Aeration control method and device based on CFD-PBM coupling model

Abstract

The invention relates to the technical field of aeration control of sewage treatment plants, and discloses an aeration control method and device based on a CFD-PBM coupling model, and the method comprises the steps: constructing a CFD-PBM coupling model of an aeration tank of a sewage treatment plant; solving the CFD-PBM coupling model of the aeration tank of the sewage treatment plant based on the operation parameters of the aeration tank of the target sewage treatment plant to obtain a comprehensive oxygen mass transfer coefficient; predicting the dissolved oxygen concentration based on the inflow water quality parameter of the target sewage treatment plant, the water temperature of the aeration tank and the comprehensive oxygen mass transfer coefficient to obtain dissolved oxygen concentration distribution prediction data; establishing an optimal aeration control strategy based on the dissolved oxygen concentration distribution prediction data and the operation parameters of the aeration tank of the target sewage treatment plant; and performing aeration control on the aeration equipment by using the optimal aeration control strategy. According to the invention, the oxygen mass transfer efficiency in the aeration control process is improved, the aeration energy consumption is reduced, and the sewage treatment effect is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of aeration control of sewage treatment plants, and particularly relates to an aeration control method and device based on a CFD-PBM coupling model. BACKGROUND

[0002] An aeration system is a key component in the process of sewage treatment, responsible for providing oxygen to microorganisms to promote the degradation of organic matter.

[0003] Due to the fact that the aeration control system of a sewage treatment plant cannot be accurately regulated according to real-time working conditions, the aeration efficiency is low, the energy consumption is high, and the sewage treatment effect is poor. SUMMARY

[0004] Therefore, the present application provides an aeration control method and device based on a CFD-PBM coupling model to solve the problems of low aeration efficiency, high energy consumption, and poor sewage treatment effect of the aeration control system of a sewage treatment plant.

[0005] In a first aspect, the present application provides an aeration control method based on a CFD-PBM coupling model, which comprises the following steps:

[0006] Constructing a CFD-PBM coupling model of an aeration tank of a sewage treatment plant;

[0007] Obtaining operation parameters of an aeration tank of a target sewage treatment plant, and solving the CFD-PBM coupling model of the aeration tank of the sewage treatment plant based on the operation parameters of the aeration tank of the target sewage treatment plant to obtain a comprehensive oxygen mass transfer coefficient;

[0008] Obtaining water quality parameters of influent water of the target sewage treatment plant and water temperature of the aeration tank, and predicting the concentration of dissolved oxygen based on the water quality parameters of the influent water of the target sewage treatment plant, the water temperature of the aeration tank, and the comprehensive oxygen mass transfer coefficient to obtain prediction data of the concentration distribution of dissolved oxygen;

[0009] Establishing an optimal aeration control strategy based on the prediction data of the concentration distribution of dissolved oxygen and the operation parameters of the aeration tank of the target sewage treatment plant;

[0010] Controlling the aeration of the aeration equipment by using the optimal aeration control strategy.

[0011] The embodiment provides an aeration control method based on a CFD-PBM coupling model, CFD-PBM coupling model of an aeration tank of a target sewage treatment plant is solved based on operation parameters of the aeration tank of the sewage treatment plant, oxygen mass transfer efficiency in an aeration process is calculated by coupling a CFD model with a PBM model, and in combination with monitoring of dissolved oxygen concentration, dissolved oxygen concentration is predicted based on water quality parameters of influent of the target sewage treatment plant and a comprehensive oxygen mass transfer coefficient, prediction data of dissolved oxygen concentration distribution is obtained, and then an optimal aeration control strategy is established, oxygen mass transfer efficiency in the aeration control process is improved, aeration energy consumption is reduced, and sewage treatment effect is improved.

[0012] In an alternative implementation, the CFD-PBM coupling model of the aeration tank of the sewage treatment plant is solved based on the operation parameters of the aeration tank of the target sewage treatment plant, and a comprehensive oxygen mass transfer coefficient is obtained, including:

[0013] The CFD model is solved based on the operation parameters of the aeration tank of the target sewage treatment plant, and flow field data is obtained;

[0014] The PBM model is solved based on the flow field data, and bubble number density distribution and interfacial area density are obtained;

[0015] The diffusion coefficient of oxygen in water, the relative speed of the bubble and the diameter of the bubble are obtained, the local liquid film mass transfer coefficient is calculated based on the diffusion coefficient of oxygen in water, the relative speed of the bubble and the diameter of the bubble;

[0016] The comprehensive oxygen mass transfer coefficient is calculated based on the local liquid film mass transfer coefficient, the bubble number density distribution and the interfacial area density.

[0017] The embodiment provides an aeration control method based on a CFD-PBM coupling model, by solving the CFD-PBM coupling model of the aeration tank of the sewage treatment plant, the oxygen mass transfer efficiency in the aeration process is calculated by coupling the CFD model with the PBM model, the flow field and the bubble distribution in the aeration tank are accurately described, the dissolved oxygen distribution is more uniform, and the aeration precision is improved.

[0018] In an alternative implementation, the dissolved oxygen concentration is predicted based on the water quality parameters of influent of the target sewage treatment plant, the water temperature of the aeration tank and the comprehensive oxygen mass transfer coefficient, and prediction data of dissolved oxygen concentration distribution is obtained, including:

[0019] The saturated dissolved oxygen concentration is calculated based on the water temperature of the aeration tank;

[0020] The carbon oxidation oxygen consumption rate and the ammonia oxidation oxygen consumption rate are calculated based on the water quality parameters of influent of the target sewage treatment plant, and the total oxygen consumption rate is calculated based on the carbon oxidation oxygen consumption rate and the ammonia oxidation oxygen consumption rate;

[0021] The dynamic equation of dissolved oxygen is constructed based on the comprehensive oxygen mass transfer coefficient, flow field data, diffusion coefficient of oxygen in water, saturated dissolved oxygen concentration and total oxygen consumption rate.

[0022] The dynamic equation of dissolved oxygen is solved to obtain the dissolved oxygen concentration data.

[0023] The dissolved oxygen distribution prediction is performed based on the dissolved oxygen concentration data to obtain the dissolved oxygen concentration distribution prediction data.

[0024] The aeration control method based on the CFD-PBM coupling model provided in the embodiment fully considers the complex fluid dynamics characteristics and bubble distribution in the aeration tank, improves the aeration efficiency and reduces the energy consumption.

[0025] In an optional implementation, the optimal aeration control strategy is established based on the dissolved oxygen concentration distribution prediction data and the operation parameters of the target wastewater treatment plant aeration tank, including:

[0026] The aeration energy consumption and the energy consumption change rate are determined based on the operation parameters of the target wastewater treatment plant aeration tank.

[0027] The dissolved oxygen concentration set value is obtained, and the dissolved oxygen concentration control deviation is determined based on the dissolved oxygen concentration distribution prediction data and the dissolved oxygen concentration set value.

[0028] The weight coefficient is obtained, and the comprehensive objective function is constructed based on the aeration energy consumption, the energy consumption change rate, the dissolved oxygen concentration control deviation and the weight coefficient.

[0029] The comprehensive objective function is solved to obtain the optimal aeration amount and the optimal aeration device opening degree vector.

[0030] The optimal aeration amount and the optimal aeration device opening degree vector are optimized to obtain the optimal aeration control strategy.

[0031] The aeration control method based on the CFD-PBM coupling model provided in the embodiment realizes the minimum energy consumption operation under the premise of meeting the process requirements, and effectively reduces the energy consumption.

[0032] In an optional implementation, the optimal aeration control strategy is obtained by optimizing the optimal aeration amount and the optimal aeration device opening degree vector, including:

[0033] The actual aeration device opening degree, the actual aeration amount and the blower outlet air pressure are obtained, the actual aeration device opening degree, the actual aeration amount and the blower outlet air pressure are taken as PID basic parameters, and the PID correction amount is determined by using the PID control algorithm.

[0034] determining the optimized aeration quantity based on the optimal aeration quantity and the PID correction quantity;

[0035] determining the optimal aeration opening of the aerator based on the optimal aeration opening vector and the PID correction quantity;

[0036] determining the optimal aeration control strategy based on the optimized aeration quantity and the optimal aeration opening of the aerator.

[0037] The aeration control method based on the CFD-PBM coupling model provided in this embodiment further optimizes the optimal aeration quantity and the optimal aeration opening vector, so that the related parameters of the aerator and the blower are more accurate, the accurate regulation and control of the aerator and the blower are realized, and the aeration efficiency is improved.

[0038] In an alternative embodiment, the method further comprises:

[0039] obtaining an actual value of dissolved oxygen after aeration control of the aeration equipment, and updating and optimizing the CFD-PBM coupling model of the aeration tank of the wastewater treatment plant based on the actual value of dissolved oxygen.

[0040] The aeration control method based on the CFD-PBM coupling model provided in this embodiment updates and optimizes the CFD-PBM coupling model of the aeration tank of the wastewater treatment plant based on the actual value of dissolved oxygen, so that the system can adapt to changes in water quality, load fluctuations, etc., is suitable for various types of wastewater treatment processes, and has enhanced adaptability.

[0041] In a second aspect, the present application provides an aeration control device based on a CFD-PBM coupling model, which comprises:

[0042] a construction module for constructing a CFD-PBM coupling model of an aeration tank of a wastewater treatment plant;

[0043] a solution module for obtaining target wastewater treatment plant aeration tank operation parameters, solving the CFD-PBM coupling model of the wastewater treatment plant aeration tank based on the target wastewater treatment plant aeration tank operation parameters, and obtaining a comprehensive oxygen transfer coefficient;

[0044] a prediction module for obtaining target wastewater treatment plant influent water quality parameters and aeration tank water temperature, predicting the dissolved oxygen concentration based on the target wastewater treatment plant influent water quality parameters, the aeration tank water temperature, and the comprehensive oxygen transfer coefficient, and obtaining dissolved oxygen concentration distribution prediction data;

[0045] an establishment module for establishing an optimal aeration control strategy based on the dissolved oxygen concentration distribution prediction data and the target wastewater treatment plant aeration tank operation parameters;

[0046] a control module for aeration control of aeration equipment using the optimal aeration control strategy.

[0047] In a third aspect, the present application provides a computer device, comprising a memory and a processor, which are connected to each other in communication, and the memory stores computer instructions, and the processor executes the aeration control method based on the CFD-PBM coupling model of the first aspect or any of the corresponding embodiments thereof by executing the computer instructions.

[0048] In a fourth aspect, the present application provides a computer readable storage medium, which stores computer instructions for causing a computer to execute the aeration control method based on the CFD-PBM coupling model of the first aspect or any of the corresponding embodiments thereof.

[0049] In a fifth aspect, the present application provides a computer program product, which comprises computer instructions for causing a computer to execute the aeration control method based on the CFD-PBM coupling model of the first aspect or any of the corresponding embodiments thereof. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed in the specific embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0051] Figure 1 is a flowchart of an aeration control method based on a CFD-PBM coupling model according to an embodiment of the present application;

[0052] Figure 2 is a schematic diagram of a learning and model updating mechanism according to an embodiment of the present application;

[0053] Figure 3 is a flowchart of another aeration control method based on a CFD-PBM coupling model according to an embodiment of the present application;

[0054] Figure 4 is a flowchart of CFD-PBM coupling calculation according to an embodiment of the present application;

[0055] Figure 5 is a flowchart of still another aeration control method based on a CFD-PBM coupling model according to an embodiment of the present application;

[0056] Figure 6 is a flowchart of yet another aeration control method based on a CFD-PBM coupling model according to an embodiment of the present application;

[0057] Figure 7 is a flowchart of a multi-objective optimization control algorithm according to an embodiment of the present application;

[0058] Figure 8 is a comparison diagram of aeration air volume before and after aeration control according to an embodiment of the present application;

[0059] Figure 9 is a schematic diagram of the overall architecture of a system according to an embodiment of the present application;

[0060] Figure 10 is a structural block diagram of an aeration control device based on a CFD-PBM coupling model according to an embodiment of the present application;

[0061] Figure 11 is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0062] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described below in connection with the drawings of the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0063] The related aeration control system mainly adopts the following technical solutions:

[0064] Dissolved oxygen (DO) concentration-based feedback control: the aeration amount is adjusted according to the deviation between the measured value and the target value by setting the dissolved oxygen concentration target value; however, this method fails to consider the complex fluid dynamics characteristics and bubble distribution in the aeration tank, resulting in low aeration efficiency and high energy consumption.

[0065] Fuzzy logic control: aeration control is combined with expert experience and fuzzy rules, but the control accuracy is limited by the accuracy of the experience rules, and it is difficult to adapt to changes in different working conditions and tank types.

[0066] Simplified computational fluid dynamics model: although the fluid dynamics characteristics are considered, the influence of bubble size distribution on mass transfer efficiency is ignored, and the oxygen mass transfer process cannot be accurately described.

[0067] The main problems existing in the above-mentioned aeration control systems include: uneven aeration leading to local anoxia or hyperoxia; low aeration efficiency and high energy consumption; unable to accurately regulate according to real-time working conditions; poor system adaptability, difficult to cope with fluctuations in water quality and load, etc.

[0068] To solve the above technical problems, the embodiment of the present application provides an aeration control method based on a CFD-PBM coupling model, which determines the oxygen mass transfer efficiency in the aeration process by coupling a CFD (Computational Fluid Dynamics) model with a PBM (Population Balance Model) model, combines with the monitoring of the dissolved oxygen concentration, and then realizes the accurate control of aeration by using a multi-objective optimization control algorithm, the method comprising the steps of data acquisition and preprocessing, construction and solution of the CFD-PBM coupling model based on the aeration tank of the sewage treatment plant, prediction of the dissolved oxygen concentration, fan air volume algorithm, feedback regulation of the fan air volume and air pressure, and optimization and update of the CFD-PBM coupling model, and has the advantages of improving the oxygen mass transfer efficiency, reducing the aeration energy consumption, and improving the sewage treatment effect.

[0069] The embodiment of the present application provides an aeration control method based on a CFD-PBM coupling model, and it should be noted that the aeration control method based on the CFD-PBM coupling model provided by the embodiment of the present application can be an aeration control device based on the CFD-PBM coupling model, which can be realized by software, hardware or a combination of software and hardware to become part or all of an electronic device, wherein the electronic device can be a server or a terminal, wherein the server in the embodiment of the present application can be a server, or a server cluster composed of multiple servers, and the terminal in the embodiment of the present application can be a smart phone, a personal computer, a tablet computer, a wearable device, a smart robot and other smart hardware devices. In the following method embodiment, the execution subject is taken as an example to be described.

[0070] According to the embodiment of the present application, an aeration control method based on a CFD-PBM coupling model is provided, and it should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a group of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.

[0071] In the present embodiment, an aeration control method based on a CFD-PBM coupling model is provided, which can be used in the above-mentioned electronic device, Figure 1 is a flowchart of an aeration control method based on a CFD-PBM coupling model according to the embodiment of the present application, as Figure 1 shown, the flowchart comprises the following steps:

[0072] Step S101, constructing a CFD-PBM coupling model of the aeration tank of the sewage treatment plant.

[0073] In step S102, the operation parameters of the target wastewater treatment plant aeration tank are acquired, and a CFD-PBM coupling model of the wastewater treatment plant aeration tank is solved based on the operation parameters of the target wastewater treatment plant aeration tank, so as to obtain a comprehensive oxygen mass transfer coefficient.

[0074] Specifically, the operation parameters of the target wastewater treatment plant aeration tank include a length (L), a width (W), and a height (H) of the aeration tank, coordinates (x i , y i , z i ) of an aerator, an aeration amount (Q_air), a blower outlet air pressure (P_sys), and an aeration energy consumption (E_energy).

[0075] Further, the operation parameters of the target wastewater treatment plant aeration tank are preprocessed, and the preprocessing steps include abnormal value identification and elimination by using a 3σ rule (a statistical method based on normal distribution), missing value filling by using a linear interpolation method or a nearest neighbor value, data noise elimination by using a Kalman filtering method, and data standardization, so as to obtain a standardized operation parameter matrix, which can be expressed as:

[0076] X_equipment=[Q_air_norm,P_sys_norm,E_energy_norm] (1)

[0077] wherein Q_air_norm represents the standardized aeration amount, P_sys_norm represents the standardized blower outlet air pressure, and E_energy_norm represents the standardized aeration energy consumption.

[0078] In step S103, the influent water quality parameters of the target wastewater treatment plant and the water temperature of the aeration tank are acquired, and the dissolved oxygen concentration is predicted based on the influent water quality parameters of the target wastewater treatment plant, the water temperature of the aeration tank, and the comprehensive oxygen mass transfer coefficient, so as to obtain dissolved oxygen concentration distribution prediction data.

[0079] Specifically, the influent water quality parameters of the target wastewater treatment plant include a pH value, an ammonia nitrogen concentration (NH4 + -N), and a COD (Chemical Oxygen Demand) concentration.

[0080] Further, the water quality parameter of the influent of the target sewage treatment plant is preprocessed, and the data preprocessing steps include 3σ criterion for outlier identification and elimination, linear interpolation or nearest neighbor value for missing value filling, Kalman filter for data noise elimination, and data standardization, to obtain a water quality parameter matrix after standardization. The matrix X_quality corresponding to the normalized influent water quality parameters, aeration tank water temperature (T) and dissolved oxygen concentration (DO) can be expressed as:

[0081] X_quality = [DO_norm, T_norm, pH_norm, NH4_norm, COD_norm] (2)

[0082] wherein DO_norm represents the normalized dissolved oxygen concentration, T_norm represents the normalized aeration tank water temperature, pH_norm represents the normalized pH value, NH4_norm represents the normalized ammonia nitrogen concentration, and COD_norm represents the normalized COD concentration.

[0083] Further, the timestamp matrix A corresponding to the aeration tank operation parameters and the influent water quality parameters of the target sewage treatment plant can be expressed as:

[0084] A = [t1, t2,..., tn] (3) n ] (3)

[0085] wherein t n represents n time points.

[0086] Step S104, an optimal aeration control strategy is established based on the dissolved oxygen concentration distribution prediction data and the aeration tank operation parameters of the target sewage treatment plant.

[0087] Step S105, the optimal aeration control strategy is used to control the aeration of the aeration equipment.

[0088] Specifically, the optimal aeration control strategy is used to control the aeration of the blower and the aeration implement.

[0089] Further, the actual value of the dissolved oxygen after the aeration control of the aeration equipment is obtained, and the CFD-PBM coupling model of the aeration tank of the sewage treatment plant is updated based on the actual value of the dissolved oxygen.

[0090] Further, as Figure 2As shown, according to the deviation between the actual operation data and the model predicted value, the model parameters are automatically updated to improve the prediction accuracy, that is, after the optimal aeration control strategy is executed, the actual value C_actual(x, y, z, t) of dissolved oxygen is collected, and the predicted value C_predict(x, y, z, t) of dissolved oxygen is obtained, the error of the CFD-PBM coupling model of the aeration tank of the sewage treatment plant is evaluated, and the model prediction error e model The calculation formula of the relative prediction error e

[0091]

[0092] In the above formula, C_predict(b) represents the predicted value of the dissolved oxygen at the bth sampling point, C_actual(b) represents the actual value of the dissolved oxygen at the bth sampling point, and B represents the number of sampling points. predict,b actual,b

[0093] Further, the calculation formula of the absolute prediction error e model,relative

[0094]

[0095] In the above formula, e represents the absolute prediction error, C_set represents the set value of the dissolved oxygen concentration, and C_actual represents the actual value of the dissolved oxygen. model set

[0096] Further, error statistical analysis: calculate the root mean square error RMSE, the mean absolute error MAE, and the determination coefficient R 2 , and the specific calculation formula is as follows:

[0097]

[0098]

[0099] In the above formula, C_actual represents the mean value of the actual value of the dissolved oxygen.

[0100] Further, the expression of the error threshold judgment is:

[0101] UPDATE=TRUE,if e model >ε threshold (9)

[0102] In the above formula, ε represents the model prediction error threshold. threshold

[0103] Further, the time interval judgment can be represented as:

[0104] UPDATE=TRUE,if t current -t last_update >T update ​​​​​​​(10)

[0105] In the above formula, t current represents the current time, t last_update represents the latest update time, T update represents the time difference threshold.

[0106] Further, the working condition change judgment can be represented as:

[0107]

[0108] In the above formula, X current represents the current system state parameter, X last_update represents the last updated system state parameter, δ threshold represents the state parameter relative error threshold; wherein the system state parameters include aerator opening, aeration quantity, dissolved oxygen concentration monitoring value and blower outlet air pressure.

[0109] Further, data incremental learning is performed, and the new data merging can be represented as:

[0110] D updated = D history ∪(X new ,C actual_new ) (12)

[0111] In the above formula, D ipdated represents the system state parameter data set, D history represents the system state parameter historical running data set, X new represents the latest system state parameter, C actual_new represents the latest dissolved oxygen concentration.

[0112] The data importance weighting can be represented as:

[0113] W data,b = exp(-λ·(t current -t b )) (13)

[0114] In the above formula, W data,b represents the weight of the bth sampling point, λ represents the time decay coefficient, t current represents the current time, t b represents the time stamp of the bth sampling point.

[0115] Further, the model parameter update, the specific steps of which include gradient descent-based parameter update, the calculation formula of the loss function L(θ) is as follows:

[0116]

[0117] In the above formula, θ represents the model parameter, hθ (X b ) represents the prediction of the model for the input data of the h-th sampling point.

[0118] The calculation formula of parameter update is as follows:

[0119]

[0120] In the above formula, θ new represents the updated parameter, θ current represents the current parameter, and η represents the learning rate, represents the gradient of the loss function with respect to the model parameters.

[0121] Annealing learning rate adjustment:

[0122]

[0123] In the above formula, η represents the learning rate of the current iteration, η0 represents the initial learning rate, decay rate represents the decay rate, t iteration represents the number of iterations.

[0124] Further, the update of the key parameters of the CFD-PBM coupling model of the sewage treatment plant aeration tank can be represented as:

[0125]

[0126] In the above formula, θ CFD-PBM,new represents the updated key parameter of the CFD-PBM coupling model, θ CFD-PBM,current represents the current parameter of the CFD-PBM coupling model, represents the model prediction error under the current state parameter, f update represents the update function.

[0127] Further, model performance verification: use the validation set to evaluate the performance of the updated model, and the verification error e validation The calculation formula is as follows:

[0128]

[0129] In the above formula, Q represents the number of validation set samples, represents the prediction of the updated model for the validation set, C actual,q represents the actual DO value in the validation set.

[0130] The performance improvement judgment can be represented as:

[0131] ACCEPT = TRUE, if e validation <e validation,old (19)

[0132] In the above equation, e validation,old denotes the error threshold.

[0133] The update acceptance / rejection can be expressed as:

[0134] θ final = θ new , if ACCEPT = TRUE (20)

[0135] θ final = θ current , if ACCEPT = FALSE (21)

[0136] In the above equation, θ final denotes the final model parameters.

[0137] Further, the model parameter library and the working condition-parameter mapping relationship are updated, and the expressions are:

[0138] Θknowledge = Θknowledge U θ final (22)

[0139] Map condition (X) → θ optimal (23)

[0140] In the above equation, knowledge denotes the model parameter library, Map condition (X) denotes the working condition data, and θ optimal denotes the model optimal parameter data corresponding to the working condition.

[0141] Further, the updated model parameters θ final , the model performance evaluation report [RMSE, MAE, R 2 , e validation ], the update timestamp t last_update , and the knowledge base state [Θknowledge, Map condition ] are output.

[0142] The embodiment provides an aeration control method based on a CFD-PBM coupling model, CFD-PBM coupling model of an aeration tank of a target sewage treatment plant is solved based on operation parameters of the aeration tank of the target sewage treatment plant, oxygen transfer efficiency in an aeration process is judged by using the CFD model coupled with the PBM model, and in combination with dissolved oxygen concentration monitoring, dissolved oxygen concentration is predicted based on water quality parameters and comprehensive oxygen transfer coefficients of influent of the target sewage treatment plant, so that dissolved oxygen concentration distribution prediction data are obtained, and then an optimal aeration control strategy is established, oxygen transfer efficiency in the aeration control process is improved, aeration energy consumption is reduced, and sewage treatment effect is improved; and the CFD-PBM coupling model of the aeration tank of the sewage treatment plant is optimized and updated through actual values of the dissolved oxygen, so that the system can adapt to changes such as water quality and load fluctuation, is applicable to various types of sewage treatment processes, and adaptability is enhanced.

[0143] In the embodiment, an aeration control method based on a CFD-PBM coupling model is provided, which can be used for the electronic device described above, Figure 3 is a flowchart of an aeration control method based on a CFD-PBM coupling model according to the embodiment of the application, as Figure 3 shown, the flowchart comprises the following steps:

[0144] Step S301, constructing a CFD-PBM coupling model of an aeration tank of a sewage treatment plant. For details, refer to step S101 of the embodiment shown in Figure 1 , which will not be repeated here.

[0145] Step S302, obtaining operation parameters of an aeration tank of a target sewage treatment plant, and solving the CFD-PBM coupling model of the aeration tank of the sewage treatment plant based on the operation parameters of the aeration tank of the target sewage treatment plant to obtain a comprehensive oxygen transfer coefficient.

[0146] Specifically, as shown in the coupling calculation process of the CFD model and the PBM model, boundary condition setting, alternating iteration calculation and convergence judgment are included; and the step S302 includes: Figure 4

[0147] Step S3021, solving the CFD model based on the operation parameters of the aeration tank of the target sewage treatment plant to obtain flow field data.

[0148] Specifically, CFD calculation initialization: calculation domain grid division: the aeration tank is divided into N*M*K grid units according to the length, width and height of the aeration tank; boundary condition setting: inlet flow rate v_in, outlet pressure p_out and wall surface no-slip condition; initial condition setting: initial gas holdup a_g,0 and initial liquid phase velocity v_l,0.

[0149] Further, the CFD model is solved, the continuity equation is solved to obtain a density field p(x,y,z), and the expression of the continuity equation is:​

[0150]

[0151] In the above formula, p represents the fluid density, represents the velocity vector, represents the divergence operator, represents the partial derivative of density with respect to time.

[0152] Further, the velocity field u(x, y, z) and the pressure field p(x, y, z) are obtained by solving the momentum equation, and the expression of the momentum equation is as follows:

[0153]

[0154] In the above formula, p represents the pressure, represents the viscous stress tensor, represents the gravitational acceleration, represents the external volume force.

[0155] Further, the turbulent parameters are calculated, that is, the turbulent kinetic energy k(x, y, z) and the turbulent dissipation rate ε(x, y, z) are calculated, and the calculation formula is as follows:

[0156]

[0157] In the above formula, k represents the turbulent kinetic energy, ε represents the turbulent dissipation rate, μ represents the dynamic viscosity, μ t represents the turbulent viscosity, σ k and σ ε represent the turbulent Prandtl number, G k represents the turbulent kinetic energy generated by the average velocity gradient, C 1ε , C 2ε , C μ represent empirical constants.

[0158] Further, the calculated flow field data u(x, y, z), p(x, y, z), k(x, y, z) and ε(x, y, z) are transmitted to the PBM model.

[0159] In step S3022, the PBM model is solved based on the flow field data, and the bubble number density distribution and the interface area density are obtained.

[0160] Specifically, the initial bubble spectrum distribution n0(v) is set based on the characteristics of the aeration equipment; the bubble dynamics calculation includes calculating the bubble rising velocity u b (v), and the calculation formula is as follows:

[0161]

[0162] In the above formula, g represents the gravitational acceleration, db represents the bubble diameter, p l represents the liquid phase density, p g represents the gas phase density, C D represents the drag coefficient.

[0163] Further, the bubble number density distribution n(v, x, y, z) is obtained by solving the bubble population balance equation, and the expression of the bubble population balance equation is as follows:

[0164]

[0165] In the above formula, n(v, x, y, z) represents the bubble number density of the bubble with the volume v at the position (x, y, z) and the time t, v represents the velocity vector, S birt represents the bubble generation source term, S deat represents the bubble extinction source term, S breakup represents the bubble breakup source term, S coalescence represents the bubble coalescence source term.

[0166] In the above formula, the calculation formula of the bubble breakup source term and the bubble coalescence source term is as follows:

[0167]

[0168] In the above formula, g(v', v) represents the frequency of the bubble with the volume v' breaking up to form the bubble with the volume v, β(v, v') represents the frequency of the bubble with the volumes v and v' coalescing, g(v) represents the bubble breakup frequency, β(v-v', v') represents the sub-bubble distribution, represents the bubble number density.

[0169] Further, the gas holdup is obtained by integrating the bubble number density distribution:

[0170]

[0171] In the above formula, a(x, y, z) represents the gas holdup. g (x, y, z) represents the gas holdup.

[0172] Further, the interface area density a(x, y, z) is calculated, and the calculation formula is as follows:

[0173]

[0174] In the above formula, a(x, y, z) represents the interface area density at the position (x, y, z), v represents the surface area of a single bubble, represents the bubble number density of the bubble with the volume v at the position (x, y, z), v​​min and v max represents the volume integral range of the bubble.

[0175] Further, the interfacial force and mass exchange term are calculated, and the calculation formula is as follows:

[0176]

[0177] In the above formula, represents the drag force, represents the lift force, C D represents the drag coefficient, C L represents the lift coefficient, represents the gas phase velocity, represents the liquid phase velocity, p l represents the liquid phase density, d b represents the bubble diameter.

[0178] Further, it is judged whether the above solving variables (i.e. gas holdup, interfacial area density and velocity field) converge, if not, the source term of the CFD model is updated, and the CFD model is solved again until convergence; wherein, the convergence is based on the following formula:

[0179]

[0180] In the above formula, φ represents the solving variable, φ i represents the value of the i-th iteration, φ i+1 represents the value of the i+1-th iteration, and θ represents the convergence threshold.

[0181] Step S3023, the diffusion coefficient of oxygen in water, bubble relative velocity and bubble diameter are obtained, and the local liquid film mass transfer coefficient is calculated based on the diffusion coefficient of oxygen in water, bubble relative velocity and bubble diameter.

[0182] Specifically, the calculation formula of the local liquid film mass transfer coefficient KL(v, x, y, z) is as follows:

[0183]

[0184] In the above formula, K L (v) represents the liquid film mass transfer coefficient of the bubble with a volume of v, D represents the diffusion coefficient of oxygen in water, u r represents the bubble relative velocity, d b represents the bubble diameter.

[0185] Step S3024, the overall oxygen mass transfer coefficient is calculated based on the local liquid film mass transfer coefficient, bubble number density distribution and interfacial area density.

[0186] Specifically, the calculation formula of the overall oxygen mass transfer coefficient KLa(x, y, z) is as follows:

[0187]

[0188] in the above formula, indicates the oxygen mass transfer coefficient at the position a(v) indicates the interface area density of the bubble with a volume of v.

[0189] In step S303, the influent water quality parameters of the target sewage treatment plant and the water temperature of the aeration tank are obtained, and the dissolved oxygen concentration is predicted based on the influent water quality parameters of the target sewage treatment plant, the water temperature of the aeration tank, and the comprehensive oxygen mass transfer coefficient, to obtain dissolved oxygen concentration distribution prediction data. For details, please refer to the step S103 of the embodiment shown in Figure 1 In step S103 of the embodiment shown in

[0190] In step S304, the optimal aeration control strategy is established based on the dissolved oxygen concentration distribution prediction data and the aeration tank operation parameters of the target sewage treatment plant. For details, please refer to the step S104 of the embodiment shown in Figure 1 In step S104 of the embodiment shown in

[0191] In step S305, the optimal aeration control strategy is used to control the aeration of the aeration equipment. For details, please refer to the step S105 of the embodiment shown in Figure 1 In step S105 of the embodiment shown in

[0192] The aeration control method based on the CFD-PBM coupling model provided in this embodiment solves the CFD-PBM coupling model of the aeration tank of the sewage treatment plant, uses the CFD model to couple the PBM model to judge the oxygen mass transfer efficiency in the aeration process, accurately describes the flow field and bubble distribution in the aeration tank, makes the dissolved oxygen distribution more uniform, and improves the aeration accuracy.

[0193] In this embodiment, an aeration control method based on a CFD-PBM coupling model is provided, which can be used in the electronic device described above, Figure 5 is a flowchart of an aeration control method based on a CFD-PBM coupling model according to an embodiment of the present application, as shown in Figure 5 The flowchart includes the following steps:

[0194] In step S501, a CFD-PBM coupling model of the aeration tank of the sewage treatment plant is constructed. For details, please refer to the step S301 of the embodiment shown in Figure 3 In step S301 of the embodiment shown in

[0195] In step S502, the operation parameters of the aeration tank of the target sewage treatment plant are obtained, and the CFD-PBM coupling model of the aeration tank of the sewage treatment plant is solved based on the operation parameters of the aeration tank of the target sewage treatment plant, to obtain the comprehensive oxygen mass transfer coefficient. For details, please refer to the step S302 of the embodiment shown in Figure 3 In step S302 of the embodiment shown in

[0196] In step S503, the influent water quality parameter of the target wastewater treatment plant and the water temperature of the aeration tank are obtained, and the dissolved oxygen concentration is predicted based on the influent water quality parameter of the target wastewater treatment plant, the water temperature of the aeration tank and the comprehensive oxygen transfer coefficient, to obtain dissolved oxygen concentration distribution prediction data.

[0197] Specifically, step S503 includes:

[0198] In step S5031, the saturated dissolved oxygen concentration is calculated based on the water temperature of the aeration tank.

[0199] Specifically, the calculation formula of the saturated dissolved oxygen concentration C s is as follows:

[0200] C s = 14.652-0.41022·T+0.007991·T 2 -0.000077774·T 3 (40)

[0201] In step S5032, the carbon oxidation oxygen consumption rate and the ammonia oxidation oxygen consumption rate are calculated based on the influent water quality parameter of the target wastewater treatment plant, and the total oxygen consumption rate is calculated based on the carbon oxidation oxygen consumption rate and the ammonia oxidation oxygen consumption rate.

[0202] Specifically, the calculation formula of the carbon oxidation oxygen consumption rate OUR C is as follows:

[0203]

[0204] In the above formula, Y H represents the heterotrophic bacteria yield coefficient, q H represents the maximum specific growth rate of heterotrophic bacteria, S S represents the easily degradable substrate concentration, K S represents the half-saturation constant, S O represents the dissolved oxygen concentration, K O,H represents the oxygen half-saturation constant of heterotrophic bacteria, and X H represents the heterotrophic bacteria concentration.

[0205] Further, the calculation formula of the ammonia oxidation oxygen consumption rate OUR N is as follows:

[0206]

[0207] In the above formula, Y A represents the autotrophic bacteria yield coefficient, q A represents the maximum specific growth rate of autotrophic bacteria, SNH represents the ammonia nitrogen concentration, KNH represents the ammonia nitrogen half-saturation constant, S Orepresents the dissolved oxygen concentration, K0,A represents the oxygen half-saturation constant of the autotrophic bacteria, and X A represents the concentration of the autotrophic bacteria, and 4.57 represents the stoichiometric coefficient of ammonia oxidation.

[0208] Further, the calculation formula of the total oxygen consumption rate OUR is as follows:

[0209] OUR = OUR C + OUR N (43)

[0210] Step S5033, a dissolved oxygen dynamic equation is constructed based on the comprehensive oxygen transfer coefficient, the flow field data, the diffusion coefficient of oxygen in water, the saturated dissolved oxygen concentration and the total oxygen consumption rate.

[0211] Specifically, the expression of the DO dynamic equation (i.e., the dissolved oxygen dynamic equation) considering the convection diffusion is as follows:

[0212]

[0213] In the above formula, C represents the dissolved oxygen concentration, represents the velocity vector, D represents the diffusion coefficient of oxygen in water, and K L a represents the comprehensive oxygen transfer coefficient, C s represents the saturated dissolved oxygen concentration, and OUR represents the total oxygen consumption rate.

[0214] Step S5034, the dissolved oxygen dynamic equation is solved to obtain the dissolved oxygen concentration data.

[0215] Specifically, the finite volume method is used to discretize and solve the dissolved oxygen dynamic equation, and the expression is as follows:

[0216]

[0217] In the above formula, represents the dissolved oxygen concentration at the grid point (i, j, k) at the (n+1) th time step, represents the dissolved oxygen concentration at the grid point (i, j, k) at the n th time step, and Δt represents the time step, represents the oxygen consumption rate at the grid point (i, j, k) at the n th time step.

[0218] Step S5035, the dissolved oxygen distribution is predicted based on the dissolved oxygen concentration data to obtain the dissolved oxygen concentration distribution prediction data.

[0219] Specifically, the dissolved oxygen distribution C(x, y, z, t+Δt) at the time t+Δt is calculated by using the above step S5033 and step S5034, and then the DO concentration time series C(x m ,y m ,zm ,t+Δt), m represents the key monitoring point, m=1,2,...,M.

[0220] Step S504: Establish an optimal aeration control strategy based on the predicted data of dissolved oxygen concentration distribution and the operating parameters of the aeration tank of the target sewage treatment plant. Figure 3 Step S304 of the illustrated embodiment will not be described in detail here.

[0221] Step S505: Use the optimal aeration control strategy to control aeration of the aeration equipment. Figure 3 Step S305 of the illustrated embodiment will not be described in detail here.

[0222] This embodiment provides an aeration control method based on a CFD-PBM coupling model. By comprehensively considering the oxygen mass transfer coefficient, flow field data, the diffusion coefficient of oxygen in water, the saturated dissolved oxygen concentration, and the total oxygen consumption rate, a dissolved oxygen dynamic equation is constructed. This dissolved oxygen dynamic equation is solved to obtain dissolved oxygen concentration data. This method fully considers the complex fluid dynamic characteristics and bubble distribution in the aeration tank, thereby improving aeration efficiency and reducing energy consumption.

[0223] In this embodiment, an aeration control method based on a CFD-PBM coupling model is provided, which can be used for the above-mentioned electronic equipment. Figure 6 FIG. 1 is a flow chart of an aeration control method based on a CFD-PBM coupling model according to an embodiment of the present invention. Figure 6 As shown, the process includes the following steps:

[0224] Step S601: Construct a CFD-PBM coupling model of the sewage treatment plant aeration tank. Figure 5 Step S501 of the illustrated embodiment will not be described in detail here.

[0225] Step S602: Obtain the target sewage treatment plant aeration tank operating parameters, solve the sewage treatment plant aeration tank CFD-PBM coupling model based on the target sewage treatment plant aeration tank operating parameters, and obtain the comprehensive oxygen mass transfer coefficient. Figure 5 Step S502 of the illustrated embodiment will not be described in detail here.

[0226] Step S603: Obtain the target sewage treatment plant's influent water quality parameters and aeration tank water temperature, and predict the dissolved oxygen concentration based on the target sewage treatment plant's influent water quality parameters, aeration tank water temperature, and comprehensive oxygen mass transfer coefficient to obtain the predicted data of dissolved oxygen concentration distribution. Figure 5 Step S503 of the illustrated embodiment will not be described in detail here.

[0227] Step S604, establishing an optimal aeration control strategy based on the dissolved oxygen concentration distribution prediction data and the target sewage treatment plant aeration tank operation parameters.

[0228] Specifically, the step S604 includes:

[0229] Step S6041, determining the aeration energy consumption and the energy consumption change rate based on the target sewage treatment plant aeration tank operation parameters.

[0230] Specifically, the aeration energy consumption E energy is calculated as follows:

[0231] E energy =k1·Q air ·P sys ·Δt (46)

[0232] In the above formula, k1 represents the energy consumption conversion coefficient, Q air represents the aeration amount, P sys represents the blower outlet air pressure, and Δt represents the time step.

[0233] Further, the energy consumption change rate E rate is calculated as follows:

[0234]

[0235] In the above formula, represents the derivative of energy consumption with respect to time.

[0236] Step S6042, obtaining a dissolved oxygen concentration set value, and determining a dissolved oxygen concentration control deviation based on the dissolved oxygen concentration distribution prediction data and the dissolved oxygen concentration set value.

[0237] Specifically, the calculation formula of the dissolved oxygen concentration control deviation E DO is as follows:

[0238]

[0239] In the above formula, C m represents the dissolved oxygen concentration of the mth key monitoring point, C set represents the dissolved oxygen concentration set value, and M represents the number of key monitoring points.

[0240] Step S6043, obtaining a weight coefficient, and constructing a comprehensive objective function based on the aeration energy consumption, the energy consumption change rate, the dissolved oxygen concentration control deviation, and the weight coefficient.

[0241] Specifically, the expression of the comprehensive objective function J is as follows:

[0242] J=w1·E energy +w2·E DO+w3·E rate (49)

[0243] In the above formula, w1, w2 and w3 represent weight coefficients, which are dynamically adjusted according to control priorities.

[0244] In step S6044, the optimal aeration quantity and the optimal aeration device opening degree vector are obtained by solving the comprehensive objective function.

[0245] Specifically, the improved particle swarm optimization algorithm is used to solve the comprehensive objective function, as shown in the following formula: Figure 7 As shown in the control parameter optimization process based on the improved particle swarm optimization algorithm, the process includes particle initialization, fitness evaluation, optimal solution update and termination condition judgment, and specifically includes:

[0246] 1) Particle coding: X = [Q_air, a1, a2,..., aK], wherein aK is the opening degree of the Kth aeration device.

[0247] 2) Particle initialization: NP particles are generated, and the expression is as follows:

[0248] X xy =X y,min +rand(0,1)·(X y,max -X y,min ) (50)

[0249] In the above formula, X xy represents the yth dimension of the xth particle, X y,min and X y,max represent the lower limit and upper limit of the yth dimension, and rand(0,1) represents a random number between 0 and 1, wherein x = 1, 2,..., NP, and y = 1, 2,..., D.

[0250] 3) Particle velocity initialization.

[0251] 4) Fitness evaluation: the objective function value J(X x ) of each particle is calculated.

[0252] 5) Update the individual optimal position P x and the global optimal position p g The calculation formulas of the individual optimal position p x and the global optimal position P g are as follows:

[0253] P x =X x ,if J(X x )<J(P x ) (51)

[0254] P g= X x , if J(X x ) < J(P g ) (52)

[0255] In the above equation, J(P x ) represents the comprehensive objective function based on the individual optimal position, and J(P g ) represents the comprehensive objective function based on the global optimal position.

[0256] 6) Velocity update:

[0257]

[0258] In the above equation, represents the velocity of the xth particle in the yth dimension in the k+1th iteration, w represents the inertia weight, c1 and c2 represent the learning factors, r1 and r2 represent random numbers between 0 and 1, P x,y represents the yth dimension of the historical optimal position of the xth particle, P g,y represents the yth dimension of the global optimal position, represents the position of the xth particle in the yth dimension in the kth iteration.

[0259] 7) Adaptive inertia weight calculation, the calculation formula is as follows:

[0260]

[0261] In the above equation, w represents the inertia weight of the current iteration, w max and w min represent the upper and lower limits of the inertia weight, k max represents the maximum number of iterations, and k represents the current number of iterations.

[0262] 8) Position update, which can be represented as:

[0263]

[0264] In the above equation, represents the position of the xth particle in the yth dimension in the k+1th iteration, represents the position of the xth particle in the yth dimension in the kth iteration.

[0265] 9) Boundary handling:

[0266]

[0267] 10) Termination condition check: the number of iterations reaches the maximum number of iterations or the number of consecutive optimal solutions exceeds the threshold.

[0268] 11) Based on the optimal particle P gGenerate aeration control instructions, which include the optimal aeration volume and the optimal aerator opening vector, and the expression is:

[0269] Q air,opt =P g [1] (58)

[0270] α opt =[P g [2],P g [3],…,P g [k+1]] (59)

[0271] In the above formula, Q air,opt represents the optimal aeration rate, α opt represents the optimal aerator opening vector, α opt =[α 1_opt ,α 2_opt ...,α K_opt ].

[0272] Step S6045: Optimize the optimal aeration volume and the optimal aerator opening vector to obtain the optimal aeration control strategy.

[0273] In some optional implementations, the above step S6045 includes:

[0274] Step a1: obtain the actual aerator opening, actual aeration volume and blower outlet pressure, use the actual aerator opening, actual aeration volume and blower outlet pressure as PID basic parameters, and use the PID control algorithm to determine the PID correction amount.

[0275] Specifically, the specific steps of determining the PID correction amount using the PID control algorithm include:

[0276] 1) PID controller parameter adaptive adjustment: the actual aerator opening, actual aeration volume and blower outlet pressure are used as the PID basic parameter K p0 , K i0 and K d0 , adjust the PID parameters according to the actual aerator opening, actual aeration volume and blower outlet pressure. The expression is as follows:

[0277] K p =K p0 +ΔK p ·f1(e) (60)

[0278] K i =K i0 +ΔK i ·f2(e) (61)

[0279] K d =Kd0 +ΔK d ·f3(e) (62)

[0280] Among them, K p , K i and K d Represents the proportional, integral, and differential coefficients, i.e., PID controller parameters, ΔK p , ΔK i and ΔK d represents the PID parameter adjustment amount, f1(e), f2(e), and f3(e) represent adaptive functions based on the control deviation, and e represents the control deviation.

[0281] 2) Feedback correction calculation: Calculate the feedback correction amount (i.e., PID correction amount). The calculation formula is as follows:

[0282] e(t)=C set -C actual (63)

[0283]

[0284] In the above formula, e(t) represents the control deviation at time t, C set Indicates DO setting value, C actual Indicates the actual DO value, ΔQ corr Indicates the PID correction amount.

[0285] Step a2: determining the optimized aeration volume based on the optimal aeration volume and the PID correction amount.

[0286] Specifically, the optimized aeration volume Q air,final The calculation formula is as follows:

[0287] Q air,final =Q air,opt +ΔQ corr (65)

[0288] Furthermore, the expression of the aeration rate boundary constraint is:

[0289] Q air,final =max(Q air,min ,min(Q air,final ,Q air,max )) (66)

[0290] In the above formula, Q air,min and Q air,max Indicates the upper and lower limits of aeration volume.

[0291] Step a3: determining the optimal aerator opening based on the optimal aerator opening vector and the PID correction value.

[0292] Specifically, the expression of the correction function f(P sys ,Q air,final ) based on the air pressure at the air outlet of the blower and the optimized aeration amount is as follows:

[0293]

[0294] Further, the calculation formula of the aeration opening degree (i.e., the optimal opening degree of the aerator) under the influence of the air pressure at the air outlet of the blower is as follows:

[0295] α k,final =α k,opt ·f(P sys ,Q air,final ) (68)

[0296] In the above formula, α k,final represents the final opening degree of the kth aerator, and α k,opt represents the optimal opening degree of the kth aerator.

[0297] Further, the expression of the opening degree boundary constraint is as follows:

[0298] α k,final =max(α min ,min(α k,final ,α max )) (69)

[0299] In the above formula, α min and α max represent the upper and lower limits of the aeration opening degree.

[0300] Step a4, determining the optimal aeration control strategy based on the optimized aeration amount and the optimal opening degree of the aerator.

[0301] Specifically, the optimized aeration amount is converted into the blower frequency f blower :

[0302] f blower =g1(Q air,final ,P sys ) (70)

[0303] Further, the optimal opening degree of the aerator is converted into the actuator control signal S actuator,k :

[0304] S actuator,k =g2(α k,final ) (71)

[0305] wherein g1 and g2 are device characteristic conversion functions.

[0306] Step S605, aeration control is performed on the aeration equipment using the optimal aeration control strategy. For details, please refer to Figure 5 Step S505 of the embodiment shown will not be described here.

[0307] The aeration control method based on the CFD-PBM coupling model provided in this embodiment realizes minimum energy consumption operation under the premise of meeting process requirements, effectively reducing energy consumption, through a multi-objective optimization control algorithm.

[0308] The specific steps of the aeration control method based on the CFD-PBM coupling model will be described below through a specific embodiment.

[0309] Embodiment 1

[0310] Microalgae and microorganisms are co-cultured using domestic sewage as the substrate, and microalgae biofertilizer is produced using the cultured microalgae and activated sludge. The domestic sewage is treated while producing the microalgae biofertilizer, achieving the goal of saving and recycling water and nitrogen, phosphorus, and potassium fertilizer resources. In order to achieve the above goal, a certain municipal wastewater treatment plant is taken as an example. The design treatment scale of the certain municipal wastewater treatment plant is 80,000 tons per day. The design influent water quality is COD≤330 mg / L, BOD5 (Biochemical Oxygen Demand) ≤180 mg / L, SS (Suspended Substance) ≤250 mg / L, ammonia nitrogen≤35 mg / L, TN (Total Nitrogen) ≤45 mg / L, and TP (Total Phosphorus) ≤8.0 mg / L. The effluent executes the first level A discharge standard. The A 2 O treatment process (a secondary sewage treatment process) is used. The length, width, and height of the aeration tank of the wastewater treatment plant, the spatial coordinate position of the aerator, historical water quality, water quantity, aeration quantity, and other working condition data are input into the CFD-PBM coupling model in the aeration control method. The dissolved oxygen concentration (DO), water temperature (T), pH value, ammonia nitrogen concentration, and COD concentration monitoring data of the aeration tank are input into the CFD-PBM coupling model in the aeration control method through a data collector. The aeration precise control method based on the CFD-PBM coupling model includes the following steps:

[0311] S1, data acquisition and preprocessing;

[0312] S2, CFD-PBM coupling model construction and prediction based on the aeration tank of the wastewater treatment plant;

[0313] S3, oxygen transfer coefficient calculation;

[0314] S4, dissolved oxygen concentration prediction;

[0315] S5, fan air volume algorithm;

[0316] S6, fan air volume control and feedback adjustment;

[0317] S7, dissolved oxygen concentration prediction model optimization update.

[0318] According to the above aeration control method, continuous aeration control is carried out for 1 month. During the aeration control period, the influent water quantity is 2911 t / d-3340 t / d, the influent COD concentration is 223-329 mg / L, the ammonia nitrogen concentration is 15.4-26.8 mg / L, the total nitrogen is 23.6-52.2 mg / L, the effluent COD concentration is 10-16 mg / L, the ammonia nitrogen is 0.07-1.30 mg / L, and the total nitrogen is 5.58-9.65 mg / L; as Figure 8 Compared with the related aeration control method, the aeration control method of the embodiment saves 26.55% of the aeration air volume.

[0319] Example 2:

[0320] The system architecture corresponding to the aeration control method based on the CFD-PBM coupling model is shown in Figure 9 The system architecture includes a data acquisition module, a CFD-PBM coupling calculation module, an intelligent optimization control module, an execution control module, and a man-machine interaction module. The data acquisition module is used for data acquisition and preprocessing. The CFD-PBM coupling calculation module is used for CFD-PBM coupling model construction and prediction based on the aeration tank of the sewage treatment plant, oxygen transfer coefficient calculation, dissolved oxygen concentration prediction, and fan air volume algorithm. The intelligent optimization control module is used for fan air volume control and feedback adjustment. The execution control module is used to execute the optimal aeration control strategy. The man-machine interaction module provides a friendly interface, and the operator can easily monitor and adjust the system parameters.

[0321] In the above embodiment, the following advantages are achieved:

[0322] (1) Improved aeration accuracy: The CFD-PBM coupling model accurately describes the flow field and bubble distribution in the aeration tank, making the dissolved oxygen distribution more uniform, and the control accuracy improved by more than 30%.

[0323] (2) Significant energy saving: Based on the multi-objective optimization control algorithm, the minimum energy consumption operation is realized under the premise of meeting the process requirements. In practical application, the aeration energy consumption can be reduced by 15%-25%.

[0324] (3) Strong adaptability: The self-learning and model updating mechanism enables the system to adapt to changes in water quality, load fluctuations, and other changes, and is suitable for various types of sewage treatment processes.

[0325] (4) Easy to operate: The man-machine interaction module provides a friendly interface, and the operator can easily monitor and adjust the system parameters, reducing the difficulty of operation.

[0326] (5) Low maintenance cost: high system stability, reduced equipment wear and tear, extended equipment life, and reduced maintenance cost.

[0327] In this embodiment, an aeration control device based on the CFD-PBM coupling model is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and will not be described again. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware, or a combination of software and hardware implementation is also possible and contemplated.

[0328] The present embodiment provides an aeration control device based on the CFD-PBM coupling model, as shown in Figure 10 , comprising:

[0329] The construction module 1001 is configured to construct a CFD-PBM coupling model of an aeration tank of a sewage treatment plant.

[0330] The solving module 1002 is configured to obtain operation parameters of an aeration tank of a target sewage treatment plant, and solve the CFD-PBM coupling model of the aeration tank of the sewage treatment plant based on the operation parameters of the aeration tank of the target sewage treatment plant, to obtain a comprehensive oxygen mass transfer coefficient.

[0331] The prediction module 1003 is configured to obtain water quality parameters of influent of the target sewage treatment plant and water temperature of the aeration tank, and predict the dissolved oxygen concentration based on the water quality parameters of the influent of the target sewage treatment plant, the water temperature of the aeration tank, and the comprehensive oxygen mass transfer coefficient, to obtain prediction data of dissolved oxygen concentration distribution.

[0332] The establishing module 1004 is configured to establish an optimal aeration control strategy based on the prediction data of dissolved oxygen concentration distribution and the operation parameters of the aeration tank of the target sewage treatment plant.

[0333] The control module 1005 is configured to control aeration of aeration equipment by using the optimal aeration control strategy.

[0334] In some optional embodiments, the solving module 1002 comprises:

[0335] The first solving unit is configured to solve the CFD model based on the operation parameters of the aeration tank of the target sewage treatment plant, to obtain flow field data.

[0336] The second solving unit is configured to solve the PBM model based on the flow field data, to obtain bubble number density distribution and interfacial area density.

[0337] The first calculation unit is configured to obtain a diffusion coefficient of oxygen in water, a bubble relative velocity, and a bubble diameter, and calculate a local liquid film mass transfer coefficient based on the diffusion coefficient of oxygen in water, the bubble relative velocity, and the bubble diameter;

[0338] The second calculation unit is configured to calculate an overall oxygen mass transfer coefficient based on the local liquid film mass transfer coefficient, a bubble number density distribution, and an interfacial area density.

[0339] In some optional embodiments, the prediction module 1003 comprises:

[0340] The third calculation unit is configured to determine a water temperature based on the influent water quality parameter of the target wastewater treatment plant, and calculate a saturated dissolved oxygen concentration based on the water temperature;

[0341] The fourth calculation unit is configured to calculate a carbon oxidation oxygen consumption rate and an ammonia oxidation oxygen consumption rate based on the influent water quality parameter of the target wastewater treatment plant, and calculate a total oxygen consumption rate based on the carbon oxidation oxygen consumption rate and the ammonia oxidation oxygen consumption rate;

[0342] The first construction unit is configured to construct a dissolved oxygen dynamic equation based on the overall oxygen mass transfer coefficient, flow field data, the diffusion coefficient of oxygen in water, the saturated dissolved oxygen concentration, and the total oxygen consumption rate;

[0343] The third solving unit is configured to solve the dissolved oxygen dynamic equation to obtain dissolved oxygen concentration data;

[0344] The prediction unit is configured to perform dissolved oxygen distribution prediction based on the dissolved oxygen concentration data to obtain dissolved oxygen concentration distribution prediction data.

[0345] In some optional embodiments, the establishment module 1004 comprises:

[0346] The first determination unit is configured to determine an aeration energy consumption and an energy consumption change rate based on the operation parameter of the target wastewater treatment plant;

[0347] The second determination unit is configured to obtain a dissolved oxygen concentration set value, and determine a dissolved oxygen concentration control deviation based on the dissolved oxygen concentration distribution prediction data and the dissolved oxygen concentration set value;

[0348] The second construction unit is configured to obtain a weight coefficient, and construct an overall objective function based on the aeration energy consumption, the energy consumption change rate, the dissolved oxygen concentration control deviation, and the weight coefficient;

[0349] The fourth solving unit is configured to solve the overall objective function to obtain an optimal aeration amount and an optimal aerator opening degree vector;

[0350] The tuning unit is configured to tune the optimal aeration amount and the optimal aerator opening degree vector to obtain an optimal aeration control strategy.

[0351] In some optional embodiments, the tuning unit comprises:

[0352] a first determining subunit configured to acquire the actual aerator opening degree, the actual aeration amount and the air pressure at the air outlet of the air blower, take the actual aerator opening degree, the actual aeration amount and the air pressure at the air outlet of the air blower as PID basic parameters, and determine a PID correction amount by using a PID control algorithm;

[0353] a second determining subunit configured to determine an optimized aeration amount based on the optimal aeration amount and the PID correction amount;

[0354] a third determining subunit configured to determine the optimal opening degree of the aerator based on the optimal aerator opening degree vector and the PID correction amount;

[0355] a fourth determining subunit configured to determine the optimal aeration control strategy based on the optimized aeration amount and the optimal opening degree of the aerator.

[0356] In some optional embodiments, the aeration control device further comprises:

[0357] an optimization updating module configured to acquire the actual value of dissolved oxygen after the aeration control of the aeration equipment, and perform optimization updating on the CFD-PBM coupled model of the aeration tank of the wastewater treatment plant based on the actual value of dissolved oxygen.

[0358] The further function descriptions of the above-mentioned various modules and units are the same as those of the above-mentioned corresponding embodiments, and will not be described here again.

[0359] The aeration control device based on the CFD-PBM coupled model in the embodiment is presented in the form of a functional unit. The unit herein refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory executing one or more software or fixed programs, and / or other devices that can provide the above-mentioned functions.

[0360] The embodiment of the present application also provides a computer device with the aeration control device based on the CFD-PBM coupled model. Figure 10

[0361] Please refer to Figure 11 , Figure 11 is a structural schematic diagram of a computer device provided by an optional embodiment of the present application, as Figure 11 ​As shown, the computer device includes one or more processors 10, memory 20, and interfaces 30 for external devices such as a keyboard and a mouse and peripheral devices such as disk devices or other storage devices. One or more busses 10 can be used to implement the interface between the various internal and external components and can be implemented using any one or more of a variety of bus technologies including a System bus, PCI, SCSI, AGP, Super- I / O bus, etc. Furthermore, various buses can be used in front side buses, back side buses, and other bus configurations based on any bus or messaging technology known to those skilled in the art. Figure 11 The processor 10 is used in the embodiments below as an example.

[0362] The processor 10 can be a central processing unit, a network processor, or a combination thereof. The processor 10 can further include a hardware chip. The hardware chip can be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device can be a complex programmable logic device, a field programmable logic device, a general array logic, or any combination thereof.

[0363] The memory 20 stores instructions that can be executed by the at least one processor 10, so that the at least one processor 10 can perform the method shown in the above embodiments.

[0364] The memory 20 can include a program region and a data region. The program region can store an operating system and application programs required by at least one function. The data region can store data created according to the use of the computer device, etc. In addition, the memory 20 can include a high-speed random access memory, and can further include a non-transitory memory such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some alternative embodiments, the memory 20 can optionally include a memory disposed remotely with respect to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0365] The memory 20 can include a volatile memory such as a random access memory, and can also include a non-volatile memory such as a flash memory, a hard disk, or a solid state disk. The memory 20 can further include a combination of the above-mentioned types of memories.

[0366] The computer device further includes a communication interface 30 for communication with other devices or communication networks.

[0367] The embodiments of the present application further provide a computer readable storage medium, and the method according to the embodiments of the present application can be implemented in hardware, firmware, or recorded in a storage medium, or stored in a remote storage medium or a non-transitory machine readable storage medium and downloaded to a local storage medium through network, so that the method described herein can be processed by such software on a storage medium using a general purpose computer, a special purpose processor, or programmable or special hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid state disk, etc. Further, the storage medium can also include a combination of the above-mentioned memories. It can be understood that the computer, the processor, the microprocessor controller, or the programmable hardware includes a storage component that can store or receive software or computer code, when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the method shown in the above embodiments is implemented.

[0368] Part of the present application can be applied as a computer program product, for example, computer program instructions, when executed by a computer, the operation of the computer can invoke or provide the method and / or technical solutions according to the present application. Those skilled in the art should understand that the form of computer program instructions in computer readable medium includes but is not limited to source file, executable file, installation package file, etc. Correspondingly, the way of computer program instructions executed by computer includes but is not limited to: the computer directly executes the instructions, or the computer compiles the instructions and then executes the corresponding compiled program, or the computer reads and executes the instructions, or the computer reads and installs the instructions and then executes the corresponding installed program. Here, the computer readable medium can be any available computer readable storage medium or communication medium accessible to the computer.

[0369] Although the embodiments of the present application are described in conjunction with the accompanying drawings, various modifications and changes can be made by those skilled in the art without departing from the spirit and scope of the present application, and such modifications and changes fall within the scope defined by the appended claims.

Claims

1. An aeration control method based on CFD-PBM coupling model, characterized in that: The method comprises: Construct a CFD-PBM coupling model of aeration tank in sewage treatment plant; Obtaining operating parameters of an aeration tank of a target sewage treatment plant, and solving a CFD-PBM coupling model of the aeration tank of the sewage treatment plant based on the operating parameters of the aeration tank of the target sewage treatment plant to obtain a comprehensive oxygen mass transfer coefficient; Obtaining influent water quality parameters and aeration tank water temperature of a target sewage treatment plant, and predicting dissolved oxygen concentration based on the influent water quality parameters, aeration tank water temperature, and the comprehensive oxygen mass transfer coefficient to obtain predicted dissolved oxygen concentration distribution data; Establishing an optimal aeration control strategy based on the predicted dissolved oxygen concentration distribution data and the operating parameters of the aeration tank of the target sewage treatment plant; The optimal aeration control strategy is used to control aeration of the aeration equipment.

2. The method according to claim 1, characterized in that The CFD-PBM coupling model of the aeration tank of the target sewage treatment plant is solved based on the operating parameters of the aeration tank of the target sewage treatment plant to obtain a comprehensive oxygen mass transfer coefficient, including: Solving the CFD model based on the operating parameters of the aeration tank of the target sewage treatment plant to obtain flow field data; Solving the PBM model based on the flow field data to obtain the bubble number density distribution and the interface area density; Obtaining a diffusion coefficient of oxygen in water, a relative velocity of bubbles, and a bubble diameter, and calculating a local liquid film mass transfer coefficient based on the diffusion coefficient of oxygen in water, the relative velocity of bubbles, and the bubble diameter; The comprehensive oxygen mass transfer coefficient is calculated based on the local liquid film mass transfer coefficient, the bubble number density distribution, and the interfacial area density.

3. The method according to claim 2, characterized in that The dissolved oxygen concentration is predicted based on the influent water quality parameters of the target sewage treatment plant, the water temperature of the aeration tank, and the comprehensive oxygen mass transfer coefficient to obtain the predicted data of the dissolved oxygen concentration distribution, including: Calculating the saturated dissolved oxygen concentration based on the water temperature of the aeration tank; Calculating a carbon oxidation oxygen consumption rate and an ammonia oxidation oxygen consumption rate based on the influent water quality parameters of the target sewage treatment plant, and calculating a total oxygen consumption rate based on the carbon oxidation oxygen consumption rate and the ammonia oxidation oxygen consumption rate; constructing a dissolved oxygen dynamic equation based on the comprehensive oxygen mass transfer coefficient, the flow field data, the diffusion coefficient of oxygen in water, the saturated dissolved oxygen concentration, and the total oxygen consumption rate; Solving the dissolved oxygen dynamic equation to obtain dissolved oxygen concentration data; Dissolved oxygen distribution prediction is performed based on the dissolved oxygen concentration data to obtain dissolved oxygen concentration distribution prediction data.

4. The method according to claim 3, characterized in that The establishing of an optimal aeration control strategy based on the predicted dissolved oxygen concentration distribution data and the operating parameters of the aeration tank of the target sewage treatment plant includes: Determining aeration energy consumption and energy consumption change rate based on the operating parameters of the aeration tank of the target sewage treatment plant; Obtaining a dissolved oxygen concentration set value, and determining a dissolved oxygen concentration control deviation based on the dissolved oxygen concentration distribution prediction data and the dissolved oxygen concentration set value; Obtaining a weight coefficient, and constructing a comprehensive objective function based on the aeration energy consumption, the energy consumption change rate, the dissolved oxygen concentration control deviation, and the weight coefficient; Solving the comprehensive objective function to obtain the optimal aeration volume and the optimal aerator opening vector; The optimal aeration volume and the optimal aerator opening vector are tuned to obtain the optimal aeration control strategy.

5. The method according to claim 4, characterized in that The optimizing the optimal aeration volume and the optimal aerator opening vector to obtain the optimal aeration control strategy includes: Obtaining an actual aerator opening, an actual aeration volume, and a blower outlet pressure, using the actual aerator opening, the actual aeration volume, and the blower outlet pressure as PID basic parameters, and determining a PID correction amount using a PID control algorithm; Determining an optimized aeration amount based on the optimal aeration amount and the PID correction amount; Determining an optimal aerator opening based on the optimal aerator opening vector and the PID correction amount; The optimal aeration control strategy is determined based on the optimized aeration amount and the optimal opening of the aerator.

6. The method according to claim 1, characterized in that Also includes: An actual value of dissolved oxygen after aeration control of the aeration equipment is obtained, and a CFD-PBM coupling model of the sewage treatment plant aeration tank is optimized and updated based on the actual value of dissolved oxygen.

7. An aeration control device based on a CFD-PBM coupling model, characterized in that: The device comprises: Construction module for constructing CFD-PBM coupled model of aeration tank in sewage treatment plant; A solution module, configured to obtain operating parameters of an aeration tank of a target sewage treatment plant, and solve a CFD-PBM coupling model of the aeration tank of the sewage treatment plant based on the operating parameters of the aeration tank of the target sewage treatment plant to obtain a comprehensive oxygen mass transfer coefficient; A prediction module is used to obtain the influent water quality parameters and aeration tank water temperature of a target sewage treatment plant, and predict the dissolved oxygen concentration based on the influent water quality parameters of the target sewage treatment plant, the aeration tank water temperature and the comprehensive oxygen mass transfer coefficient to obtain dissolved oxygen concentration distribution prediction data; Establishing a module for establishing an optimal aeration control strategy based on the predicted dissolved oxygen concentration distribution data and the operating parameters of the aeration tank of the target sewage treatment plant; The control module is used to control the aeration of the aeration equipment using the optimal aeration control strategy.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the aeration control method based on the CFD-PBM coupling model according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the aeration control method based on the CFD-PBM coupling model according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the aeration control method based on the CFD-PBM coupling model according to any one of claims 1 to 6.

Images