Parameter optimization method and device of tail gas aftertreatment model, equipment and medium

Abstract

The application discloses a tail gas aftertreatment model parameter optimization method, device, equipment and medium, wherein the method comprises: obtaining initial kinetic parameters of a tail gas aftertreatment model corresponding to a target tail gas aftertreatment system and chemical reaction curves of the target tail gas aftertreatment system under each working state; determining to-be-applied kinetic parameters corresponding to the working states based on the chemical reaction curves and the initial kinetic parameters; and determining target kinetic parameters based on the to-be-applied kinetic parameters and the chemical reaction curves. Based on the above technical solution, the corresponding to-be-applied kinetic parameters are calculated based on the initial kinetic parameters and the chemical reaction curves, and then the target kinetic parameters are determined based on the to-be-applied kinetic parameters, thereby solving the problem of poor optimization efficiency of the parameter optimization method based on the element rate in the prior art, and achieving the technical effect of improving the parameter optimization efficiency.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, apparatus, equipment and medium for optimizing parameters of an exhaust gas aftertreatment model. Background Technology

[0002] With the continuous development of vehicle technology, in order to ensure that the exhaust gas emitted by vehicles during their journey meets the preset emission standards, it is necessary to simulate the exhaust gas treatment process based on a preset model before designing the vehicle.

[0003] Traditional simulation methods for chemical reaction mechanisms are divided into global rate expression and elementary rate expression. Although global rate expression can describe the reaction network with fewer chemical reaction equations, elementary rate expression expresses the reaction network in more detail with more elementary reaction equations. In other words, the elementary rate expression method is inevitably accompanied by more adjustment parameters, which reduces the efficiency of the simulation. Summary of the Invention

[0004] This invention provides a parameter optimization method, apparatus, device, and medium for an exhaust gas aftertreatment model. By calculating the corresponding kinetic parameters to be applied based on initial kinetic parameters and chemical reaction curves, and then determining the target kinetic parameters based on the kinetic parameters to be applied, this invention solves the problem of poor optimization efficiency of parameter optimization methods based on elementary rates in the prior art, thereby achieving the technical effect of improving parameter optimization efficiency.

[0005] According to one aspect of the present invention, a method for optimizing parameters of an exhaust gas aftertreatment model is provided, the method comprising:

[0006] The initial kinetic parameters of the exhaust gas aftertreatment model corresponding to the target exhaust gas aftertreatment system and the chemical reaction curves of the target exhaust gas aftertreatment system under various working conditions are obtained.

[0007] Based on the chemical reaction curves and the initial kinetic parameters, the kinetic parameters to be applied corresponding to each working state are determined; wherein, the chemical reaction curves include low-temperature pollutant conversion curves, by-product generation and consumption curves, and high-temperature pollutant conversion curves;

[0008] The target kinetic parameters are determined based on the kinetic parameters to be applied and the chemical reaction curve.

[0009] According to another aspect of the present invention, a parameter optimization apparatus for an exhaust gas aftertreatment model is provided, the apparatus comprising:

[0010] The parameter acquisition module is used to acquire the initial kinetic parameters of the exhaust gas after-treatment model corresponding to the target exhaust gas after-treatment system, and the chemical reaction curves of the target exhaust gas after-treatment system under various working conditions.

[0011] The parameter determination module is used to determine the kinetic parameters to be applied corresponding to each working state based on the chemical reaction curve and the initial kinetic parameters; wherein, the chemical reaction curve includes a low-temperature pollutant conversion curve, a by-product generation and consumption curve, and a high-temperature pollutant conversion curve;

[0012] The target parameter determination module is used to determine the target kinetic parameters based on the kinetic parameters to be applied and the chemical reaction curve.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the parameter optimization method of the exhaust gas aftertreatment model according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the parameter optimization method of the exhaust gas aftertreatment model according to any embodiment of the present invention.

[0018] The technical solution of this invention obtains the initial kinetic parameters of the exhaust gas aftertreatment model corresponding to the target exhaust gas aftertreatment system and the chemical reaction curves of the target exhaust gas aftertreatment system under various operating states. Then, based on the chemical reaction curves and the initial kinetic parameters, it determines the kinetic parameters to be applied corresponding to each operating state. Finally, it determines the target kinetic parameters based on the kinetic parameters to be applied and the chemical reaction curves. Based on this technical solution, by calculating the corresponding kinetic parameters to be applied based on the initial kinetic parameters and the chemical reaction curves, and then determining the target kinetic parameters based on the kinetic parameters to be applied, the problem of poor optimization efficiency in existing parameter optimization methods based on elementary rates is solved, thereby achieving the technical effect of improving parameter optimization efficiency.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating a parameter optimization method for an exhaust gas aftertreatment model provided in an embodiment of the present invention.

[0022] Figure 2 This is a flowchart of a parameter optimization method for an exhaust gas aftertreatment model provided in an embodiment of the present invention;

[0023] Figure 3 This is a flowchart of the parameter optimization method for the exhaust gas aftertreatment model provided in the embodiments of the present invention;

[0024] Figure 4 This is a flowchart of the parameter optimization method for the exhaust gas aftertreatment model provided in the embodiments of the present invention;

[0025] Figure 5 This is a structural block diagram of a parameter optimization device for an exhaust gas aftertreatment model provided in an embodiment of the present invention;

[0026] Figure 6 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] Example 1

[0030] Figure 1 This is a flowchart illustrating a parameter optimization method for an exhaust gas aftertreatment model provided in an embodiment of the present invention. This embodiment is applicable to situations where the target kinetic parameters are determined based on the initial kinetic parameters and chemical reaction curves under various operating conditions of the target exhaust gas aftertreatment system model. This method can be executed by a parameter optimization device for the exhaust gas aftertreatment model. The parameter optimization device for the exhaust gas aftertreatment model can be implemented in hardware and / or software. The parameter optimization device for the exhaust gas aftertreatment model can be configured in an electronic device, such as a PC or a server.

[0031] like Figure 1 As shown, the method includes:

[0032] S110. Obtain the initial kinetic parameters of the exhaust gas aftertreatment model corresponding to the target exhaust gas aftertreatment system, and the chemical reaction curves of the target exhaust gas aftertreatment system under various operating conditions.

[0033] The target exhaust aftertreatment system can be an exhaust aftertreatment system selected by technicians that requires parameter optimization. The exhaust aftertreatment model can be understood as a mathematical model constructed by staff based on relevant data from the target exhaust aftertreatment system. Initial kinetic parameters can be parameter information obtained from the target exhaust aftertreatment model during experiments. Operating state can be the current operating state of the vehicle. The chemical reaction curve can be understood as the reaction curve of fuel during combustion. Initial kinetic parameters can be pre-selected parameter values, such as adsorption terms, pre-factors, and activation energy.

[0034] It should be noted that the vehicle in the embodiments of the present invention can be a vehicle fueled by natural gas. That is, the exhaust aftertreatment model in this application is a natural gas engine exhaust aftertreatment model. Furthermore, in order to facilitate parameter optimization, a representative working state can be selected from all vehicle working states, such as the vehicle's cold start state, steady state, and high load and high torque state.

[0035] Specifically, the initial kinetic parameters of the exhaust aftertreatment model corresponding to the target exhaust aftertreatment system and the chemical reaction curves of the target exhaust aftertreatment system under various operating conditions are obtained. For example, technicians may select the target exhaust aftertreatment system that needs parameter optimization according to requirements, and obtain the initial kinetic parameter values ​​of the exhaust aftertreatment model corresponding to the target exhaust aftertreatment system, as well as the chemical reaction curves under various operating conditions.

[0036] Based on the above technical solution, before obtaining the initial kinetic parameter values ​​of the exhaust gas aftertreatment model corresponding to the target exhaust gas aftertreatment system, the method includes: obtaining the chemical reaction rate corresponding to the target exhaust gas aftertreatment system based on preset temperature conditions, determining the kinetic curve based on the chemical reaction rate, and processing the kinetic curve according to the Arrhenius equation to determine the initial kinetic parameters corresponding to the target exhaust gas aftertreatment system.

[0037] The preset temperature conditions can be multiple pre-set reaction temperature conditions. The chemical reaction rate can be understood as the relative reaction rate information of each chemical reaction during the experiment. The kinetic curve can be a chemical kinetic curve determined based on the chemical reaction rate. The Arrhenius equation can be understood as the equation used to solve the chemical kinetic curve to obtain the initial kinetic parameters.

[0038] Specifically, the chemical reaction rate corresponding to the target exhaust gas aftertreatment system is obtained based on preset temperature conditions. A kinetic curve is determined based on the chemical reaction rate, and then the kinetic curve is processed according to the Arrhenius equation to determine the initial kinetic parameters corresponding to the target exhaust gas aftertreatment system. For example, the reaction rates of each reaction under different temperature conditions can be measured separately, chemical kinetic curves can be plotted, and the initial kinetic parameters of the relevant reactions can be calculated according to the Arrhenius equation. It should be noted that the Arrhenius equation is also known as the Arrhenius formula, and is written as k = Ae^(-k / A ... -Ea / RT Where k is the rate constant, R is the molar gas constant, T is the thermodynamic temperature, Ea is the apparent activation energy, and A is the pre-exponential factor (also known as the frequency factor).

[0039] Based on the above technical solution, before obtaining the initial kinetic parameter values ​​of the exhaust gas aftertreatment model corresponding to the target exhaust gas aftertreatment system, the method includes: obtaining the treatment parameter values ​​corresponding to the target exhaust gas aftertreatment system; and determining the exhaust gas aftertreatment model based on the treatment parameter values, a preset mass and heat transfer equation, and a preset chemical reaction mechanism. The preset chemical reaction mechanism is the Langmuir-Hinscherwood mechanism.

[0040] The processing parameter values ​​can be the various parameter values ​​of the target exhaust gas aftertreatment system during the design process. The preset mass and heat transfer equations can be understood as the equations used to determine the exhaust gas aftertreatment model. The preset chemical reaction mechanism can be the reaction mechanism followed by the construction of the exhaust gas aftertreatment model; the preset chemical reaction mechanism is the Langmuir-Hinssheelwood mechanism.

[0041] Specifically, the processing parameter values ​​corresponding to the target exhaust gas aftertreatment system are obtained, and then the exhaust gas aftertreatment model is determined based on the processing parameter values, the preset mass and heat transfer equations, and the preset chemical reaction mechanism. It should be noted that the heat transfer equations include solid-phase heat transfer equations and gas-phase heat transfer equations. The solid-phase energy conservation equation is as follows:

[0042]

[0043] f sb =1-ε……(2);

[0044]

[0045]

[0046] λ g =2.66×10 -4 ×Tg 0.805 ……(5);

[0047]

[0048] In equation (1), the term on the left side of the equation represents the overall heat transfer of the solid phase, and the three terms on the right side represent the one-dimensional axial thermal conductivity of the carrier, the thermal conductivity between the gas and solid phases, and the heat released by the reaction, respectively. In the expression for the one-dimensional axial thermal conductivity of the carrier, fsb represents the proportion of the solid portion of the carrier, which can be calculated using equation (2). ∈ represents the porosity of the catalyst, specifically calculated using equation (3), where D represents the pore size of the catalyst, w1 represents the thickness of cordierite, and w2 represents the thickness of the coating. λsb represents the thermal conductivity of the solid phase, specifically 1.5 J / (msK). In the expression for the thermal conductivity between the gas and solid phases, h represents the thermal conductivity between the gas and solid phases. For laminar fluids, this coefficient depends on the Nusselt number, and the calculation formula is shown in equation (4). λg The coefficient represents the heat transfer coefficient of the gas phase, which depends on the temperature of the gas phase. The specific value is calculated by equation (5). In the equation, Dh represents the characteristic length of heat transfer between the gas and solid phases. The specific value is calculated by equation (6), and for square channels, the Nu value is taken as 2.98. S represents the specific surface area in each channel. In the expression for the heat released by the reaction, Δhj represents the enthalpy change of reaction j, and rj represents the reaction rate of reaction j.

[0049]

[0050] In the gas-phase energy conservation expression, the left side of the equation represents the total heat transfer in the gas phase, and the right side represents the heat transferred from the solid phase to the gas phase. In the expression for the total gas-phase heat transfer, ∈ represents the porosity of the catalyst, and ρ... s C represents the solid density. p,g u represents the constant-pressure specific heat capacity of a gaseous fluid. z This represents the axial velocity of the gaseous fluid.

[0051] The mass conservation equation is as follows:

[0052]

[0053]

[0054]

[0055] In the mass conservation expression, the term on the left side of the equation represents the total mass change of species i in the gas phase, the term on the right side of the first equation represents the mass transfer between the solid and gas phases, and the term on the right side of the second equation represents the mass change of species i due to the chemical reaction. K mi The mass transfer coefficient between the gas and solid phases is represented by equation (9), where Sh represents the Sherwood number. In a square pore, the Sherwood number of the fluid is approximately equal to the Nusselt number, which is 3 here. Dim is the diffusion coefficient, which is calculated by equation (10), η is the stoichiometric coefficient, and Xgi and Xst represent the concentrations of species i in the gas phase and the solid phase, respectively.

[0056] S120. Based on the chemical reaction curve and the initial kinetic parameters, determine the kinetic parameters to be applied corresponding to each working state.

[0057] The chemical reaction curves include a low-temperature pollutant conversion curve, a byproduct formation and consumption curve, and a high-temperature pollutant conversion curve. The kinetic parameters to be applied can be kinetic parameters that satisfy the initial conditions, determined based on the chemical reaction curves and initial kinetic parameters. The low-temperature pollutant conversion curve can be understood as the pollutant conversion rate curve under low-temperature conditions; similarly, the high-temperature pollutant conversion curve can be understood as the pollutant conversion rate curve under high-temperature conditions. The byproduct formation and consumption curves can be understood as the byproduct formation and consumption curves under all conditions.

[0058] Specifically, based on the chemical reaction curve and initial kinetic parameters, the kinetic parameters to be applied corresponding to each working state are determined. It should be noted that the chemical reaction curve can be data obtained by the staff based on prior experiments. Those skilled in the art can complete the preliminary test of the target exhaust gas treatment system according to the requirements, obtain the reaction data during the experiment, and then determine the chemical reaction curve based on the reaction data.

[0059] Based on the above technical solution, the step of determining the kinetic parameters to be applied corresponding to each working state based on the chemical reaction curve and the initial kinetic parameters includes: determining the low-temperature reaction variance value based on the low-temperature pollutant conversion curve and the initial kinetic parameters; if the low-temperature reaction variance value does not meet the preset variance range, adjusting the initial kinetic parameters based on a genetic algorithm and re-determining the low-temperature reaction variance value; if the low-temperature reaction variance meets the preset variance range, using the initial kinetic parameters as the first kinetic parameter, and determining the kinetic parameters to be applied based on the first kinetic parameter and the byproduct generation and consumption curve.

[0060] The low-temperature reaction variance can be calculated based on the low-temperature pollutant conversion curve and initial kinetic parameters. The preset variance range can be understood as a pre-defined variance condition, such as 0.9-1.1. The genetic algorithm can be an algorithm used to iteratively solve for kinetic parameters that do not meet the preset variance range. The first kinetic parameter can be understood as the initial kinetic parameter for which the low-temperature reaction variance meets the preset condition.

[0061] Specifically, the low-temperature reaction variance value is determined based on the low-temperature pollutant conversion curve and the initial kinetic parameters. Furthermore, it is determined whether the low-temperature reaction variance value meets a preset variance range. If the low-temperature reaction variance value does not meet the preset variance range, the initial kinetic parameters are adjusted based on a genetic algorithm, and the low-temperature reaction variance value is re-determined. If the low-temperature reaction variance meets the preset variance range, the initial kinetic parameters are used as the first kinetic parameter, and the kinetic parameters to be applied are determined based on the first kinetic parameter and the byproduct generation and consumption curve.

[0062] Based on the above technical solution, the step of determining the kinetic parameters to be applied based on the first kinetic parameter and the by-product generation and consumption curve includes: determining the variance of the by-product generation curve based on the first kinetic parameter and the by-product generation and consumption curve; if the variance of the by-product generation curve does not meet the preset variance range, then adjusting the first kinetic parameter based on a genetic algorithm and returning to determine the variance value of the low-temperature reaction; if the variance of the by-product generation curve meets the preset variance range, then determining the kinetic parameters to be applied based on the first kinetic parameter and the high-temperature pollutant conversion curve.

[0063] The variance of the by-product formation curve can be calculated based on the first kinetic parameter and the by-product formation and consumption curve.

[0064] Specifically, the variance of the by-product formation curve is determined based on the first kinetic parameter and the by-product formation and consumption curve. Further, it is determined whether the variance of the by-product formation curve meets a preset variance range. If the variance of the by-product formation curve does not meet the preset variance range, the first kinetic parameter is adjusted based on a genetic algorithm, and the variance value of the low-temperature reaction is determined. If the variance of the by-product formation curve meets the preset variance range, the kinetic parameter to be applied is determined based on the first kinetic parameter and the high-temperature pollutant conversion curve.

[0065] Based on the above technical solution, the step of determining the kinetic parameter to be applied based on the first kinetic parameter and the high-temperature pollutant conversion curve includes: determining the high-temperature conversion rate variance based on the first kinetic parameter and the high-temperature pollutant conversion curve; if the high-temperature conversion rate variance meets a preset variance range, then the first kinetic parameter is used as the kinetic parameter to be applied; if the high-temperature conversion rate variance does not meet the preset variance range, then the first kinetic parameter is re-determined.

[0066] The variance of the high-temperature conversion rate can be calculated based on the first kinetic parameter and the high-temperature pollutant conversion curve.

[0067] Specifically, the high-temperature section conversion rate variance is determined based on the first kinetic parameter and the high-temperature section pollutant conversion curve, and it is determined whether the high-temperature section conversion rate variance meets a preset variance range. If the high-temperature section conversion rate variance meets the preset variance range, the first kinetic parameter is used as the kinetic parameter to be applied. If the high-temperature section conversion rate variance does not meet the preset variance range, the first kinetic parameter is re-determined.

[0068] S130. Determine the target kinetic parameters based on the kinetic parameters to be applied and the chemical reaction curve.

[0069] The target dynamic parameters can be the target parameter values ​​determined after the conditions are met.

[0070] Specifically, the target kinetic parameters are determined based on the kinetic parameters to be applied and the chemical reaction curve. For example, the kinetic parameters to be applied can be further verified based on the chemical reaction curve, and the kinetic parameters to be applied that meet the verification conditions can be used as the target kinetic parameters. Then, the parameters of the exhaust gas treatment model can be optimized based on the target kinetic parameters.

[0071] Based on the above technical solution, the step of determining the target kinetic parameter based on the kinetic parameter to be applied and the chemical reaction curve includes: determining the average reaction variance group corresponding to the current kinetic parameter to be applied based on the current kinetic parameter to be applied and the chemical reaction curve; if the variances in the average reaction variance group all meet the preset variance range, then the current kinetic parameter to be applied is taken as the target kinetic parameter.

[0072] The currently applied kinetic parameter can be the kinetic parameter corresponding to the current operating state. The average reaction variance set can be understood as the variance data set determined based on the currently applied kinetic parameter and the chemical reaction curves under other operating states.

[0073] Specifically, based on the current kinetic parameters to be applied and the chemical reaction curve, a set of average reaction variances corresponding to the current kinetic parameters to be applied is determined. If any variance in the set of average reaction variances meets the preset variance range, then the current kinetic parameters to be applied are taken as the target kinetic parameters. It should be noted that the combustion reaction of fuel in the engine is not the same under different operating conditions, and the content of exhaust gas that the exhaust gas after-treatment system needs to treat is also different. Therefore, in order to ensure that the target kinetic parameters can meet the treatment conditions under any operating condition, the current kinetic parameters to be applied are considered as the target kinetic parameters.

[0074] The technical solution of this invention obtains the initial kinetic parameters of the exhaust gas aftertreatment model corresponding to the target exhaust gas aftertreatment system and the chemical reaction curves of the target exhaust gas aftertreatment system under various operating states. Then, based on the chemical reaction curves and the initial kinetic parameters, it determines the kinetic parameters to be applied corresponding to each operating state. Finally, it determines the target kinetic parameters based on the kinetic parameters to be applied and the chemical reaction curves. Based on this technical solution, by calculating the corresponding kinetic parameters to be applied based on the initial kinetic parameters and the chemical reaction curves, and then determining the target kinetic parameters based on the kinetic parameters to be applied, the problem of poor optimization efficiency in existing parameter optimization methods based on elementary rates is solved, thereby achieving the technical effect of improving parameter optimization efficiency.

[0075] Example 2

[0076] Figure 2 This is a flowchart illustrating a parameter optimization method for an exhaust gas aftertreatment model provided in this embodiment of the invention. This embodiment further optimizes the parameter optimization method for the exhaust gas aftertreatment model based on the above embodiments. Specific implementation details can be found in the technical solution of this embodiment. Technical terms that are the same as or corresponding to those in the above embodiments will not be repeated here.

[0077] like Figure 2 As shown, the method includes:

[0078] Constructing an exhaust gas aftertreatment model: Specifically, to ensure the model's prediction accuracy, it is necessary to determine the mass and heat transfer equations of the aftertreatment system. The heat transfer equations include the solid-phase heat transfer equations and the gas-phase heat transfer equations. The solid-phase energy conservation equation is as follows:

[0079]

[0080] f sb =1-ε……(2);

[0081]

[0082]

[0083] λ g =2.66×10 -4 ×Tg 0.805 ……(5);

[0084]

[0085] In equation (1), the term on the left side of the equation represents the overall heat transfer of the solid phase, and the three terms on the right side represent the one-dimensional axial thermal conductivity of the carrier, the thermal conductivity between the gas and solid phases, and the heat released by the reaction, respectively. In the expression for the one-dimensional axial thermal conductivity of the carrier, fsb represents the proportion of the solid portion of the carrier, which can be calculated using equation (2). ∈ represents the porosity of the catalyst, specifically calculated using equation (3), where D represents the pore size of the catalyst, w1 represents the thickness of cordierite, and w2 represents the thickness of the coating. λsb represents the thermal conductivity of the solid phase, specifically 1.5 J / (msK). In the expression for the thermal conductivity between the gas and solid phases, h represents the thermal conductivity between the gas and solid phases. For laminar fluids, this coefficient depends on the Nusselt number, and the calculation formula is shown in equation (4). λ g The coefficient represents the heat transfer coefficient of the gas phase, which depends on the temperature of the gas phase. The specific value is calculated by equation (5). In the equation, Dh represents the characteristic length of heat transfer between the gas and solid phases. The specific value is calculated by equation (6), and for square channels, the Nu value is taken as 2.98. S represents the specific surface area in each channel. In the expression for the heat released by the reaction, Δhj represents the enthalpy change of reaction j, and rj represents the reaction rate of reaction j.

[0086]

[0087] In the gas-phase energy conservation expression, the left side of the equation represents the total heat transfer in the gas phase, and the right side represents the heat transferred from the solid phase to the gas phase. In the expression for the total gas-phase heat transfer, ∈ represents the porosity of the catalyst, and ρ... s C represents the solid density. p,g u represents the constant-pressure specific heat capacity of a gaseous fluid. z This represents the axial velocity of the gaseous fluid.

[0088] The mass conservation equation is as follows:

[0089]

[0090]

[0091]

[0092] In the mass conservation expression, the term on the left side of the equation represents the total mass change of species i in the gas phase, the term on the right side of the first equation represents the mass transfer between the solid and gas phases, and the term on the right side of the second equation represents the mass change of species i due to the chemical reaction. K miThe mass transfer coefficient between the gas and solid phases is represented by equation (9), where Sh represents the Sherwood number. In a square pore, the Sherwood number of the fluid is approximately equal to the Nusselt number, which is 3 here. Dim is the diffusion coefficient, which is calculated by equation (10), η is the stoichiometric coefficient, and Xgi and Xst represent the concentrations of species i in the gas phase and the solid phase, respectively.

[0093] It should be noted that the chemical reaction mechanism adopted in the technical solution provided in this invention is the Langmuir-Hinshelwood mechanism, also known as the Langmuir-Hinshelwood reaction mechanism. This mechanism introduces a byproduct generation and consumption reaction mechanism based on the chemical reaction mechanism for the removal of major pollutants, improving the model's prediction accuracy while aligning it with the upcoming National VII emission standards. Reactions occurring within the pores of the CNG catalyst are heterogeneous gas-solid surface reactions. The catalyst's composition and active components are complex, and its operating conditions are relatively variable. Therefore, a key step in establishing a kinetic model for the CNG catalyst is determining a suitable kinetic mechanism, which can improve the predictive accuracy of the kinetic model. The Langmuir-Hinshelwood reaction mechanism posits that gaseous species first adsorb onto the surface of the catalyst's active centers, then migrate through surface collisions to form chemical bonds, leading to a chemical reaction. To improve computational efficiency, a total reaction is used as the chemical reaction representation. In the model, the key to reaction kinetics is determining the reaction rate of each reaction at known temperatures and pressures. Since the selected reaction mechanism is the Langmuir-Hinshelwood reaction mechanism, there are three main factors determining this parameter: the adsorption rate of gas phase molecules, the number of molecular collisions, and the number of effective molecular collisions. These factors determine the expression for the reaction rate constant, and these three rate-controlling factors correspond to three kinetic parameters: G is the adsorption term, representing the adsorption rate of gas molecules onto the catalyst surface; A... j E is the pre-exponential factor, representing the probability of collisions between molecules after adsorption. aj The activation energy represents the probability of an effective collision. The specific value of the adsorption term G is generally calculated using empirical formulas.

[0094] Optimizing model parameters: Specifically, the model calibration process essentially involves finding the optimal combination of kinetic parameters to satisfy the model's predictions under all operating conditions. Therefore, variance needs to be introduced to quantify the difference between the model's predictions and experimental results. The optimal variance value is 1, and the acceptable range is 0.9-1.1. Since the kinetic parameters included in the model need to have practical physical meaning, their numerical values ​​cannot deviate too much from reality. Therefore, the initial values ​​of the kinetic parameters need to be obtained experimentally, i.e., by measuring the reaction rates of each reaction under different temperature conditions, plotting chemical kinetic curves, and calculating the kinetic parameters of the relevant reactions according to the Arrhenius curve. These are used as the initial values ​​for parameter calibration. Furthermore, since byproducts have a significant impact on the conversion efficiency of major pollutants at low temperatures, model calibration is divided into two stages. The first stage optimization process is as follows: Figure 3 As shown, the first stage only considers the prediction accuracy of the conversion rate curve of the main pollutants in the low temperature range and the generation and consumption curve of by-products in the entire temperature range. The second stage optimization process is as follows: Figure 4 As shown, the second stage considers the prediction accuracy of the conversion rate of major pollutants in the high-temperature section. This segmented correction method can directly eliminate some unreasonable combinations of kinetic parameters, thereby improving the model's correction efficiency. In addition, the model needs to predict the post-treatment effect under multiple operating conditions. Therefore, it is necessary to integrate the variances of the predicted values ​​under different operating conditions and finally determine the most suitable combination of kinetic parameters through the total variance value.

[0095] The technical solution of this invention obtains the initial kinetic parameters of the exhaust gas aftertreatment model corresponding to the target exhaust gas aftertreatment system and the chemical reaction curves of the target exhaust gas aftertreatment system under various operating states. Then, based on the chemical reaction curves and the initial kinetic parameters, it determines the kinetic parameters to be applied corresponding to each operating state. Finally, it determines the target kinetic parameters based on the kinetic parameters to be applied and the chemical reaction curves. Based on this technical solution, by calculating the corresponding kinetic parameters to be applied based on the initial kinetic parameters and the chemical reaction curves, and then determining the target kinetic parameters based on the kinetic parameters to be applied, the problem of poor optimization efficiency in existing parameter optimization methods based on elementary rates is solved, thereby achieving the technical effect of improving parameter optimization efficiency.

[0096] Example 3

[0097] Figure 5 This is a structural block diagram of a parameter optimization device for an exhaust gas aftertreatment model provided in an embodiment of the present invention. Figure 5 As shown, the device includes: a parameter acquisition module 510, a parameter determination module 520, and a target parameter determination module 530.

[0098] The parameter acquisition module 510 is used to acquire the initial kinetic parameters of the exhaust gas after-treatment model corresponding to the target exhaust gas after-treatment system, and the chemical reaction curves of the target exhaust gas after-treatment system under various working conditions.

[0099] The parameter determination module 520 is used to determine the kinetic parameters to be applied corresponding to each working state based on the chemical reaction curve and the initial kinetic parameters; wherein, the chemical reaction curve includes a low-temperature pollutant conversion curve, a by-product generation and consumption curve, and a high-temperature pollutant conversion curve;

[0100] The target parameter determination module 530 is used to determine the target kinetic parameters based on the kinetic parameters to be applied and the chemical reaction curve.

[0101] Based on the above technical solution, the device further includes: an initial parameter determination module, used to obtain the chemical reaction rate corresponding to the target exhaust gas aftertreatment system based on preset temperature conditions before obtaining the initial kinetic parameter values ​​of the exhaust gas aftertreatment model corresponding to the target exhaust gas aftertreatment system, and to determine the kinetic curve based on the chemical reaction rate; and to process the kinetic curve according to the Arrhenius equation to determine the initial kinetic parameters corresponding to the target exhaust gas aftertreatment system.

[0102] Based on the above technical solution, the parameter determination module is used to determine the low-temperature reaction variance value based on the low-temperature pollutant conversion curve and the initial kinetic parameters; if the low-temperature reaction variance value does not meet the preset variance range, the initial kinetic parameters are adjusted based on a genetic algorithm, and the low-temperature reaction variance value is re-determined; if the low-temperature reaction variance meets the preset variance range, the initial kinetic parameters are used as the first kinetic parameters, and the kinetic parameters to be applied are determined based on the first kinetic parameters and the byproduct generation and consumption curve.

[0103] Based on the above technical solution, the parameter determination module is used to determine the variance of the by-product generation curve based on the first kinetic parameter and the by-product generation and consumption curve; if the variance of the by-product generation curve does not meet the preset variance range, the first kinetic parameter is adjusted based on the genetic algorithm, and the variance value of the low-temperature reaction is returned; if the variance of the by-product generation curve meets the preset variance range, the kinetic parameter to be applied is determined based on the first kinetic parameter and the high-temperature pollutant conversion curve.

[0104] Based on the above technical solution, the parameter determination module is used to determine the high-temperature section conversion rate variance based on the first kinetic parameter and the high-temperature section pollutant conversion curve; if the high-temperature section conversion rate variance meets the preset variance range, then the first kinetic parameter is used as the kinetic parameter to be applied; if the high-temperature section conversion rate variance does not meet the preset variance range, then the first kinetic parameter is re-determined.

[0105] Based on the above technical solution, the target parameter determination module is used to determine the average reaction variance group corresponding to the current kinetic parameter to be applied based on the current kinetic parameter to be applied and the chemical reaction curve; if the variances in the average reaction variance group all meet the preset variance range, then the current kinetic parameter to be applied is used as the target kinetic parameter.

[0106] Based on the above technical solution, the device includes a model building module, used to acquire processing parameter values ​​corresponding to the target exhaust gas aftertreatment system before acquiring the initial kinetic parameter values ​​of the exhaust gas aftertreatment model corresponding to the target exhaust gas aftertreatment system; and to determine the exhaust gas aftertreatment model based on the processing parameter values, a preset mass and heat transfer equation, and a preset chemical reaction mechanism; wherein the preset chemical reaction mechanism is the Langmuir-Hinscherwood mechanism.

[0107] The technical solution of this invention obtains the initial kinetic parameters of the exhaust gas aftertreatment model corresponding to the target exhaust gas aftertreatment system and the chemical reaction curves of the target exhaust gas aftertreatment system under various operating states. Then, based on the chemical reaction curves and the initial kinetic parameters, it determines the kinetic parameters to be applied corresponding to each operating state. Finally, it determines the target kinetic parameters based on the kinetic parameters to be applied and the chemical reaction curves. Based on this technical solution, by calculating the corresponding kinetic parameters to be applied based on the initial kinetic parameters and the chemical reaction curves, and then determining the target kinetic parameters based on the kinetic parameters to be applied, the problem of poor optimization efficiency in existing parameter optimization methods based on elementary rates is solved, thereby achieving the technical effect of improving parameter optimization efficiency.

[0108] The parameter optimization device for the exhaust gas aftertreatment model provided in this embodiment of the invention can execute the parameter optimization method for the exhaust gas aftertreatment model provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0109] Example 4

[0110] Figure 6A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0111] like Figure 6 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0112] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0113] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as parameter optimization methods for exhaust gas aftertreatment models.

[0114] In some embodiments, the parameter optimization method for the exhaust aftertreatment model can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the parameter optimization method for the exhaust aftertreatment model described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the parameter optimization method for the exhaust aftertreatment model by any other suitable means (e.g., by means of firmware).

[0115] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0116] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0117] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0118] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0119] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0120] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0121] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0122] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A parameter optimization method for an exhaust gas aftertreatment model, characterized in that, include: The initial kinetic parameters of the exhaust gas aftertreatment model corresponding to the target exhaust gas aftertreatment system and the chemical reaction curves of the target exhaust gas aftertreatment system under various working conditions are obtained. Based on the chemical reaction curves and the initial kinetic parameters, the kinetic parameters to be applied corresponding to each working state are determined; wherein, the chemical reaction curves include low-temperature pollutant conversion curves, by-product generation and consumption curves, and high-temperature pollutant conversion curves; The target kinetic parameters are determined based on the kinetic parameters to be applied and the chemical reaction curve.

2. The method according to claim 1, characterized in that, Before obtaining the initial dynamic parameter values ​​of the exhaust aftertreatment model corresponding to the target exhaust aftertreatment system, the process includes: The chemical reaction rate corresponding to the target exhaust gas aftertreatment system is obtained based on the preset temperature conditions, and the kinetic curve is determined based on the chemical reaction rate. The dynamic curves are processed according to the Arrhenius equation to determine the initial dynamic parameters corresponding to the target exhaust aftertreatment system.

3. The method according to claim 1, characterized in that, The determination of the kinetic parameters to be applied corresponding to each working state based on the chemical reaction curve and the initial kinetic parameters includes: The low-temperature reaction variance value is determined based on the pollutant transformation curve in the low-temperature range and the initial kinetic parameters. If the variance value of the low-temperature response does not meet the preset variance range, the initial kinetic parameters are adjusted based on the genetic algorithm, and the variance value of the low-temperature response is re-determined. If the variance of the low-temperature reaction meets the preset variance range, the initial kinetic parameter is used as the first kinetic parameter, and the kinetic parameter to be applied is determined based on the first kinetic parameter and the byproduct generation and consumption curve.

4. The method according to claim 3, characterized in that, The step of determining the kinetic parameters to be applied based on the first kinetic parameter and the byproduct generation and consumption curve includes: The variance of the byproduct formation curve is determined based on the first kinetic parameter and the byproduct formation and consumption curve. If the variance of the byproduct generation curve does not meet the preset variance range, the first kinetic parameter is adjusted based on the genetic algorithm, and the variance value of the low-temperature reaction is returned. If the variance of the byproduct generation curve meets the preset variance range, then the kinetic parameters to be applied are determined based on the first kinetic parameter and the high-temperature pollutant conversion curve.

5. The method according to claim 4, characterized in that, The determination of the kinetic parameters to be applied based on the first kinetic parameter and the high-temperature pollutant conversion curve includes: The variance of the high-temperature conversion rate is determined based on the first kinetic parameter and the high-temperature pollutant conversion curve. If the variance of the conversion rate in the high-temperature section meets the preset variance range, then the first kinetic parameter is used as the kinetic parameter to be applied. If the variance of the conversion rate in the high-temperature section does not meet the preset variance range, then the first kinetic parameter is redefined.

6. The method according to claim 1, characterized in that, The determination of the target kinetic parameters based on the kinetic parameters to be applied and the chemical reaction curve includes: Based on the current kinetic parameters to be applied and the chemical reaction curve, determine the average reaction variance group corresponding to the current kinetic parameters to be applied; If the variances within the average response variance group all meet the preset variance range, then the current kinetic parameter to be applied is used as the target kinetic parameter.

7. The method according to claim 1, characterized in that, Before obtaining the initial dynamic parameter values ​​of the exhaust aftertreatment model corresponding to the target exhaust aftertreatment system, the process includes: Obtain the processing parameter values ​​corresponding to the target exhaust gas aftertreatment system; The exhaust gas aftertreatment model is determined based on the processing parameter values, the preset mass and heat transfer equations, and the preset chemical reaction mechanism; wherein, the preset chemical reaction mechanism is the Langmuir-Hinscherwood mechanism.

8. A parameter optimization device for an exhaust gas aftertreatment model, characterized in that, include: The parameter acquisition module is used to acquire the initial kinetic parameters of the exhaust gas after-treatment model corresponding to the target exhaust gas after-treatment system, and the chemical reaction curves of the target exhaust gas after-treatment system under various working conditions. The parameter determination module is used to determine the kinetic parameters to be applied corresponding to each working state based on the chemical reaction curve and the initial kinetic parameters; wherein, the chemical reaction curve includes a low-temperature pollutant conversion curve, a by-product generation and consumption curve, and a high-temperature pollutant conversion curve; The target parameter determination module is used to determine the target kinetic parameters based on the kinetic parameters to be applied and the chemical reaction curve.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the parameter optimization method of the exhaust gas aftertreatment model according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the parameter optimization method of the exhaust gas aftertreatment model according to any one of claims 1-7.

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