Method and device for screening of reformate selective hydrogenation alloy catalyst
By constructing surface and kinetic models of alloy catalysts, the catalytic performance was predicted, solving the screening problem of alloy catalysts for the selective hydrogenation of macromolecular olefins in reformed oils, and realizing rapid and economical catalyst screening.
Patent Information
- Application Number
- CN202311468939.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-11-07
- Publication Date
- 2026-01-27
- Estimated Expiration
- 2043-11-07
AI Technical Summary
Existing technologies make it difficult to quickly and economically screen alloy catalysts suitable for the selective hydrogenation of macromolecular olefins in reformed oils, as traditional methods are time-consuming and uneconomical.
By acquiring information on reactants and small molecules, a surface model of the alloy catalyst is built to predict the surface hydrogen atom coverage and adsorption energy. A kinetic model is then constructed to predict the catalytic performance, and alloy catalysts that meet the preset conditions are screened out.
This enables the rapid and precise screening of high-performance and low-cost alloy catalysts, significantly shortening the R&D cycle and reducing development costs.
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Figure CN119964658B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of catalyst materials technology, specifically to a method and apparatus for screening alloy catalysts for selective hydrogenation of reformed oil. Background Technology
[0002] Aromatic hydrocarbons (benzene, toluene, xylene) are important organic chemical raw materials, second only to ethylene and propylene in terms of production volume and scale. They are widely used in the production of synthetic fibers, synthetic resins, synthetic rubber, and various fine chemicals. Catalytic reforming / aromatic extraction is one of the main processes for producing aromatic hydrocarbons. However, in addition to being rich in aromatic hydrocarbons and solvent oil, catalytic reforming products also contain small amounts of olefins (1%–3%). To produce qualified aromatic hydrocarbons and solvent oil, the olefins must be removed; otherwise, the bromine value, corrosiveness, and acid wash color of the aromatic products will fail to meet the requirements. At the same time, the small amount of olefins present in the reforming product will polymerize in the extraction solvent, contaminating the extraction solvent and causing corrosion of the extraction system equipment, which will have varying degrees of impact on the performance of downstream equipment, adsorbents, and catalysts.
[0003] One method for removing olefins from reforming products is hydrorefining using a hydrocatalyst, selectively hydrogenating saturated reforming to remove olefins from the product. Currently widely used noble metal catalysts (Pd, Pt, etc.) suffer from drawbacks such as high cost, limited reserves, and low selectivity. In contrast, some non-noble metal catalysts (such as Ni) have demonstrated catalytic performance comparable to noble metal catalysts in certain reduction reactions. Furthermore, preparing alloy catalysts by doping active metals with other metal elements can not only improve the selectivity of the selective hydrogenation reaction of olefins while maintaining its activity as much as possible, but also enhance the stability of the catalytic material.
[0004] However, due to the wide variety of metals and the different mechanisms by which various alloys act in reactions, the traditional trial-and-error method of screening for a relatively ideal catalyst through extensive experimentation is time-consuming and uneconomical. Recent research shows that theoretical calculations can be used to study catalytic reaction mechanisms, evaluate catalytic performance, and thus predict the catalytic performance of unknown alloy materials. This method of effectively screening alloy catalytic materials using theoretical calculations can significantly reduce the catalyst development cycle.
[0005] In recent years, although many researchers have made some progress in the field of first-principles calculations for screening the design of alloy catalysts for selective hydrogenation of olefins, most of these studies focus on the selective hydrogenation reactions of small molecules (C2-C4), with very limited computational research on selective hydrogenation catalysts for reformed oil components (C6 macromolecules). This is because calculations related to the selective hydrogenation of macromolecules suffer from problems such as large computational systems and long computation times. Summary of the Invention
[0006] To address the shortcomings of existing technologies, this invention proposes a method and apparatus for screening alloy catalysts for selective hydrogenation of reforming products, which enables rapid screening of catalysts.
[0007] To achieve the above objectives, the present invention provides a method for screening alloy catalysts for selective hydrogenation of reforming oil, comprising:
[0008] To obtain information on reactants, information on small molecules that 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] Based on the relationship between the adsorption capacity of transition metals and hydrogen atoms, an alloy catalyst surface model that satisfies the first preset condition is generated for each alloy catalyst system.
[0010] Based on the surface model of each alloy catalyst, the surface hydrogen atom coverage is predicted, and a kinetic model is constructed in combination with the information of the small molecules to obtain the intrinsic reaction kinetic information of the surface model of each alloy catalyst, wherein the intrinsic reaction kinetic information includes catalytic performance.
[0011] A predictive model is constructed using the predicted surface hydrogen atom coverage and hydrogen atom adsorption energy as input data and the catalytic performance as output data.
[0012] Based on the prediction model and the intrinsic reaction kinetics information of each alloy catalyst surface model, the hydrogenation rate of macromolecular olefins and the loss rate of aromatics are predicted, and the predicted catalytic performance value of each alloy catalyst surface model is obtained.
[0013] Based on the predicted catalytic performance values, alloy catalysts are screened to obtain selective hydrogenation alloy catalysts for reformed oil that meet the second preset conditions, which are related to the olefin hydrogenation rate and the aromatic loss rate.
[0014] In one embodiment, the relationship between the adsorption capacity of the transition metal and hydrogen atoms is obtained as follows:
[0015] For transition metal systems, a transition metal surface model was built to determine how the adsorption capacity of hydrogen atoms on the transition metal surface changes with the hydrogen atom coverage.
[0016] The transition metals are divided into strongly adsorbed metals and weakly adsorbed metals. The surface hydrogen atom coverage of the strongly adsorbed metal is set to a first coverage value, and the hydrogen atom adsorption capacity is set to 1. The surface hydrogen atom coverage of the weakly adsorbed metal is set to a second coverage value, there is no hydrogen atom adsorption energy, and the hydrogen atom adsorption capacity is set to 0. The first coverage value is greater than the second coverage value.
[0017] In one embodiment, the method for predicting the surface hydrogen atom coverage based on the surface model of each alloy catalyst is as follows:
[0018] Based on the ratio of strongly adsorbed metals to weakly adsorbed metals in the surface model of the alloy catalyst, the surface hydrogen atom coverage of the strongly adsorbed metals and the weakly adsorbed metals is calculated respectively.
[0019] The predicted surface hydrogen atom coverage is obtained by weighting the surface hydrogen atom coverage of strongly adsorbed metals and weakly adsorbed metals.
[0020] In one embodiment, the method further comprises:
[0021] First-principles calculations were performed to verify the selective hydrogenation alloy catalyst for reformed oil that met the second preset conditions.
[0022] If the verification does not meet the third preset condition, the prediction model and the predicted catalytic performance value are corrected.
[0023] The third preset condition is determined based on the deviation between the calculated catalytic performance value and the predicted catalytic performance value.
[0024] In one embodiment, obtaining intrinsic reaction kinetic information for each alloy catalyst surface model includes:
[0025] Based on the chemical reaction pathway network of the small molecule information, the catalytic reaction process is simulated using first-principles calculations to determine the adsorption energy of reactants, intermediate products, final products, 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 to obtain the maximum activation energy barrier in the corresponding reaction process;
[0027] Based on the adsorption energy of the reactants, intermediate products, final products, hydrogen atoms, transition state structure, and maximum activation energy barrier, a microscopic kinetic model is established. Through microscopic reaction kinetic analysis, the intrinsic reaction kinetic information of the surface model of each alloy catalyst is obtained.
[0028] In one embodiment, constructing a prediction model includes:
[0029] Based on the correlation between the adsorption energy of reactants, the adsorption energy of products, and the heat of reaction, a first correlation model between adsorption energy and heat of reaction is established.
[0030] Based on 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] A third correlation model is established based on the proportional relationship between the adsorption energies of reactants, intermediate products, and final products.
[0032] A fourth correlation model is established based on the proportional relationship between surface hydrogen atom coverage, hydrogen atom adsorption energy, and reactant adsorption energy.
[0033] Based on the first correlation model, the second correlation model, the third correlation model, the fourth correlation model, and the kinetic model, catalytic performance information is determined, and the prediction model regarding surface hydrogen atom coverage, hydrogen atom adsorption energy, and catalytic performance is generated.
[0034] Optionally, the predicted surface hydrogen atom coverage and hydrogen atom adsorption energy of the alloy catalyst to be screened can be input into the prediction model.
[0035] The equilibrium constants of adsorption and desorption processes are corrected, the hydrogenation energy barrier of benzene and the hydrogenation reaction energy barrier are calculated, the hydrogenation rate of macromolecular olefins and the loss rate of aromatics are predicted, and the predicted catalytic performance values of the surface models of each alloy catalyst are output.
[0036] In one embodiment, obtaining reactant information and small molecule information that reflects the structural characteristics of the reactants includes:
[0037] Based on the functional groups and active sites in the reactant information, information on small molecules that can reflect the structural characteristics of each reactant is screened out.
[0038] In one embodiment, the first preset conditions include: reaction temperature, atmosphere pressure, and model stability;
[0039] For each of the aforementioned alloy catalyst systems, an alloy catalyst surface model satisfying a first preset reaction condition is generated, comprising:
[0040] For each of the aforementioned alloy catalyst systems,
[0041] Considering the pre-adsorption of H on the catalyst surface in a hydrogenation environment,
[0042] By calculating the surface Gibbs free energy under different H coverage, the surface model of the alloy catalyst that satisfies the first preset reaction conditions is determined, wherein the lower the surface free energy, the higher the stability of the surface model of the alloy catalyst.
[0043] In one embodiment, the alloy catalyst comprises a non-precious metal catalyst.
[0044] Catalyst materials with the same doped metal but different alloy ratios are selected as the same alloy catalyst system.
[0045] In one embodiment, the second preset conditions include: olefin hydrogenation rate ≥ 95% and aromatic loss rate ≤ 0.3%.
[0046] Another aspect of the present invention provides a screening device for selective hydrogenation alloy catalysts of reforming product oil, comprising at least:
[0047] The acquisition module is used to acquire reactant information and information on small molecules that reflect the structural characteristics of the reactants; and,
[0048] Obtain all alloy catalyst systems to be screened;
[0049] To determine the relationship between the adsorption capacity of transition metals and hydrogen atoms;
[0050] The model building module is used to generate an alloy catalyst surface model that satisfies the first preset reaction conditions for each alloy catalyst system based on the relationship between the adsorption capacity of transition metals and hydrogen atoms.
[0051] Based on the surface model of each alloy catalyst, the surface hydrogen atom coverage is predicted. A kinetic model is constructed by combining this with the small molecule information to obtain the intrinsic reaction kinetic information of each alloy catalyst surface model. This intrinsic reaction kinetic information includes catalytic performance; and...
[0052] A predictive model is constructed using the predicted surface hydrogen atom coverage and hydrogen atom adsorption energy as input data and the catalytic performance as output data.
[0053] The prediction module is used to predict the hydrogenation rate of macromolecular olefins and the loss rate of aromatics based on the prediction model, and to obtain the predicted catalytic performance value of each alloy catalyst surface model.
[0054] The screening module is used to screen the alloy catalysts according to the predicted catalytic performance values to obtain reformate selective hydrogenation alloy catalysts that meet the second preset conditions, which are related to the olefin hydrogenation rate and the aromatic loss rate.
[0055] As can be seen from the above solutions, the advantages of the present invention are:
[0056] The present invention provides a method for screening alloy catalysts for selective hydrogenation of reformed oil. This method involves acquiring reactant information, information on small molecules reflecting the structural characteristics of the reactants, all alloy catalyst systems to be screened, and the relationship between the adsorption capacity of transition metals and hydrogen atoms. Then, based on the relationship between the adsorption capacity of transition metals and hydrogen atoms, an alloy catalyst surface model satisfying a first preset condition is generated for each alloy catalyst system. Based on each alloy catalyst surface model, the surface hydrogen atom coverage is predicted, and a kinetic model is constructed by combining this with small molecule information to obtain the intrinsic reaction kinetics information of each alloy catalyst surface model. Using the predicted surface hydrogen atom coverage and hydrogen atom adsorption energy as input data and catalytic performance as output data, a prediction model is constructed. Based on the prediction model, the hydrogenation rate of macromolecular olefins and the aromatic hydrocarbon loss rate are predicted to obtain the predicted catalytic performance value of each alloy catalyst surface model. The alloy catalysts are then screened based on the predicted catalytic performance values. This method has high accuracy and screening speed. Utilizing this prediction model, alloy catalysts with high target performance and low cost can be quickly screened, greatly accelerating the research and development speed of alloy catalysts for selective hydrogenation of reformed oil and effectively reducing catalyst development and production costs. Attached Figure Description
[0057] Figure 1 This is a schematic diagram of the overall process for screening alloy catalysts for selective hydrogenation of reformed oil according to the present invention.
[0058] Figure 2 for Figure 1 The detailed flowchart of step S5;
[0059] Figure 3 for Figure 1 The detailed flowchart of step S6;
[0060] Figure 4 A schematic diagram of the structure of a screening device for selective hydrogenation of reforming product oil alloy catalysts;
[0061] The accompanying figure is labeled as follows:
[0062] Screening device for 400-selective hydrogenation alloy catalysts for reformed oil;
[0063] 401 - Get Module;
[0064] 402 - Model Building Module;
[0065] 403 - Prediction Module;
[0066] 404 - Filtering module;
[0067] S1-S8, S51-S53, S61-S64: Steps. Detailed Implementation
[0068] To make the above features and effects of the present invention clearer and easier to understand, specific embodiments are described below, and detailed descriptions are provided in conjunction with the accompanying drawings.
[0069] As previously mentioned, alloy catalysts are chosen for selective hydrogenation of reformate due to their low cost and good catalytic performance. This requires a set of small molecules that reflect the reaction characteristics of macromolecules in the reformate components, and rapid screening of alloy catalysts based on their catalytic site structure and intrinsic kinetic information. Therefore, this invention provides a method for screening alloy catalysts for selective hydrogenation of reformate, which can complete the catalyst formulation screening without conducting extensive experiments, and offers high accuracy and speed. This screening method will be described in detail below.
[0070] A method for screening alloy catalysts for selective hydrogenation of reforming oil, comprising:
[0071] S1. Obtain information about reactants, including information about small molecules that reflect the structural characteristics of the reactants.
[0072] In this embodiment, based on the functional groups and active sites in the reactant information, information on small molecules that reflect the structural characteristics of each reactant can be screened. The reactant information refers to the types of olefins that will be generated from the reforming process, such as n-hexene and cyclohexene. Information on small molecules that reflect the structural characteristics of the reactants refers to small molecules with similar functional groups or reaction sites to the reactants, such as ethylene and cyclopropylene.
[0073] S2. Obtain all alloy catalyst systems to be screened.
[0074] In this embodiment, the alloy catalyst includes non-precious metal catalysts such as Ni. Catalyst materials with the same doped metal but different alloy ratios can be selected as the same alloy catalyst system.
[0075] Taking Ni-based alloy catalysts as an example, a Ni-based alloy catalyst system refers to pure Ni doped with different types and proportions of metals. Ni-based catalyst materials with different alloy ratios of the same doped metal can be considered as a single alloy catalyst system. For example, the Ni-Pd alloy ratio can be 1:3, 1:1, or 3:1, and these three different alloy ratios of NiPd catalysts can be regarded as the same alloy catalyst system. Similarly, the structures of Ni with different alloy ratios of 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 the adsorption capacity of transition metals and hydrogen atoms.
[0077] In this embodiment, a surface model is built for the transition metal system, and the change in the adsorption capacity of surface hydrogen atoms with the hydrogen atom coverage is calculated. The transition metals are divided into strongly adsorbing metals and weakly adsorbing metals according to the surface hydrogen atom coverage. The surface hydrogen atom coverage of strongly adsorbing metals is set to a first coverage value (e.g., 1.0), and the hydrogen atom adsorption capacity is set to 1. The surface hydrogen atom coverage of weakly adsorbing metals is set to a second coverage value (e.g., 0), there is no adsorption energy of hydrogen atoms, and the hydrogen atom adsorption capacity is set to 0. The first coverage value is greater than the second coverage value.
[0078] S4. Generate an alloy catalyst surface model that meets the first preset conditions for each alloy catalyst system.
[0079] In this embodiment, after obtaining all alloy catalyst systems to be screened in step S2, and combining the relationship between the adsorption capacity of transition metals and hydrogen atoms obtained in step S3, an alloy catalyst surface model satisfying a first preset condition is further generated for each alloy catalyst system. Specifically, the setting of the first preset condition is related to the reaction temperature, atmosphere pressure, and model stability. For each alloy catalyst system, considering the H pre-adsorption situation on the catalyst surface in the hydrogenation environment, 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 alloy catalyst surface model.
[0080] In practical implementation, for example, the first preset conditions are set as follows: reducing atmosphere, reaction temperature 150-200℃, and pressure 1-2 MPa, which results in the highest model stability. To obtain an alloy catalyst surface model that meets these first preset conditions, the crystal structure of a common Ni-based alloy is first obtained from the Materials Project website. This structure is then cut along the direction of the most stable crystal plane index to construct a Ni-based alloy catalyst surface model. Subsequently, considering the H pre-adsorption on the catalyst surface in a hydrogenation environment, the surface Gibbs free energy under different H coverage is calculated. The alloy catalyst surface model corresponding to the lowest surface free energy value is determined to be the most stable alloy catalyst surface model, i.e., the alloy catalyst surface model that meets the first preset conditions.
[0081] S5. Based on the surface model of each alloy catalyst, predict the surface hydrogen atom coverage and construct a kinetic model by combining small molecule information to obtain the intrinsic reaction kinetic information of the surface model of each alloy catalyst, wherein the intrinsic reaction kinetic information includes catalytic performance.
[0082] In this embodiment, after obtaining information on small molecules in step S1 and generating alloy catalyst surface models in steps S2-S4, the surface hydrogen atom coverage is predicted based on each alloy catalyst surface model to obtain the adsorption energy of hydrogen atoms. Furthermore, by combining the surface hydrogen atom coverage, the hydrogen atom adsorption energy, the information on small molecules, and the structure of the alloy catalyst surface model, a kinetic model is constructed to obtain the intrinsic reaction kinetics information of each alloy surface model, thus obtaining the relevant catalytic performance information.
[0083] In this embodiment, the surface hydrogen atom coverage of the strongly adsorbed metal and the weakly adsorbed metal is calculated according to the ratio of strongly adsorbed metal to 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 and averaged to obtain the predicted surface hydrogen atom coverage.
[0084] In addition, specifically, such as Figure 2 As shown, Figure 2 A detailed flowchart of step S5 is shown. First, based on the chemical reaction pathway network of small molecule information, the catalytic reaction process is simulated using first-principles calculations to determine the adsorption energies of reactants, intermediate products, final products, and hydrogen atoms on the surface model of each alloy catalyst, as well as the rate-controlling step (S51). Then, the transition state structure of the rate-controlling step is searched to obtain the corresponding maximum activation energy barrier in the reaction process (S52). Finally, based on the adsorption energies of reactants, intermediate products, final products, and hydrogen atoms, the transition state structure, and the maximum activation energy barrier, a micro-kinetic model is established. Through micro-reaction kinetic analysis, the intrinsic reaction kinetic information of each alloy catalyst surface model is obtained (S53).
[0085] In practical implementation, firstly, computational simulation software such as VASP (ViennaAb Initio Simulation Package) can be used to perform first-principles calculations to simulate the catalytic reaction process. Based on the adsorbed species contained in the reforming-to-olefin hydrogenation reaction network, the adsorption energies of reactants, intermediates, final products, and hydrogen atoms on different surface models are calculated. The initial assumption is that C / C double bond breaking is the rate-determining step, and the transition state structure of the rate-determining step is searched preferentially to obtain the maximum activation energy barrier in the corresponding reaction process. Specifically, CI-NEB or Dimer's transition state calculation methods can be used to search for the lowest energy-consuming reaction path of the elementary reaction of this rate-determining step and obtain its saddle point energy. After determining the transition state structure through frequency calculations, the activation energy barrier of the corresponding elementary reaction can be obtained, and thus the maximum activation energy barrier can be derived. Then, after determining the adsorption energies of reactants, intermediates, final products, and hydrogen atoms, as well as the transition state structure and maximum activation energy barrier, a micro-kinetic model can be established using kinetic calculation software such as Matlab or Catmap. The adsorption energies of reactants, intermediates, final products, and hydrogen atoms, the transition state structure, and the maximum activation energy barrier are input into the micro-kinetic model to obtain the coverage of each substance and the elementary reaction steps, thereby obtaining the net reaction rate. Finally, intrinsic kinetic data such as the apparent activation energy are obtained, which is the catalytic performance information.
[0086] S6. Using the predicted surface hydrogen atom coverage and hydrogen atom adsorption energy as input data and catalytic performance as output data, construct a prediction model.
[0087] In this embodiment, as can be seen from the micro-kinetic model, the product formation rate of the chemical reaction can be jointly determined by the adsorption energy of the reactants contained in the chemical reaction pathway network and the energy barrier of each elementary reaction in the target reaction, based on the predicted surface hydrogen atom coverage and hydrogen atom adsorption energy. As can be seen from the micro-kinetic model, the chemical reaction rate can be determined by combining the adsorption energy of free species in the reaction network with the energy barrier of the elementary reaction. There are four intrinsic correlations among 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, such as Figure 3 As shown, Figure 3A detailed flowchart of step S6 is shown. Based on the correlation between the adsorption energy of reactants, the adsorption energy of products, and the heat of reaction, a first correlation model regarding adsorption energy and heat of reaction is established (S61). Based on the correlation between the adsorption energy of reactants, the adsorption energy of intermediates, and the activation energy barrier, a second correlation model regarding adsorption energy and activation energy barrier is established (S62). Simultaneously, based on the proportional relationship between the adsorption energies of reactants, intermediates, and final products, a third correlation model is established (S63). Based on the proportional relationship between surface hydrogen atom coverage, the adsorption energy of hydrogen atoms, and the adsorption energy of reactants, a fourth correlation model is established (S64). Finally, based on the first, second, third, and fourth correlation models, catalytic performance information is determined, and a predictive model regarding surface hydrogen atom coverage, the adsorption energy of hydrogen atoms, and catalytic performance is generated (S65). The catalytic performance information includes the product formation rate and the chemical reaction rate, where the product formation rate is determined based on the adsorption energy and activation energy barrier of the reactant species, and the chemical reaction rate is determined based on the adsorption energy and activation energy barrier of the free species.
[0089] S7. Based on the prediction model, predict the hydrogenation rate of macromolecular olefins and the loss rate of aromatics, and obtain the predicted catalytic performance values of the surface model of each alloy catalyst.
[0090] In this embodiment, after constructing the prediction model through step S6, the predicted surface hydrogen atom coverage and hydrogen atom adsorption energy of the alloy catalyst to be screened are input into the prediction model to correct the equilibrium constants of adsorption and desorption processes, calculate the hydrogenation energy barrier of benzene and the hydrogenation reaction energy barrier, predict the hydrogenation rate of macromolecular olefins and the loss rate of aromatics, and output the predicted catalytic performance values of the surface model of each alloy catalyst.
[0091] In this embodiment, the modeling process in steps S3-S6 establishes a predictive model for the catalytic performance of alloy catalysts for selective hydrogenation of reforming oil. This model can be used to quickly screen alloy catalysts with high target performance and low cost.
[0092] S8. Based on the predicted catalytic performance values, the alloy catalysts are subjected to high-throughput screening to obtain alloy catalysts for selective hydrogenation of reformed oil that meet the second preset conditions.
[0093] In this embodiment, after predicting the catalytic performance value through step S7, the predicted catalytic performance value can be used to perform high-throughput screening of the alloy catalyst to obtain a reformate selective hydrogenation alloy catalyst that meets the conditions. Regarding the screening conditions, this embodiment sets a second preset condition related to the olefin hydrogenation rate and aromatics loss rate. For example, in practice, the olefin hydrogenation rate can be set to ≥95%, and the aromatics loss rate to ≤0.3%.
[0094] Furthermore, in this embodiment, after selecting the reformate selective hydrogenation alloy catalyst that meets the requirements in step S8, further verification is needed. Specifically, this is achieved by performing first-principles calculations on the reformate selective hydrogenation alloy catalyst that meets the second preset condition, i.e., performing thermodynamic and kinetic calculations to obtain the first-principles calculation results. If the verification does not meet the third preset condition, the prediction model and the predicted catalytic performance value are corrected. Then, based on the corrected prediction model and the predicted catalytic performance value, the alloy catalyst is re-screened to obtain the reformate selective hydrogenation alloy catalyst that meets the second preset condition. In this embodiment, the third preset condition can be specifically determined based on the deviation between the calculated catalytic performance value and the predicted catalytic performance value. If the deviation between the obtained first-principles calculation result and the predicted catalytic performance value is less than a threshold, for example, within 10%, then the hydrogenation alloy catalyst that achieves the target catalytic performance is considered successfully screened. If the deviation between the first-principles calculation result and the predicted catalytic performance value is large, exceeding the threshold, for example, the deviation is >10%, then the relevant data of the sample needs to be added to the original dataset, the prediction model needs to be corrected again, and then the process of S7-S8 is repeated to re-screen the alloy catalyst.
[0095] In summary, the screening method for selective hydrogenation alloy catalysts for reformed oil provided by this invention involves acquiring reactant information, information on small molecules reflecting the structural characteristics of the reactants, all alloy catalyst systems to be screened, and the relationship between the adsorption capacity of transition metals and hydrogen atoms. Then, a surface model of the alloy catalyst satisfying a first preset condition is generated for each alloy catalyst system. Based on each surface model, the surface hydrogen atom coverage is predicted, and a kinetic model is constructed by combining the small molecule information to obtain the intrinsic reaction kinetics information of each surface model. Using the predicted surface hydrogen atom coverage and hydrogen atom adsorption energy as input data and catalytic performance as output data, a prediction model is constructed. Based on the prediction model, the hydrogenation rate of macromolecular olefins and the aromatic loss rate are predicted to obtain the predicted catalytic performance value of each surface model. The alloy catalysts are then screened based on the predicted catalytic performance values. This method has high accuracy and screening speed. Utilizing this prediction model, alloy catalysts with high target performance and low cost can be quickly screened, greatly accelerating the research and development speed of selective hydrogenation alloy catalysts for reformed oil and effectively reducing catalyst development and production costs.
[0096] Reference Figure 4 , Figure 4 A screening device 400 for selective hydrogenation of reforming oil alloy catalysts is shown, which can be applied to personal terminals and host computer terminal equipment. It can achieve screening by means of... Figures 1-3The screening method for selective hydrogenation alloy catalysts of reformed oil shown in this application, the apparatus provided in this embodiment can realize each process of the above method, including at least an acquisition module 401, a model building module 402, a prediction module 403, and a screening module 404, specifically:
[0097] Acquisition module 401 is used to acquire reactant information and information on small molecules that reflect the structural characteristics of the reactants; and,
[0098] Obtain all alloy catalyst systems to be screened;
[0099] To determine the relationship between the adsorption capacity of transition metals and hydrogen atoms;
[0100] The model building module 402 is used to generate an alloy catalyst surface model that satisfies the first preset reaction conditions for each alloy catalyst system based on the relationship between the adsorption capacity of transition metals and hydrogen atoms.
[0101] Based on the surface model of each alloy catalyst, the surface hydrogen atom coverage is predicted. A kinetic model is constructed by combining this with the small molecule information to obtain the intrinsic reaction kinetic information of each alloy catalyst surface model. This intrinsic reaction kinetic information includes catalytic performance; and...
[0102] A predictive model is constructed using the predicted surface hydrogen atom coverage and hydrogen atom adsorption energy as input data and the catalytic performance as output data.
[0103] The prediction module 403 is used to predict the hydrogenation rate of macromolecular olefins and the loss rate of aromatics based on the prediction model, and to obtain the predicted catalytic performance value of each alloy catalyst surface model.
[0104] The screening module 404 is used to screen the alloy catalyst according to the predicted catalytic performance value to obtain a reforming oil selective hydrogenation alloy catalyst that meets the second preset condition, which is related to the olefin hydrogenation rate and the aromatic loss rate.
[0105] It should be understood that the descriptions of the methods also apply to the screening device 400 for selective hydrogenation of reforming oil alloy catalysts according to embodiments of this application, and will not be described in detail again to avoid repetition.
[0106] Furthermore, it should be understood that the above-described division of functional modules is merely an example in the screening device 400 for selective hydrogenation of reforming oil according to the embodiments of this application. In actual applications, the above functions can be assigned to different functional modules as needed. That is, the device can be divided into functional modules different from those illustrated above to complete all or part of the functions described above.
[0107] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be applied, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.
[0108] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.
Claims
1. A method for screening alloy catalysts for selective hydrogenation of reformed oil, characterized in that, Include: Acquire information on reactants, information on small molecules that can reflect the structural characteristics of reactants, and all alloy catalyst systems to be screened; To obtain the relationship between the adsorption capacity of transition metals and hydrogen atoms, including: For transition metal systems, a transition metal surface model was built to determine how the adsorption capacity of hydrogen atoms on the transition metal surface changes with the hydrogen atom coverage. The transition metal is divided into a strong adsorption metal and a weak adsorption metal. The surface hydrogen atom coverage of the strong adsorption metal is set to a first coverage value, and the hydrogen atom adsorption capacity is set to 1. The surface hydrogen atom coverage of the weak adsorption metal is set to a second coverage value, there is no hydrogen atom adsorption energy, and the hydrogen atom adsorption capacity is set to 0. The first coverage value is greater than the second coverage value. Based on the relationship between the adsorption capacity of transition metals and hydrogen atoms, an alloy catalyst surface model that satisfies the first preset condition is generated for each alloy catalyst system. Based on the surface model of each alloy catalyst, the surface hydrogen atom coverage is predicted, including: Based on the ratio of strongly adsorbed metals to weakly adsorbed metals in the surface model of the alloy catalyst, the surface hydrogen atom coverage of the strongly adsorbed metals and weakly adsorbed metals in the surface model of the alloy catalyst is calculated respectively. The predicted surface hydrogen atom coverage is obtained by weighting the surface hydrogen atom coverage of strongly adsorbed metals and weakly adsorbed metals. A kinetic model is constructed by combining the information of the small molecule substances to obtain the intrinsic reaction kinetic information of the surface model of each alloy catalyst, wherein the intrinsic reaction kinetic information includes catalytic performance; A predictive model is constructed using the predicted surface hydrogen atom coverage and hydrogen atom adsorption energy as input data and the catalytic performance as output data. Based on the prediction model, the hydrogenation rate of macromolecular olefins and the loss rate of aromatics are predicted, and the predicted catalytic performance values of each alloy catalyst surface model are obtained. Based on the predicted catalytic performance values, alloy catalysts are screened to obtain selective hydrogenation alloy catalysts for reformed oil that meet the second preset conditions, which are related to the olefin hydrogenation rate and the aromatic loss rate.
2. The method according to claim 1, characterized in that, Further includes: First-principles calculations were performed to verify the selective hydrogenation alloy catalyst for reformed oil that met the second preset conditions, and the calculated catalytic performance values were obtained. If the verification does not meet the third preset condition, the prediction model and the predicted catalytic performance value are corrected. The third preset condition is determined based on the deviation between the calculated catalytic performance value and the predicted catalytic performance value.
3. The method according to claim 1, characterized in that, Obtain intrinsic reaction kinetics information for the surface model of each alloy catalyst, including: Based on the chemical reaction pathway network of the small molecule information, the catalytic reaction process is simulated using first-principles calculations to determine the adsorption energies of reactants, intermediate products, final products, and hydrogen atoms on the surface model of each alloy catalyst, as well as the rate control steps. Search for the transition state structure of the rate-controlling step to obtain the maximum activation energy barrier in the corresponding reaction process; Based on the adsorption energy of the reactants, intermediate products, final products, hydrogen atoms, transition state structure, and maximum activation energy barrier, the kinetic model is established, and the intrinsic reaction kinetic information of each alloy catalyst surface model is obtained through microscopic reaction kinetic analysis.
4. The method according to claim 1, characterized in that, Construct a prediction model, including: establishing a first correlation model between adsorption energy and heat of reaction based on the adsorption energy of reactants, the adsorption energy of products, and the correlation with the heat of reaction; Based on 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. A third correlation model is established based on the proportional relationship between the adsorption energies of reactants, intermediate products, and final products. A fourth correlation model is established based on the proportional relationship between surface hydrogen atom coverage, hydrogen atom adsorption energy, and reactant adsorption energy. Based on the first correlation model, the second correlation model, the third correlation model, the fourth correlation model, and the kinetic model, catalytic performance information is determined, and the prediction model regarding surface hydrogen atom coverage, hydrogen atom adsorption energy, and catalytic performance is generated.
5. The method according to claim 4, 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. The equilibrium constants of adsorption and desorption processes are corrected, the hydrogenation energy barrier of benzene and the hydrogenation reaction energy barrier are calculated, the hydrogenation rate of macromolecular olefins and the loss rate of aromatics are predicted, and the predicted catalytic performance values of the surface models of each alloy catalyst are output.
6. The method according to claim 1, characterized in that, Information on reactants, and information on small molecules that reflect the structural characteristics of the reactants, including: Based on the functional groups and active sites in the reactant information, information on small molecules that can reflect the structural characteristics of each reactant is screened out.
7. The method according to claim 1, characterized in that, The first preset conditions include: reaction temperature, atmospheric pressure, and model stability; For each of the aforementioned alloy catalyst systems, an alloy catalyst surface model satisfying a first preset reaction condition is generated, comprising: For each of the aforementioned alloy catalyst systems, Considering the pre-adsorption of H on the catalyst surface in a hydrogenation environment, By calculating the surface Gibbs free energy under different H coverage, the surface model of the alloy catalyst that satisfies the first preset reaction conditions is determined, wherein the lower the surface free energy, the higher the stability of the surface model of the alloy catalyst.
8. The method according to claim 1, characterized in that, The alloy catalyst includes a non-precious metal catalyst. Catalyst materials with the same doped metal but different alloy ratios are selected as the same alloy catalyst system.
9. The method according to claim 1, characterized in that, The second preset conditions include: olefin hydrogenation rate ≥ 95%, and aromatic loss rate ≤ 0.3%.
10. A screening device for selective hydrogenation alloy catalysts of reformed oil, characterized in that, At least include: The acquisition module is used to acquire reactant information and information on small molecules that reflect the structural characteristics of the reactants; and, Obtain all alloy catalyst systems to be screened; To obtain the relationship between the adsorption capacity of transition metals and hydrogen atoms, including: For transition metal systems, a transition metal surface model was built to determine how the adsorption capacity of hydrogen atoms on the transition metal surface changes with the hydrogen atom coverage. The transition metal is divided into a strong adsorption metal and a weak adsorption metal. The surface hydrogen atom coverage of the strong adsorption metal is set to a first coverage value, and the hydrogen atom adsorption capacity is set to 1. The surface hydrogen atom coverage of the weak adsorption metal is set to a second coverage value, there is no hydrogen atom adsorption energy, and the hydrogen atom adsorption capacity is set to 0. The first coverage value is greater than the second coverage value. The model building module is used to generate an alloy catalyst surface model that satisfies a first preset reaction condition for each alloy catalyst system based on the relationship between the adsorption capacity of transition metals and hydrogen atoms; predict the surface hydrogen atom coverage based on each alloy catalyst surface model, construct a kinetic model in combination with the small molecule information, and obtain the intrinsic reaction kinetic information of each alloy catalyst surface model, wherein the intrinsic reaction kinetic information includes catalytic performance; and construct a prediction model using the predicted surface hydrogen atom coverage and hydrogen atom adsorption energy as input data and catalytic performance as output data. The step of predicting the surface hydrogen atom coverage based on the surface model of each alloy catalyst includes: calculating the surface hydrogen atom coverage of the strongly adsorbed metal and the weakly adsorbed metal in the surface model of the alloy catalyst according to the ratio of strongly adsorbed metal to weakly adsorbed metal; and taking a weighted average of the surface hydrogen atom coverage of the strongly adsorbed metal and the weakly adsorbed metal to obtain the predicted surface hydrogen atom coverage. The prediction module is used to predict the hydrogenation rate of macromolecular olefins and the loss rate of aromatics based on the prediction model, and to obtain the predicted catalytic performance value of each alloy catalyst surface model. The screening module is used to screen the alloy catalysts according to the predicted catalytic performance values to obtain reformate selective hydrogenation alloy catalysts that meet the second preset conditions, which are related to the olefin hydrogenation rate and the aromatic loss rate.
Citation Information
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