A simulation analysis method, device, equipment and medium for porous medium packed bed

Abstract

The present application discloses a simulation analysis method, device, equipment and medium for a porous medium packed bed, which relates to the field of computer technology and is applied to a preset simulation analysis system, including: data collection based on a preset real-time database and a packed bed parameter database, and correction of the target heat transfer dimensionless coefficient to obtain collection results and correction results; model construction based on the collection results, correction results and preset simulation software to obtain a gas characteristic prediction model for a porous medium packed bed; in the process of gas transport simulation, the outlet gas characteristic information of each node is predicted based on the gas characteristic prediction model and the input packed bed inlet gas characteristic information; by comparing the obtained prediction results and the corresponding real outlet gas characteristic information, it is determined whether abnormal prompt measures are currently triggered based on the obtained analysis results. It can efficiently and accurately realize the comprehensive capture of the dynamic changes of the gas characteristics inside the packed bed.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a simulation analysis method, device, equipment and medium for a porous medium packed bed. Background Art

[0002] A porous medium packed bed is usually a porous medium area formed by the random accumulation of equal-diameter particles filled inside a cylindrical pipe. The gas flows in from the inlet of the packed bed, and after being fully developed in a section of the pipe, it flows out through the porous medium area formed by the accumulation of equal-diameter particles. However, the complex pore structure and flow characteristics inside the packed bed make it difficult to directly observe and accurately calculate its heat transfer, gas flow and mass transfer processes. At the same time, traditional experimental methods are not only time-consuming and costly, but also difficult to fully capture the dynamic changes of internal gas characteristics. Summary of the invention

[0003] In view of this, the purpose of the present invention is to provide a simulation analysis method, device, equipment and medium for a porous medium packed bed, which can efficiently and accurately capture the dynamic changes of gas characteristics inside the packed bed and reduce costs. The specific scheme is as follows:

[0004] In a first aspect, the present application provides a simulation analysis method for a porous medium packed bed, which is applied to a preset simulation analysis system, comprising:

[0005] Data is collected based on a preset real-time database and a packed bed parameter database, and the target heat transfer dimensionless coefficient is corrected to obtain corresponding collection results and correction results;

[0006] Based on the acquisition results, the correction results and the preset simulation software, a model is constructed to obtain a corresponding gas characteristic prediction model of the porous medium packed bed;

[0007] In the process of gas transportation simulation, the outlet gas characteristic information of each node is predicted based on the gas characteristic prediction model and the input packed bed inlet gas characteristic information to obtain corresponding prediction results;

[0008] The prediction result is compared with the corresponding actual outlet gas characteristic information to determine the corresponding analysis result, and based on the analysis result, it is determined whether an abnormal prompt measure is currently triggered.

[0009] Optionally, data collection is performed based on a preset real-time database and a packed bed parameter database, and the target heat transfer dimensionless coefficient is corrected to obtain corresponding collection results and correction results, including:

[0010] Experimental simulation is performed through a preset real-time database to determine the pipeline characteristic information and insulation layer characteristic information of the corresponding porous medium packed bed; wherein the real-time database is used to store the real value of the gas characteristic obtained through the preset interface;

[0011] Performing data collection on the real-time database and the packed bed parameter database to determine collection results including the true value of the gas characteristics, porous medium packed bed equipment parameters, and modeling constraints;

[0012] The target dimensionless coefficient of inertia term and the target dimensionless coefficient of viscosity term in heat transfer are corrected to obtain corresponding correction results.

[0013] Optionally, predicting the outlet gas characteristic information of each node based on the gas characteristic prediction model and the input packed bed inlet gas characteristic information includes:

[0014] The gas characteristics are collected in real time through a preset interface, and the obtained real values ​​of the gas characteristics are filtered to obtain the corresponding packed bed inlet gas characteristic information;

[0015] Or, inputting information based on a preset interface to obtain the gas characteristic information at the packed bed inlet;

[0016] Based on the gas characteristic prediction model and the packed bed inlet gas characteristic information, the outlet gas characteristic information of each node is predicted to obtain a corresponding prediction result.

[0017] Optionally, the predicting of the outlet gas characteristic information of each node based on the gas characteristic prediction model and the input packed bed inlet gas characteristic information to obtain a corresponding prediction result includes:

[0018] Segmenting the porous medium packed bed to obtain a corresponding number of segments;

[0019] Determine a segment from the plurality of segments as a current segment to be processed, and configure a corresponding initial value of the outlet pressure for the node corresponding to the current segment to be processed to obtain a corresponding initial value of the outlet pressure;

[0020] The initial value of the outlet temperature of the node corresponding to the current segment to be processed is configured to obtain the corresponding initial value of the outlet temperature;

[0021] Determine the first outlet fluid enthalpy value of the node corresponding to the current segment to be processed based on the Joule-Thomson coefficient, the initial value of the outlet pressure, and the initial value of the outlet temperature;

[0022] Determine the second outlet fluid enthalpy value of the node corresponding to the current segment to be processed based on the correction result, the outlet pressure initial value and the outlet temperature initial value;

[0023] By comparing the first outlet fluid enthalpy value with the second outlet fluid enthalpy value, a target enthalpy value of the node corresponding to the current segment to be processed is determined;

[0024] Determine a first outlet pressure value of the node corresponding to the current section to be processed based on pipeline roughness, packed bed friction pressure drop efficiency, gravitational acceleration, and packed bed length;

[0025] By comparing the first outlet pressure value with the initial outlet pressure value, a target pressure value of the node corresponding to the current segment to be processed is determined;

[0026] Determining a target temperature value based on the target enthalpy value and the target pressure value to obtain an outlet gas characteristic prediction result of the node corresponding to the current segment to be processed;

[0027] The next segment among the several segments is taken as the current segment to be processed, and the node corresponding to the current segment to be processed is jumped again to perform the corresponding step of configuring the initial value of the outlet pressure until the outlet gas characteristic prediction results of the nodes corresponding to each segment are determined to obtain the prediction results.

[0028] Optionally, comparing the first outlet fluid enthalpy value with the second outlet fluid enthalpy value to determine the target enthalpy value of the node corresponding to the current segment to be processed includes:

[0029] Determine whether a difference between the first outlet fluid enthalpy value and the second outlet fluid enthalpy value is greater than a first preset threshold value to obtain a first determination result;

[0030] If the first judgment result indicates that it is greater than, then jump back to the step of configuring the initial value of the outlet temperature of the node corresponding to the current segment to be processed;

[0031] If the first judgment result indicates that it is not greater than, then the first outlet fluid enthalpy value is determined to be the target enthalpy value of the node corresponding to the current segment to be processed.

[0032] Optionally, comparing the first outlet pressure value with the initial outlet pressure value to determine the target pressure value of the node corresponding to the current segment to be processed includes:

[0033] Determine whether the difference between the first outlet pressure value and the initial outlet pressure value is greater than a second preset threshold value to obtain a second determination result;

[0034] If the second judgment result indicates that it is greater than, then jump back to the step of configuring the corresponding initial value of the outlet pressure;

[0035] If the second judgment result indicates that it is not greater than, then the first outlet pressure value is determined to be the target pressure value of the node corresponding to the current segment to be processed.

[0036] Optionally, comparing the prediction result with the corresponding real outlet gas characteristic information to determine a corresponding analysis result, and determining whether to trigger an abnormal prompt measure based on the analysis result, includes:

[0037] For each of the nodes, comparing the difference between the outlet gas characteristic information in the prediction result and the corresponding actual outlet gas characteristic information to see whether it exceeds a preset range, so as to determine the corresponding analysis result;

[0038] If the analysis result indicates that the preset range is exceeded, corresponding abnormal prompt information is determined based on the analysis result, and a corresponding abnormal prompt information push operation is triggered.

[0039] In a second aspect, the present application provides a simulation analysis device for a porous medium packed bed, which is applied to a preset simulation analysis system, comprising:

[0040] A data acquisition module is used to collect data based on a preset real-time database and a packed bed parameter database, and to correct the target heat transfer dimensionless coefficient to obtain corresponding collection results and correction results;

[0041] A model building module, used to build a model based on the acquisition results, the correction results and the preset simulation software to obtain a corresponding gas characteristic prediction model of the porous medium packed bed;

[0042] A characteristic prediction module is used to predict the outlet gas characteristic information of each node based on the gas characteristic prediction model and the input packed bed inlet gas characteristic information during the gas transportation simulation to obtain corresponding prediction results;

[0043] The comparison and analysis module is used to determine the corresponding analysis result by comparing the prediction result with the corresponding actual outlet gas characteristic information, and determine whether to trigger abnormal prompt measures based on the analysis result.

[0044] In a third aspect, the present application provides an electronic device, including:

[0045] Memory, used to store computer programs;

[0046] The processor is used to execute the computer program to implement the steps of the aforementioned simulation analysis method of the porous medium packed bed.

[0047] In a fourth aspect, the present application provides a computer-readable storage medium for storing a computer program, which, when executed by a processor, implements the steps of the aforementioned simulation analysis method for a porous medium packed bed.

[0048] It can be seen that in this application, through the preset simulation analysis system, data is first collected based on the preset real-time database and the packed bed parameter database, and the target heat transfer dimensionless coefficient is corrected to obtain the corresponding collection results and correction results; then the model is constructed based on the collection results, the correction results and the preset simulation software to obtain the corresponding gas characteristic prediction model of the porous medium packed bed; then in the process of gas transport simulation, the outlet gas characteristic information of each node is predicted based on the gas characteristic prediction model and the input packed bed inlet gas characteristic information to obtain the corresponding prediction result; finally, by comparing the prediction result with the corresponding real outlet gas characteristic information, the corresponding analysis result is determined, and based on the analysis result, it is determined whether the abnormal prompt measure is currently triggered. That is, in this application, data is collected through the preset simulation analysis system, and a gas characteristic prediction model is created based on the collected data. Then, when performing gas transport simulation, the gas characteristics of the packed bed outlet are predicted based on the gas characteristic prediction model, and the corresponding real outlet gas characteristic information is used to compare with the obtained prediction result to determine whether the abnormal prompt measure is currently triggered. In this way, the dynamic changes of gas characteristics inside the packed bed can be fully captured efficiently and accurately, and costs can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0050] Figure 1 A flow chart of a simulation analysis method for a porous medium packed bed provided in this application;

[0051] Figure 2 A schematic diagram of the architecture of a specific preset simulation software provided in this application;

[0052] Figure 3 A schematic diagram of the structure of a simulation analysis device for a porous medium packed bed provided in this application;

[0053] Figure 4 A structural diagram of an electronic device provided for this application. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] However, the complex pore structure and flow characteristics inside the packed bed make it difficult to directly observe and accurately calculate the heat transfer, gas flow and mass transfer processes. At the same time, traditional experimental methods are not only time-consuming and costly, but also difficult to fully capture the dynamic changes of internal gas characteristics. To this end, the present application provides a simulation analysis scheme for a porous media packed bed, which can efficiently and accurately capture the dynamic changes of gas characteristics inside the packed bed and reduce costs.

[0056] See also Figure 1 As shown, an embodiment of the present invention discloses a simulation analysis method for a porous medium packed bed, which is applied to a preset simulation analysis system, comprising:

[0057] Step S11: data collection is performed based on a preset real-time database and a packed bed parameter database, and the target heat transfer dimensionless coefficient is corrected to obtain corresponding collection results and correction results.

[0058] Specifically, in this embodiment, before simulation analysis, some data required for modeling must first be collected. That is, data collection is performed based on a preset real-time database and a packed bed parameter database, and the target heat transfer dimensionless coefficient is corrected to obtain corresponding collection results and correction results, including: experimental simulation is performed through a preset real-time database to determine the corresponding pipeline characteristic information and insulation layer characteristic information of the porous medium packed bed; wherein the real-time database is used to store the true value of the gas characteristics obtained through the preset interface; data is collected from the real-time database and the packed bed parameter database to determine the collection results including the true value of the gas characteristics, porous medium packed bed equipment parameters, and modeling constraints; the target inertia term dimensionless coefficient and the target viscosity term dimensionless coefficient in heat transfer are corrected to obtain corresponding correction results. It should be understood that the pipeline characteristic information includes but is not limited to the thermal conductivity and diameter of the pipeline; the insulation layer characteristic information includes but is not limited to the thermal conductivity of the insulation layer; the true value of the gas characteristic refers to the gas physical properties such as temperature, pressure, enthalpy value, etc.; the porous medium packed bed equipment parameters refer to the porosity, particle size and other parameters of the filler; the modeling constraints are the constraints that need to be met during the modeling process, such as the inlet temperature must be 25 degrees Celsius and the inlet pressure must be 101.325Kpa.

[0059] At the same time, it should be pointed out that the true value of the gas characteristic stored in the real-time database is filtered data, and the true value of the gas characteristic in this embodiment specifically refers to the temperature value and the pressure value.

[0060] Further, regarding coefficient correction, specifically, the target dimensionless coefficient of inertia term and the target dimensionless coefficient of viscosity term in heat transfer are corrected to obtain corresponding correction results. The target dimensionless coefficient of inertia term and the target dimensionless coefficient of viscosity term can be obtained by fitting actual data.

[0061] Step S12: constructing a model based on the acquisition results, the correction results and preset simulation software to obtain a corresponding gas characteristic prediction model of the porous medium packed bed.

[0062] Specifically, in this embodiment, after completing data collection, modeling is performed based on all the data acquired in the above steps. That is, modeling is performed using the pipeline characteristic information, the insulation layer characteristic information, the true value of the gas characteristic, the porous medium packed bed equipment parameters, the modeling constraint conditions and the correction results, and the modeling is completed using the preset simulation software, and finally a gas characteristic prediction model corresponding to the porous medium packed bed is obtained.

[0063] Step S13: During the gas transport simulation, the outlet gas characteristic information of each node is predicted based on the gas characteristic prediction model and the input packed bed inlet gas characteristic information to obtain corresponding prediction results.

[0064] Specifically, in this embodiment, an initialization mechanism is provided by collecting real-time data online or inputting data on a page to obtain the physical properties of the inlet gas as the input of the model. Then, the outlet gas characteristic information of the packed bed is predicted based on the gas characteristic prediction model to obtain the outlet gas characteristic information of each node. Among them, online refers to the method of directly obtaining real-time data, that is, the true value of the gas characteristic, through the preset interface, such as obtaining the actual online flow through a DCS (Distributed Control System); page input is to directly input simulation data on the page.

[0065] Furthermore, when performing model prediction, the porous medium packed bed can be segmented. All flows flowing into each node should be equal to all flows flowing out. The pressure drop and heat transfer equations are established and solved using a multivariable iteration method. In this process, temperature, pressure and enthalpy are the three core elements of the simulation calculation. Regarding the segmented processing, the porous packed bed is divided into n nodes. The subscripts i (i∈[1, n-1]) and i+1 appearing below are the physical properties (i.e., gas characteristic information) corresponding to the i-th and i+1-th nodes, respectively. In this way, the segmentation of the porous medium packed bed can be completed, and a number of corresponding segments can be obtained. The specific processing flow for each node is shown in the following steps:

[0066] 1): First, obtain the physical parameters of the porous packed bed inlet fluid, such as temperature, pressure, composition, etc.; the packed bed equipment parameters including porosity, geometric coefficient, etc., the pipeline characteristic information including the length and roughness of the cylindrical pipeline, etc., and the insulation layer characteristic information;

[0067] 2): Determine a segment from the plurality of segments as the current segment to be processed, and configure the corresponding initial value of the outlet pressure for the node corresponding to the current segment to be processed to obtain the corresponding initial value of the outlet pressure P i+1 ;

[0068] 3): Configure the initial value of the outlet temperature of the node corresponding to the current segment to be processed to obtain the corresponding initial value of the outlet temperature T i+1 ;

[0069] 4): According to the chemical thermodynamics calculation, the outlet fluid is (P i+1 , T i+1 ) under the first outlet fluid enthalpy Hi+1 ;

[0070] Specifically, the specific heat, isentropic index, Joule-Thomson coefficient and viscosity are calculated based on chemical thermodynamic equations, including cubic state equations such as Peng-Rob, Soave-Redlich-Kwong (Soave-Redlich-Kwong equation, also known as SRK equation) and activity coefficient equations such as NRTL (Non-Random Two Liquid, i.e. non-constant exchange heat entropy) and WILSON. The calculation equations are as follows (taking Soave-Redlich-Kwong as an example):

[0071] ;

[0072] ;

[0073] ;

[0074] The initial value of the outlet temperature T i+1 And the initial value of the outlet pressure P i+1 Substituting the above formula, the enthalpy value H of the first outlet fluid can be determined: i+1 In the above formula, P is the pressure value; T is the temperature value; a and b are the parameters of the SRK equation; V m is the molar volume; R is the ideal gas constant, 8.314 J / K; H is the enthalpy; H R is the residual enthalpy value; H0 is the ideal gas enthalpy value. Since the ideal gas enthalpy value H0 is constant, the enthalpy value is a function of the temperature value T and the pressure value P, which can be expressed as:

[0075] ;

[0076] In the formula, H i is the enthalpy value of the i-th node; T i is the temperature value of the i-th node; P i is the pressure value of the i-th node;

[0077] 5): According to the heat transfer calculation, the outlet fluid is at (P i+1 , T i+1 ) under the second outlet fluid enthalpy H' i+1 ;

[0078] Since the presence of solid particles in the porous medium packed bed has a great influence on the heat transfer of the pipeline, the heat transferred from the internal fluid to the environment can be calculated by the following formula:

[0079] ;

[0080] ;

[0081] In the above formula, h i and h0 are the convective heat transfer coefficients of the fluid in the porous medium packed bed and the external environment, respectively; K is the total heat transfer coefficient; dH / dx is the change in enthalpy per unit length of the fluid; Q is the total heat transfer between the internal fluid and the environment; T in 、T out 、T a are the temperatures of the inlet, outlet and environment of the porous medium packed bed respectively; r1 is the internal radius of the pipe; D is the internal diameter of the pipe, i.e. D=r1*2; is the packed bed void ratio; is the particle shape factor; D p is the particle diameter; B is the dimensionless coefficient of the target inertia term; A is the dimensionless coefficient of the target viscosity term; π is the pi.

[0082] ;

[0083] Where L is the length of the porous medium packed bed. By substituting the numerical value, the second outlet fluid enthalpy H' can be obtained. i+1 ;

[0084] 6): Determine whether the difference between the first outlet fluid enthalpy value and the second outlet fluid enthalpy value is greater than a first preset threshold value. If the first determination result indicates that |H i+1 -H' i+1 |>Set the first preset threshold, return to step 3, otherwise continue:

[0085] ;

[0086] That is, determining the first outlet fluid enthalpy value as the target enthalpy value of the node corresponding to the current segment to be processed;

[0087] 7): Calculate the first outlet pressure value P' of the outlet fluid according to the calculation formula considering the friction pressure drop and gravity acceleration pressure drop of the porous packed bed i+1 ;

[0088] For the calculation of pure gas pipeline pressure drop, the most important calculation parameter is the specific gravity of the gas. , most of them use empirical formulas such as Smith, Weymouth, AGA (Adaptive Genetic Algorithm, an adaptive genetic algorithm). For porous media packed beds, the specific area load (gas volume flow rate per area), the dynamic viscosity of the gas and the thickness of the medium, the size, shape and arrangement of the particles that make up the medium all play a decisive role. The friction pressure drop per unit length is calculated as follows:

[0089] ;

[0090] In the above formula: Q sc is the gas flow rate under standard conditions; T sc and P sc is the standard temperature and pressure; ε is the pipe roughness; T avg is the average temperature of the pipeline; is the inner diameter of the pipe, =r 1× 2; Z avg is the average compressibility factor of the fluid in the pipeline; E is the friction pressure drop efficiency of the porous packed bed, and its calculation formula is:

[0091] ;

[0092] ;

[0093] Where: μ is the gas viscosity, v is the specific volume of the gas, that is, 1 / ρ, P is the average pressure of the pipeline (the arithmetic average of the inlet pressure and the assumed outlet pressure), and λ is the pipeline friction factor.

[0094] For the pressure drop per unit length (dP a ) / dx, the calculation formula is:

[0095] ;

[0096] In the above formula, g is the acceleration due to gravity, 9.8N / kg.

[0097] Since the internal fluid of the porous medium packed bed is gas, the pressure drop caused by its gravity is small and can be ignored. Therefore, the final pressure drop expression is:

[0098] ;

[0099] Rearrange to get:

[0100] ;

[0101] ;

[0102] The first outlet pressure value P' can be determined by substituting specific values ​​into the above formula: i+1 ;

[0103] 8): Determine whether the difference between the first outlet pressure value and the initial outlet pressure value is greater than a second preset threshold value. If the second determination result indicates that |P i+1 -P' i+1|>set the second preset threshold, then step 2, otherwise determine the first outlet pressure value as the target pressure value of the node corresponding to the current segment to be processed, determine the corresponding target temperature value based on the target enthalpy value, the target pressure value and the corresponding function, and end the calculation of this segment;

[0104] 9): Take the next segment among the several segments as the current segment to be processed, and jump again to the node corresponding to the current segment to be processed, and perform the corresponding step of configuring the initial value of the outlet pressure until the outlet gas characteristic prediction results of the nodes corresponding to each segment are determined to obtain the prediction results.

[0105] Step S14: Determine a corresponding analysis result by comparing the prediction result with the corresponding actual outlet gas characteristic information, and determine whether to trigger an abnormal prompt measure based on the analysis result.

[0106] In this embodiment, after obtaining the prediction result, the difference between the predicted result of the outlet gas characteristics of each node and the corresponding real outlet gas characteristics information is compared and analyzed, and if there is an abnormality, the abnormal situation is pushed to the relevant operation management personnel. That is, for each node, the difference between the outlet gas characteristics information in the prediction result and the corresponding real outlet gas characteristics information is compared to see whether it exceeds the preset range to determine the corresponding analysis result; if the analysis result shows that it exceeds the preset range, the corresponding abnormal prompt information is determined based on the analysis result, and the corresponding abnormal prompt information push operation is triggered.

[0107] At the same time, in this embodiment, when performing difference analysis, the predicted results of the outlet gas characteristics of each node and the corresponding actual outlet gas characteristics information are displayed on the flow chart interface, and the predicted results including the predicted results of the outlet gas characteristics of each node are stored in the simulation result database to achieve uninterrupted updating of the porous media packed bed model and data mapping as well as data backup.

[0108] In addition, this embodiment updates the gas characteristic prediction model in real time through the filtered real-time data to improve the accuracy of the model.

[0109] It can be seen that in this application, through the preset simulation analysis system, data is first collected based on the preset real-time database and the packed bed parameter database, and the target heat transfer dimensionless coefficient is corrected to obtain the corresponding collection results and correction results; then the model is constructed based on the collection results, the correction results and the preset simulation software to obtain the corresponding gas characteristic prediction model of the porous medium packed bed; then in the process of gas transport simulation, the outlet gas characteristic information of each node is predicted based on the gas characteristic prediction model and the input packed bed inlet gas characteristic information to obtain the corresponding prediction result; finally, by comparing the prediction result with the corresponding real outlet gas characteristic information, the corresponding analysis result is determined, and based on the analysis result, it is determined whether the abnormal prompt measure is currently triggered. That is, in this application, data is collected through the preset simulation analysis system, and a gas characteristic prediction model is created based on the collected data. Then, when performing gas transport simulation, the gas characteristics of the packed bed outlet are predicted based on the gas characteristic prediction model, and the corresponding real outlet gas characteristic information is used to compare with the obtained prediction result to determine whether the abnormal prompt measure is currently triggered. In this way, the dynamic changes of gas characteristics inside the packed bed can be fully captured efficiently and accurately, and costs can be reduced.

[0110] Combine the following Figure 2 The schematic diagram disclosed in the figure specifically illustrates the technical solution of the embodiment of the present application.

[0111] In this embodiment, the preset simulation analysis system includes corresponding online simulation modules, data storage, data visualization, data and analysis result push. Among them, the online simulation module is used to perform simulation analysis of porous medium packed bed, and has the functions of physical property parameter calculation, hydraulic and thermal simulation, component and calorific value tracking, parameter evaluation and correction. The data storage is used to save the prediction results obtained by the online simulation module to the simulation result database, and also has a real-time database for saving the real-time data (i.e. the preset real-time database), a packed bed parameter database for saving the parameters of the porous medium packed bed (i.e. the packed bed parameter database), and other parameter databases for saving other related parameters used in the simulation process. The data visualization is used to display the data stored in the data storage, and specifically can display the node data of each node of the packed bed, simulation data (i.e. the prediction results), data analysis (i.e. the analysis results) and actual data (i.e. the real outlet gas characteristic information, the real-time data).

[0112] It should be understood that the real-time data in the real-time database in the data storage needs to be filtered before it can be input into the system for use. The data and analysis result push can communicate with the data storage, and the pushed analysis results are saved in the data storage.

[0113] See also Figure 3 As shown, the embodiment of the present application also discloses a simulation analysis device for a porous medium packed bed, which is applied to a preset simulation analysis system, including:

[0114] The data acquisition module 11 is used to collect data based on a preset real-time database and a packed bed parameter database, and to correct the target heat transfer dimensionless coefficient to obtain corresponding collection results and correction results;

[0115] A model building module 12, used to build a model based on the acquisition results, the correction results and the preset simulation software to obtain a corresponding gas characteristic prediction model of the porous medium packed bed;

[0116] The characteristic prediction module 13 is used to predict the outlet gas characteristic information of each node based on the gas characteristic prediction model and the input packed bed inlet gas characteristic information during the gas transportation simulation to obtain the corresponding prediction result;

[0117] The comparison and analysis module 14 is used to determine the corresponding analysis result by comparing the prediction result with the corresponding real outlet gas characteristic information, and determine whether to trigger abnormal prompt measures based on the analysis result.

[0118] Among them, for more specific working processes of the above-mentioned modules, please refer to the corresponding contents disclosed in the aforementioned embodiments, which will not be repeated here.

[0119] It can be seen that in this application, data is collected through a preset simulation analysis system, and a gas characteristic prediction model is created based on the collected data. Then, when performing gas transportation simulation, the gas characteristics of the packed bed outlet are predicted based on the gas characteristic prediction model, and the corresponding real outlet gas characteristic information is compared with the obtained prediction results to determine whether abnormal prompt measures are currently triggered. In this way, the dynamic changes of the gas characteristics inside the packed bed can be captured comprehensively and efficiently, and costs can be reduced.

[0120] In some specific embodiments, the data acquisition module 11 may specifically include:

[0121] An experimental simulation unit, used to perform experimental simulation through a preset real-time database to determine the pipeline characteristic information and insulation layer characteristic information of the corresponding porous medium packed bed; wherein the real-time database is used to store the real value of the gas characteristic obtained through the preset interface;

[0122] A data acquisition unit, used for acquiring data from the real-time database and the packed bed parameter database to determine acquisition results including the true value of the gas characteristic, porous medium packed bed equipment parameters, and modeling constraints;

[0123] The coefficient correction unit is used to correct the target inertia term dimensionless coefficient and the target viscosity term dimensionless coefficient in heat transfer to obtain corresponding correction results.

[0124] In some specific embodiments, the characteristic prediction module 13 may specifically include:

[0125] A data filtering unit is used to collect gas characteristics in real time through a preset interface and filter the obtained real values ​​of the gas characteristics to obtain corresponding packed bed inlet gas characteristic information;

[0126] An interface input unit, used for inputting information based on a preset interface to obtain the gas characteristic information at the packed bed inlet;

[0127] The characteristic prediction unit is used to predict the outlet gas characteristic information of each node based on the gas characteristic prediction model and the packed bed inlet gas characteristic information to obtain a corresponding prediction result.

[0128] In some specific embodiments, the characteristic prediction module 13 may specifically include:

[0129] A packed bed segmentation unit, used for segmenting the porous medium packed bed to obtain a corresponding number of segments;

[0130] A pressure initial value configuration unit, used to determine a segment from the plurality of segments as a current segment to be processed, and to perform a corresponding outlet pressure initial value configuration for the node corresponding to the current segment to be processed, so as to obtain a corresponding outlet pressure initial value;

[0131] A temperature initial value configuration unit, used to configure the initial value of the outlet temperature of the node corresponding to the current segment to be processed to obtain a corresponding outlet temperature initial value;

[0132] A first enthalpy value determining unit, used to determine a first outlet fluid enthalpy value of the node corresponding to the current segment to be processed based on the Joule-Thomson coefficient, the outlet pressure initial value, and the outlet temperature initial value;

[0133] a second enthalpy value determining unit, configured to determine a second outlet fluid enthalpy value of the node corresponding to the current segment to be processed based on the correction result, the outlet pressure initial value, and the outlet temperature initial value;

[0134] a target enthalpy value determining unit, configured to determine a target enthalpy value of the node corresponding to the current segment to be processed by comparing the first outlet fluid enthalpy value with the second outlet fluid enthalpy value;

[0135] A first outlet pressure value determining unit, configured to determine a first outlet pressure value of the node corresponding to the current to-be-processed segment based on pipeline roughness, packed bed friction pressure drop efficiency, gravitational acceleration, and packed bed length;

[0136] a target pressure value determining unit, configured to determine a target pressure value of the node corresponding to the current segment to be processed by comparing the first outlet pressure value with the initial outlet pressure value;

[0137] An outlet gas characteristic prediction result determination unit, used to determine a target temperature value based on the target enthalpy value and the target pressure value, so as to obtain an outlet gas characteristic prediction result of the node corresponding to the current segment to be processed;

[0138] The prediction result determination unit is used to take the next segment among the several segments as the current segment to be processed, and jump again to the node corresponding to the current segment to be processed, and perform the corresponding steps of configuring the initial value of the outlet pressure until the outlet gas characteristic prediction results of the nodes corresponding to each segment are determined to obtain the prediction results.

[0139] In some specific embodiments, the target enthalpy value determination unit may specifically include:

[0140] an enthalpy comparison subunit, used to determine whether a difference between the first outlet fluid enthalpy value and the second outlet fluid enthalpy value is greater than a first preset threshold value, so as to obtain a first determination result;

[0141] The first step jumps to a subunit, which is used to jump again to the step of configuring the initial value of the outlet temperature of the node corresponding to the current segment to be processed if the first judgment result indicates that it is greater than;

[0142] The target enthalpy value determination subunit is used to determine the first outlet fluid enthalpy value as the target enthalpy value of the node corresponding to the current segment to be processed if the first judgment result indicates that it is not greater than.

[0143] In some specific embodiments, the target pressure value determination unit may specifically include:

[0144] a pressure value comparison subunit, used to determine whether the difference between the first outlet pressure value and the initial outlet pressure value is greater than a second preset threshold value, so as to obtain a second determination result;

[0145] A second step jump unit, for jumping back to the step of configuring the corresponding outlet pressure initial value if the second judgment result indicates that it is greater than;

[0146] The target pressure value determination subunit is used to determine the first outlet pressure value as the target pressure value of the node corresponding to the current segment to be processed if the second judgment result indicates that it is not greater than.

[0147] In some specific embodiments, the comparison and analysis module 14 may specifically include:

[0148] A gas characteristic comparison unit, for comparing, for each of the nodes, whether the difference between the outlet gas characteristic information in the prediction result and the corresponding actual outlet gas characteristic information exceeds a preset range, so as to determine a corresponding analysis result;

[0149] The information push unit is used to determine corresponding abnormal prompt information based on the analysis result and trigger a corresponding abnormal prompt information push operation if the analysis result indicates that the abnormal prompt information exceeds a preset range.

[0150] Furthermore, the present application also discloses an electronic device. Figure 4 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content in the diagram cannot be regarded as any limitation on the scope of use of the present application.

[0151] Figure 4 A schematic diagram of the structure of an electronic device 20 provided in an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the simulation analysis method of the porous medium packed bed disclosed in any of the aforementioned embodiments. In addition, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0152] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0153] In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0154] The operating system 221 is used to manage and control the hardware devices and computer program 222 on the electronic device 20, and can be Windows Server, Netware, Unix, Linux, etc. In addition to the computer program that can be used to complete the simulation analysis method of the porous medium packed bed performed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 can further include computer programs that can be used to complete other specific tasks.

[0155] Furthermore, the present application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the simulation analysis method of the porous medium packed bed disclosed above is implemented. The specific steps of the method can be referred to the corresponding contents disclosed in the above embodiments, and will not be repeated here.

[0156] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0157] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0158] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0159] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article 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, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0160] The technical solution provided by the present application is introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A simulation analysis method for a porous medium packed bed, characterized in that: Applied to preset simulation analysis systems, including: Data is collected based on a preset real-time database and a packed bed parameter database, and a target heat transfer dimensionless coefficient is corrected to obtain corresponding collection results and correction results; the target heat transfer dimensionless coefficient is a target inertia term dimensionless coefficient and a target viscosity term dimensionless coefficient in heat transfer; Based on the acquisition results, the correction results and the preset simulation software, a model is constructed to obtain a corresponding gas characteristic prediction model of the porous medium packed bed; In the process of gas transportation simulation, the outlet gas characteristic information of each node is predicted based on the gas characteristic prediction model and the input packed bed inlet gas characteristic information to obtain corresponding prediction results; By comparing the prediction result with the corresponding real outlet gas characteristic information, a corresponding analysis result is determined, and based on the analysis result, whether an abnormal prompt measure is currently triggered; The method of predicting the outlet gas characteristic information of each node based on the gas characteristic prediction model and the input packed bed inlet gas characteristic information to obtain corresponding prediction results includes: Segmenting the porous medium packed bed to obtain a corresponding number of segments; Determine a segment from the plurality of segments as a current segment to be processed, and configure a corresponding initial value of the outlet pressure for a node corresponding to the current segment to be processed to obtain a corresponding initial value of the outlet pressure; The initial value of the outlet temperature of the node corresponding to the current segment to be processed is configured to obtain the corresponding initial value of the outlet temperature; Determine the first outlet fluid enthalpy value of the node corresponding to the current segment to be processed based on the Joule-Thomson coefficient, the initial value of the outlet pressure, and the initial value of the outlet temperature; Determine the second outlet fluid enthalpy value of the node corresponding to the current segment to be processed based on the correction result, the outlet pressure initial value and the outlet temperature initial value; By comparing the first outlet fluid enthalpy value with the second outlet fluid enthalpy value, a target enthalpy value of the node corresponding to the current segment to be processed is determined; Determine a first outlet pressure value of the node corresponding to the current section to be processed based on pipeline roughness, packed bed friction pressure drop efficiency, gravitational acceleration, and packed bed length; By comparing the first outlet pressure value with the initial outlet pressure value, a target pressure value of the node corresponding to the current segment to be processed is determined; Determining a target temperature value based on the target enthalpy value and the target pressure value to obtain an outlet gas characteristic prediction result of the node corresponding to the current segment to be processed; The next segment among the several segments is taken as the current segment to be processed, and the process jumps again to the node corresponding to the current segment to be processed to perform the corresponding steps of configuring the initial value of the outlet pressure until the outlet gas characteristic prediction results of the nodes corresponding to each segment are determined to obtain the prediction results.

2. The simulation analysis method of porous medium packed bed according to claim 1, characterized in that: The data collection is performed based on the preset real-time database and the packed bed parameter database, and the target heat transfer dimensionless coefficient is corrected to obtain corresponding collection results and correction results, including: Experimental simulation is performed through a preset real-time database to determine the pipeline characteristic information and insulation layer characteristic information of the corresponding porous medium packed bed; wherein the real-time database is used to store the real value of the gas characteristic obtained through the preset interface; Performing data collection on the real-time database and the packed bed parameter database to determine collection results including the true value of the gas characteristics, porous medium packed bed equipment parameters, and modeling constraints; The target dimensionless coefficient of inertia term and the target dimensionless coefficient of viscosity term in heat transfer are corrected to obtain corresponding correction results.

3. The simulation analysis method of porous medium packed bed according to claim 1, characterized in that: The predicting of the outlet gas characteristic information of each node based on the gas characteristic prediction model and the input packed bed inlet gas characteristic information includes: The gas characteristics are collected in real time through a preset interface, and the obtained real values ​​of the gas characteristics are filtered to obtain the corresponding packed bed inlet gas characteristic information; Or, input information based on a preset interface to obtain the packed bed inlet gas characteristic information; Based on the gas characteristic prediction model and the packed bed inlet gas characteristic information, the outlet gas characteristic information of each node is predicted to obtain corresponding prediction results.

4. The simulation analysis method of porous medium packed bed according to claim 1, characterized in that: The step of comparing the first outlet fluid enthalpy value with the second outlet fluid enthalpy value to determine the target enthalpy value of the node corresponding to the current segment to be processed includes: Determine whether a difference between the first outlet fluid enthalpy value and the second outlet fluid enthalpy value is greater than a first preset threshold value to obtain a first determination result; If the first judgment result indicates that it is greater than, then jump back to the step of configuring the initial value of the outlet temperature of the node corresponding to the current segment to be processed; If the first judgment result indicates that it is not greater than, then the first outlet fluid enthalpy value is determined to be the target enthalpy value of the node corresponding to the current segment to be processed.

5. The simulation analysis method of porous medium packed bed according to claim 1, characterized in that: The step of comparing the first outlet pressure value with the initial outlet pressure value to determine the target pressure value of the node corresponding to the current segment to be processed includes: Determine whether the difference between the first outlet pressure value and the initial outlet pressure value is greater than a second preset threshold value to obtain a second determination result; If the second judgment result indicates that it is greater than, then jump back to the step of configuring the corresponding initial value of the outlet pressure; If the second judgment result indicates that it is not greater than, then the first outlet pressure value is determined to be the target pressure value of the node corresponding to the current segment to be processed.

6. The simulation analysis method of porous medium packed bed according to claim 1, characterized in that: The method of comparing the prediction result with the corresponding real outlet gas characteristic information to determine the corresponding analysis result, and determining whether to trigger abnormal prompt measures based on the analysis result, includes: For each of the nodes, comparing the difference between the outlet gas characteristic information in the prediction result and the corresponding actual outlet gas characteristic information to see whether it exceeds a preset range, so as to determine the corresponding analysis result; If the analysis result indicates that the preset range is exceeded, corresponding abnormal prompt information is determined based on the analysis result, and a corresponding abnormal prompt information push operation is triggered.

7. A simulation analysis device for a porous medium packed bed, characterized in that: Applied to preset simulation analysis systems, including: A data acquisition module, used for performing data acquisition based on a preset real-time database and a packed bed parameter database, and correcting a target heat transfer dimensionless coefficient to obtain corresponding acquisition results and correction results; the target heat transfer dimensionless coefficient is a target inertia term dimensionless coefficient and a target viscosity term dimensionless coefficient in heat transfer; A model building module, used to build a model based on the acquisition results, the correction results and the preset simulation software to obtain a corresponding gas characteristic prediction model of the porous medium packed bed; A characteristic prediction module is used to predict the outlet gas characteristic information of each node based on the gas characteristic prediction model and the input packed bed inlet gas characteristic information during the gas transportation simulation to obtain corresponding prediction results; A comparison and analysis module, used to determine a corresponding analysis result by comparing the prediction result with the corresponding real outlet gas characteristic information, and determine whether to trigger an abnormal prompt measure based on the analysis result; The characteristic prediction module comprises: A packed bed segmentation unit, used for segmenting the porous medium packed bed to obtain a corresponding number of segments; A pressure initial value configuration unit, used to determine a segment from the plurality of segments as a current segment to be processed, and to perform a corresponding outlet pressure initial value configuration for the node corresponding to the current segment to be processed, so as to obtain a corresponding outlet pressure initial value; A temperature initial value configuration unit, used to configure the initial value of the outlet temperature of the node corresponding to the current segment to be processed to obtain a corresponding outlet temperature initial value; A first enthalpy value determining unit, used to determine a first outlet fluid enthalpy value of the node corresponding to the current segment to be processed based on the Joule-Thomson coefficient, the outlet pressure initial value, and the outlet temperature initial value; a second enthalpy value determining unit, configured to determine a second outlet fluid enthalpy value of the node corresponding to the current segment to be processed based on the correction result, the outlet pressure initial value, and the outlet temperature initial value; a target enthalpy value determining unit, configured to determine a target enthalpy value of the node corresponding to the current segment to be processed by comparing the first outlet fluid enthalpy value with the second outlet fluid enthalpy value; A first outlet pressure value determining unit, configured to determine a first outlet pressure value of the node corresponding to the current to-be-processed segment based on pipeline roughness, packed bed friction pressure drop efficiency, gravitational acceleration, and packed bed length; a target pressure value determining unit, configured to determine a target pressure value of the node corresponding to the current segment to be processed by comparing the first outlet pressure value with the initial outlet pressure value; An outlet gas characteristic prediction result determination unit, used to determine a target temperature value based on the target enthalpy value and the target pressure value, so as to obtain an outlet gas characteristic prediction result of the node corresponding to the current segment to be processed; The prediction result determination unit is used to take the next segment among the several segments as the current segment to be processed, and jump again to the node corresponding to the current segment to be processed, and perform the corresponding steps of configuring the initial value of the outlet pressure until the outlet gas characteristic prediction results of the nodes corresponding to each segment are determined to obtain the prediction results.

8. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the simulation analysis method for a porous medium packed bed as claimed in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: Used to store a computer program, which, when executed by a processor, implements the simulation analysis method for a porous medium packed bed as described in any one of claims 1 to 6.

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