Fischer-Tropsch reactor strengthening method based on microsystem catalyst screening and kinetic research

Abstract

The invention relates to the technical field of chemical engineering and machine learning, and provides a Fischer-Tropsch reactor strengthening method based on microsystem catalyst screening and kinetic research. The method comprises four main steps of catalyst screening, kinetic model establishment, reactor size optimization and performance verification. The method comprises the following steps: firstly, screening Fe9Cr3 as an optimal bimetallic catalyst through molecular dynamics simulation; then, establishing a kinetic model by utilizing an Arrhenius equation, and calculating a reaction rate constant and activation energy; secondly, optimizing the length-diameter ratio L / D of the reactor through macroscopic simulation and computational fluid dynamics (CFD) simulation; finally, the performance of the optimized reactor is verified through experiments, it is ensured that the activity and the synthesis rate of the catalyst reach expected targets, and CO2 emission reduction is achieved. According to the method, microscopic simulation and macroscopic simulation are combined, so that the overall efficiency and performance of F-T synthesis are improved, and meanwhile, the research and development cost is reduced, and the research and development time is shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of chemical engineering and machine learning, and in particular to a Fischer-Tropsch reactor enhancement method based on microscopic system catalyst screening and kinetic research. Background Art

[0002] Short-chain hydrocarbon fuels (SC-HCFs) extracted from petroleum are vital in various fields such as power generation, transportation and industrial production. With the rapid development of the global economy and the continuous growth of the population, the demand for SC-HCFs is also growing rapidly. However, the extraction and utilization of conventional oil not only brings a huge environmental burden, but also faces the problem of limited reserves. Fischer-Tropsch synthesis (FT) technology uses renewable resources such as natural gas and biomass to convert synthesis gas (CO and H2) into liquid fuels through catalytic reactions, providing a promising solution to these problems.

[0003] In FT synthesis, catalysts play a key role in increasing reaction rates and reducing activation energy, thereby promoting the development of the chemical industry. Therefore, catalyst screening as a key step is the focus of FT synthesis research. The traditional trial and error method is time-consuming and labor-intensive, which has prompted the development of computer simulation to find the best catalyst. At present, computer simulation methods mainly include density functional theory (DFT) calculations and molecular dynamics (MD) simulations. However, both DFT and traditional MD simulations usually involve predetermined reaction paths, which require prior definition and expert knowledge. Reaction force field molecular dynamics (ReaxFF-MD) simulations are different from traditional molecular dynamics simulations in that they dynamically capture reactions through adaptive adjustments of atomic interactions, thereby avoiding the need to predefine reaction paths. Its unique ability to simulate bond formation and breaking makes it ideal for studying complex systems with chemical reactions, providing a detailed understanding of dynamic reaction processes.

[0004] The simultaneous occurrence of reaction and mass transfer in the FT reactor is crucial to the performance of FT synthesis. The first traditional method is to screen Fischer-Tropsch synthesis (FT) catalysts by trial and error, which is time-consuming, labor-intensive and inefficient. Second, the research on the kinetic parameters of the FT synthesis reaction is not in-depth enough, and there is a lack of accurate kinetic models to support macroscopic simulation and optimize reactor performance. Third, the reactor size design lacks a scientific basis, and the design of key dimensions such as the reactor's aspect ratio (L / D) often relies on experience or trial and error, and lacks scientific optimization methods. Fourth, the traditional catalyst screening and reactor optimization process requires a lot of experimental verification, which is costly and time-consuming. Fifth, traditional experimental methods are difficult to efficiently handle multivariate and high-dimensional data, and identify nonlinear relationships and complex patterns in the data.

[0005] Based on this, it is of great significance to study the Fischer-Tropsch reactor intensification method based on microscopic system catalyst screening and kinetic research. Summary of the invention

[0006] In order to solve the problems existing in the background technology, the present invention provides a Fischer-Tropsch reactor strengthening method based on microscopic system catalyst screening and kinetic research, which comprises the following steps:

[0007] S1. Catalyst screening: Screening the best bimetallic catalyst for FT synthesis of short-chain hydrocarbon fuels SC-HCFs from the bimetallic catalyst combination;

[0008] S2. Kinetic model establishment: Study the reaction kinetic parameters of the optimal catalytic system and establish a kinetic model to support macroscopic simulation and optimize reactor performance;

[0009] S3. Reactor size optimization: Optimize the aspect ratio L / D of the FT reactor through mutual verification of process simulation and computational fluid dynamics (CFD) simulation;

[0010] S4. Reactor performance verification: Verify the performance of the optimized reactor to ensure that the expected catalyst activity and synthesis rate are achieved and CO 2 Emission reduction.

[0011] In the preferred solution, in step S1, the specific process includes:

[0012] S11, catalyst selection: using compounds of different valences composed of bimetallic catalysts Fe / Cr, Fe / Cu, and Cu / Cr as candidate catalysts;

[0013] S12, Reaction system box construction: Construct a reaction system box for each catalyst as a microscopic model of the FT reactor; use the "ball-and-stick" model of molecular dynamics simulation to display the molecules, and optimize the structure through the integration of force field and simulation algorithm;

[0014] S13, preliminary optimization: preliminary optimization of the reaction system box, including annealing process, canonical ensemble NVT and constant pressure-temperature ensemble NPT relaxation, to achieve energy minimization and structural stability;

[0015] S14, simulation and analysis: Run ReaxFF-MD simulation to observe the effects of different catalysts on product distribution and carbon conversion rate; select the best catalyst Fe through comparison and analysis. 9 Cr 3 .

[0016] In a preferred solution, the specific steps of step S2 include:

[0017] S21. Simulation at different temperatures: Fe 9 Cr 3 The catalytic system was simulated by ReaxFF-MD, and the trend of the number of reactants was recorded;

[0018] S22, Kinetic parameter calculation: Use the Arrhenius equation to fit the simulation data and calculate the reaction rate constant k and activation energy Ea;

[0019] S23. Kinetic model establishment: Based on the calculated kinetic parameters, a kinetic model of FT synthesis is established for subsequent macroscopic simulation and optimization.

[0020] In a preferred solution, the specific process of step S21 includes:

[0021] The ReaxFF-MD simulation method was used to simulate the Fe 9 Cr 3 Catalytic system simulation;

[0022] At each temperature point, record the key reactant H in the reaction process in detail. 2 The changing trend of quantity; by monitoring H 2 The change in the number of molecules over time indirectly reflects the rate and progress of the catalytic reaction.

[0023] In a preferred solution, the specific process of step S22 includes:

[0024] The Arrhenius equation is an empirical formula that describes the relationship between the reaction rate constant and temperature. The Arrhenius equation is used to fit the simulated data;

[0025] By analyzing the changes in reaction rates at different temperatures, the reaction rate constant k under a specific catalytic system is calculated; the reaction rate constant reflects the speed of the catalytic reaction;

[0026] The activation energy Ea of the reaction is calculated based on the slope of the Arrhenius equation; the activation energy is the minimum energy threshold that the reactant molecules can reach for effective collision, reflecting the difficulty of the catalytic reaction.

[0027] In a preferred solution, the specific process of step S23 includes:

[0028] The calculated reaction rate constant k and activation energy Ea are used as key input parameters, and combined with the basic mechanism and steps of the FT synthesis reaction, a kinetic model is established to describe the rate change law of the catalytic reaction under different conditions, as well as the influence of factors such as temperature and pressure on the reaction rate. During the macroscopic simulation process, the kinetic model predicts and optimizes the performance of the reactor through numerical simulation methods.

[0029] In a preferred solution, the specific process of step S3 includes:

[0030] S31. Macroscopic simulation: Use reaction kinetic parameters to perform macroscopic simulation and analyze the effect of different L / D ratios on reactor performance; by adjusting the length and diameter of the reactor, observe the changes in the mole fraction of each component in the product gas;

[0031] S32, CFD simulation: Establish a CFD model to perform numerical simulation on the bubbling fluidized bed reactor; combine the fluid mechanics model and the chemical reaction kinetics model to simulate the fluid flow and reaction process in the reactor;

[0032] S33. Result comparison and verification: Compare the results of macro simulation and CFD simulation to evaluate the accuracy and reliability of the model; based on the simulation results, optimize the L / D ratio of the reactor to achieve the best performance.

[0033] In a preferred solution, the specific process of step S4 includes:

[0034] S41. Experimental preparation: Experimental preparation is carried out in the optimized reactor, including the catalyst Fe 9 Cr 3 loading, charging of reaction gas, and heating of the reactor to a preset temperature;

[0035] S42, production verification: Start the reactor and start actual production verification; operate the reactor under optimized operating conditions and monitor the gas product CO 2 and the mass content of the target product;

[0036] S43. Data collection and analysis: Collect gas product samples regularly and determine the content of each component by chromatographic analysis;

[0037] S44, Result confirmation: According to the experimental data, confirm the CO content in the gas product 2 Whether the quality content of

[0038] The beneficial effects achieved by the present invention are:

[0039] The present invention designs a Fischer-Tropsch reactor strengthening method based on microscopic system catalyst screening and kinetic research, realizes efficient screening of catalysts, accurate establishment of kinetic models, scientific optimization of reactor size and reliable verification of performance, significantly improves the overall efficiency and performance of Fischer-Tropsch synthesis, and adopts the ReaxFF-MD simulation method to screen out the best catalyst Fe9Cr3 from the bimetallic catalyst combination, significantly improves the efficiency and accuracy of catalyst screening; through ReaxFF-MD simulation and Arrhenius equation fitting, a kinetic model of FT synthesis is established. Accurate kinetic parameters are provided, macroscopic simulation and optimization of reactor performance are supported, and the scientificity and reliability of reactor design are enhanced. Combining macroscopic simulation and CFD simulation, the aspect ratio L / D of the FT reactor is optimized through mutual verification, and the scientific optimization of the reactor size is realized, and the performance of the reactor is improved, including product distribution, conversion rate and energy efficiency, etc., while unnecessary experimental verification is reduced and the cost is reduced. Through actual production verification and data analysis, the performance of the optimized reactor is confirmed, including catalyst activity and synthesis rate, as well as CO 2 The emission reduction effect ensures the reliability and practicability of the optimization results, provides strong support for industrial applications, and at the same time achieves environmental protection goals and reduces greenhouse gas emissions.

[0040] The present invention combines microscopic system catalyst screening, kinetic model establishment, reactor size optimization and performance verification to form a complete set of Fischer-Tropsch reactor enhancement methods. This method not only improves the overall efficiency and performance of Fischer-Tropsch synthesis, but also reduces R&D costs and time, providing new ideas and solutions for the application of chemical and machine learning technologies in the field of catalytic reactions. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 Framework diagram of the method of the present invention.

[0042] Figure 2 Construction of high-temperature FT reaction system box under different catalysts.

[0043] Figure 3 Preliminary optimization of the energy of the catalyst-free reaction system.

[0044] Figure 4 Preliminary selection of catalysts for high temperature FT reaction.

[0045] Figure 5 Construction of high-temperature FT reaction system box under small-proportion catalyst.

[0046] Figure 6 Catalyst selection for high temperature FT reaction based on small iron / chromium ratio.

[0047] Figure 7 H at different temperatures2 The trend of the number of molecules over time.

[0048] Figure 8 Fe 9 Cr 3 Kinetic fitting of catalytic reaction system.

[0049] Fig. 9 Fe 9 Cr 3 Catalytic system reactor model.

[0050] Fig.10 Variation of the mole fraction of CO in the product gas with length and diameter. Fig.11 CO in product gas 2 and CH 4 The mole fraction varies with length and diameter.

[0051] Fig.12 CO in product gas 2 and CH 4 Variation of the mole fraction with length and diameter (single variable).

[0052] Fig.13 Schematic geometry of the bubbling fluidized bed reactor model.

[0053] Fig.14 Solid and gas volume fraction curves.

[0054] Fig.15 Different heights of CO at different diameter ratios 2 Distributed CFD simulation. DETAILED DESCRIPTION

[0055] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. In addition, the forms of the various structures recorded in the following embodiments are merely illustrative, and the present invention is not limited to the various structures recorded in the following embodiments. All other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0056] Reference Figure 1-Figure 15 In order to solve the problems existing in the background technology, the present invention provides a Fischer-Tropsch reactor strengthening method based on microscopic system catalyst screening and kinetic research, which comprises the following steps:

[0057] S1. Catalyst screening: Screening the best bimetallic catalyst for FT synthesis of short-chain hydrocarbon fuels SC-HCFs from the bimetallic catalyst combination;

[0058] S2. Kinetic model establishment: Study the reaction kinetic parameters of the optimal catalytic system and establish a kinetic model to support macroscopic simulation and optimize reactor performance;

[0059] S3. Reactor size optimization: Optimize the aspect ratio L / D of the FT reactor through mutual verification of process simulation and computational fluid dynamics (CFD) simulation;

[0060] S4. Reactor performance verification: Verify the performance of the optimized reactor to ensure that the expected catalyst activity and synthesis rate are achieved and CO 2 Emission reduction.

[0061] In the preferred solution, in step S1, the specific process includes:

[0062] S11, catalyst selection: using compounds of different valences composed of bimetallic catalysts Fe / Cr, Fe / Cu, and Cu / Cr as candidate catalysts;

[0063] S12, Reaction system box construction: Construct a reaction system box for each catalyst as a microscopic model of the FT reactor; use the "ball-and-stick" model of molecular dynamics simulation to display the molecules, and optimize the structure through the integration of force field and simulation algorithm;

[0064] S13, preliminary optimization: preliminary optimization of the reaction system box, including annealing process, canonical ensemble NVT and constant pressure-temperature ensemble NPT relaxation, to achieve energy minimization and structural stability;

[0065] S14, simulation and analysis: Run ReaxFF-MD simulation to observe the effects of different catalysts on product distribution and carbon conversion rate; select the best catalyst Fe through comparison and analysis. 9 Cr 3 .

[0066] The specific steps of step S2 include:

[0067] S21. Simulation at different temperatures: ReaxFF-MD simulations were performed on the Fe9Cr3 catalytic system at different temperatures, and the trend of the number of reactants was recorded;

[0068] S22, Kinetic parameter calculation: Use the Arrhenius equation to fit the simulation data and calculate the reaction rate constant k and activation energy Ea;

[0069] S23. Kinetic model establishment: Based on the calculated kinetic parameters, a kinetic model of FT synthesis is established for subsequent macroscopic simulation and optimization.

[0070] The specific process of step S21 includes:

[0071] The ReaxFF-MD simulation method was used to simulate the Fe 9 Cr 3 Catalytic system simulation;

[0072] At each temperature point, record the key reactant H in the reaction process in detail. 2 The changing trend of quantity; by monitoring H 2 The change in the number of molecules over time indirectly reflects the rate and progress of the catalytic reaction.

[0073] In a preferred solution, the specific process of step S22 includes:

[0074] The Arrhenius equation is an empirical formula that describes the relationship between the reaction rate constant and temperature. The Arrhenius equation is used to fit the simulated data;

[0075] By analyzing the changes in reaction rates at different temperatures, the reaction rate constant k under a specific catalytic system is calculated; the reaction rate constant reflects the speed of the catalytic reaction;

[0076] The activation energy Ea of the reaction is calculated based on the slope of the Arrhenius equation; the activation energy is the minimum energy threshold that the reactant molecules can reach for effective collision, reflecting the difficulty of the catalytic reaction.

[0077] The specific process of step S23 includes:

[0078] The calculated reaction rate constant k and activation energy Ea are used as key input parameters, and combined with the basic mechanism and steps of the FT synthesis reaction, a kinetic model is established to describe the rate change law of the catalytic reaction under different conditions, as well as the influence of factors such as temperature and pressure on the reaction rate. During the macroscopic simulation process, the kinetic model predicts and optimizes the performance of the reactor through numerical simulation methods.

[0079] The specific process of step S3 includes:

[0080] S31. Macroscopic simulation: Use reaction kinetic parameters to perform macroscopic simulation and analyze the effect of different L / D ratios on reactor performance; by adjusting the length and diameter of the reactor, observe the changes in the mole fraction of each component in the product gas;

[0081] S32, CFD simulation: Establish a CFD model to perform numerical simulation on the bubbling fluidized bed reactor; combine the fluid mechanics model and the chemical reaction kinetics model to simulate the fluid flow and reaction process in the reactor;

[0082] S33. Result comparison and verification: Compare the results of macro simulation and CFD simulation to evaluate the accuracy and reliability of the model; based on the simulation results, optimize the L / D ratio of the reactor to achieve the best performance.

[0083] The specific process of step S4 includes:

[0084] S41. Experimental preparation: Experimental preparation is carried out in the optimized reactor, including the catalyst Fe 9 Cr 3 loading, charging of reaction gas, and heating of the reactor to a preset temperature;

[0085] S42, production verification: Start the reactor and start actual production verification; operate the reactor under optimized operating conditions and monitor the gas product CO 2 and the mass content of the target product;

[0086] S43. Data collection and analysis: Collect gas product samples regularly and determine the content of each component by chromatographic analysis;

[0087] S44, Result confirmation: According to the experimental data, confirm the CO content in the gas product 2 Whether the quality content of

[0088] Example 1: This example illustrates a method for strengthening a Fischer-Tropsch reactor based on microscopic system catalyst screening and kinetic research through a specific case. The theoretical framework is as follows: Figure 1 As shown, the specific steps are as follows:

[0089] (1) ReaxFF-MD simulation was used to determine the optimal bimetallic catalyst for FT synthesis of SC-HCFs.

[0090] Iron-based, cobalt-based and nickel-based catalysts are commonly used in FT synthesis. Among them, iron-based catalysts have been more widely used in actual industry due to their low cost, abundant resources and high catalytic activity. However, monometallic catalysts usually show low selectivity, which poses a challenge in controlling the quality of product distribution. In contrast, the advantage of bimetallic catalysts is that the composition and ratio of different metals can be adjusted to achieve precise control of the product. In the present invention, three types of bimetallic catalysts are considered for the synthesis of FT, and the effects of different ratios between them on the reaction performance are deeply studied. In order to fully visualize the FT synthesis process, a reaction system box is constructed as a microscopic model of the FT reactor. The integration of various force fields and simulation algorithms allows structural optimization and energy calculation to improve the accuracy and reliability of ReaxFF-MD simulation. Considering a total of 12 catalyst configurations including Fe / Cr, Fe / Cu, and Cu / Cr with a ratio of 0 to 1, the system box construction process is summarized as follows: First, the structure of the single molecule is optimized to obtain a more suitable reaction geometry. Then 12 reaction system boxes are established, such as Figure 2 The molecules are shown using a ball-and-stick model, with their relative sizes determined by their atomic radius. In addition, the catalyst model uses a block structure rather than a single molecule, which better reflects the composition and structure of the actual catalyst, allowing for a more accurate simulation of its behavior in the reaction.

[0091] When designing and optimizing the FT synthesis box, the choice of force field directly affects the accuracy and reliability of the ReaxFF-MD simulation results. In the present invention, CVFF is used to construct and optimize the FT system box. Taking the FT synthesis system of pure iron catalyst as an example, the energy convergence process of the system is described in detail. The annealing process adjusts the position of the atoms in the box to reach an equilibrium state, thereby determining the ground state structure of the system. Figure 3 As shown in (a), the energy reaches the lowest point in the configuration of the 78th frame. Subsequently, the canonical ensemble (NVT) and constant pressure-temperature ensemble (NPT) relaxations are performed, with a step size of 0.25 fs and a total time of 100 ps. The trends of temperature and density in the two relaxation processes are shown in Figure 3 (b) and (c). It is observed that the system box temperature gradually stabilizes at around 300°C. Finally, based on the final configuration of NVT relaxation, the system box density is stabilized by NPT relaxation to achieve preliminary optimization of the system box energy. By adopting NVT and NPT relaxation, the internal structure and dynamic behavior of the system box are further considered to simulate more realistic reaction conditions.

[0092] After achieving system box energy optimization and obtaining configuration data, ReaxFF-MD simulation is performed. During the simulation process, in order to ensure the reliability and accuracy of the simulation results, the parameters and analysis results must be carefully selected. Initially, the XX.Data file is generated based on the configuration data, which describes the molecular structure in the box and contains information such as atomic coordinates, types, and charges. In the preliminary processing of the XX.Data file, the writing of the in.XX file is for easy reading and execution. The key writing steps are listed below:

[0093] ① Implementing periodic boundary conditions allows simulation of infinitely large repetitive structures, which is very similar to the real reaction environment. This approach alleviates the unnecessary boundary effects introduced by non-periodic boundary conditions, thereby better characterizing intermolecular interactions and obtaining more reliable simulation results;

[0094] ② Selecting Gaussian distribution to define the initial velocity of atoms avoids velocity extremes and improves the accuracy and credibility of ReaxFF-MD simulations;

[0095] ③ Using NVT system to maintain temperature and volume stability during simulation, thus facilitating further reaction kinetics calculations;

[0096] ④ Set a heating interval of 50K to ensure a steady rise in the box temperature and prevent instability caused by sudden temperature changes.

[0097] Finally, other parameters are set as follows:

[0098] ①Force field file selection: C / H / O / Fe / Al / Ni / Cu / S / Cr.ff;

[0099] ②Simulation step size: 0.1fs, a total of 20,000,000 steps, simulation time 2000ps;

[0100] ③Definition of output file: bond.tatb, containing key information at different time steps, such as number of bonds, type, length and potential energy;

[0101] ④Define the output file: species.Out, which mainly includes information such as particle type, corresponding atomic or molecular identification number, coordinate position at different time steps, etc.;

[0102] ⑤Define the output file: dump.Lammpstrj, which stores the simulation trajectory information of each time step.

[0103] The output files were processed and analyzed to extract information such as particle type, quantity, bond type, atom ID, etc. at each step of the reaction simulation. Finally, the simplified molecular input line input system (SMILES) expression of the particles was obtained and compared with the standard SMILES to determine the final reaction product.

[0104] The choice of catalyst significantly affects the distribution of molecules of different chain lengths in the reaction products, such as Figure 4 (a) For example, the product of a pure iron-based catalyst is mainly composed of C 1 The products of pure copper-based and chromium-based catalysts are mainly composed of C 1 In its production, C 1 The main component of carbon is methane, which has low energy density, and the production of methane is associated with energy loss. Therefore, reducing methane production is essential to improve carbon utilization and reduce energy loss. Therefore, this study does not consider it to be the best catalyst. Figure 4 (b) illustrates the relationship between catalyst type and carbon conversion, which facilitates comparative analysis of the effects of different catalysts. Each catalytic reaction system initially contains 100 carbon atoms, with a carbon atom conversion rate of 80% as a reference indicator. Both Fe9Cr3 and Fe6Cr6 catalysts can produce products containing more than 80 carbon atoms. Specifically, Fe9Cr3 can convert 94 carbon atoms, while Fe6Cr6 can convert 88 carbon atoms, such as Figure 4 (b) is shown. The product distribution under the action of the two catalysts is shown in Figure 4 (c) and (d). It can be clearly seen that the proportion of C1 in the Fe9Cr3 catalytic system is lower than that in the Fe6Cr6 catalytic system. Therefore, Fe9Cr3 was selected from the 12 catalysts for further study.

[0105] In order to more accurately determine the Fe / Cr ratio for the best catalytic effect, catalytic systems with other ratios close to Fe9Cr3 were simulated and analyzed. Figure 5 Four catalytic systems with different ratios of Fe7Cr5, Fe8Cr4, Fe10Cr2 and Fe11Cr1 are given, and the corresponding box dimensions are shown in Table 2. In order to ensure the reliability of the analysis results, the parameter settings used in the simulation process are consistent with those of the Fe9Cr3 catalytic system simulation.

[0106] from Figure 6 As can be seen from (a), the production of Fe11Cr1, Fe10Cr2 and Fe9Cr3 catalysts is higher. Figure 6When the number of converted carbon atoms is shown in (b), Fe7Cr6, Fe8Cr4 and Fe9Cr3 show higher conversion ratios. Considering both the number of products and carbon conversion rate, Fe9Cr3 is the most suitable catalyst for high-temperature FT synthesis among the 16 bimetallic catalysts.

[0107] (2) Study the reaction kinetic parameters of the optimal catalytic system and apply them to macroscopic simulations to improve the performance and efficiency of the reactor.

[0108] Reaction kinetic parameters play a key role in the design, optimization and scale-up of FT synthesis processes. In particular, the reaction rate constant (k) and activation energy (E a ) are two key parameters that determine the hydrocarbon production rate and energy efficiency of the FT reactor. An increase in temperature increases the probability of atomic collisions, thereby reducing Ea for both exothermic and endothermic reactions. Temperature also has a significant effect on k, but the effect varies depending on the type of reaction. Therefore, it is crucial to consider the type of reaction when analyzing the effect of temperature on k. As temperature increases, k tends to decrease for exothermic reactions, while k tends to increase for endothermic reactions. The heat transfer properties, k, and Ea of Fe9Cr3 catalyzed FT synthesis were calculated by fitting the simulation data using the Arrhenius equation. a These parameters serve as essential inputs for kinetic modeling of FT reactors and provide valuable insights into the temperature and pressure dependence of catalytic activity.

[0109] k represents the rate at which the concentration of the reactant decreases over time and is a proportional constant that controls the reaction rate and concentration. As a highly complex process involving multiple steps, FT synthesis is generally considered to be a complete first-order reaction. Therefore, k for FT synthesis can be expressed as formula (1)-(2).

[0110] r=k·[CO][H 2 ] (1)

[0111] [H 2 ]=[H 2 ] 0 ·e -kt (2)

[0112] k is a measure of how fast a reaction proceeds. In industrial settings, k provides valuable insights into reaction mechanisms. Therefore, understanding k is essential for developing production plans, designing reactors, and effectively controlling reaction processes. Since the reaction rate constant is affected by factors such as temperature, pressure, and catalyst composition, optimizing these parameters can improve production efficiency and product quality.

[0113] The Fe9Cr3 catalytic system at different temperatures was simulated using the ReaxFF-MD simulation method. The components of the reaction system were simulated in parallel by keeping the pressure, volume and other relevant parameters constant. The temperature interval was systematically increased by 200 K. The reactant H 2 The change in the number of molecules is Figure 7 As shown, time is expressed in ps. In order to determine k at different temperatures, referring to equation (2), an equivalent reaction rate equation is derived, as shown in equation (3).

[0114]

[0115] Referring to formula (3), for each temperature, H 2 The trend of the change in the number of molecules is exponentially fitted, such as Figure 5 The reaction rate equations at different temperatures are shown in Table 3. The time dimension of the reaction rate constant is expressed in seconds. In order to evaluate the quality of the fit, the coefficient of determination (R 2 The calculation formula is shown in formula (4), and the value range is 0 to 1. 2 The higher the value, the higher the fitting accuracy.

[0116]

[0117] The calculation methods of SSR, SST and SSE are shown in equations (5)-(7):

[0118]

[0119] E a Represents the energy barrier that a reaction must exceed, as the minimum energy required by the reactants to initiate the reaction. It fundamentally affects the rate and temperature sensitivity of the reaction, and lower Ea generally favors higher reaction rates. The k and E of a system a The relationship between is described by the Arrhenius formula, as shown in formula (8):

[0120]

[0121] Equation (8) reveals a remarkable inference: there is a linear correlation between the inverse of the reaction temperature and the logarithm of k. Therefore, the linear fit of k at different temperatures is as follows: Figure 8 shown.

[0122] Under the catalysis of Fe9Cr3, the Arrhenius expression controlling the FT synthesis is shown in Eq. (9), where E a Calculated to be 116.40 kJ / mol.

[0123] lnk=12.98+14000.74 / T (8)

[0124] (3) Through mutual verification of process simulation and CFD simulation, the aspect ratio of the FT reactor is optimized to further improve its performance.

[0125] Fluidized bed reactors are widely used in large-scale production due to their excellent structural characteristics. Fig. 9 As shown in the figure, this study uses macroscopic simulation technology to replicate the actual production process and optimize the size of the FT reactor to improve product yield and quality. Macroscopic simulation uses the reaction kinetics obtained in the early stage to determine the reactor parameters and ensure that the simulation results are accurate and profound.

[0126] The length-to-diameter ratio (L / D) of the reactor has a significant impact on the flow dynamics and mass transfer of the FT reactor, and thus has an important impact on the overall performance of the reactor. An appropriate L / D ratio is essential to promote uniform mixing of the gas and solid phases, thereby reducing the occurrence of dead zones and concentration gradients. Therefore, taking L / D as an example, the FT reactor is enhanced to achieve efficient operation. Fig.10 The change in the CO mole fraction in the product gas when the reactor length and diameter are adjusted simultaneously. As these two parameters increase, the CO content decreases significantly and tends to stabilize. This observation suggests that increasing the length and diameter can increase the diffusion rate and contact time of the reactants, thereby promoting the reaction and reducing the residual CO level.

[0127] In addition, CO in the product gas 2 and CH 4 The mole fraction of is used as an indicator to determine the optimal L / D of the reactor, such as Fig.11 As shown. Fig.11 (a) It can be seen that CO in the product gas 2 The mole fraction increases with increasing FT reactor length and is not affected by changes in reactor diameter. Fig.11 (b) It can be seen that CH 4 The mole fraction of increases and tends to be stable with the increase of reactor length or diameter, but the overall variation range is small. The observed phenomenon can be explained as follows:

[0128] ① Increasing the reactor length means that the reactants have a longer residence time in the reactor, allowing primary and secondary reactions to further develop toward products;

[0129] ② As the reactor diameter increases, the concentration of reactants per unit volume is relatively low, resulting in a limited reaction rate, making CH 4 The generation rate is faster than that of CO 2 , which eventually leads to an increase in the mole fraction of methane;

[0130] ③The characteristics of the fluidized bed reactor provide a relatively uniform gas distribution across the entire cross section, resulting in negligible effect of diameter size on the reaction.

[0131] The previous analysis showed that there is a weak correlation between reactor length and diameter. To provide a brief overview of their impact, further investigations were conducted, such as Fig.12 Compared with the observed CO 2 and CH 4 In contrast to the fluctuations in CO content, the range of CO content is significantly sensitive to changes in reactor size. This phenomenon can be attributed to the significant effect of reactor size on the overall extent of the reaction, rather than causing significant changes in the product composition ratios. The main goal of the optimization is to minimize the CO content in the product gas. 2 and CH 4 molar ratio of CO while ensuring that the molar fraction of CO remains below 5%. Therefore, the size of the FT reactor is carefully determined to achieve a delicate balance between efficiency and cost-effectiveness. Therefore, the final size of the FT reactor is set to 5.5m long, 0.5m in diameter, and an L / D ratio of 11. This configuration not only achieves the optimization goals, but also reduces unnecessary capital costs associated with oversized equipment. Ultimately, the mass fraction of carbon dioxide is reduced to 16.19%, and the product distribution is shown in Table 4.

[0132] The above macro reactor models rely on assumptions and empirical parameters. CFD simulations can more accurately describe the flow of complex fluids. By comparing the results of the two methods, the accuracy and reliability of the models can be evaluated, providing valuable insights for the advancement of FT technology. Modeling of reaction flows within fluidized bed reactors combines bed fluid dynamics (hydrodynamics) and chemical reaction kinetic models. The hydrodynamic model uses a conventional fluidization model, and the kinetic model is supported by molecular simulation data. The subsequent sections outline the specific parameters and configuration of the CFD modeling process:

[0133] ① The Euler-Euler multiphase flow model is adopted, and the gas phase adopts the standard k-ε turbulence dispersion model;

[0134] ② The gas-solid tracer coefficient adopts the Gidaspow model;

[0135] ③ The Gunn model is used for gas-solid heat transfer;

[0136] ④The restitution coefficient of elastic collision between particles is set to 0.9;

[0137] ⑤ The simulation process uses velocity inlet and pressure outlet boundary conditions;

[0138] ⑥ The adiabatic wall adopts the conditions of no velocity slip and no mass intrusion;

[0139] ⑦Use the standard wall function method to compensate for the wall boundary layer effect;

[0140] ⑧ The reactor type is a bubbling fluidized bed, and the geometric model of the bubbling fluidized bed is established, and meshing is performed through meshing;

[0141] ⑨During the reaction, assuming that the particle size remains unchanged, the average diameter of Fe9Cr3 particles is 500 mm and is evenly distributed;

[0142] ⑩Particle density is 2600kg / m 3 , belonging to Geldart Class B. Using the pressure-based PC-SIMPLE algorithm.

[0143] Using the Euler multiphase flow model, particle dynamics, and gas-solid heterogeneous chemical reaction kinetics, the reactor specifications were scaled down 100 times to match the actual industrial processing capacity. On this basis, the reactor wall was reinforced to improve the realism of the flow simulation, and the FT reaction in the bubbling fluidized bed was numerically simulated. The specific simulation geometry is as follows Fig.13 The main input parameters are shown in Table 5.

[0144] The CFD model generates a graph depicting the catalyst and CO 2 Distribution cloud. Fig.14 (ae) shows the distribution cloud of the catalyst bed at different L / H ratios (11, 9, 7, 5, 3), and the dimensions of the CFD model are shown in Table 6. Fig.14 (fj) is the same L / H interval CO 2 Gas distribution cloud map.

[0145] Fig.15 Describes the different lengths of CO in the CFD model 2 The red, blue, and black curves represent the CO distribution when the reactor length is 0.5x, 0.75x, and 1x, respectively. 2 When the L / D ratio is 3 and 5, CO 2 The mole fraction of CO shows an initial increase and then decreases with increasing length, indicating insufficient mixing within the reactor at these ratios. In contrast, when the L / D ratio is 7, CO 2 The mole fraction of CO increases with increasing length, indicating insufficient mixing and contact area between the reactants and the catalyst. However, when the L / D ratio is 9 and 11, CO 2 The mole fraction of increases with the length before stabilizing, indicating that these ratios are more appropriate choices. At this ratio, the gas flow in the reactor is more uniform, ensuring sufficient contact between the reactants and the catalyst, thereby improving the reaction efficiency and product selectivity. When the simulation time reaches about 4.5 seconds, CO at 0.75 reactor length 2The mole fraction is lower than 0.5 reactor length, and the L / H ratio is 9, indicating that there is a local diffusion effect in the reactor. Therefore, an L / H ratio of 11 is selected as the final size of the FT reactor. These CFD simulation results are consistent with the sensitivity analysis results, achieving mutual verification of mesoscale and macroscale simulations. Tables 1-5 are shown below:

[0146] Table 1 Reaction system box size and optimized potential energy information

[0147]

[0148] Table 2: Box size and optimized potential energy information for small-scale reaction systems

[0149]

[0150] Table 3 Reaction rate equations at different temperatures

[0151]

[0152] Table 4 Gas distribution in the reactor after optimization

[0153]

[0154] Table 5 Input parameter contents

[0155]

[0156] Table 6 Input parameter contents

[0157]

[0158]

[0159] At this point, the Fischer-Tropsch reactor enhancement method based on microscopic system catalyst screening and kinetic research was successfully completed.

[0160] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A Fischer-Tropsch reactor enhancement method based on microscopic system catalyst screening and kinetic research, characterized in that: It includes the following steps: S1. Catalyst screening: Screening the best bimetallic catalyst for FT synthesis of short-chain hydrocarbon fuels SC-HCFs from the bimetallic catalyst combination; S2. Kinetic model establishment: Study the reaction kinetic parameters of the optimal catalytic system and establish a kinetic model to support macroscopic simulation and optimize reactor performance; S3. Reactor size optimization: Optimize the aspect ratio L / D of the FT reactor through mutual verification of process simulation and computational fluid dynamics (CFD) simulation; S4. Reactor performance verification: Verify the performance of the optimized reactor to ensure that the expected catalyst activity and synthesis rate are achieved and CO2 emission reduction is achieved.

2. The Fischer-Tropsch reactor strengthening method based on microscopic system catalyst screening and kinetic research according to claim 1, characterized in that: In step S1, the specific process includes: S11, catalyst selection: using compounds of different valences composed of bimetallic catalysts Fe / Cr, Fe / Cu, and Cu / Cr as candidate catalysts; S12, Reaction system box construction: Construct a reaction system box for each catalyst as a microscopic model of the FT reactor; use the "ball-and-stick" model of molecular dynamics simulation to display the molecules, and optimize the structure through the integration of force field and simulation algorithm; S13, preliminary optimization: preliminary optimization of the reaction system box, including annealing process, canonical ensemble NVT and constant pressure-temperature ensemble NPT relaxation, to achieve energy minimization and structural stability; S14. Simulation and analysis: Run ReaxFF-MD simulation to observe the effects of different catalysts on product distribution and carbon conversion rate; select the best catalyst Fe9Cr3 through comparison and analysis.

3. The Fischer-Tropsch reactor strengthening method based on microscopic system catalyst screening and kinetic research according to claim 2, characterized in that: The specific steps of step S2 include: S21. Simulation at different temperatures: ReaxFF-MD simulations were performed on the Fe9Cr3 catalytic system at different temperatures, and the trend of the number of reactants was recorded; S22, Kinetic parameter calculation: Use the Arrhenius equation to fit the simulation data and calculate the reaction rate constant k and activation energy Ea; S23. Kinetic model establishment: Based on the calculated kinetic parameters, a kinetic model of FT synthesis is established for subsequent macroscopic simulation and optimization.

4. The Fischer-Tropsch reactor strengthening method based on microscopic system catalyst screening and kinetic research according to claim 3, characterized in that: The specific process of step S21 includes: From 2500K to 3500K, a temperature point was set every 200K. At a series of preset temperature points, the Fe9Cr3 catalytic system was simulated using the ReaxFF-MD simulation method. At each temperature point, the changing trend of the amount of key reactant H2 during the reaction process is recorded in detail; by monitoring the change in the number of H2 molecules over time, the rate and progress of the catalytic reaction are indirectly reflected.

5. The Fischer-Tropsch reactor strengthening method based on microscopic system catalyst screening and kinetic research according to claim 3, characterized in that: The specific process of step S22 includes: The Arrhenius equation is an empirical formula that describes the relationship between the reaction rate constant and temperature. The Arrhenius equation is used to fit the simulated data; By analyzing the changes in reaction rates at different temperatures, the reaction rate constant k under a specific catalytic system is calculated; the reaction rate constant reflects the speed of the catalytic reaction; The activation energy Ea of the reaction is calculated based on the slope of the Arrhenius equation; the activation energy is the minimum energy threshold that the reactant molecules can reach for effective collision, reflecting the difficulty of the catalytic reaction.

6. The Fischer-Tropsch reactor enhancement method based on microscopic system catalyst screening and kinetic research according to claim 3, characterized in that: The specific process of step S23 includes: The calculated reaction rate constant k and activation energy Ea are used as key input parameters, and combined with the basic mechanism and steps of the FT synthesis reaction, a kinetic model is established to describe the rate change law of the catalytic reaction under different conditions, as well as the influence of factors such as temperature and pressure on the reaction rate. During the macroscopic simulation process, the kinetic model predicts and optimizes the performance of the reactor through numerical simulation methods.

7. The Fischer-Tropsch reactor strengthening method based on microscopic system catalyst screening and kinetic research according to claim 1, characterized in that: The specific process of step S3 includes: S31. Macroscopic simulation: Use reaction kinetic parameters to perform macroscopic simulation and analyze the effect of different L / D ratios on reactor performance; by adjusting the length and diameter of the reactor, observe the changes in the mole fraction of each component in the product gas; S32, CFD simulation: Establish a CFD model to perform numerical simulation on the bubbling fluidized bed reactor; combine the fluid mechanics model and the chemical reaction kinetics model to simulate the fluid flow and reaction process in the reactor; S33. Result comparison and verification: Compare the results of macro simulation and CFD simulation to evaluate the accuracy and reliability of the model; based on the simulation results, optimize the L / D ratio of the reactor to achieve the best performance.

8. The Fischer-Tropsch reactor strengthening method based on microscopic system catalyst screening and kinetic research according to claim 2, characterized in that: The specific process of step S4 includes: S41, experimental preparation: performing experimental preparation in the optimized reactor, including loading of the catalyst Fe9Cr3, charging of the reaction gas, and heating the reactor to a preset temperature; S42, production verification: start the reactor and start the actual production verification; operate the reactor under the optimized operating conditions and monitor the mass content of the gas product CO2 and the target product; S43. Data collection and analysis: Collect gas product samples regularly and determine the content of each component by chromatographic analysis; S44. Result confirmation: Based on the experimental data, confirm whether the mass content of CO2 in the gas product is significantly reduced to the expected level.

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