Prediction method and system for internal diffusion coefficient of special-shaped catalyst

Abstract

The embodiment of the invention provides a method and system for predicting the internal diffusion coefficient of a special-shaped catalyst, and belongs to the field of petrochemical chemical reaction engineering. The prediction method comprises the following steps: acquiring operation parameters of a reactor and geometric parameters of a to-be-tested special-shaped catalyst under actual working conditions; respectively inputting the operation parameters and the geometric parameters into a pre-constructed prediction model to obtain an internal diffusion coefficient of the to-be-tested special-shaped catalyst; wherein the prediction model is constructed based on each operation parameter of the reactor, each geometric parameter of the special-shaped catalyst and a corresponding relation data set between each operation parameter and an internal diffusion coefficient corresponding to each geometric parameter under an actual working condition. Therefore, a prediction model can be pre-constructed based on each operation parameter of the reactor, each geometric parameter of the special-shaped catalyst and a corresponding relation data set between each operation parameter and an internal diffusion coefficient corresponding to each geometric parameter under an actual working condition; and the internal diffusion coefficient of the special-shaped catalyst under a given operation condition can be obtained more quickly, efficiently and accurately.

Description

Technical Field

[0001] The invention relates to the technical field of petrochemical chemical reaction engineering, and in particular to a method and system for predicting the internal diffusion coefficient of a heterogeneous catalyst. Background Art

[0002] Fixed bed reactors are widely used in various hydrogenation processes in the field of petrochemical production. In order to improve the utilization efficiency of catalysts and reduce the pressure drop of the bed, industrial fixed bed hydrogenation reactors are usually loaded with a large number of shaped catalysts. Different from spherical catalysts, shaped catalysts have special geometric shapes, which makes it difficult to describe the mass transfer-diffusion behavior of reaction components on shaped catalysts by traditional simplified models. The direct impact of mass transfer-diffusion behavior on catalysts is reflected in the difference between the average reaction rate on the catalyst surface and inside.

[0003] At present, the prediction of the internal diffusion coefficient of catalysts is usually targeted at spherical catalysts, and can only describe simple reaction processes, with poor scalability. For the calculation of the internal diffusion coefficient of various shaped catalysts other than spherical ones, the spherical equivalent method is often used. This has the disadvantages of poor model reliability and limited accuracy, and it is impossible to accurately predict the size of the internal diffusion coefficient of shaped catalysts. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a method and system for predicting the internal diffusion coefficient of a special-shaped catalyst, so as to solve the technical problems existing in the above-mentioned prior art.

[0005] In order to achieve the above-mentioned object, an embodiment of the present invention provides a method for predicting the internal diffusion coefficient of a shaped catalyst, comprising obtaining operating parameters of a reactor under actual working conditions and geometric parameters of the shaped catalyst to be tested;

[0006] The operating parameters and the geometric parameters are respectively input into a pre-constructed prediction model to obtain the internal diffusion coefficient of the special-shaped catalyst to be tested; wherein the prediction model is constructed based on a data set of corresponding relationships between the operating parameters of the reactor under actual operating conditions, the geometric parameters of the special-shaped catalyst, and the internal diffusion coefficients corresponding to the operating parameters and the geometric parameters.

[0007] Optionally, the construction of the prediction model includes:

[0008] Acquiring reaction kinetics experimental data, and fitting the reaction kinetics experimental data to obtain initial values ​​of reaction kinetics parameters;

[0009] Based on the initial values ​​of the reaction kinetic parameters and a pre-constructed three-dimensional calculation model of the shaped catalyst particles, a calculated value of the internal diffusion coefficient of the shaped catalyst is obtained, wherein in the pre-constructed three-dimensional calculation model of the shaped catalyst particles, the interior of the shaped catalyst is set as a porous medium, and the outer surface of the shaped catalyst is set as a wall condition that allows component concentration transmission;

[0010] Determine whether the calculated value of the internal diffusion coefficient obtained satisfies a first preset condition, and if so, output the initial value of the reaction kinetics parameter and the calculated value of the internal diffusion coefficient, wherein the first preset condition is a convergence criterion for obtaining a stable solution of the internal diffusion coefficient;

[0011] Based on the initial values ​​of the reaction kinetic parameters, the corresponding calculated values ​​of the internal diffusion coefficient, the operating parameters of the reactor and the geometric parameters of the heterogeneous catalyst, a corresponding relationship data set is constructed, and based on the corresponding relationship data set, the prediction model is constructed.

[0012] Optionally, the reaction kinetics experimental data is fitted according to the following formula:

[0013]

[0014] Where SSE is the relative deviation, and are the calculated and experimental values ​​of the mass concentration of each element in the reaction products, respectively.

[0015] Optionally, the convergence criterion is:

[0016]

[0017] Where η i,j , η i,j-1 represents the calculated values ​​of the internal diffusion coefficient obtained at the j-th and j-1-th times, and m is the number of components.

[0018] Optionally, obtaining a calculated value of the internal diffusion coefficient based on the initial value of the reaction kinetics parameter and a pre-constructed three-dimensional calculation model of the heterogeneous catalyst particles includes:

[0019] Performing geometric modeling on the special-shaped catalyst and performing mesh division to obtain a plurality of single-mesh special-shaped catalysts;

[0020] According to the initial values ​​of the reaction kinetic parameters and the preset initial value of the internal diffusion coefficient, a transport equation for the heterogeneous catalyst components is established, and the transport equation for the heterogeneous catalyst components is solved to obtain the reaction rate of each component in the heterogeneous catalyst in each single grid, wherein the preset initial value of the internal diffusion coefficient is 1;

[0021] Substituting the reaction rate into the following equation, the calculated value of the internal diffusion coefficient is obtained;

[0022]

[0023] In the formula, is the internal diffusion coefficient, V p and S p are the volume and surface area of ​​the catalyst, r i,cell is the reaction rate of component i in each single grid.

[0024] Optionally, the component transport equation is:

[0025]

[0026] In the formula, C1~C m-2 represents the concentration of the first to the m-2th components, ρ is the density of the porous medium, U is the main flow velocity of the porous medium, μ is the viscosity of the porous medium, r0~r m-2 Represents the reaction rate of the 1st to m-2th components.

[0027] Optionally, the determining whether the calculated value of the internal diffusion coefficient satisfies a first preset condition includes:

[0028] Based on the calculated values ​​of the internal diffusion coefficients obtained at the jth and j-1th times, the error vector Eps is constructed:

[0029]

[0030] Taking an average value of the error vector, determining whether the average value meets the first preset condition, and if so, outputting the initial value of the reaction kinetics parameter and the internal diffusion coefficient;

[0031] If it is not satisfied, the internal diffusion coefficient obtained for the jth time is used as a new initial value of the internal diffusion coefficient, and the initial value of the reaction kinetics parameter is corrected;

[0032] Based on the new initial value of the internal diffusion coefficient and the corrected initial value of the reaction kinetic parameters, a new transport equation for the shaped catalyst components is established, and based on the new transport equation for the shaped catalyst components, a stable solution for the internal diffusion coefficient and the intrinsic kinetic parameters of the reaction are obtained.

[0033] Optionally, the construction of the corresponding relationship data set includes:

[0034] Construct an input parameter matrix Para containing the operating parameters and the geometric parameters of the shaped catalyst:

[0035]

[0036] The operating parameters, the geometric parameters, the stable solution of the internal diffusion coefficient, and the intrinsic kinetic parameters of the reaction are input into the pre-constructed three-dimensional calculation model of the heterogeneous catalyst particles to obtain a data set of the internal diffusion coefficient corresponding to the input parameter matrix Para, and based on the operating parameters, the geometric parameters and the data set of the internal diffusion coefficient, the corresponding relationship data set θ is constructed:

[0037]

[0038] Where A1…A j Indicates operating parameters, B1…B j Represents geometric parameters, I1…I j Dataset representing the internal diffusion coefficient.

[0039] Optionally, constructing the prediction model according to the corresponding relationship data set includes:

[0040] According to the corresponding relationship data set, a two-dimensional or multi-dimensional map of the internal diffusion coefficient of any component of the shaped catalyst with respect to any input parameter or multiple input parameters is constructed, wherein the input parameters include the operating parameters of the reactor and the geometric parameters of the shaped catalyst.

[0041] Optionally, the prediction model is constructed according to the following formula:

[0042] η i =f(a1,a2,...,b1,b2,...)

[0043] Wherein, a1, a2... are reactor operating parameters, b1, b2... are geometric parameters of the shaped catalyst;

[0044] The Siler modulus Φ is introduced to construct the quantitative relationship between the internal diffusion coefficient and the Siler modulus:

[0045]

[0046] Where η i =(Φ i ) represents the internal diffusion coefficient η i and the Schiller modulus Φ i The quantitative relationship between V p and S p are the volume and surface area of ​​the catalyst, k is the reaction kinetic constant, D i,eff is the effective diffusion coefficient of component i, is the concentration of component i on the outer surface of the catalyst.

[0047] Optionally, the effective diffusion coefficient of component i in the heterogeneous catalyst is determined according to the following formula:

[0048]

[0049] Where D i, is the effective diffusion coefficient of component i, D i is the diffusion coefficient of component i, ε b and τ are the porosity and tortuosity factor of the shaped catalyst, respectively.

[0050] Optionally, the method further includes:

[0051] Predicting the reaction performance of the reactor based on the obtained internal diffusion coefficient of the heteromorphic catalyst to be tested; or,

[0052] If the obtained initial values ​​of the reaction kinetic parameters satisfy the Weisz-Prater criterion, then based on the initial values ​​of the reaction kinetic parameters, predicting the reaction performance of the reactor;

[0053] If the obtained initial values ​​of the reaction kinetic parameters do not satisfy the Weisz-Prater criterion, the reaction performance of the reactor is predicted based on the obtained internal diffusion coefficient of the special-shaped catalyst to be tested.

[0054] Optionally, the Weisz-Prater criterion is:

[0055]

[0056] Where V p and A p are the volume and surface area of ​​the heterogeneous catalyst, n is the reaction order, r i is the apparent reaction rate, d i,eff is the effective diffusion coefficient of component i in the heterogeneous catalyst, C i,0 is the concentration of component i on the outer surface of the heteromorphic catalyst.

[0057] In another aspect, the present invention provides a system for predicting the internal diffusion coefficient of a heterogeneous catalyst, comprising:

[0058] Memory; and

[0059] The processor is configured to execute the above-mentioned method for predicting the diffusion coefficient within the heterogeneous catalyst.

[0060] Through the above technical scheme, a prediction model pre-constructed based on the data set of corresponding relationships between the operating parameters of the reactor under actual operating conditions, the geometric parameters of the shaped catalyst, and the internal diffusion coefficients corresponding to the operating parameters and the geometric parameters can be used to obtain the internal diffusion coefficient of the shaped catalyst under given operating conditions more quickly, efficiently and accurately.

[0061] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0063] Figure 1 It is a flow chart of an implementation method of predicting the internal diffusion coefficient of a heterogeneous catalyst provided in an embodiment of the present application;

[0064] Figure 2 It is a flowchart of an implementation method for predicting the internal diffusion coefficient of a butterfly catalyst provided in an embodiment of the present application;

[0065] Figure 3 It is a schematic diagram of a flow chart of a diffusion simulation in a heterogeneous catalyst provided in an embodiment of the present application;

[0066] Figure 4 It is a two-dimensional prediction map of the internal diffusion coefficient of the catalyst equivalent size obtained in the examples of the present application;

[0067] Figure 5 It is a three-dimensional prediction map of the internal diffusion coefficient regarding hydrogen partial pressure and hydrogen-to-oil ratio provided in the embodiment of the present application;

[0068] Figure 6 is a comparison chart of the calculated results and experimental results of the mass fractions of sulfur and nitrogen in the reaction products of Examples 1-4 of the present application;

[0069] Figure 7 It is a schematic diagram of the structure of a device for predicting the internal diffusion coefficient of a special-shaped catalyst provided in an embodiment of the present application. DETAILED DESCRIPTION

[0070] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0071] In addition, the terms used in the present application specification are only for describing specific embodiments and are not intended to limit the present application. Those skilled in the art should understand that the terms "internal diffusion coefficient", "internal diffusion effective factor", "internal diffusion efficiency factor" and "internal diffusion factor" have the same meaning when describing the effect of catalyst internal diffusion on the reaction process.

[0072] See also Figure 1As shown, it is a flowchart of an implementation method for predicting the internal diffusion coefficient of a heterogeneous catalyst provided in an embodiment of the present application, comprising the following execution steps:

[0073] Step 100: Obtain the operating parameters of the reactor under actual working conditions and the geometric parameters of the special-shaped catalyst to be tested.

[0074] Step 101: input the operating parameters and the geometric parameters into a pre-built prediction model respectively to obtain the internal diffusion coefficient of the special-shaped catalyst to be tested.

[0075] The prediction model is constructed based on a data set of corresponding relationships between various operating parameters of the reactor under actual working conditions, various geometric parameters of the special-shaped catalyst, and internal diffusion coefficients corresponding to various operating parameters and various geometric parameters.

[0076] In some embodiments, the construction of the prediction model comprises the following steps:

[0077] S1: Acquire reaction kinetics experimental data, and fit the reaction kinetics experimental data to obtain initial values ​​of reaction kinetics parameters.

[0078] In some embodiments, the reaction kinetics experimental data is fitted according to the following formula:

[0079]

[0080] Where SSE is the relative deviation, and are the calculated and experimental values ​​of the mass concentration of each element in the reaction products, respectively.

[0081] S2: Based on the initial values ​​of the reaction kinetic parameters and a pre-constructed three-dimensional calculation model of the shaped catalyst particles, a calculated value of the internal diffusion coefficient of the shaped catalyst is obtained.

[0082] Wherein, in the pre-constructed three-dimensional computational model of the shaped catalyst particles, the interior of the shaped catalyst is set as a porous medium, and the outer surface of the shaped catalyst is set as a wall condition that allows the transmission of component concentration.

[0083] In some embodiments, the outer surface of the catalyst is set to a wall condition that only allows component concentration transmission, which is divided into two cases. In one case, for the flow equation, the outer surface of the catalyst is set to a non-slip wall condition with no momentum and mass transfer. In the other case, for the constructed component transport equation, m-1 group component concentration C0□C m-2 The interfacial flux conservation condition is set on the outer surface of the catalyst, that is, the component concentrations on both sides of the catalyst surface are equal:

[0084]

[0085] The three-dimensional calculation model of the heterogeneous catalyst is solved by iterative calculation using the transient method, and the concentration of each component inside the catalyst is initialized to 0. More preferably, the flow field is iterated to a steady state without activating the component transport equation, and the component transport equation is activated on this basis for solution.

[0086] In some implementations, when executing step S2, the following steps may be performed:

[0087] Step 1: geometrically modeling the special-shaped catalyst and performing mesh division to obtain a plurality of single-mesh special-shaped catalysts;

[0088] Step 2: According to the initial values ​​of the reaction kinetic parameters and the preset initial value of the internal diffusion coefficient, a transport equation for the heterogeneous catalyst components is established, and the transport equation for the heterogeneous catalyst components is solved to obtain the reaction rate of each component in the heterogeneous catalyst in each single grid, wherein the preset initial value of the internal diffusion coefficient is 1;

[0089] Step 3: Substitute the reaction rate into the following equation to obtain the calculated value of the internal diffusion coefficient;

[0090]

[0091] In the formula, is the internal diffusion coefficient, V p and S p are the volume and surface area of ​​the catalyst, r i,cell is the reaction rate of component i in each single grid.

[0092] In some embodiments, the component transport equation is:

[0093]

[0094] In the formula, C1~C m-2 represents the concentration of the first to the m-2th components, ρ is the density of the porous medium, U is the main flow velocity of the porous medium, μ is the viscosity of the porous medium, r0~r m-2 Represents the reaction rate of the 1st to m-2th components.

[0095] S3: Determine whether the calculated value of the internal diffusion coefficient obtained satisfies a first preset condition. If so, output the initial value of the reaction kinetics parameter and the calculated value of the internal diffusion coefficient.

[0096] The first preset condition is a convergence criterion for obtaining a stable solution setting for the internal diffusion coefficient.

[0097] In some embodiments, the convergence criterion is:

[0098]

[0099] Where η i,j , η i,j-1 represents the calculated values ​​of the internal diffusion coefficient obtained at the j-th and j-1-th times, and m is the number of components.

[0100] In some embodiments, it can be determined whether the calculated value of the internal diffusion coefficient satisfies the first preset condition in the following manner:

[0101] Based on the calculated values ​​of the internal diffusion coefficients obtained at the jth and j-1th times, the error vector Eps is constructed:

[0102]

[0103] Taking an average value of the error vector, determining whether the average value meets the first preset condition, and if so, outputting the initial value of the reaction kinetics parameter and the internal diffusion coefficient;

[0104] If it is not satisfied, the internal diffusion coefficient obtained for the jth time is used as a new initial value of the internal diffusion coefficient, and the initial value of the reaction kinetics parameter is corrected;

[0105] Based on the new initial value of the internal diffusion coefficient and the corrected initial value of the reaction kinetic parameters, a new transport equation for the shaped catalyst components is established, and based on the new transport equation for the shaped catalyst components, a stable solution for the internal diffusion coefficient and the intrinsic kinetic parameters of the reaction are obtained.

[0106] S4: constructing a corresponding relationship data set based on the initial values ​​of the reaction kinetic parameters, the corresponding calculated values ​​of the internal diffusion coefficient, the operating parameters of the reactor and the geometric parameters of the heterogeneous catalyst, and constructing the prediction model based on the corresponding relationship data set.

[0107] In some implementations, the construction of the correspondence data set includes:

[0108] Construct an input parameter matrix Para containing the operating parameters and the geometric parameters of the shaped catalyst:

[0109]

[0110] The operating parameters, the geometric parameters, the stable solution of the internal diffusion coefficient, and the intrinsic kinetic parameters of the reaction are input into the pre-constructed three-dimensional calculation model of the heterogeneous catalyst particles to obtain a data set of the internal diffusion coefficient corresponding to the input parameter matrix Para, and based on the operating parameters, the geometric parameters and the data set of the internal diffusion coefficient, the corresponding relationship data set θ is constructed:

[0111]

[0112] Where A1…A j Indicates operating parameters, B1…B j Represents geometric parameters, I1…I j Dataset representing the internal diffusion coefficient.

[0113] In some embodiments, constructing the prediction model based on the correspondence data set includes: constructing a two-dimensional or multi-dimensional map of the diffusion coefficient within any component of the shaped catalyst with respect to any input parameter or multiple input parameters based on the correspondence data set, wherein the input parameters include operating parameters of the reactor and geometric parameters of the shaped catalyst.

[0114] In some implementations, the prediction model may also be constructed according to the following formula:

[0115] η i =f(a1,a2,...,b1,b2,...)

[0116] Wherein, a1, a2... are reactor operating parameters, b1, b2... are geometric parameters of the shaped catalyst;

[0117] The Siler modulus Φ is introduced to construct the quantitative relationship between the internal diffusion coefficient and the Siler modulus:

[0118]

[0119] Where η i =(Φ i ) represents the internal diffusion coefficient η i and the Schiller modulus Φ i The quantitative relationship between V p and S p are the volume and surface area of ​​the catalyst, k is the reaction kinetic constant, D i,eff is the effective diffusion coefficient of component i, is the concentration of component i on the outer surface of the catalyst.

[0120] In some embodiments, the effective diffusion coefficient of the i component in the heterogeneous catalyst is determined according to the following formula:

[0121]

[0122] Where D i, is the effective diffusion coefficient of component i, D i is the diffusion coefficient of component i, ε b and τ are the porosity and tortuosity factor of the shaped catalyst, respectively.

[0123] In some embodiments, the prediction method execution process further includes:

[0124] Predicting the reaction performance of the reactor based on the obtained internal diffusion coefficient of the heteromorphic catalyst to be tested; or,

[0125] If the obtained initial values ​​of the reaction kinetic parameters satisfy the Weisz-Prater criterion, then based on the initial values ​​of the reaction kinetic parameters, predicting the reaction performance of the reactor;

[0126] If the obtained initial values ​​of the reaction kinetic parameters do not satisfy the Weisz-Prater criterion, the reaction performance of the reactor is predicted based on the obtained internal diffusion coefficient of the special-shaped catalyst to be tested.

[0127] Optionally, the reactor is any one of a fixed bed reactor, a trickle bed reactor, a fluidized bed reactor and a moving bed reactor.

[0128] In some embodiments, the Weisz-Prater criterion is:

[0129]

[0130] Where V p and A p are the volume and surface area of ​​the heterogeneous catalyst, n is the reaction order, r i is the apparent reaction rate, d i,eff is the effective diffusion coefficient of component i in the heterogeneous catalyst, C i,0 is the concentration of component i on the outer surface of the heteromorphic catalyst.

[0131] In some embodiments, the internal diffusion coefficient is predicted by taking diesel hydrodesulfurization and denitrification reaction on butterfly catalyst as an example, see Figure 2 As shown, it is a flowchart of an implementation method for predicting the internal diffusion coefficient of a butterfly catalyst provided in an embodiment of the present application, which includes the following execution steps:

[0132] Embodiment 1:

[0133] S200: Acquire reaction kinetics experimental data and obtain initial values ​​of reaction kinetics parameters by fitting.

[0134] Specifically, the geometric parameters of the butterfly catalyst are shown in Table 1. The diesel hydrodesulfurization and denitrification reactions can be expressed as:

[0135] S+H2→H2S

[0136] 2N+3H2→2H2N

[0137] Table 1 Geometric parameters of butterfly catalyst

[0138] Section size / mm Length / mm Equivalent size / mm Sphericity 1.6 5 1.324 0.552

[0139] Assuming that the internal diffusion coefficients of diesel hydrodesulfurization and denitrification reactions are both 1, based on the experimental data of diesel hydrogenation reaction kinetics, the ant colony algorithm with multi-point optimization is used to fit the apparent kinetic parameters of hydrodesulfurization and denitrification reactions. The objective function used for fitting is to minimize the relative deviation between the calculated value and the experimental value of the mass concentration of sulfur or nitrogen in the product, as shown in the following formula:

[0140]

[0141] In the formula and are the calculated and experimental values ​​of the mass concentration of sulfur or nitrogen in the reaction products, respectively.

[0142] Based on the obtained apparent kinetic parameters, reaction kinetic equations and geometric parameters of the butterfly catalyst shown in Table 1, taking the operating temperature of 370°C, the hydrogen partial pressure of 6.4 MPa and the hydrogen-to-oil ratio of 300 as an example, the influence of the internal diffusion resistance is determined by the Weisz-Prater criterion:

[0143]

[0144] Where V P and A P are the volume and surface area of ​​the catalyst, n is the reaction order, r i is the apparent reaction rate, D i,eff is the effective diffusion coefficient of sulfur or nitrogen in the catalyst, C i,0 is the molar concentration of sulfur or nitrogen on the outer surface of the catalyst.

[0145] S201: Construct and solve a three-dimensional computational model of a shaped catalyst to obtain a numerical solution of the internal diffusion coefficient.

[0146] In some embodiments, the schematic diagram of the process of simulating diffusion within a heterogeneous catalyst is as follows: Figure 3 Specifically, the component transport equations of sulfur and nitrogen are established, the reaction kinetic parameters and internal diffusion coefficients obtained in step S200 are introduced into the reaction rate equation, and added to the component transport equation in the form of source terms:

[0147]

[0148] The geometric model of the butterfly catalyst was drawn in sequence using Design Modeler and Meshing, and the hexahedral computational grid was divided. A three-dimensional computational model was set up in CFX software based on the finite volume method and solved in transient mode. In the three-dimensional computational model, the interior of the catalyst was set as a porous medium, and the outer surface of the catalyst was set as a wall condition that only allowed component concentration transmission: for the flow equation, the outer surface of the catalyst was set as a non-slip wall condition with no momentum and mass transfer; for the constructed component transport equation, the outer surface of the catalyst was set as an interface flux conservation condition, that is, the concentrations of sulfur and nitrogen elements on the inner and outer sides of the catalyst surface were equal.

[0149] The effective diffusion coefficient of sulfur or nitrogen in the catalyst is calculated as:

[0150]

[0151] Where D i,eff is the effective diffusion coefficient of sulfur or nitrogen, D i is the diffusion coefficient of sulfur or nitrogen, ε b and τ are the porosity and tortuosity factor of the butterfly catalyst, respectively.

[0152] The initialization condition for the transient solution of the three-dimensional computational model is that the concentrations of sulfur and nitrogen elements inside the catalyst are both 0. At the same time, the flow field is iterated to a steady state without activating the component transport equation, and the component transport equation is activated on this basis for solution.

[0153] After the component transport equation is iteratively stabilized, the internal diffusion coefficient of the hydrodesulfurization reaction is calculated using the following formula:

[0154]

[0155] In the formula, r i,cell The magnitude of the hydrodesulfurization or denitrification reaction rate for each computational grid point.

[0156] S202: Based on the calculated internal diffusion coefficient, steps S200 and S201 are repeated to obtain a stable solution of the internal diffusion coefficient and intrinsic kinetic parameters of the reaction.

[0157] Compare the calculation results of the j-th and j-1-th internal diffusion coefficients, and construct the error function:

[0158]

[0159] Define the following convergence criteria:

[0160]

[0161] Correct the reaction kinetic parameters and repeat the S200 and S201 processes until the above criteria are met. i,j ,i=S,N as the stable solution of the internal diffusion coefficient of sulfur element, η obtained under the conditions of operating temperature of 370℃, hydrogen partial pressure of 6.4MPa, and hydrogen-to-oil ratio of 300 S =0.825,η N =1.0.

[0162] S203: Calculate and obtain a series of data sets including operating parameters, geometric parameters and internal diffusion coefficients, and construct a prediction map and mathematical model of the internal diffusion coefficient.

[0163] Construct the input parameter matrix Para containing the operating parameters and catalyst geometry parameters:

[0164]

[0165] The columns of the parameter matrix Para are operating temperature (°C), hydrogen partial pressure (MPa), hydrogen-to-oil ratio, catalyst cross-sectional size (mm), catalyst length (mm), catalyst equivalent size (mm) and sphericity.

[0166] The above parameters and the intrinsic kinetic parameters obtained in step S103 are applied to step S102 to obtain a data set I of the internal diffusion coefficient corresponding to the input parameter matrix Para, forming a total θ data set:

[0167]

[0168] The columns of the data set Θ are operating temperature (°C), hydrogen partial pressure (MPa), hydrogen-to-oil ratio, catalyst cross-sectional size (mm), catalyst length (mm), catalyst equivalent size (mm), sphericity, and internal diffusion coefficients for desulfurization and denitrification reactions.

[0169] The predicted map of the diffusion coefficients in the desulfurization and denitrification reactions was obtained by plotting the Θ data set. Figure 4 This is a two-dimensional prediction map of the internal diffusion coefficient for the catalyst equivalent size obtained in the examples of the present application. The ordinate represents the internal diffusion coefficient, the abscissa represents the catalyst equivalent size, the black line represents the internal diffusion coefficient for the desulfurization reaction, and the red line represents the internal diffusion coefficient for the denitrification reaction. Figure 5 The three-dimensional prediction map of the internal diffusion coefficient for hydrogen partial pressure and hydrogen-to-oil ratio obtained in the examples of the present application; for a given catalyst equivalent size and operating conditions, the internal diffusion coefficients of the hydrodesulfurization and denitrification reactions can be quickly obtained through the prediction map.

[0170] S204: Given the operating parameters and catalyst geometric parameters, the magnitude of the internal diffusion coefficient is obtained through a prediction map or mathematical model of the internal diffusion coefficient, which is used to predict the reactor-scale reaction performance.

[0171] For the given operating parameters (operating temperature of 350 °C, hydrogen partial pressure of 3.2 MPa, volume space velocity of 2 h -1 , hydrogen-to-oil ratio of 150) and catalyst geometric parameters (catalyst cross-sectional size of 1.6 mm, catalyst length of 5 mm, catalyst equivalent size of 1.324 mm and sphericity of 0.552), the internal diffusion coefficient η is obtained by the method established in step S104. S =0.939 and η N =1.0, and it is applied to the prediction of the reaction performance of the fixed bed reactor. The prediction deviations of the mass fractions of nitrogen and sulfur in the products are 8.128% and -6.836% respectively.

[0172] Example 2: Steps S200-S203 of this example are the same as those of Example 1, except that the volumetric air velocity in step S204 is different. The volumetric air velocity of this example is 1.3 h -1 The catalyst geometric parameters are: catalyst cross-sectional size is 1.6 mm, catalyst length is 5 mm, catalyst equivalent size is 1.324 mm and sphericity is 0.552. The internal diffusion coefficient η is obtained by the method established in step S203. S =0.939 and η N =1.0, and it is applied to the prediction of the reaction performance of the fixed bed reactor. The prediction deviations of the mass fractions of nitrogen and sulfur in the products are -1.339% and 17.482%, respectively.

[0173] Example 3, steps S200-S202 of this example are the same as those of Example 1, except that in step S203, in order to more efficiently construct the corresponding relationship between the internal diffusion coefficient and the operating parameters and the catalyst geometric parameters, a mathematical model for predicting the internal diffusion coefficient is constructed using the Θ data set:

[0174]

[0175] For the given operating parameters (operating temperature of 370 °C, hydrogen partial pressure of 6.4 MPa, volumetric space velocity of 1.3 h -1 , hydrogen-to-oil ratio of 150) and catalyst geometric parameters (catalyst cross-sectional size of 1.6 mm, catalyst length of 5 mm, catalyst equivalent size of 1.324 mm and sphericity of 0.552), the internal diffusion coefficient η is obtained by the method established in step S104. S =0.846 and η N =1.0, and it is applied to the prediction of reaction performance of fixed bed reactor, the prediction deviations of mass fractions of nitrogen and sulfur in products are -18.828% and -3.239% respectively.

[0176] Example 4, steps S200-S203 of this example are the same as those of Example 3, except that the hydrogen-to-oil ratio in step S204 is different from that in Example 2, and the hydrogen-to-oil ratio of this example is 300. The catalyst geometric parameters are the same as those of Example 3, and the internal diffusion coefficient η is obtained by the method established in step S203. S =0.825 and η N =1.0, and it is applied to the prediction of the reaction performance of the fixed bed reactor. The prediction deviations of the mass fractions of nitrogen and sulfur in the products are 27.381% and -2.015% respectively.

[0177] Comparative Example 1

[0178] The operating conditions of this comparative example are exactly the same as those of Example 3, except that the internal diffusion coefficient is calculated by the spherical equivalent method, and the calculation formula is as follows:

[0179]

[0180] The calculated internal diffusion coefficient is η S =0.806 and η N =1.0, and it is applied to the prediction of the reaction performance of the fixed bed reactor. The prediction deviations of the mass fractions of nitrogen and sulfur in the products are -18.826% and -79.074%, respectively.

[0181] Comparative Example 2

[0182] The operating conditions of this comparative example are exactly the same as those of Example 4, except that the calculation method of the internal diffusion coefficient is obtained by the spherical equivalent method, and the calculation formula is:

[0183]

[0184] The calculated internal diffusion coefficient is η S =0.711 and η N =1.0, and it is applied to the prediction of reaction performance of fixed bed reactor, the prediction deviations of mass fractions of nitrogen and sulfur in products are 27.381% and -130.257% respectively.

[0185] The comparison between the calculated results and the experimental results of the mass fractions of sulfur and nitrogen in the reaction products of Examples 1-4 of the present application is as follows: Figure 6 The calculation results of the hydrodesulfurization and denitrification reaction performance of the fixed bed reactor of Examples 1-4 and Comparative Examples 1-2 are shown in Table 2.

[0186] Table 2 Calculation results of hydrodesulfurization and denitrification reaction performance in fixed bed reactors of Examples 1-4 and Comparative Examples 1-2

[0187]

[0188] From the comparison results, it can be seen that the method and system provided by the present invention can accurately calculate the internal diffusion coefficient of the special-shaped catalyst, thereby realizing the accurate prediction of the reaction performance of the reactor.

[0189] In some embodiments, see Figure 7 As shown, it is a structural schematic diagram of a device for predicting the internal diffusion coefficient of a shaped catalyst provided in an embodiment of the present application, comprising five parts: a kinetic parameter preprocessing module, a shaped catalyst internal diffusion simulation module, a kinetic parameter correction and preprocessing module, a shaped catalyst internal diffusion coefficient post-processing module and an application module.

[0190] The kinetic parameter preprocessing module is used to obtain reaction kinetic experimental data and obtain initial values ​​of reaction kinetic parameters by fitting;

[0191] The shaped catalyst internal diffusion simulation module is used to construct and solve the shaped catalyst three-dimensional calculation model to obtain the numerical solution of the internal diffusion coefficient;

[0192] The kinetic parameter correction and preprocessing module is used to cyclically calculate the internal diffusion coefficient to obtain the stable solution of the internal diffusion coefficient and the intrinsic kinetic parameters of the reaction;

[0193] The post-processing module for the internal diffusion coefficient of the heterogeneous catalyst is used to calculate a series of data sets including operating parameters, geometric parameters and internal diffusion coefficients, and to construct a prediction map and mathematical model for the internal diffusion coefficients;

[0194] The application module is used to obtain the size of the internal diffusion coefficient by given operating parameters and catalyst geometric parameters through a prediction map or mathematical model of the internal diffusion coefficient, which is used to predict the reactor-scale reaction performance.

[0195] On the other hand, the present application also provides a system for predicting the diffusion coefficient of a heterogeneous catalyst, comprising:

[0196] Memory; and

[0197] The processor is configured to execute the method for predicting the diffusion coefficient within a heterogeneous catalyst described in any of the above embodiments.

[0198] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0199] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0200] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0201] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0202] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0203] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0204] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0205] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0206] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for predicting the internal diffusion coefficient of a heterogeneous catalyst, characterized in that: include: Obtaining the operating parameters of the reactor under actual working conditions and the geometric parameters of the special-shaped catalyst to be tested; The operating parameters and the geometric parameters are respectively input into a pre-constructed prediction model to obtain the internal diffusion coefficient of the special-shaped catalyst to be tested; wherein the prediction model is constructed based on a data set of corresponding relationships between the operating parameters of the reactor under actual operating conditions, the geometric parameters of the special-shaped catalyst, and the internal diffusion coefficients corresponding to the operating parameters and the geometric parameters.

2. The method according to claim 1, characterized in that The construction of the prediction model includes: Acquiring reaction kinetics experimental data, and fitting the reaction kinetics experimental data to obtain initial values ​​of reaction kinetics parameters; Based on the initial values ​​of the reaction kinetic parameters and a pre-constructed three-dimensional calculation model of the shaped catalyst particles, a calculated value of the internal diffusion coefficient of the shaped catalyst is obtained, wherein in the pre-constructed three-dimensional calculation model of the shaped catalyst particles, the interior of the shaped catalyst is set as a porous medium, and the outer surface of the shaped catalyst is set as a wall condition that allows component concentration transmission; Determine whether the calculated value of the internal diffusion coefficient obtained satisfies a first preset condition, and if so, output the initial value of the reaction kinetics parameter and the calculated value of the internal diffusion coefficient, wherein the first preset condition is a convergence criterion for obtaining a stable solution of the internal diffusion coefficient; Based on the initial values ​​of the reaction kinetic parameters, the corresponding calculated values ​​of the internal diffusion coefficient, the operating parameters of the reactor and the geometric parameters of the heterogeneous catalyst, a corresponding relationship data set is constructed, and based on the corresponding relationship data set, the prediction model is constructed.

3. The method according to claim 2, characterized in that The reaction kinetics experimental data were fitted according to the following formula: Where SSE is the relative deviation, and are the calculated and experimental values ​​of the mass concentration of each element in the reaction products, respectively.

4. The method according to claim 2, characterized in that: The convergence criterion is: Where η i,j , η i,j-1 represents the calculated values ​​of the internal diffusion coefficient obtained at the j-th and j-1-th times, and m is the number of components.

5. The method according to claim 2, characterized in that: The method of obtaining a calculated value of the internal diffusion coefficient based on the initial value of the reaction kinetics parameter and a pre-constructed three-dimensional calculation model of the heterogeneous catalyst particles includes: Performing geometric modeling on the special-shaped catalyst and performing mesh division to obtain a plurality of single-mesh special-shaped catalysts; According to the initial values ​​of the reaction kinetic parameters and the preset initial value of the internal diffusion coefficient, a transport equation for the heterogeneous catalyst components is established, and the transport equation for the heterogeneous catalyst components is solved to obtain the reaction rate of each component in the heterogeneous catalyst in each single grid, wherein the preset initial value of the internal diffusion coefficient is 1; Substituting the reaction rate into the following equation, the calculated value of the internal diffusion coefficient is obtained; In the formula, is the internal diffusion coefficient, V p and S p are the volume and surface area of ​​the catalyst, r i,cell is the reaction rate of component i in each single grid.

6. The method according to claim 5, characterized in that The component transport equation is: In the formula, C1~C m-2 represents the concentration of the first to the m-2th components, ρ is the density of the porous medium, U is the main flow velocity of the porous medium, μ is the viscosity of the porous medium, r0~r m-2 Represents the reaction rate of the 1st to m-2th components.

7. The method according to claim 2, characterized in that The determining whether the calculated value of the internal diffusion coefficient satisfies a first preset condition includes: Based on the calculated values ​​of the internal diffusion coefficients obtained at the jth and j-1th times, the error vector Eps is constructed: Taking an average value of the error vector, determining whether the average value meets the first preset condition, and if so, outputting the initial value of the reaction kinetics parameter and the internal diffusion coefficient; If it is not satisfied, the internal diffusion coefficient obtained for the jth time is used as a new initial value of the internal diffusion coefficient, and the initial value of the reaction kinetics parameter is corrected; Based on the new initial value of the internal diffusion coefficient and the corrected initial value of the reaction kinetic parameters, a new transport equation for the shaped catalyst components is established, and based on the new transport equation for the shaped catalyst components, a stable solution for the internal diffusion coefficient and the intrinsic kinetic parameters of the reaction are obtained.

8. The method according to claim 5, characterized in that The construction of the corresponding relationship data set includes: Construct an input parameter matrix Para containing the operating parameters and the geometric parameters of the shaped catalyst: The operating parameters, the geometric parameters, the stable solution of the internal diffusion coefficient, and the intrinsic kinetic parameters of the reaction are input into the pre-constructed three-dimensional calculation model of the heterogeneous catalyst particles to obtain a data set of the internal diffusion coefficient corresponding to the input parameter matrix Para, and based on the operating parameters, the geometric parameters and the data set of the internal diffusion coefficient, the corresponding relationship data set θ is constructed: Where A1…A j Indicates operating parameters, B1…B j Represents geometric parameters, I1…I j Dataset representing the internal diffusion coefficient.

9. The method according to claim 6, characterized in that The step of constructing the prediction model according to the corresponding relationship data set includes: According to the corresponding relationship data set, a two-dimensional or multi-dimensional map of the internal diffusion coefficient of any component of the shaped catalyst with respect to any input parameter or multiple input parameters is constructed, wherein the input parameters include the operating parameters of the reactor and the geometric parameters of the shaped catalyst.

10. The method according to claim 1, characterized in that The prediction model is constructed according to the following formula: <h2 style=";text-align:left;direction:ltr">η<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> =f(a1,a2,...,b1,b2,...) Wherein, a1, a2... are reactor operating parameters, b1, b2... are geometric parameters of the shaped catalyst; The Siler modulus Φ is introduced to construct the quantitative relationship between the internal diffusion coefficient and the Siler modulus: Where η i =(Φ i ) represents the internal diffusion coefficient η i and the Schiller modulus Φ i The quantitative relationship between V p and S p are the volume and surface area of ​​the catalyst, k is the reaction kinetic constant, D i,eff is the effective diffusion coefficient of component i, is the concentration of component i on the outer surface of the catalyst.

11. The method according to claim 10, characterized in that The effective diffusion coefficient of component i in the heterogeneous catalyst is determined according to the following formula: Where D i, is the effective diffusion coefficient of component i, D i is the diffusion coefficient of component i, ε b and τ are the porosity and tortuosity factor of the shaped catalyst, respectively.

12. The method according to claim 2, characterized in that: The method further comprises: Predicting the reaction performance of the reactor based on the obtained internal diffusion coefficient of the heteromorphic catalyst to be tested; or, If the obtained initial values ​​of the reaction kinetic parameters satisfy the Weisz-Prater criterion, then based on the initial values ​​of the reaction kinetic parameters, predicting the reaction performance of the reactor; If the initial values ​​of the reaction kinetic parameters obtained do not satisfy the Weisz-Prater criterion, the reaction performance of the reactor is predicted based on the internal diffusion coefficient of the special-shaped catalyst to be tested.

13. The method according to claim 12, characterized in that The Weisz-Prater criterion is: Where V p and A p are the volume and surface area of ​​the heterogeneous catalyst, n is the reaction order, r i is the apparent reaction rate, D i,eff is the effective diffusion coefficient of component i in the heterogeneous catalyst, C i,0 is the concentration of component i on the outer surface of the heteromorphic catalyst.

14. A prediction system for the internal diffusion coefficient of a heterogeneous catalyst, characterized in that: include: Memory; as well as A processor is configured to execute the method for predicting the diffusion coefficient in a heterogeneous catalyst as described in any one of claims 1-13.

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