CN114117954A - Dynamic real-time visualization method for three-dimensional reaction field in reactor - Google Patents

Dynamic real-time visualization method for three-dimensional reaction field in reactor Download PDF

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CN114117954A
CN114117954A CN202111316547.9A CN202111316547A CN114117954A CN 114117954 A CN114117954 A CN 114117954A CN 202111316547 A CN202111316547 A CN 202111316547A CN 114117954 A CN114117954 A CN 114117954A
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杨宏燕
韩华云
韩红桂
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Abstract

The invention relates to a dynamic real-time visualization method for a three-dimensional reaction field in a reactor. Based on an operation database access module, historical operation data and a real-time operation database of the reactor are constructed; determining typical reference working conditions of the reactor based on a typical working condition analysis module; based on a numerical simulation module, performing off-line three-dimensional CFD numerical simulation calculation of a reactor internal reaction field under a reference operation condition; based on a data clustering and standardization module, dividing a data set of the established reactor CFD three-dimensional simulation database by adopting a data mining and principal component analysis method, processing parameters, integrating redundancy, reducing dimension, and obtaining 8-10 principal components after processing; and calculating to obtain the three-dimensional reaction field data of the reactor under the full-operation working condition by adopting a regression prediction algorithm and a neural network model based on a deep learning prediction module. The invention solves the problem of 'black boxes' in the reactor in the field of petrochemical industry, and realizes accurate optimized operation and intelligent regulation and control of the operation process of the reactor.

Description

Dynamic real-time visualization method for three-dimensional reaction field in reactor
Technical Field
The invention relates to a dynamic real-time visualization method for a three-dimensional reaction field in a reactor, which is suitable for the crossing field of petrochemical reactor three-dimensional visualization and process control.
Background
The reactor in the fields of petrochemical industry and the like is a complex nonlinear chemical reaction system and is often used for meeting various requirements of synthetic materials, medical reagents, dyes and the like. Because the reaction conditions inside the reactor directly determine the quality and performance of reaction products, but the reaction process is complex, most of the reactions are carried out in a high-temperature and high-pressure environment, three-dimensional reaction field information including flow fields, gas-phase reactant concentrations, particle concentration distribution and the like is difficult to obtain, and intelligent control and optimal adjustment aiming at the reaction process cannot be realized.
In order to obtain multi-field coupling data inside the reactor and ensure that the flow and reaction process inside the reactor are within a controllable range, reaction field information inside the reactor needs to be accurately known, but because complex operating conditions such as tightness, high temperature and high pressure, low visibility and the like exist inside the reactor, the traditional measuring methods such as endoscopes, component concentration measuring sensors and the like have limited measurable parameters and area ranges, the measuring precision and efficiency are required to be improved, the influence of human factors is large, and accurate three-dimensional reaction field information is difficult to obtain. In recent years, data-driven methods represented by various machine learning methods are applied to data prediction of industrial processes, but conventional deep learning prediction methods based on neural networks are often highly dependent on input of historical operating data sets, neglect reaction mechanism models and have inherent defects in the aspects of predictable data types and accuracy. With the continuous maturity of the three-dimensional numerical simulation (CFD) technology, the actual reaction mechanism can be coupled, and the flow, heat transfer and chemical reaction processes in the reactor can be accurately calculated and obtained.
Aiming at solving the problems, the invention provides a dynamic real-time visualization method for a three-dimensional reaction field in a reactor, aiming at solving the problem of a 'black box' in the reactor commonly existing in the field of petrochemical industry and meeting the requirement of obtaining the information of complex flow and reaction process in the reactor in real time.
Disclosure of Invention
The invention provides a dynamic real-time visualization method for a three-dimensional reaction field in a reactor, aiming at the problem of 'black boxes' in the reactor commonly existing in the field of petrochemical industry and aiming at meeting the requirement of obtaining complex flow and reaction process information in the reactor in real time.
A dynamic real-time visualization method for a three-dimensional reaction field in a reactor comprises the following modules:
the system comprises an operation database access module, a typical working condition analysis module, a numerical simulation module, a data clustering and standardization module, a deep learning prediction module and a three-dimensional visualization module which are totally six modules;
the operation database access module, the typical working condition analysis module and the numerical simulation module are all offline modules; the data clustering and standardizing module, the deep learning prediction module and the three-dimensional visualization module are all online modules;
the method comprises the following specific steps:
the method comprises the following steps: historical operation data and real-time operation database of reactor are constructed based on operation database access module
a) Extracting historical operating data of the reactor from a historical operating database of the reactor according to a certain time interval delta t, wherein the historical operating data comprises reactor inlet parameters, reactor internal monitoring parameters and reactor outlet parameters;
reactor inlet parameters including composition of the reactor inlet feed, flow rate Q of the inlet feedIVelocity v of the inlet feedITemperature T of the inlet feedIWherein, I represents different inlet reactant species;
reactor internal monitoring parameters, including temperature values T of the reaction zonex,y,zPressure valuePx,y,zGas component concentration Cx,y,zWherein x, y and z represent the three-dimensional space coordinate position in the reactor under a Cartesian coordinate system;
reactor outlet parameters including gas flow Q at the reactor outletOAverage temperature TOAverage pressure PO；
b) Extracting real-time operation data of the reactor according to a certain time interval delta t in the operation process of the reactor, and analyzing working conditions;
reactor inlet parameters including composition of the reactor inlet feed, flow rate Q of the inlet feedIVelocity v of the inlet feedITemperature T of the inlet feedIWherein I represents a different inlet reactant type;
reactor internal monitoring parameters, including temperature values T of the reaction zonexPressure value PxGas component concentration CxWherein x, y and z represent the three-dimensional space coordinate position in the reactor under a Cartesian coordinate system;
reactor outlet parameters including gas flow Q at the reactor outletOAverage temperature TOAverage pressure PO；
c) And storing historical operating data and real-time operating data of the reactor in a database, constructing the operating database of the reactor, removing data abnormal points and carrying out data standardization processing. The method specifically comprises the following steps:
processing by using a K-means algorithm to reduce data dimensionality; judging whether the data contains null values, 0 values or abnormal values, and if so, filling the data by a Lagrange interpolation method; then, the data is normalized. Wherein the normalization processing formula is expressed as:
Figure BDA0003343843930000031
in the formula (I), the compound is shown in the specification,
Figure BDA0003343843930000032
representing the production parameters of the kth sample under n-dimensional data normalization,
Figure BDA0003343843930000033
n-dimensional metadata representing that the kth sample is arranged in time series, K being the number of data sets,
Figure BDA0003343843930000034
n-dimensional data representing the kth sample, where k is 1,2, …, and N is the number of datasets;
step two: determining typical reference conditions of a reactor based on a typical condition analysis module
a) Because mass data exist in the actual operation of the reactor, the number of the operation working conditions is huge, and therefore representative reference operation working condition data in the reactor needs to be selected preferably. Specifically, calculating to obtain data of a reference operation condition in the reactor operation process by adopting a K-means mean value clustering algorithm in the reactor operation database constructed in the step one;
step three: based on a numerical simulation module, off-line three-dimensional CFD computational fluid dynamics numerical simulation calculation of the reactor internal reaction field under the reference operation condition is carried out
a) Calculating and solving the chemical reaction process among the materials by using Chemkin software according to the components of the materials at the inlet of the reactor and the chemical reaction conditions to obtain the reaction composition of each element, the corresponding chemical equation and the reaction kinetic parameters of the element reaction in each step;
b) establishing a three-dimensional calculation domain physical model of the reactor according to the operation process, structure and size parameters of the reactor;
c) adopting Gambit computational domain meshing software to perform meshing on a three-dimensional computational domain model of the reactor, and performing local encryption on meshes of an inlet, an outlet and a structurally complex region of the reactor to obtain a high-precision simulation result; setting boundary condition types of an inlet, an outlet and a wall surface of the reactor, wherein the boundary condition of the inlet of the reactor is a mass inlet, the boundary condition of the outlet of the reactor is a pressure outlet, and the boundary condition of the wall surface of the reactor is a constant temperature wall surface;
d) setting inlet feeding components and inlet feeding flow Q of the three-dimensional calculation domain model of the reactor according to the reactor reference working condition operation data obtained in the step twoIVelocity v of the inlet feedITemperature T of the feedIWherein I represents a different inlet reactant type;
e) utilizing a high-performance computer and a CFD numerical simulation platform, adopting the chemical elementary reaction obtained in the step three a), selecting a turbulent flow model, a multiphase flow model and a radiation and reaction mechanism model, utilizing the reactor computational domain grid model obtained in the step three c) to carry out solving simulation on the three-dimensional chemical reaction process in the reactor, and simulating to obtain the speed (v) corresponding to the coordinate (x, y, z) of the three-dimensional space in the reactorx，vy，vzB), temperature Tx,y,zPressure Px,y,zReaction product component concentration Cx,y,zThe distribution data of (2); wherein, the turbulent flow model, the multiphase flow model, the radiation model and the reaction mechanism model are respectively expressed as:
in the turbulent flow model, a k-epsilon two-pass model is used for simulating the turbulent flow process in the reactor;
in the multiphase flow model, a Mixture model based on an Euler-Euler method is adopted, wherein a continuity equation and a momentum equation are respectively expressed as follows:
Figure BDA0003343843930000041
Figure BDA0003343843930000042
in the formula, ρmIs density,. is Hamiltonian,. vmIn order to obtain a mass-averaged velocity,
Figure BDA0003343843930000043
in the form of a vector of mass-averaged velocities, μmFor the mixed viscosity coefficient, T is the temperature of the phase,
Figure BDA0003343843930000044
representing the mass-averaged velocity vector form for a temperature T, F is the volumetric force,
Figure BDA0003343843930000045
in the form of a vector of volume forces, B is the total number of phases, k is the kth phase, gmIn order to be the acceleration of the gravity,
Figure BDA0003343843930000046
in the form of a vector of gravitational acceleration, αkVolume fraction of the k-th phase, pkIs the density of the k-th phase, vdr,kIs the slip speed of the k-th phase,
Figure BDA0003343843930000047
in the form of a slip velocity vector of the k-th phase;
in the radiation model, P-1 radiation model is used to simulate radiation heat transfer, and the model also includes the mutual radiation among ions and describes radiation flux
Figure BDA0003343843930000048
The equation of (c) can be expressed as follows:
Figure BDA0003343843930000049
wherein beta is the absorption coefficient, CsIs the scattering coefficient, h is the coefficient of the linear anisotropy phase function, v is the hamiltonian, G is the incident radiation;
in the reaction mechanism model, the chemical reaction rate constants follow the arrhenius formula and are expressed as follows:
Figure BDA0003343843930000051
in the formula, kaFor reaction rate constant, Z is the molar gas constant, TaIs a thermodynamic temperature, ZaIs apparent activation energy, A is a pre-exponential factor, e is a natural constant;
f) based on the CFD three-dimensional numerical simulation result obtained in the step three e), performing data verification and CFD calculation model optimization by adopting the reactor operation database constructed in the step one until the calculation error of the CFD three-dimensional numerical simulation result is less than 5%;
g) repeating the step three a) to the step three f), calculating to obtain all CFD three-dimensional numerical simulation results of all working conditions in the reactor reference operation working condition, and establishing a simulation database of the reactor reference operation working condition reaction process;
step four: based on a data clustering and standardization module, a reactor CFD three-dimensional simulation database established in the third step is subjected to data set division, parameter processing, redundancy integration and dimensionality reduction by adopting a data mining and principal component analysis method, and 8-10 principal components are obtained after processing
a) Aiming at a CFD three-dimensional simulation database of reactors, in order to reduce the number of subsequent data modeling, the data quantity randomly extracted in the reference operation condition of each reactor is 2-3 ten thousand groups, and in the data extraction process, the data extraction coordinates of each condition are ensured to be the same;
b) the extracted CFD simulation data information includes: coordinate position (x, y, z) of each calculation node in the reactor, and fluid velocity (v) corresponding to each nodex，vy，vzB,) rotation phix,y,zReaction product component concentration Cx,y,zTemperature Tx,y,zAnd pressure Tx,y,z；
c) Because the data extraction amount of each working condition is huge, the direct grouping use can cause the problems of low prediction precision and long time, therefore, in order to improve the calculation precision and speed of the full operation working condition of the reactor, a data mining and principal component analysis method is adopted to carry out correlation analysis on the data; specifically, a K-means mean clustering algorithm and a dimensionality reduction algorithm are combined to divide a data set and process parameters so as to integrate redundant data and reduce dimensionality of the data, so that a high-efficiency data set is obtained, and main parameters influencing the chemical reaction process in the reactor are obtained. Obtaining 8 to 10 main components after data processing;
step five: based on a deep learning prediction module, three-dimensional reaction field data under the full-operation working condition of the reactor is obtained by adopting a regression prediction algorithm and a neural network model for calculation
a) Combining a regression prediction algorithm and a neural network model to establish a main parameter rapid prediction model of the internal reaction process of the reactor, wherein the parameters comprise: velocity (v) corresponding to three-dimensional space arbitrary position coordinate (x, y, z)x，vy，vzB), temperature Tx,y,zPressure Px,y,zReaction product component concentration Cx,y,zThe distribution data of (2). The neural network model adopts a BP neural network model, the regression prediction algorithm adopts a lasso regression prediction algorithm, and in the lasso regression prediction algorithm, the loss function of the lasso model is assumed to be as follows:
Figure BDA0003343843930000061
wherein J (β) is a loss function, βjThe regression parameters are s, the total data quantity is s, the characteristic variable is L, the calculated data quantity is p, the actual output is G, and the lambda is a regular term coefficient; beta is ajThe first part is subjected to partial derivation by using a sub-gradient method to obtain:
Figure BDA0003343843930000062
optimizing and continuously iterating each time to obtain an optimal regression coefficient;
in the BP neural network model, the output layer is the speed (v) corresponding to the reaction field parameters (including the coordinates (x, y, z) of any position in three-dimensional space) in the reactorx，vy，vzB), temperature Tx,y,zPressure Px,y,zReaction product component concentration Cx,y,z) Recording the number of hidden layer nodes of each layer, limiting the number of nodes of the input layer and the number of iterations, defining the weight from the input layer to the hidden layer and the weight from the hidden layer to the output layer, the threshold value of the hidden layer and the threshold value of the output layer, and setting the learning rate and the iteration numberMinimum error, wherein the learning rate setting range is between 0.1 and 0.6, and the minimum error is 1%;
b) taking the inlet parameters of the reactor in the database as an input data set based on the database of the reference operation conditions of the reactor obtained in the fourth step, wherein the inlet parameters comprise: inlet feed composition, inlet feed flow rate QIVelocity v of the inlet feedITemperature T of the inlet feedI(ii) a Training a neural network model by using 8 to 10 principal components obtained by data processing as an output data set, wherein the principal components comprise velocities (v) corresponding to three-dimensional space arbitrary position coordinates (x, y, z)x，vy，vzB), temperature Tx,y,zPressure Px,y,zReaction product component concentration Cx,y,z；
c) The above processes are circulated until the error meets the precision requirement or the learning times is more than the set maximum times, and the training process is ended; the accuracy requirement is that the error of a predicted value is within 5 percent, and the maximum learning frequency to be set is 1000 times;
d) and adopting the trained neural network model to carry out three-dimensional reaction field prediction under all working conditions of the reactor, and specifically taking the difference value between the inlet parameter of the reactor under the atypical working condition to be predicted and the reference operation working condition as the input variable of the neural network prediction model. Using the principal component difference value of the non-reference operation condition and the reference operation condition as an output variable of a prediction model, and carrying out prediction of principal components in a reaction field;
step six: based on a three-dimensional visualization module, the three-dimensional visualization under the full operation condition of the reactor is realized
a) Acquiring a reactor three-dimensional reaction field database based on the reactor three-dimensional reaction field data under the non-reference operation condition obtained by model prediction in the step five;
b) integrating the three-dimensional reaction field database under the non-reference operation condition with the three-dimensional reaction field database under the reference operation condition to form a three-dimensional reaction field database under the full operation condition;
c) drawing three-dimensional reaction by using OpenGL software based on three-dimensional reaction field database under full operation conditionA field image for constructing a velocity (v) corresponding to an arbitrary position coordinate (x, y, z) in a three-dimensional spacex，vy，vzB), temperature Tx,y,zPressure Px,y,zReaction product component concentration Cx,y,zThe three-dimensional distribution cloud picture and the numerical scale are displayed on a computer-side software interface in real time, and are displayed in a three-dimensional visual mode for operators of the reactor to visually observe and check;
according to the three-dimensional visualization result of the reactor, the operating personnel of the reactor can visually know the distribution conditions of the eddy current distribution, the high-temperature area position and the insufficient reaction area in the reactor in real time, so that the operating personnel can conveniently adjust the operating conditions of the reactor in a targeted manner, and the required high-concentration and high-quality reaction product can be obtained while the safe and reliable operation of the reactor is realized.
According to the dynamic real-time visualization method for the three-dimensional reaction field in the reactor, the three-dimensional reaction field in the reactor is established by the system, the three-dimensional real-time monitoring of the reaction in the reactor is realized, the three-dimensional reaction field is displayed on a visualization interface in real time, the state of a black box in the reactor is broken, a technical method for knowing and visualizing the state in the reactor is provided for the technology and operating personnel of the reactor, and the intelligent control and optimized operation capability of the reaction process are improved.
The invention has the beneficial effects that:
(1) based on chemical reaction dynamics simulation, and in combination with a computational fluid dynamics three-dimensional numerical simulation method, numerical simulation is carried out on three-dimensional flow, heat transfer and chemical reaction processes in the reactor, so that three-dimensional reaction field parameter distribution of any position of the internal space of the reactor under a typical reference operation condition is obtained, and a three-dimensional reaction field database under a typical operation condition based on a reaction mechanism model is provided;
(2) by combining a numerical simulation database based on a reaction mechanism model, a historical operation database of the reactor and real-time operation data of the reactor, the prediction of the reaction field of the reactor under the full operation condition is realized by utilizing a neural network algorithm, so that a large number of working conditions, operation steps and calculation time generated by the traditional CFD three-dimensional numerical calculation are saved, and high-precision three-dimensional reaction field data under the full operation condition of the reactor can be obtained in real time;
(3) based on the obtained reaction field data under the full-operation working condition of the reactor, the reaction field under the full-operation working condition of the reactor can be displayed on a computer-side display in real time through visual software, three-dimensional visualization is realized, the technology and operators of the reactor are facilitated, the cause analysis is carried out on the problems occurring in the operation process of the reactor, a solution is provided, meanwhile, the problems about to occur in the operation process of the reactor are predicted, and a corresponding optimization control scheme is proposed in advance.
Drawings
FIG. 1 is a logic framework diagram of a dynamic real-time visualization method of a three-dimensional reaction field in a reactor according to the present invention.
FIG. 2 is a schematic diagram of a neural network of a dynamic real-time visualization method of a three-dimensional reaction field inside a reactor according to the present invention.
FIG. 3 is a logic control diagram of a neural network for a dynamic real-time visualization method of a three-dimensional reaction field inside a reactor according to the present invention.
FIG. 4 shows the predicted three-dimensional axial velocity v of coal gasifier under the condition of changing the incident angle of air distributionzAnd (4) distributing cloud pictures.
FIG. 5 shows the predicted three-dimensional temperature T in the coal gasifier under the condition of changing the incident angle of the air distributionx,y,zAnd (4) distributing cloud pictures.
Detailed Description
In order to clarify the objects, technical solutions and advantages of the present invention, the following embodiments further illustrate the method for dynamically visualizing the three-dimensional reaction field inside the reactor in real time. It should be noted that the specific embodiments described herein are only for explaining the present invention and are not used to limit the present invention.
In order to explain the embodiments of the present invention, a specific embodiment of the present invention will be described below by taking a coal gasifier as an example.
As shown in fig. 1, a method for dynamically visualizing a three-dimensional reaction field inside a reactor in real time includes the following modules:
the system comprises an operation database access module, a typical working condition analysis module, a numerical simulation module, a data clustering and standardization module, a deep learning prediction module and a three-dimensional visualization module which are totally six modules;
the operation database access module, the typical working condition analysis module and the numerical simulation module are all offline modules; the data clustering and standardizing module, the deep learning prediction module and the three-dimensional visualization module are all online modules;
the method comprises the following specific steps:
as shown in fig. 1, step one: historical operation data and real-time operation database of coal gasifier reactor are constructed based on operation database access module
a) Extracting historical operating data of a reactor from a historical operating database of the coal gasifier reactor according to a certain time interval delta t of 10s, wherein the historical operating data comprises coal gasifier reactor inlet parameters, coal gasifier reactor internal monitoring parameters and coal gasifier reactor outlet parameters;
coal gasifier reactor inlet parameters, including the composition of the reactor inlet feed (including air, CH)4Pulverized coal), inlet feed flow QI(including air flow, steam flow, pulverized coal feed flow), inlet feed velocity vI(including air incidence velocity, steam incidence velocity, pulverized coal incidence velocity), inlet feed temperature TI(including air temperature, steam temperature, coal dust temperature), wherein I represents different inlet reactant species;
internal monitoring parameters of the coal gasifier reactor, including the temperature value T of the violent reaction zonex,y,zPressure value Px,y,zGas component concentration Cx,y,z(including CO, H)2、CO2、N2、H2O、H2S and CH4Concentration), wherein x, y and z represent the three-dimensional space coordinate position in the reactor under a Cartesian coordinate system;
coal gasifier reactor outlet parameters, includingGas flow Q at the outlet of the reactorO(mainly CO flux and H)2Flow rate), average temperature TOAverage pressure PO；
b) Extracting real-time operation data of the reactor according to a certain time interval delta t of 10s in the operation process of the coal gasifier flute reactor, and analyzing working conditions;
coal gasifier reactor inlet parameters, including the composition of the reactor inlet feed (including air, CH)4Pulverized coal), inlet feed flow QI(including air flow, steam flow, pulverized coal feed flow), inlet feed velocity vI(including air incidence velocity, steam incidence velocity, pulverized coal incidence velocity), inlet feed temperature TI(including air temperature, steam temperature, coal dust temperature), wherein I represents different inlet reactant species;
internal monitoring parameters of the coal gasifier reactor, including the temperature value T of the violent reaction zonex,y,zPressure value Px,y,zGas component concentration Cx,y,z(including CO, H)2、CO2、N2、H2O、H2S and CH4Concentration), wherein x, y and z represent the three-dimensional space coordinate position in the reactor under a Cartesian coordinate system;
coal gasifier reactor outlet parameters including gas flow Q at the reactor outletO(mainly CO flux and H)2Flow rate), average temperature TOAverage pressure PO；
c) And storing historical operating data and real-time operating data of the coal gasifier reactor in a database, constructing an operating database of the reactor, removing data abnormal points and carrying out data standardization processing. The method specifically comprises the following steps:
processing by using a K-means algorithm to reduce data dimensionality; judging whether null values, zero values or abnormal values exist in the data, and if so, supplementing the data by using a Lagrange interpolation method; then, carrying out standardization processing on the data; wherein the normalization processing formula is expressed as:
Figure BDA0003343843930000101
in the formula (I), the compound is shown in the specification,
Figure BDA0003343843930000102
representing the production parameters of the kth sample under n-dimensional data normalization,
Figure BDA0003343843930000103
n-dimensional metadata representing that the kth sample is arranged in time series, K being the number of data sets,
Figure BDA0003343843930000104
n-dimensional data representing the kth sample, where k is 1,2, …, and N is the number of datasets;
as shown in fig. 1, step two: determining typical reference working conditions of coal gasifier reactor based on typical working condition analysis module
a) Because the coal gasifier reactor has massive data in actual operation, the number of operation working conditions is huge, and therefore representative reference operation working condition data in the reactor needs to be selected preferably. Specifically, calculating to obtain representative standard operation condition data in the reactor operation process by adopting a K-means mean value clustering algorithm based on the coal gasifier reactor operation database constructed in the step one;
as shown in fig. 1, step three: based on a numerical simulation module, off-line three-dimensional CFD computational fluid dynamics numerical simulation calculation of the internal reaction field of the coal gasifier reactor under the reference operation condition is carried out
a) According to the composition of the coal gasifier reactor inlet material (including air flow and CH)4Flow and pulverized coal flow) and chemical reaction conditions, and adopting Chemkin software to calculate and solve the chemical reaction process among all materials, and obtaining the reaction composition of each element, the corresponding chemical equation and the reaction kinetic parameters of each step of element reaction. In the reaction, after the pulverized coal is heated to separate out volatile components, the coal coke and the gas are subjected to out-of-phase reaction, and the specific out-of-phase reaction is considered to comprise:
C(s)+0.5O2(g)＝CO(g) (2)
C(s)+H2O(g)＝CO(g)+H2(g) (3)
C(s)+CO2(g)＝2CO(g) (4)
C(s)+2H2(g)＝CH4(g) (5)
corresponding reaction rate constant kaApparent activation energy ZaAnd the value of the reaction order q is as follows:
Figure BDA0003343843930000111
b) establishing a three-dimensional calculation domain physical model of the reactor according to the operation process, structure and size parameters of the coal gasifier reactor;
c) adopting Gambit computational domain meshing software to perform meshing on a three-dimensional computational domain model of a coal gasifier reactor, and performing local encryption on meshes of an inlet, an outlet and a structurally complex region of the coal gasifier reactor to obtain a high-precision simulation result; setting boundary condition types of an inlet, an outlet and a wall surface of the reactor, wherein the boundary condition of the inlet of the reactor is a mass inlet, the boundary condition of the outlet of the reactor is a pressure outlet, and the boundary condition of the wall surface of the reactor is a constant temperature wall surface;
d) setting inlet feeding components and inlet feeding flow Q of the three-dimensional calculation domain model of the reactor according to the reference working condition operation data of the coal gasifier reactor obtained in the step twoIVelocity v of the inlet feedITemperature T of the feedIWherein I represents a different inlet reactant type;
e) utilizing a high-performance computer and a CFD numerical simulation platform, adopting the chemical elementary reaction obtained in the step three a), selecting a turbulent flow model, a multiphase flow model and a radiation and reaction mechanism model, utilizing the coal gasifier reactor computational domain grid model obtained in the step three c) to carry out solving simulation on the three-dimensional chemical reaction process in the reactor, and simulating to obtain any three-dimensional space in the reactorVelocity (v) corresponding to position coordinates (x, y, z)x，vy，vzB), temperature Tx,y,zPressure Px,y,zReaction product component concentration Cx,y,zThe distribution data of (2). Wherein, the turbulent flow model, the multiphase flow model, the radiation model and the reaction mechanism model are respectively expressed as:
in the turbulent flow model, a model which can better simulate non-rotational flow and can realize k-epsilon double-stroke is used for simulating the turbulent flow process in the reactor;
in the multiphase flow model, a Mixture model based on an Euler-Euler method is adopted, wherein a continuity equation and a momentum equation are respectively expressed as follows:
Figure BDA0003343843930000121
Figure BDA0003343843930000122
in the formula, ρmIs density,. is Hamiltonian,. vmIn order to obtain a mass-averaged velocity,
Figure BDA0003343843930000123
in the form of a vector of mass-averaged velocities, μmFor the mixed viscosity coefficient, T is the temperature of the phase,
Figure BDA0003343843930000124
representing the mass-averaged velocity vector form for a temperature T, F is the volumetric force,
Figure BDA0003343843930000125
in the form of a vector of volume forces, B is the total number of phases, k is the kth phase, gmIn order to be the acceleration of the gravity,
Figure BDA0003343843930000126
in the form of a vector of gravitational acceleration, αkVolume fraction of the k-th phase, pkIs as followsDensity of k phase, vdr,kIs the slip speed of the k-th phase,
Figure BDA0003343843930000127
in the form of a slip velocity vector of the k-th phase;
in the radiation model, P-1 radiation model is used to simulate radiation heat transfer, and the model also includes the mutual radiation among ions and describes radiation flux
Figure BDA0003343843930000128
The equation of (c) can be expressed as follows:
Figure BDA0003343843930000129
wherein beta is the absorption coefficient, CsIs the scattering coefficient, h is the coefficient of the linear anisotropy phase function, v is the hamiltonian, G is the incident radiation;
in the reaction mechanism model, the chemical reaction rate constants follow the arrhenius formula and are expressed as follows:
Figure BDA00033438439300001210
in the formula, kaFor reaction rate constant, Z is the molar gas constant, TaIs a thermodynamic temperature, ZaIs apparent activation energy, A is a pre-exponential factor, e is a natural constant;
f) based on the CFD three-dimensional numerical simulation result obtained in the step three e), performing data verification and CFD calculation model optimization by adopting the coal gasifier reactor operation database constructed in the step one until the calculation error of the CFD three-dimensional numerical simulation result is less than 5%;
g) repeating the step three a) to the step three f), calculating to obtain all CFD three-dimensional numerical simulation results of all working conditions in the reference operation working condition of the coal gasifier reactor, and establishing a simulation database of the reaction process of the reference operation working condition of the coal gasifier reactor;
as shown in fig. 1, step four: based on a data clustering and standardization module, a CFD three-dimensional simulation database of the coal gasifier reactor established in the third step is subjected to data set division, parameter processing, redundancy integration and dimensionality reduction by adopting a data mining and principal component analysis method, and 8 principal components are obtained after processing
a) Aiming at a CFD three-dimensional simulation database of a coal gasifier reactor, in order to reduce the number of subsequent data modeling, the data quantity randomly extracted in the reference operation condition of each coal gasifier reactor is 2 ten thousand groups, and in the data extraction process, the data extraction coordinates of each working condition are ensured to be the same;
b) the extracted CFD simulation data information includes: coordinate position (x, y, z) of each calculated node in the coal gasifier reactor, and fluid velocity (v) corresponding to each nodex，vy，vzB,) rotation phix,y,zTemperature Tx,y,zPressure Px,y,zCO concentration, CO2Concentration, H2Concentration, N2Concentration, H2S concentration, H2Concentration of O, CH4Concentration;
c) because the data extraction amount of each working condition is huge, the direct grouping use can cause the problems of low prediction precision and long time, therefore, in order to improve the calculation precision and speed of the full operation working condition of the coal gasifier reactor, a data mining and principal component analysis method is adopted to carry out correlation analysis on the data; specifically, a K-means mean value clustering algorithm and a dimensionality reduction algorithm are combined to divide a data set and process parameters so as to integrate redundant data and reduce dimensionality of the data, a high-efficiency data set is obtained, and main parameters influencing the internal chemical reaction process of the coal gasifier reactor are obtained. After data processing, 8 main components are obtained, including flow velocity (v)x，vy，vzB), temperature Tx,y,zPressure Px,y,zCO concentration, H2Concentration, N2Concentration, H2Concentration of O, CH4Concentration;
as shown in fig. 1, fig. 2 and fig. 3, step five: based on a deep learning prediction module, three-dimensional reaction field data under the full-operation working condition of the reactor is obtained by adopting a regression prediction algorithm and a neural network model for calculation
a) Combining a regression prediction algorithm and a neural network model to establish a main parameter rapid prediction model of the internal reaction process of the coal gasifier reactor, wherein the parameters comprise: flow velocity (v) corresponding to three-dimensional space arbitrary position coordinate (x, y, z)x，vy，vzB), temperature Tx,y,zPressure Px,y,zCO concentration, H2Concentration, N2Concentration, H2Concentration of O, CH4Distribution data of concentration. The neural network model adopts a BP neural network model, the regression prediction algorithm adopts a lasso regression prediction algorithm, and in the lasso regression prediction algorithm, the loss function of the lasso model is assumed to be as follows:
Figure BDA0003343843930000141
wherein J (β) is a loss function, βjThe regression parameters are s, the total data quantity is s, the characteristic variable is L, the calculated data quantity is p, the actual output is G, and the lambda is a regular term coefficient; beta is ajThe first part of RSS is subjected to partial derivation by using a sub-gradient method to obtain:
Figure BDA0003343843930000142
optimizing and continuously iterating each time to obtain an optimal regression coefficient;
in the BP neural network model, the output layer is the speed (v) corresponding to the reaction field parameters (including the coordinates (x, y, z) of any position in three-dimensional space) in the reactorx，vy，vzB), temperature Tx,y,zPressure Px,y,zReaction product component concentration Cx,y,z) Recording the number of nodes of each hidden layer, limiting the number of nodes of an input layer and the number of iterations, defining the weight from the input layer to the hidden layer and the weight from the hidden layer to an output layer, the threshold value of the hidden layer and the threshold value of the output layer, and setting the learning rate and the minimum error; wherein, the learning rate setting range is between 0.1 and 0.6, and the minimum error is 1 percent;
b) taking the inlet parameters of the reactor in the database as an input data set based on the database of the reference operation conditions of the coal gasifier reactor obtained in the fourth step, wherein the inlet parameters comprise: reactor inlet feed composition (including air, CH)4Pulverized coal), inlet feed flow QI(including air flow, steam flow, pulverized coal feed flow), inlet feed velocity vI(including air incidence velocity, steam incidence velocity, pulverized coal incidence velocity), inlet feed temperature TI(including air temperature, steam temperature, buggy temperature), will pass through 8 principal components that data processing obtained as the output data set, carry out the training of neural network model, the principal component includes: flow velocity (v)x，vy，vzB), temperature Tx,y,zPressure Px,y,zCO concentration, H2Concentration, N2Concentration, H2Concentration of O, CH4Concentration;
c) the above processes are circulated until the error meets the precision requirement or the learning times is more than the set maximum times, and the training process is ended; the accuracy requirement is that the error of a predicted value is within 5 percent, and the maximum learning frequency to be set is 1000 times;
d) adopting the trained neural network model to carry out three-dimensional reaction field prediction of the coal gasifier reactor under all working conditions, and specifically taking the difference value between the reactor inlet parameter of the atypical working condition to be predicted and the reference operation working condition as the input variable of the neural network prediction model; using the principal component difference value of the non-reference operation condition and the reference operation condition as an output variable of a prediction model, and carrying out prediction of principal components in a reaction field;
as shown in fig. 1, 4 and 5, step six: based on three-dimensional visualization module, three-dimensional visualization under full-operation working condition of coal gasifier reactor is realized
a) Acquiring three-dimensional reaction field data of the coal gasifier reactor under the non-reference operation condition based on the model prediction in the step five to obtain a three-dimensional reaction field database of the reactor;
b) integrating the three-dimensional reaction field database under the non-reference operation condition with the three-dimensional reaction field database under the reference operation condition to form a three-dimensional reaction field database of the coal gasifier under the full operation condition;
c) based on a coal gasifier three-dimensional reaction field database under the full-operation working condition, drawing a three-dimensional reaction field image by using OpenGL software, and constructing a flow velocity (v) corresponding to any position coordinate (x, y, z) in a three-dimensional spacex，vy，vzB), temperature Tx,y,zPressure Px,y,zCO concentration, H2Concentration, N2Concentration, H2Concentration of O, CH4The three-dimensional distribution cloud picture and the numerical scale of the concentration are displayed on a software interface of a computer end in real time for three-dimensional visual display for operators of the reactor to visually observe and check;
according to the three-dimensional visualization result of the coal gasifier reactor, the operating personnel of the reactor can visually know the distribution conditions of the eddy current distribution, the high-temperature area position and the insufficient reaction area in the reactor in real time, the operating personnel can conveniently and pertinently adjust the operating conditions of the reactor, and the required high-concentration and high-quality reaction products can be obtained while the safe and reliable operation of the reactor is realized.
FIG. 4 shows the predicted three-dimensional axial velocity v of coal gasifier under the condition of changing the incident angle of air distributionzAnd (4) distributing cloud pictures. FIG. 5 shows the predicted three-dimensional temperature T in the coal gasifier under the condition of changing the incident angle of the air distributionx,y,zAnd (4) distributing cloud pictures. Wherein, the working conditions of 0 degree and 50 degrees are typical reference working conditions, the cloud chart in the figure is the CFD simulation result obtained through the step three, the working conditions of 10 degrees, 20 degrees, 30 degrees and 40 degrees are non-reference working conditions, and the cloud chart in the figure is the prediction result obtained through the step five.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (1)

1. A dynamic real-time visualization method for a three-dimensional reaction field in a reactor is characterized by comprising the following modules:
the system comprises an operation database access module, a typical working condition analysis module, a numerical simulation module, a data clustering and standardization module, a deep learning prediction module and a three-dimensional visualization module which are totally six modules;
the operation database access module, the typical working condition analysis module and the numerical simulation module are all offline modules; the data clustering and standardizing module, the deep learning prediction module and the three-dimensional visualization module are all online modules;
the method comprises the following specific steps:
the method comprises the following steps: historical operation data and real-time operation database of reactor are constructed based on operation database access module
a) Extracting historical operating data of the reactor from a historical operating database of the reactor according to a certain time interval delta t, wherein the historical operating data comprises reactor inlet parameters, reactor internal monitoring parameters and reactor outlet parameters;
reactor inlet parameters including composition of the reactor inlet feed, flow rate Q of the inlet feedIVelocity v of the inlet feedITemperature T of the inlet feedIWherein, I represents different inlet reactant species;
reactor internal monitoring parameters, including temperature values T of the reaction zonex,y,zPressure value Px,y,zGas component concentration Cx,y,zWherein x, y and z represent the three-dimensional space coordinate position in the reactor under a Cartesian coordinate system;
reactor outlet parameters including gas flow Q at the reactor outletOAverage temperature TOAverage pressure PO；
b) Extracting real-time operation data of the reactor according to a certain time interval delta t in the operation process of the reactor, and analyzing working conditions;
reactor inlet parameters including composition of the reactor inlet feed, flow rate Q of the inlet feedIVelocity v of the inlet feedITemperature T of the inlet feedIWherein I represents a different inlet reactant type;
reactor internal monitoring parameters, including temperature values T of the reaction zonexPressure value PxGas component concentration CxWherein x, y and z represent the three-dimensional space coordinate position in the reactor under a Cartesian coordinate system;
reactor outlet parameters including gas flow Q at the reactor outletOAverage temperature TOAverage pressure PO；
c) Storing historical operating data and real-time operating data of the reactor in a database, constructing an operating database of the reactor, removing data abnormal points and carrying out data standardization processing; the method specifically comprises the following steps:
processing by using a K-means algorithm to reduce data dimensionality; judging whether the data contains null values, 0 values or abnormal values, and if so, filling the data by a Lagrange interpolation method; then, carrying out standardization processing on the data; wherein the normalization processing formula is expressed as:
Figure FDA0003343843920000021
in the formula (I), the compound is shown in the specification,
Figure FDA0003343843920000022
representing the production parameters of the kth sample under n-dimensional data normalization,
Figure FDA0003343843920000023
n-dimensional metadata representing that the kth sample is arranged in time series, K being the number of data sets,
Figure FDA0003343843920000024
n-dimensional data representing the kth sampleK is 1,2, …, N is the number of datasets;
step two: determining typical reference conditions of a reactor based on a typical condition analysis module
a) Calculating to obtain data of a reference operation condition in the reactor operation process by adopting a K-means mean value clustering algorithm in the reactor operation database constructed in the step one;
step three: based on a numerical simulation module, off-line three-dimensional CFD computational fluid dynamics numerical simulation calculation of the reactor internal reaction field under the reference operation condition is carried out
a) Calculating and solving the chemical reaction process among the materials by using Chemkin software according to the components of the materials at the inlet of the reactor and the chemical reaction conditions to obtain the reaction composition of each element, the corresponding chemical equation and the reaction kinetic parameters of the element reaction in each step;
b) establishing a three-dimensional calculation domain physical model of the reactor according to the operation process, structure and size parameters of the reactor;
c) adopting Gambit computational domain meshing software to perform meshing on a three-dimensional computational domain model of the reactor, and performing local encryption on meshes of an inlet, an outlet and a structurally complex region of the reactor to obtain a high-precision simulation result; setting boundary condition types of an inlet, an outlet and a wall surface of the reactor, wherein the boundary condition of the inlet of the reactor is a mass inlet, the boundary condition of the outlet of the reactor is a pressure outlet, and the boundary condition of the wall surface of the reactor is a constant temperature wall surface;
d) setting inlet feeding components and inlet feeding flow Q of the three-dimensional calculation domain model of the reactor according to the reactor reference working condition operation data obtained in the step twoIVelocity v of the inlet feedITemperature T of the feedIWherein I represents a different inlet reactant type;
e) utilizing a high-performance computer and a CFD numerical simulation platform, adopting the chemical elementary reaction obtained in the step three a), selecting a turbulent flow model, a multiphase flow model and a radiation and reaction mechanism model, and utilizing the reactor computational domain grid model obtained in the step three c) to carry out three-dimensional chemical reaction process solving simulation in the reactorTrue, simulation obtains the speed (v) corresponding to the coordinate (x, y, z) of the arbitrary position in the three-dimensional space in the reactorx，vy，vzB), temperature Tx,y,zPressure Px,y,zReaction product component concentration Cx,y,zThe distribution data of (2); wherein, the turbulent flow model, the multiphase flow model, the radiation model and the reaction mechanism model are respectively expressed as:
in the turbulent flow model, a k-epsilon two-pass model is used for simulating the turbulent flow process in the reactor;
in the multiphase flow model, a Mixture model based on an Euler-Euler method is adopted, wherein a continuity equation and a momentum equation are respectively expressed as follows:
Figure FDA0003343843920000031
Figure FDA0003343843920000032
in the formula, ρmIn order to be the density of the mixture,
Figure FDA0003343843920000033
as Hamiltonian, vmIn order to obtain a mass-averaged velocity,
Figure FDA0003343843920000034
in the form of a vector of mass-averaged velocities, μmFor the mixed viscosity coefficient, T is the temperature of the phase,
Figure FDA0003343843920000035
representing the mass-averaged velocity vector form for a temperature T, F is the volumetric force,
Figure FDA0003343843920000036
in the form of a vector of volume forces, B is the total number of phases, k is the kth phase, gmIn order to be the acceleration of the gravity,
Figure FDA0003343843920000037
in the form of a vector of gravitational acceleration, αkVolume fraction of the k-th phase, pkIs the density of the k-th phase, vdr,kIs the slip speed of the k-th phase,
Figure FDA0003343843920000038
in the form of a slip velocity vector of the k-th phase;
in the radiation model, P-1 radiation model is used to simulate radiation heat transfer, and the model also includes the mutual radiation among ions and describes radiation flux
Figure FDA0003343843920000039
The equation of (c) can be expressed as follows:
Figure FDA00033438439200000310
wherein beta is the absorption coefficient, CsIs the scattering coefficient, h is the linear anisotropy phase function coefficient,
Figure FDA00033438439200000311
is Hamiltonian, G is incident radiation;
in the reaction mechanism model, the chemical reaction rate constants follow the arrhenius formula and are expressed as follows:
Figure FDA0003343843920000041
in the formula, kaFor reaction rate constant, Z is the molar gas constant, TaIs a thermodynamic temperature, ZaIs apparent activation energy, A is a pre-exponential factor, e is a natural constant;
f) based on the CFD three-dimensional numerical simulation result obtained in the step three e), performing data verification and CFD calculation model optimization by adopting the reactor operation database constructed in the step one until the calculation error of the CFD three-dimensional numerical simulation result is less than 5%;
g) repeating the step three a) to the step three f), calculating to obtain all CFD three-dimensional numerical simulation results of all working conditions in the reactor reference operation working condition, and establishing a simulation database of the reactor reference operation working condition reaction process;
step four: based on a data clustering and standardization module, a reactor CFD three-dimensional simulation database established in the third step is subjected to data set division, parameter processing, redundancy integration and dimensionality reduction by adopting a data mining and principal component analysis method, and 8-10 principal components are obtained after processing
a) Aiming at a CFD three-dimensional simulation database of reactors, in order to reduce the number of subsequent data modeling, the data quantity randomly extracted in the reference operation condition of each reactor is 2-3 ten thousand groups, and in the data extraction process, the data extraction coordinates of each condition are ensured to be the same;
b) the extracted CFD simulation data information includes: coordinate position (x, y, z) of each calculation node in the reactor, and fluid velocity (v) corresponding to each nodex，vy，vzB,) rotation phix,y,zReaction product component concentration Cx,y,zTemperature Tx,y,zAnd pressure Tx,y,z；
c) Performing correlation analysis on the data by adopting a data mining and principal component analysis method; specifically, a K-means mean clustering algorithm and a dimensionality reduction algorithm are combined to carry out data set division and parameter processing so as to integrate redundant data and carry out data dimensionality reduction to obtain 8-10 principal components;
step five: based on a deep learning prediction module, three-dimensional reaction field data under the full-operation working condition of the reactor is obtained by adopting a regression prediction algorithm and a neural network model for calculation
a) Combining a regression prediction algorithm and a neural network model to establish a main parameter rapid prediction model of the internal reaction process of the reactor, wherein the parameters comprise: velocity (v) corresponding to three-dimensional space arbitrary position coordinate (x, y, z)x，vy，vzB), temperature Tx,y,zPressure Px,y,zGroup of reaction productsConcentration by weight Cx,y,zThe distribution data of (2); the neural network model adopts a BP neural network model, the regression prediction algorithm adopts a lasso regression prediction algorithm, and in the lasso regression prediction algorithm, the loss function of the lasso model is assumed to be as follows:
Figure FDA0003343843920000051
wherein J (β) is a loss function, βjThe regression parameters are s, the total data quantity is s, the characteristic variable is L, the calculated data quantity is p, the actual output is G, and the lambda is a regular term coefficient; beta is ajThe first part is subjected to partial derivation by using a sub-gradient method to obtain:
Figure FDA0003343843920000052
optimizing and continuously iterating each time to obtain an optimal regression coefficient;
in the BP neural network model, the output layer is the speed (v) corresponding to the reaction field parameters (including the coordinates (x, y, z) of any position in three-dimensional space) in the reactorx，vy，vzB), temperature Tx,y,zPressure Px,y,zReaction product component concentration Cx,y,z) Recording the number of nodes of each hidden layer, limiting the number of nodes of an input layer and the number of iterations, defining the weight from the input layer to the hidden layer and the weight from the hidden layer to an output layer, the threshold value of the hidden layer and the threshold value of the output layer, and setting the learning rate and the minimum error; wherein, the learning rate setting range is between 0.1 and 0.6, and the minimum error is 1 percent;
b) taking the inlet parameters of the reactor in the database as an input data set based on the database of the reference operation conditions of the reactor obtained in the fourth step, wherein the inlet parameters comprise: inlet feed composition, inlet feed flow rate QIVelocity v of the inlet feedITemperature T of the inlet feedI(ii) a Using 8 to 10 principal components obtained by data processing as an output data setTraining a neural network model, wherein the principal component comprises a speed (v) corresponding to any position coordinate (x, y, z) in three-dimensional spacex，vy，vzB), temperature Tx,y,zPressure Px,y,zReaction product component concentration Cx,y,z；
c) The above processes are circulated until the error meets the precision requirement or the learning times is more than the set maximum times, and the training process is ended; the accuracy requirement is that the error of a predicted value is within 5 percent, and the maximum learning frequency to be set is 1000 times;
d) adopting the trained neural network model to carry out three-dimensional reaction field prediction under all working conditions of the reactor, and specifically taking the difference value between the inlet parameter of the reactor under the atypical working condition to be predicted and the reference operation working condition as the input variable of the neural network prediction model; using the principal component difference value of the non-reference operation condition and the reference operation condition as an output variable of a prediction model, and carrying out prediction of principal components in a reaction field;
step six: based on a three-dimensional visualization module, the three-dimensional visualization under the full operation condition of the reactor is realized
a) Acquiring a reactor three-dimensional reaction field database based on the reactor three-dimensional reaction field data under the non-reference operation condition obtained by model prediction in the step five;
b) integrating the three-dimensional reaction field database under the non-reference operation condition with the three-dimensional reaction field database under the reference operation condition to form a three-dimensional reaction field database under the full operation condition;
c) based on a three-dimensional reaction field database under the full-operation working condition, drawing a three-dimensional reaction field image by using OpenGL software, and constructing a speed (v) corresponding to any position coordinate (x, y, z) in a three-dimensional spacex，vy，vzB), temperature Tx,y,zPressure Px,y,zReaction product component concentration Cx,y,zThe three-dimensional distribution cloud picture and the numerical scale are displayed on a computer-side software interface in real time to carry out three-dimensional visual display.
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