There is also a certain correlation between the surface hydrogen coverage, the adsorption energy of hydrogen atoms, and the adsorption energy of reactants.

[0088] Specifically, Figure 3 As shown in Figure 3The specific flow diagram of step S6 is shown. According to the correlation between the adsorption energy of the reactants and the adsorption energy of the products and the reaction heat, a first correlation model (S61) about the adsorption energy and the reaction heat is established; according to the correlation between the adsorption energy of the reactants and the adsorption energy of the intermediates and the activation energy barrier, a second correlation model (S62) about the adsorption energy and the activation energy barrier is established; at the same time, according to the proportional relationship between the adsorption energies of the reactants, the intermediates and the final products, a third correlation model (S63) is established; according to the proportional relationship between the surface hydrogen atom coverage, the adsorption energy of hydrogen atoms and the adsorption energy of the reactants, a fourth correlation model (S64) is established. Finally, according to the first correlation model, the second correlation model, the third correlation model and the fourth correlation model, the catalytic performance information is determined, and a prediction model (S65) about the surface hydrogen atom coverage, the adsorption energy of hydrogen atoms and the catalytic performance is generated, wherein the catalytic performance information includes the product generation rate of the chemical reaction, the chemical reaction rate, etc., wherein the product generation rate is determined according to the adsorption energy and the activation energy barrier of the reaction species, and the chemical reaction rate is determined according to the adsorption energy and the activation energy barrier of the free species.

[0089] S7. According to the prediction model, the hydrogenation rate of macromolecular olefins and the loss rate of aromatics are predicted to obtain the predicted value of the catalytic performance of each alloy catalyst surface model.

[0090] In this embodiment, after the prediction model is constructed through step S6, the predicted surface hydrogen atom coverage and the adsorption energy of hydrogen atoms of the alloy catalyst to be screened are input into the prediction model, the equilibrium constants of the adsorption and desorption processes are corrected, the benzene hydrogenation energy barrier and the hydrogenation reaction energy barrier are calculated, the macromolecular olefin hydrogenation rate and the aromatic hydrocarbon loss rate are predicted, and the catalytic performance prediction value of each alloy catalyst surface model is output.

[0091] In this embodiment, through the modeling process of steps S3-S6, a process of establishing a prediction model for the catalytic performance of reforming oil selective hydrogenation alloy catalysts is achieved. The model can be used to quickly screen out alloy catalysts with high target performance and low cost.

[0092] S8. Perform high-throughput screening on the alloy catalyst according to the predicted value of the catalytic performance to obtain a reforming oil selective hydrogenation alloy catalyst that meets the second preset condition.

[0093] In this embodiment, after the catalytic performance prediction value is predicted in step S7, the catalytic performance prediction value can be used to perform high-throughput screening on the alloy catalyst to obtain a reforming oil selective hydrogenation alloy catalyst that meets the conditions. For the screening conditions, this embodiment sets a second preset condition related to the olefin hydrogenation rate and the aromatic loss rate. For example, in practice, the olefin hydrogenation rate can be set to ≥95% and the aromatic loss rate can be set to ≤0.3%.

[0094] In addition, in this embodiment, after the reforming oil selective hydrogenation alloy catalyst that meets the requirements is screened out in step S8, it is necessary to further verify it, specifically by performing first principle calculation verification on the reforming oil selective hydrogenation alloy catalyst that meets the second preset condition, that is, performing thermodynamic and kinetic calculations to obtain the first principle calculation results; if the verification does not meet the third preset condition, the prediction model and the catalytic performance prediction value are corrected. Then, according to the corrected prediction model and the catalytic performance prediction value, the alloy catalyst is re-screened to obtain the reforming oil selective hydrogenation alloy catalyst that meets the second preset condition. In this embodiment, for the third preset condition, it can be determined specifically based on the deviation between the catalytic performance calculation value and the catalytic performance prediction value. If the deviation between the obtained first principle calculation result and the catalytic performance prediction value is less than a threshold value, for example, the deviation is within 10%, it is considered that the hydrogenation alloy catalyst that achieves the target catalytic performance is successfully screened out. If the first principle calculation result and the catalytic performance prediction value deviate greatly, exceeding the threshold value, for example, the deviation is >10%, then the relevant data of the sample needs to be added to the original data set, the prediction model is re-corrected, and then, the process of S7-S8 is repeated, and the alloy catalyst is re-screened.

[0095] In summary, the screening method of the reforming oil selective hydrogenation alloy catalyst provided by the present invention is to obtain the reactant information, the small molecule substance information that can reflect the structural characteristics of the reactant, all the alloy catalyst systems to be screened, and the relationship between the transition metal and the hydrogen atom adsorption capacity; then, generate an alloy catalyst surface model that meets the first preset condition for each alloy catalyst system; according to each alloy catalyst surface model, predict the surface hydrogen atom coverage, and build a kinetic model in combination with the small molecule substance information to obtain the intrinsic reaction kinetic information of each alloy catalyst surface model; use the predicted surface hydrogen atom coverage and the adsorption energy of hydrogen atoms as input data, and the catalytic performance as output data to build a prediction model; according to the prediction model, predict the hydrogenation rate of macromolecular olefins and the aromatic loss rate, and obtain the catalytic performance prediction value of each alloy catalyst surface model; according to the catalytic performance prediction value, screen the alloy catalyst. This method has high accuracy and screening speed, and uses this prediction model to quickly screen out alloy catalysts with high target performance and low cost, which greatly accelerates the research and development speed of reforming oil selective hydrogenation alloy catalysts, and effectively reduces the development cost and production cost of the catalyst.

[0096] Reference Figure 4 , Figure 4 A screening device 400 for selective hydrogenation alloy catalyst of reforming oil is shown, which can be applied to personal terminals and host terminal devices, and can be realized by Figure 1-Figure 3The screening method for the reforming oil selective hydrogenation alloy catalyst shown in the embodiment of the present application can realize each process of the above method, and at least includes an acquisition module 401, a model building module 402, a prediction module 403, and a screening module 404, that is, specifically:

[0097] An acquisition module 401 is used to acquire reactant information and small molecule substance information that can reflect the structural characteristics of the reactants; and

[0098] Obtain all alloy catalyst systems to be screened;

[0099] Obtain the relationship between the adsorption capacity of transition metals and hydrogen atoms;

[0100] A model building module 402 is used to generate an alloy catalyst surface model that meets the first preset reaction condition for each of the alloy catalyst systems according to the relationship between the adsorption capacity of the transition metal and the hydrogen atom;

[0101] According to each of the alloy catalyst surface models, the surface hydrogen atom coverage is predicted, and a kinetic model is constructed in combination with the small molecule substance information to obtain the intrinsic reaction kinetic information of each of the alloy catalyst surface models, wherein the intrinsic reaction kinetic information includes catalytic performance; and,

[0102] The prediction model was constructed using the predicted surface hydrogen atom coverage and hydrogen atom adsorption energy as input data and the catalytic performance as output data;

[0103] A prediction module 403 is used to predict the macromolecular olefin hydrogenation rate and the aromatic hydrocarbon loss rate according to the prediction model, and obtain the catalytic performance prediction value of each alloy catalyst surface model;

[0104] The screening module 404 is used to screen the alloy catalysts according to the predicted catalytic performance value to obtain the reforming oil selective hydrogenation alloy catalyst that meets the second preset condition, where the second preset condition is related to the olefin hydrogenation rate and the aromatic loss rate.

[0105] It should be understood that the descriptions of the methods are also applicable to the screening device 400 for the selective hydrogenation alloy catalyst for reformed oil according to the embodiment of the present application, and will not be described in detail to avoid repetition.

[0106] In addition, it should be understood that in the screening device 400 for the selective hydrogenation alloy catalyst of reforming oil according to the embodiment of the present application, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the device can be divided into functional modules different from the modules illustrated above to complete all or part of the functions described above.

[0107] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises one..." does not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be noted that the scope of the method and device in the embodiment of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be applied, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0108] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A method for screening alloy catalysts for selective hydrogenation of reformed oil, characterized in that: Include: Obtain information on reactants, small molecules that can reflect the structural characteristics of reactants, all alloy catalyst systems to be screened, and the relationship between the adsorption capacity of transition metals and hydrogen atoms; According to the relationship between the adsorption capacity of transition metals and hydrogen atoms, generating an alloy catalyst surface model satisfying a first preset condition for each of the alloy catalyst systems; According to each of the alloy catalyst surface models, the surface hydrogen atom coverage is predicted, and a kinetic model is constructed in combination with the small molecule substance information to obtain the intrinsic reaction kinetic information of each of the alloy catalyst surface models, wherein the intrinsic reaction kinetic information includes catalytic performance; The prediction model was constructed using the predicted surface hydrogen atom coverage and hydrogen atom adsorption energy as input data and the catalytic performance as output data; According to the prediction model, the hydrogenation rate of macromolecular olefins and the loss rate of aromatic hydrocarbons are predicted to obtain the catalytic performance prediction value of each alloy catalyst surface model; According to the predicted value of catalytic performance, alloy catalysts are screened to obtain reforming oil selective hydrogenation alloy catalysts that meet the second preset condition, where the second preset condition is related to the olefin hydrogenation rate and the aromatic loss rate.

2. The method according to claim 1, characterized in that The relationship between the adsorption capacity of transition metals and hydrogen atoms is obtained as follows: For transition metal systems, a transition metal surface model is built to determine how the adsorption capacity of hydrogen atoms on the transition metal surface changes with the coverage of hydrogen atoms; The transition metal is divided into a strongly adsorbed metal and a weakly adsorbed metal. The surface hydrogen atom coverage of the strongly adsorbed metal is set to a first coverage value, and the adsorption capacity of hydrogen atoms is set to 1; the surface hydrogen atom coverage of the weakly adsorbed metal is set to a second coverage value, there is no adsorption energy of hydrogen atoms, the adsorption capacity of hydrogen atoms is set to 0, and the first coverage value is greater than the second coverage value.

3. The method according to claim 2, characterized in that According to each of the alloy catalyst surface models, the method for predicting the surface hydrogen atom coverage is: According to the ratio of strongly adsorbed metal to weakly adsorbed metal in the alloy catalyst surface model, the surface hydrogen atom coverage of the strongly adsorbed metal and the weakly adsorbed metal in the alloy catalyst surface model is calculated respectively; The surface hydrogen atom coverage of the strongly adsorbed metal and the surface hydrogen atom coverage of the weakly adsorbed metal are weighted averaged to obtain the predicted surface hydrogen atom coverage.

4. The method according to claim 1, characterized in that Further including: Performing first-principles calculation verification on the reforming oil selective hydrogenation alloy catalyst that meets the second preset condition to obtain a calculated value of catalytic performance; If the verification does not satisfy the third preset condition, modifying the prediction model and the catalytic performance prediction value; The third preset condition is determined according to the deviation between the calculated value of the catalytic performance and the predicted value of the catalytic performance.

5. The method according to claim 1, characterized in that Obtaining intrinsic reaction kinetic information of each alloy catalyst surface model comprises: Based on the chemical reaction path network of the small molecule information, the catalytic reaction process is simulated by first-principles calculation to determine the adsorption energy of reactants, intermediates, final products, and hydrogen atoms on each of the alloy catalyst surface models, as well as the rate-controlling step; Search for the transition state structure of the rate-controlling step and obtain the maximum activation energy barrier in the corresponding reaction process; The kinetic model is established based on the reactants, intermediate products, final products, adsorption energy of hydrogen atoms, transition state structure, and maximum activation energy barrier, and the intrinsic reaction kinetic information of each alloy catalyst surface model is obtained through microscopic reaction kinetic analysis.

6. The method according to claim 1 or 5, characterized in that: Constructing a prediction model, including: establishing a first correlation model between adsorption energy and reaction heat according to the correlation between adsorption energy of reactants, adsorption energy of products, and reaction heat; According to the correlation between the adsorption energy of reactants, the adsorption energy of intermediates and the activation energy barrier, a second correlation model between adsorption energy and activation energy barrier is established; According to the proportional relationship between the adsorption energies of reactants, intermediate products and final products, a third correlation model is established; A fourth correlation model is established based on the proportional relationship between the surface hydrogen atom coverage, the adsorption energy of hydrogen atoms and the adsorption energy of reactants; Based on the first correlation model, the second correlation model, the third correlation model, the fourth correlation model and the kinetic model, the catalytic performance information is determined to generate the prediction model regarding the surface hydrogen atom coverage, the adsorption energy of hydrogen atoms and the catalytic performance.

7. The method according to claim 6, characterized in that Also includes: The predicted surface hydrogen atom coverage and hydrogen atom adsorption energy of the alloy catalyst to be screened are input into the prediction model, Correct the equilibrium constants of the adsorption and desorption processes, calculate the benzene hydrogenation energy barrier and the hydrogenation reaction energy barrier, predict the macromolecular olefin hydrogenation rate and aromatic hydrocarbon loss rate, and output the catalytic performance prediction value of each alloy catalyst surface model.

8. The method according to claim 1, characterized in that Obtain information about reactants and small molecules that can reflect the structural characteristics of reactants, including: Based on the functional groups and active sites in the reactant information, small molecule substance information that can reflect the structural characteristics of each reactant is screened out.

9. The method according to claim 1 or 3, characterized in that: The first preset conditions include: reaction temperature, atmosphere pressure, and model stability; Generating an alloy catalyst surface model satisfying a first preset reaction condition for each of the alloy catalyst systems, comprising: For each of the alloy catalyst systems, Considering the H pre-adsorption on the catalyst surface in the hydrogenation environment, By calculating the surface Gibbs free energy at different H coverages, the surface model of the alloy catalyst that meets the first preset reaction condition is determined, wherein the lower the surface free energy, the higher the stability of the corresponding alloy catalyst surface model.

10. The method according to claim 1, characterized in that The alloy catalyst comprises a non-noble metal catalyst, Catalyst materials with the same doping metal and different alloy ratios are selected as the same alloy catalyst system.

11. The method according to claim 1, characterized in that: The second preset conditions include: olefin hydrogenation rate ≥ 95%, and aromatic loss rate ≤ 0.3%.

12. A screening device for alloy catalysts for selective hydrogenation of reformed oil, characterized in that: At least: An acquisition module, used to acquire reactant information and small molecule substance information that can reflect the structural characteristics of the reactants; and Obtain all alloy catalyst systems to be screened; Obtain the relationship between the adsorption capacity of transition metals and hydrogen atoms; A model building module, for generating an alloy catalyst surface model satisfying a first preset reaction condition for each of the alloy catalyst systems according to the relationship between the adsorption capacity of the transition metal and the hydrogen atom; According to each of the alloy catalyst surface models, the surface hydrogen atom coverage is predicted, and a kinetic model is constructed in combination with the small molecule substance information to obtain the intrinsic reaction kinetic information of each of the alloy catalyst surface models, wherein the intrinsic reaction kinetic information includes catalytic performance; and, The prediction model was constructed using the predicted surface hydrogen atom coverage and hydrogen atom adsorption energy as input data and the catalytic performance as output data; A prediction module, used to predict the macromolecular olefin hydrogenation rate and the aromatic hydrocarbon loss rate according to the prediction model, and obtain the catalytic performance prediction value of each alloy catalyst surface model; The screening module is used to screen the alloy catalyst according to the predicted value of catalytic performance to obtain the reforming oil selective hydrogenation alloy catalyst that meets the second preset condition, and the second preset condition is related to the olefin hydrogenation rate and the aromatic loss rate.

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