Optimization Method, Device, Equipment, Medium and Product for Process Parameters of Double-Pump System

Abstract

The present application discloses an optimization method, device, equipment, medium and product for process parameters of a dual-pump system, which relates to the field of optimization of process parameters of lift pumps. The method includes: obtaining multiple groups of simulation data of the dual-pump system under different working conditions, where the multiple groups of simulation data are obtained by performing flow field fluid simulation on the dual-pump system under different combinations of process parameter values using a dual-pump system simulation model; constructing a multi-objective function with the optimal outlet pressure and outlet flow as the objectives; and based on the multiple groups of simulation data, using a multi-objective particle swarm optimization algorithm to solve the multi-objective function to obtain multiple groups of optimal combinations of process parameter values. The present application can achieve precise control of the dual-pump system and simultaneously optimize multiple process parameter values, improving the system performance.

Description

Technical Field

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

[0002] As a key fluid transportation device, lift pumps are widely used in industries such as petroleum, natural gas, deep-sea drilling, and chemical engineering. 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, becomes the key to improving the performance of the pump system.

[0003] Traditional methods for designing lift pump parameters usually rely on manual experience and experimental data, and it is difficult to obtain the best combination of process parameters under the conditions of multi-variable and multi-objective optimization. Especially in a dual-pump series / parallel system, involving the interactive effects of multiple optimization parameters, how to perform precise control and optimization in a complex fluid flow field has become an urgent problem to be solved. Summary of the Invention

[0004] The purpose of the present 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, improving the system performance.

[0005] To achieve the above purpose, the present application provides the following solutions:

[0006] In a first aspect, the present application provides a method for optimizing the process parameters of a dual-pump system, including:

[0007] Obtaining multiple groups of simulation data of the dual-pump system under different working conditions, where the multiple groups of simulation data are obtained by performing fluid flow simulation on the dual-pump system under different combinations of process parameter values using a dual-pump system simulation model; wherein, the multiple groups of simulation data include combinations of each process parameter value and the corresponding outlet flow rate and outlet pressure, the different working conditions include dual-pump parallel connection and dual-pump series connection, and the process parameters include valve opening angle, valve opening speed, inlet pressure, and inlet flow rate;

[0008] Constructing a multi-objective function with the optimal outlet pressure and outlet flow rate as the objectives; wherein, the multi-objective function includes an outlet pressure objective function and an outlet flow rate objective function;

[0009] Based on the multiple groups of simulation data, using a multi-objective particle swarm optimization algorithm to solve the multi-objective function to obtain multiple groups of optimal process parameter value combinations.

[0010] Furthermore, the outlet pressure objective function and the outlet flow objective function are respectively:

[0011] ;

[0012] ;

[0013] wherein, is the combination of process parameter values, is the outlet flow objective function, is the outlet pressure objective function, and are the outlet flow value and the outlet pressure value respectively, and are the outlet flow target value and the outlet pressure target value respectively.

[0014] Furthermore, based on the multiple groups of simulation data, the multi-objective particle swarm optimization algorithm is used to solve the multi-objective function, and multiple groups of optimal process parameter value combinations are obtained, including:

[0015] Determine the parameter range constraint conditions of each process parameter according to the multiple groups of simulation data;

[0016] Randomly generate a group of initial particles according to the parameter range constraint conditions, and each particle represents a group of process parameter value combinations;

[0017] Calculate the outlet pressure value and the outlet flow value of each particle according to the combination of process parameter values;

[0018] Determine the current best position and the global best position of the particle according to the outlet pressure value and the outlet flow value of each particle;

[0019] Update the velocity of each particle according to the current best position and the global best position of the particle, and update the position of each particle according to the updated velocity;

[0020] Determine the non-dominated solution set according to the position of the updated particle, and calculate the crowding distance of each particle in the non-dominated solution set;

[0021] Determine the optimal solution set based on the crowding distance, and the optimal solution set contains multiple groups of optimal process parameter value combinations.

[0022] Furthermore, the calculation formula of the crowding distance is:

[0023] ;

[0024] wherein, is the th particle 's crowding distance, and are respectively the maximum and minimum values of the k th objective function. Particles and are adjacent particles after sorting. is the th objective function value of particle , and k is the th objective function value of particle . k

[0025] Further, the parameter range constraint conditions are:

[0026] ;

[0027] where , is the valve opening angle, is the valve opening speed, is the inlet flow rate, and is the inlet pressure.

[0028] Second, this application provides an optimization device for process parameters of a double-pump system, including:

[0029] An acquisition module that acquires multiple sets of simulation data of the double-pump system under different working conditions. The multiple sets of simulation data are obtained by performing a flow field simulation on the double-pump system with different combinations of process parameter values using a double-pump system simulation model. Among them, 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 double-pump parallel connection and double-pump series connection, and the process parameters include valve opening angle, valve opening speed, inlet pressure, and inlet flow rate;

[0030] A construction module for constructing a multi-objective function with the optimization of outlet pressure and outlet flow rate as the goal. Among them, the multi-objective function includes an outlet pressure objective function and an outlet flow rate objective function;

[0031] An optimization module for solving the multi-objective function based on the multiple sets of simulation data using a multi-objective particle swarm optimization algorithm to obtain multiple sets of optimal process parameter value combinations.

[0032] Third, this application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the computer program to implement the above-mentioned optimization method for process parameters of the double-pump system.

[0033] Fourth, this application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the above-mentioned optimization method for process parameters of the double-pump system.

[0034] 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 optimization method for process parameters of a dual-pump system.

[0035] According to the specific embodiments provided by the present application, the following technical effects are disclosed:

[0036] The present application provides an optimization method, device, equipment, medium and product for process parameters of a dual-pump system. By using a dual-pump system simulation model to simulate the flow field fluid of the dual-pump system under different combinations of process parameter values, multiple groups of simulation data of the dual-pump system under different working conditions are obtained. Based on this, a multi-objective particle swarm optimization algorithm is adopted to optimize the process parameter values of the dual-pump system with the optimal outlet pressure and outlet flow as the objectives. This solution uses the dual-pump system simulation model to simulate the working process of the dual-pump system, which can precisely control the dual-pump system. At the same time, the multi-objective particle swarm optimization algorithm is used to optimize the process parameter values, without relying on manual experience and experimental data, realizing a higher-precision optimization of the process parameter values of the dual-pump system and improving the performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0038] Figure 1 It is an application environment diagram of an optimization method for process parameters of a dual-pump system in an embodiment of the present application;

[0039] Figure 2 It is a schematic flowchart of an optimization method for process parameters of a dual-pump system provided in an embodiment of the present application;

[0040] Figure 3 It is a schematic connection diagram of a dual-pump system model provided in an embodiment of the present application;

[0041] Figure 4 It is a schematic functional module diagram of an optimization device for process parameters of a dual-pump system provided in another embodiment of the present application;

[0042] Figure 5 It is a schematic structural diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present application.

[0044] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below in conjunction with the drawings and specific embodiments.

[0045] The optimization method for the process parameters of the dual-pump system provided by the embodiments of the present application can be applied to, for example, Figure 1 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 groups of simulation data of the dual-pump system under different working conditions to the server 104. After receiving the multiple groups of simulation data of the dual-pump system under different working conditions, for the multiple groups of simulation data of the dual-pump system under different working conditions, the server 104 constructs a multi-objective function with the optimal outlet pressure and outlet flow as the goal, and uses the multi-objective particle swarm optimization algorithm to solve the multi-objective function to obtain multiple groups of optimal process parameter value combinations. The server 104 can feedback the obtained multiple groups of optimal process parameter value combinations to the terminal 102. In addition, in some embodiments, the optimization method for the process parameters of the dual-pump system can also be implemented by the server 104 or the terminal 102 alone. For example, the terminal 102 can directly process multiple groups of simulation data of the dual-pump system under different working conditions, or the server 104 can obtain multiple groups of simulation data of the dual-pump system under different working conditions from the data storage system and process the multiple groups of simulation data of the dual-pump system under different working conditions.

[0046] Among them, the terminal 102 can be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, etc. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0047] In an exemplary embodiment, as Figure 2 shown, an optimization method for the process parameters of a dual-pump system is provided. This method is executed by a computer device, and can be specifically executed by a computer device such as a terminal or a server alone, or jointly executed by a terminal and a server. In the embodiments of the present application, taking this method as being applied toFigure 1 Taking the server 104 in

[0048] Step 201, obtaining multiple groups of simulation data of the dual-pump system under different working conditions, where the multiple groups of simulation data are obtained by performing a flow field fluid simulation on the dual-pump system under different combinations of process parameter values using a dual-pump system simulation model; wherein, the multiple groups of simulation data include each combination of process parameter values and the corresponding outlet flow rate and outlet pressure, and the different working conditions include dual-pump parallel connection and dual-pump series connection, and the process parameters include valve opening angle, valve opening speed, inlet pressure, and inlet flow rate.

[0049] Step 201 specifically includes the following steps:

[0050] Step 1: Establishing a dual-pump system simulation model.

[0051] Using SolidWorks software to establish 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, pipelines, and flow channels, etc. When designing the dual-pump system simulation model, the smoothness of the fluid channel and the working characteristics of the pump are considered, providing a basis for subsequent fluid simulation.

[0052] Step 2: Flow field simulation.

[0053] After the SolidWorks modeling is completed, use the Fluent module in Ansys software to perform a three-dimensional simulation of the flow field. Through the flow field simulation, key fluid parameters such as the flow velocity, pressure, and flow rate inside the dual-pump system can be obtained. In particular, during the simulation process, the changes in the outlet flow rate and outlet pressure at the outlet end of the dual-pump system are focused on to ensure the reliability of the simulation results. Set different combinations of process parameter values under different working conditions for multiple simulation simulations, where the different working conditions include dual-pump parallel connection and dual-pump series connection. As Figure 3 shown, when the dual pumps are in parallel: open valve 1, valve 2, valve 3, and valve 5, and close valve 4; when the dual pumps are in series: open valve 4, valve 3, and valve 5; and close valve 1 and valve 2.

[0054] Through the simulation, the influence of factors such as different valve openings and dual-pump operation modes on the system performance can be analyzed. Among them, during one simulation process, the opening angles and opening speeds of the opened valves in the dual-pump system under the same working condition are the same. For example, when the dual pumps are 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 the simulation, multiple groups of simulation data of the dual-pump system under different combinations of process parameter values are obtained.

[0055] Step 3: Mesh generation.

[0056] After the flow field simulation is completed, the entire flow channel is then meshed. The mesh size selected in this application is 7 mm, which ensures the accuracy of the calculation and can fully capture the details in the flow field. During meshing, special attention was paid to the mesh refinement of key areas such as the inlets, outlets, valves, and pipes of the dual-pump system.

[0057] Step 4: Fluid simulation.

[0058] After completing the meshing, fluid simulation is carried out using Ansys Fluent. During the fluid simulation process, different process parameters (such as flow rate, pressure, etc.) are used for testing, and key data is calculated. Among them, the key data includes the distribution of the outlet pressure and the change of the outlet flow rate, etc.

[0059] In a specific application example, first, a simulation model of the dual-pump system is established, and the geometric model and mesh are created and set: Use Ansys Fluent to create the geometric model of the fluid domain and generate high-quality meshes. Set the physical and fluid models: Define the fluid properties and boundary conditions. Set the boundary conditions related to the design variables. The design variables in this embodiment are process parameters, and the process parameters include the inlet flow rate, valve opening angle, valve opening speed, and inlet pressure. The boundary conditions are the parameter range constraint conditions of each process parameter.

[0060] When setting the boundary conditions, two UDF (User Defined Function) codes are involved to control the speed and pressure simultaneously and define the process of the valve opening linearly. Through this method, more complex boundary conditions can be set in Fluent, such as the situation where the inlet flow rate and inlet pressure change with position. Adjust the logic in the UDF code according to specific simulation requirements.

[0061] Thread structure: The Thread structure contains the geometric and physical information of the boundary conditions or fluid regions, such as faces, nodes, boundary condition type, etc.

[0062] Thread parameter: In the UDF, the Thread parameter is passed to the DEFINE_PROFILE function to indicate on which boundary condition the UDF is to be applied.

[0063] Integrate the Multiple Objective Particle Swarm Optimization (MOPSO) algorithm: Select the optimization tool, use Matlab, Python, or other optimization tools that support MOPSO. In this embodiment, Python is selected.

[0064] Write the MOPSO algorithm: Initialize the particle swarm, including the initial values of process parameters. Define the multi-objective function, which calculates the outlet flow rate value and outlet pressure value by calling Ansys Fluent. The specific steps are as follows:

[0065] (1) Set boundary conditions: Set the boundary conditions of Fluent through the combination of process parameter values X.

[0066] (2) Start the simulation: Call Ansys Fluent through the subprocess module of Python and run the simulation script.

[0067] (3) Extract results: Use the post-processing function of Fluent to extract the results (including the outlet flow rate and outlet pressure).

[0068] Step 202: Construct a multi-objective function with the optimal outlet pressure and outlet flow rate as the objectives.

[0069] In the multi-objective particle swarm optimization algorithm, the process parameters to be optimized include the following:

[0070] ① Valve opening angle: A key parameter controlling the fluid flow rate, which affects the pressure and flow rate distribution of the double-pump system.

[0071] ② Valve opening speed: Determines the speed of valve opening and closing, thus affecting the instantaneous response of the double-pump system.

[0072] ③ Inlet flow velocity: Directly affects the flow rate of the double-pump system and the operating efficiency of the double-pump system.

[0073] ④ Inlet pressure: Has a direct impact on the working state and stability of the double-pump system.

[0074] To optimize the performance of the double-pump system, the multi-objective function needs to be defined according to two optimization objectives: the outlet flow rate and the outlet pressure.

[0075] The multi-objective function includes an outlet pressure objective function and an outlet flow rate objective function; the outlet pressure objective function and the outlet flow rate objective function are respectively:

[0076] ;

[0077] ;

[0078] Where is the combination of process parameter values, is the outlet flow rate objective function, is the outlet pressure objective function, and are the outlet flow rate value and the outlet pressure value respectively, and They are the target value of the outlet flow rate and the target value of the outlet pressure respectively.

[0079] After each iterative calculation, the performance of the current combination of process parameter values of the dual-pump system is evaluated using multi-objective optimization. The evaluation formula is as follows:

[0080] ① Outlet flow rate evaluation formula:

[0081] ;

[0082] Among them, is the evaluation value of the outlet flow rate, is the target value of the outlet flow rate under the r-th group of process parameter value combinations, is the outlet flow rate value of the dual-pump system under the r-th group of process parameter value combinations, N is the total number of combinations of process parameter values.

[0083] ② Outlet pressure evaluation formula:

[0084] ;

[0085] Among them, is the evaluation value of the outlet pressure, is the target value of the outlet pressure under the r-th group of process parameter value combinations, is the outlet pressure value of the dual-pump system under the r-th group of process parameter value combinations.

[0086] ③ Multi-objective optimization evaluation formula:

[0087] The final optimization goal is the weighted sum of the outlet flow rate and the outlet pressure:

[0088] ;

[0089] Among them, and are the weight coefficients. The weight coefficients represent the importance of the outlet flow rate and the outlet pressure in multi-objective optimization. Usually, + = 1.

[0090] and The smaller the values of

[0091] In a specific application example, a fitness function, i.e., a multi-objective function, is written: a Python script is written to call Ansys Fluent to calculate the model, and the outlet flow rate value and the outlet pressure value are returned as the objective function values corresponding to the multi-objective function.

[0092] Step 203: Based on the multiple groups of simulation data, use the multi-objective particle swarm optimization algorithm to solve the multi-objective function to obtain multiple groups of optimal process parameter value combinations. Specifically, it includes the following steps 31 - step 37:

[0093] Step 31: Determine the parameter range constraint conditions for each process parameter according to the multiple groups of simulation data.

[0094] Through the multiple groups of simulation data of the double-pump system under different working conditions obtained by the above simulation, by statistically analyzing the multiple groups of simulation data, the variation ranges of each process parameter under different working conditions are obtained. For example, the reasonable range of the outlet flow rate of the double-pump system can be obtained by multiple simulations and comparing the pump performance under different valve opening angles, opening speeds, inlet flow rates, and inlet pressures, ensuring that within these ranges, the double-pump system can operate stably and achieve the optimization goal.

[0095] According to the variation ranges of each process parameter under different working conditions, determine the parameter range constraint conditions for each process parameter. The process parameters include the valve opening angle, valve opening speed, inlet flow rate, and inlet pressure.

[0096] The parameter range constraint conditions are:

[0097] ;

[0098] Among them, is the valve opening angle, in degrees, is the valve opening speed, in ° / s, is the inlet flow rate, in m / s, is the inlet pressure, in mPa.

[0099] Step 32: Randomly generate a group of initial particles according to the parameter range constraint conditions. Each particle represents a group of process parameter value combinations.

[0100] Initialize the objective function value: By calling a simulation tool (such as Fluent), calculate the objective function value of each particle and

[0101] Initialize the velocity and position: The initial position of each particle is the initial value of the process parameter value combination, and the velocity is random.

[0102] Step 33: Calculate the outlet pressure value and the outlet flow rate value of each particle according to the process parameter value combination.

[0103] Step 34: Determine the current best position and the global best position of each particle according to the outlet pressure value and the outlet flow value of each particle.

[0104] ;

[0105] Among them, 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 best position of the particle (i.e., the best combination of process parameter values of the current particle), is the global best position (i.e., the best combination of process parameter values among all particles), is the inertia weight, and the inertia weight controls the inertia of the particle, is the acceleration constant, and the acceleration constant controls the degree to which the particle follows the current best position and the global best position, , are random numbers, and the value range is [0, 1], introducing randomness.

[0106] Step 35: Update the velocity of each particle according to the current best position and the global best position of the particle, and update the position of each particle according to the updated velocity.

[0107] ;

[0108] Among them, is the updated position of the particle.

[0109] Step 36: Determine the non-dominated solution set according to the updated positions of the particles, and calculate the crowding distance of each particle in the non-dominated solution set.

[0110] After the positions of the updated particles, use the dominance relationship comparison to help screen out a set of non-dominated solution sets, representing the Pareto optimal solution set.

[0111] Given two particles and , if the following conditions are met, then dominates , indicating that particle is not worse than in all objectives, and is better in at least one objective.

[0112] ;

[0113] ;

[0114] Among them, Represents a particle The k th objective function value, Is the particle Of k th objective function value.

[0115] Step 37, determine the optimal solution set based on the crowding distance, and the optimal solution set contains multiple groups of optimal process parameter value combinations.

[0116] Among all non-dominated solutions, further sort by the crowding distance. Particles with a larger crowding distance usually represent the "sparse" region in the non-dominated solution set. Select the non-dominated solution with a larger crowding distance in the "sparse" region as the Pareto optimal solution set.

[0117] The calculation formula for the crowding distance is:

[0118] ;

[0119] Where, Is the crowding distance of the th particle , And Are respectively the maximum and minimum values of the k th objective function. Particles And Are adjacent particles after sorting , Is the th k th objective function value of particle , Is the k th objective function value of particle

[0120] Through multiple iterations, finally, 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 numbers. When the number of iterations is greater than the preset maximum number of iterations, stop the iteration. The final output result is:

[0121]

[0122] Where, Is the output process parameter value combination, Is the optimal value of the valve opening angle, Is the optimal value of the valve opening speed, Is the optimal value of the inlet flow rate, Is the optimal value of the inlet pressure.

[0123] The combination of the output process parameter values can maximize the outlet flow rate and outlet pressure performance of the dual-pump system, ensuring the stability and efficiency of the dual-pump system under different operating conditions.

[0124] The optimal solution set of the dual-pump system under the condition of dual-pump series connection is shown in Table 1 below.

[0125] Table 1

[0126]

[0127] The solution sets within the optimal solution set show a set of optimal solution sets for the outlet flow rate and outlet pressure under the constraint conditions of the parameter ranges that satisfy each design variable. The optimal solutions of each design variable at the 212th iteration are selected, and the design variables are: inlet flow velocity 3.916 m / s, valve opening angle 74.1°, valve opening speed 78.6° / s, inlet pressure 17.4 mpa; outlet flow rate 1841.7 kg / s and outlet pressure 8.3 mpa; compared with the original valve opening of 30°, the outlet flow rate is increased by 180.4% and the outlet pressure is increased by 124.1%. This optimization is effective.

[0128] For design variable 1, the inlet flow velocity, its range in the optimal solution set is 3.876 - 3.938 m / s, accounting for 1.6% of the entire feasible domain.

[0129] For 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.

[0130] For design variable 3, the valve opening speed, its range in the optimal solution set is 75.8 - 79.2°, accounting for 3.7% of the entire feasible domain.

[0131] For design variable 4, the inlet pressure, its range in the optimal solution set is 16.1 - 18.3 mpa, accounting for 22% of the entire feasible domain.

[0132] The optimal solution set of the dual-pump system under the condition of dual-pump parallel connection is shown in Table 2 below:

[0133] Table 2

[0134]

[0135] The solution set within the optimal solution set shows a set of solutions where the outlet flow rate and outlet pressure are optimized while satisfying the parameter range constraints of each design variable. The optimal solutions for each design variable at the 283rd iteration are as follows: inlet flow velocity of 4.121 m / s, valve opening angle of 64.1°, valve opening speed of 78.6° / s, and inlet pressure of 17.4 mpa; the outlet flow rate of the lift pump is 2042.9 kg / s and the pressure is 2.9 mpa; compared with the original valve opening of 30 degrees, the flow rate is increased by 119.5% and the pressure is increased by 116%, indicating that this optimization is effective.

[0136] For design variable 1, the inlet flow velocity, its range in the optimal solution set is 4.081 - 4.208 m / s, accounting for 3.1% of the entire feasible domain.

[0137] For design variable 2, the valve opening angle, its range in the optimal solution set is 61.3 - 64.5°, accounting for 3.6% of the entire feasible domain.

[0138] For design variable 3, the valve opening speed, its range in the optimal solution set is 75 - 78.6°, accounting for 4% of the entire feasible domain.

[0139] For design variable 4, the inlet pressure, its range in the optimal solution set is 17.3 - 18.8 mpa, accounting for 5% of the entire feasible domain.

[0140] After multiple iterations of optimization, the optimization results are analyzed, and the final optimized process parameter value combinations obtained by selecting the best results according to the requirements are shown in Table 3.

[0141] Table 3

[0142]

[0143] In the case of the double - pump system in series compared with the original valve opening of 30°, the outlet flow rate is increased by 180.4% and the outlet pressure is increased by 124.1%; in the case of the double - pump system in parallel compared with the original valve opening of 30°, the outlet flow rate is increased by 119.5% and the outlet pressure is increased by 116%.

[0144] Based on the same inventive concept, the embodiment of the present application also provides an optimization device for the process parameters of a double - pump system for implementing the above - mentioned optimization method of the process parameters of the double - pump system. The implementation solutions provided by this device to solve problems are similar to the implementation solutions described in the above - mentioned method. Therefore, the specific limitations in one or more embodiments of the optimization device for the process parameters of the double - pump system provided below can refer to the limitations for the optimization method of the process parameters of the double - pump system in the above text and will not be repeated here.

[0145] In an exemplary embodiment, as Figure 4As shown, an optimization device for process parameters of a dual-pump system includes:

[0146] An acquisition module 61 that acquires multiple groups of simulation data of the dual-pump system under different working conditions. The multiple groups of simulation data are obtained by performing a flow field simulation on the dual-pump system under different combinations of process parameter values using a dual-pump system simulation model. Among them, the multiple groups 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-pump parallel connection and dual-pump series connection, and the process parameters include valve opening angle, valve opening speed, inlet pressure, and inlet flow rate.

[0147] A construction module 62 for constructing a multi-objective function with the optimization of outlet pressure and outlet flow rate as the goal. Among them, the multi-objective function includes an outlet pressure objective function and an outlet flow rate objective function.

[0148] An optimization module 63 for solving the multi-objective function based on the multiple groups of simulation data using a multi-objective particle swarm optimization algorithm to obtain multiple groups of optimal process parameter value combinations.

[0149] In an exemplary embodiment, the optimization module 63 includes:

[0150] A constraint determination sub-module for determining the constraint conditions of each process parameter according to the multiple groups of simulation data.

[0151] An initialization sub-module for randomly generating a group of initial particles according to the constraint conditions, and each particle represents a group of process parameter value combinations.

[0152] A first calculation sub-module for calculating the outlet pressure value and outlet flow rate value of each particle according to the process parameter value combination.

[0153] A best position determination sub-module for determining the current best position and global best position of the particle according to the outlet pressure value and outlet flow rate value of each particle.

[0154] An update sub-module for updating the velocity 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 velocity.

[0155] A second calculation sub-module for determining a non-dominated solution set according to the position of the updated particle and calculating the crowding distance of each particle in the non-dominated solution set.

[0156] An optimal solution determination sub-module for determining an optimal solution set based on the crowding distance. The optimal solution set contains multiple groups of optimal process parameter value combinations.

[0157] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal, and its internal structure diagram can be asFigure 5 As shown in the figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated 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 sets of simulation data of the double-pump system under different working conditions. The input / output interface of the computer device is used to exchange information between the processor and external devices. 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, it realizes an optimization method for the process parameters of a double-pump system.

[0158] Those skilled in the art can understand that Figure 5 the structure shown in the figure is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0159] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are realized.

[0160] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by the processor, the steps in the above method embodiments are realized.

[0161] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by the processor, the steps in the above method embodiments are realized.

[0162] 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 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 need to comply with relevant regulations.

[0163] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. 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 methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0164] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0165] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, 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, it should be considered as the scope described in this specification.

[0166] Specific examples are used in this article to elaborate on the principles and implementation manners of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present 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 sets of simulation data, the multi-objective particle swarm optimization algorithm is used to solve the multi-objective function to obtain multiple sets of optimal process parameter value combinations; during the optimization process, the outlet flow evaluation formula is: ； The outlet pressure assessment formula is: ； The multi-objective optimization evaluation formula is: ； in, is the process parameter value combination, 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, is the outlet pressure assessment value, is the outlet pressure target value under the rth group of process parameter value combination, is the outlet pressure value of the dual pump system under the rth combination of process parameter values, and is the weight coefficient.

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 sets of simulation data using a multi-objective particle swarm optimization algorithm to obtain multiple sets of optimal process parameter value combinations; during the optimization process, the outlet flow evaluation formula is: ； The outlet pressure assessment formula is: ； The multi-objective optimization evaluation formula is: ； in, is the process parameter value combination, 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, is the outlet pressure assessment value, is the outlet pressure target value under the rth group of process parameter value combination, is the outlet pressure value of the dual pump system under the rth combination of process parameter values, and is the weight coefficient.

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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