Method and device for determining intelligent pressure optimization and control parameters of a ground handling system

Abstract

The application provides a method and device for determining intelligent pressure optimization and control parameters of a apron fuel supply system, and relates to the technical field of apron fuel supply system control. The method comprises the following steps: establishing a first target model; the first target model is used for minimizing the energy consumption of the apron fuel supply system; determining a target outlet pressure of the apron fuel supply system according to the first target model; the target outlet pressure represents the outlet pressure of the apron fuel supply system corresponding to the minimized energy consumption of the apron fuel supply system; and determining the control parameters corresponding to the apron fuel supply system according to the target outlet pressure of the apron fuel supply system. The method of the above-mentioned embodiment realizes accurate determination of the control parameters, ensures the energy consumption optimization and outlet pressure optimization of the fuel supply system, and reduces the risk of pipe explosion, leakage and equipment damage.

Description

Technical Field

[0001] This invention relates to the field of apron fuel supply system control technology, and in particular to a method and apparatus for intelligent pressure optimization and determination of control parameters for an apron fuel supply system. Background Technology

[0002] In the control process of the apron fuel supply system, the proportional-integral-derivative (PID) parameters are the core content of the control system design. It is necessary to determine the proportional coefficient, integral time and derivative time of the PID controller according to the characteristics of the controlled process.

[0003] In related technologies, research on optimization and adjustment methods for PID parameters of apron refueling systems is relatively scarce. Therefore, how to accurately optimize the PID parameters of apron refueling systems and achieve precise control of these systems is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] This invention provides a method and apparatus for intelligent pressure optimization and determination of control parameters for an apron fuel supply system. By optimizing the energy consumption of the apron fuel supply system, the optimal pipeline pressure value is determined. Based on the optimal pipeline pressure value, the corresponding control parameters of the apron fuel supply system are determined, thereby achieving accurate determination of control parameters, ensuring optimal energy consumption and outlet pressure of the fuel supply system, and reducing the risk of pipe bursts, leaks, and equipment damage.

[0005] This invention provides a method for intelligent pressure optimization and determination of control parameters for an apron fuel supply system, comprising the following steps.

[0006] Establish a first objective model; the first objective model is used to minimize the energy consumption of the apron fuel supply system.

[0007] Based on the first target model, the target outlet pressure of the apron fuel supply system is determined; the target outlet pressure represents the outlet pressure of the apron fuel supply system when minimizing the energy consumption of the apron fuel supply system.

[0008] Based on the target outlet pressure of the apron fuel supply system, determine the corresponding control parameters for the apron fuel supply system.

[0009] According to the present invention, a method for intelligent pressure optimization and determination of control parameters for an apron fuel supply system is provided, wherein the first target model is established based on the following formula:

[0010]

[0011] in, This indicates the energy consumption of the apron fuel supply system; Indicates rotational speed; Indicates the outlet pressure of the apron fuel supply system; This indicates the number of pumps started in the apron fuel supply system; This indicates the peak flow rate of the pump in the apron fuel supply system.

[0012] According to the present invention, a method for intelligent pressure optimization and determination of control parameters for an apron fuel supply system, wherein determining the target outlet pressure of the apron fuel supply system based on the first target model includes:

[0013] Based on a genetic algorithm, the first target model is solved to determine the target outlet pressure of the apron fuel supply system.

[0014] According to the present invention, a method for intelligent pressure optimization and determination of control parameters for an apron fuel supply system includes determining the control parameters corresponding to the apron fuel supply system based on the target outlet pressure of the apron fuel supply system, comprising:

[0015] A second target model is established based on the target outlet pressure of the apron fuel supply system; the second target model is used to minimize the difference between the simulation result of the outlet pressure of the apron fuel supply system and the target outlet pressure of the apron fuel supply system.

[0016] Based on the second target model, the control parameters corresponding to the apron fuel supply system are determined.

[0017] According to the intelligent pressure optimization and control parameter determination method for an apron fuel supply system provided by the present invention, the second target model is established based on the following formula:

[0018] ；

[0019] Among them, the This represents the difference between the simulation results of the outlet pressure of the apron fuel supply system and the target outlet pressure of the apron fuel supply system. The simulation results represent the outlet pressure of the apron fuel supply system; This indicates the target outlet pressure of the apron fuel supply system.

[0020] According to the present invention, a method for intelligent pressure optimization and determination of control parameters for an apron fuel supply system is provided. The step of determining the control parameters corresponding to the apron fuel supply system based on the second target model includes:

[0021] The second target model is solved using the particle swarm optimization algorithm to determine the control parameters corresponding to the apron fuel supply system.

[0022] This invention also provides a device for intelligent pressure optimization and control parameter determination of an apron fuel supply system, comprising the following modules:

[0023] A module is established to build the first objective model; the first objective model is used to minimize the energy consumption of the apron fuel supply system.

[0024] The first determining module is used to determine the target outlet pressure of the apron fuel supply system based on the first target model; the target outlet pressure represents the outlet pressure of the apron fuel supply system when minimizing the energy consumption of the apron fuel supply system.

[0025] The second determining module is used to determine the control parameters corresponding to the apron fuel supply system based on the target outlet pressure of the apron fuel supply system.

[0026] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a method for intelligent pressure optimization and determination of control parameters for any of the above-described apron fuel supply systems.

[0027] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for intelligent pressure optimization and determination of control parameters for an apron fuel supply system as described above.

[0028] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements a method for intelligent pressure optimization and determination of control parameters for any of the above-described apron fuel supply systems.

[0029] The present invention provides a method and apparatus for intelligent pressure optimization and control parameter determination of an apron fuel supply system. By optimizing the energy consumption of the apron fuel supply system, the optimal pipeline pressure value is determined. Based on the optimal pipeline pressure value, the corresponding control parameters of the apron fuel supply system are determined, thereby achieving accurate determination of control parameters, ensuring optimal energy consumption and outlet pressure of the fuel supply system, and reducing the risk of pipe bursts, leaks and equipment damage. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0031] Figure 1This is a flowchart illustrating the method for determining intelligent pressure optimization and control parameters of the apron fuel supply system provided by the present invention.

[0032] Figure 2 This is a schematic diagram showing the change of centrifugal pump power with frequency provided by the present invention.

[0033] Figure 3 This is a schematic diagram illustrating the change in flow rate of a centrifugal pump with frequency, provided by the present invention.

[0034] Figure 4 This is a schematic diagram showing the change of centrifugal pump outlet pressure with frequency provided by the present invention.

[0035] Figure 5 This is a schematic diagram of the process for determining the optimal pipeline outlet pressure provided by the present invention.

[0036] Figure 6 This is a schematic diagram comparing the measured data and simulation data of the centrifugal pump outlet flow provided by the present invention.

[0037] Figure 7 This is a schematic diagram comparing the measured data and simulation data of the centrifugal pump outlet pressure provided by the present invention.

[0038] Figure 8 This is a flowchart of the process for determining PID controller parameters based on particle swarm optimization algorithm provided by the present invention.

[0039] Figure 9 This is a diagram illustrating the iterative process of centrifugal pump outlet pressure based on PID control, provided by the present invention.

[0040] Figure 10 This is a diagram illustrating the iterative process of PID control parameters provided by this invention.

[0041] Figure 11 This is another flowchart illustrating the method for determining intelligent pressure optimization and control parameters of the apron fuel supply system provided by the present invention.

[0042] Figure 12 This is a schematic diagram of the intelligent pressure optimization and control parameter determination device for the apron fuel supply system provided by the present invention.

[0043] Figure 13 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0045] The following is combined Figures 1-13 The present invention describes a method and apparatus for intelligent pressure optimization and determination of control parameters for an apron fuel supply system.

[0046] To facilitate a clearer understanding of the technical solutions of the various embodiments of this application, some technical content related to the various embodiments of this application will be introduced first.

[0047] In the operation and maintenance of apron refueling systems, optimizing the outlet pressure of the refueling pipelines (inlet pressure of the apron pipeline network) is crucial. It not only affects refueling efficiency and energy consumption but also directly relates to the safe operation of pipelines and equipment. Appropriate pressure reduces flow resistance in the pipelines, improving refueling efficiency and enabling efficient and rapid refueling. Conversely, excessive pressure can cause pipelines and related equipment to be subjected to excessive stress, increasing the risk of pipe bursts, leaks, and equipment damage. By properly adjusting the pressure, this can be avoided, reducing wear and tear on pipelines and equipment and ensuring the safety and reliability of the system. Furthermore, optimizing the pressure effectively reduces energy consumption and operating costs during refueling. By reducing unnecessary energy waste, the overall energy demand and expenses of the system are lowered. Stable pressure also ensures the continuity and reliability of refueling supply, providing sufficient fuel to aircraft in a timely and stable manner to meet their refueling needs and maintain normal airport operations.

[0048] In the operation and maintenance of pipelines for apron fuel supply systems, optimizing and regulating pressure is a crucial aspect. By scientifically and rationally controlling the pressure in the pipelines, efficient fuel flow can be ensured, reducing resistance and energy consumption, thereby improving fuel delivery efficiency. Appropriate pressure also effectively prevents wear and damage to pipelines caused by excessive pressure, ensuring the safe operation of pipelines and equipment and extending their service life. Simultaneously, maintaining stable pressure helps ensure the continuity and reliability of fuel supply, avoiding supply interruptions due to abnormal pressure. Through these measures, preventative maintenance of the pipeline system can be achieved, allowing for timely detection and adjustment of abnormalities, preventing potential failures, and ensuring the efficient, reliable, and economical operation of the fuel supply system.

[0049] Research on PID optimization control methods for existing apron refueling systems in related fields is relatively scarce, and related research is still in the exploratory and initial stages. CN118399844A, "A PID-based Motor Control Method," employs a PID enhanced control model to control motor speed. A temperature correction is added to the PID enhanced control model to reduce its output, decrease motor speed, wait for temperature to drop, extend motor lifespan, and improve the stability and reliability of the entire automated system. This addresses the problem that existing PID control models do not consider the impact of temperature changes on motor performance, leading to performance degradation or damage in high-temperature environments. CN114564052A, "Pressure Control Method and Device, Electronic Equipment, and Storage Medium for Apron Refueling Systems," first establishes and stabilizes a computer-based apron network model to obtain steady-state operating information. Then, based on the steady-state information and a refueling event list, transient simulation analysis is performed to analyze the changes in flow rate and pressure over time. Finally, by setting controller parameters and adjusting pump speed and start / stop operations, the refueling pressure is ensured to remain stable within the preset range, thus solving the problem of transient hydraulic simulation and automatic control of refueling pressure in the apron pipeline network, enabling the refueling pressure of the apron fuel supply system to remain stable.

[0050] Traditional PID control-based regulation methods optimize motor speed in real time only from an algorithmic improvement perspective, neglecting the interrelationships between production line equipment. While this method can improve the performance of individual motors to some extent, it ignores the synergistic effects and mutual influences between production line equipment, potentially leading to poor overall system efficiency. Furthermore, relying solely on PID control algorithms cannot dynamically adapt to changing operating conditions and external disturbances during production, easily resulting in unstable regulation effects. The method proposed in "Pressure Control Method and Device for Apron Fuel Supply System, Electronic Equipment, and Storage Medium" establishes a steady-state model of the apron pipeline network and ensures pipeline pressure values ​​during refueling through transient simulation and controller adjustment. However, this method fails to fully consider the system's real-time requirements and the optimization of pressure values ​​and PID control parameters.

[0051] Figure 1 This is one of the flowcharts illustrating the intelligent pressure optimization and control parameter determination method for the apron fuel supply system provided by the present invention, such as... Figure 1 As shown, the method includes the following:

[0052] Step 101: Establish the first objective model; the first objective model is used to minimize the energy consumption of the apron fuel supply system.

[0053] Specifically, in this embodiment, a first target model is first established, which is used to minimize the energy consumption of the apron refueling system. That is, in the process of optimizing the PID parameters of the apron refueling system, this application needs to minimize the energy consumption of the apron refueling system, thereby achieving energy consumption optimization and precise control of the apron refueling system.

[0054] Step 102: Based on the first target model, determine the target outlet pressure of the apron fuel supply system; the target outlet pressure represents the outlet pressure of the apron fuel supply system when minimizing the energy consumption of the apron fuel supply system.

[0055] Specifically, since the energy consumption of the apron fuel supply system is related to the outlet pressure of the fuel supply pipeline, the first objective model can be used to determine the outlet pressure of the apron fuel supply system that minimizes its energy consumption, thus obtaining the optimal pipeline pressure value. Based on this optimal pipeline pressure value, not only can the energy consumption of the apron fuel supply system be effectively controlled and optimized, but the risks of pipe bursts, leaks, and equipment damage can also be reduced.

[0056] Step 103: Determine the control parameters corresponding to the apron fuel supply system based on the target outlet pressure of the apron fuel supply system.

[0057] Specifically, after determining the target outlet pressure of the apron fuel supply system based on the first target model, the corresponding control parameters of the apron fuel supply system can then be determined. In other words, by using the control parameters of the apron fuel supply system, the outlet pressure of the system can be made to the optimal pipeline pressure value, thereby achieving the effect of controlling and optimizing the energy consumption of the apron fuel supply system and reducing the risk of pipe bursts, leaks, and equipment damage.

[0058] The method described in the above embodiments determines the optimal pipeline pressure value by optimizing the energy consumption of the apron fuel supply system; based on the optimal pipeline pressure value, the corresponding control parameters of the apron fuel supply system are determined, thereby achieving accurate determination of the control parameters, ensuring optimal energy consumption and outlet pressure of the fuel supply system, and reducing the risk of pipe bursts, leaks, and equipment damage.

[0059] In one embodiment, the first target model is established based on the following formula:

[0060]

[0061] in, This indicates the energy consumption of the apron fuel supply system; Indicates rotational speed; Indicates the outlet pressure of the apron fuel supply system; This indicates the number of pumps started in the apron fuel supply system; This indicates the peak flow rate of the pump in the apron fuel supply system.

[0062] Specifically, by simulating the power and flow rate of a single centrifugal pump as a function of pump frequency, Figure 2 shows that the power of the centrifugal pump increases with increasing pump frequency. Comparing Figures 3 and 4, it can be observed that the mass flow rate and outlet pressure of the centrifugal pump increase with increasing pump frequency, with the outlet pressure showing a higher trend than the outlet flow rate. Therefore, through the above experiments and simulations, it can be determined that the energy consumption and fuel delivery efficiency of the airport fuel depot supply system are most significantly affected by the outlet pressure of the centrifugal pump pipeline; that is, the energy consumption of the airport fuel depot supply system is strongly correlated with the outlet pressure of the apron fuel supply system.

[0063] Furthermore, energy loss occurs during oil transportation, primarily due to friction between the oil in the centrifugal pump and the pipeline. Optionally, equipment data for centrifugal pumps in airport oil depots is shown in Table 1.

[0064] Table 1

[0065]

[0066] Therefore, in establishing the first objective model, in addition to incorporating the outlet pressure value of the apron fuel supply system, parameters such as the maximum instantaneous flow rate, number of pumps in operation, and rotational speed of the centrifugal pumps also need to be included. Optionally, the first objective model is as follows:

[0067] (1)

[0068] in, This indicates the changed rotational speed, in r / min; Indicates the number of pumps started; This indicates the pump's actual maximum peak flow rate. .

[0069] Optionally, the inlet pressure of the centrifugal pump The constraints are as follows:

[0070]

[0071] According to the similarity law, if the shape of the performance curve of a pump at a certain speed or impeller diameter is known, the similarity law can be used to predict the performance of the same pump at different speeds or with different impeller diameters with high accuracy. Therefore, the pressure output of a centrifugal pump at different speeds can be calculated, as shown in the following formula:

[0072] (2)

[0073] in, For centrifugal pumps at speed The outlet pressure value, MPa.

[0074] Substituting formula (2) into formula (1), we can obtain the first objective model with the goal of minimizing the energy consumption of the oil supply pipeline system:

[0075] .

[0076] In other words, based on the output characteristics of the centrifugal pump in the apron fuel supply system, the outlet pressure of its pipeline is determined as the decision variable, and the minimum energy consumption of the fuel supply system is taken as the optimization objective to establish the first objective model.

[0077] The method described in the above embodiment determines the factors affecting the energy consumption of the apron fuel supply system, and then establishes a first target model with the minimum energy consumption of the fuel supply pipeline system as the optimization objective. The optimal pipeline pressure value and control parameters of the apron fuel supply system can be determined through the first target model, thereby achieving accurate determination and precise control of the control parameters of the apron fuel supply system.

[0078] In one embodiment, determining the target outlet pressure of the apron fuel supply system based on a first target model includes:

[0079] Based on the genetic algorithm, the first objective model is solved to determine the target outlet pressure of the apron fuel supply system.

[0080] Specifically, in this embodiment of the application, a genetic evolution algorithm is used to adjust the output flow rate, number of pumps in operation, and rotation speed of the centrifugal pump through mutation, crossover, and selection of the population to obtain the optimal outlet pressure value. The main process is shown in Figure 5.

[0081] 1) Initialize population-related parameters: Set the maximum instantaneous flow rate, number of pumps in operation, and rotational speed of the centrifugal pumps as variables with a value range of [value range missing]. , , Population size Maximum number of iterations Crossover probability Probability of mutation Establishing a relationship between variables and binary strings using binary encoding:

[0082]

[0083] in This is the decimal number corresponding to the binary string. The minimum value of the variable. The maximum value of the variable is determined, and then the fitness value of each individual in the population is calculated. A sorting method is used to select individuals from the population. First, the fitness values ​​of the n=10 individuals are sorted in ascending order to obtain the sorted individual positions. Based on the sorted individual positions, a linear scaling method is used to assign higher fitness values ​​to individuals with higher rankings.

[0084]

[0085] in This represents the position number of the nth individual after sorting. Then, the percentage of fitness value is calculated, which is the proportion of each individual's fitness value in the total fitness value, and is used for subsequent selection operations.

[0086] Based on the sorted population and the calculated fitness values, a roulette wheel selection method is used to generate a new generation of individuals.

[0087] The generated new generation of individuals undergoes a crossover operation. Individuals are randomly paired within the existing population, and binary gene segments are exchanged between each pair under a certain crossover probability, thus generating new individuals. The specific process includes extracting individual parameters, random pairing, and crossover based on the probability. It decides whether to perform crossover, generates crossover points and exchanges gene fragments, and updates and returns to a new population.

[0088] After the crossover operation, the final step is mutation. This involves iterating through each individual in the population and randomly mutating each bit of its binary code with a certain mutation probability. Changing 0 to 1 or 1 to 0 is done by updating the mutated individuals into the new population.

[0089] The fitness value of each individual in the population is recalculated, the optimal solution is updated by binary decoding, and then the current average fitness value is calculated. The process continues until the change in the average fitness value falls below a set threshold. Or it exceeds the maximum number of iterations If the best solution is found, stop the iteration; otherwise, continue with selection, crossover, and mutation operations.

[0090] Therefore, the optimal individual is obtained. ,Right now Then the optimal outlet pressure of the pipeline is:

[0091]

[0092] The method described in the above embodiments, based on a genetic algorithm, determines the optimal outlet pressure value of the apron fuel supply system that minimizes the energy consumption of the apron fuel supply system. This achieves the optimization of the outlet pressure value of the apron fuel supply system, thereby enabling precise control of the energy consumption of the apron fuel supply system and reducing the risk of pipe bursts, leaks, and equipment damage.

[0093] In one embodiment, the control parameters corresponding to the apron fuel supply system are determined based on the target outlet pressure of the apron fuel supply system, including:

[0094] A second objective model is established based on the target outlet pressure of the apron fuel supply system. The second objective model is used to minimize the difference between the simulation result of the outlet pressure of the apron fuel supply system and the target outlet pressure of the apron fuel supply system.

[0095] Based on the second objective model, determine the control parameters corresponding to the apron fuel supply system.

[0096] Specifically, in this embodiment of the application, after determining the target outlet pressure of the apron fuel supply system, the minimum difference between the simulation result of the outlet pressure of the apron fuel supply system and the target outlet pressure of the apron fuel supply system is used as the second target model; then, by solving the second target model, the optimal control parameters of the apron fuel supply system can be obtained, thereby realizing the accurate determination and precise control of the control parameters of the apron fuel supply system.

[0097] Optionally, if the target outlet pressure of the apron fuel supply system is determined to be 0.78 MPa by solving the first objective model based on a genetic algorithm, then the second objective model is:

[0098] =

[0099] in, This represents the difference between the simulation results of the outlet pressure of the apron fuel supply system and the target outlet pressure of the apron fuel supply system. The simulation results represent the outlet pressure of the apron fuel supply system; This indicates the target outlet pressure of the apron fuel supply system.

[0100] Optionally, in this embodiment of the application, a real-time simulation model of the apron fuel supply system is constructed based on the mechanistic characteristics of the centrifugal pump to simulate the outlet pressure of its pipeline. Optionally, the real-time simulation model mainly consists of three parts: a centrifugal pump module, a pipeline loss module, and a condition monitoring subsystem.

[0101] The input parameters of a centrifugal pump are the motor control frequency (Hz) and inlet pressure (MPa); the output parameters are the outlet pressure (MPa) and flow rate. The centrifugal pump is driven by an electric motor. The pump body and suction pipe are filled with liquid. The motor drives the impeller to rotate at high speed, and the impeller, in turn, rotates the liquid between the blades. Due to centrifugal force, the liquid is thrown from the center of the impeller to the outer edge and flows out at a higher pressure through the discharge outlet. Simultaneously, a vacuum is created at the center of the impeller due to the liquid being thrown out. The pressure at the liquid surface in the inlet storage tank (heat well, water tank, storage tank, etc.) is higher than that at the center of the impeller. Therefore, the liquid in the storage tank enters the pump under the pressure difference. The impeller rotates continuously, and the liquid is continuously drawn in and discharged.

[0102] By querying the performance curve data of the centrifugal pump, its performance curve can be obtained through fitting, thus completing the hydraulic characteristic modeling of the centrifugal pump. The centrifugal pump module mainly includes: an oil tank, a solver, fluid characteristic settings, a centrifugal pump, a general piping module, a centrifugal angular velocity source, a gain module, and a variable area orifice module in the blockage subsystem. The general piping module simulates ordinary, unbent pipes; its diameter and length are derived from the process installation diagram of the pump room. The variable area orifice module simulates pipe blockage and allows control over the degree of blockage. The motor providing the centrifugal pump's rotational speed is an asynchronous motor, with the shaft speed controlled by frequency control. The gain module converts the frequency into shaft speed. The ideal angular velocity source outputs the rotational speed based on the control signal 's', inputting the speed into the centrifugal pump module. To simulate energy loss caused by friction between the oil and the pipe, a piping loss module is built after the centrifugal pump module. This module mainly consists of a pipe interface module, a general piping module, an elbow-shaped pipe module, a constant area orifice module, a flow monitoring module, liquid transfer, and a filter. The pipeline interface module is mainly used to simulate the connection between pipes of different diameters; the elbow-shaped pipe module is mainly used to simulate bends in the pipeline system; the ordinary pipe module is used to simulate ordinary pipes without bends; the constant area orifice module is used to control the leakage area. The filter subsystem consists of two parts: the coalescing filter element and the separating filter element subsystem, corresponding to the coalescing filter element and the separating filter element in the equipment, respectively. The material inside the filter undergoes four processes: filtration, coalescence, sedimentation, and separation, thereby removing impurities, moisture, alkali, and water-soluble organic acid salts. This subsystem is mainly used to monitor the output pressure and flow rate of the centrifugal pump, simulating real pressure and flow sensors. Depending on the actual sensor installation location, the module is inserted into different positions on the model.

[0103] In the apron fuel supply system, the parameters of the control variable output are mainly determined by setting the error between the optimal pipeline outlet pressure of the centrifugal pump and the actual outlet pressure. This determines whether the entire control system should increase or decrease the pressure, thereby realizing the dynamic adjustment of the motor rotation frequency in the centrifugal pump simulation model.

[0104] Optionally, after building a real-time simulation model of the apron fuel supply system using Simulink, the simulated outlet flow rate and outlet pressure can be compared with the measured signal outlet flow rate and outlet pressure, as shown in Figure 6. Figure 7 As shown, this determines the accuracy of the simulation results and allows for correction of the simulation model. Optionally, if the simulated signal is numerically similar to the measured signal and has the same basic trend, it indicates that the simulation accuracy of the model is high. (The error between the two is: ).

[0105] The method in the above embodiment, after determining the target outlet pressure of the apron fuel supply system, minimizes the difference between the simulation result of the outlet pressure of the apron fuel supply system and the target outlet pressure of the apron fuel supply system as the second objective model; then, by solving the second objective model, the optimal control parameters of the apron fuel supply system can be obtained, so as to realize the accurate determination and precise control of the control parameters of the apron fuel supply system.

[0106] In one embodiment, the control parameters corresponding to the apron fuel supply system are determined based on the second target model, including:

[0107] Based on the particle swarm optimization algorithm, the second objective model is solved to determine the control parameters corresponding to the apron fuel supply system.

[0108] Specifically, in this embodiment, the particle swarm optimization algorithm is used to solve the second objective model to obtain the optimal control parameters of the apron fuel supply system, as detailed below. Figure 8 As shown:

[0109] 1) First, set the dimension of the particle swarm. The number of particles acceleration constant Inertia factor The particle velocity is Maximum number of iterations .

[0110] 2) Calculate the particle's fitness value: Compare the calculation result with the individual extreme value and the population extreme value to obtain the optimal solution, which includes the individual optimal solution. The algorithm formula for simulating the PID controller output is as follows:

[0111]

[0112] The simulated sampling time Output Let e ​​be the discrete error over the time scale. Then, the pipe outlet pressure output of the real-time simulation model of the centrifugal pump is:

[0113]

[0114] in, For the mapping relationship of the real-time simulation model of the centrifugal pump, the objective function is the fitness function, which for the PID controller is the function that measures the performance index of the PID controller. Therefore, the integral of squared deviation (ISE) function, which measures the system's regulation quality, is adopted:

[0115]

[0116] 3) Adjusting particle speed and position: This is done by comparing the current particle speed with a specified speed range. The same method is used to adjust particle position. If a particle's position exceeds the set maximum range, it is taken as the maximum boundary of the particle swarm; if the particle's position does not exceed the set minimum range, it is taken as the minimum boundary of the particle swarm. Particle speed and position can be adjusted using the following formula:

[0117]

[0118]

[0119] 4) Determine if the algorithm has reached the termination stage: If the particle swarm algorithm has reached the maximum number of updates and iterations, stop the operation and finally obtain the optimal solution; otherwise, continue to step 2 for iteration.

[0120] Real-time simulation results of centrifugal pumps based on PID control are as follows: Figure 9 As shown, after the particle swarm optimization algorithm has iterated more than four times, the outlet pressure of the main pipeline of the aviation fuel production line, i.e., the simulated output of the pump, stabilizes at 0.78 MPa. PID control parameters The iteration results are as follows Figure 10 As shown, the parameters are visible. The system gradually stabilizes as the number of iterations increases, indicating that the PID control parameters tuned using the particle swarm optimization algorithm are searched relatively quickly.

[0121] The method described in the above embodiments establishes an energy loss model (first objective model) based on the characteristics of centrifugal pumps during oil transportation, calculates the optimal outlet pressure value using a genetic algorithm, and constructs a real-time simulation model of the centrifugal pump using Simulink to simulate the real centrifugal pump equipment and the PID control module of the motor. Finally, based on the optimized outlet pressure value, and taking the error between the optimal pressure value and the pipeline pressure value output by the real-time simulation as the optimization objective, the PID control parameters are optimized using a particle swarm optimization algorithm to achieve energy consumption optimization and precise control of the oil depot supply system, ensuring the efficiency and safety of pipeline transportation and reducing pipeline transportation energy consumption.

[0122] For example, in the fuel supply system of a large international airport, centrifugal pumps are responsible for transporting fuel from storage tanks to various refueling stations. To ensure the stability of fuel supply and the efficient operation of the system, the centrifugal pumps must maintain optimal operating conditions under different operating circumstances. Traditional control methods struggle to simultaneously consider system energy consumption and equipment interdependencies, resulting in high energy consumption and poor operating efficiency. To address this need, this application provides an intelligent pressure optimization and control parameter determination method for an apron fuel supply system, applied to the optimization and control of the apron fuel supply system. Figure 11 As shown, the specific process is as follows:

[0123] (1) Based on the output characteristics of the centrifugal pump in the apron fuel supply system, the outlet pressure of its pipeline is determined as the decision variable, i.e., the inlet pressure of the apron pipeline network. The first objective model is established with the minimum energy consumption of the fuel supply system as the optimization objective. That is, based on the characteristics of the centrifugal pump in the fuel transportation process, an energy loss model is established, and the maximum instantaneous flow rate, number of pumps in operation, and speed of the centrifugal pump are used as constraints. The optimization objective is to minimize the system energy consumption through power relationships.

[0124] (2) Using a genetic evolution algorithm, the output flow rate, number of pumps in operation, and rotation speed of the centrifugal pump are adjusted by mutation, crossover, and selection of the population to obtain the optimal individual and calculate the optimal outlet pressure of the pipeline. That is, the optimal pipeline outlet pressure value is calculated using a genetic evolution algorithm.

[0125] (3) Based on the mechanistic characteristics of the centrifugal pump, a real-time simulation model is constructed to simulate the outlet pressure of its pipeline. A second objective model is established with the optimal outlet pressure of the oil supply pipeline as the optimization objective. Optionally, based on the mechanistic characteristics of the centrifugal pump, a high-fidelity real-time simulation model of the centrifugal pump is constructed using Simulink, significantly improving the real-time performance of the centrifugal pump control. This simulation model can accurately simulate the operating state of the centrifugal pump under different working conditions, providing a reliable foundation for subsequent control strategy optimization. Based on this, a PID controller for motor frequency control is simulated to regulate the outlet pressure of the centrifugal pump in real time.

[0126] (4) Construct a PID controller for controlling the motor input of the centrifugal pump. Use the particle swarm optimization algorithm to determine the search space of the particles and obtain the optimal parameters of the centrifugal pump PID controller. That is, optimize the PID control parameters through the particle swarm optimization algorithm so that the output pressure of the centrifugal pump is always kept at the optimal pressure value, ensuring the energy consumption optimization and precise control of the oil supply system.

[0127] The method described in the above embodiments significantly reduces the energy consumption of the oil supply system and improves the operating efficiency and stability of the centrifugal pump. Furthermore, this method not only improves operating efficiency and reduces energy consumption in airport oil depot supply systems, but also has broad application prospects in industries such as transportation, oil and gas, chemicals, water treatment, energy and power, intelligent manufacturing, and environmental protection. By optimizing the operating parameters of the fluid transport system, this method can improve the overall system efficiency, reduce energy consumption, ensure the safe operation of equipment, and promote its sustainable development, providing strong technical support for the intelligent and efficient operation of various industries.

[0128] The intelligent pressure optimization and control parameter determination device for an apron fuel supply system provided by the present invention will be described below. The intelligent pressure optimization and control parameter determination device for an apron fuel supply system described below can be referred to in correspondence with the intelligent pressure optimization and control parameter determination method for an apron fuel supply system described above. The intelligent pressure optimization and control parameter determination device for an apron fuel supply system according to an embodiment of this application is as follows: Figure 12 As shown, it includes:

[0129] Module 1210 is established to create the first objective model; the first objective model is used to minimize the energy consumption of the apron fuel supply system.

[0130] The first determining module 1220 is used to determine the target outlet pressure of the apron fuel supply system according to the first target model; the target outlet pressure represents the outlet pressure of the apron fuel supply system when minimizing the energy consumption of the apron fuel supply system.

[0131] The second determining module 1230 is used to determine the control parameters corresponding to the apron fuel supply system based on the target outlet pressure of the apron fuel supply system.

[0132] Figure 13 A schematic diagram of the physical structure of an electronic device is provided. This electronic device may include a processor 1310, a communications interface 1320, a memory 1330, and a communication bus 1340. The processor 1310, communications interface 1320, and memory 1330 communicate with each other via the communication bus 1340. The processor 1310 can call logical instructions from the memory 1330 to execute a method for intelligent pressure optimization and determination of control parameters for an apron refueling system. This method includes: establishing a first target model; the first target model is used to minimize the energy consumption of the apron refueling system; determining the target outlet pressure of the apron refueling system based on the first target model; the target outlet pressure represents the outlet pressure of the apron refueling system corresponding to minimizing the energy consumption of the apron refueling system; and determining the corresponding control parameters of the apron refueling system based on the target outlet pressure.

[0133] Furthermore, the logical instructions in the aforementioned memory 1330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0134] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the intelligent pressure optimization and control parameter determination method for the apron refueling system provided by the above methods. The method includes: establishing a first target model; the first target model is used to minimize the energy consumption of the apron refueling system; determining the target outlet pressure of the apron refueling system according to the first target model; the target outlet pressure represents the outlet pressure of the apron refueling system corresponding to minimizing the energy consumption of the apron refueling system; and determining the corresponding control parameters of the apron refueling system according to the target outlet pressure of the apron refueling system.

[0135] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a method for intelligent pressure optimization and determination of control parameters for an apron refueling system provided by the methods described above. This method includes: establishing a first target model; the first target model being used to minimize the energy consumption of the apron refueling system; determining a target outlet pressure of the apron refueling system based on the first target model; the target outlet pressure representing the outlet pressure of the apron refueling system corresponding to minimizing its energy consumption; and determining the corresponding control parameters of the apron refueling system based on the target outlet pressure of the apron refueling system.

[0136] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0137] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent pressure optimization and determination of control parameters for an apron fuel supply system, characterized in that, The method comprises: establishing a first target model; the first target model is used for minimizing energy consumption of the apron fuel supply system; wherein the energy consumption of the apron fuel supply system and the outlet pressure of the apron fuel supply system are highly relevant; determining a target outlet pressure of the apron fuel supply system according to the first target model; the target outlet pressure represents the outlet pressure of the apron fuel supply system corresponding to the minimized energy consumption of the apron fuel supply system; determining the corresponding control parameter of the apron fuel supply system according to the target outlet pressure of the apron fuel supply system; the first target model is established based on the following formula: Wherein, f represents the energy consumption of the apron fuel supply system; n represents the rotation speed; p out represents the outlet pressure of the apron fuel supply system; n 台 represents the number of starting stations of the pump in the apron fuel supply system; Q 需 represents the peak flow of the pump in the apron fuel supply system.

2. The method of determining the intelligent pressure optimization and control parameters of the airport fuelling system according to claim 1, characterized in that, the determining of the target outlet pressure of the apron fuel supply system according to the first target model comprises: solving the first target model based on a genetic algorithm to determine the target outlet pressure of the apron fuel supply system.

3. The method of determining the intelligent pressure optimization and control parameters of a ramp fueling system of claim 1 or 2, wherein, the determining of the corresponding control parameter of the apron fuel supply system according to the target outlet pressure of the apron fuel supply system comprises: establishing a second target model according to the target outlet pressure of the apron fuel supply system; the second target model is used for minimizing the difference between the simulation result of the outlet pressure of the apron fuel supply system and the target outlet pressure of the apron fuel supply system; determining the corresponding control parameter of the apron fuel supply system according to the second target model.

4. The method of determining the intelligent pressure optimization and control parameters of the airport fuelling system according to claim 3, characterized in that, the second target model is established based on the following formula: Min e(t) = P sim - P; where e(t) represents the difference between the simulation result of the outlet pressure of the airport fuel supply system and the target outlet pressure of the airport fuel supply system; P sim represents the simulation result of the outlet pressure of the airport fuel supply system; P represents the target outlet pressure of the airport fuel supply system.

5. The method of determining the intelligent pressure optimization and control parameters of the airport fuelling system according to claim 4, characterized in that, the determining of the corresponding control parameter of the apron fuel supply system according to the second target model comprises: solving the second target model based on a particle swarm algorithm to determine the corresponding control parameter of the apron fuel supply system.

6. A device for intelligent pressure optimization and determination of control parameters for an apron fuel supply system, characterized in that, The method comprises: a establishing module, configured to establish a first target model; the first target model is used for minimizing energy consumption of the apron fuel supply system; wherein the energy consumption of the apron fuel supply system and the outlet pressure of the apron fuel supply system are highly relevant; a first determining module, configured to determine a target outlet pressure of the apron fuel supply system according to the first target model; the target outlet pressure represents the outlet pressure of the apron fuel supply system corresponding to the minimized energy consumption of the apron fuel supply system; the first target model is established based on the following formula: Wherein, f represents the energy consumption of the apron fuel supply system; n represents the rotating speed; p out represents the outlet pressure of the apron fuel supply system; n 台 represents the number of starting pumps in the apron fuel supply system; Q 需 represents the peak flow of the pump in the apron fuel supply system; a second determining module, configured to determine the corresponding control parameter of the apron fuel supply system according to the target outlet pressure of the apron fuel supply system.

7. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to realize the intelligent pressure optimization and control parameter determination method of the apron fuel supply system according to any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the intelligent pressure optimization and control parameter determination method of the apron fuel supply system according to any one of claims 1 to 5.

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