Air compressor load distribution method and device, computer equipment and storage medium

Abstract

The invention provides an air compressor load distribution method and device, computer equipment and a storage medium, and belongs to the field of equipment control. The method comprises the steps that physical property parameters of compressed gas in multiple parallel air compressors are obtained; establishing a load distribution model by taking the load distribution proportion of each compressor as a decision variable and minimizing the total energy consumption of the compressor station as a target function and combining the equipment characteristics of the compressors and the gas physical property parameters, and setting outlet pressure constraint and outlet temperature constraint; performing iterative optimization solution on the load distribution model by adopting a dogvessel colony algorithm to generate an optimal load distribution scheme meeting the outlet pressure constraint and the outlet temperature constraint; and according to the optimal load distribution scheme, dynamically distributing the operation load of the plurality of parallel air compressors. According to the method, efficiency attenuation caused by long-term overload operation of a single device is avoided, the overall energy efficiency of the system is improved through collaborative optimization, and dual benefits of energy efficiency optimization and device service life prolonging are achieved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of device control, and particularly relates to an air compressor load distribution method and device, computer equipment and a storage medium. BACKGROUND

[0002] As a key equipment in industrial production, the energy consumption of an air compressor accounts for a large proportion of the total energy consumption. The traditional air compressor control method has problems such as high energy consumption and low operation efficiency. In particular, in the fields of mining, railway hump and large-scale manufacturing, the working efficiency and energy consumption management of an air compressor group are particularly important. In the actual operation process of a multi-machine joint control air compressor system, the system is often affected by various complex factors, such as frequent fluctuations in working conditions, objective differences in performance between compressors, and continuous changes in load demand of production processes. These factors interact with each other, often leading to uneven load distribution in the air compressor system, which cannot achieve an ideal state. Uneven load distribution reduces system energy efficiency, significantly reduces energy utilization rate, increases energy consumption cost and maintenance cost, and thus adversely affects the economic benefits of enterprises. Therefore, relevant intelligent load distribution methods have attracted widespread attention from scholars.

[0003] In the prior art, the operating parameters and energy consumption of a compressor group are monitored in real time, and the load distribution ratio is dynamically adjusted to minimize energy consumption. However, due to the performance differences of the compressor group, changes in raw gas flow and pressure, and the influence of external environment, the load distribution often cannot achieve an ideal state. Some compressors are operated at high load for a long time, while other compressors are idle, resulting in a decrease in system energy efficiency and reliability.

[0004] In summary, although the existing method improves the energy efficiency of the compressor system to some extent, it still has problems such as uneven load distribution and limited energy efficiency improvement. SUMMARY

[0005] To solve the above problems, the present application provides an air compressor load distribution method, device, computer equipment and storage medium.

[0006] To achieve the above purpose, the present application provides the following technical solutions: An air compressor load distribution method, the method comprising: obtaining the physical property parameters of compressed gas in a plurality of parallel air compressors; taking the load distribution ratio of each compressor as a decision variable, taking the minimization of total energy consumption of the compressor station as an objective function, establishing a load distribution model for compressor operation based on the equipment characteristics of the compressor and the gas physical property parameters, and setting outlet pressure constraints and outlet temperature constraints; The barrel sea slug colony algorithm is used to iteratively optimize and solve the load distribution model, so as to generate an optimal load distribution scheme meeting the outlet pressure constraint and the outlet temperature constraint. According to the optimal load distribution scheme, the operating load of the plurality of parallel air compressors is dynamically allocated.

[0007] Optionally, the physical property parameters include gas density and gas compressibility factor, and the obtaining of the physical property parameters of the compressed gas in the plurality of parallel air compressors comprises: A BWRS gas state equation of the compressed gas inside the air compressor is constructed, the temperature, pressure and universal gas constant inside the air compressor are brought in, and the molar volume of the compressed gas inside the air compressor is determined, the BWRS gas state equation being: ; wherein, represents a universal gas constant, represents pressure, represents temperature, represents molar volume, , , and represent coefficients of the equation. The molar mass of air is obtained, and the gas density is determined based on the molar volume and the density formula; Based on the molar volume and the universal gas constant, the gas compressibility factor is calculated.

[0008] Optionally, the calculation formula of the gas compressibility factor is: ; wherein, represents a universal gas constant, represents pressure, represents temperature, represents molar volume.

[0009] Optionally, the formula of the objective function is: ; wherein, represents total energy consumption of the air compression station, represents power of the i-th compressor, represents flow rate, , , represent density, temperature and compressibility factor of the gas, represents total number of compressors, represents operating time.

[0010] Optionally, the outlet pressure constraint and the outlet temperature constraint are respectively defined as: ; wherein, and represent the outlet and inlet pressure of the i-th compressor, respectively, and represent the volume flow rate of the inlet and outlet, respectively, represents the adiabatic index, represents the maximum allowable pressure of the i-th compressor; ; wherein, and represent the outlet and inlet temperature of the i-th compressor, respectively, represents the maximum allowable temperature of the i-th compressor.

[0011] Optionally, the adopting the sea cucumber colony algorithm to iteratively optimize and solve the load distribution model comprises: initializing a colony, and randomly generating a set of load distribution ratios as sea cucumber positions; defining a fitness function to comprehensively evaluate total energy consumption, system stability, and load balance; sorting according to the fitness value and selecting the optimal solution and the worst solution, and iteratively optimizing according to a position update rule; performing constraint verification on the updated load distribution ratio until the optimal scheme is output after the termination condition is met.

[0012] Optionally, the fitness function is defined as: ; wherein, represents the total energy consumption of the i-th load distribution scheme; represents the system stability index, represents the load balance index. is a weight coefficient.

[0013] An air compressor load distribution device, the device comprising: an acquisition module for acquiring the physical property parameters of compressed gas in a plurality of parallel air compressors; a construction module for taking the load distribution ratio of each compressor as a decision variable, taking the minimization of the total energy consumption of the compression station as an objective function, combining the equipment characteristics of the compressor and the gas physical property parameters to establish a load distribution model for the operation of the compressor, and setting outlet pressure constraints and outlet temperature constraints; an optimization module for adopting the sea cucumber colony algorithm to iteratively optimize and solve the load distribution model, and generating an optimal load distribution scheme that satisfies the outlet pressure constraints and the outlet temperature constraints; ​The allocation module is configured to dynamically allocate the operation load of the plurality of parallel air compressors according to the optimal load allocation scheme.

[0014] A computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the air compressor load allocation method.

[0015] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the air compressor load allocation method when executing the program.

[0016] The air compressor load allocation method provided by the application has the following beneficial effects: The application first acquires the physical property parameters of compressed gas in parallel air compressors, provides reliable data basis for load allocation, fully considers the coupling relationship between compressor equipment characteristics and gas physical property parameters when constructing an optimization model with load allocation ratio as a decision variable, ensures operation safety by setting outlet pressure and temperature constraints, then iteratively optimizes by using a sea cucumber colony algorithm, effectively jumps out of a local optimal solution by using the colony intelligence characteristics, quickly converges to a globally optimal solution under complex constraints, and finally generates a load allocation scheme that can significantly improve the load unevenness phenomenon caused by a traditional experience allocation mode. Finally, by adjusting the operation load of each compressor, the total energy consumption of the air compression station is minimized, which not only avoids the efficiency decay caused by long-term overload operation of a single device, but also improves the overall energy efficiency of the system through collaborative optimization. The technical scheme realizes the dual benefits of energy efficiency optimization and device life extension under the premise of ensuring stable outlet parameters, and is especially suitable for air compressor group intelligent control scenes under variable working conditions. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the application and the design scheme thereof, the following will briefly introduce the drawings required by the embodiments. The drawings in the following description are only part of the embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0018] Figure 1 A flowchart of an air compressor load allocation method provided by the application according to an exemplary embodiment.

[0019] Figure 2 A flowchart of another air compressor load allocation method provided by the application according to an exemplary embodiment.

[0020] Figure 3 A flowchart of an optimal load allocation scheme solving method based on a sea cucumber colony algorithm provided by the application according to an exemplary embodiment.

[0021] Figure 4 A partial compressor typical daily load fluctuation data chart is provided according to an exemplary embodiment of the present application.

[0022] Figure 5 A different load distribution model convergence curve comparison schematic diagram is provided according to an exemplary embodiment of the present application.

[0023] Figure 6 A schematic diagram of an air compressor load distribution device is provided according to an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to make the technical solution of the present application better understood by those skilled in the art and to enable them to implement it, the present application will be described in detail below in conjunction with the drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present application, and cannot be used to limit the protection scope of the present application.

[0025] In the present application, as shown in Figure 1 , the gas property parameters are first calculated based on the BWRS equation, which is the basis of the load distribution model and involves molar volume, density and compression factor, etc., which is crucial for the calculation of compressor energy consumption and efficiency. Subsequently, a load distribution model is established by minimizing the energy consumption of the compression station, combining the compressor characteristics and gas parameters, and setting the outlet pressure and temperature as constraint conditions. The jellyfish colony algorithm is used to solve this model, including initialization parameters, generating an initial load distribution scheme, calculating fitness values, sorting and selecting optimal and worst solutions, updating jellyfish positions, and ensuring that the new positions meet the constraints. The iteration process continues until the termination condition is met, and finally the optimal load distribution scheme is output. The whole process is efficient and systematic.

[0026] The technical solutions provided by the embodiments of the present application will be described in detail below in conjunction with the drawings.

[0027] Firstly, the present application provides an air compressor load distribution method, as shown in Figure 2 , comprising the following steps: S101, obtaining the property parameters of the compressed gas in the plurality of parallel air compressors.

[0028] In the multi-compressor control system, the load of each compressor needs to be dynamically adjusted according to the overall demand of the system and the actual capacity of each compressor. This process must be based on the accurate description of the work of the compressor on the gas, otherwise the load distribution will not achieve the optimal. In the process of energy-saving air compressor working on gas, the physical parameters of the gas not only determine the difficulty of the work of the compressor on the gas, but also affect the energy consumption and efficiency of the compressor. Therefore, in order to accurately describe the work of the compressor on the gas, and then build a reasonable objective function to optimize the load distribution, the present application calculates the physical parameters of the gas in combination with the BWRS gas equation.

[0029] The BWRS equation is a semi-empirical cubic equation of state, which is used to describe the behavior of gas under high pressure and low temperature conditions. It takes into account the interaction between gas molecules and can accurately predict the relationship between pressure, volume and temperature of the gas. The BWRS gas equation of the compressed gas in the air compressor is constructed, the temperature, pressure and universal gas constant in the air compressor are brought in, and the molar volume of the compressed gas in the air compressor is determined. The specific equation expression is as follows:

[0030] ; Among them, represents the universal gas constant, represents the pressure, represents the temperature, represents the molar volume, , , and represent the coefficients of the equation, which depend on the type of gas and temperature, which are obtained by simplifying the calculation of air composition. According to the type of gas and temperature, the coefficients of the BWRS equation are calculated based on the energy-saving air compressor as the research object.

[0031] In one embodiment, the coefficients of the BWRS equation are calculated according to the molar fraction of nitrogen and oxygen in the gas composition, the molar volume is determined by substituting the coefficients into the BWRS equation, and the gas density under given pressure and temperature is solved based on the density formula; based on the gas volume and the variable of the BWRS equation, the gas compression factor is calculated.

[0032] Air is mainly composed of nitrogen and oxygen, and also contains a small amount of argon, carbon dioxide and the like. In order to simplify the calculation, only the main contribution of nitrogen and oxygen is considered here. According to the molar fraction of nitrogen and oxygen in the air, the weighted average of the coefficients of their BWRS equation is calculated. For the coefficient , the corresponding weighted average calculation formula is as follows:

[0033] ; Among them, and respectively represent the mole fraction of nitrogen and oxygen in air, and respectively represent the BWRS equation coefficients of nitrogen and oxygen under the internal environment of the air compressor. The equation coefficients of nitrogen and oxygen are , , , respectively substituted into the above formula to determine the BWRS equation coefficients of the gas in the air compressor. The equation coefficient solution is substituted into the above BWRS equation, so that the gas density can be calculated under the given pressure and temperature conditions, and the specific calculation formula is as follows:

[0034] ; wherein, represents the molar mass of the gas. The compressibility factor is an important parameter describing the deviation of the gas from the ideal gas behavior. The specific calculation formula is as follows:

[0035] .

[0036] The , in the BWRS equation obtained by the above solution is substituted into the compressibility factor calculation formula, and the compressibility factor calculation result can be obtained.

[0037] The effective calculation of the gas property parameters can be completed through the above steps, considering the interaction between gas molecules, constructing the BWRS gas state equation, and deducing the calculation method of the molar volume, density and compressibility factor and other property parameters.

[0038] S102, taking the load distribution ratio of each compressor as the decision variable, taking the minimization of the total energy consumption of the compression station as the objective function, combining the equipment characteristics of the compressor and the gas property parameters to establish a load distribution model for the operation of the compressor, and setting the outlet pressure constraint and the outlet temperature constraint.

[0039] In this step, the multi-stage cascade control load distribution model of the air compressor is constructed by taking the minimization of the energy consumption of the compression station as the optimization target, and the constraint conditions are designed in combination with the gas property parameters solved in the above.

[0040] First, it is assumed that all air compressors in the gas compression station are connected in parallel, and each compressor operates independently without interference. In the parallel system, each compressor adjusts the intake volume according to the set load distribution, achieving control of the overall pressure or flow. This assumption ignores the possible slight mutual influence between compressors. Then, the operating state of a single compressor is simplified into two states: normal and off, without considering intermediate states (such as partial load operation). It is also assumed that the intake temperature of each compressor is the ambient temperature, and the temperature change of the gas during pipeline flow is ignored. Based on the above assumptions, the optimization goal is set as minimizing the energy consumption of the gas compression station, and the specific objective function expression is as follows:

[0041] ; wherein, represents the total energy consumption of the gas compression station, represents the power of the i-th compressor, represents the flow rate, , , represents the gas property parameters obtained in the above, i.e. density, temperature and compression factor, represents the total number of compressors, represents the operating time.

[0042] In one embodiment, first, initialization can be performed, setting known conditions such as the total number of compressors, operating time, gas property parameters (density, temperature, compression factor), etc. The load distribution (decision variable) of each stage of compressor can be randomly generated or set according to experience. Then, the objective function value is calculated, and the power of each compressor is calculated according to the current load distribution. The total energy consumption of the gas compression station is obtained by adding the power of all compressors and multiplying by the operating time, and the objective function is to minimize the total energy consumption of the gas compression station. Finally, the constraint conditions are checked, and the outlet pressure and temperature of each stage of compressor are calculated. Check whether the outlet pressure and temperature meet the set maximum allowable value. If the constraint condition is not met, adjust the load distribution and return to the previous step.

[0043] wherein the decision variable is the load distribution of each stage of compressor, and then the above function is subjected to constraint processing. The outlet pressure of each stage of compressor should meet certain requirements, which depends on the gas property parameters and the design of the compressor. The specific pressure limit expression is as follows:

[0044] ; wherein, and represent the outlet and inlet pressures of the i-th compressor, respectively, and represent the volume flow rates at the inlet and outlet, respectively, represents the adiabatic index, represents the maximum allowable pressure of the i-th stage compressor. At the same time, it is also necessary to ensure that the outlet temperature of each stage compressor should not exceed the maximum allowable temperature. When the outlet temperature is too high, the energy consumption of the compressor will increase and the efficiency will decrease. By controlling the outlet temperature, the compressor can be kept running at a higher efficiency, thereby reducing energy consumption. The temperature limit expression is as follows:

[0045] ; wherein, and represent the outlet and inlet temperatures of the i-th stage compressor, respectively, represents the maximum allowable temperature of the i-th stage compressor.

[0046] Through the above steps, with the goal of minimizing the energy consumption of the gas compression station, considering the structure, composition of the compressor equipment and the gas physical property parameters, by accurately calculating the power, flow, pressure and temperature parameters of each stage compressor, it is ensured that the outlet temperature and power of each stage compressor do not exceed the limit value while meeting the total flow and pressure requirements, thereby realizing the minimization of energy consumption.

[0047] S103, adopt the TSP algorithm to iteratively optimize and solve the load distribution model, and generate an optimal load distribution scheme that meets the outlet pressure constraint and the outlet temperature constraint.

[0048] For the load distribution model constructed above, the TSP algorithm is used to optimize and solve it, so as to obtain the optimal load distribution scheme. The solving process of the algorithm is as shown in Figure 3 .

[0049] First, a set of initial solutions (i.e. load distribution schemes) is randomly generated as the positions of the TSP population wherein, represents the number of TSPs, represents the position of the i-th TSP, i.e. the load distribution scheme. The initialization population expression is as follows:

[0050] ; wherein, represents the dimension of the search space, represents the population size. and represent the upper and lower boundaries of the search space, respectively.

[0051] In order to evaluate the pros and cons of each load distribution scheme, a fitness function is defined as which considers multiple factors, including total energy consumption, system stability, load balance, etc. The specific expression is as follows:

[0052] ; wherein, represents the total energy consumption of the jth load distribution scheme; represents the system stability index, represents the load balancing index. is a weight coefficient, used to adjust the influence degree of different factors in the fitness function.

[0053] According to the fitness value, the sea squirts are sorted, and the optimal solution and the worst solution are selected. For each sea squirt except the worst solution, according to its current position and the position of the optimal solution, its position is updated according to the updating rule of the sea squirt swarm algorithm. The updating rule expression is as follows:

[0054] ; wherein, represents the new position of the jth sea squirt, represents the position of the optimal solution, represents the position of the randomly selected sea squirt, represents the position of the worst solution, and respectively represent random numbers. After updating the position, it is necessary to ensure that the new position is within the feasible region. If the new position exceeds the constraint condition (such as total flow, outlet temperature, power, etc.), adjustment needs to be made until the constraint is met. Iterate the above steps until the termination condition is met, and finally output the optimal load distribution scheme. The above-mentioned gas property parameter calculation and load distribution model construction and other related contents are combined, and thus the energy-saving air compressor multi-machine joint control load intelligent distribution method design is completed.

[0055] S104, according to the optimal load distribution scheme, dynamically distributing the operation load of the multiple parallel air compressors.

[0056] In the present application, the steps of the above method are also experimentally demonstrated.

[0057] ​The experiment takes a compressor station with six air compressors as the experimental object. The simulation load distribution control of the equipment in the compressor station is carried out to verify the actual application effect of the method. The total area of the compressor station is about 5000 m2, and the total installed capacity reaches 3000 kW. The average daily gas processing capacity is about 1 million m2. The compressor station is mainly composed of compressor area, control room, gas tank area, cooling system, drying system, filtering system and pipeline network. The compressor area is equipped with six parallel air compressors, each of which is equipped with an independent control system and monitoring instrument. In addition, the experiment uses MATLAB software platform to write and run the program of evolutionary algorithm. By writing the code of sea cucumber colony algorithm and setting the corresponding parameters and constraints, the solution of the load distribution model is realized. The specific parameters of the compressor are shown in Table 1.

[0058] Table 1 Specific parameters of the compressor Each compressor is equipped with a variable frequency speed regulation device, which can adjust the speed and power output according to the actual demand. The loading and unloading time of the compressor is 120s and 60s respectively to ensure the stability and continuity of gas supply.

[0059] In order to realize the simulation load distribution control, the experiment retrieves the original load flow curve of the compressor, and the typical daily load fluctuation of some compressors is shown in Figure 4

[0060] From Figure 4 It can be seen that the current compressor station has unbalanced load of the compressors, and the effective work of the same level compressors cannot be effectively complemented, resulting in waste of resources. In the simulation experiment, by adjusting the load distribution ratio of each compressor, the energy consumption and gas supply of the whole compressor station are observed. The load distribution ratio ranges from 0% to 100%, which can be flexibly adjusted according to the actual demand. In addition, the parameter configuration of sea cucumber colony algorithm is shown in Table 2.

[0061] Table 2 Parameter configuration of sea cucumber colony algorithm Based on the parameter configuration results in Table 2, the proposed method is used to simulate and optimize the load of the six compressors, and the compressor model after distribution is run to verify the effectiveness of the proposed method. In addition, two conventional compressor load control methods are selected as the experimental control group, which are the fuzzy reinforcement learning based control method (conventional method A) and the real-time data based control method (conventional method B). By comparing the actual distribution effect under different strategies, the performance of the method is effectively compared.

[0062] The convergence curves of the three load distribution models are shown in​Figure 5 As shown in the convergence curves of the three compressor load distribution strategies, the load distribution method based on the sea cucumber colony algorithm is superior to the other two methods in terms of convergence speed, energy consumption reduction, and stability improvement. The sea cucumber colony algorithm can quickly converge to the optimal solution, achieve load balancing of the compressors, reduce system energy consumption, and improve stability indicators. The reason why the proposed method converges faster is that the sea cucumber colony algorithm has a unique search mechanism and efficient optimization strategy. Compared with the other two methods, the sea cucumber colony algorithm can more quickly explore the solution space and effectively avoid falling into a local optimal solution during the solving process. The experimental results show that the sea cucumber colony algorithm can quickly locate a better load distribution scheme at the initial stage, and continuously approach the global optimal solution as the number of iterations increases. This fast convergence characteristic not only improves the calculation efficiency, but also ensures the timeliness and accuracy of the load distribution scheme, providing strong support for the stable operation and energy efficiency improvement of the gas compression station.

[0063] The load distribution results of the six compressors after applying the method proposed in the application are shown in Table 3.

[0064] Table 3 Load distribution results of compressors As can be seen from Table 3, although the load of some compressors increases after distribution, the overall load distribution is more balanced. This balanced load distribution helps to reduce the energy consumption difference between compressors and improve the energy efficiency of the entire system.

[0065] In addition, the application also compares the energy efficiency of the gas compression station. The overall energy efficiency ratio (COP) of the gas compression station under different load distribution strategies is used as a comparison index to measure the actual distribution effect of the method. COP measures the ratio of refrigeration capacity (or heating capacity) to input electric power. The higher the value, the more reasonable the distribution strategy and the higher the energy utilization efficiency of the system. The specific experimental results are shown in Table 4.

[0066] Table 4 COP comparison results As can be seen from the experimental results in Table 4, after using the proposed method, the COP value of the gas compression station increases from the initial 3.2 to 4.5, with a high increase rate of 40.6%. This increase is much higher than that of the other two strategies, showing the significant advantage of the proposed method in improving energy efficiency. Although the conventional method A and the conventional method B also improve the energy efficiency ratio of the gas compression station to a certain extent, the increase is relatively small. In contrast, the proposed method optimizes the load distribution strategy, enabling the gas compression station to produce more refrigeration capacity under the same input electric power, thereby achieving a significant increase in energy efficiency ratio. This experimental result not only verifies the effectiveness and feasibility of the proposed method, but also provides a new idea and method for improving the energy efficiency of the gas compression station.

[0067] Load balancing degree is an important indicator to measure the uniformity of load distribution among different compressors. During the operation of the gas compression station, if the load distribution is uneven, some compressors will be overloaded, while others will be underloaded or idle, which not only reduces the overall energy efficiency of the system, but also accelerates the wear and aging of the equipment. Response speed is an important indicator to measure the ability of load distribution strategy to adapt to new demands. In industrial production, due to the continuous change of gas demand, the gas compression station needs to quickly adjust the load distribution strategy to meet the new demand. If the strategy response speed is too slow, it will cause the system to fail to respond to demand changes in time, thereby affecting production efficiency and product quality. In order to comprehensively evaluate the superiority of the proposed method, load balancing degree and response speed are introduced as key indicators. The deviation between the actual load of each compressor and the average load is calculated to evaluate the effect of different load distribution strategies on load balancing. The response time of the system is observed by changing the gas demand to evaluate the response speed of different load distribution strategies. The experimental results are shown in Table 5.

[0068] Table 5 Comparison results of load balancing degree and response speed From Table 5, it can be seen that after using the proposed method, the initial load balancing degree of the gas compression station is 0.26, which is reduced to 0.06 after optimization, with a load balancing degree improvement rate of 85%, which is significantly better than the conventional method A and the conventional method B. This shows that the proposed method performs well in load balancing and can more effectively achieve uniform load distribution among compressors, avoiding the occurrence of overload and underload, which helps to improve the overall energy efficiency of the system and prolong the service life of the equipment. At the same time, in terms of response speed, the proposed method also shows obvious advantages. When the gas demand changes, the average response time of the gas compression station using the proposed method is only 9s, which is much lower than the conventional method A and the conventional method B. This shows that the proposed method can adapt to new demand changes faster and adjust the load distribution strategy in time, thereby ensuring the stability of production efficiency and product quality. Therefore, by combining the experimental results of load balancing degree and response speed, the proposed method has significant advantages in improving the superiority and adaptability of the load distribution strategy of the gas compression station.

[0069] By optimizing the load distribution model, the energy efficiency ratio is significantly improved and uniform load distribution is achieved. Experiments show that this method can accurately control the state of the compressor group, reducing energy consumption by 10%-15%. In the future, the optimization algorithm will be optimized to adapt to larger scale systems and expanded to other industrial equipment collaborative optimization, providing effective solutions for industrial intelligentization and energy saving and emission reduction.

[0070] With the above method, the property parameters of the compressed gas in the parallel air compressor are accurately calculated by the BWRS gas state equation, and reliable data basis is provided for load distribution. When the load distribution ratio is used as the decision variable to build an optimization model, the coupling relationship between the compressor equipment characteristics and the gas property parameters is fully considered, the safety of operation is ensured by setting the outlet pressure and temperature constraints, and then the zooid colony algorithm is used for iterative optimization. The global intelligence characteristics can effectively jump out of the local optimal solution, quickly converge to the global optimal solution under complex constraint conditions, and finally generate a load distribution scheme that can significantly improve the load imbalance phenomenon caused by the traditional experience distribution mode. Finally, by adjusting the running load of each compressor, the total energy consumption of the air compression station is minimized, which not only avoids the efficiency decay caused by long-term overload operation of a single device, but also improves the overall energy efficiency of the system through collaborative optimization. The technical scheme realizes the dual benefits of energy efficiency optimization and equipment life extension under the premise of ensuring stable outlet parameters, and is especially suitable for intelligent regulation and control of air compressor groups under variable working conditions.

[0071] Secondly, the air compressor load distribution device provided by the present application is shown as follows, comprising: Figure 6 The acquisition module 201 is configured to establish the BWRS gas state equation of the compressed gas inside the air compressor, and further calculate the property parameters of the compressed gas in the plurality of parallel air compressors.

[0072] The construction module 202 is configured to take the load distribution ratio of each compressor as a decision variable, take the minimization of the total energy consumption of the air compression station as an objective function, and establish a load distribution model in combination with the equipment characteristics of the compressor and the property parameters of the gas, and set the outlet pressure constraint and the outlet temperature constraint.

[0073] The optimization module 203 is configured to use the zooid colony algorithm to iteratively optimize and solve the load distribution model, and generate an optimal load distribution scheme that satisfies the outlet pressure constraint and the outlet temperature constraint. The distribution module 204 is configured to dynamically distribute the running load of the plurality of parallel air compressors according to the optimal load distribution scheme.

[0074] ​Using the aforementioned device, the physical properties of compressed gas in parallel air compressors are accurately calculated using the BWRS gas state equation, providing a reliable data foundation for load allocation. When constructing an optimization model with the load allocation ratio as the decision variable, the coupling relationship between compressor equipment characteristics and gas physical properties is fully considered. Operational safety is ensured by setting outlet pressure and temperature constraints. Then, the swarm optimization algorithm is used for iterative optimization. Its swarm intelligence characteristics can effectively escape local optima and quickly converge to a globally optimal solution under complex constraints. The resulting load allocation scheme significantly improves the load unevenness caused by traditional experience-based allocation methods. Finally, by adjusting the operating load of each compressor, the total energy consumption of the compressor station is minimized. This avoids efficiency degradation caused by long-term overload operation of a single unit and improves the overall system energy efficiency through collaborative optimization. This technical solution achieves the dual benefits of energy efficiency optimization and equipment life extension while ensuring stable outlet parameters, and is particularly suitable for intelligent control scenarios of air compressor groups under varying operating conditions.

[0075] The present invention also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 2 The steps of the provided air compressor load distribution method.

[0076] This invention also provides a computer device. At the hardware level, the computer device includes a processor, an internal bus, a network interface, memory, and non-volatile memory, and may also include other hardware required for various operations. The processor reads the corresponding computer program from the non-volatile memory into the memory and then executes it to achieve the above-mentioned functions. Figure 2 The steps of the provided air compressor load distribution method.

[0077] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0078] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0079] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0080] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0081] It should be noted that the above-mentioned detailed embodiments can enable those skilled in the art to more fully understand the present application, but in no way limit the present application. Therefore, although the present application has been described in detail in the present specification, those skilled in the art should understand that the present application can still be modified or replaced by equivalents; and all technical solutions and improvements that do not deviate from the spirit and scope of the present application are covered in the protection scope of the present application. Any reference signs in the claims should not be considered as limiting the claims.

Claims

1. A method for distributing load on an air compressor, characterized in that, The method includes: Obtain the physical property parameters of compressed gas in multiple parallel air compressors; Using the load distribution ratio of each compressor as the decision variable and minimizing the total energy consumption of the compressor station as the objective function, a load distribution model for compressor operation is established based on the equipment characteristics of the compressor and the gas physical property parameters, and outlet pressure constraints and outlet temperature constraints are set. The load allocation model is iteratively optimized and solved using the tunic group algorithm to generate the optimal load allocation scheme that satisfies the outlet pressure constraint and the outlet temperature constraint. The operating load of multiple parallel air compressors is dynamically allocated according to the optimal load allocation scheme.

2. The method according to claim 1, characterized in that, The physical properties include gas density and gas compressibility factor. Obtaining the physical properties of the compressed gas in multiple parallel air compressors includes: A BWRS gas equation of state is constructed for the compressed gas inside the air compressor. Substituting the temperature, pressure, and universal gas constant within the air compressor, the molar volume of the compressed gas is determined. The BWRS gas equation of state is as follows: ; in, Represents the universal gas constant. Represents pressure, Represents temperature. Represents molar volume. , , and The coefficients of the equation; Obtain the molar mass of air, and determine the gas density based on the molar volume and density formula; The gas compressibility factor is calculated based on the molar volume and the universal gas constant.

3. The method according to claim 2, characterized in that, The formula for calculating the gas compressibility factor is: ； in, Represents the universal gas constant. Represents pressure, Represents temperature. Represents molar volume.

4. The method according to claim 1, characterized in that, The formula for the objective function is: ； in, This represents the total energy consumption of the compressor station. This represents the power of the i-th stage compressor. Represents traffic, , , These represent the density, temperature, and compressibility factor of the gas. Represents the total number of compressors. This represents the runtime.

5. The method according to claim 1, characterized in that, The outlet pressure constraint and outlet temperature constraint are defined as follows: ； in, and These represent the outlet and inlet pressures of the i-th stage compressor, respectively. and These represent the volumetric flow rates at the inlet and outlet, respectively. Represents the adiabatic index. This represents the maximum permissible pressure of the i-th stage compressor; ； in, and These represent the outlet and inlet temperatures of the i-th stage compressor, respectively. This represents the maximum permissible temperature of the i-th stage compressor.

6. The method according to claim 1, characterized in that, The iterative optimization solution of the load allocation model using the *Slugoides swarm algorithm* includes: Initialize the population and randomly generate a set of load allocation ratios as the locations of the salps; Define a fitness function to comprehensively evaluate total energy consumption, system stability, and load balance. Sort by fitness value and select the best and worst solutions, then iteratively optimize according to the position update rule; The updated load allocation ratio is constrained and verified until the termination condition is met, at which point the optimal solution is output.

7. The method according to claim 6, characterized in that, The fitness function is defined as follows: ； in, Indicates the first Total energy consumption of each load allocation scheme; Indicates system stability index, This indicates the load balance index. These are the weighting coefficients.

8. A load distribution device for an air compressor, characterized in that, The device includes: The acquisition module is used to acquire the physical property parameters of compressed gas in multiple parallel air compressors; The module is used to establish a load distribution model for compressor operation, taking the load distribution ratio of each compressor as the decision variable and minimizing the total energy consumption of the compressor station as the objective function, and combining the equipment characteristics of the compressor and the gas physical property parameters, and setting outlet pressure constraints and outlet temperature constraints. The optimization module is used to iteratively optimize and solve the load allocation model using the tunic group algorithm to generate the optimal load allocation scheme that satisfies the outlet pressure constraint and the outlet temperature constraint. The allocation module is used to dynamically allocate the operating load of multiple parallel air compressors according to the optimal load allocation scheme.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the method described in any one of claims 1 to 7.

10. A computer device, characterized in that, The method includes 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 the method described in any one of claims 1 to 7.

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