A method for predicting and controlling erosion of coal gasifier pipeline

Abstract

The present invention discloses a method for predicting and controlling erosion of a coal gasifier pipeline, which belongs to the technical field of safe transportation of industrial pipelines and is divided into four layers, wherein the first layer is a perception layer; the second layer is a monitoring layer; the third layer is a digital twin computing layer, and the fourth layer is a decision layer; the erosion morphology of the pipeline is displayed in real time, the erosion position of the pipeline is predicted, and the data of the collision of solid impurities in the pipeline with the pipe wall is collected, transmitted, analyzed and fed back in real time, so as to find the erosion position of the gas pipeline wall in advance and take protective measures as soon as possible. The core of the present invention is to connect CFD numerical simulation with the digital twin system, and use the intelligent solver to generate an intelligent algorithm. Complex physical behaviors can be monitored as the real process progresses, so as to respond to changes in real-time input data, which not only meets the needs of calculating real-time data, but also can improve the simulation accuracy of the digital twin, so that the constructed digital twin can be quickly applied to the erosion prediction of the pipeline.

Description

Technical Field

[0001] The invention relates to the technical field of safe transportation of industrial pipelines, and relates to a method for predicting and controlling erosion of a coal gasifier pipeline. Background Art

[0002] In Shell's pulverized coal gasification process, the pulverized coal is ground, mixed with combustion-supporting gas, and passed through high pressure into the reaction chamber of the gasifier. It is heated to 1350-1600°C to react and generate synthesis gas, coal slag, and fly ash. The ash in the coal is discharged from the outlet of the gasifier in the form of slag and fly ash. The discharged coal slag can be removed by filtering, but the ash has extremely high viscosity under high temperature conditions. When passing through the outlet pipe of the gasifier, it will adhere to the inner wall of the pipe. When the synthesis gas generated by the reaction of the gasifier carries the ash and passes through the elbow of the outlet pipe of the gasifier, the flow direction of the fluid in the pipe changes, thereby colliding and impacting the inner wall of the pipe elbow, causing erosion at the elbow of the pipe, seriously reducing the safety of the gasifier outlet pipeline transportation and reducing the production efficiency of coal chemical industry. Pipeline erosion failure is a common failure form in the current industry and is one of the hot issues that need to be solved in the safe operation of pipelines.

[0003] At present, the prevention and control measures for the erosion failure of the synthesis gas transmission pipeline at the outlet of the gasifier are still in the "passive treatment after the event" stage, and cannot achieve "active prevention before the event". The technical bottleneck lies in the inability to obtain the movement trajectory and state of the particles in the pipeline in real time, and the inability to accurately obtain the erosion rate of solid impurities on various parts of the pipeline, and thus it is impossible to integrate various data to conduct intelligent real-time monitoring and early warning of the pipeline erosion morphology. Summary of the invention

[0004] Based on the technical problems existing in the background technology, the present invention proposes a method for predicting and controlling erosion of a gasifier pipeline and a digital twin system, which can obtain the movement trajectory of solid impurities in the pipeline in real time over a long period of time, obtain the erosion rate of each part of the pipeline, use digital twin technology to obtain the erosion morphology at the pipeline elbow in real time, intelligently judge the health status of the pipeline, and automatically alarm for abnormal problems.

[0005] The technical solution adopted by the present invention is:

[0006] A method for predicting and controlling erosion of a coal gasifier pipeline, characterized in that it comprises the following steps:

[0007] The first step is to build a cloud database to store input data and store, analyze, and make decisions on the simulated data later;

[0008] The second step is to collect particle phase and gas phase signals:

[0009] 2.1 Use the IoT-connected velocity meter and density meter to collect the data of the dense phase of coal particles in the coal transportation pipeline, that is, the mass flow rate of particles;

[0010] q m =v*A*c*ρ p

[0011] ρ=ρ p *c+(1-c)*ρ g

[0012] Where q m is the mass flow rate of particles; v is the particle velocity; A is the pipe cross-sectional area; c is the particle concentration; ρ p represents the particle density; ρ represents the density measured by the densitometer; ρ g Indicates particle concentration;

[0013] 2.2 For the dilute coal particles at the outlet pipe of the gasifier, arrange an optical pulsation probe to measure the particle size, concentration and velocity data of the dilute coal particles in real time using the principle of optical pulsation transmission signal, and input the collected data into the digital twin system;

[0014] 2.3 The gas phase velocity is approximately the solid phase velocity. The extinction principle is to use a parallel light beam to pass through the medium area of ​​the particles. The random changes in the number and size of particles entering and leaving the measurement volume cause the fluctuation of the transmitted light. The statistical characteristics of the particles in the measurement area are associated with the pulsation of the transmitted light intensity. The average size of the particles and the particle number concentration can be obtained by collecting and analyzing them through the sensor.

[0015] The third step is modeling and meshing. Use SolidWorks software to build a pipeline model according to the drawing size and divide the mesh. Due to the special flow characteristics of the elbow, the mesh needs to be encrypted at the elbow.

[0016] The fourth step is to set up multiple monitoring points on the built model to monitor and record the erosion rate at the location;

[0017] Step 5: Set up the numerical calculation model and call the model parameters stored in the database; update the boundary conditions according to the data obtained in step 2; the erosion of the elbow can be simulated by combining the particle-wall rebound model and the E / CRC erosion model:

[0018] ER=C(BH) -0.59 F s V p F(θ)

[0019]

[0020] e n =0.993-1.76α+1.56α 2 -0.49α 3

[0021] e t =0.998-1.66α+2.11α 2 -0.67α 3

[0022] Where, BH represents the Brinell hardness of the pipe material; F s Represents the shape factor of the particle, where sharp particles are F s =1.0; semicircular particles are F s =0.53; Full-circle particle F s =0.2; V p represents the particle velocity; F(θ) represents the function of the impact angle; θ is the particle incident angle; n and C are empirical constants, which are 2.41 and 2.17×10-7 respectively; e n and e t are the restitution coefficients of the normal velocity component and the tangential velocity component, respectively; α is the particle incident angle;

[0023] The sixth step is to simulate with the data transmitted physically. During the simulation process, optimization iterations are continuously performed to finally obtain an applicable erosion model. The flow field analysis, particle tracking and erosion calculation are performed on the simulation of solid particle erosion.

[0024] Step 7: Upload the simulated data of the model built in step 5 to the terminal network, and use the intelligent solver to connect the model built in step 5 with the digital twin system;

[0025] Step 8: Analyze the factors affecting the pipeline erosion rate based on the erosion information and summarize the erosion rate change curve; comprehensively study the pipeline diameter, bend ratio, pipeline bending angle, pipeline orientation, particle diameter, particle flow rate and particle flow rate, and conclude that the bend ratio is the most significant factor affecting the maximum pipeline erosion rate. Therefore, in order to predict the pipeline erosion position, the relationship between the particle inlet velocity and the erosion rate under different bend ratios is mainly obtained;

[0026] In the ninth step, the formula obtained in the eighth step is input into the digital twin system, connected with the measured data obtained in the second step to calculate the erosion rate and predict the erosion location;

[0027] The tenth step is to take protective measures for the predicted erosion locations. When solid particles collide with the wall and cause pipeline erosion exceeding the threshold, start the acoustic field control to change the movement trajectory of the particles and alleviate the erosion of the solid wall.

[0028] Furthermore, the setting of the numerical calculation model described in the fifth step, calling the model parameters stored in the database, specifically includes the following steps: the solid particle volume fraction in the gas-solid two-phase flow is small, so the discrete phase model is selected for calculation; for the gas-solid two-phase flow field analysis, the Eulerian-Lagrangian method is used to solve the Navier-Stokes equation in the Eulerian coordinate system to obtain the gas continuous phase flow field, and the particle trajectory of the discrete phase particles is calculated in the Lagrangian coordinate system; the numerical simulation adopts the standard k-ε turbulence model; wherein the continuous phase equation sets the gas phase control equation and the turbulence equation, and the solution of the particle trajectory is obtained by integrating the particle motion equation in the Lagrangian coordinate system; when solving the particle motion trajectory and erosion rate, it is assumed that: I. the incident particles are independent of each other, and the collision between particles and the fragmentation of particles are not considered; II. the pipeline deformation caused by particle collision is not considered;

[0029]

[0030] In the formula represent drag force, buoyancy force, pressure gradient force and added mass force respectively;

[0031]

[0032]

[0033]

[0034]

[0035]

[0036] In the formula, represents the velocity of the fluid; is the velocity of the particle; ρ represents the density of the fluid; ρ p Represents the density of the particle; Re is the relative Reynolds number, C D is the drag coefficient; C VM Represents a virtual quality factor with a value of 0.5; the boundary conditions are set as follows: continuous phase: the standard k-ε turbulence model is used for fluid calculation, and the standard wall function is used for the near-wall area; the velocity inlet boundary condition is used at the inlet, and the free outflow boundary is used at the outlet; the tube wall is the wall boundary, and the wall boundary is set as a "no-slip wall"; discrete phase: the escape condition is used at the inlet and outlet of the DPM model, and the rebound condition is used on the wall, and the initial velocity of the particles is the same as the fluid inlet velocity.

[0037] Furthermore, the construction method of the intelligent solver described in the seventh step is:

[0038] 7.1 Input the physical parameters of coal sample: particle size, inlet velocity, particle density, using three-factor three-level orthogonal method;

[0039] 7.2 The model built in the fifth step simulates the relationship between pipeline erosion and factors such as particle size, inlet velocity, and particle density;

[0040] 7.3 Based on the numerical results, a second-order multi-response surface model is established to generate an intelligent algorithm; the optimal model is obtained through response surface fitting, and the predicted response of the response surface model is compared with the simulation results of the model built in the five steps to verify the correctness of the response surface model; finally, it is input into the intelligent solver and converted into a digital model to connect the simulation of the model built in the five steps with the digital twin system.

[0041] The advantages of the present invention are:

[0042] The present invention performs CFD simulation according to actual engineering data, generates intelligent algorithms through intelligent solvers, connects digital twin systems to respond to changes in real-time data, and completes the prediction of pipeline erosion through decision analysis of real-time data. At present, CFD numerical simulation can well display the morphology and position of pipeline erosion, but the simulation time is too long and cannot meet the needs of changes in real-time pipeline data; while the digital twin system can meet the needs of changes in real-time data, but the accuracy of erosion state simulation is not high. Therefore, the core of the present invention is to connect CFD numerical simulation with the digital twin system, use intelligent solvers to generate intelligent algorithms, and complex physical behaviors can be monitored as the real process progresses, so as to respond to changes in real-time input data, which not only meets the needs of calculating real-time data, but also can improve the simulation accuracy of digital twins, so that the constructed digital twins can be quickly applied to pipeline erosion prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a system structure flow chart of the present invention.

[0044] Figure 2 It is a schematic diagram of the erosion morphology of a pipeline elbow simulated by CFD in an example of the present invention.

[0045] Figure 3 This is the relationship diagram between the inlet gas velocity and erosion rate of the elbow. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0047] Example 1.

[0048] A method for predicting and controlling erosion of coal gasifier pipelines is used to predict and prevent pipeline erosion caused by the collision of syngas and ash-containing solid particles on the inner wall of the pipe at the outlet of the coal gasifier. The real-time data of the operation process of the coal gasifier outlet pipeline is analyzed using digital twin technology, and the CFD system is used to simulate the pipeline. The pipeline erosion is monitored and predicted. By controlling the flow direction of solid particles, pipeline erosion is reduced, and the transportation safety of the coal gasifier outlet pipeline is improved.

[0049] The digital twin system structure flow chart of the present invention is as follows: Figure 1 As shown in the figure, the prediction and adjustment method of coal gasifier pipeline erosion is divided into four layers. The first layer is the perception layer, which is described in the first step; the second layer is the monitoring layer, which is described in the second step; the third layer is the digital twin computing layer, which is described in the third to sixth layers; the fourth layer is the decision-making layer, which is described in the seventh to tenth steps.

[0050] A method for predicting and controlling erosion of a coal gasifier pipeline comprises the following steps:

[0051] The first step is to build a cloud database to store the input data, and to store, analyze, and make decisions on the simulated data later. The basic data structure of the cloud database is as follows:

[0052]

[0053]

[0054] The second step is to collect particle phase and gas phase signals:

[0055] 2.1 Use the IoT-connected velocity meter and density meter to collect the data of the dense phase of coal particles in the coal transportation pipeline, that is, the mass flow rate of particles;

[0056] q m =v*A*c*ρ p

[0057] ρ=ρ p *c+(1-c)*p g

[0058] Where q m is the mass flow rate of particles; v is the particle velocity; A is the pipe cross-sectional area; c is the particle concentration; ρ p represents the particle density; ρ represents the density measured by the densitometer; ρ g Indicates particle concentration;

[0059] 2.2 For the dilute coal particles at the outlet pipe of the gasifier, arrange an optical pulsation probe to measure the particle size, concentration and velocity data of the dilute coal particles in real time using the principle of optical pulsation transmission signal, and input the collected data into the digital twin system;

[0060] 2.3 The gas phase velocity is approximately the solid phase velocity. The extinction principle is to use a parallel light beam to pass through the medium area of ​​the particles. The random changes in the number and size of particles entering and leaving the measurement volume cause the fluctuation of the transmitted light. The statistical characteristics of the particles in the measurement area are associated with the pulsation of the transmitted light intensity. The average size of the particles and the particle number concentration can be obtained by collecting and analyzing them through the sensor.

[0061] The third step is modeling and meshing. Use SolidWorks software to build a pipeline model according to the drawing size and divide the mesh. Due to the special flow characteristics of the elbow, the mesh needs to be encrypted at the elbow.

[0062] The fourth step is to set up multiple monitoring points on the built model to monitor and record the erosion rate at the location;

[0063] Step 5: Set up the numerical calculation model and call the model parameters stored in the database; update the boundary conditions according to the data obtained in step 2; the erosion of the elbow can be simulated by combining the particle-wall rebound model and the E / CRC erosion model:

[0064] ER=C(BH) -0.59 F s V p F(θ)

[0065]

[0066] e n =0.993-1.76α+1.56α 2 -0.49α 3

[0067] e t =0.998-1.66α+2.11α 2 -0.67α 3

[0068] Where, BH represents the Brinell hardness of the pipe material; F s Represents the shape factor of the particle, where sharp particles are F s =1.0; semicircular particles are F s =0.53; Full-circle particle F s =0.2; V p represents the particle velocity; F(θ) represents the function of the impact angle; θ is the particle incident angle; n and C are empirical constants, which are 2.41 and 2.17×10-7 respectively; e n and e t are the restitution coefficients of the normal velocity component and the tangential velocity component, respectively; α is the particle incident angle;

[0069] Specifically: the volume fraction of solid particles in gas-solid two-phase flow is small, so the discrete phase model (DPM) is used for calculation; for the flow field analysis of gas-solid two-phase flow, the Eulerian-Lagrangian method is used to solve the Navier-Stokes equation in the Eulerian coordinate system to obtain the gas continuous phase flow field, and the particle trajectory of the discrete phase particles is calculated in the Lagrangian coordinate system; the numerical simulation adopts the standard k-ε turbulence model; the continuous phase equation sets the gas phase control equation and the turbulence equation, and the solution of the particle trajectory is obtained by integrating the particle motion equation in the Lagrangian coordinate system; when solving the particle motion trajectory and erosion rate, it is assumed that: I. The incident particles are independent of each other, and the collision between particles and the fragmentation of particles are not considered; II. The pipeline deformation caused by particle collision is not considered;

[0070]

[0071] In the formula represent drag force, buoyancy force, pressure gradient force and added mass force respectively;

[0072]

[0073]

[0074]

[0075]

[0076]

[0077] In the formula, represents the velocity of the fluid; is the velocity of the particle; ρ represents the density of the fluid; ρ p Represents the density of the particle; Re is the relative Reynolds number, C D is the drag coefficient; C VM Represents a virtual quality factor with a value of 0.5; the boundary conditions are set as follows: continuous phase: the standard k-ε turbulence model is used for fluid calculation, and the standard wall function is used for the near-wall area; the velocity inlet boundary condition is used at the inlet, and the free outflow boundary is used at the outlet; the tube wall is the wall boundary, and the wall boundary is set as a "no-slip wall"; discrete phase: the DPM model uses the escape condition at the inlet and outlet, and the rebound condition is used on the wall, and the initial velocity of the particles is the same as the fluid inlet velocity.

[0078] The sixth step is to simulate with the data transmitted physically. During the simulation process, optimization iterations are continuously performed to finally obtain an applicable erosion model. The flow field analysis, particle tracking and erosion calculation are performed on the simulation of solid particle erosion.

[0079] Step 7: Upload the simulated data of the model built in step 5 to the terminal network, and use the intelligent solver to connect the model built in step 5 with the digital twin system;

[0080] The core of the present invention is to combine the model built in the five steps with the digital twin system, accurately display the pipeline erosion information through the model built in the five steps, and connect the digital twin system to quickly display the real-time status of pipeline erosion. Figure 3 Erosion morphology of 90° and 135° pipe elbows in the model built in the fifth step;

[0081] Among them, the construction method of the intelligent solver is:

[0082] 7.1 Input the physical parameters of coal sample: particle size, inlet velocity, particle density, using three-factor three-level orthogonal method;

[0083] 7.2 The model built in the fifth step simulates the relationship between pipeline erosion and factors such as particle size, inlet velocity, and particle density;

[0084] 7.3 Based on the numerical results, a second-order multi-response surface model is established to generate an intelligent algorithm; the optimal model is obtained through response surface fitting, and the predicted response of the response surface model is compared with the simulation results of the model built in the five steps to verify the correctness of the response surface model; finally, it is input into the intelligent solver and converted into a digital model to connect the simulation of the model built in the five steps with the digital twin system.

[0085] Step 8: Analyze the factors affecting the pipeline erosion rate based on the erosion information and summarize the erosion rate change curve; comprehensively study the pipeline diameter, bend ratio, pipeline bending angle, pipeline orientation, particle diameter, particle flow rate and particle flow rate, and conclude that the bend ratio is the most significant factor affecting the maximum pipeline erosion rate. Therefore, in order to predict the pipeline erosion position, the relationship between the particle inlet velocity and the erosion rate under different bend ratios is mainly obtained;

[0086] In the ninth step, the formula obtained in the eighth step is input into the digital twin system, connected with the measured data obtained in the second step to calculate the erosion rate and predict the erosion location;

[0087] The tenth step is to take protective measures for the predicted erosion locations. When solid particles collide with the wall and cause pipeline erosion exceeding the threshold, start the acoustic field control to change the movement trajectory of the particles and alleviate the erosion of the solid wall.

[0088] Among them, sound field control is an effective means to reduce pipeline erosion. The sound field can be used to make solid particles complete the agglomeration effect and be easy to remove. The principle is that under the action of the sound field, the speed of the gas medium increases rapidly, which leads to an increase in the direct relative movement speed of the particles, forming an agglomeration effect. However, if the sound pressure level is too high, the agglomerates are prone to separation, so it is necessary to control the sound pressure level at an optimal balance between agglomeration and separation. Experiments have shown that for solid particles around 10μm, low frequency (500-3000Hz) is more effective.

[0089] In summary, the present invention discloses a method for predicting and controlling erosion of a coal gasifier pipeline. By combining CFD numerical simulation with a digital twin system, the system can present real-time data on the operating status of the pipeline throughout its life cycle, analyze and simulate the data of the coal gasifier outlet pipeline itself and the operating process, monitor and protect against pipeline erosion, and improve the safety of the coal gasifier outlet pipeline.

[0090] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for predicting and controlling erosion of a coal gasifier pipeline, characterized in that: The following steps are involved: The first step is to build a cloud database to store input data and store, analyze, and make decisions on the simulated data later; The second step is to collect particle phase and gas phase signals: 2.1 Use the speed meter and density meter connected by the Internet of Things to collect the data of the dense phase of coal particles in the coal transportation pipeline, that is, the mass flow rate of particles; what m =v*A*c*ρ p p=p p *c+(1-c)*p g Where q m is the mass flow rate of particles; v is the particle velocity; A is the pipe cross-sectional area; c is the particle concentration; ρ p represents the particle density; ρ represents the density measured by the densitometer; ρ g Indicates gas density; 2.2 For the dilute coal particles at the outlet pipe of the gasifier, arrange an optical pulsation probe to measure the particle size, concentration and velocity data of the dilute coal particles in real time using the principle of optical pulsation transmission signal, and input the collected data into the digital twin system; 2.3 The gas phase velocity is the solid phase velocity. The extinction principle is to use a parallel light beam to pass through the medium area of ​​the particles. The random changes in the number and size of particles entering and leaving the measurement volume cause the fluctuation of the transmitted light. The statistical characteristics of the particles in the measurement area are associated with the pulsation of the transmitted light intensity. The data are collected and analyzed by the sensor to obtain the average size of the particles and the particle number concentration. The third step is modeling and meshing. Use SolidWorks software to build a pipeline model according to the drawing size and divide the mesh. Due to the special flow characteristics of the elbow, the mesh needs to be encrypted at the elbow. The fourth step is to set up multiple monitoring points on the built model to monitor and record the erosion rate at the location; Step 5: Set up the numerical calculation model and call the model parameters stored in the database; update the boundary conditions according to the data obtained in step 2; the erosion of the elbow is simulated by combining the particle-wall rebound model and the E / CRC erosion model: ER=C(BH) -0.59 F s V p F(θ) e n ＝0.993-1.76α+1.56α 2 -0.49α 3 e t ＝0.998-1.66α+2.11α 2 -0.67α 3 Where, BH represents the Brinell hardness of the pipe material; F s Represents the shape factor of the particle, where sharp particles are F s =1.0; semicircular particles are F s =0.53; Full-circle particle F s =0.2; V p represents the particle velocity; F(θ) represents the function of the impact angle; θ is the particle incident angle; n and C are empirical constants, which are 2.41 and 2.17×10-7 respectively; e n and e t are the restitution coefficients of the normal velocity component and the tangential velocity component, respectively; α is the particle incident angle; The sixth step is to simulate with the data transmitted physically. During the simulation process, optimization and iteration are continuously performed to finally obtain the applied erosion model. The flow field analysis, particle tracking and erosion calculation are performed for the simulation of solid particle erosion. Step 7: Upload the simulated data of the model built in step 5 to the terminal network, and use the intelligent solver to connect the model built in step 5 with the digital twin system; Step 8: Analyze the factors affecting the pipeline erosion rate based on the erosion information and summarize the erosion rate change curve; comprehensively study the pipeline diameter, bend ratio, pipeline bending angle, pipeline orientation, particle diameter, particle flow rate and particle flow rate, and conclude that the bend ratio is the most significant factor affecting the maximum pipeline erosion rate. Therefore, in order to predict the pipeline erosion position, the relationship between the particle inlet velocity and the erosion rate under different bend ratios is obtained; Step 9: Input the formula obtained in step 8 into the digital twin system, connect it with the measured data obtained in step 2 to calculate the erosion rate and predict the erosion location; The tenth step is to take protective measures for the predicted erosion locations. When solid particles collide with the wall and cause pipeline erosion exceeding the threshold, start the acoustic field control to change the movement trajectory of the particles and alleviate the erosion of the solid wall.

2. A coal gasifier pipeline erosion prediction and control method according to claim 1, characterized in that: The setting of the numerical calculation model described in the fifth step, calling the model parameters stored in the database, specifically includes the following steps: the solid particle volume fraction in the gas-solid two-phase flow is small, so the discrete phase model is used for calculation; for the gas-solid two-phase flow field analysis, the Eulerian-Lagrangian method is used to solve the Navier-Stokes equation in the Eulerian coordinate system to obtain the gas continuous phase flow field, and the particle trajectory of the discrete phase particles is calculated in the Lagrangian coordinate system; the numerical simulation adopts the standard k-ε turbulence model; the continuous phase equation sets the gas phase control equation and the turbulence equation, and the solution of the particle trajectory is obtained by integrating the particle motion equation in the Lagrangian coordinate system; when solving the particle motion trajectory and erosion rate, it is assumed that: I. The incident particles are independent of each other, and the collision between particles and the fragmentation of particles are not considered; II. The pipeline deformation caused by particle collision is not considered; In the formula represent drag force, buoyancy force, pressure gradient force and added mass force respectively; In the formula, represents the velocity of the fluid; is the velocity of the particle; ρ represents the density of the fluid; ρ p Represents the density of the particle; Re is the relative Reynolds number, C D is the drag coefficient; C VM Represents a virtual quality factor with a value of 0.5; the boundary conditions are set as follows: continuous phase: the standard k-ε turbulence model is used for fluid calculation, and the standard wall function is used for the near-wall area; the velocity inlet boundary condition is used at the inlet, and the free outflow boundary is used at the outlet; the tube wall is the wall boundary, and the wall boundary is set as "no-slip wall"; discrete phase: the escape condition is used at the inlet and outlet of the DPM model, and the rebound condition is used on the wall, and the initial velocity of the particle is the same as the fluid inlet velocity.

3. A coal gasifier pipeline erosion prediction and control method according to claim 1, characterized in that: The construction method of the intelligent solver described in step 7 is: 7.1 Input the physical parameters of coal sample: particle size, inlet velocity, particle density, using three-factor three-level orthogonal method; 7.2 The model built in the fifth step simulates the relationship between pipeline erosion and particle size, inlet velocity, and particle density; 7.3 Based on the numerical results, a second-order multi-response surface model is established to generate an intelligent algorithm. The optimal model is obtained through response surface fitting, and the predicted response of the response surface model is compared with the simulation results of the model built in the fifth step to verify the correctness of the response surface model. Finally, it is input into the intelligent solver and converted into a digital model to connect the simulation of the model built in the fifth step with the digital twin system.

Images