Method, device and equipment for optimizing process parameters of double-pump system, medium and product

Abstract

The invention discloses an optimization method and device for technological parameters of a double-pump system, equipment, a medium and a product, and relates to the field of lifting pump technological parameter optimizing.The method comprises the steps that multiple sets of simulation data of the double-pump system under different working conditions are obtained, the multiple groups of simulation data are obtained by performing flow field fluid simulation on a double-pump system under different process parameter value combinations by using a double-pump system simulation model; constructing a multi-objective function by taking optimal outlet pressure and outlet flow as objectives; and based on the multiple groups of simulation data, solving the multi-objective function by adopting a multi-objective particle swarm optimization algorithm to obtain multiple groups of optimal process parameter value combinations. According to the method and the device, the double-pump system can be accurately controlled, various process parameter values are optimized at the same time, and the system performance is improved.

Description

Technical Field

[0001] The present application relates to the field of optimization of process parameters of lift pumps, and in particular to a method, device, equipment, medium and product for optimizing process parameters of a dual pump system. Background Art

[0002] As a key fluid delivery equipment, lift pumps are widely used in the oil, natural gas, deep sea drilling, chemical industry and other industries. In particular, their important role in deep sea oil and gas exploitation and industrial production cannot be ignored. Since lift pumps need to operate in a complex fluid environment, their performance directly affects the working efficiency and economic benefits of the entire system. Therefore, optimizing the process parameters of lift pumps, such as valve opening angle, valve opening speed, input pressure and input flow rate, has become the key to improving the performance of the pump system.

[0003] Traditional lift pump parameter design methods usually rely on manual experience and experimental data, and it is difficult to obtain the best process parameter combination under multi-variable and multi-objective optimization conditions. Especially in the dual-pump series / parallel system, which involves the interactive influence of multiple optimization parameters, how to accurately control and optimize in a complex fluid flow field has become an urgent problem to be solved. Summary of the invention

[0004] The purpose of this application is to provide a method, device, equipment, medium and product for optimizing the process parameters of a dual pump system, which can achieve precise control of the dual pump system and simultaneously optimize multiple process parameter values, thereby improving system performance.

[0005] To achieve the above objectives, this application provides the following solutions: In a first aspect, the present application provides a method for optimizing process parameters of a dual pump system, comprising: Acquire multiple sets of simulation data of the dual-pump system under different working conditions, wherein the multiple sets of simulation data are obtained by using a dual-pump system simulation model to perform flow field fluid simulation on the dual-pump system under different combinations of process parameter values; wherein the multiple sets of simulation data include each process parameter value combination and the corresponding outlet flow rate and outlet pressure, the different working conditions include dual pumps in parallel and dual pumps in series, and the process parameters include valve opening angle, valve opening speed, inlet pressure and inlet flow rate; Constructing a multi-objective function with the goal of optimizing the outlet pressure and the outlet flow rate; wherein the multi-objective function includes an outlet pressure objective function and an outlet flow objective function; Based on the multiple groups of simulation data, a multi-objective particle swarm optimization algorithm is used to solve the multi-objective function to obtain multiple groups of optimal process parameter value combinations.

[0006] Furthermore, the outlet pressure objective function and the outlet flow objective function are respectively: ; ; in, is the process parameter value combination, is the export flow objective function, is the outlet pressure objective function, and are the outlet flow value and outlet pressure value respectively, and They are the outlet flow target value and outlet pressure target value respectively.

[0007] Furthermore, based on the multiple sets of simulation data, a multi-objective particle swarm optimization algorithm is used to solve the multi-objective function to obtain multiple sets of optimal process parameter value combinations, including: Determining parameter range constraints of each process parameter according to the multiple sets of simulation data; Randomly generate a group of initial particles according to the parameter range constraint, each particle represents a group of process parameter value combinations; Calculating the outlet pressure value and outlet flow value of each particle according to the process parameter value combination; Determine the current optimal position and the global optimal position of the particle according to the outlet pressure value and the outlet flow value of each particle; updating the speed of each particle according to the current best position of the particle and the global best position, and updating the position of each particle according to the updated speed; Determine a non-dominated solution set according to the updated position of the particle, and calculate the crowding distance of each particle in the non-dominated solution set; An optimal solution set is determined based on the crowding distance, and the optimal solution set includes multiple groups of optimal process parameter value combinations.

[0008] Furthermore, the calculation formula of crowding distance is: ; in, For the Particles The crowding distance, and Respectively k The maximum and minimum values ​​of the objective function, the particle and For the sorted particles Adjacent particles, For particles No. k The objective function value, For particles No. k The objective function value.

[0009] Furthermore, the parameter range constraints are: ; in, is the valve opening angle, is the valve opening speed, is the inlet flow rate, is the inlet pressure.

[0010] In a second aspect, the present application provides a device for optimizing process parameters of a dual pump system, comprising: An acquisition module is used to acquire multiple sets of simulation data of the dual-pump system under different working conditions, wherein the multiple sets of simulation data are obtained by using a dual-pump system simulation model to simulate the flow field of the dual-pump system under different combinations of process parameter values; wherein the multiple sets of simulation data include each combination of process parameter values ​​and the corresponding outlet flow rate and outlet pressure, the different working conditions include dual pumps in parallel and dual pumps in series, and the process parameters include valve opening angle, valve opening speed, inlet pressure and inlet flow rate; A construction module is used to construct a multi-objective function with the outlet pressure and the outlet flow being optimized as the objectives; wherein the multi-objective function includes an outlet pressure objective function and an outlet flow objective function; The optimization module is used to solve the multi-objective function based on the multiple groups of simulation data by using a multi-objective particle swarm optimization algorithm to obtain multiple groups of optimal process parameter value combinations.

[0011] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned method for optimizing the process parameters of the dual-pump system.

[0012] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for optimizing the process parameters of the dual-pump system.

[0013] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned method for optimizing the process parameters of the dual-pump system.

[0014] According to the specific embodiments provided in this application, this application discloses the following technical effects: The present application provides a method, device, equipment, medium and product for optimizing the process parameters of a dual-pump system. A dual-pump system simulation model is used to simulate the flow field fluid of the dual-pump system under different combinations of process parameter values ​​to obtain multiple groups of simulation data of the dual-pump system under different working conditions. Based on this, a multi-objective particle swarm optimization algorithm is used to optimize the process parameter values ​​of the dual-pump system with the goal of optimizing the outlet pressure and outlet flow. The scheme uses a dual-pump system simulation model to simulate the working process of the dual-pump system, and can accurately control the dual-pump system. At the same time, a multi-objective particle swarm optimization algorithm is used to optimize the process parameter values. It does not rely on manual experience and experimental data, so as to achieve more precise optimization of the process parameter values ​​of the dual-pump system and improve the performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 This is an application environment diagram of a method for optimizing process parameters of a dual-pump system in one embodiment of the present application; Figure 2 A schematic flow chart of a method for optimizing process parameters of a dual-pump system provided in one embodiment of the present application; Figure 3 A schematic diagram of the connection relationship of a dual pump system model provided in one embodiment of the present application; Figure 4 A schematic diagram of functional modules of a device for optimizing process parameters of a dual-pump system provided in another embodiment of the present application; Figure 5 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0018] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0019] The method for optimizing the process parameters of the dual pump system provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send multiple sets of simulation data of the dual-pump system under different working conditions to the server 104. After the server 104 receives the multiple sets of simulation data of the dual-pump system under different working conditions, for the multiple sets of simulation data of the dual-pump system under different working conditions, the server 104 constructs a multi-objective function based on the multiple sets of simulation data of the dual-pump system under different working conditions, with the outlet pressure and outlet flow as the optimal target, and uses a multi-objective particle swarm optimization algorithm to solve the multi-objective function to obtain multiple sets of optimal process parameter value combinations. The server 104 can feedback the obtained multiple sets of optimal process parameter value combinations to the terminal 102. In addition, in some embodiments, the method for optimizing the process parameters of the dual-pump system can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly process multiple sets of simulation data of the dual-pump system under different working conditions, or the server 104 can obtain multiple sets of simulation data of the dual-pump system under different working conditions from the data storage system and process the multiple sets of simulation data of the dual-pump system under different working conditions.

[0020] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, and tablet computers, etc. The portable wearable device may be a smart watch, a smart bracelet, a head-mounted device, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0021] In an exemplary embodiment, Figure 2 As shown, a method for optimizing the process parameters of a dual pump system is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate, including the following steps 201 to 203. Among them: Step 201, obtaining multiple sets of simulation data of the dual pump system under different working conditions, wherein the multiple sets of simulation data are obtained by using a dual pump system simulation model to perform flow field fluid simulation on the dual pump system under different combinations of process parameter values; wherein the multiple sets of simulation data include each combination of process parameter values ​​and the corresponding outlet flow rate and outlet pressure, the different working conditions include dual pumps in parallel and dual pumps in series, and the process parameters include valve opening angle, valve opening speed, inlet pressure and inlet flow rate.

[0022] Step 201 specifically includes the following steps: Step 1: Establish a dual-pump system simulation model.

[0023] SolidWorks software was used to build a three-dimensional model of the dual pump and its valves to obtain a dual pump system simulation model. The dual pump system simulation model accurately simulates the geometric structure of the dual pump system, including pumps, valves, pipes, and flow channels. The dual pump system simulation model was designed with the smoothness of the fluid channel and the working characteristics of the pump in mind, providing a basis for subsequent fluid simulation.

[0024] Step 2: Flow field simulation.

[0025] After the SolidWorks modeling is completed, the Fluent module in the Ansys software is used to perform a three-dimensional simulation of the flow field. Through the flow field simulation, the key fluid parameters such as flow velocity, pressure, and flow rate inside the dual-pump system can be obtained. In particular, the simulation process focuses on the changes in the outlet flow rate and outlet pressure at the outlet of the dual-pump system to ensure the reliability of the simulation results. Different process parameter value combinations are set under different working conditions for multiple simulations. Different working conditions include dual pumps in parallel and dual pumps in series, such as Figure 3 As shown, when two pumps are connected in parallel: open valve 1, valve 2, valve 3 and valve 5, and close valve 4; when two pumps are connected in series: open valve 4, valve 3 and valve 5; and close valve 1 and valve 2.

[0026] Through simulation, we can analyze the influence of different valve openings, dual pump operation modes and other factors on system performance. In one simulation process, the opening angles and valve opening speeds of each valve opened in the dual pump system under the same working condition are the same. For example, when the dual pumps are connected in series: open valve 4, valve 3 and valve 5. During the simulation process, the opening angles and opening speeds of valve 4, valve 3 and valve 5 are the same. Through simulation, multiple groups of simulation data of the dual pump system under different combinations of process parameter values ​​are obtained.

[0027] Step 3: Meshing.

[0028] After the flow field simulation is completed, the entire flow channel is meshed. The mesh size selected in this application is 7mm, which ensures the accuracy of the calculation and can fully capture the details in the flow field. When meshing, special attention is paid to the mesh refinement of key areas such as the inlet, outlet, valves and pipes of the dual pump system.

[0029] Step 4: Fluid simulation.

[0030] After the meshing is completed, Ansys Fluent is used to perform fluid simulation. During the fluid simulation process, different process parameters (such as flow rate, pressure, etc.) are used for testing and key data are calculated, including the distribution of outlet pressure and changes in outlet flow.

[0031] In a specific application example, a dual-pump system simulation model is first established, and a geometric model and mesh are created and set: Ansys Fluent is used to create a geometric model of the fluid domain and generate a high-quality mesh. Physical and fluid models are set: fluid properties and boundary conditions are defined. Boundary conditions related to design variables are set. In this embodiment, the design variables are process parameters, which include inlet flow rate, valve opening angle, valve opening speed, and inlet pressure. Boundary conditions are parameter range constraints for each process parameter.

[0032] Setting boundary conditions involves two UDF (User Defined Function) codes to control both velocity and pressure and define the linear opening of the valve. This method allows more complex boundary conditions to be set in Fluent, such as inlet velocity and inlet pressure that vary with position. Adjust the logic in the UDF code according to specific simulation requirements.

[0033] Thread structure: The Thread structure contains the geometric and physical information of the boundary conditions or fluid regions, such as faces, nodes, boundary condition types, etc.

[0034] Thread parameter: In a UDF, the Thread parameter is passed to the DEFINE_PROFILE function to indicate on which boundary condition the UDF is to be applied.

[0035] Integrated Multiple Objective Particle Swarm Optimization (MOPSO) algorithm: Select an optimization tool, use Matlab, Python or other optimization tools that support MOPSO. In this embodiment, python is selected.

[0036] Write the MOPSO algorithm: Initialize the particle swarm, including the initial values ​​of the process parameters. Define the multi-objective function, which calculates the outlet flow and outlet pressure values ​​by calling Ansys Fluent. The specific steps are as follows: (1) Setting boundary conditions: Set the boundary conditions of Fluent by combining the process parameter values ​​X.

[0037] (2) Start simulation: Call Ansys Fluent through Python's subprocess module and run the simulation script.

[0038] (3) Extract results: Use Fluent’s post-processing function to extract the results (including outlet flow and outlet pressure).

[0039] Step 202, constructing a multi-objective function with the outlet pressure and outlet flow being optimized as the objectives.

[0040] In the multi-objective particle swarm optimization algorithm, the optimized process parameters include the following: ① Valve opening angle: a key parameter for controlling fluid flow, affecting the pressure and flow distribution of the dual pump system.

[0041] ② Valve opening speed: determines the speed of valve opening and closing, thus affecting the instantaneous response of the dual pump system.

[0042] ③Inlet flow rate: directly affects the flow rate of the dual pump system and the operating efficiency of the dual pump system.

[0043] ④ Inlet pressure: has a direct impact on the working state and stability of the dual pump system.

[0044] In order to optimize the performance of the dual-pump system, a multi-objective function needs to be defined based on the two optimization objectives of outlet flow and outlet pressure.

[0045] The multi-objective function includes an outlet pressure objective function and an outlet flow objective function; the outlet pressure objective function and the outlet flow objective function are respectively: ; ; in, is the process parameter value combination, is the export flow objective function, is the outlet pressure objective function, and are the outlet flow value and outlet pressure value respectively, and They are the outlet flow target value and outlet pressure target value respectively.

[0046] After each iterative calculation, multi-objective optimization is used to evaluate the performance of the current combination of process parameter values ​​of the dual pump system. The evaluation formula is as follows: ①Export flow evaluation formula: ; in, is the estimated value of the export flow, is the outlet flow target value under the rth group of process parameter value combination, is the outlet flow rate of the dual pump system under the rth combination of process parameter values, N is the total number of process parameter value combinations.

[0047] ②Export pressure assessment formula: ; in, is the outlet pressure assessment value, is the outlet pressure target value under the rth group of process parameter value combination, It is the outlet pressure value of the dual pump system under the rth combination of process parameter values.

[0048] ③Multi-objective optimization evaluation formula: The final optimization goal is the weighted sum of outlet flow and outlet pressure: ; in, and is the weight coefficient, which indicates the importance of outlet flow and outlet pressure in multi-objective optimization. + =1.

[0049] and The smaller the value, the higher the degree of fit of the current process parameter value combination to the outlet flow target value or the outlet pressure target value, the closer the outlet flow value is to the target outlet flow value, or the closer the outlet pressure value is to the outlet pressure target value, the better the optimization effect, and vice versa.

[0050] In a specific application example, a fitness function, that is, a multi-objective function, is written: a Python script is written to call the Ansys Fluent calculation model, and the outlet flow value and the outlet pressure value are returned as the objective function values ​​corresponding to the multi-objective function.

[0051] Step 203, based on the multiple sets of simulation data, a multi-objective particle swarm optimization algorithm is used to solve the multi-objective function to obtain multiple sets of optimal process parameter value combinations. Specifically, the following steps 31 to 37 are included: Step 31, determining parameter range constraints of each process parameter according to the multiple sets of simulation data.

[0052] Through the above simulation, multiple sets of simulation data of the dual-pump system under different working conditions are obtained, and the variation range of each process parameter under different working conditions is obtained by statistics and analysis of multiple sets of simulation data. For example, the reasonable range of the outlet flow rate of the dual-pump system can be obtained by multiple simulations, comparing the pump performance under different valve opening angles, opening speeds, inlet flow rates, and inlet pressures, to ensure that within these ranges, the dual-pump system can operate stably and achieve the optimization goal.

[0053] The parameter range constraints of each process parameter are determined according to the variation range of each process parameter under different working conditions. The process parameters include valve opening angle, valve opening speed, inlet flow rate and inlet pressure.

[0054] The parameter range constraints are: ; in, is the valve opening angle, in degrees, is the valve opening speed, unit: ° / s, is the inlet flow velocity, in m / s, is the inlet pressure, in mPa.

[0055] Step 32: randomly generate a group of initial particles according to the parameter range constraint, each particle representing a group of process parameter value combinations.

[0056] Initialize the objective function value: Calculate the objective function value of each particle by calling a simulation tool (such as Fluent) and

[0057] Initialization speed and position: The initial position of each particle is the initial value of the process parameter combination, and the speed is random.

[0058] Step 33, calculating the outlet pressure value and outlet flow value of each particle according to the process parameter value combination.

[0059] Step 34, determining the current optimal position and the global optimal position of the particle according to the outlet pressure value and the outlet flow value of each particle.

[0060] ; in, is the updated velocity of the particle, is the current velocity of the particle, is the current position of the particle (i.e. the current combination of process parameter values), is the current optimal position of the particle (i.e. the optimal process parameter value combination of the current particle), is the global optimal position (i.e., the best combination of process parameter values ​​among all particles), is the inertia weight, which controls the inertia of the particle. is the acceleration constant, which controls the degree to which the particle follows the current best position and the global best position. , is a random number with a value range of [0,1], which introduces randomness.

[0061] Step 35, updating the speed of each particle according to the current optimal position of the particle and the global optimal position, and updating the position of each particle according to the updated speed.

[0062] ; in, is the updated position of the particle.

[0063] Step 36, determining a non-dominated solution set according to the updated position of the particle, and calculating the crowding distance of each particle in the non-dominated solution set.

[0064] After updating the positions of the particles, the domination relation comparison is used to help screen out a set of non-dominated solutions, which represent the Pareto optimal solution set.

[0065] Given two particles and , if the following conditions are met, then Dominate , indicating the particle Compare It is not inferior in all objectives and is better in at least one objective.

[0066] ; ; in, Represents particles No. k The objective function value, For particles of k The objective function value.

[0067] Step 37: determining an optimal solution set based on the crowding distance, wherein the optimal solution set includes multiple groups of optimal process parameter value combinations.

[0068] Among all non-dominated solutions, they are further sorted by crowding distance. Particles with larger crowding distance usually represent the "sparse" area in the non-dominated solution set. The non-dominated solutions with larger crowding distance in the "sparse" area are selected as the Pareto optimal solution set.

[0069] The calculation formula of crowding distance is: ; in, For the Particles The crowding distance, and Respectively k The maximum and minimum values ​​of the objective function, the particle and For the sorted particles Adjacent particles, For particles No. k The objective function value, For particles No. k The objective function value.

[0070] Through multiple iterations, multiple groups of optimal process parameter value combinations can be obtained through the multi-objective particle swarm optimization algorithm. In this embodiment, the maximum number of iterations is set to 500 times, and it can also be set to other times. When the number of iterations is greater than the preset maximum number of iterations, the iteration is stopped. The final output result is:

[0071] in, is the output process parameter value combination, is the optimal value of the valve opening angle, is the optimal value of valve opening speed, is the optimal value of the inlet flow velocity, is the optimal value of the inlet pressure.

[0072] The output process parameter value combination can maximize the outlet flow and outlet pressure performance of the dual pump system, ensuring the stability and efficiency of the dual pump system under different working conditions.

[0073] The optimal solution set for the dual pump system under the condition of dual pumps in series is shown in Table 1.

[0074] Table 1

[0075] The solution set in the optimal solution set shows a set of solutions that meet the parameter range constraints of each design variable, and the outlet flow and outlet pressure reach the optimal solution set. The design variables at the 212th iteration are selected as the optimal solution, and the design variables are: inlet flow rate 3.916m / s, valve opening angle 74.1°, valve opening speed 78.6° / s, inlet pressure 17.4mpa; outlet flow 1841.7kg / s and outlet pressure 8.3mpa; compared with the original valve opening of 30°, the outlet flow increased by 180.4% and the outlet pressure increased by 124.1%. This optimization is effective.

[0076] For the inlet flow velocity of design variable 1, its range in the optimal solution set is 3.876~3.938m / s, accounting for 1.6% of the entire feasible domain.

[0077] For the design variable 2, the valve opening angle, its range in the optimal solution set is 71.8~74.5°, accounting for 3% of the entire feasible domain.

[0078] For the design variable 3, valve opening speed, its range in the optimal solution set is 75.8~79.2°, accounting for 3.7% of the entire feasible domain.

[0079] For the design variable 4 inlet pressure, its range in the optimal solution set is 16.1~18.3 MPa, accounting for 22% of the entire feasible domain.

[0080] The optimal solution set of the dual pump system under the condition of dual pumps in parallel is shown in Table 2: Table 2

[0081] The solution set in the optimal solution set shows a set of solutions that meet the parameter range constraints of each design variable, and the outlet flow and outlet pressure reach the optimal solution set. The design variables at the 283rd iteration are selected as the optimal solution, and the design variables are: inlet flow rate 4.121m / s, valve opening angle 64.1°, valve opening speed 78.6° / s, inlet pressure 17.4mpa; lift pump outlet flow 2042.9kg / s and pressure 2.9mpa; compared with the original valve opening of 30 degrees, the flow rate increased by 119.5% and the pressure increased by 116%. This optimization is effective.

[0082] For the inlet flow velocity of design variable 1, its range in the optimal solution set is 4.081-4.208m / s, accounting for 3.1% of the entire feasible domain.

[0083] For the design variable 2, valve opening angle, its range in the optimal solution set is 61.3-64.5°, accounting for 3.6% of the entire feasible domain.

[0084] For the design variable 3, valve opening speed, its range in the optimal solution set is 75-78.6°, accounting for 4% of the entire feasible domain.

[0085] For the design variable 4 inlet pressure, its range in the optimal solution set is 17.3-18.8 MPa, accounting for 5% of the entire feasible domain.

[0086] After multiple iterations of optimization, the optimization results were analyzed and the final optimized process parameter value combination obtained by selecting the best result according to the requirements is shown in Table 3.

[0087] Table 3

[0088] When the dual pumps are connected in series, the outlet flow rate increases by 180.4% and the outlet pressure increases by 124.1% compared with the original solution with a valve opening of 30°; when the dual pumps are connected in parallel, the outlet flow rate increases by 119.5% and the outlet pressure increases by 116% compared with the original solution with a valve opening of 30°.

[0089] Based on the same inventive concept, the embodiment of the present application also provides a dual-pump system process parameter optimization device for implementing the dual-pump system process parameter optimization method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more dual-pump system process parameter optimization device embodiments provided below can refer to the limitations of the dual-pump system process parameter optimization method above, and will not be repeated here.

[0090] In an exemplary embodiment, Figure 4 As shown, a device for optimizing process parameters of a dual pump system is provided, comprising: Acquisition module 61 acquires multiple sets of simulation data of the dual pump system under different working conditions, wherein the multiple sets of simulation data are obtained by using a dual pump system simulation model to simulate the flow field of the dual pump system under different combinations of process parameter values; wherein the multiple sets of simulation data include each combination of process parameter values ​​and the corresponding outlet flow rate and outlet pressure, the different working conditions include dual pumps in parallel and dual pumps in series, and the process parameters include valve opening angle, valve opening speed, inlet pressure and inlet flow rate.

[0091] The construction module 62 is used to construct a multi-objective function with the outlet pressure and the outlet flow being optimized as the objectives; wherein the multi-objective function includes an outlet pressure objective function and an outlet flow objective function.

[0092] The optimization module 63 is used to solve the multi-objective function based on the multiple groups of simulation data by using a multi-objective particle swarm optimization algorithm to obtain multiple groups of optimal process parameter value combinations.

[0093] In an exemplary embodiment, the optimization module 63 includes: The constraint determination submodule is used to determine the constraint conditions of each process parameter according to the multiple groups of simulation data.

[0094] The initialization submodule is used to randomly generate a group of initial particles according to the constraint conditions, and each particle represents a group of process parameter value combinations.

[0095] The first calculation submodule is used to calculate the outlet pressure value and outlet flow value of each particle according to the process parameter value combination.

[0096] The optimal position determination submodule is used to determine the current optimal position of the particle and the global optimal position according to the outlet pressure value and outlet flow value of each particle.

[0097] The updating submodule is used to update the speed of each particle according to the current optimal position of the particle and the global optimal position, and to update the position of each particle according to the updated speed.

[0098] The second calculation submodule is used to determine a non-dominated solution set according to the updated position of the particle, and calculate the crowding distance of each particle in the non-dominated solution set.

[0099] The optimal solution determination submodule is used to determine an optimal solution set based on the crowding distance, wherein the optimal solution set includes multiple groups of optimal process parameter value combinations.

[0100] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store multiple groups of simulation data of the dual pump system under different working conditions. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for optimizing the process parameters of a dual pump system is implemented.

[0101] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0102] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0103] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0104] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0105] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0106] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0107] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0108] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0109] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for optimizing process parameters of a dual pump system, characterized in that: The method for optimizing the process parameters of the dual pump system includes: Acquire multiple sets of simulation data of the dual-pump system under different working conditions, wherein the multiple sets of simulation data are obtained by using a dual-pump system simulation model to perform flow field fluid simulation on the dual-pump system under different combinations of process parameter values; wherein the multiple sets of simulation data include each process parameter value combination and the corresponding outlet flow rate and outlet pressure, the different working conditions include dual pumps in parallel and dual pumps in series, and the process parameters include valve opening angle, valve opening speed, inlet pressure and inlet flow rate; Constructing a multi-objective function with the goal of optimizing the outlet pressure and the outlet flow rate; wherein the multi-objective function includes an outlet pressure objective function and an outlet flow objective function; Based on the multiple groups of simulation data, a multi-objective particle swarm optimization algorithm is used to solve the multi-objective function to obtain multiple groups of optimal process parameter value combinations.

2. The method for optimizing process parameters of a dual pump system according to claim 1, characterized in that: The outlet pressure objective function and the outlet flow objective function are respectively: ； ； in, is the process parameter value combination, is the export flow objective function, is the outlet pressure objective function, and are the outlet flow value and outlet pressure value respectively, and They are the outlet flow target value and outlet pressure target value respectively.

3. The method for optimizing process parameters of a dual pump system according to claim 1, characterized in that: Based on the multiple sets of simulation data, a multi-objective particle swarm optimization algorithm is used to solve the multi-objective function to obtain multiple sets of optimal process parameter value combinations, including: Determining parameter range constraints of each process parameter according to the multiple sets of simulation data; Randomly generate a group of initial particles according to the parameter range constraint, each particle represents a group of process parameter value combinations; Calculating the outlet pressure value and outlet flow value of each particle according to the process parameter value combination; Determine the current optimal position and the global optimal position of the particle according to the outlet pressure value and the outlet flow value of each particle; updating the speed of each particle according to the current best position of the particle and the global best position, and updating the position of each particle according to the updated speed; Determine a non-dominated solution set according to the updated position of the particle, and calculate the crowding distance of each particle in the non-dominated solution set; An optimal solution set is determined based on the crowding distance, and the optimal solution set includes multiple groups of optimal process parameter value combinations.

4. The method for optimizing the process parameters of a dual pump system according to claim 3, characterized in that: The calculation formula of crowding distance is: ； in, For the Particles The crowding distance, and Respectively k The maximum and minimum values ​​of the objective function, the particle and For the sorted particles Adjacent particles, For particles No. k The objective function value, For particles No. k The objective function value.

5. The method for optimizing process parameters of a dual pump system according to claim 3, characterized in that: The parameter range constraints are: ； in, is the valve opening angle, is the valve opening speed, is the inlet flow rate, is the inlet pressure.

6. A device for optimizing process parameters of a dual pump system, characterized in that: The device for optimizing the process parameters of the dual pump system comprises: An acquisition module is used to acquire multiple sets of simulation data of the dual-pump system under different working conditions, wherein the multiple sets of simulation data are obtained by using a dual-pump system simulation model to perform flow field fluid simulation on the dual-pump system under different combinations of process parameter values; wherein the multiple sets of simulation data include each process parameter value combination and the corresponding outlet flow rate and outlet pressure, the different working conditions include dual pumps in parallel and dual pumps in series, and the process parameters include valve opening angle, valve opening speed, inlet pressure and inlet flow rate; A construction module is used to construct a multi-objective function with the outlet pressure and the outlet flow being optimized as the objectives; wherein the multi-objective function includes an outlet pressure objective function and an outlet flow objective function; The optimization module is used to solve the multi-objective function based on the multiple groups of simulation data by using a multi-objective particle swarm optimization algorithm to obtain multiple groups of optimal process parameter value combinations.

7. The device for optimizing process parameters of a dual pump system according to claim 6, characterized in that: The optimization module includes: A constraint determination submodule, used to determine the constraint conditions of each process parameter according to the multiple sets of simulation data; An initialization submodule, used to randomly generate a group of initial particles according to the constraint conditions, each particle representing a group of process parameter value combinations; A first calculation submodule, used for calculating the outlet pressure value and outlet flow value of each particle according to the process parameter value combination; The optimal position determination submodule is used to determine the current optimal position and the global optimal position of the particle according to the outlet pressure value and the outlet flow value of each particle; An updating submodule, used for updating the speed of each particle according to the current best position of the particle and the global best position, and updating the position of each particle according to the updated speed; A second calculation submodule is used to determine a non-dominated solution set according to the updated position of the particle, and calculate the crowding distance of each particle in the non-dominated solution set; The optimal solution determination submodule is used to determine an optimal solution set based on the crowding distance, wherein the optimal solution set includes multiple groups of optimal process parameter value combinations.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for optimizing the process parameters of the dual pump system according to any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for optimizing the process parameters of the dual pump system according to any one of claims 1 to 5 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for optimizing the process parameters of the dual pump system according to any one of claims 1 to 5 is implemented.

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