Aeration control method and system for sewage treatment

Abstract

The invention discloses an aeration control method and system for sewage treatment, the method comprising the following steps: deriving historical data; calculating the aeration volume under standard conditions at historical moments corresponding to the historical data based on an air volume frequency conversion formula; constructing a standard fuzzy gas-water ratio feature; constructing a prediction model based on the historical data and taking the gas-water ratio as a prediction value; collecting real-time data; inputting the real-time data into the prediction model to predict the fuzzy gas-water ratio GWR at the next moment required to reach a target DO value; calculating the blower frequency f by combining the predicted fuzzy gas-water ratio GWR with a water flow rate w; adjusting the blower frequency in real time according to the calculated blower frequency f; the invention can realize real-time automatic prediction and control of the blower air volume in the absence of an air flow meter, thus avoiding the cost accumulation caused by the expensive air flow meter, and realizing full-automatic algorithm control at the same time, significantly reducing labor costs, and having stable operation and accurate control.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage treatment, and in particular to an aeration control method and system for sewage treatment. Background Art

[0002] Biological treatment process is one of the technologies widely used in sewage treatment. Its process involves complex biochemical reactions, and aeration control (dissolved oxygen control) is a key link. The existing aeration control methods are mainly divided into two types: manual and automatic control.

[0003] In manual control, operators rely on manually adjusting the air volume of the blower to ensure the appropriate dissolved oxygen concentration. However, this method has poor stability, high labor intensity, and is prone to operating errors, resulting in excessive or insufficient aeration, high energy consumption, unstable treatment effects, and other problems. With the development of technology, automatic control can monitor a variety of parameters in the sewage treatment process in real time, such as dissolved oxygen concentration, influent flow, COD (chemical oxygen demand), ammonia nitrogen concentration, etc., and use PLC automatic control programs for processing, adjust the aeration volume in real time, improve the accuracy and response speed of control, and significantly improve the effect and energy efficiency of sewage treatment. However, even the existing automatic aeration control system is still highly dependent on gas flow meters to monitor and control the aeration volume.

[0004] As a key device, gas flow meter is expensive, complicated to install and maintain, and increases the overall budget and operating cost of the system. Especially in practical applications. For many sewage treatment plants, especially small and medium-sized sewage treatment plants, this additional budget and maintenance burden has become a major obstacle to the widespread adoption of AI automatic control systems. The existing technology has the following defects and deficiencies in practical applications:

[0005] 1) Insufficient control accuracy and response speed: Manual adjustment depends on the operator's experience and judgment, and it is difficult to respond to changes in water quality and flow in real time. The dissolved oxygen concentration fluctuates greatly, and there are often problems of excessive or insufficient aeration. It is difficult to ensure the consistency and reliability of the treatment effect. Due to the slow response speed of manual control, it is difficult for the system to adjust the aeration volume in time to respond to water quality changes, resulting in difficulty in maintaining the dissolved oxygen concentration in the biochemical pool within the ideal range, affecting the efficiency and effect of biological treatment.

[0006] 2) High energy consumption and high labor intensity: In order to ensure that the effluent quality meets the standards, operators usually choose excessive aeration, which causes energy waste and significantly increases operating costs. In addition, manual PLC control requires frequent monitoring and manual adjustment of system parameters, which is labor-intensive and prone to operational errors. Frequent manual intervention increases labor costs and operational burdens, reducing the overall operating efficiency of the system.

[0007] 3) High equipment costs: Gas flow meters are expensive, increasing the initial investment and operating costs of the system. Installing a high-precision gas flow meter not only requires expensive equipment purchase costs, but also requires professional installation and commissioning, which will increase the overall implementation costs. For small and medium-sized enterprises, expensive equipment and complex installation requirements have become the main obstacles for them to adopt advanced aeration control technology.

[0008] 4) Strong dependence on gas flow meter: The existing aeration control system is highly dependent on gas flow meter for data monitoring and control. Once the flow meter fails or requires maintenance, the operation of the entire system will be affected, and continuous and stable aeration control cannot be achieved. Summary of the invention

[0009] The object of the present invention is to provide an aeration control method and system for sewage treatment, which can realize real-time automatic predictive control of the blower air volume in the absence of an air flow meter, avoiding the cost accumulation caused by the expensive air flow meter, and realizing fully automatic algorithm control, significantly reducing labor costs, stable operation and accurate control.

[0010] To achieve this object, the present invention adopts the following technical solutions:

[0011] A method for controlling aeration in sewage treatment is provided, comprising the following steps:

[0012] Export historical data;

[0013] Based on the air volume frequency conversion formula, the fuzzy volume flow of the blower under the standard state at the historical moment corresponding to the historical data is calculated. ;

[0014] Construct standard fuzzy gas-water ratio feature: ,in, is the fuzzy gas-water ratio, is the water inflow corresponding to that moment: m³ / min;

[0015] Based on historical data, a prediction model is constructed with fuzzy gas-water ratio as the prediction value;

[0016] Collect real-time data;

[0017] Inputting the real-time data into the prediction model to predict the fuzzy gas-water ratio GWR at the next moment required to reach the target DO value;

[0018] Based on the air volume frequency conversion formula, the predicted fuzzy gas-water ratio GWR is combined with the water inlet flow w to calculate the blower frequency f;

[0019] The frequency of the blower is adjusted in real time according to the calculated blower frequency f.

[0020] As a preferred solution of the aeration control method for sewage treatment, the historical data includes: absolute temperature , intake pressure , blower frequency, water inlet flow w, air humidity , dissolved oxygen DO, inlet COD, effluent COD, biochemical pool water temperature, inlet total nitrogen concentration, inlet ammonia nitrogen concentration, sludge concentration MLSS.

[0021] As a preferred solution of the aeration control method for sewage treatment, the real-time data includes: absolute temperature , intake pressure , blower frequency, water inlet flow w, air humidity , dissolved oxygen DO, inlet COD, effluent COD, biochemical pool water temperature, inlet total nitrogen concentration, inlet ammonia nitrogen concentration, sludge concentration MLSS.

[0022] As a preferred solution of the aeration control method for sewage treatment, the fuzzy volume flow of the blower under the standard state at the historical moment corresponding to the historical data is calculated based on the air volume frequency conversion formula. In the step, the fuzzy volume flow of the blower under the standard state at the historical moment corresponding to the historical data for:

[0023]

[0024] Where:

[0025] ——Fuzzy volume flow of the blower under standard conditions: ;

[0026] ——Fuzzy volume flow of the blower in use: ;

[0027] - absolute temperature of the intake air in the operating state, ;

[0028] - The intake pressure of the intake air in the use state, measured by the pressure sensor;

[0029] ——absolute temperature of intake air under standard conditions, ;

[0030] ——the intake pressure of the intake air under standard conditions, ;

[0031] ——humidity content of air in use, (in, is the relative humidity, with values ​​between 0 and 1 (%); is the partial pressure of water vapor in saturated moist air ( ))、 ;

[0032]

[0033] Where Q 额定 is the rated air volume of the blower (m³ / min), n s is the speed of the fan (rpm), n 额定 is the rated speed of the blower (rpm);

[0034]

[0035] where f s is the fuzzy power supply frequency (Hz), p is the number of motor pole pairs;

[0036] Fuzzy volume flow of the blower in use The actual blower air volume Q real There is a certain percentage of error: fuzzy ratio value .

[0037] As a preferred solution of the aeration control method for sewage treatment, in the step of calculating the blower frequency f by combining the predicted fuzzy gas-water ratio GWR with the water inlet flow w based on the air volume frequency conversion formula, the blower frequency f is:

[0038] Among them, n 所需 is the required speed, ; p is the number of pole pairs of the motor; ; ; .

[0039] As a preferred solution of the aeration control method for sewage treatment, after the step of collecting real-time data, the method includes:

[0040] The collected real-time data is stored as historical data.

[0041] As a preferred solution of the aeration control method for sewage treatment, after the step of exporting historical data, the method includes:

[0042] Eliminate abnormal historical data.

[0043] The present invention also provides an aeration control system for sewage treatment, comprising:

[0044] Data collection and storage module, used to export historical data;

[0045] The air volume frequency conversion module is used to calculate the fuzzy volume flow of the blower under the standard state at the historical moment corresponding to the historical data based on the air volume frequency conversion formula. ;

[0046] Model building module, used to build standard fuzzy gas-water ratio features: ,in, is the fuzzy gas-water ratio, is the water inflow corresponding to that moment: m³ / min;

[0047] The model building module is also used to build a prediction model based on historical data and using the fuzzy gas-water ratio as a prediction value;

[0048] The data acquisition and storage module is also used to collect real-time data;

[0049] A model prediction module, used for inputting the real-time data into the prediction model to predict the fuzzy gas-water ratio GWR at the next moment required to reach the target DO value;

[0050] The air volume frequency conversion module is further used to calculate the blower frequency f by combining the predicted fuzzy gas-water ratio GWR with the water inlet flow w based on the air volume frequency conversion formula;

[0051] The control module is used to adjust the frequency of the blower in real time according to the calculated blower frequency f.

[0052] Beneficial effects of the present invention: The aeration control method and system for sewage treatment proposed in the present invention eliminate the fuzzy error ratio through forward and reverse operations of the air volume frequency conversion formula, realize real-time automatic prediction and control of the blower air volume in the absence of an air flow meter, avoid the cost accumulation caused by the expensive air flow meter, and realize full automatic control of the algorithm, which significantly reduces the labor cost; at the same time, real-time data is collected in real time and input into the prediction model to predict the fuzzy air-water ratio at the next moment required to achieve the target DO value, and the response speed to water quality changes is fast, highly adaptive, and fully automated operation control is carried out, without worrying about unexpected situations such as distraction of on-site workers, stable operation, and high control accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0054] Figure 1 is a flow chart of an aeration control method for sewage treatment according to an embodiment of the present invention;

[0055] Figure 2 It is a schematic block diagram of the structure of an aeration control system for sewage treatment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0057] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0058] Reference Figure 1 An embodiment of the present invention provides an aeration control method for sewage treatment, comprising the following steps:

[0059] S1: Export historical data;

[0060] S2: Based on the air volume frequency conversion formula, calculate the fuzzy volume flow of the blower under the standard state at the historical moment corresponding to the historical data ;

[0061] S3: Construct standard fuzzy gas-water ratio feature: ,in, is the fuzzy gas-water ratio, is the water inflow corresponding to that moment: m³ / min;

[0062] S4: Based on historical data, a prediction model is constructed with fuzzy gas-water ratio as the prediction value;

[0063] S5: collect real-time data;

[0064] S6: inputting the real-time data into the prediction model to predict the fuzzy gas-water ratio GWR at the next moment required to reach the target DO value;

[0065] S7: Based on the air volume frequency conversion formula, the predicted fuzzy gas-water ratio GWR is combined with the water inlet flow w to calculate the blower frequency f;

[0066] S8: Adjust the frequency of the blower in real time according to the calculated blower frequency f.

[0067] As described in step S1 above, the data storage and acquisition module collects the monitoring and operation data of each PLC hardware device, such as: the acquisition frequency is in minutes; including: absolute temperature , intake pressure , blower frequency, water inlet flow w, air humidity , dissolved oxygen DO, inlet COD, effluent COD, biochemical pool water temperature, inlet total nitrogen concentration, inlet ammonia nitrogen concentration, sludge concentration MLSS, etc. are stored in the database to facilitate subsequent model training and historical data backtracking; the data storage and acquisition module exports historical data.

[0068] As described in step S2 above, the air volume frequency conversion module calculates the fuzzy volume flow of the blower under the standard state at the historical moment corresponding to the historical data based on the air volume frequency conversion formula. :

[0069] Where:

[0070] ——Fuzzy volume flow of the blower under standard conditions: ;

[0071] ——Fuzzy volume flow of the blower in use: ;

[0072] - absolute temperature of the intake air in the operating state, ;

[0073] - The intake pressure of the intake air in the use state, measured by the pressure sensor;

[0074] ——absolute temperature of intake air under standard conditions, ;

[0075] ——the intake pressure of the intake air under standard conditions, ;

[0076] ——humidity content of air in use, (in, is the relative humidity, with values ​​between 0 and 1 (%); is the partial pressure of water vapor in saturated moist air ( ))、 ;

[0077]

[0078] Where Q 额定 is the rated air volume of the blower (m³ / min), n s is the speed of the fan (rpm), n 额定 is the rated speed of the blower (rpm).

[0079]

[0080] where f s is the fuzzy power supply frequency (Hz), p is the number of motor pole pairs;

[0081] There will be a certain percentage error between the fuzzy blower air volume under the use state calculated by the above formula and the actual blower air volume. The actual blower air volume is Q real , then:

[0082]

[0083] Where P ac The ratio of the calculated fuzzy blower air volume to the actual blower air volume reflects the error ratio between the calculated air volume and the actual air volume, and is called the fuzzy ratio value.

[0084] As described in the above step S3, the model building module builds a standard fuzzy gas-water ratio feature. The gas-water ratio represents the ratio of aeration volume to water intake volume under standard conditions per unit volume. The calculation formula is as follows.

[0085]

[0086] in, is the fuzzy gas-water ratio, is the fuzzy volume flow rate of the blower at a certain moment in use: m³ / min, is the water inflow corresponding to that moment: m³ / min.

[0087] After step S1 of exporting historical data, the following steps are included:

[0088] S11: Eliminate abnormal historical data.

[0089] As described in the above step S11, the model building module analyzes and removes abnormalities in historical data based on aeration-related knowledge and hardware equipment abnormalities, as well as abnormal data analysis methods related to statistical distribution.

[0090] As described in the above step S4, the model building module is based on a large amount of historical data, and performs feature engineering by means of time window sliding and feature fusion with parameters such as influent flow w, dissolved oxygen DO, influent COD, effluent COD, biochemical pool water temperature, influent total nitrogen concentration, influent ammonia nitrogen concentration, sludge concentration MLSS, etc., and builds and trains a prediction model with fuzzy gas-water ratio as the prediction value. After the model training is completed, it is deployed on the cloud.

[0091] As described in step S5 above, the data acquisition and storage collects real-time data of monitoring and operation of each PLC hardware device in real time, such as: the acquisition frequency is in minutes; the real-time data includes: absolute temperature , intake pressure , blower frequency, water inlet flow w, air humidity , dissolved oxygen DO, inlet COD, effluent COD, biochemical pool water temperature, inlet total nitrogen concentration, inlet ammonia nitrogen concentration, sludge concentration MLSS.

[0092] After the step S5 of collecting real-time data, the following steps are included:

[0093] S51: storing the collected real-time data as historical data.

[0094] As described in the above step S51, the real-time data collected in real time is stored in the database as historical data to facilitate subsequent model training and historical data backtracking.

[0095] As described in step S6, the model prediction module constructs the feature values ​​required by the model acquired in real time and inputs them into the model, and sets the target DO value. The model predicts the fuzzy gas-water ratio GWR at the next moment required to reach the target DO value in real time.

[0096] As described in step S7, the air volume frequency conversion module calculates the blower frequency f based on the air volume frequency conversion formula by combining the predicted fuzzy gas-water ratio GWR with the water inlet flow w. The blower frequency f is:

[0097] where n 所需 is the required speed, and p is the number of pole pairs of the motor.

[0098]

[0099] in

[0100]

[0101] Where:

[0102] ——Required fuzzy volume flow of the blower under standard conditions: ;

[0103] ——Required fuzzy volume flow of the blower in use: ;

[0104] - absolute temperature of the intake air in the operating state, ;

[0105] - The intake pressure of the intake air in the use state, measured by the pressure sensor;

[0106] ——absolute temperature of intake air under standard conditions, ;

[0107] ——the intake pressure of the intake air under standard conditions, ;

[0108] ——humidity content of air in use, (in, is the relative humidity, with values ​​between 0 and 1 (%); is the partial pressure of water vapor in saturated moist air ( ))、

[0109]

[0110] Where GWR is the fuzzy gas-water ratio required for the target DO value predicted by the model, and w is the water flow rate at the current moment. The actual required volume flow rate of the blower under the use state is Q real , then there should also be

[0111]

[0112] Therefore, P ac is the ratio of the fuzzy blower air volume predicted by the model to the actual required blower air volume, which is equal to the fuzzy ratio value P in the above step S2. ac equal, so the required blower frequency calculated in this way actually corresponds to the actual blower air volume Q real , and the fuzzy ratio value P ac After the forward calculation and the reverse calculation in step S2 and step S7 are eliminated, the blower frequency value required when the actual blower air volume is required is obtained.

[0113] As described in the above step S8, the control module sends the calculated blower frequency f to the on-site control actuator through the cloud to adjust the fan frequency f, thereby adjusting the frequency of the blower in real time.

[0114] After the fan frequency is adjusted based on the predicted air-water ratio and air volume frequency conversion formula, the data acquisition and storage module continues to collect real-time data on the monitoring and operation of each PLC hardware device, re-enters the real-time parameters of the biochemical pool at the next moment into the cloud model, and repeats steps S5-S8 to achieve stable control of the dissolved oxygen DO value in the biochemical pool without an air flow meter throughout the process.

[0115] The above-mentioned embodiment of the present invention eliminates the fuzzy error ratio through the forward operation and reverse operation of the air volume frequency conversion formula, realizes real-time automatic prediction and control of the blower air volume in the absence of an air flow meter, avoids the cost accumulation caused by the expensive air flow meter, and realizes full automatic control of the algorithm, which significantly reduces the labor cost; at the same time, real-time data is collected in real time and input into the prediction model to predict the fuzzy air-water ratio at the next moment required to achieve the target DO value. It has a fast response speed to changes in water quality, is highly adaptive, and has fully automated operation control. There is no need to worry about unexpected situations such as distraction of on-site workers, and the operation is stable and the control accuracy is high.

[0116] Reference Figure 2 An embodiment of the present invention further provides an aeration control system for sewage treatment, comprising:

[0117] Data collection and storage module, used to export historical data;

[0118] The air volume frequency conversion module is used to calculate the fuzzy volume flow of the blower under the standard state at the historical moment corresponding to the historical data based on the air volume frequency conversion formula. ;

[0119] Model building module, used to build standard fuzzy gas-water ratio features: ,in, is the fuzzy gas-water ratio, is the water inflow corresponding to that moment: m³ / min;

[0120] The model building module is also used to build a prediction model based on historical data and using the fuzzy gas-water ratio as a prediction value;

[0121] The data acquisition and storage module is also used to collect real-time data;

[0122] A model prediction module, used for inputting the real-time data into the prediction model to predict the fuzzy gas-water ratio GWR at the next moment required to reach the target DO value;

[0123] The air volume frequency conversion module is further used to calculate the blower frequency f by combining the predicted fuzzy gas-water ratio GWR with the water inlet flow w based on the air volume frequency conversion formula;

[0124] The control module is used to adjust the frequency of the blower in real time according to the calculated blower frequency f.

[0125] In the description of the present invention, it is necessary to understand that the terms "middle", "length", "up", "down", "front", "back", "vertical", "horizontal", "inside", "outside", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0126] In the present invention, unless otherwise clearly specified and limited, the first feature "on" the second feature may be that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. "Multiple" means at least two, such as two, three, etc., unless otherwise clearly and specifically limited.

[0127] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral one; it can be a mechanical connection, an electrical connection, or communication with each other; it can be a direct connection, or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0128] The above is only for explaining the implementation mode of the present invention and is not intended to limit the present invention. For those skilled in the art, any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention without creative work should be included in the protection scope of the present invention.

Claims

1. An aeration control method for sewage treatment, characterized in that: The following steps are involved: Export historical data, wherein the historical data includes: absolute temperature , intake pressure , blower frequency, water inlet flow w, air humidity , dissolved oxygen DO, inlet COD, effluent COD, biochemical pool water temperature, inlet total nitrogen concentration, inlet ammonia nitrogen concentration, sludge concentration MLSS; Based on the air volume frequency conversion formula, the fuzzy volume flow of the blower under the standard state at the historical moment corresponding to the historical data is calculated. ; Among them, the historical data corresponds to the fuzzy volume flow of the blower under the historical standard state for: Where: ——Fuzzy volume flow of the blower under standard conditions ; ——Fuzzy volume flow of the blower in use ; - absolute temperature of the intake air in use, , is the intake air temperature in degrees Celsius under use; ——The intake pressure of the intake air in the use state is measured by the pressure sensor, the unit is ; ——absolute temperature of intake air under standard conditions, ; ——the intake pressure of the intake air under standard conditions, ; ——humidity content of air in use, ,in, is the relative humidity, with values ​​between 0% and 100%; is the partial pressure of water vapor in saturated moist air, in units of , The unit is kg [water vapor] / kg [dry air]; Where Q 额定 is the rated air volume of the blower in m³ / min, n s is the speed of the fan in revolutions per minute, n 额定 is the rated speed of the blower in revolutions per minute; where f s is the fuzzy power supply frequency in Hz, and p is the number of pole pairs of the motor; Fuzzy volume flow of the blower in use The actual blower air volume Q real There is a certain percentage of error: fuzzy ratio value ; Construct standard fuzzy gas-water ratio feature: ,in, is the fuzzy gas-water ratio, is the water inflow corresponding to that moment, in m³ / min; Based on historical data, a prediction model is constructed with fuzzy gas-water ratio as the prediction value; Collect real-time data, where the real-time data includes: absolute temperature , intake pressure , blower frequency, water inlet flow w, air humidity , dissolved oxygen DO, inlet COD, effluent COD, biochemical pool water temperature, inlet total nitrogen concentration, inlet ammonia nitrogen concentration, sludge concentration MLSS; Inputting the real-time data into the prediction model to predict the fuzzy gas-water ratio GWR at the next moment required to reach the target DO value; Based on the air volume frequency conversion formula, the predicted fuzzy gas-water ratio GWR is combined with the water inlet flow w to calculate the blower frequency f, which is: Among them, n 所需 is the required speed, ; p is the number of pole pairs of the motor; ; ; ; The frequency of the blower is adjusted in real time according to the calculated blower frequency f.

2. The aeration control method for sewage treatment according to claim 1, characterized in that: After the step of collecting real-time data, the following steps are included: The collected real-time data is stored as historical data.

3. The aeration control method for sewage treatment according to claim 1, characterized in that: After the step of exporting the historical data, the following steps are included: Eliminate abnormal historical data.

4. A control system using the aeration control method for sewage treatment according to any one of claims 1 to 3, characterized in that: include: The data acquisition and storage module is used to export historical data, wherein the historical data includes: absolute temperature , intake pressure , blower frequency, water inlet flow w, air humidity , dissolved oxygen DO, inlet COD, effluent COD, biochemical pool water temperature, inlet total nitrogen concentration, inlet ammonia nitrogen concentration, sludge concentration MLSS; The air volume frequency conversion module is used to calculate the fuzzy volume flow of the blower under the standard state at the historical moment corresponding to the historical data based on the air volume frequency conversion formula. ; Among them, the historical data corresponds to the fuzzy volume flow of the blower under the standard state at the historical moment for: Where: ——Fuzzy volume flow of the blower under standard conditions ; ——Fuzzy volume flow of the blower in use ; - absolute temperature of the intake air in use, , is the intake air temperature in degrees Celsius under use; ——The intake pressure of the intake air in the use state is measured by the pressure sensor in units of ; ——absolute temperature of intake air under standard conditions, ; ——the intake pressure of the intake air under standard conditions, ; ——humidity content of air in use, ,in, is the relative humidity, with values ​​between 0% and 100%; is the partial pressure of water vapor in saturated moist air, in units of , The unit is kg [water vapor] / kg [dry air]; Where Q 额定 is the rated air volume of the blower in m³ / min, n s is the speed of the fan in revolutions per minute, n 额定 is the rated speed of the blower in revolutions per minute; where f s is the fuzzy power supply frequency in Hz, and p is the number of pole pairs of the motor; Fuzzy volume flow of the blower in use The actual blower air volume Q real There is a certain percentage of error: fuzzy ratio value ; Model building module, used to build standard fuzzy gas-water ratio features: ,in, is the fuzzy gas-water ratio, is the water inflow corresponding to that moment, in m³ / min; The model building module is also used to build a prediction model based on historical data and using the fuzzy gas-water ratio as a prediction value; The data acquisition storage module is also used to collect real-time data, wherein the real-time data includes: absolute temperature , intake pressure , blower frequency, water inlet flow w, air humidity , dissolved oxygen DO, inlet COD, effluent COD, biochemical pool water temperature, inlet total nitrogen concentration, inlet ammonia nitrogen concentration, sludge concentration MLSS; A model prediction module, used for inputting the real-time data into the prediction model to predict the fuzzy gas-water ratio GWR at the next moment required to reach the target DO value; The air volume frequency conversion module is further used to calculate the blower frequency f by combining the predicted fuzzy gas-water ratio GWR with the water inlet flow w based on the air volume frequency conversion formula. The blower frequency f is: Among them, n 所需 is the required speed, ; p is the number of pole pairs of the motor; ; ; ; The control module is used to adjust the frequency of the blower in real time according to the calculated blower frequency f.

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