Intermittent aeration parameter optimization method based on numerical simulation

Abstract

The invention discloses an intermittent aeration parameter optimization method based on numerical simulation, which comprises the following steps: by taking a statistical method for optimizing a random process as a means, collecting data through multiple linear regression, establishing a multi-dimensional space curved surface, and constructing a mathematical model to find a quantitative rule between aeration rate and aeration-stop ratio; therefore, the optimal aeration scheme and operation parameters are determined according to the water inlet load and the water outlet quality requirements. The regression model constructed by the method can accurately predict the removal effect of water pollutants under different aeration parameters. Meanwhile, the optimal aeration rate and the optimal aeration stop ratio can be determined according to the regression model when the certain effluent quality requirement is met, when the aeration rate is 3.528 L / min and the aeration stop ratio is 1.046 h / h, the removal rates of all pollutants in the sewage reach the highest, and at the moment, the removal rates of COD, NH3-N and TP reach 84.198%, 87.025% and 95.544% respectively.

Description

Technical Field

[0001] The invention relates to a method for optimizing intermittent aeration parameters based on numerical simulation, and belongs to the field of environmental protection. Background Art

[0002] The proposal of the "dual carbon" strategic goal has put forward new requirements for the development of high-energy-consuming industries. As a traditional high-energy-consuming industry, the sewage treatment industry often brings about higher carbon emissions while achieving high-standard effluent discharge. According to the United Nations data, the carbon emissions of the global water treatment industry account for about 2% of the global total carbon emissions. Under the background of the national "dual carbon" strategy, reducing energy consumption and taking the path of green and low-carbon development are the inevitable goals of the transformation and upgrading of the sewage treatment industry. With the rapid growth of the number of sewage treatment plants and sewage treatment capacity in my country, carbon reduction methods such as upgrading sewage treatment systems, energy saving and consumption reduction of equipment, and improving the accuracy and intelligence of equipment have become research hotspots in recent years.

[0003] The high-energy-consuming equipment in the sewage treatment unit mainly comes from the conveying system and the aeration system, among which the aeration system accounts for the largest proportion of overall energy consumption. Data show that the power consumption of the aeration system accounts for more than half of the total power consumption of the entire plant. Therefore, under the condition of ensuring the effluent quality, the aeration volume is adjusted in real time according to the inlet load, and the amount of oxygen required for microbial degradation of organic matter is accurately controlled. It is necessary to prevent energy waste caused by excessive aeration and insufficient aeration from failing to meet treatment requirements; oxygen supply on demand is the key to energy conservation and consumption reduction in sewage treatment plants, and has important practical significance for energy conservation and emission reduction in the sewage treatment industry.

[0004] Based on market competition and the need for energy conservation and consumption reduction in sewage treatment plants, there are many studies on how to reduce energy consumption of aeration equipment, mainly focusing on the optimization of the underwater structure and improving the oxygenation efficiency of aeration equipment. However, the control systems of most sewage treatment plants are still unable to adjust the aeration volume in real time according to the influent load to achieve the effect of precise aeration. For intermittent aeration, the current implementation method is mainly to use two valves in parallel to control the opening time of different aeration pipelines to achieve intermittent aeration; while the method of using valve groups to open and close to achieve alternating aeration affects the service life due to the frequent opening and closing of valves, and it is also difficult to achieve precise aeration. The most critical issue for intermittent aeration is how long the interval should be. The "interval" duration is different under different aeration volumes. If the interval time is too long, the aeration volume cannot meet the design requirements, and the longer the aeration stop time, the longer the system needs to recover; if the interval time is too short, it may cause excessive aeration and fail to achieve the purpose of energy conservation and consumption reduction.

[0005] The accuracy and efficiency of aeration equipment are often affected by factors such as the influent load, aeration volume, and aeration duration, and there are complex interactions between these parameters. However, the control systems of most sewage treatment plants are unable to adjust the aeration volume in real time according to the influent load, resulting in excessive energy consumption in the sewage treatment plant. This is also the technical challenge and research direction facing my country's future sewage treatment plants. Summary of the invention

[0006] Purpose of the invention: The purpose of the present invention is to provide a method for optimizing intermittent aeration parameters based on numerical simulation. The present invention uses the statistical method of optimizing random processes as a means, collects data through multivariate linear regression, establishes a multidimensional space surface, and constructs a mathematical model to find the quantitative law between "aeration volume" and "exposure-stop ratio", thereby determining the optimal aeration scheme and operating parameters according to the inlet load and outlet water quality requirements.

[0007] Technical solution: The intermittent aeration parameter optimization method based on numerical simulation of the present invention comprises the following steps:

[0008] (1) Determine the inlet water quality parameter values ​​and set the effluent water quality parameter monitoring indicators;

[0009] (2) According to the set effluent water quality parameter monitoring indicators, aeration volume or aeration-to-stop ratio is selected as the influencing factor for single factor experiment to determine the central value of aeration volume and aeration-to-stop ratio;

[0010] (3) According to the central values ​​of aeration volume and aeration-stop ratio, the two influencing factors of aeration volume and aeration-stop ratio (the ratio of aeration time to aeration-stop time) were set as the value range of the independent variables of the multi-factor response surface experiment, so as to design a two-factor three-level pilot experiment using the response surface method (RSM), construct a binary second-order polynomial regression model, and draw a 3D surface graph; and according to the influent water quality parameter values, the binary second-order polynomial regression model and the 3D surface graph were used to determine the optimal aeration parameter combination and the optimal response value;

[0011] (4) Significance analysis of regression model: According to the R of the binary second-order polynomial regression model 2 The value, F value and P value were used to analyze the goodness of fit of the binary second-order polynomial regression model and the consistency between the predicted results and the actual results;

[0012] (5) According to the monitoring indicators of effluent water quality parameters and aeration energy, the aeration volume and aeration-stop ratio were optimized using a binary second-order polynomial regression model, and the linear relationship of the binary second-order polynomial regression model was verified using the optimized "aeration volume" and "aeration-stop ratio";

[0013] (6) Use the constructed binary second-order polynomial regression model to predict the removal effect of water quality parameters under different aeration parameters, or control the aeration volume and aeration-stop ratio according to the water quality parameter monitoring indicators.

[0014] Furthermore, the intermittent aeration parameter optimization method based on numerical simulation described in claim 1 is characterized in that in step (1), the water quality parameters include chemical oxygen demand (COD), ammonia nitrogen (NH3-N) and / or total phosphorus (TP).

[0015] Furthermore, in step (2), when the aeration amount is used as a single factor variable, the change in the water quality parameter is used as the response value to determine the central value of the aeration amount.

[0016] Furthermore, in step (2), when the exposure-stop ratio is used as a single factor variable, the aeration volume is fixed at the central value of the aeration volume, and the change of the water quality parameter is used as the response value to determine the central value of the exposure-stop ratio.

[0017] Furthermore, in step (3), the aeration parameters are aeration volume and aeration-stop ratio, the aeration volume value range is set to the aeration volume center value ±2L / min, and the aeration-stop ratio value range is set to the aeration-stop ratio center value ±0.5.

[0018] Furthermore, in step (3), in order to reflect the interaction between the two factors of "aeration volume" and "exposure-to-stop ratio" in the aeration system, the Box-Behnken Design (BBD) model in the response surface method (RSM) software was used to design a two-factor three-level experiment, a binary second-order polynomial regression model was constructed and a 3D surface graph was drawn to determine the linear relationship between the effluent water quality parameters and the aeration parameters (aeration volume and exposure-to-stop ratio), thereby determining the optimal aeration parameter combination and the optimal response value.

[0019] Furthermore, in step (3), the binary second-order polynomial regression model is as shown in formula (1):

[0020] Y=b0+b1A+b2B+b3AB+b4A 2 +b5B 2 (1)

[0021] Among them, Y is, b0 is the constant term, b1, b2, b3, b4, b5 are regression coefficients, A is the aeration volume, and B is the aeration-to-stop ratio.

[0022] Furthermore, in step (3), according to the central combination principle in the BBD model, a cross-group design experiment scheme is carried out for the three levels of the two independent variable factors "aeration amount" and "exposure-stop ratio", and experimental data are collected. The binary second-order regression model of the response value of each water quality parameter is constructed using RSM software. The significance of the binary second-order regression model of the response value of each water quality parameter is tested by regression analysis and response surface analysis to determine the interaction effect between "aeration amount" and "exposure-stop ratio" and the degree of influence on the removal rate of water quality parameters, and to determine the optimal "aeration amount" and "exposure-stop ratio" to achieve the goals of energy saving, carbon reduction and synergistic efficiency improvement while ensuring the effluent water quality.

[0023] Furthermore, the binary second-order regression model of the water quality parameter chemical oxygen demand is shown in formula (2):

[0024] Y COD =84.77+2.6A+1.55B-2.01AB-1.77A 2 -5.63B 2 (2)

[0025] Among them, A is the aeration volume and B is the aeration-stop ratio.

[0026] Furthermore, the binary second-order regression model of the water quality parameter ammonia nitrogen is shown in formula (3):

[0027] Y NH3-N =83.92-15.96A+4.86B+13.7AB-10.38A 2 -16.05B 2 (3)

[0028] Among them, A is the aeration volume and B is the aeration-stop ratio.

[0029] Furthermore, the binary second-order regression model of the water quality parameter total phosphorus is shown in formula (4):

[0030] Y TP =95.19-2.12A+0.35B+0.90AB-2.2A 2 -3.93B 2 (4)

[0031] Among them, A is the aeration volume and B is the aeration-to-stop ratio.

[0032] Furthermore, in step (4), when the P value is less than 0.0001, R 2 Values ​​close to 1 confirm that the goodness of fit and consistency meet the requirements.

[0033] Furthermore, in step (4), R 2The F value, determination coefficient, is an indicator used to measure the fit of the model to the data. The F value is a statistic used to test the significance of the regression model. The P value is a statistic used to determine the probability value of the hypothesis test result.

[0034] Starting from a completely new perspective, the present invention establishes an interaction model among various influencing factors, optimizes parameters such as aeration volume and aeration duration according to the inlet load and outlet water quality requirements; determines the optimal aeration scheme and operating parameters, thereby improving the accuracy and aeration efficiency of aeration equipment; explores the feasibility of low-intensity intermittent aeration technology, greatly reduces aeration energy consumption, and provides basic data and technical support for the refined and intelligent control of aeration systems in engineering practice.

[0035] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages:

[0036] (1) The method of the present invention can achieve precise aeration by optimizing the "aeration volume" and "aeration-stop ratio" using the response surface methodology while ensuring the effluent quality, thereby achieving the goals of energy saving, carbon reduction, and synergistic efficiency improvement.

[0037] (2) The present invention uses RSM software to construct a binary second-order regression model with "aeration volume" and "exposure-to-stop ratio" as independent variables and COD, NH3-N and TP removal rates as response values. The P values ​​of these models are all less than 0.0001, and the determination coefficients are all close to 1. Each model has strong reliability, and the error between the prediction results and the experimental verification results is less than 2%. This shows that the model has a good fit, and the prediction results of the model are highly consistent with the actual results. The three models can better reflect the linear relationship between the removal rates of pollutants COD, NH3-N and TP and "aeration volume" and "exposure-to-stop ratio".

[0038] (3) The regression model constructed by the method of the present invention can more accurately predict the removal effect of pollutants under different aeration parameters. At the same time, the optimal "aeration volume" and "aeration-to-stop ratio" (i.e., intermittent duration) under certain effluent water quality requirements can also be determined based on the regression model.

[0039] (4) The regression model constructed by the method of the present invention has the highest removal rate of pollutants in sewage when the "aeration volume" is 3.528L / min and the "aeration-stop ratio" is 1.046h / h. At this time, the removal rates of COD, NH3-N and TP are 84.198%, 87.025% and 95.544% respectively. The monitoring indicators of the three parameters of COD, NH3-N and TP in the effluent are in line with the Class A standard of "Pollutant Discharge Standard for Urban Wastewater Treatment Plants" GB18918-2002. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1The 3D response surface diagram and contour diagram of the interaction between the two parameters of "aeration rate" and "exposure-to-stop ratio" on the COD removal rate drawn by RSM software in Example 1;

[0041] Figure 2 The 3D response surface diagram and contour diagram of the interaction between the two parameters of "aeration rate" and "exposure-to-stop ratio" on the NH3-N removal rate drawn by RSM software in Example 1;

[0042] Figure 3 The 3D response surface diagram and contour diagram of the interaction between the two parameters "aeration rate" and "exposure-to-stop ratio" on TP removal efficiency drawn by RSM software in Example 1 are shown. DETAILED DESCRIPTION

[0043] The technical solution of the present invention is further described below in conjunction with the accompanying drawings.

[0044] Example 1

[0045] 1. Raw water quality

[0046] The raw water for the experiment was taken from the aeration tank of a sewage treatment plant in J City, Jiangsu Province. The raw water was yellow-brown in appearance and had no unpleasant odor. Since the main indicator parameters of the sewage treatment plant effluent that are of concern and have the risk of exceeding the standard are chemical oxygen demand (COD), ammonia nitrogen (NH3-N) and total phosphorus (TP), the monitoring parameters selected for the experiment are COD, NH3-N, TP and dissolved oxygen (DO), which is an indicator reflecting aeration performance.

[0047] The monitoring methods of COD, NH3-N and TP and DO adopt the standard methods specified in the "Water and Wastewater Monitoring and Analysis Methods" (4th edition). The specific analysis and measurement methods and monitoring instruments and equipment for each indicator are shown in Table 1.

[0048] Table 1 Monitoring indicators and methods

[0049]

[0050]

[0051] After monitoring and analysis, the monitoring values ​​of the above four indicators of raw water are shown in Table 2.

[0052] Table 2 Raw water quality (mg / L)

[0053]

[0054] 2. Experimental setup and operation

[0055] The experimental simulation device uses a rectangular plastic water tank with a length of 60cm, a width of 40cm, and a height of 33cm. The effective volume is 60L. The bottom of the tank is paved with 3-5cm thick inoculated activated sludge (taken from the aeration tank of a sewage treatment plant in J City, Jiangsu Province). Two aeration heads are arranged in the sewage tank, and the aeration volume is adjusted according to environmental conditions such as water quality and temperature to simulate the operation of the aeration tank in the sewage treatment plant. At the same time, 5 2L open glass containers are equipped, and 1L of the mud-water mixture in the aeration tank is placed in each of the 5 glass containers. Single-factor experiments are carried out, and the set aeration volumes are 1L / min, 2L / min, 4L / min, 6L / min, and 8L / min, respectively. Taking into account the effluent quality (COD, NH3-N and TP removal effect) and energy consumption (increasing the aeration volume will increase energy consumption), the central value of the "aeration volume" of the multi-factor response surface experiment is determined to be 4L / min. At an aeration rate of 4 L / min, 1 L of the mud-water mixture in the aeration box was taken and placed in 6 glass containers, and the exposure-stop ratio was set to 3:1, 3:1.5, 3:2, 3:3, 3:4 and 3:6, respectively, for single factor experiments. Taking into account the effluent quality (COD, NH3-N and TP removal effect) and energy consumption, the central value of the "exposure-stop ratio" of the multi-factor response surface experiment was determined to be 3:3.

[0056] 3. Optimization design of aeration conditions

[0057] In order to reflect the interaction between the two factors of "aeration volume" and "exposure-stop ratio" in the aeration system, the Box-Behnken Design (BBD) model in the RSM software was used to design a two-factor three-level experiment. The experiment selected the two influencing factors of "aeration volume" and "exposure-stop ratio" as independent variables, and took the removal rates of COD, NH3-N and TP as response values, respectively, to construct a binary second-order polynomial regression model, see Formula 1, and draw a 3D surface graph to explore the linear relationship between effluent water quality and aeration parameters to determine the optimal aeration parameter combination and the optimal response value. The results are shown in Figure 1-3 The coding levels of each factor are low level (-1), medium level (0), and high level (1), as shown in Table 3.

[0058] Table 3 Impact factors and levels

[0059]

[0060] The general form of the binary second-order polynomial regression model is shown in Formula 1:

[0061] Y=b0+b1A+b2B+b3AB+b4A 2 +b5B 2 (1)

[0062] In the formula: Y is the response value, b0 is the constant term, b1, b2, b3, b4, b5 are regression coefficients, A and B are the independent variables of "aeration volume" and "exposure-to-stop ratio" respectively.

[0063] Figure 1 The 3D response surface diagram and contour diagram of the interaction between the two parameters "aeration rate" and "exposure-to-stop ratio" on the COD removal rate drawn by RSM software in Example 1; Figure 1 From the surface rate and contour lines, we can see that both the "aeration volume" and "exposure-stop ratio" parameters have an impact on COD removal rate. The COD removal rate increases first and then decreases with the increase of the "exposure-stop ratio", and gradually increases with the increase of the "aeration volume". However, the steepness of the curve with the "aeration volume" is slightly greater than that with the "exposure-stop ratio", indicating that the effect of the "aeration volume" on the COD removal rate is slightly more significant than that of the "exposure-stop ratio". Figure 1 It can also be seen that when the "exposure-to-stop ratio" is larger, the slope of the COD removal rate curve slows down with the increase of "aeration volume", which means that the two parameters have a certain interactive effect on the COD removal rate. When the "aeration volume" is 4-6L / min and the "exposure-to-stop ratio" is in the range of 0.7-1.3h / h, the COD removal rate is relatively large.

[0064] Figure 2 The 3D response surface diagram and contour diagram of the interaction between the two parameters "aeration rate" and "exposure-to-stop ratio" on the NH3-N removal rate drawn by RSM software in Example 1; Figure 2 The surface rate and the steep trend of the contour lines show that both the "aeration volume" and the "exposure-to-stop ratio" parameters have an impact on the NH3-N removal rate, and there is a significant interaction. The effect of the "aeration volume" on the NH3-N removal rate is more significant than that of the "exposure-to-stop ratio". The slope of the curve in the figure increases first and then slowly decreases with the increase of the "exposure-stop ratio". When the "exposure-stop ratio" is low, the NH3-N removal rate decreases with the increase of the "aeration volume", and shows a negative correlation with the "aeration volume". Especially when the "exposure-stop ratio" is 0.5h / h and the "aeration volume" is 6L / min, the NH3-N removal rate is only 25.65%, and the removal effect is the worst at this time. This is because excessive aeration increases the activity of filamentous bacteria, causing the activated sludge to expand, and the NH3-N concentration will increase instead. Considering the interaction between the two, when the "aeration volume" is 2-4L / min and the "exposure-stop ratio" is in the range of 0.7-1.3h / h, the NH3-N removal rate is relatively large.

[0065] Figure 3 The 3D response surface diagram and contour diagram of the interaction between the two parameters of "aeration rate" and "exposure-to-stop ratio" on TP removal efficiency drawn by RSM software in Example 1; Figure 3The surface rate and the slope of the contour lines show that the TP removal rate is also affected by the "aeration volume", "exposure-stop ratio" and their interaction. The slope of the TP removal rate curve increases first and then decreases with the increase of the "exposure-stop ratio", and decreases first slowly and then quickly with the increase of the "aeration volume". The steepness of the curve with the "aeration volume" is greater than that of the "exposure-stop ratio", indicating that the effect of the "aeration volume" on the TP removal rate is greater than that of the "exposure-stop ratio". Considering the interaction between the two, when the "aeration volume" is 2-4L / min and the "exposure-stop ratio" is in the range of 0.7-1.3h / h, the TP removal rate is relatively large.

[0066] According to the central combination principle in the BBD model, the three levels of the two independent variable factors "aeration amount" and "exposure-to-stop ratio" were cross-grouped, and a total of 13 experimental schemes were designed. The experimental data were collected, and the binary second-order regression models of each response value were constructed using RSM software. The significance of each model was tested through regression analysis and response surface analysis, and the interaction effect between "aeration amount" and "exposure-to-stop ratio" and the degree of influence on the removal rates of COD, NH3-N and TP were explored. The optimal "aeration amount" and "exposure-to-stop ratio" while ensuring the effluent quality were determined, thereby providing supporting data for energy saving, carbon reduction and synergistic efficiency improvement of sewage treatment plants.

[0067] 5. Model construction and regression analysis

[0068] According to the 13 experimental schemes designed by the above BBD model, multi-factor experiments were carried out to determine the response values ​​of each experimental group, namely, the removal rates of COD, NH3-N, and TP; in order to reduce errors and ensure the accuracy of experimental data, three parallel measurements were performed for each group of experiments, and the results are shown in Table 4. In order to simplify the mathematical processing process and make the regression model easier to explain and apply, the codes of the two independent variable factors, "aeration volume" and "exposure-to-stop ratio", were represented by A and B, respectively.

[0069] Table 4 Response surface experimental design and results

[0070]

[0071] RSM software was used to fit the experimental data in Table 4, and a binary second-order regression model of the removal rate of the three response values ​​COD, NH3-N and TP shown in Table 5 was constructed. In order to test the significance of the three regression models, variance analysis was performed, and the R 2 The F value and P value are shown in Table 5.

[0072] Table 5 Response surface variance analysis table

[0073]

[0074]

[0075] From the variance analysis in Table 5, we can see that the F values ​​of the binary second-order regression models for COD, NH3-N and TP removal rates are 30.14, 38.59 and 71.47, respectively, which are all large; and the P values ​​are all less than 0.0001, indicating that the three models are all highly significant. The determination coefficients R 2 The values ​​are 0.9556, 0.9650 and 0.9808, respectively, all close to 1, indicating that the model fit is very good, and the prediction results of the model are highly consistent with the actual results. The three models can better reflect the linear relationship between the removal rates of pollutants COD, NH3-N and TP and the "aeration volume" and "exposure-stop ratio"; these models can be used to more accurately predict the removal effect of pollutants under different aeration parameters or to accurately control the aeration volume and intermittent duration to achieve a certain removal effect.

Claims

1. A method for optimizing intermittent aeration parameters based on numerical simulation, characterized in that: The following steps are involved: (1) Determine the inlet water quality parameter values ​​and set the effluent water quality parameter monitoring indicators; (2) According to the set effluent water quality parameter monitoring indicators, aeration volume or aeration-to-stop ratio is selected as the influencing factor for single factor experiment to determine the central value of aeration volume and aeration-to-stop ratio; (3) According to the central values ​​of aeration volume and exposure-stop ratio, the two influencing factors of aeration volume and exposure-stop ratio are set as the value range of the independent variables of the multi-factor response surface experiment, so as to design a two-factor three-level pilot experiment using the response surface method, construct a binary second-order polynomial regression model, and draw a 3D surface graph; and according to the influent water quality parameter values, the binary second-order polynomial regression model and the 3D surface graph are used to determine the optimal aeration parameter combination and the optimal response value; (4) Significance analysis of regression model: According to the R of the binary second-order polynomial regression model 2 The value, F value and P value were used to analyze the goodness of fit of the binary second-order polynomial regression model and the consistency between the predicted results and the actual results; (5) According to the monitoring indicators of effluent water quality parameters and aeration energy, the aeration volume and aeration-stop ratio were optimized using a binary second-order polynomial regression model, and the linear relationship of the binary second-order polynomial regression model was verified using the optimized "aeration volume" and "aeration-stop ratio"; (6) Use the constructed binary second-order polynomial regression model to predict the removal effect of water quality parameters under different aeration parameters, or control the aeration volume and aeration-stop ratio according to the water quality parameter monitoring indicators.

2. The method for optimizing intermittent aeration parameters based on numerical simulation according to claim 1, characterized in that: In step (1), the water quality parameters include chemical oxygen demand, ammonia nitrogen and / or total phosphorus.

3. The method for optimizing intermittent aeration parameters based on numerical simulation according to claim 1, characterized in that: In step (2), when the aeration volume is used as a single factor variable, the change in water quality parameters is used as the response value to determine the central value of the aeration volume.

4. The method for optimizing intermittent aeration parameters based on numerical simulation according to claim 1, characterized in that: In step (2), when the exposure-stop ratio is used as a single factor variable, the aeration volume is fixed to the central value of the aeration volume, and the change in water quality parameters is used as the response value to determine the central value of the exposure-stop ratio.

5. The method for optimizing intermittent aeration parameters based on numerical simulation according to claim 1, characterized in that: In step (3), the aeration parameters are the aeration volume and the aeration-to-stop ratio. The aeration volume has a value range of ±2 L / min from the center value of the aeration volume, and the aeration-to-stop ratio has a value range of ±0.5 from the center value of the aeration-to-stop ratio.

6. The method for optimizing intermittent aeration parameters based on numerical simulation according to claim 1, characterized in that: In step (3), a two-factor three-level experiment is designed using the Box-Behnken Design model in the response surface methodology software, a binary second-order polynomial regression model is constructed and a 3D surface graph is drawn to determine the linear relationship between the effluent water quality parameters and the aeration parameters, thereby determining the optimal aeration parameter combination and the optimal response value.

7. The method for optimizing intermittent aeration parameters based on numerical simulation according to claim 1, characterized in that: In step (3), the binary second-order polynomial regression model is shown in formula (1): <h2 style=";text-align:left;direction:ltr">Y = b0 + b1A + b2B + b3AB + b4A<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> +b5B<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> (1) Among them, Y is, b0 is the constant term, b1, b2, b3, b4, b5 are regression coefficients, A is the aeration volume, and B is the exposure-to-stop ratio.

8. The method for optimizing intermittent aeration parameters based on numerical simulation according to claim 7, characterized in that: In step (3), according to the central combination principle in the BBD model, a cross-group design experiment was conducted for the three levels of the two independent variable factors "aeration amount" and "exposure-stop ratio". The binary second-order regression model of the response values ​​of each water quality parameter was constructed using RSM software. The significance of the binary second-order regression model of the response values ​​of each water quality parameter was tested through regression analysis and response surface analysis to determine the interaction effect between "aeration amount" and "exposure-stop ratio" and the degree of influence on the removal rate of water quality parameters, and to determine the optimal "aeration amount" and "exposure-stop ratio" while ensuring the effluent water quality.

9. The method for optimizing intermittent aeration parameters based on numerical simulation according to claim 8, characterized in that: The binary second-order regression model of water quality parameter chemical oxygen demand is shown in formula (2): <h2 style=";text-align:left;direction:ltr">Y<h2 style=";text-align:left;direction:ltr"> COD <h2 style=";text-align:left;direction:ltr"> =84.77+2.6A+1.55B-2.01AB-1.77A<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> -5.63B<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> (2) Among them, A is the aeration volume, and B is the aeration-stop ratio; The binary second-order regression model of water quality parameter ammonia nitrogen is shown in formula (3): <h2 style=";text-align:left;direction:ltr">Y<h2 style=";text-align:left;direction:ltr"> NH3 <h2 style=";text-align:left;direction:ltr"> -N=83.92-15.96A+4.86B+13.7AB-10.38A<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> -16.05B<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> (3) Among them, A is the aeration volume, and B is the aeration-stop ratio; The binary second-order regression model of water quality parameter total phosphorus is shown in formula (4): <h2 style=";text-align:left;direction:ltr">Y<h2 style=";text-align:left;direction:ltr"> TP <h2 style=";text-align:left;direction:ltr"> =95.19-2.12A+0.35B+0.90AB-2.2A<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> -3.93B<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> (4) Among them, A is the aeration volume and B is the aeration-to-stop ratio.

10. The method for optimizing intermittent aeration parameters based on numerical simulation according to claim 1, characterized in that: In step (4), when the P value is less than 0.0001, R 2 Values ​​close to 1 confirm that the goodness of fit and consistency meet the requirements.

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