Air blower optimization control system and method for wastewater treatment

Abstract

The invention discloses an air blower optimization control system and method for waste water treatment, and relates to the technical field of waste water treatment.The method comprises the steps that predetermined waste water indexes are read, feature collection is conducted on target waste water, and target waste water feature information is obtained; taking the target wastewater characteristic information as input information of a dissolved oxygen demand prediction model to obtain a target demand dissolved oxygen concentration; reading a predetermined aeration index, and analyzing the predetermined aeration index to obtain a target aeration control space; introducing a predetermined control fitness function as an optimization evaluation strategy, and performing global optimization in the target aeration control space by taking the target demand dissolved oxygen concentration as an optimization constraint to obtain an optimal aeration control scheme; and performing energy-saving aeration treatment on the target blower of the target wastewater according to the optimal aeration control scheme. The technical problems of low treatment efficiency and serious energy consumption during biological treatment of wastewater in the prior art are solved, and the technical effects of efficient wastewater treatment and remarkable energy conservation are achieved.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, and specifically to an optimized control system and method for a blower used in wastewater treatment. Background Technology

[0002] With the acceleration of industrialization and urbanization, wastewater discharge is constantly increasing, and wastewater treatment has become an important part of environmental protection and resource management. However, traditional wastewater treatment systems have many shortcomings in energy management and are unable to meet the demands of modern society for high efficiency, energy saving, and environmental protection.

[0003] Biological treatment processes in wastewater treatment, especially aeration, typically consume significant amounts of energy. Traditional aeration control methods result in low wastewater treatment efficiency and substantial energy waste. As wastewater treatment scales expand and processes become more complex, these problems become increasingly prominent, urgently requiring a more efficient and intelligent control method to optimize wastewater treatment processes and reduce energy consumption. Summary of the Invention

[0004] This application provides an optimized control system and method for a blower used in wastewater treatment, which addresses the technical problems of low treatment efficiency and high energy consumption in the biological treatment of wastewater in existing technologies.

[0005] In view of the above problems, this application provides an optimized control system and method for a blower used in wastewater treatment.

[0006] A first aspect of this application provides an optimized control system for a blower used in wastewater treatment, the system comprising:

[0007] The system comprises the following modules: a feature collection module, which reads predetermined wastewater indicators and collects features of the target wastewater based on these indicators to obtain target wastewater feature information; a dissolved oxygen demand prediction module, which uses the target wastewater feature information as input to a dissolved oxygen demand prediction model to obtain a target required dissolved oxygen concentration, wherein the target required dissolved oxygen concentration refers to the minimum dissolved oxygen concentration required for biological treatment of the target wastewater; a predetermined aeration indicator reading module, which reads predetermined aeration indicators and analyzes these indicators to obtain a target aeration control space; an optimization module, which introduces a predetermined control fitness function as an optimization evaluation strategy and uses the target required dissolved oxygen concentration as an optimization constraint to perform global optimization within the target aeration control space to obtain an optimal aeration control scheme; and an aeration treatment module, which controls a target blower to aerate the target wastewater according to the optimal aeration control scheme.

[0008] A second aspect of this application provides a method for optimizing the control of a blower for wastewater treatment, the method comprising: A predetermined wastewater index is read, and feature collection of the target wastewater is performed based on the predetermined wastewater index to obtain target wastewater feature information. The target wastewater feature information is used as input information for a dissolved oxygen demand prediction model to obtain the target required dissolved oxygen concentration, wherein the target required dissolved oxygen concentration refers to the minimum dissolved oxygen concentration for biological treatment of the target wastewater. A predetermined aeration index is read, and the predetermined aeration index is analyzed to obtain a target aeration control space. A predetermined control fitness function is introduced as an optimization evaluation strategy, and the target required dissolved oxygen concentration is used as an optimization constraint to perform global optimization in the target aeration control space to obtain the optimal aeration control scheme. The target blower is controlled to aerate the target wastewater according to the optimal aeration control scheme.

[0009] The technical solution provided in this application has at least the following technical effects or advantages: This application reads predetermined wastewater indicators and collects features of target wastewater based on these indicators to obtain target wastewater feature information. The target wastewater feature information is used as input to a dissolved oxygen demand prediction model to obtain the target required dissolved oxygen concentration, where the target required dissolved oxygen concentration refers to the minimum dissolved oxygen concentration required for biological treatment of the target wastewater. Predetermined aeration indicators are read and analyzed to obtain a target aeration control space. A predetermined control fitness function is introduced as an optimization evaluation strategy, and the target required dissolved oxygen concentration is used as an optimization constraint to perform global optimization within the target aeration control space to obtain the optimal aeration control scheme. The target blower is then controlled to aerate the target wastewater according to the optimal aeration control scheme. This invention solves the technical problems of low treatment efficiency and high energy consumption in existing biological wastewater treatment technologies. By acquiring wastewater and aeration indicators, using big data analysis and prediction models to calculate dissolved oxygen demand, and optimizing aeration parameters through a global optimization algorithm, the blower is controlled for energy-saving aeration treatment, achieving efficient wastewater treatment and significant energy savings. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A schematic diagram of an optimized control system for a blower used in wastewater treatment is provided in an embodiment of this application. Figure 2 This is a schematic flowchart of a blower optimization control method for wastewater treatment provided in an embodiment of this application.

[0012] Figure labeling: Feature collection module 11, dissolved oxygen demand prediction module 12, predetermined aeration index reading module 13, optimization module 14, aeration treatment module 15. Detailed Implementation

[0013] This application provides an optimized control system and method for a blower used in wastewater treatment. It addresses the technical problems of low treatment efficiency and high energy consumption in the biological treatment of wastewater in existing technologies. By acquiring wastewater and aeration indicators, using big data analysis and prediction models to calculate dissolved oxygen demand, and optimizing aeration parameters through a global optimization algorithm, the blower is controlled to perform energy-saving aeration treatment, achieving the technical effects of efficient wastewater treatment and significant energy saving.

[0014] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0015] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0016] Example 1, as Figure 1 As shown, this application provides an optimized control system for a blower used in wastewater treatment, for performing actions such as... Figure 2 The method for optimizing the control of a blower for wastewater treatment is shown, the system comprising: The feature collection module 11 reads a predetermined wastewater index and collects features of the target wastewater based on the predetermined wastewater index to obtain the target wastewater feature information.

[0017] Furthermore, in the system provided in the application embodiment, the predetermined wastewater indicators include sludge concentration, water flow velocity, water temperature, influent flow rate, and organic matter concentration.

[0018] In this embodiment, the feature collection module first reads predetermined wastewater indicators. These predetermined wastewater indicators include sludge concentration, water flow velocity, water temperature, influent flow rate, and organic matter concentration. Sludge concentration refers to the content of suspended solids in the wastewater, reflecting the density and volume of sludge; water flow velocity refers to the speed at which wastewater flows through a pipeline or treatment system; water temperature is the temperature of the wastewater; influent flow rate represents the speed and quantity of wastewater entering the treatment system; and organic matter concentration is the content of organic pollutants in the wastewater.

[0019] After reading the predetermined wastewater indicators, the feature collection module collects features of the target wastewater based on these indicators. By measuring and analyzing various physical and chemical parameters of the wastewater, it extracts key information that accurately reflects the wastewater's state and treatment requirements. Specifically, during feature collection, multiple sensors and measuring devices are used, such as a suspended solids sensor to measure sludge concentration, a flow meter to measure water flow velocity, a thermometer to measure water temperature, a flow meter to measure influent flow rate, and a chemical analysis instrument to measure organic matter concentration. Through this process, the characteristic information of the target wastewater is obtained.

[0020] The dissolved oxygen demand prediction module 12 uses the target wastewater characteristic information as input information for the dissolved oxygen demand prediction model to obtain the target required dissolved oxygen concentration, wherein the target required dissolved oxygen concentration refers to the minimum dissolved oxygen concentration for biological treatment of the target wastewater.

[0021] In this embodiment, the dissolved oxygen demand prediction module inputs the target wastewater characteristic information as input information into the dissolved oxygen demand prediction model. The dissolved oxygen demand prediction model is a pre-built machine learning model used to predict the minimum dissolved oxygen concentration required by the wastewater during the biological treatment process. By processing and analyzing the input target wastewater characteristic information, the target required dissolved oxygen concentration is obtained.

[0022] Furthermore, the dissolved oxygen demand prediction module 12 in the system provided in the application embodiment is also used for: The system arbitrarily acquires the first historical record from the historical wastewater biological treatment records; performs normalized weighted analysis on the first historical treatment quality information extracted from the first historical record to obtain the first historical treatment quality index; if the first historical treatment quality index reaches a predetermined quality index threshold, it issues a data assembly instruction; based on the data assembly instruction, it extracts the first historical wastewater feature information and the first historical dissolved oxygen concentration from the first historical record and assembles the first training data; based on the neural network principle, it performs supervised learning on the first training data and tests it to obtain the dissolved oxygen demand prediction model.

[0023] In this embodiment, a first historical record is first randomly selected from the historical wastewater biological treatment records in the storage database. The storage database contains a large amount of wastewater treatment data, which is collected and saved in real time during the wastewater treatment process through sensors and monitoring equipment. The first historical record refers to a record randomly selected from this data.

[0024] Next, the extracted historical processing quality information is subjected to normalized weighted analysis to obtain the first historical processing quality index. Specifically, data standardization methods are used to normalize indicators of different dimensions, bringing them to the same scale. Then, a weighted algorithm is used to assign pre-defined weights to each indicator based on its importance in processing quality, and a comprehensive quality evaluation value is calculated, which is the first historical processing quality index. If the first historical processing quality index reaches a predetermined quality index threshold, a data assembly instruction is issued. The predetermined quality index threshold is a quality standard pre-set by technical experts to ensure that only high-quality data can be used for model training. Based on the data assembly instruction, first historical wastewater characteristic information and first historical dissolved oxygen concentration are extracted from the first historical records. Wastewater characteristic information includes sludge concentration, water flow velocity, water temperature, influent flow rate, and organic matter concentration, while dissolved oxygen concentration refers to the oxygen content in the water during wastewater treatment. This extracted information is then integrated into the first training data.

[0025] The initial training data is then input into the neural network model for supervised learning. The neural network model, such as a Long Short-Term Memory (LSTM) network, continuously adjusts the network weights using backpropagation, learning the mapping relationship between input and output, by inputting the training data and corresponding output labels. During training, a portion of historical data is retained as a validation dataset. This validation dataset is a randomly selected portion of data from historical wastewater biological treatment records; it is not used in training but only for evaluating the model's performance during and after training. After training, this validation dataset is used to test the model, evaluating its prediction accuracy and generalization ability. By comparing the prediction results on the validation dataset with the actual results, it is determined whether the model has good predictive ability, ensuring that it can accurately predict the minimum dissolved oxygen concentration required for wastewater biological treatment.

[0026] Finally, through the above steps, the dissolved oxygen demand prediction model is constructed.

[0027] Furthermore, the dissolved oxygen demand prediction module 12 in the system provided in the application embodiment is also used for:

[0028] A predetermined quality feature is read, and the first historical treatment quality information is extracted based on the predetermined quality feature to obtain the first historical quality feature parameter; the first historical quality feature parameter is processed and analyzed using the coefficient of variation principle to obtain the first historical treatment quality index; wherein, the predetermined quality feature includes at least a water quality dimension, a biochemical quality dimension, and a microbial quality dimension, the water quality dimension includes suspended solids ratio, total nitrogen ratio, total phosphorus ratio, and pH value, the biochemical quality dimension includes sludge settling ratio and sludge concentration, and the microbial quality dimension includes microbial species, microbial quantity, and biological activity.

[0029] In this embodiment, predetermined quality characteristics are first read. These predetermined quality characteristics are predefined indicators used to evaluate wastewater treatment quality, including water quality dimensions, biochemical quality dimensions, and microbiological quality dimensions. Specifically, the water quality dimensions include suspended solids ratio, total nitrogen ratio, total phosphorus ratio, and pH value. Suspended solids ratio refers to the content of solid particles in water; total nitrogen ratio and total phosphorus ratio represent the content of total nitrogen and total phosphorus in water, respectively, both of which are important indicators reflecting water quality; pH ​​value is the acidity or alkalinity of water, affecting the survival and activity of microorganisms. The biochemical quality dimensions include sludge settling ratio and sludge concentration. Sludge settling ratio refers to the settling rate of sludge over a certain period; sludge concentration is the content of sludge in water. The microbiological quality dimensions include microbial species, microbial quantity, and biological activity. Microbial species and microbial quantity reflect the diversity and abundance of microorganisms involved in degrading organic matter during wastewater treatment; biological activity refers to the metabolic activity of microorganisms, directly affecting the efficiency of wastewater treatment.

[0030] Based on predetermined quality characteristics, the first historical treatment quality information is extracted traversally to obtain the first historical quality characteristic parameters. Traversal extraction refers to sequentially reading the treatment quality information from each historical record and extracting data related to the predetermined quality characteristics. For example, parameters such as suspended solids ratio, total nitrogen ratio, total phosphorus ratio, pH value, sludge settling ratio, sludge concentration, microbial species, microbial quantity, and biological activity are extracted from historical records as the first historical quality characteristic parameters. Then, the first historical quality characteristic parameters are processed and analyzed using the coefficient of variation principle to obtain the first historical treatment quality index. The coefficient of variation is the ratio of the standard deviation to the mean, used to measure the dispersion of data. Specifically, the mean and standard deviation of each quality characteristic parameter are first calculated, and then the coefficient of variation is calculated using the coefficient of variation formula. For example, for suspended solids ratio, its mean and standard deviation are calculated, and then the coefficient of variation is obtained. After calculating the coefficient of variation for all quality characteristic parameters, a weighted average is performed according to the importance of each characteristic, finally obtaining a comprehensive treatment quality index, i.e., the first historical treatment quality index.

[0031] The predetermined aeration index reading module 13 reads the predetermined aeration index and analyzes the predetermined aeration index to obtain the target aeration control space.

[0032] Furthermore, in the system provided in the application embodiment, the predetermined aeration indicators include air pressure, air flow rate, aeration duration, and bubble size.

[0033] In this embodiment of the application, the predetermined aeration index reading module first reads the predetermined aeration index, which refers to the key parameters that need to be monitored and adjusted during the wastewater treatment process, including air pressure, air flow rate, aeration time and bubble size.

[0034] Next, the predetermined aeration parameters are analyzed to obtain the target aeration control space. Specifically, a large number of historical wastewater treatment records are collected from the database, including data on all predetermined aeration parameters in past treatment processes. Historical data collection involves extracting data such as air pressure, air flow rate, aeration duration, and bubble size recorded in all past treatment processes for analysis. Data mining techniques are then used to analyze this historical data to identify the impact of different combinations of air pressure, air flow rate, aeration duration, and bubble size on wastewater treatment effectiveness. Regression analysis is used to find the relationship between different parameter combinations and treatment effects. For example, the analysis reveals that a certain range of air pressure and air flow rate combinations has the best treatment effect under specific aeration duration and bubble size conditions. Based on the data analysis results, the target aeration control space is constructed. The target aeration control space refers to the range of aeration control parameters that can be used in actual wastewater treatment processes, determined by analyzing the predetermined aeration parameters.

[0035] The optimization module 14 introduces a predetermined control fitness function as an optimization evaluation strategy and uses the target required dissolved oxygen concentration as an optimization constraint to perform global optimization in the target aeration control space to obtain the optimal aeration control scheme.

[0036] In this embodiment, the optimization module introduces a predetermined control fitness function as an optimization evaluation strategy, and then uses the target required dissolved oxygen concentration as an optimization constraint. The target required dissolved oxygen concentration refers to the minimum dissolved oxygen concentration required for the biological treatment of the target wastewater.

[0037] After determining the control fitness function and optimization constraints, a global optimization is performed in the target aeration control space. By using some optimization algorithms, such as particle swarm optimization or genetic algorithm, the optimal aeration control scheme is found.

[0038] Furthermore, the optimization module 14 in the system provided in the application embodiment is also used for: Step a: Traverse the first aeration control scheme extracted from the target aeration control space in the aeration database to obtain the first control log of the first most similar aeration control scheme; Step b: Determine whether the first dissolved oxygen concentration in the first control log meets the optimization constraint; Step c: If it meets the constraint, retrieve the optimization evaluation strategy to analyze and evaluate the first control log to obtain the first control fitness; Step d: Obtain the second control fitness, which refers to the control fitness of the second aeration control scheme extracted from the target aeration control space; Step e: Obtain the optimal aeration control scheme based on the comparison result between the first control fitness and the second control fitness; Step f: Repeat steps a to e until a predetermined number of iterations is reached, and output the optimal aeration control scheme at that time.

[0039] In this embodiment, the optimization module first begins step a, by traversing and searching the aeration database for the first aeration control scheme extracted from the target aeration control space. Through this traversal search, database query technology is used to find the scheme most similar to the first aeration control scheme, and its first control log is recorded. The control log contains data such as dissolved oxygen concentration and aeration rate monitored by the dissolved oxygen sensor during the actual operation of the scheme. In step b, it is determined whether the first dissolved oxygen concentration in the first control log meets the optimization constraints. Dissolved oxygen concentration refers to the oxygen content in the water, and the optimization constraints are the minimum dissolved oxygen concentration requirements used to ensure that aerobic microorganisms in the wastewater can carry out normal metabolic activities. If the first dissolved oxygen concentration meets the optimization constraints, it indicates that the scheme has reached the minimum dissolved oxygen concentration requirement in actual operation. In step c, if the first dissolved oxygen concentration meets the requirements, the optimization evaluation strategy is invoked to analyze and evaluate the first control log to obtain the first control fitness. The optimization evaluation strategy is an evaluation method based on a predetermined control fitness function. By calculating the fitness function and comprehensively considering indicators such as dissolved oxygen concentration, energy consumption, and treatment effect, the control fitness is obtained.

[0040] Then, in step d, using the same method as described above, the second control log of the second most similar aeration control scheme is obtained. The optimization evaluation strategy is then retrieved to analyze and evaluate the second control log, obtaining the second control fitness. In step e, the first control fitness and the second control fitness are compared. By comparing these two fitness values, it is determined which scheme is better.

[0041] In step f, steps a through e are repeated multiple times for iteration. In each iteration, a new control scheme is extracted from the target aeration control space, and this scheme is traversed, judged, evaluated, and compared. Iterative algorithms, such as genetic algorithms or particle swarm optimization, are used to continuously update and optimize the scheme during the iteration process. This process continues until the predetermined number of iterations is reached.

[0042] Finally, when the predetermined number of iterations is reached, the optimal aeration control scheme at that time will be output.

[0043] Furthermore, in the system provided in the application embodiment, the expression of the predetermined control fitness function is as follows: ;

[0044] in, This refers to the aeration control scheme Control fitness, This refers to the aeration control scheme. Aeration volume, This refers to the energy consumption coefficient per unit of aeration. This refers to the aeration control scheme. Dissolved oxygen concentration, This refers to the target required dissolved oxygen concentration. and These refer to the first fitness coefficient and the second fitness coefficient, respectively. .

[0045] In this embodiment of the application, when calculating the control fitness, the calculation is performed using the aforementioned predetermined control fitness function, wherein the unit aeration energy consumption coefficient represents the energy consumed per unit air flow rate, which is preset by technical experts. The first fitness coefficient and the second fitness coefficient are also preset by technical experts according to the performance requirements of the specific wastewater treatment system.

[0046] The control fitness is calculated using a predetermined control fitness function.

[0047] The aeration treatment module 15 is used to control the target blower to aerate the target wastewater according to the optimal aeration control scheme.

[0048] In this embodiment, the parameters of the optimal aeration control scheme are input into the control system of the target blower. The target blower is a device used to deliver air into the wastewater, and its operating parameters include air pressure, air flow rate, aeration time, and bubble size. Based on the optimal aeration control scheme, the operating parameters of the blower are adjusted to perform energy-saving aeration treatment of the target wastewater using the target blower.

[0049] Furthermore, the aeration treatment module 15 in the system provided in the application embodiment is also used for: Extract the optimal bubble size from the optimal aeration control scheme and determine a predetermined bubble threshold based on the optimal bubble size; obtain any aeration control scheme from the aeration database, the arbitrary aeration control scheme including any aerator control scheme; when any bubble size in the arbitrary aerator control scheme meets the predetermined bubble threshold, issue an optimal pickup command; read the predetermined bubble control index based on the optimal pickup command, and perform traversal matching in the arbitrary aerator control scheme based on the predetermined bubble control index to obtain arbitrary bubble control parameters; use the arbitrary bubble control parameters as the optimal aerator control scheme, and perform energy-saving aeration treatment on the target aerator of the target wastewater according to the optimal aerator control scheme.

[0050] Furthermore, in the system provided in the application embodiment, the predetermined bubble control indicators include the installation depth and orifice diameter of the target aerator.

[0051] In this embodiment, the optimal bubble size in the optimal aeration control scheme is first read, and then a predetermined bubble threshold is determined based on the optimal bubble size according to a preset rule, for example, 1 ± 5% of the optimal bubble size is set as the predetermined bubble threshold.

[0052] Then, arbitrary aeration control schemes are retrieved from the aeration database using database query technology. The aeration database stores historical aeration records and control parameters under different operating conditions, including air pressure, air flow rate, aeration duration, and bubble size. Next, it is checked whether any bubble size in any aeration control scheme meets a predetermined bubble threshold. If any bubble size is detected to be within the threshold range, an optimal pick-up command is issued.

[0053] Next, based on the optimal pickup instruction, the predetermined bubble control indicators are read, including the installation depth and orifice diameter of the target aerator. Using database query technology, control parameters that meet the predetermined bubble control indicators are extracted from any aeration control scheme. Then, a traversal matching process is performed on any aerator control scheme to obtain arbitrary bubble control parameters. The traversal matching is performed using a heuristic search algorithm to ensure that the selected parameter combinations can generate bubble sizes that meet the predetermined bubble threshold. These parameters include air pressure, air flow rate, and aeration duration.

[0054] Then, these arbitrary bubble control parameters are used as the optimal aerator control scheme, and energy-saving aeration treatment is carried out on the target aerator for the target wastewater according to the optimal aerator control scheme.

[0055] During the aeration process, sensors monitor factors such as air velocity, water velocity, and additives in real time. Air velocity refers to the speed of air entering the wastewater, water velocity refers to the flow rate of wastewater in the treatment tank, and additives refer to chemical substances added to the wastewater that can alter the surface tension of the water, affecting bubble formation. Sensors collect this data in real time and transmit it to the control system via wireless transmission technology. Based on the sensor data, a feedback control algorithm is used for dynamic adjustments. The feedback control algorithm is an automatic adjustment method that adjusts control parameters in real time based on the deviation between actual measured values ​​and set values ​​to achieve the desired effect. Specifically, it uses a fuzzy control algorithm to automatically reduce airflow when the sensor detects excessively high air velocity; adjust the aerator angle or increase air pressure when the water velocity is too low; and automatically add an appropriate amount of chemical substances when the additive concentration is insufficient. This process ensures that the bubble size remains within a predetermined threshold range, guaranteeing optimal oxygen transfer efficiency and treatment effect while reducing energy consumption.

[0056] In summary, the embodiments of this application have at least the following technical effects: This application reads predetermined wastewater indicators and collects features of the target wastewater based on these indicators to obtain target wastewater feature information. This feature information is then used as input to a dissolved oxygen demand prediction model to obtain the target required dissolved oxygen concentration, which is the minimum dissolved oxygen concentration required for biological treatment of the target wastewater. Predetermined aeration indicators are read and analyzed to obtain the target aeration control space. A predetermined control fitness function is introduced as an optimization evaluation strategy, and the target required dissolved oxygen concentration is used as an optimization constraint to perform global optimization within the target aeration control space, obtaining the optimal aeration control scheme. Based on the optimal aeration control scheme, the target blower for the target wastewater is subjected to energy-saving aeration treatment. This invention solves the technical problems of low treatment efficiency and high energy consumption in existing biological wastewater treatment technologies. By acquiring wastewater and aeration indicators, using big data analysis and prediction models to calculate dissolved oxygen demand, and optimizing aeration parameters through a global optimization algorithm, the blower is controlled for energy-saving aeration treatment, achieving both high-efficiency wastewater treatment and significant energy savings.

[0057] Example 2 is based on the same inventive concept as the blower optimization control system for wastewater treatment in the previous examples, such as... Figure 2 As shown in the figure, this application provides an optimized control method for a blower used in wastewater treatment, the method comprising: A predetermined wastewater index is read, and feature collection of the target wastewater is performed based on the predetermined wastewater index to obtain target wastewater feature information. The target wastewater feature information is used as input information for a dissolved oxygen demand prediction model to obtain the target required dissolved oxygen concentration, wherein the target required dissolved oxygen concentration refers to the minimum dissolved oxygen concentration for biological treatment of the target wastewater. A predetermined aeration index is read, and the predetermined aeration index is analyzed to obtain a target aeration control space. A predetermined control fitness function is introduced as an optimization evaluation strategy, and the target required dissolved oxygen concentration is used as an optimization constraint to perform global optimization in the target aeration control space to obtain the optimal aeration control scheme. The target blower is controlled to aerate the target wastewater according to the optimal aeration control scheme.

[0058] Furthermore, the method also includes: The predetermined wastewater parameters include sludge concentration, water flow velocity, water temperature, influent flow rate, and organic matter concentration.

[0059] Furthermore, the method also includes:

[0060] The system arbitrarily acquires the first historical record from the historical wastewater biological treatment records; performs normalized weighted analysis on the first historical treatment quality information extracted from the first historical record to obtain the first historical treatment quality index; if the first historical treatment quality index reaches a predetermined quality index threshold, it issues a data assembly instruction; based on the data assembly instruction, it extracts the first historical wastewater feature information and the first historical dissolved oxygen concentration from the first historical record and assembles the first training data; based on the neural network principle, it performs supervised learning on the first training data and tests it to obtain the dissolved oxygen demand prediction model.

[0061] Furthermore, the method also includes: A predetermined quality feature is read, and the first historical treatment quality information is extracted based on the predetermined quality feature to obtain the first historical quality feature parameter; the first historical quality feature parameter is processed and analyzed using the coefficient of variation principle to obtain the first historical treatment quality index; wherein, the predetermined quality feature includes at least a water quality dimension, a biochemical quality dimension, and a microbial quality dimension, the water quality dimension includes suspended solids ratio, total nitrogen ratio, total phosphorus ratio, and pH value, the biochemical quality dimension includes sludge settling ratio and sludge concentration, and the microbial quality dimension includes microbial species, microbial quantity, and biological activity.

[0062] Furthermore, the method also includes: The predetermined aeration parameters include air pressure, air flow rate, aeration duration, and bubble size.

[0063] Furthermore, the method also includes: Step a: Traverse the first aeration control scheme extracted from the target aeration control space in the aeration database to obtain the first control log of the first most similar aeration control scheme; Step b: Determine whether the first dissolved oxygen concentration in the first control log meets the optimization constraint; Step c: If it meets the constraint, retrieve the optimization evaluation strategy to analyze and evaluate the first control log to obtain the first control fitness; Step d: Obtain the second control fitness, which refers to the control fitness of the second aeration control scheme extracted from the target aeration control space; Step e: Obtain the optimal aeration control scheme based on the comparison result between the first control fitness and the second control fitness; Step f: Repeat steps a to e until a predetermined number of iterations is reached, and output the optimal aeration control scheme at that time.

[0064] Furthermore, the expression for the predetermined control fitness function is as follows: ;

[0065] in, This refers to the aeration control scheme Control fitness, This refers to the aeration control scheme. Aeration volume, This refers to the energy consumption coefficient per unit of aeration. This refers to the aeration control scheme. Dissolved oxygen concentration, This refers to the target required dissolved oxygen concentration. and These refer to the first fitness coefficient and the second fitness coefficient, respectively. .

[0066] Furthermore, the method also includes: Extract the optimal bubble size from the optimal aeration control scheme and determine a predetermined bubble threshold based on the optimal bubble size; obtain any aeration control scheme from the aeration database, the arbitrary aeration control scheme including any aerator control scheme; when any bubble size in the arbitrary aerator control scheme meets the predetermined bubble threshold, issue an optimal pickup command; read the predetermined bubble control index based on the optimal pickup command, and perform traversal matching in the arbitrary aerator control scheme based on the predetermined bubble control index to obtain arbitrary bubble control parameters; use the arbitrary bubble control parameters as the optimal aerator control scheme, and perform energy-saving aeration treatment on the target aerator of the target wastewater according to the optimal aerator control scheme.

[0067] Furthermore, the method also includes: The predetermined bubble control parameters include the installation depth and orifice diameter of the target aerator.

[0068] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0069] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0070] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. An optimized control system for a blower used in wastewater treatment, characterized in that, include: The feature collection module is used to read predetermined wastewater indicators and collect features of the target wastewater based on the predetermined wastewater indicators to obtain target wastewater feature information. A dissolved oxygen demand prediction module is used to take the target wastewater characteristic information as input information of the dissolved oxygen demand prediction model to obtain the target required dissolved oxygen concentration, wherein the target required dissolved oxygen concentration refers to the minimum dissolved oxygen concentration for biological treatment of the target wastewater. A predetermined aeration index reading module is used to read predetermined aeration indexes and analyze the predetermined aeration indexes to obtain the target aeration control space. The optimization module is used to introduce a predetermined control fitness function as an optimization evaluation strategy and use the target required dissolved oxygen concentration as an optimization constraint to perform global optimization in the target aeration control space to obtain the optimal aeration control scheme. An aeration treatment module is used to control a target blower to aerate the target wastewater according to the optimal aeration control scheme.

2. The blower optimization control system for wastewater treatment according to claim 1, characterized in that, The predetermined wastewater parameters include sludge concentration, water flow velocity, water temperature, influent flow rate, and organic matter concentration.

3. The blower optimization control system for wastewater treatment according to claim 1, characterized in that, The dissolved oxygen demand prediction module is also used for: Arbitrarily retrieve the first historical record from historical wastewater biological treatment records; The first historical processing quality information extracted from the first historical record is subjected to normalized weighted analysis to obtain the first historical processing quality index. If the first historical processing quality index reaches the predetermined quality index threshold, a data assembly instruction is issued. Based on the data assembly instructions, extract the first historical wastewater feature information and the first historical dissolved oxygen concentration from the first historical record, and assemble them into the first training data; The dissolved oxygen demand prediction model is obtained by performing supervised learning and testing on the first training data based on neural network principles.

4. The blower optimization control system for wastewater treatment according to claim 3, characterized in that, The dissolved oxygen demand prediction module is also used for: Read the predetermined quality features, and extract the first historical processing quality information based on the predetermined quality features to obtain the first historical quality feature parameters; The first historical quality characteristic parameter is processed and analyzed using the principle of coefficient of variation to obtain the first historical processing quality index. The predetermined quality characteristics include at least water quality dimensions, biochemical quality dimensions, and microbiological quality dimensions. The water quality dimensions include suspended solids ratio, total nitrogen ratio, total phosphorus ratio, and pH value. The biochemical quality dimensions include sludge settling ratio and sludge concentration. The microbiological quality dimensions include microbial species, microbial quantity, and biological activity.

5. The blower optimization control system for wastewater treatment according to claim 1, characterized in that, The predetermined aeration parameters include air pressure, air flow rate, aeration duration, and bubble size.

6. The blower optimization control system for wastewater treatment according to claim 1, characterized in that, The optimization module is also used for: Step a: Traverse the first aeration control scheme extracted from the target aeration control space in the aeration database to obtain the first control log of the first most similar aeration control scheme; Step b: Determine whether the first dissolved oxygen concentration in the first control log meets the optimization constraint; Step c: If the conditions are met, retrieve the optimization evaluation strategy to analyze and evaluate the first control log to obtain the first control fitness. Step d: Obtain the second control fitness, which refers to the control fitness of the second aeration control scheme extracted from the target aeration control space; Step e: Based on the comparison results between the first control fitness and the second control fitness, the optimal aeration control scheme is obtained; Step f: Repeat steps a to e until the predetermined number of iterations is reached, and output the optimal aeration control scheme at that time.

7. The blower optimization control system for wastewater treatment according to claim 6, characterized in that, The expression for the predetermined control fitness function is as follows: ； in, This refers to the aeration control scheme Control fitness, This refers to the aeration control scheme. Aeration volume, This refers to the energy consumption coefficient per unit of aeration. This refers to the aeration control scheme. Dissolved oxygen concentration, This refers to the target required dissolved oxygen concentration. and These refer to the first fitness coefficient and the second fitness coefficient, respectively. .

8. The blower optimization control system for wastewater treatment according to claim 6, characterized in that, The aeration treatment module is also used for: Extract the optimal bubble size from the optimal aeration control scheme, and determine a predetermined bubble threshold based on the optimal bubble size; Obtain any aeration control scheme from the aeration database, wherein the arbitrary aeration control scheme includes any aerator control scheme; When the size of any bubble in the arbitrary aerator control scheme meets the predetermined bubble threshold, an optimal pickup command is issued. Based on the optimal picking instruction, the predetermined bubble control index is read, and based on the predetermined bubble control index, the arbitrary aerator control scheme is traversed and matched to obtain the arbitrary bubble control parameters. The arbitrary bubble control parameters are used as the optimal aerator control scheme, and the target aerator for the target wastewater is subjected to energy-saving aeration treatment according to the optimal aerator control scheme.

9. The blower optimization control system for wastewater treatment according to claim 8, characterized in that, The predetermined bubble control parameters include the installation depth and orifice diameter of the target aerator.

10. A method for optimizing the control of a blower used in wastewater treatment, characterized in that, The blower optimization control method for wastewater treatment is implemented by the blower optimization control system for wastewater treatment as described in any one of claims 1 to 9, wherein the blower optimization control method for wastewater treatment includes: Read the predetermined wastewater index, and collect the characteristics of the target wastewater based on the predetermined wastewater index to obtain the target wastewater characteristic information; The target wastewater characteristic information is used as input information for the dissolved oxygen demand prediction model to obtain the target required dissolved oxygen concentration, wherein the target required dissolved oxygen concentration refers to the minimum dissolved oxygen concentration for biological treatment of the target wastewater. Read the predetermined aeration index and analyze the predetermined aeration index to obtain the target aeration control space; A predetermined control fitness function is introduced as an optimization evaluation strategy, and the target required dissolved oxygen concentration is used as an optimization constraint. Global optimization is performed in the target aeration control space to obtain the optimal aeration control scheme. The target blower is controlled to aerate the target wastewater according to the optimal aeration control scheme.

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