Water pump optimization regulation and control method based on differential evolution algorithm, electronic equipment and medium

Abstract

The invention discloses a water pump optimization regulation and control method based on a differential evolution algorithm, electronic equipment and a medium. The method comprises the following steps: acquiring operation data of a water pump system of a sewage treatment plant in real time; calibrating a characteristic curve of the water pump according to historical operation data, and establishing a mathematical model of system energy efficiency; the system energy efficiency is optimized through a differential evolution algorithm based on the operation data; and adjusting operation parameters of the water pump according to the optimal solution obtained by the differential evolution algorithm so as to carry out optimal regulation and control on the water pump. Through an intelligent optimization algorithm, the operation parameters of the water pump are dynamically adjusted, the energy efficiency of the system is maximized, the energy consumption in the sewage treatment process is reduced, and the service life of equipment is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage treatment, and more specifically, to a water pump optimization control method based on a differential evolution algorithm, an electronic device and a medium. Background Art

[0002] With the acceleration of urbanization and increasingly stringent environmental protection requirements, sewage treatment plants play a vital role in water resource management and ecological environment protection. However, during the operation of sewage treatment plants, the water pump system, as a key energy-consuming equipment, consumes huge amounts of energy and is one of the main sources of electricity consumption in the entire sewage treatment process. Therefore, how to improve the energy efficiency of the water pump system and reduce operating costs while ensuring the quality of sewage treatment has become an important topic of concern in the industry.

[0003] The control of the pump system in traditional sewage treatment plants mostly adopts fixed frequency control or simple variable frequency regulation to cope with different treatment needs. However, these control methods usually have many problems: 1) Low energy efficiency: Traditional control methods often lack the ability to respond to actual sewage treatment needs in real time, resulting in the pump system operating in non-optimal conditions for a long time and low energy efficiency. For example, when the treatment load fluctuates greatly, the pump may still maintain high frequency operation, resulting in unnecessary increase in energy consumption; 2) Short equipment life: Frequent start-stop operations and long-term non-optimal operation not only aggravate the wear of the pump and related equipment, but also shorten its service life and increase the cost of equipment maintenance and replacement. In addition, high energy consumption operation will cause additional thermal stress inside the equipment, which will further affect its stability and reliability; 3) Limited control accuracy: Traditional control methods lack intelligent means and it is difficult to achieve precise control of the pump system. Especially when facing the complex and changeable working conditions of sewage treatment plants, traditional methods often need to rely on experience for adjustment and cannot make full use of real-time data for optimization, resulting in difficulty in improving the overall efficiency of the system; 4) Difficult to cope with complex working conditions: The operating environment of sewage treatment plants is complex and changeable, and working parameters such as processing volume, flow rate, and pressure often fluctuate. Traditional control strategies are difficult to effectively cope with such dynamic changes. Usually, they can only make rough adjustments and cannot achieve comprehensive optimization of the operating status of the water pump, which in turn affects the stability and efficiency of the entire system. In recent years, with the development of intelligent control technology, more and more studies have begun to explore how to improve the energy efficiency of water pump systems through optimization algorithms. For example, intelligent optimization methods such as genetic algorithms and particle swarm optimization algorithms have been applied to water pump control to automatically adjust the operating parameters of the pump to achieve energy-saving effects. However, the application of these methods in multi-pump parallel systems still faces challenges such as slow convergence speed and difficulty in ensuring the global optimal solution, which limits their application effect in actual engineering. .

[0004] Therefore, it is necessary to develop a water pump optimization control method, electronic equipment and medium based on differential evolution algorithm.

[0005] The information disclosed in the background technology section of the present invention is only intended to deepen the understanding of the general background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0006] The present invention proposes a water pump optimization and control method, electronic equipment and medium based on a differential evolution algorithm, which can dynamically adjust the operating parameters of the water pump through an intelligent optimization algorithm, maximize the energy efficiency of the system, reduce energy consumption in the sewage treatment process, and extend the service life of the equipment.

[0007] In a first aspect, an embodiment of the present disclosure provides a water pump optimization control method based on a differential evolution algorithm, comprising:

[0008] Real-time collection of operating data of the sewage treatment plant pump system;

[0009] Calibrate the pump characteristic curve based on historical operating data and establish a mathematical model of system energy efficiency;

[0010] Optimizing system energy efficiency through a differential evolution algorithm based on the operating data;

[0011] The operating parameters of the water pump are adjusted according to the optimal solution obtained by the differential evolution algorithm, and then the water pump is optimized and controlled.

[0012] Preferably, the operating data includes flow rate, head, power and start / stop status of each pump.

[0013] Preferably, the mathematical model is:

[0014] P(t)=Q(t)·H(t) / η(t)

[0015] Among them, η(t) represents the efficiency of the pump, Q(t) is the flow rate, H(t) is the head, and P(t) is the power.

[0016] Preferably, optimizing the system energy efficiency by using a differential evolution algorithm based on the operating data includes:

[0017] Assume that the population size is N, and each individual is represented by the operating parameter x of the water pump system. i constitute;

[0018] Construct a fitness function based on the system energy efficiency, update the population through mutation, crossover and selection operations, and iteratively optimize the operating parameters x i Maximize the fitness function.

[0019] Preferably, the fitness function is:

[0020]

[0021] Where n is the number of pumps, Q i is the flow rate of the ith pump, H i is the head of the ith pump, P i is the power consumption of the i-th pump.

[0022] Preferably, the mutation operation is:

[0023] v i =x r1 +F·(x r2 -x r3 )

[0024] Among them, v i is the new individual, F is the scaling factor, x r1 、x r2 、x r3 is an individual randomly selected from the population.

[0025] Preferably, the crossover operation converts the new individual v i With the original individual x i The candidate solutions are generated by combination, namely:

[0026]

[0027] Among them, u i is the candidate solution, Cr is the crossover probability, j rand is the index of a randomly selected gene.

[0028] Preferably, the optimal solution is:

[0029]

[0030] In a second aspect, an embodiment of the present disclosure further provides an electronic device, the electronic device comprising:

[0031] A memory storing executable instructions;

[0032] A processor runs the executable instructions in the memory to implement the water pump optimization control method based on the differential evolution algorithm.

[0033] In a third aspect, an embodiment of the present disclosure further provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the water pump optimization and control method based on the differential evolution algorithm is implemented.

[0034] Its beneficial effects are:

[0035] The present invention applies the differential evolution algorithm to the optimization and control of the water pump system in the sewage treatment plant, which can realize the intelligent adjustment of key parameters such as water pump start and stop, speed, and head, thereby significantly improving the energy efficiency of the system, extending the service life of the equipment, and reducing energy consumption. Therefore, the present invention can not only adapt to complex working conditions, but also dynamically optimize through the feedback of real-time data to ensure that the water pump system always operates in the optimal state, aiming to provide a set of efficient and reliable solutions for energy saving and consumption reduction in sewage treatment plants, with broad application prospects and significant economic benefits.

[0036] The methods and apparatus of the present invention have other features and advantages that will be apparent from, or will be described in detail in, the accompanying drawings and subsequent detailed descriptions incorporated herein, which together serve to explain the specific principles of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present invention.

[0038] Figure 1 A flow chart showing the steps of a water pump optimization control method based on a differential evolution algorithm according to an embodiment of the present invention.

[0039] Figure 2 A differential evolution algorithm solution flow chart according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0040] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0041] To facilitate understanding of the solutions and effects of the embodiments of the present invention, three specific application examples are given below. Those skilled in the art should understand that the examples are only for facilitating understanding of the present invention, and any specific details thereof are not intended to limit the present invention in any way.

[0042] Example 1

[0043] Figure 1 A flow chart showing the steps of a water pump optimization control method based on a differential evolution algorithm according to an embodiment of the present invention.

[0044] like Figure 1As shown, the water pump optimization control method based on differential evolution algorithm includes:

[0045] Step 101, collecting operation data of a water pump system of a sewage treatment plant in real time;

[0046] Step 102, calibrating the characteristic curve of the water pump according to the historical operation data, and establishing a mathematical model of system energy efficiency;

[0047] Step 103, optimizing system energy efficiency through a differential evolution algorithm based on the operating data;

[0048] Step 104, adjusting the operating parameters of the water pump according to the optimal solution obtained by the differential evolution algorithm, and then optimizing and controlling the water pump.

[0049] In one example, the operation data includes flow rate, head, power, and start / stop status of each pump.

[0050] In one example, the mathematical model is:

[0051] P(t)=Q(t)·H(t) / η(t)

[0052] Among them, η(t) represents the efficiency of the pump, Q(t) is the flow rate, H(t) is the head, and P(t) is the power.

[0053] In one example, optimizing system energy efficiency by using a differential evolution algorithm based on operating data includes:

[0054] Assume that the population size is N, and each individual is represented by the operating parameter x of the water pump system. i constitute;

[0055] Construct a fitness function based on the system energy efficiency, update the population through mutation, crossover and selection operations, and iteratively optimize the operating parameters x i Maximize the fitness function.

[0056] In one example, the fitness function is:

[0057]

[0058] Where n is the number of pumps, Q i is the flow rate of the ith pump, H i is the head of the ith pump, P i is the power consumption of the i-th pump.

[0059] In one example, the mutation operation is:

[0060] v i =x r1 +F·(x r2 -x r3 )

[0061] Among them, v i is the new individual, F is the scaling factor, x r1 、x r2 、x r3 is an individual randomly selected from the population.

[0062] In one example, the crossover operation converts the new individual v i With the original individual x i The candidate solutions are generated by combination, namely:

[0063]

[0064] Among them, u i is the candidate solution, Cr is the crossover probability, j rand is the index of a randomly selected gene.

[0065] In one example, the optimal solution is:

[0066]

[0067] Specifically, during the operation of a sewage treatment plant, the water pump system is the main source of energy consumption. This embodiment optimizes and regulates a water pump system with multiple pumps running in parallel based on a differential evolution algorithm to improve system energy efficiency, reduce energy consumption, and extend equipment life.

[0068] The sensor network collects the operating data of the sewage treatment plant pump system in real time, including flow rate Q(t), head H(t), power P(t) and the start and stop status of each pump. The collected data is preprocessed to remove abnormal values ​​and complete the missing data to ensure the accuracy of subsequent modeling. The characteristic curve of the pump is calibrated using historical operating data to establish a mathematical model that describes the system energy efficiency:

[0069] P(t)=Q(t)·H(t) / η(t)

[0070] Among them, η(t) represents the efficiency of the water pump, which is an important reference indicator for the optimization algorithm.

[0071] Figure 2 A differential evolution algorithm solution flow chart according to an embodiment of the present invention is shown.

[0072] like Figure 2 As shown, the population of the differential evolution algorithm is initialized, the population size is set to N, and each individual is determined by the operating parameters x of the water pump system. i The fitness function is defined as the comprehensive energy efficiency of the system:

[0073]

[0074] Where n is the number of pumps, Q i is the flow rate of the i-th pump, H i is the head of the ith pump, P i is the power consumption of the i-th pump. The goal is to optimize x by iterative i Maximize f(x).

[0075] During the iteration process, the differential evolution algorithm updates the population through mutation, crossover, and selection operations.

[0076] For each individual x i , first perform mutation operation to generate new individual v i :

[0077] v i =x r1 +F·(x r2 -x r3 )

[0078] Where F is the scaling factor, x r1 、x r2 、x r3 is an individual randomly selected from the population.

[0079] The crossover operation transforms the new individual v i With the original individual x i Perform combination to generate candidate solution u i :

[0080]

[0081] Among them, Cr is the crossover probability, j rand is the index of a randomly selected gene.

[0082] Among them, the scaling factor F in the mutation operation ranges from 0.5 to 1.0, and the crossover probability C r The value range of is 0.7 to 1.0 to ensure that the algorithm has better global search ability and convergence speed.

[0083] In the selection operation, according to the fitness function value f(u i ) and f(x i ), select individuals with higher fitness values ​​to enter the next generation:

[0084]

[0085] Through the above iterative process, the algorithm gradually optimizes the individuals in the population so that the system operating parameters tend to be optimal.

[0086] The optimal solution x* obtained by the differential evolution algorithm is applied to the actual operation of the water pump system, and the operating parameters of the water pump are adjusted so that the system can meet the sewage treatment needs while achieving the goal of minimizing energy consumption. The optimal solution can be expressed as:

[0087]

[0088] The system performance indicators after application, such as energy consumption, head, flow rate, etc., are monitored in real time, and the results are fed back to the differential evolution algorithm to further optimize and calibrate the model parameters.

[0089] Considering the complexity and variability of the operating environment of the sewage treatment plant, the present invention also sets up a dynamic adjustment mechanism. Regularly re-collect real-time data and adjust the population and fitness function of the differential evolution algorithm to ensure that the system can maintain the optimal state under different working conditions. Adaptive adjustment of algorithm parameters is performed according to the real-time operating data of the water pump to cope with dynamic changes under different load conditions.

[0090] During long-term operation, the accumulated historical data is used to continuously improve the system's energy efficiency model and optimization strategy, thereby further improving the energy-saving effect of the water pump system and extending the service life of the equipment.

[0091] Example 2

[0092] The present disclosure provides an electronic device, which includes: a memory storing executable instructions; a processor, which runs the executable instructions in the memory to implement the above-mentioned water pump optimization control method based on the differential evolution algorithm.

[0093] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0094] The memory is used to store non-temporary computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0095] The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of the present disclosure, the processor is used to run the computer-readable instructions stored in the memory.

[0096] Those skilled in the art should be able to understand that in order to solve the technical problem of how to obtain a good user experience, the present embodiment may also include well-known structures such as a communication bus and an interface, and these well-known structures should also be included in the protection scope of the present disclosure.

[0097] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0098] Example 3

[0099] An embodiment of the present disclosure provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the water pump optimization control method based on the differential evolution algorithm is implemented.

[0100] According to the computer-readable storage medium of the embodiment of the present disclosure, non-transitory computer-readable instructions are stored thereon. When the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the above-mentioned methods of each embodiment of the present disclosure are executed.

[0101] The above-mentioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or mobile hard disk), media with built-in rewritable non-volatile memory (e.g., memory card) and media with built-in ROM (e.g., ROM box).

[0102] Those skilled in the art should understand that the purpose of the above description of the embodiments of the present invention is only to exemplarily illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any given examples.

[0103] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A water pump optimization control method based on differential evolution algorithm, characterized in that: include: Real-time collection of operating data of the sewage treatment plant pump system; Calibrate the pump characteristic curve based on historical operating data and establish a mathematical model of system energy efficiency; Optimizing system energy efficiency through a differential evolution algorithm based on the operating data; The operating parameters of the water pump are adjusted according to the optimal solution obtained by the differential evolution algorithm, and then the water pump is optimized and controlled.

2. The water pump optimization control method based on differential evolution algorithm according to claim 1, wherein: The operating data includes flow rate, head, power and start / stop status of each pump.

3. The water pump optimization control method based on differential evolution algorithm according to claim 2, wherein: The mathematical model is: P(t)=Q(t)·H(t) / η(t) Among them, η(t) represents the efficiency of the pump, Q(t) is the flow rate, H(t) is the head, and P(t) is the power.

4. The water pump optimization control method based on differential evolution algorithm according to claim 1, wherein: Optimizing the system energy efficiency by using a differential evolution algorithm based on the operating data includes: Assume that the population size is N, and each individual is represented by the operating parameter x of the water pump system. i constitute; Construct a fitness function based on the system energy efficiency, update the population through mutation, crossover and selection operations, and iteratively optimize the operating parameters x i Maximize the fitness function.

5. The water pump optimization control method based on differential evolution algorithm according to claim 4, wherein: The fitness function is: Where n is the number of pumps, Q i is the flow rate of the ith pump, H i is the head of the ith pump, P i is the power consumption of the i-th pump.

6. The water pump optimization control method based on differential evolution algorithm according to claim 4, wherein: The mutation operation is: v i =x r1 +F·(x r2 -x r3 ) Among them, v i is the new individual, F is the scaling factor, x r1 、x r2 、x r3 is an individual randomly selected from the population.

7. The water pump optimization control method based on differential evolution algorithm according to claim 6, wherein: The crossover operation transforms the new individual v i With the original individual x i The candidate solutions are generated by combination, namely: Among them, u i is the candidate solution, Cr is the crossover probability, j rand is the index of a randomly selected gene.

8. The water pump optimization control method based on differential evolution algorithm according to claim 1, wherein: The optimal solution is:

9. An electronic device, characterized in that: The electronic device comprises: A memory storing executable instructions; A processor, wherein the processor runs the executable instructions in the memory to implement the water pump optimization control method based on the differential evolution algorithm according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the water pump optimization control method based on a differential evolution algorithm described in any one of claims 1 to 8 is implemented.

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