Simulation-based multi-stage segmented spray drying joint modeling simulation parameter optimization method and system, terminal and storage medium

Abstract

The invention relates to the technical field of spray drying, and particularly provides a simulation-based multi-stage segmented spray drying joint modeling simulation parameter optimization method and system, a terminal and a storage medium, and the method comprises the steps: building a three-dimensional model of a drying tower and a fluidized bed by using SOLIDWORKS; importing the three-dimensional models of the drying tower and the fluidized bed into FLUENT, establishing a fluid dynamic simulation model, and setting initial parameters of the three-dimensional models of the drying tower and the fluidized bed in the fluid dynamic simulation model; response surface test design is conducted on the initial parameters through DESIGN-EXPERT, a test design optimization scheme is obtained, and the test design optimization scheme comprises test design optimization parameters; performing finite element simulation on each group of test design optimization parameters by using FLUENT to obtain each group of simulation data; and performing response surface analysis on each group of simulation data by using DESIGN-EXPERT to obtain an optimal parameter combination mode, and completing optimization design. According to the method, modeling, simulation and optimization are integrated, a standardized operation process is formed, and the influence of human intervention on a result is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of spray drying, and particularly relates to a simulation-based multi-stage segmented spray drying combined modeling and simulation parameter optimization method, system, terminal, and storage medium. Background Art

[0002] Spray drying is an important technology widely used in fields such as food, medicine, and chemical industry. Its core is to spray liquid materials into mist-like particles, which are quickly dried into powder or granular products after contacting with hot air. This method can achieve efficient water removal while maintaining the properties of the materials, so it is widely used in the production of easily soluble and storable dry powder products. There are many factors affecting the spray drying effect, and the interaction relationships between these factors are complex and many influencing factors are difficult to directly measure. Currently, research is mainly carried out through methods such as engineering practice and single-factor test methods.

[0003] However, there are still some deficiencies in the existing technologies. First, most of the existing technologies rely on a single software platform, and these tools have limitations in dealing with complex three-dimensional geometric models and multi-physics field coupling problems. For example, the modeling of complex equipment usually requires a large amount of manual operations and is difficult to complete efficiently. At the same time, the process of modifying and optimizing the geometric model is rather cumbersome and cannot flexibly adapt to different working conditions. Second, the existing optimization methods mainly rely on the single analysis of experimental data or simulation results, and have insufficient ability for collaborative optimization of multi-parameters and multi-working conditions, making it difficult to achieve a global optimal design. Third, simulation analysis often requires multiple rounds of iterative calculations, and the entire process requires manual intervention, resulting in an extended R & D cycle and low efficiency. In the field of spray drying, complex phenomena such as heat transfer, mass transfer, and airflow movement make the simulation accuracy crucial, and it is difficult for the existing technologies to balance accuracy and efficiency. Summary of the Invention

[0004] Aiming at the problems existing in the prior art, such as relying on a single software platform, cumbersome modification and optimization of geometric models, manual intervention required during simulation analysis, and low efficiency, the present invention provides a simulation-based multi-stage segmented spray drying combined modeling and simulation parameter optimization method, system, terminal, and storage medium. By using SOLIDWORKS software to establish a three-dimensional geometric model and importing the three-dimensional geometric model into FLUENT software to establish a fluid mechanics simulation model, and using Design expert to optimize the parameters of the fluid mechanics model in FLUENT software to solve the above technical problems.

[0005] In the first aspect, the present invention provides a simulation-based multi-stage segmented spray drying combined modeling and simulation parameter optimization method, including: Using SOLIDWORKS to establish three-dimensional models of a drying tower and a fluidized bed, and saving them as editable files; Import the 3D models of the drying tower and the fluidized bed into FLUENT to establish a hydrodynamic simulation model, and set the initial parameters of the 3D models of the drying tower and the fluidized bed in the hydrodynamic simulation model; Use DESIGN-EXPERT to conduct a response surface experimental design for the initial parameters to obtain an experimental design optimization scheme, where the experimental design optimization scheme includes experimental design optimization parameters; Use FLUENT to perform finite element simulations on each group of experimental design optimization parameters to obtain each group of simulation data; Use DESIGN-EXPERT to conduct a response surface analysis on each group of simulation data to obtain the best parameter combination method and complete the optimization design.

[0006] Furthermore, import the 3D models of the drying tower and the fluidized bed into FLUENT to establish a hydrodynamic simulation model, and set the initial parameters of the 3D models of the drying tower and the fluidized bed in the hydrodynamic simulation model, including: Use Fluent software to set the initial parameters of the Energy energy source term, DPM model, nozzle type, number of streams, feed temperature, flow rate, and diameter.

[0007] Furthermore, use Fluent software to set the initial parameters of the Energy energy source term, DPM model, nozzle type, number of streams, feed temperature, flow rate, and diameter, including: Use Fluent software to set the Energy energy source term, where the Energy energy source term includes water vapor, liquid feed, and particles; Open the option box of the DPM model in Fluent software, click on unsteady particle tracking, set the time step size to 0.001 s, set the DPM model iteration interval to 20, set the physical model to fragmentation, close the option of "considering sub-particles in the same tracking step", and set the parallel method to hybrid to complete the setting of the DPM model; Use Fluent software to set the nozzle type of the injection source to cone, set the number of streams to 180, set the feed temperature to 293.15 K, set the cone angle size to 30°, set the total flow rate to 0.005 kg / s, and set the diameter to 5 mm; Use Fluent software to select the SIMPLEC algorithm in the pressure-based coupling solver for solution, set the number of Skewness Correction to 1, and set the time step to 0.005 s.

[0008] Furthermore, establish the 3D models of the drying tower and the fluidized bed, including: Using SOLIDWORKS software, a cylindrical-conical spray drying tower is established. A pressure nozzle position is set at the top of the cylinder of the spray drying tower, and an outlet position is set at the bottom of the cone of the spray drying tower. A first pressure outlet Outlet 1 is also set at the top of the cylinder of the spray drying tower, which is used to discharge part of the gas generated by the slight negative pressure in the tower during the spray drying process. A first inlet Inlet 1 is set on the left side of the cylinder of the spray drying tower, which is used to input the first dry hot air; Using SOLIDWORKS software, a fluidized bed is established. An inlet position is set at the upper left end of the fluidized bed, an inlet position is set at the lower left end, an outlet exhaust position Outlet 2 is set at the upper end, which is used to discharge part of the waste gas, and an outlet discharge position Outlet3 is set at the right end, which is used to output the dried material. The lower left inlet position Inlet 2 is used to input the dry hot air to perform secondary drying when the material particles enter the fluidized bed; Connect the upper left inlet position of the fluidized bed to the outlet position set at the bottom of the cone of the spray drying tower through an airtight feeder.

[0009] Furthermore, set the initial parameters of the three-dimensional models of the drying tower and the fluidized bed in the hydrodynamic simulation model, including: Using Fluent software, set Outlet 1 to -110 Pa, set Outlet 2 and Outlet 3 as outflow boundaries, and do not set parameter constraints; Set the surfaces of the drying tower and the fluidized bed as the wall wall, do not set the slip condition, set the component diffusion to 0, set the boundary condition of the wall wall as "reflection", set the reflection angle to be determined by the reflection coefficient, set the normal direction to 0.9, set the tangential direction to 0.5, and set the ambient temperature to 293.15 K.

[0010] Furthermore, use DESIGN-EXPERT to conduct a response surface experimental design on the initial parameters to obtain an experimental design optimization plan. The experimental design optimization plan includes experimental design optimization parameters, including: According to engineering practice verification, the experimental design optimization parameters include the temperature of the first dry hot air, the feed flow rate, and the temperature of the second dry hot air. Among them, the temperature of the first dry hot air includes the first low temperature, the first medium temperature, and the first high temperature, the feed flow rate includes the low flow rate, the medium flow rate, and the high flow rate, and the temperature of the second dry hot air includes the second low temperature, the second medium temperature, and the second high temperature; Use the Box-Behnken experimental design optimization method to determine the outlet air temperature as the response value, with the unit of °C.

[0011] Furthermore, use DESIGN-EXPERT to conduct a response surface analysis on each group of simulation data to obtain the best parameter combination method and complete the optimization design, including: Numerically encode each group of simulation data. Encode the first low temperature, low flow rate, and second low temperature as -1, encode the first medium temperature, medium flow rate, and second medium temperature as 0, and encode the first high temperature, high flow rate, and second high temperature as 1; Input the encoded values into the response value box and click Analysis for data analysis; Solve for the response target value to obtain the best parameter combination after each factor takes the optimal value.

[0012] In a second aspect, the present invention provides a simulation-based multi-stage segmented spray drying combined modeling and simulation parameter optimization system, including: A three-dimensional model establishment module for using SOLIDWORKS to establish a three-dimensional model of the drying tower and the fluidized bed and saving it as an editable file; A fluid dynamics simulation model establishment module for importing the three-dimensional models of the drying tower and the fluidized bed into FLUENT to establish a fluid dynamics simulation model and setting the initial parameters of the three-dimensional models of the drying tower and the fluidized bed in the fluid dynamics simulation model; A scheme design module for using DESIGN-EXPERT to perform a response surface experimental design on the initial parameters to obtain an experimental design optimization scheme, where the experimental design optimization scheme includes experimental design optimization parameters; A simulation module for using FLUENT to perform finite element simulations on each group of experimental design optimization parameters to obtain each group of simulation data; A response surface analysis module for using DESIGN-EXPERT to perform response surface analysis on each group of simulation data to obtain the best parameter combination method and complete the optimization design.

[0013] In a third aspect, a terminal is provided, including: A processor and a memory, where, The memory is used to store a computer program, The processor is used to call and run the computer program from the memory, so that the terminal executes the method of the above terminal.

[0014] In a fourth aspect, a computer storage medium is provided. Instructions are stored in the computer-readable storage medium, and when it runs on a computer, it causes the computer to execute the methods described in the above aspects.

[0015] The beneficial effects of the present invention are as follows. The method, system, terminal and storage medium for optimizing simulation parameters of multi-stage segmented spray drying based on simulation provided by the present invention perform three-dimensional geometric modeling through SOLIDWORKS software, establish an accurate spray drying equipment model, import it into FLUENT for fluid dynamics simulation analysis, and use DESIGN-EXPERT to conduct response surface experimental design on the initial parameters. This method avoids the repeated construction and verification work of traditional experimental methods and greatly shortens the R & D cycle.

[0016] By integrating modeling, simulation and optimization into one, the present application forms a standardized operation process, reducing the influence of human intervention on the results. The FLUENT simulation file can run automatically under the optimized parameters, thus realizing efficient simulation and optimization of the spray drying process. In addition, the design principle of the present invention is reliable and the process is clear, having a very broad application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a schematic flow chart of the method according to an embodiment of the present invention.

[0019] Figure 2 It is a schematic block diagram of the system according to an embodiment of the present invention.

[0020] Figure 3 It is a schematic structural diagram of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] In order to enable those skilled in the art of the present technology to better understand the technical solutions in the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field of the present invention. The terms used in the description of the present invention in this specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0023] The simulation-based multi-stage segmented spray drying joint modeling and simulation parameter optimization method provided by the embodiments of the present invention is executed by a computer device. Correspondingly, the simulation-based multi-stage segmented spray drying joint modeling and simulation parameter optimization system runs in the computer device.

[0024] Figure 1 It is a schematic flowchart of the method of an embodiment of the present invention. Among them, Figure 1 The execution subject can be a simulation-based multi-stage segmented spray drying joint modeling and simulation parameter optimization system. According to different requirements, the order of the steps in this flowchart can be changed, and some can be omitted.

[0025] For the convenience of understanding the present invention, the principle of the simulation-based multi-stage segmented spray drying joint modeling and simulation parameter optimization method of the present invention is described below in combination with the process of managing pluggable module materials in the embodiments to further describe the simulation-based multi-stage segmented spray drying joint modeling and simulation parameter optimization method provided by the present invention.

[0026] Specifically, as Figure 1 shown, the simulation-based multi-stage segmented spray drying joint modeling and simulation parameter optimization method includes: S1. Use SOLIDWORKS to establish three-dimensional models of the drying tower and the fluidized bed and save them as editable files. The editable files can be XT files.

[0027] Use Fluent software to set the initial parameters of the Energy energy source term, DPM model, nozzle type, number of streams, feed temperature, flow rate, and diameter, including steps S11 - S13.

[0028] S11. Use SOLIDWORKS software to establish a cylindrical-conical spray drying tower, set a pressure nozzle position at the top of the cylinder of the spray drying tower, set an outlet position at the bottom of the cone of the spray drying tower, and also set a first pressure outlet Outlet 1 at the top of the cylinder of the spray drying tower for discharging part of the gas generated by the micro-negative pressure in the tower during the spray drying process, and set a first inlet Inlet 1 on the left side of the cylinder of the spray drying tower for inputting the first drying hot gas.

[0029] S12. Use SOLIDWORKS software to establish a fluidized bed, set an inlet position at the upper left end, an inlet position at the lower left end, set an outlet exhaust position Outlet 2 at the upper end for discharging part of the waste gas, set an outlet discharge position Outlet 3 at the right end for outputting the dried material, and set an inlet position Inlet 2 at the lower left end for inputting the drying hot gas to perform secondary drying when the material particles enter the fluidized bed.

[0030] S13. Connect the left upper end inlet position of the fluidized bed with the outlet position set at the conical bottom of the spray drying tower through an air lock.

[0031] Specifically, this application focuses on the elaboration of the physical three-dimensional model. According to the actual working conditions of the drying tower and the fluidized bed, the model assembly drawing is drawn by SOLIDWORKS software, and appropriate simplification is carried out according to the simulation requirements. The naming selection operation is performed by Design expert software, and then the mesh is divided through the mesh module of the ANSYS collection.

[0032] The cylinder-conical spray drying tower is a relatively common geometric shape of the spray drying tower at present. In the present invention, the simulation calculation domain selects the internal flow field of the spray drying tower, and the physical models of the spray drying tower and the fluidized bed are established according to the actual machine model.

[0033] The pressure nozzle of the drying tower is located at the top of the drying tower, and the hot air enters the drying tower through the annular horizontal inlet on the upper side. The outlet is located at the conical bottom. The left upper end inlet of the fluidized bed is connected with the outlet of the spray drying tower through an air lock. The right end outlet discharges materials, and the upper end outlet exhausts gas. The hot air for baking the secondary material particles enters at the left lower end inlet, and the upper end of the fluidized bed exhausts gas. According to the three-dimensional modeling of the drying tower and the fluidized bed, a fluid domain model is established. And when carrying out the numerical simulation work, in order to converge the simulation solution calculation, the following aspects are mainly simplified in the construction of the fluid model: (1) The interval between the pressure nozzle and the main body of the spray drying tower is very small, and the spacing between the two is ignored during modeling.

[0034] (2) Ignore the influence of the angle distribution baffle at the hot air inlet on the hot air at the inlet.

[0035] (3) In order to ensure convergence, the air lock is satisfied through the setting of boundary conditions.

[0036] S2. Import the three-dimensional models of the drying tower and the fluidized bed into FLUENT to establish a computational fluid dynamics simulation model, and set the initial parameters of the three-dimensional models of the drying tower and the fluidized bed in the computational fluid dynamics simulation model.

[0037] Set the Energy source term using Fluent software. The Energy source term includes water vapor, liquid feed, and particles. Open the option box of the DPM model in the Fluent software, click on unsteady particle tracking, set the time step size to 0.001 s, set the DPM model iteration interval to 20, set the physical model to fragmentation, turn off the option of "considering sub-particles in the same tracking step", set the parallel method to hybrid, and complete the setting of the DPM model. Use Fluent software to set the nozzle type of the injection source to cone, set the flow number to 180, set the feed temperature to 293.15 K, set the cone angle size to 30°, set the total flow rate to 0.005 kg / s, and set the diameter to 5 mm. Use Fluent software to select the SIMPLEC algorithm in the pressure-based coupled solver for solving, set the number of Skewness Correction to 1, and set the time step to 0.005 s.

[0038] It also includes: Use Fluent software to set Outlet 1 to -110 Pa, set Outlet 2 and Outlet 3 as outflow boundaries without setting parameter constraints. Set the surfaces of the drying tower and the fluidized bed as wall surfaces without setting slip conditions, set the component diffusion to 0, set the boundary condition of the wall surface to "reflection", set the reflection angle to be determined by the reflection coefficient, set the normal direction to 0.9, set the tangential direction to 0.5, and set the ambient temperature to 293.15 K.

[0039] Specifically, the simulation study of the present invention is based on the following steps: By separately changing the material injection pressure and the drying hot air temperature, complete the optimization design of the parameters during the spray drying process. Secondly, under the same spray drying simulation parameters, conduct simulation studies on a single drying tower and a drying tower combined with a fluidized bed (multi-stage drying) respectively, analyze the advantages of multi-stage drying, and optimize the process coefficients of the pressure spray drying of soy protein to meet the invention indicators.

[0040] The DPM model of Fluent can simulate particles or droplets. When different particle types are selected, different Laws will be activated. For the present invention, the Droplet particle type is selected to describe the relationships of heating, evaporation, and boiling during the spray evaporation process. The Droplet particles need to activate the following laws: The Inert Heating or Cooling law, which is applicable when the particle temperature is lower than the vaporization temperature or when the particles have not completely volatilized, uses a heat balance model to relate the particle temperature, convective heat transfer, and radiative absorption / emission at the particle surface. In this law, the particles / droplets do not exchange mass with the continuous phase and do not participate in any chemical reactions. Therefore, the Droplet Vaporization law is added to predict the evaporation of discrete-phase droplets, starting when the droplet temperature reaches the evaporation temperature and continuing until the droplet reaches the boiling point or until all the volatile components in the droplet have volatilized. Once vaporization begins (when the droplet reaches the temperature threshold), it will continue to vaporize (even if the droplet temperature drops below). During this period, the droplet will always follow this law, but evaporation cannot be predicted. When the droplet temperature reaches its boiling point, droplet vaporization is predicted by the boiling rate, and at this time, the Droplet Boiling law is followed.

[0041] First, turn on the Energy source term. The spray drying process is a typical heat and mass transfer process. Second, turn on component transport. Three source terms are set, namely water vapor, feed liquid, and particles. Open the DPM model option box and make the following setting adjustments. Turn on unsteady particle tracking, set the time step size to 0.001 s, turn on the interaction with the continuous phase, set the DPM iteration interval to 20, select "fragmentation" for the physical model, and to reduce the difficulty of computational convergence, turn off "consider sub-particles in the same tracking step". Select "hybrid" for the parallel method. Complete the settings of the DPM model.

[0042] To create a spray source, first determine the nozzle type. Fluent has a dedicated incidence model for droplet spray simulation. Different from other conventional injector models that require specifying the particle size, position, and initial velocity, the spray injector does not need to specify these parameters. The atomization model uses the physical parameters of the nozzle (such as nozzle diameter and mass flow rate) to calculate the initial droplet size, velocity, and position. In a real spray simulation, the spatial dispersion angle and release time of the droplets must be randomly distributed. For non-atomizer type particle incidence in Fluent, all droplets are injected into the computational domain along a fixed trajectory at the beginning of the time step. The atomizer model uses random trajectories to achieve random distribution. Select a cone type nozzle, set the flow number to 180, the feed temperature to 293.15 K, the cone angle to 30°, the total flow rate to 0.005 kg / s, and the diameter to 5 mm.

[0043] The boundary conditions are set as follows. The named Outlet 1 is set as a pressure outlet. Since the inside of the tower is at a slightly negative pressure during the spray drying process, this outlet is set to -110 Pa to discharge some gas. Inlet 1 is set as a velocity inlet to input the drying hot gas. Outlet 2 / 3 is set as an outflow boundary without other parameter constraints. Among them, Outlet 2 discharges some waste gas, and Outlet 3 outputs the dried completed material. Inlet 2 inputs another set of drying hot gas for secondary drying when the material particles enter the fluidized bed. The Wall is set as a wall with no-slip conditions, the component diffusion is 0, and the convective heat transfer coefficient at room temperature is taken. Considering the heat exchange between the entire system and the environment, the boundary conditions of the DPM model for each wall are set as reflect, that is, the particles are reflected at a certain angle after hitting the wall and then enter the computational domain. The reflection angle is determined by the Reflection Coefficients. The normal direction is set to 0.9 and the tangential direction is set to 0.5. The environmental temperature is 293.15 K. The Surface is set as the exhaust port, only considering the migration of the material and blocking the exchange of the gas continuous phase. The second-order upwind scheme is used for spatial discretization. Finally, the SIMPLEC (Semi-Implicit Method for Pressure-Linked Equations-Consistent) algorithm in the pressure-based coupled solver is selected for solution. The number of Skewness Correction is 1, and the time step is set to 0.005 s.

[0044] S3. Use DESIGN-EXPERT to conduct a response surface experimental design for the initial parameters to obtain an optimized experimental design scheme, and the optimized experimental design scheme includes optimized experimental design parameters.

[0045] According to engineering practice verification, the optimized experimental design parameters include the temperature of the first drying hot gas, the feed flow rate, and the temperature of the second drying hot gas. Among them, the temperature of the first drying hot gas includes the first low temperature, the first medium temperature, and the first high temperature. The feed flow rate includes a low flow rate, a medium flow rate, and a high flow rate. The temperature of the second drying hot gas includes the second low temperature, the second medium temperature, and the second high temperature. Use the Box-Behnken experimental design optimization method to determine the outlet temperature as the response value, with the unit of °C.

[0046] For example, the first low temperature, the first medium temperature, and the first high temperature in the temperature of the first drying hot gas are 140 °C, 160 °C, and 180 °C respectively. The low flow rate, the medium flow rate, and the high flow rate in the feed flow rate are 135 m / s, 165 m / s, and 191 m / s respectively. The second low temperature, the second medium temperature, and the second high temperature in the temperature of the second drying hot gas are 100 °C, 120 °C, and 140 °C respectively.

[0047] S4. Use FLUENT to perform finite element simulations on the optimized parameters of each group of experimental designs to obtain the simulation data of each group.

[0048] Automatically generate an experimental design table through software. The first column of the experimental design table is the experimental run order. Use the FLUENT software to perform simulation tests on the parameters of each group in the experimental design table respectively, and collect the simulation test data of each group.

[0049] S5. Use DESIGN-EXPERT to perform response surface analysis on the simulation data of each group to obtain the best parameter combination method and complete the optimization design.

[0050] Perform numerical coding on the simulation data of each group. Code the first low temperature, low flow rate, and second low temperature as -1, code the first medium temperature, medium flow rate, and second medium temperature as 0, and code the first high temperature, high flow rate, and second high temperature as 1. Enter the coded values into the response value box and click Analysis for data analysis. Solve the response target value to obtain the best parameter combination after each factor takes the optimal value.

[0051] In some embodiments, the simulation-based multi-stage segmented spray drying combined modeling and simulation parameter optimization system may include multiple functional modules composed of computer program segments. The computer programs of each program segment in the simulation-based multi-stage segmented spray drying combined modeling and simulation parameter optimization system can be stored in the memory of a computer device and executed by at least one processor to execute (see Figure 1 description) the functions of simulation-based multi-stage segmented spray drying combined modeling and simulation parameter optimization.

[0052] In this embodiment, according to the functions it performs, the simulation-based multi-stage segmented spray drying combined modeling and simulation parameter optimization system can be divided into multiple functional modules, as Figure 2 shown. The functional modules of system 200 may include: a three-dimensional model establishment module 210, a fluid dynamics simulation model establishment module 220, a simulation analysis module 230, a scheme design module 240, a simulation module 250, and a response surface analysis module. The module referred to in the present invention means a series of computer program segments that can be executed by at least one processor and can complete fixed functions, and are stored in the memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0053] The three-dimensional model establishment module is used to establish a three-dimensional model of the drying tower and the fluidized bed using SOLIDWORKS and save it as an editable file.

[0054] A hydrodynamic simulation model establishment module is used to import the three-dimensional models of the drying tower and the fluidized bed into FLUENT, establish a hydrodynamic simulation model, and set the initial parameters of the three-dimensional models of the drying tower and the fluidized bed in the hydrodynamic simulation model.

[0055] A scheme design module is used to perform response surface experimental design on the initial parameters using DESIGN-EXPERT to obtain an experimental design optimization scheme, and the experimental design optimization scheme includes experimental design optimization parameters.

[0056] A simulation module is used to perform finite element simulation on each group of experimental design optimization parameters using FLUENT to obtain each group of simulation data.

[0057] A response surface analysis module is used to perform response surface analysis on each group of simulation data using DESIGN-EXPERT to obtain the best parameter combination method and complete the optimization design.

[0058] Figure 3 FIG. 13 is a schematic structural diagram of a terminal 300 provided by an embodiment of the present invention, and the terminal 300 can be used to execute the simulation-based multi-stage segmented spray drying joint modeling and simulation parameter optimization method provided by the embodiment of the present invention.

[0059] Among them, the terminal 300 may include: a processor 310, a memory 320, and a communication unit 330. These components communicate through one or more buses. Those skilled in the art can understand that the structure of the server shown in the figure does not constitute a limitation to the present invention. It can be a bus structure, a star structure, and may also include more or fewer components than shown in the figure, or combine some components, or have different component arrangements.

[0060] Among them, the memory 320 can be used to store the execution instructions of the processor 310, and the memory 320 can be implemented by any type of volatile or non-volatile storage terminal or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disc. When the execution instructions in the memory 320 are executed by the processor 310, the terminal 300 can execute some or all of the steps in the above method embodiments.

[0061] The processor 310 is the control center of the storage terminal, connecting various parts of the entire electronic terminal through various interfaces and circuits. By running or executing software programs and / or modules stored in the memory 320, and by invoking data stored in the memory, it performs various functions of the electronic terminal and / or processes data. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions connected together. For example, the processor 310 can include only a central processing unit (CPU). In the embodiments of the present invention, the CPU can be a single arithmetic core or can include multiple arithmetic cores.

[0062] The communication unit 330 is used to establish a communication channel so that the storage terminal can communicate with other terminals. It receives user data sent by other terminals or sends user data to other terminals.

[0063] The present invention also provides a computer storage medium. Among them, the computer storage medium can store a program, and when the program is executed, it can include some or all of the steps in the embodiments provided by the present invention. The storage medium can be a magnetic disk, an optical disc, a read-only memory (ROM), a random access memory (RAM), etc.

[0064] Therefore, for the simulation-based multi-stage segmented spray drying combined modeling and simulation parameter optimization method, system, terminal, and storage medium provided by the present invention, three-dimensional geometric modeling is carried out through SOLIDWORKS software to establish an accurate spray drying equipment model, and it is imported into FLUENT for fluid dynamics simulation analysis. This method avoids the repeated setup and verification work of traditional experimental methods, and greatly shortens the R & D cycle. This application integrates modeling, simulation, and optimization into one, forming a standardized operation process, reducing the influence of human intervention on the results. The FLUENT simulation file can automatically run under the optimized parameters, thereby realizing the efficient simulation and optimization of the spray drying process. The technical effects that can be achieved by this embodiment can be seen in the above description and will not be elaborated here.

[0065] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solutions in the embodiments of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk, or an optical disc, etc., various media that can store program codes, including several instructions for causing a computer terminal (which can be a personal computer, a server, or a second terminal, a network terminal, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0066] For the same or similar parts among the various embodiments in this specification, reference can be made to each other. In particular, for the terminal embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the descriptions in the method embodiments.

[0067] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of systems or modules can be in electrical, mechanical, or other forms.

[0068] The modules described as separate components may or may not be physically separated. The components displayed as modules may or may not be physical modules, that is, they can be located in one place, or they can be distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0069] In addition, in each embodiment of the present invention, the various functional modules can be integrated in a processing module, or each module can exist physically alone, or two or more modules can be integrated in one module.

[0070] Although the present invention has been described in detail by reference to the accompanying drawings and in conjunction with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, those of ordinary skill in the art can make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions should all be within the scope covered by the present invention. / Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be determined by the protection scope of the claims.

Claims

1. A simulation-based multi-stage spray drying joint modeling simulation parameter optimization method, characterized in that: include: Use SOLIDWORKS to build the 3D model of the drying tower and fluidized bed and save it as an editable file; Import the three-dimensional models of the drying tower and the fluidized bed into FLUENT, establish a fluid dynamics simulation model, and set the initial parameters of the three-dimensional models of the drying tower and the fluidized bed in the fluid dynamics simulation model; Perform response surface experimental design on the initial parameters using DESIGN-EXPERT to obtain an experimental design optimization scheme, wherein the experimental design optimization scheme includes experimental design optimization parameters; Use FLUENT to perform finite element simulation on the optimization parameters of each group of experimental design to obtain each group of simulation data; DESIGN-EXPERT was used to perform response surface analysis on each group of simulation data to obtain the best parameter combination and complete the optimization design.

2. The method according to claim 1, characterized in that Import the three-dimensional model of the drying tower and fluidized bed into FLUENT, establish a fluid dynamics simulation model, and set the initial parameters of the three-dimensional model of the drying tower and fluidized bed in the fluid dynamics simulation model, including: Fluent software was used to set the initial parameters of Energy source term, DPM model, nozzle type, flow number, feed temperature, flow rate and diameter.

3. The method according to claim 2, characterized in that Use Fluent software to set the initial parameters of Energy source term, DPM model, nozzle type, flow number, feed temperature, flow rate and diameter, including: Use Fluent software to set Energy source items, where the Energy source items include water vapor, liquid and particles; Open the DPM model option box of Fluent software, click Unsteady Particle Tracking, set the time step size to 0.001s, set the DPM model iteration interval to 20, set the physical model to fragmentation, turn off the "Consider sub-particles in the same tracking step" option, set the parallel method to hybrid, and complete the DPM model settings; Use Fluent software to set the nozzle type of the injection source to cone, the flow number to 180, the feed temperature to 293.15K, the cone angle to 30°, the total flow rate to 0.005kg / s, and the diameter to 5mm; Use Fluent software to select the SIMPLEC algorithm in the pressure-based coupling solver for solving, set the number of Skewness Corrections to 1, and set the time step to 0.005s.

4. The method according to claim 1, characterized in that: Build a 3D model of the drying tower and fluidized bed, including: Using SOLIDWORKS software, a cylindrical-conical spray drying tower is established, a pressure nozzle position is set at the top of the cylinder of the spray drying tower, an outlet position is set at the bottom of the cone of the spray drying tower, a first pressure outlet Outlet 1 is also set at the top of the cylinder of the spray drying tower, which is used to discharge part of the gas generated in the tower due to the slight negative pressure during the spray drying process, and a first inlet Inlet 1 is set on the left side of the cylinder of the spray drying tower, which is used to input the first drying hot gas; Use SOLIDWORKS software to build a fluidized bed, set an inlet position at the upper left end of the fluidized bed, set an inlet position at the lower left end, set an outlet exhaust position Outlet 2 at the upper end to discharge part of the exhaust gas, set an outlet material position Outlet 3 at the right end to output the dried material, and set an inlet position Inlet 2 at the lower left end to input dry hot air, so that the material particles are secondary dried when entering the fluidized bed; The upper left inlet of the fluidized bed is connected to the outlet arranged at the cone bottom of the spray drying tower through an air lock.

5. The method according to claim 2, characterized in that: Set the initial parameters of the 3D model of the drying tower and fluidized bed in the fluid dynamics simulation model, including: Use Fluent software, set Outlet 1 to -110Pa, set Outlet 2 and Outlet 3 as outflow boundaries, and do not set parameter constraints; Set the surface of the drying tower and the fluidized bed to wall, do not set the slip condition, set the component diffusion to 0, set the boundary condition of the wall to "reflection", set the reflection angle to be determined by the reflection coefficient, set the normal to 0.9, set the tangent to 0.5, and set the ambient temperature to 293.15K.

6. The method according to claim 1, characterized in that DESIGN-EXPERT is used to perform response surface experimental design on the initial parameters to obtain an experimental design optimization scheme, wherein the experimental design optimization scheme includes experimental design optimization parameters, including: According to engineering practice verification, the experimental design optimization parameters include the first drying hot gas temperature, the feed flow rate and the second drying hot gas temperature, wherein the first drying hot gas temperature includes a first low temperature, a first medium temperature and a first high temperature, the feed flow rate includes a low flow rate, a medium flow rate and a high flow rate, and the second drying hot gas temperature includes a second low temperature, a second medium temperature and a second high temperature; The Box-Behnken experimental design optimization method was used to determine the outlet air temperature as the response value in °C.

7. The method according to claim 1, characterized in that DESIGN-EXPERT was used to perform response surface analysis on each set of simulation data to obtain the best parameter combination and complete the optimization design, including: Numerical encoding is performed on each group of simulation data, the first low temperature, low flow rate and second low temperature are encoded as -1, the first medium temperature, medium flow rate and second medium temperature are encoded as 0, and the first high temperature, high flow rate and second high temperature are encoded as 1; Enter the coded value into the response value box and click Analysis to perform data analysis; The response target value is solved to obtain the best parameter combination after each factor takes the optimal value.

8. A simulation-based multi-stage spray drying joint modeling simulation parameter optimization system, characterized in that: include: 3D model building module, used to build 3D models of drying tower and fluidized bed using SOLIDWORKS and save them as editable files; A fluid dynamics simulation model building module is used to import the three-dimensional models of the drying tower and the fluidized bed into FLUENT, build a fluid dynamics simulation model, and set the initial parameters of the three-dimensional models of the drying tower and the fluidized bed in the fluid dynamics simulation model; A scheme design module, used for performing response surface experimental design on the initial parameters using DESIGN-EXPERT to obtain an experimental design optimization scheme, wherein the experimental design optimization scheme includes experimental design optimization parameters; The simulation module is used to use FLUENT to perform finite element simulation on each group of experimental design optimization parameters to obtain each group of simulation data; The response surface analysis module is used to use DESIGN-EXPERT to perform response surface analysis on each group of simulation data, obtain the best parameter combination, and complete the optimization design.

9. A terminal, characterized in that: include: A memory for storing a simulation-based multi-stage segmented spray drying joint modeling simulation parameter optimization program; A processor is used to implement the steps of the simulation-based multi-stage spray-drying joint modeling simulation parameter optimization method as described in any one of claims 1 to 7 when executing the simulation-based multi-stage spray-drying joint modeling simulation parameter optimization program.

10. A computer-readable storage medium storing a computer program, characterized in that: The readable storage medium stores a simulation-based multi-stage spray-drying joint modeling simulation parameter optimization program, which, when executed by a processor, implements the steps of the simulation-based multi-stage spray-drying joint modeling simulation parameter optimization method as described in any one of claims 1 to 7.

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