Reaction process model construction method and reaction safety prediction method

Abstract

The invention relates to a reaction process model construction method and a reaction safety prediction method. The method comprises the following steps: acquiring catalyst particle parameters and reactor reaction section parameters in the current reaction process; simulating the filling process of the catalyst particles in the fixed bed reactor based on the catalyst particle parameters and the reactor reaction section parameters to obtain a catalyst particle accumulation model; constructing a reaction process model of the current reaction process according to the particle position of the catalyst particle accumulation model; the reaction process model comprises a multiphase flow field model and a chemical reaction model which are coupled with each other. By adopting the method, the comprehensiveness and accuracy of a prediction result can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of safety monitoring, in particular to a reaction process model construction method and a reaction safety prediction method. BACKGROUND

[0002] With the increasing demand for safe production and process optimization in the chemical industry, numerical simulation technology of fixed bed reactors has become an indispensable part of the safety monitoring and reaction condition optimization process of catalytic process.

[0003] Due to the high complexity of the catalytic reaction system, the traditional technology uses a porous medium model or a single-phase flow direct solution method to simulate the reaction process. However, the porous medium model uses macroscopic averaging assumption for simulation, which cannot reflect the influence of catalyst particle characteristics on the chemical reaction process, and the single-phase flow direct solution method is a simulation calculation for single-phase fluid, which cannot handle the case of multi-phase fluid, so the above methods are difficult to accurately predict the reaction process inside the reactor.

[0004] Therefore, the existing simulation method for the reaction process still has the problem of low comprehensiveness and accuracy of the prediction result. SUMMARY

[0005] Therefore, it is necessary to provide a reaction process model construction method and a reaction safety prediction method capable of improving the comprehensiveness and accuracy of the prediction result.

[0006] In a first aspect, the present application provides a reaction process model construction method applied to a catalytic process of a fixed bed reactor, the reaction process model construction method comprising:

[0007] obtaining catalyst particle parameters and reactor reaction section parameters of a current reaction process;

[0008] simulating a packing process of catalyst particles in the fixed bed reactor based on the catalyst particle parameters and the reactor reaction section parameters to obtain a catalyst particle packing model;

[0009] constructing a reaction process model of the current reaction process according to the particle position of the catalyst particle packing model; the reaction process model comprises a coupled multi-phase flow field model and a chemical reaction model.

[0010] In one embodiment, the multi-phase flow field model comprises at least two kinds of partially or completely immiscible liquids, or comprises partially or completely immiscible gas and liquid.

[0011] In one embodiment, the obtaining of the catalyst particle parameters and the reactor reaction section parameters of the current reaction process comprises:

[0012] acquiring material parameters of the catalyst particles, material parameters of the reactor, and material parameters between the catalyst particles and the reactor;

[0013] determining catalyst particle parameters and reactor reaction section parameters of the current reaction process according to the material parameters.

[0014] In one embodiment, the constructing a reaction process model of the current reaction process according to the catalyst particle positions in the catalyst particle accumulation model comprises:

[0015] constructing a multiphase flow field model of the current reaction process according to the positions of the catalyst particles in the catalyst particle accumulation model and preset initial flow field conditions;

[0016] determining a chemical reaction source term based on the positions of the catalyst particles in the catalyst particle accumulation model, the component concentration distribution of each phase in the multiphase flow field model, reaction kinetics parameters, and relative molecular masses of reactants and products;

[0017] applying the chemical reaction source term to a component transport equation of the multiphase flow field model to construct a chemical reaction model of the current reaction process.

[0018] In one embodiment, the multiphase flow field model comprises continuity equations, momentum equations, and energy equations, and the constructing a multiphase flow field model of the current reaction process according to the positions of the catalyst particles in the catalyst particle accumulation model and preset initial flow field conditions comprises:

[0019] performing grid division on a geometric structure of the reactor according to the positions of the catalyst particles in the catalyst particle accumulation model to obtain spatial distribution data of the catalyst particles and the reactor;

[0020] calculating volume fraction distributions of each phase according to the spatial distribution data and preset initial flow field conditions;

[0021] weighting calculating mixed phase densities and viscosities based on the volume fraction distributions;

[0022] constructing continuity equations, momentum equations, and energy equations based on the mixed phase densities and viscosities; each phase in the continuity equations shares one velocity field.

[0023] In one embodiment, the determining a chemical reaction source term based on the positions of the catalyst particles in the catalyst particle accumulation model, the component concentration distribution of each phase in the multiphase flow field model, reaction kinetics parameters, and relative molecular masses of reactants and products comprises:

[0024] calculating a reaction component concentration distribution on the surface of the catalyst particle based on the component concentration distribution of each phase in the multiphase flow field model when the fluid position corresponding to the position of the catalyst particle in the catalyst particle packing model is the surface of the catalyst particle;

[0025] calculating a chemical reaction rate based on the reaction component concentration distribution and the reaction kinetics parameter;

[0026] determining the chemical reaction source term based on the chemical reaction rate and the relative molecular mass of the reactants and products.

[0027] In one embodiment, the determining the chemical reaction source term based on the position of the catalyst particle in the catalyst particle packing model, the component concentration distribution of each phase in the multiphase flow field model, the reaction kinetics parameter, and the relative molecular mass of the reactants and products further comprises:

[0028] setting the chemical reaction source term to zero when the fluid position corresponding to the position of the catalyst particle in the catalyst particle packing model is not the surface of the catalyst particle.

[0029] In a second aspect, the present application provides a reaction safety prediction method, which comprises:

[0030] solving the mass conservation, momentum conservation and energy conservation equations of the current reaction process based on the reaction process model as described above to obtain temperature field, pressure field, velocity field and other data;

[0031] calculating the flow field data and chemical reaction data of the current reaction process by taking the obtained temperature field, pressure field, velocity field and other data as initial flow field conditions;

[0032] iteratively solving the stable simulation result of the current reaction process based on the flow field data and the chemical reaction data;

[0033] determining the safety prediction result of the current reaction process based on the stable simulation result.

[0034] In one embodiment, the iteratively solving the stable simulation result of the current reaction process based on the flow field data and the chemical reaction data comprises:

[0035] solving the flow field simulation result based on the flow field data and the chemical reaction data;

[0036] if the flow field simulation result meets the preset stability condition, taking the flow field simulation result as the stable simulation result; the preset stability condition comprises one or more of the fluctuation amplitude of the flow of each component at the outlet of the reactor, the fluctuation amplitude of the temperature at the outlet of the reactor, and the fluctuation amplitude of the reaction rate on the surface of the catalyst particle within a preset time step;

[0037] If the flow field simulation result does not meet the preset stability condition, the flow field simulation result is taken as an initial flow field condition, and the flow field simulation result is solved again based on the initial flow field condition.

[0038] In a third aspect, the present application provides a computer device, comprising a memory and a processor, the memory stores a computer program, and the processor implements the method as described above when executing the computer program.

[0039] The reaction process model construction method and the reaction safety prediction method described above, by simulating the filling process of the catalyst particles in the fixed bed reactor based on the catalyst particle parameters and the reactor reaction section parameters, obtaining the catalyst particle accumulation model, and then constructing the reaction process model of the current reaction process according to the particle position of the catalyst particle accumulation model, the reaction process model includes a multiphase flow field model and a chemical reaction model coupled, by simulating the filling process by using the catalyst particle parameters and the reactor reaction section parameters to generate the catalyst particle accumulation model reflecting the real geometric structure, and constructing the reaction process model containing the coupling mechanism of the multiphase flow field and the chemical reaction based on the particle position information in the accumulation model, thereby avoiding the limitations of the traditional technology for macro-averaging assumption and single-phase simulation, effectively considering the real geometric structure of the catalyst particles, to more accurately simulate the fluid flow, heat and mass transfer and surface reaction, and to improve the comprehensiveness and accuracy of the prediction of the internal reaction process of the reactor. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 It is a flowchart of the reaction process model construction method in one embodiment;

[0041] Figure 2 It is a flowchart of the reaction safety prediction method in one embodiment;

[0042] Figure 3 It is a flowchart of the reaction safety prediction method in another embodiment;

[0043] Figure 4 It is a catalyst particle accumulation and grid division diagram in one embodiment;

[0044] Figure 5 It is a mass fraction profile diagram of methyl aniline generated in the reactor in one embodiment;

[0045] Figure 6 It is a temperature cross-section diagram in the reactor in one embodiment;

[0046] Figure 7 It is an internal structure diagram of the computer device in one embodiment. DETAILED DESCRIPTION

[0047] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application.

[0048] In one embodiment, as shown in Figure 1 A reaction process model construction method is provided, applied to a catalytic process of a fixed bed reactor, the reaction process model construction method comprising:

[0049] In step S110, catalyst particle parameters and reactor reaction section parameters of a current reaction process are obtained.

[0050] The catalyst particle parameters can be a set of parameters describing the physical and chemical properties of the catalyst particles, which can be used to define the behavior characteristics of the catalyst particles in simulation. For example, it can include size, shape, density and other attributes. In one exemplary embodiment, the catalyst particle parameters can include but are not limited to one or more of particle size parameters, particle shape parameters, particle density parameters, and porosity parameters.

[0051] The reactor reaction section parameters can be a set of parameters describing the geometry and operating conditions of the fixed bed reactor reaction section, which can be used to define the structure of the reactor to affect the simulation of the packing process. For example, the reactor reaction section parameters can include size. In one exemplary embodiment, the reactor reaction section parameters can include but are not limited to one or more of reactor diameter parameters and reactor length parameters.

[0052] Obtaining the catalyst particle parameters and the reactor reaction section parameters can be reading or inputting parameter information from a data source. Further, the obtaining process can be realized by reading from a file, user input, database query, sensor measurement, etc.

[0053] In step S120, based on the catalyst particle parameters and the reactor reaction section parameters, the packing process of the catalyst particles in the fixed bed reactor is simulated to obtain a catalyst particle packing model.

[0054] The catalyst particles can be particles formed by solid catalyst materials, which are used to promote chemical reactions. In the packing process, the catalyst particles can serve as active sites for catalytic reactions, affecting reaction efficiency and selectivity.

[0055] A fixed bed reactor can be a type of reactor for containing catalyst particles that can provide a stable reaction environment for catalytic processes by fixing catalyst particles in a bed through which a fluid flows for reaction. Exemplarily, a fixed bed reactor can include one or more of a co-current fixed bed reactor, a counter-current fixed bed reactor, an adiabatic fixed bed reactor, a heat exchange fixed bed reactor, a cylindrical fixed bed reactor.

[0056] A catalyst particle packing model can be a model simulating the packing structure of catalyst particles after being packed in a reactor, which can provide particle geometry information representing the spatial arrangement of particles, and thus can be used to build a reaction process model based on particle positions. Exemplarily, a catalyst particle packing model can include a random packing model.

[0057] The reactor reaction section parameters and the catalyst particle parameters jointly participate in the packing process simulation. Simulating the packing process of catalyst particles in a fixed bed reactor can be to simulate the particle packing behavior using a calculation algorithm. Further, the packing simulation can be performed by discrete element method, computational fluid dynamics, etc., to generate a catalyst particle packing model representing the real geometry of the particles.

[0058] At step S130, a reaction process model of the current reaction process is built according to the particle positions of the catalyst particle packing model; the reaction process model includes a coupled multiphase flow field model and a chemical reaction model.

[0059] The particle positions can be the spatial coordinate information of the catalyst particles in the packing model, which is part of the catalyst particle packing model, and can define the geometric basis of the reaction process model, which is extracted from the catalyst particle packing model.

[0060] The reaction process model can be a comprehensive model describing the entire reaction process, including fluid flow and chemical reaction, which includes a multiphase flow field model and a chemical reaction model, and can be used to simulate and optimize the reaction process, predict performance and safety. Exemplarily, it can be built by coupling a multiphase flow field model and a chemical reaction model based on particle positions.

[0061] The multiphase flow field model can be a model simulating the behavior of multiphase fluid flow in the reactor, such as the interaction of gas and liquid. The chemical reaction model coupled in the reaction process model can be used to predict fluid flow behavior, affecting mass transfer and reaction efficiency. In some exemplary embodiments, the multiphase flow field model can employ one or more of the Euler-Euler model, the Euler-Lagrange model, and the hybrid model.

[0062] The chemical reaction model can be a model that simulates the rate and kinetics of chemical reactions, which can predict the reaction rate, product distribution, and the like. Illustratively, the chemical reaction model can include one or more of intrinsic kinetics models, macro-kinetics models, surface reaction models. Building the reaction process model can be based on creating a coupling model based on particle location.

[0063] The reaction process model building method provided by the embodiment simulates the packing process of the catalyst particles in the fixed bed reactor based on the catalyst particle parameters and the reactor reaction section parameters, obtains a catalyst particle packing model, and then builds a reaction process model of the current reaction process according to the particle location of the catalyst particle packing model. The reaction process model includes a coupled multiphase flow field model and a chemical reaction model. The packing process is simulated by using the catalyst particle parameters and the reactor reaction section parameters to generate a catalyst particle packing model reflecting the real geometric structure, and the reaction process model including the coupling mechanism of the multiphase flow field and the chemical reaction is built based on the particle location information in the packing model. Thus, the limitations of the macro-averaging assumption and the single-phase simulation in the conventional technology are avoided, the real geometric structure of the catalyst particles is effectively considered, the fluid flow, heat and mass transfer, and surface reaction are more accurately simulated, and the technical effects of improving the comprehensiveness and accuracy of the prediction of the internal reaction process of the reactor are achieved.

[0064] In one embodiment, the multiphase flow field model includes at least two partially or completely immiscible liquids, or includes partially or completely immiscible gas and liquid.

[0065] In the embodiment, the multiphase flow field model can be a model that simulates the flow, heat transfer, and mass transfer of fluids in a multiphase system, supports the description of the coexistence and interaction of multiple phases, and can be used to describe the interfacial behavior, flow distribution, and momentum exchange between immiscible phases.

[0066] Illustratively, the multiphase flow field model can be realized by solving the Navier-Stokes equation combined with the mass conservation and energy conservation equation. Illustratively, the multiphase flow field model can use Euler-Euler model, Euler-Lagrange model, interface tracking model, and the like.

[0067] In the embodiment, the multiphase flow field model can be extended to support non-miscible combinations including at least two partially or completely immiscible liquids, or gas and liquid, thereby enhancing the adaptability to complex multiphase systems.

[0068] Further, when the multiphase flow field involves immiscible gas or liquid, a phase interface tracking method can be introduced for modeling, thereby creating a comprehensive model to handle multiphase flow interaction and reaction kinetics. In one specific embodiment, for the case where immiscible phases exist, the modeling process is enhanced in the description capability of interfacial tension between phases, phase separation behavior, and local concentration gradient, which can more realistically reflect the influence of multiphase distribution in the reaction environment on catalytic reactions.

[0069] The reaction process modeling method provided by the embodiment includes partial or complete immiscible gas and liquid in the multiphase flow field model, extends the applicability of the multiphase flow field model to partial or complete immiscible gas or liquid combinations, and enhances the modeling capability of interfacial tension between phases, phase separation behavior, and local concentration gradient in the coupling process, so that the model can cover a wider range of industrial reaction scenarios, such as gas-liquid two-phase catalytic reaction and multi-liquid phase catalytic reaction system, and improve the ability to capture interfacial mass transfer resistance and local reactant enrichment effect, thereby further achieving the technical effect of improving the prediction comprehensiveness and accuracy of the model in complex multiphase catalytic systems.

[0070] In one of the embodiments, obtaining the catalyst particle parameters and the reactor reaction section parameters of the current reaction process includes:

[0071] Obtaining the material parameters of the catalyst particles, the material parameters of the reactor, and the material parameters between the catalyst particles and the reactor;

[0072] According to the material parameters, determining the catalyst particle parameters and the reactor reaction section parameters of the current reaction process.

[0073] The material parameters can be a set of physical and chemical properties that describe the characteristics of the catalyst particles, the materials of the reactor structure, and their interaction characteristics, and can be used to determine the behavior characteristics of the reactor and the catalyst in the physical and chemical environment. The material parameters can be obtained through experiments, database queries, or literature. In one specific embodiment, the material parameters of the reactor include but are not limited to the size, shape, material density, Poisson's ratio, and shear modulus of the reactor reaction section; the material parameters of the catalyst particles include but are not limited to the rolling friction coefficient, sliding friction coefficient, and recovery coefficient between the catalyst particles; and the material parameters between the catalyst particles and the reactor include but are not limited to the rolling friction coefficient, sliding friction coefficient, and recovery coefficient between the reaction tube and the catalyst particles. Further, if the catalyst particles are non-uniform, the material parameters can also include the particle size fluctuation range, mean particle size, and standard deviation of the catalyst particles.

[0074] Further, the material parameters can include topography parameters and / or size parameters, wherein the topography parameters can be information describing the distribution of the topography of the material, and the size parameters can be information describing the size specifications of the material. In an exemplary embodiment, the material parameters and the topography parameters of the catalyst particles, the material parameters and the size parameters of the reactor, and the material parameters between the catalyst particles and the reactor can be obtained; and the catalyst particle parameters and the reactor reaction section parameters of the current reaction process can be determined according to the material parameters.

[0075] The catalyst particle parameters and the reactor reaction section parameters of the current reaction process can be determined according to the material parameters, which can be a combination of the material parameters of the catalyst particles, the material parameters of the reactor, and the material parameters between the catalyst particles and the reactor, and the parameters obtained are denoted as the catalyst particle parameters and the reactor reaction section parameters.

[0076] The reaction process model construction method provided in the embodiment combines the material characteristics of the catalyst particles and the material characteristics of the reactor, and considers the interaction relationship between the catalyst particles and the reactor reaction section, so that the accuracy of the actual catalyst particle accumulation model is improved, and the technical effect of improving the accuracy of the reaction process model construction is achieved.

[0077] In one of the embodiments, the reaction process model of the current reaction process is constructed according to the particle positions of the catalyst particle accumulation model, which includes:

[0078] A multiphase flow field model of the current reaction process is constructed according to the positions of the catalyst particles in the catalyst particle accumulation model and the preset initial flow field conditions.

[0079] A chemical reaction source term is determined based on the positions of the catalyst particles in the catalyst particle accumulation model, the component concentration distribution of each phase in the multiphase flow field model, the reaction kinetics parameters, and the relative molecular masses of the reactants and the products.

[0080] The chemical reaction source term is applied to the component transport equation of the multiphase flow field model to construct a chemical reaction model of the current reaction process.

[0081] The particle positions in the catalyst particle accumulation model can be the geometric positions of the catalyst particles in the spatial arrangement of the reactor, and can define the boundary conditions of the calculation domain of the multiphase flow field model.

[0082] The preset initial flow field condition can be an initial setting simulating a flow state of the multiphase fluid in the reactor at the beginning of the reaction process, which is used in combination with the particle position in the catalyst particle accumulation model to construct the multiphase flow field model, and can be used to provide an initial boundary input for the multiphase flow field model, thereby supporting the evolution calculation of the flow field. For example, the preset initial flow field condition can include one or more of a velocity initial condition, a pressure initial condition, a temperature initial condition, a phase volume fraction initial condition, and the like.

[0083] Constructing the multiphase flow field model of the current reaction process can be to define the calculation domain boundary based on the particle position in the catalyst particle accumulation model, and to solve the multiphase flow control equation in combination with the preset initial flow field condition. Further, constructing the multiphase flow field model of the current reaction process can be to use the VOF method to process the gas-liquid interface, and can also be to use the Euler-Euler method to process the multiphase flow, in combination with the particle surface as a solid phase boundary condition, so as to realize the fine simulation of the flow behavior of the multiphase fluid under complex geometry.

[0084] The component concentration distribution can be the mass or molar concentration distribution of each chemical component in the spatial domain in the reaction system. In this embodiment, the component concentration distribution can be provided by the multiphase flow field model, and can be obtained by solving the flow field model or according to the initial setting. For example, the component concentration distribution can include one or more of a reactant concentration distribution, an intermediate product concentration distribution, and a final product concentration distribution.

[0085] The reaction kinetics parameter can be a model parameter of the relationship between the chemical reaction rate and the concentration, temperature, and the like. For example, the reaction kinetics parameter can include one or more of a reaction order, an activation energy, and a pre-exponential factor.

[0086] The relative molecular mass can be the molecular mass value of the substance participating in the chemical reaction in the stoichiometric relationship. In this embodiment, the relative molecular mass can include one or more of a reactant relative molecular mass, a product relative molecular mass, and an intermediate relative molecular mass.

[0087] The chemical reaction source term can be a mathematical expression term of the component generation or consumption rate per unit volume caused by chemical reaction. In this embodiment, the chemical reaction source term is used as a source term embedded in the component transport equation, to reflect the influence of the reaction on the flow field. For example, the chemical reaction source term can be calculated based on the reaction kinetics model and the surface concentration data. For example, the chemical reaction source term can include one or more of a surface reaction source term and a volume reaction source term.

[0088] Determining the chemical reaction source term can be to identify the reaction active region according to the catalyst particle surface position, and to calculate the local reaction rate in combination with the component concentration distribution, the reaction kinetics parameter, and the relative molecular mass of the reactant and the product output by the multiphase flow field model.

[0089] The chemical reaction source term is applied to the component transport equation of the multiphase flow field model, which can be introducing the chemical reaction source term as a source term in the component mass conservation equation, thereby forming a coupled solving model.

[0090] The reaction process model construction method provided by the embodiment defines the geometric boundary of the multiphase flow field model by using the particle position information and completes the flow field modeling in combination with the initial flow field condition, determines the chemical reaction source term based on the position of the catalyst particles in the catalyst particle accumulation model, the component concentration distribution of each phase in the multiphase flow field model, the reaction kinetics parameters, the relative molecular mass of the reactants and products, and embeds the chemical reaction source term in the component transport equation to realize the coupling of the flow field and the reaction, which can achieve the technical effects of realizing the fine correlation of the multiphase flow field and the surface reaction kinetics on the basis of the constructed catalyst particle accumulation model, simulating the interaction of the fluid distribution and the reaction behavior under the real particle geometry, and improving the prediction comprehensiveness and accuracy of the complex catalytic process in the fixed bed reactor.

[0091] In one of the embodiments, the multiphase flow field model includes continuity equation, momentum equation and energy equation, and the multiphase flow field model of the current reaction process is constructed according to the position of the catalyst particles in the catalyst particle accumulation model and the preset initial flow field condition, which includes:

[0092] According to the position of the catalyst particles in the catalyst particle accumulation model, the grid division is performed on the geometric structure of the reactor to obtain the spatial distribution data of the catalyst particles and the reactor;

[0093] According to the spatial distribution data and the preset initial flow field condition, the volume fraction distribution of each phase is calculated;

[0094] Based on the volume fraction distribution, the mixed phase density and viscosity are calculated by weighting;

[0095] Based on the mixed phase density and viscosity, the continuity equation, momentum equation and energy equation are constructed; and each phase in the continuity equation shares one velocity field.

[0096] The spatial distribution data can be a data set including the three-dimensional spatial position of the catalyst particles in the reactor and the relative relationship with the wall surface, which can be used for grid division and volume fraction initialization, so as to determine the solid phase occupied area, and can be obtained by extracting the particle center coordinates, surface profile and contact relationship from the catalyst particle accumulation model. For example, the spatial distribution data can include particle coordinate data.

[0097] The meshing can be discretizing the geometric space formed by the reactor and the catalyst particles into a finite number of computational cells, thereby providing a basis for spatial discretization of the multiphase flow field model. Illustratively, the meshing can be performed according to the spatial position information of the catalyst particle packing model using a structured or unstructured method, thereby achieving meshing. Illustratively, the divided mesh can include one or more of a structured hexahedral mesh, an unstructured tetrahedral mesh, a hybrid polyhedral mesh, and the like.

[0098] The meshing of the reactor can be generating a complete computational domain containing the particles and the void regions and discretizing according to the spatial distribution data. In some exemplary embodiments, the boundary layer mesh can be generated by closely attaching to the particle surface through the body-fitted mesh, thereby achieving high-fidelity discretization of the complex particle packing structure and improving the spatial accuracy of the flow field simulation.

[0099] The volume fraction distribution can be a spatial distribution of the volume proportion of each phase fluid in the computational cell, which can be used to distinguish the presence of the fluid phase. Further, in the present embodiment, the volume fraction distribution can be used as an input parameter for weighted calculation of the mixed phase density and viscosity. Illustratively, the volume fraction distribution can include one of a gas phase volume fraction and a liquid phase volume fraction.

[0100] The mixed phase density can be an equivalent density of the multiphase fluid in the local mesh cell weighted by the volume fraction, which can be calculated from the volume fraction distribution and the density of each phase, and used as a physical property parameter in the continuity equation, momentum equation, and energy equation for solving. The mixed phase viscosity can be an equivalent dynamic viscosity of the multiphase fluid in the local mesh cell weighted by the volume fraction, which can be calculated from the volume fraction distribution and the viscosity of each phase, thereby being used as a transport parameter in the momentum equation to affect the evolution of the velocity field.

[0101] Illustratively, the weighted calculation of the mixed phase density and viscosity can be a weighted average operation of the density and viscosity of each phase with the volume fraction of each phase as the weight. Further, the calculation can be performed by linear weighting, harmonic averaging, or the like, thereby providing spatially variable equivalent physical property parameters for the continuity equation, momentum equation, and energy equation.

[0102] In the present embodiment, the continuity equation can be a partial differential equation describing the mass conservation in the fluid system, which can be constructed by the mixed phase density and the common velocity field or the independently solved velocity field. The continuity equation and the momentum equation are jointly solved, which can be used to simulate the mass transport and accumulation process of the multiphase fluid in space.

[0103] The momentum equation governs the conservation of momentum during fluid motion. It can be constructed from the density and viscosity of the mixture and solved in conjunction with the continuity equation. The momentum equation can be used to calculate the evolution of the fluid velocity field and pressure distribution.

[0104] The shared velocity field can be regarded as the assumption that all fluid phases in the multiphase mixing model share the same velocity vector field. As the velocity variable in the continuity equation, momentum equation, and energy equation, it is solved together with the density and viscosity of the mixed phase. By sharing the velocity field, the computational complexity can be effectively reduced, making it possible to simplify the momentum transport calculation in the case of multiphase flow with relatively small relative velocities.

[0105] The continuity equation, momentum equation and energy equation are constructed. For example, the density of the mixed phase, the viscosity of the mixed phase and the common velocity field are substituted into the mass and momentum conservation equations to form a closed set of control equations.

[0106] This embodiment provides a method for constructing a reaction process model. This method performs high-fidelity meshing based on particle position information to obtain accurate spatial distribution data, initializes the volume fraction distribution accordingly, and uses the volume fraction distribution weighting to obtain spatially variable mixed phase density and viscosity. A closed system of continuity equations, momentum equations, and energy equations is constructed by combining the assumption of a shared velocity field or independently solving the velocity field. This method achieves refined modeling of the multiphase fluid mass and momentum transport process based on the actual geometric structure of the catalyst particles, thereby achieving the technical effect of improving the modeling accuracy of local flow behavior.

[0107] In one embodiment, determining the chemical reaction source term based on the positions of catalyst particles in the catalyst particle accumulation model, the component concentration distribution of each phase in the multiphase flow field model, the reaction kinetic parameters, and the relative molecular masses of reactants and products includes:

[0108] When the fluid position corresponding to the position of the catalyst particle in the catalyst particle accumulation model is the catalyst particle surface, the reaction component concentration distribution on the catalyst particle surface is calculated based on the component concentration distribution of each phase in the multiphase flow field model;

[0109] Calculate chemical reaction rates based on reaction component concentration distribution and reaction kinetic parameters;

[0110] Determine the chemical reaction source term based on the chemical reaction rate and the relative molecular masses of the reactants and products.

[0111] The catalyst particle surface can be an outer boundary region of the catalyst particle in contact with the fluid phase, which can be used to determine the source term activation region. For example, the catalyst particle surface can be obtained by surface meshing of the particle geometry and identifying the interface exposed to the fluid domain. For example, the catalyst particle surface can be an exposed surface or an active site-rich surface.

[0112] The reaction component concentration distribution can be a spatial concentration distribution of the chemical components participating in the reaction in the surface region of the catalyst particle, which is used to calculate the chemical reaction rate. In this embodiment, the reaction component concentration distribution can be obtained by linear interpolation method from the flow field grid to the surface node. In one specific embodiment, the reaction component concentration distribution can be obtained by concentration interpolation of the fluid nodes or grid cells near the surface of the catalyst particle in the multiphase flow field model.

[0113] Calculating the reaction component concentration distribution on the surface of the catalyst particle can be identifying the grid where the catalyst particle surface is located, extracting the concentration values of each component in the adjacent fluid phase and performing spatial interpolation processing. Further, the concentration can be mapped from the flow field grid to the surface node by using the linear interpolation method.

[0114] The chemical reaction rate can be the number of moles of reactants consumed or products generated per unit time per unit reaction surface, which can be used as an input parameter for calculating the chemical reaction source term, and can be calculated by combining the reaction component concentration distribution and the reaction kinetics parameters based on the reaction kinetics model.

[0115] Calculating the chemical reaction rate, for example, the reaction component concentration distribution and the reaction kinetics parameters can be substituted into the preset reaction kinetics model for point-by-point calculation.

[0116] Further, the chemical reaction source term can be obtained by superimposing the source terms of each step after calculating the source terms of each step in a multi-step reaction system, or by using total package reactions and other processing methods for complex reaction networks, so as to generate a source term expression that meets the principle of mass conservation, in order to adapt to the component transport equation in the multiphase flow field model.

[0117] The reaction process model construction method provided in this embodiment can identify the position of the catalyst particle surface and obtain the local reactant concentration input by combining the flow field concentration distribution, and use the reaction kinetics parameters and the kinetics model to realize the spatially resolved reaction rate calculation.

[0118] In one embodiment, based on the position of the catalyst particle in the catalyst particle packing model, the concentration distribution of each phase in the multiphase flow field model, the reaction kinetics parameters, the relative molecular mass of the reactants and the products, the chemical reaction source term is further determined as follows:

[0119] When the position of the catalyst particle in the catalyst particle packing model corresponds to a fluid position other than the surface of the catalyst particle, the chemical reaction source term is set to zero.

[0120] In the above embodiment, the chemical reaction source term is set to zero in the fluid region other than the surface of the catalyst particle. If the current calculation unit is located in the fluid region far from the surface, the source term contribution can be shielded, thereby ensuring that the chemical reaction only occurs on the surface of the catalyst, avoiding the introduction of the reaction source term, and improving the physical consistency of the model.

[0121] The reaction process model construction method provided in the embodiment identifies the surface region of the catalyst actually participating in the reaction by combining the spatial position of the catalyst particle in the catalyst particle packing model, and explicitly sets the chemical reaction source term to zero in the fluid calculation unit at a position other than the surface, to limit the reaction to occur only on the surface region with catalytic activity. The matching degree of the reaction model and the real catalytic process can be enhanced during the determination of the chemical reaction source term, and the accuracy and reliability of the reaction process model in the simulation of the coupling of the multiphase flow field and the surface reaction can be improved.

[0122] On the basis of the reaction process model construction method provided in the above embodiment, the present application further provides a reaction safety prediction method applying the reaction process model obtained in the above embodiment.

[0123] In one embodiment, as shown in Figure 2 a reaction safety prediction method is provided, and the reaction safety prediction method comprises:

[0124] In step S210, based on the reaction process model, the continuity equation, momentum equation and energy equation of the current reaction process are solved to obtain temperature field, pressure field, velocity field and other data.

[0125] The continuity equation can be a control equation describing the mass conservation of the system, which can be derived based on the mass change rate of the fluid microelement and the mass flux relationship, and is used to constrain the spatiotemporal evolution of the fluid density and velocity field, and ensure the mass balance in the simulation process.

[0126] The momentum equation can be a control equation describing the relationship between the force and the motion state change of the fluid microelement, and is used to calculate the distribution and evolution of the fluid velocity field and reflect the flow resistance and shear effect.

[0127] The energy equation can be a control equation describing the energy transfer and conversion process in the system, including heat conduction, convection and reaction heat effect, and is used to solve the temperature field distribution and identify the possible hot spots or low temperature zones in the reactor.

[0128] Solving the continuity equation, momentum equation and energy equation of the current reaction process can be based on the geometry and physical properties of the reaction process model, and the conservation equations are discretized and solved by numerical methods.

[0129] The pressure field can be the distribution state of the pressure of each point in the internal space of the reactor, which can be calculated by solving the momentum conservation equation and the continuity equation, and is used to evaluate the fluid transport resistance, pressure drop distribution and equipment pressure safety. The velocity field can be the distribution of the fluid velocity vector at each point in the internal space of the reactor, which can be solved by the momentum conservation equation, and is used to reveal the fluid flow path and stagnant area, and affect the uniformity of reactant distribution. The temperature field can be the distribution state of the temperature of each point in the internal space of the reactor, which can be obtained by solving the energy conservation equation, and is used to reflect the thermodynamic state in the reactor and judge the risk of thermal runaway.

[0130] In step S220, the temperature field, pressure field, velocity field and other data are obtained as initial flow field conditions, and the flow field data and chemical reaction data of the current reaction process are calculated.

[0131] The initial flow field conditions can be a set of state parameters of the fluid system at the beginning of the simulation, including temperature field, pressure field, velocity field and other data.

[0132] The flow field data can be a set of numerical results describing the flow state of the fluid in the reaction process, which can be calculated by iterative calculation of the control equation based on the initial flow field conditions, so as to provide velocity, pressure, turbulence intensity and other information, and analyze the flow stability and mass transfer characteristics.

[0133] The chemical reaction data can be data describing the reaction rate, component concentration change and surface reaction behavior, which can be calculated based on the reaction kinetics model and the local flow field conditions, and is used to reflect the surface reaction activity of the catalyst and the material conversion process.

[0134] In step S230, the stable simulation result of the current reaction process is iteratively solved based on the flow field data and the chemical reaction data.

[0135] The stable simulation result can be the output result of the reaction process after reaching the convergence state after multiple iterations, which is used to represent the steady-state operation state of the reactor under the current conditions. Iterative solving of the stable simulation result of the current reaction process can be repeated to update the flow field and reaction data until the system variable change is lower than the convergence threshold, so as to obtain a converged solution that can represent the actual operating state.

[0136] In step S240, the safety prediction result of the current reaction process is determined based on the stable simulation result.

[0137] The safety prediction result can be a conclusion of whether the reaction process has a safety hazard based on the steady simulation result, can be determined by analyzing the change trend of the key parameters in the steady simulation result, and is used for early warning of abnormal conditions such as local overheating, blockage, and temperature runaway. For example, the safety prediction result can include one or more of thermal runaway risk prediction, overpressure prediction, and flow passage blockage prediction.

[0138] Based on the steady simulation result, the safety prediction result of the current reaction process can be an analysis of the spatial and temporal evolution characteristics of temperature, pressure, concentration, and other parameters in the steady simulation result. Further, the safety prediction result can be obtained by setting a threshold judgment rule, identifying abnormal trends by a pattern recognition method, and comparing and analyzing historical data, so as to output a safety judgment that can support operation decision.

[0139] The reaction safety prediction method provided in this embodiment generates a data set reflecting the basic physical law of the system by solving the conservation equation based on the reaction process model, integrates the conservation data to form an initial state to drive the coupled calculation of the flow field and the reaction, realizes system convergence by repeatedly updating the flow field and reaction data, and outputs a safety judgment by analyzing the evolution characteristics of key parameters in the steady state. The method fully considers the interaction of multiphase fluids, the influence of real geometric structure, and the surface reaction kinetics behavior, can more comprehensively capture the complex physical and chemical processes inside the reactor, achieves the technical effects of improving the identification ability and accuracy of local abnormal phenomena, and improving the operation safety of the fixed bed reactor.

[0140] In one of the embodiments, based on the flow field data and the chemical reaction data, the iterative solving of the steady simulation result of the current reaction process includes:

[0141] Solving the flow field simulation result based on the flow field data and the chemical reaction data;

[0142] If the flow field simulation result meets the preset steady condition, the flow field simulation result is taken as the steady simulation result;

[0143] If the flow field simulation result does not meet the preset steady condition, the flow field simulation result is taken as the initial flow field condition, and the flow field simulation result is solved again based on the initial flow field condition.

[0144] The flow field simulation result can be a fluid state output calculated based on the current flow field data and the chemical reaction data at a specific time step, used to reflect the flow and reaction state of the reactor during the transient simulation process. By judging whether the preset stability condition is met according to the flow field simulation judgment result, it can be determined whether it is used as a stable simulation result output. For example, the flow field simulation result can be obtained by numerically solving the multiphase flow control equation and coupling the local reaction rate. In some exemplary embodiments, the flow field simulation result can include one or more of a steady-state velocity field result, a steady-state pressure distribution result, a steady-state component concentration distribution result, etc.

[0145] Based on the flow field data and the chemical reaction data, the flow field simulation result is solved, which can be a transient flow field output obtained by running a multiphase flow and reaction coupling solver in the current iteration step, combining the flow field data and the chemical reaction data. Further, the transient simulation result that can be used for stability judgment can be realized by synchronously updating all variables by adopting a separate solving strategy to update the velocity field and the reaction field respectively, adopting a full-coupling solving method, etc.

[0146] The preset stability condition can be a standard set for judging whether the simulation process has reached a steady-state convergence, which is compared with the flow field simulation result to trigger the convergence determination, used as a judgment basis for iteration termination, to ensure that the simulation result has engineering credibility. The preset stability condition includes one or more of the fluctuation amplitude of the flow of each component at the outlet of the reactor, the fluctuation amplitude of the temperature at the outlet of the reactor, and the fluctuation amplitude of the reaction rate on the surface of the catalyst particles within a preset time step.

[0147] The fluctuation amplitude of the flow of each component at the outlet of the reactor can be the degree of change of the mass flow or molar flow of different chemical components at the outlet of the reactor over time. The fluctuation amplitude of the temperature at the outlet of the reactor can be the change range of the temperature at the outlet cross section in the time sequence, used to reflect the stability of the heat balance state, to identify the potential heat accumulation or temperature runaway risk. The fluctuation amplitude of the reaction rate on the surface of the catalyst particles can be the degree of change of the reaction rate at each position or the overall average reaction rate on the surface of the catalyst over time, used to characterize the stability of the reaction.

[0148] Judging whether the flow field simulation result meets the preset stability condition can be extracting the time sequence data of the flow of each component at the outlet of the reactor, the temperature, and the surface reaction rate, calculating the fluctuation amplitudes and comparing them with the preset threshold.

[0149] The reaction safety prediction method provided by the embodiment can enhance the identification capability of the dynamic stability of the reaction process, make the simulation result closer to the steady state characteristics in actual operation, and thus improve the accuracy and reliability of the safety prediction.

[0150] In order to more clearly illustrate the technical solutions of the present application, a detailed embodiment is further provided.

[0151] In one embodiment, as shown in Figure 3 A reaction safety prediction method is provided, comprising:

[0152] In step S310, based on the catalyst particle parameters and the reactor reaction section parameters, the filling process of the catalyst particles in the fixed bed reactor under the action of gravity is simulated, and a catalyst particle free stacking model is established.

[0153] The catalyst particle free stacking model is used to receive the reaction tube parameters and the particle parameters, and perform particle free stacking, and then perform particle attribute output.

[0154] In step S320, based on the catalyst particle free stacking model, the particle position is automatically identified, and based on the initial flow field condition, a multiphase flow field model of the fixed bed reactor is established.

[0155] The multiphase flow field model is used to perform flow field model establishment according to the particle attribute output, and calculate the flow field variables in combination with the initial flow field condition.

[0156] In step S330, based on the multiphase flow field model of the fixed bed reactor, the chemical reaction source term is calculated, and a chemical reaction model is established.

[0157] The chemical reaction model is used to determine whether the reaction is located on the surface of the catalyst particles according to the flow field variables, if yes, the reaction rate is calculated, the chemical reaction source term is calculated, and it is determined whether the simulation result reaches a stable state; if not, the flow field variables are calculated again through the multiphase flow field model; if yes, the result is output.

[0158] The catalyst particle parameters and the reactor reaction section parameters include but are not limited to the size, shape, material density, Poisson ratio, shear modulus, rolling friction coefficient, sliding friction coefficient and recovery coefficient between catalyst particles, rolling friction coefficient, sliding friction coefficient and recovery coefficient between the reaction tube and the catalyst particles. If the catalyst particles are non-uniform, the particle size fluctuation range, mean particle size and standard deviation of the catalyst particles are also included in the conditions. For example,Figure 4 Figure 1 shows the catalyst particle packing and meshing in one embodiment.

[0159] The initial flow field conditions include, but are not limited to, the feed flow rates and feed temperatures of the various materials entering the reactor, the reaction temperature and pressure in the reaction section of the reactor, the reactor outlet pressure, and the external heat exchange rate.

[0160] In this embodiment, the continuity equation of the multiphase flow field model uses one velocity field for all phases, the density of the mixture phase is calculated according to the volume fraction of each phase, and the viscosity in the momentum equation is calculated according to the volume fraction between phases.

[0161] The calculation formulas of the continuity equation and the momentum equation are as follows:

[0162]

[0163]

[0164] wherein, is the mass transfer rate, a is the phase fraction, and p and m are the density and viscosity, respectively, is the velocity component, is the surface tension, is the convection acceleration. The density of the mixture phase is calculated according to the volume fraction of each phase, and the viscosity in the momentum equation is calculated according to the volume fraction between phases. The calculation formulas of the mixture phase density and the mixture phase viscosity are as follows:

[0165]

[0166]

[0167] wherein, a i is the volume fraction of the i-th phase, p i is the density of the i-th phase, and m i is the viscosity of the i-th phase.

[0168] Further, the energy equation of the multiphase flow field model is:

[0169]

[0170] wherein, E is the internal energy per unit mass, k eff is the effective thermal conductivity, T is the temperature, and S r is the energy source term generated by the reaction.

[0171] Further, the calculation process of the chemical reaction source term includes: judging whether the flow field position is the surface of the catalyst particle according to the multiphase flow field model; if the position is the surface of the catalyst particle, reading the corresponding reaction component concentration, calculating the real-time reaction rate according to the reaction kinetics parameters and the reaction component concentration, and calculating the chemical reaction source term according to the relative molecular mass of the reactants and products and the real-time reaction rate; if the position is not the surface of the catalyst particle, the chemical reaction source term is set to zero.

[0172] Further, the multiphase flow field model involves two or more liquids that are mutually soluble or completely insoluble, or one or more gases and one or more liquids that are partially soluble or completely insoluble.

[0173] In the embodiment, the chemical reaction source term is added on the surface of the catalyst particle, and the chemical reaction source term is calculated by the reaction kinetics parameters, the component concentration in the flow field, and the relative molecular mass of the reactants and products. The chemical reaction source term is applied to the component transport equation, and the component transport equation is as follows:

[0174]

[0175] wherein C j is the component concentration field, D i represents the diffusion coefficient, Φ j is a discontinuous term, which aims to solve the discontinuity caused by the concentration difference of the phase interface affecting mass transfer in the multiphase system, and the concentration difference only takes a non-zero value at the phase interface; R is the reaction source term; the above parameters can be calculated according to the following formula:

[0176]

[0177]

[0178]

[0179]

[0180] wherein H is a step function, M w，i is the molar mass of component i.

[0181] Further, after the chemical reaction model is established, the following steps are further included:

[0182] Based on the multiphase flow field model and the chemical reaction model, the mass conservation, momentum conservation and energy conservation equations are solved, and the solving process is repeated until the reaction process in the fixed bed reactor is stable.

[0183] Specifically, the following steps are included:

[0184] Step S340, based on the above reaction process model, the mass conservation, momentum conservation, and energy conservation equations are calculated;

[0185] Step S350, based on the mass conservation, momentum conservation, and energy conservation equations, the flow field and chemical reaction process are calculated, and the stable simulation results of the flow field are solved;

[0186] Step S360, taking the stable simulation results of the flow field as the initial condition, the mass conservation, momentum conservation, and energy conservation equation solving process is repeated until the reaction process in the fixed bed reactor is stable.

[0187] Among them, the judgment standard of the reaction process stability in the fixed bed reactor is that the flow field characteristics and reaction characteristics in the reaction system no longer change greatly, and tend to be stable. For example, the flow field stability condition can be that the product component flow fluctuation at the reactor outlet is less than a certain threshold value within a certain time, and the calculation formula is as follows:

[0188]

[0189] Wherein, q w (t0) represents the mass flow of the key product at t0; Δt is the time step; n is the time step interval, for example, the time step interval can be 20; m is the set component flow fluctuation threshold, for example, the set threshold can be 1%.

[0190] For example, the reaction process stability condition can be that the reaction rate fluctuation amplitude of the catalyst particle surface is less than a certain threshold value within a certain time, and the calculation formula is as follows:

[0191]

[0192] Wherein, k(t0) represents the integral of the reaction rate of the catalyst particle surface at t0; Δt is the time step; n is the time step interval, for example, the time step interval can be 20; A is the set reaction rate fluctuation threshold, for example, the set threshold can be 1%.

[0193] In one embodiment, the determination criteria of the reaction process stability in the fixed bed reactor include: (1) the fluctuation amplitude of the component flow at the reactor outlet is ≤1% in the continuous 20 time steps; (2) the temperature fluctuation amplitude at the reactor outlet is ≤2% in the continuous 20 time steps; (3) the reaction rate fluctuation amplitude of the catalyst particle surface is ≤1% in the continuous 20 time steps.

[0194] In one embodiment, the simulation and prediction of the continuous flow hydrogenation of o-nitrotoluene to methyl aniline in a fixed bed reactor is used as an example. The fixed bed reactor is divided into two parts, a mixing section and a reaction section. The actual length of the reaction section is 50 mm, the reaction pressure is about 1 MPa, and the reaction temperature is 303.15 K. The reaction liquid injection speed is 0.8 ml / min, and the hydrogen feed speed is 50 ml / min. The catalyst used is a spherical palladium / carbon catalyst with a particle diameter of 1 mm. The method of this embodiment includes:

[0195] A reaction tube model is established, and on the basis of the model, the catalyst particle packing simulation of the reactor is performed. The simulation method used is the discrete element method, and the centroid coordinates of all catalyst particles in the simulation packing process are calculated.

[0196] On the basis of the reaction tube model, a complete reactor model is established according to the particle diameter and centroid coordinates of the catalyst particles. The fluid volume method is used, and the reaction liquid feed speed, hydrogen feed speed, reaction pressure, etc. are used as boundary conditions for mass conservation, momentum conservation, and energy conservation calculations. The corresponding flow field variables, including velocity field, pressure field, phase fraction field, and component mass fraction, are calculated.

[0197] Based on the concentration field data of o-nitrotoluene in the liquid phase and the reaction rate constant, the chemical reaction rate is calculated, and based on the relative molecular mass and reaction heat of the product, the mass and energy changes caused by the target reaction process are calculated, thereby obtaining the chemical reaction source term and the energy source term, and updating the component transport equation and the energy equation.

[0198] The flow field simulation results described above are used as the initial conditions for the next flow field calculation, and the calculation process of the flow field variables and the chemical reaction source term and the energy source term is repeated until the results meet the preset convergence conditions. In this embodiment, the convergence conditions are that the fluctuation amplitude of the reactor outlet component flow at 20 time steps is ≤1%, the fluctuation amplitude of the reactor outlet temperature is ≤2%, and the fluctuation amplitude of the catalyst surface reaction rate is ≤1%.

[0199] The conversion rate obtained by simulation is stabilized at 36.83%, and the experimental conversion rate of the reactor under the working conditions of this embodiment is 35.12%. The relative error between the simulation value and the actual value of the conversion rate is 4.64%.

[0200] In one embodiment, as Figure 5As shown in the figure, the reaction safety prediction method of the embodiment is used to simulate the process of generating methyl aniline by hydrogenation of o-nitrotoluene. As can be seen from the figure, the mass fraction of the product methyl aniline at the inlet section of the reactor is 0, and as the fluid flows and the reaction occurs, the mass fraction of methyl aniline gradually increases from top to bottom, and reaches the maximum at the outlet section of the reactor. In addition, the mass fraction of methyl aniline in the horizontal direction shows the characteristics of high in the middle and low on both sides.

[0201] In one embodiment, as Figure 6 shown, the reaction safety prediction method of the embodiment is used to monitor the temperature change in the generation of methyl aniline by hydrogenation of o-nitrotoluene. The temperature of the reaction section shows an increasing trend from top to bottom; on the same horizontal line, the temperature of the reactor does not change significantly, and the temperature distribution is uniform. In addition, there is no hot spot in this embodiment, and it can be considered that the safety of the reaction under the working condition corresponding to this embodiment is good.

[0202] The reaction safety prediction method provided in this embodiment generates a three-dimensional model reflecting the real particle packing structure by simulating the filling process based on the catalyst particle parameters and the reactor parameters, which is different from the traditional macroscopic averaging assumption, and can realize the fine simulation of fluid flow, heat transfer and mass transfer and surface reaction; expand the applicability of the multiphase flow model to non-miscible fluids, strengthen the modeling ability of interfacial tension, phase separation and concentration gradient, and can simulate gas-liquid two-phase, multi-liquid phase distribution scenarios, which can improve the capture accuracy of mass transfer resistance and local enrichment effect; by establishing a material characteristic representation system, combining particle size distribution statistics, dynamically introducing particle scale parameters, and flexibly adjusting the modeling accuracy through conditional parameter mechanism, the calculation efficiency and the accurate representation of physical characteristics can be balanced; by using the particle position information to define the flow field boundary and initialize the concentration distribution, calculating the spatially resolved reaction rate through the reaction kinetics model, and constructing a physically consistent flow field-reaction source mapping relationship, the dynamic coupling of multiphase fluid and surface reaction can be realized; by high-precision grid division based on particle position, by volume fraction weighted calculation of mixed phase physical property parameters, a closed momentum and continuity equation is established, which can improve the modeling accuracy of local flow behavior; by identifying the catalyst surface area and limiting the reaction to occur only on the active surface, and by setting zero source term to enhance the matching degree of the reaction model and the actual process, the reliability of the coupling of multiphase flow field and surface reaction can be ensured; by monitoring the stability of the simulation in real time in the iterative solution, and combining the pre-set threshold to determine the convergence state, the false judgment caused by simply relying on the residual decrease is avoided, and the simulation result is close to the actual stable result, so that the false convergence judgment caused by simply relying on the residual decrease is avoided, thereby improving the accuracy and reliability of the safety prediction.

[0203] It should be understood that although the steps in the flowcharts involved in the embodiments described above are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the embodiments described above can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be alternately executed with at least part of other steps or steps or stages in other steps.

[0204] In one embodiment, a computer device, which can be a terminal, is provided, and an internal structure diagram of the computer device can be as shown in Figure 7 The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected by a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The communication interface of the computer device is configured to perform wired or wireless communication with an external terminal. The wireless communication can be achieved through WIFI, mobile cellular network or other technologies. The computer program is executed by the processor to implement a reaction process model construction method or a reaction safety prediction method. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or can be a key, trackball or touchpad arranged on the shell of the computer device, or can be an external keyboard, touchpad or mouse, etc.

[0205] Those skilled in the art can understand that Figure 7 The structure shown in the above

[0206] In one embodiment, a computer device is provided, which includes a memory and a processor, and the memory stores a computer program. When the processor executes the computer program, the reaction process model construction method of any of the above embodiments is implemented.

[0207] Obtaining catalyst particle parameters and reactor reaction section parameters of a current reaction process;

[0208] Based on the catalyst particle parameters and the reactor reaction section parameters, a filling process of the catalyst particles in the fixed bed reactor is simulated to obtain a catalyst particle accumulation model;

[0209] According to the particle positions of the catalyst particle accumulation model, a reaction process model of the current reaction process is constructed; the reaction process model includes a multiphase flow field model and a chemical reaction model which are coupled.

[0210] In one embodiment, a computer device is provided, including a memory and a processor, the memory stores a computer program, and the processor executes the computer program to implement the reaction safety prediction method of any of the above embodiments:

[0211] Based on the reaction process model as described above, mass conservation data, momentum conservation data and energy conservation data of the current reaction process are calculated;

[0212] The mass conservation data, momentum conservation data and energy conservation data are taken as initial flow field conditions to calculate flow field data and chemical reaction data of the current reaction process;

[0213] Based on the flow field data and the chemical reaction data, a stable simulation result of the current reaction process is iteratively solved;

[0214] Based on the stable simulation result, a safety prediction result of the current reaction process is determined.

[0215] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties.

[0216] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0217] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0218] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for constructing a reaction process model, characterized in that: The catalytic process applied to a fixed bed reactor, the reaction process model construction method includes: Obtain the catalyst particle parameters and reactor reaction section parameters of the current reaction process; Based on the catalyst particle parameters and the reactor reaction section parameters, a catalyst particle packing process in the fixed bed reactor is simulated to obtain a catalyst particle accumulation model; A reaction process model of the current reaction process is constructed according to the particle positions of the catalyst particle accumulation model; the reaction process model includes a coupled multiphase flow field model and a chemical reaction model.

2. The reaction process model construction method according to claim 1, characterized in that: The multiphase flow field model includes at least two partially or completely immiscible liquids, or includes partially or completely immiscible gas and liquid.

3. The reaction process model construction method according to claim 1, characterized in that: The acquisition of catalyst particle parameters and reactor reaction section parameters of the current reaction process includes: Obtaining material parameters of catalyst particles, material parameters of the reactor, and material parameters between the catalyst particles and the reactor; The catalyst particle parameters and reactor reaction section parameters of the current reaction process are determined based on the material parameters.

4. The reaction process model construction method according to claim 1, characterized in that: The constructing of the reaction process model of the current reaction process according to the particle positions of the catalyst particle accumulation model includes: Constructing a multiphase flow field model of the current reaction process according to the positions of the catalyst particles in the catalyst particle accumulation model and the preset initial flow field conditions; Determine the chemical reaction source term based on the position of catalyst particles in the catalyst particle accumulation model, the component concentration distribution of each phase in the multiphase flow field model, the reaction kinetic parameters, and the relative molecular masses of reactants and products; The chemical reaction source term is applied to the component transport equation of the multiphase flow field model to construct a chemical reaction model of the current reaction process.

5. The reaction process model construction method according to claim 4, characterized in that: The multiphase flow field model includes a continuity equation, a momentum equation, and an energy equation. The multiphase flow field model of the current reaction process is constructed according to the positions of the catalyst particles in the catalyst particle accumulation model and the preset initial flow field conditions. Obtaining spatial distribution data of the catalyst particles and the reactor according to positions of the catalyst particles in the catalyst particle accumulation model, and meshing the geometric structure of the reactor according to the spatial distribution data to obtain a computational grid; Calculating the volume fraction distribution of each phase according to the calculation grid and the preset initial flow field conditions; Based on the volume fraction distribution, weighted calculation of the density and viscosity of the mixed phase; Based on the density and viscosity of the mixed phase, the continuity equation, momentum equation and energy equation are constructed.

6. The reaction process model construction method according to claim 4, characterized in that: The determination of the chemical reaction source term based on the positions of the catalyst particles in the catalyst particle accumulation model, the component concentration distribution of each phase in the multiphase flow field model, the reaction kinetic parameters, and the relative molecular masses of the reactants and products includes: When the fluid position corresponding to the position of the catalyst particle in the catalyst particle accumulation model is the catalyst particle surface, calculating the reaction component concentration distribution on the catalyst particle surface based on the component concentration distribution of each phase in the multiphase flow field model; Calculating a chemical reaction rate based on the reaction component concentration distribution and reaction kinetic parameters; The chemical reaction source term is determined based on the chemical reaction rate and the relative molecular masses of reactants and products.

7. The reaction process model construction method according to claim 4, characterized in that: The determination of the chemical reaction source term based on the positions of the catalyst particles in the catalyst particle accumulation model, the component concentration distribution of each phase in the multiphase flow field model, the reaction kinetic parameters, and the relative molecular masses of the reactants and products further includes: When the fluid position corresponding to the position of the catalyst particle in the catalyst particle accumulation model is not the surface of the catalyst particle, the chemical reaction source term is set to zero.

8. A reaction safety prediction method, characterized in that: The reaction safety prediction method comprises: Based on the reaction process model described in any one of claims 1 to 7, solving the continuity equation, momentum equation, and energy equation of the current reaction process to obtain data such as the temperature field, pressure field, and velocity field; The obtained temperature field, pressure field, velocity field and other data are used as initial flow field conditions to calculate the flow field data and chemical reaction data of the current reaction process; Iteratively solving a stable simulation result of the current reaction process based on the flow field data and the chemical reaction data; Based on the stability simulation results, a safety prediction result of the current reaction process is determined.

9. The reaction safety prediction method according to claim 8, characterized in that: The iteratively solving the stable simulation result of the current reaction process based on the flow field data and the chemical reaction data includes: solving a flow field simulation result based on the flow field data and the chemical reaction data; If the flow field simulation result meets the preset stability conditions, the flow field simulation result is used as a stable simulation result; the preset stability conditions include one or more of the fluctuation amplitude of the flow of each component at the reactor outlet, the fluctuation amplitude of the reactor outlet temperature, and the fluctuation amplitude of the catalyst particle surface reaction rate within a preset time step; If the flow field simulation result does not meet the preset stability condition, the flow field simulation result is used as the initial flow field condition, and the flow field simulation result is solved again based on the initial flow field condition.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 9 is implemented.

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