Sewage treatment methods and sewage treatment systems

Abstract

The present invention discloses a sewage treatment method and sewage treatment system. The sewage treatment method includes: at each moment, the piecewise affine exogenous autoregressive model predicts the effluent pollutant concentration and energy consumption based on past sewage treatment parameters; based on economic model predictive control, adjusts the balance between pollutant removal, energy consumption cost, and emission cost; and selects the optimal control input at the current moment. Thus, the sewage treatment system is divided into multiple linear subsystems using the piecewise affine exogenous autoregressive model, each linear subsystem representing a typical operating condition, and each linear subsystem corresponds to a submodel in the piecewise affine exogenous autoregressive model. The operating condition is determined based on relevant parameters of the sewage treatment detected in real time, and the submodel corresponding to the operating condition is selectively and dynamically activated to predict the pollutant concentration and energy consumption. Based on this, the optimal control input at the current moment is generated based on the economic model prediction.

Description

Technical Field

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

[0002] Wastewater treatment is a key link in protecting the ecological environment and human health. With the acceleration of urbanization and the improvement of environmental protection requirements, the operating efficiency and economy of wastewater treatment facilities have become particularly important.

[0003] In the sewage treatment process, traditional control methods usually rely on mechanism models. However, the sewage treatment system has strong nonlinearity, time-varying and uncertainty, which makes it difficult for traditional mechanism models to accurately describe the nonlinear dynamic behavior of the sewage treatment process. Especially when faced with complex influent conditions and changeable operating conditions, not only is the computing time and resource consumption large, but the model accuracy often cannot meet the needs of real-time control.

[0004] Furthermore, traditional control methods often rely on setpoint tracking, making it difficult to optimize economic costs and energy consumption while ensuring effluent quality. Existing control strategies often fail to achieve multi-objective optimization under complex operating conditions. Wastewater treatment plant operating conditions frequently change, and traditional control methods struggle to adapt quickly to these changes, resulting in unstable control results and potentially even system failures. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, one object of the present invention is to provide a sewage treatment method that can optimize economic costs while ensuring the quality of effluent.

[0006] The present invention further proposes a sewage treatment system.

[0007] According to an embodiment of the present invention, a method for treating sewage includes: obtaining relevant parameters for sewage treatment; using a mixed integer piecewise affine system to construct a piecewise affine exogenous autoregressive model in the sewage treatment process to identify the nonlinear dynamic characteristics of multiple working conditions; at each moment, the piecewise affine exogenous autoregressive model predicts the effluent pollutant concentration and energy consumption based on past sewage treatment parameters; according to the predicted effluent pollutant concentration and energy consumption, based on the economic model predictive control, adjust the balance between the pollutant removal amount, energy consumption cost and emission cost; select the optimal control input at the current moment and adjust the relevant parameters for sewage treatment; enter the next moment and repeat the above steps.

[0008] Therefore, since sewage has strong nonlinear characteristics during the treatment process, for example, there is a nonlinear relationship between the metabolic rate of microorganisms in sewage and the concentration of substrates that can be degraded by microorganisms, and since the sewage treatment process may have different working conditions, for example, the fluctuation of the sewage inlet load of the sewage treatment system in different time periods will cause the sewage treatment system to be in different working conditions to cope with, these two problems will make traditional models difficult to describe.

[0009] The present invention constructs a piecewise affine exogenous autoregressive model in the sewage treatment process by utilizing a mixed integer piecewise affine system. The piecewise affine exogenous autoregressive model can divide the sewage treatment system into multiple linear subsystems according to the mixed integer partitioning rule. Each linear subsystem represents that the sewage treatment system is in a typical working condition, and a linear subsystem corresponds to a submodel in the piecewise affine exogenous autoregressive model, that is, multiple submodels can accurately describe multiple subsystems in a one-to-one correspondence. According to the relevant parameters of the sewage treatment detected in real time, the working condition of the sewage treatment system at this time is judged, thereby judging the linear subsystem corresponding to the working condition, and then selectively and dynamically activating the corresponding submodel to predict the effluent pollutant concentration and energy consumption.

[0010] On this basis, predictive control based on the economic model adjusts the balance between pollutant removal, energy consumption cost and emission cost, generates the optimal control input at the current moment, and adjusts the operating parameters of the sewage treatment system in real time. This not only ensures the stable operation of the sewage treatment system under different working conditions, but also optimizes costs while improving the decontamination accuracy of the sewage treatment system.

[0011] In some examples of the present invention, the step of selecting the optimal control input at the current moment and adjusting relevant parameters of sewage treatment further includes: the relevant parameters of sewage treatment include but are not limited to aeration volume and sludge return ratio.

[0012] In some examples of the present invention, the piecewise affine exogenous autoregressive model is in the form of: ;in, is the output, that is, the effluent pollutant concentration; is the regression vector; is the state vector, and , including water inlet flow , influent pollutant concentration and control inputs ; is noise or disturbance; is a piecewise affine function, Inlet flow The historical data order, Influent pollutant concentration The historical data order, For control input The historical data order determines how many past input values ​​the model uses to predict the current output.

[0013] In some examples of the present invention, the piecewise affine function is in the form of:

[0014] ;in, are the parameters of each sub-model, and the sub-model is in the form of: ; Defines the polyhedral region of each submodel; each region Constitute a polyhedral partition, The activation area of ​​the j-th sub-model is defined by the linear inequality The state space is divided to realize dynamic modeling of multiple working conditions. The region is a convex polyhedron in the state space. When the state vector φ falls into this region, the jth sub-model is activated.

[0015] In some examples of the present invention, the step of constructing a piecewise affine exogenous autoregressive model in a sewage treatment process using a mixed integer piecewise affine system (MI-PWA) to identify the nonlinear dynamic characteristics of multiple working conditions may include: using an "articulated hyperplane model" to approximate the piecewise affine exogenous autoregressive model, wherein the articulated hyperplane model is composed of a set of articulated functions, each articulated function is composed of two half hyperplanes, and a nonlinear approximation is formed by taking the maximum value, wherein the basic form of the articulated hyperplane model is:

[0016] ;in, and are the parameters of the two hinge hyperplanes; The symbol is used to represent convex or non-convex functions; by taking the max operation, the articulated hyperplane model can adapt to nonlinear systems with broken line characteristics.

[0017] In some examples of the present invention, the articulated hyperplane model approximates a piecewise affine exogenous autoregressive model, and the articulated hyperplane model can be extended to a piecewise affine exogenous autoregressive model, and the piecewise affine exogenous autoregressive model is in the form of:

[0018] ; M represents the number of sub-models of the piecewise affine exogenous autoregressive model; is the weight parameter that determines which sub-models are activated. are the global linear basis parameters of the model, Provides the basic linear response of the model, even if no submodel is activated, i.e. all =0, the output is still Decide, and are all state vectors The weight parameter, This is the global linear part and is always effective. is the nonlinear correction parameter of the ith sub-model, which is effective only when the corresponding region is activated. The two together constitute the complete model dynamics, that is, Capturing commonalities, Capture operating condition specificity.

[0019] The structure ensures that the piecewise affine exogenous autoregressive model can handle nonlinear systems;

[0020] make ;

[0021] Binary variables: , represents the activation state of the piecewise affine exogenous autoregressive model;

[0022] Continuous variable definition: , used to implement the piecewise affine exogenous autoregressive model.

[0023] In some examples of the present invention, the parameters of the piecewise affine exogenous autoregressive model are learned by mixed integer programming. , the mixed integer programming is in the form of: ; Among them, the error definition: ,error The constraints are: , and They are The upper and lower boundaries of and The constraints are: ;

[0024] Due to the redundancy of the max operation, the redundancy constraint of the piecewise affine exogenous autoregressive model is: , is any non-zero vector.

[0025] In some examples of the present invention, the objective function of the economic model predictive control is in the form of:

[0026] ;in, is the target pollutant concentration, is the deviation between the effluent pollutant concentration and the target pollutant concentration; Energy consumption cost of sewage treatment process; The cost of pollutant emissions; is a weight coefficient suitable for adjusting the balance among pollutant removal amount, energy consumption cost and emission cost.

[0027] In some examples of the present invention, the water inlet flow rate The constraints are: ; and / or the influent pollutant concentration The constraints are: ; and / or the effluent pollutant concentration Constraints: ; and / or the control input The constraints are:

[0028] make , in order to prevent the control variable from mutating, the control input Use a smoothness constraint: .

[0029] The sewage treatment system according to the embodiment of the present invention is applicable to the above-mentioned sewage treatment method.

[0030] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments with reference to the accompanying drawings, in which:

[0032] Figure 1 is a flow chart of a sewage treatment method according to an embodiment of the present invention;

[0033] Figure 2 is a comparison chart of the predicted value and the actual value of the effluent water quality according to an embodiment of the present invention;

[0034] Figure 3 is a diagram showing the relationship between an articulation hyperplane and an articulation function according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The embodiments of the present invention will be described in detail below. The embodiments described with reference to the accompanying drawings are exemplary. The embodiments of the present invention will be described in detail below.

[0036] Reference below Figure 1-Figure 3 A method for treating sewage according to an embodiment of the present invention is described.

[0037] Combine Figure 1-Figure 3 As shown, the sewage treatment method according to the present invention may mainly include the following steps:

[0038] S1. Obtain relevant parameters of sewage treatment;

[0039] S2. Using mixed integer piecewise affine systems (MI-PWA) to construct a piecewise affine exogenous autoregressive model in the sewage treatment process to identify the nonlinear dynamic characteristics of multiple working conditions;

[0040] S3. At each moment, the piecewise affine exogenous autoregressive model predicts the effluent pollutant concentration and energy consumption based on past sewage treatment parameters;

[0041] S4. Based on the predicted effluent pollutant concentration and energy consumption, adjust the balance between pollutant removal, energy consumption cost and emission cost based on economic model predictive control (EMPC);

[0042] S5. Select the optimal control input at the current moment and adjust the relevant parameters of sewage treatment;

[0043] S6. Enter the next moment and repeat the above steps.

[0044] Specifically, sewage enters a sewage treatment plant, flows through an activated sludge reactor to remove large impurities, and then flows to a secondary sedimentation tank for further impurity treatment. This process exhibits strong nonlinear characteristics. For example, due to the coupling of parameters such as dissolved oxygen (DO) and pH, the metabolic rate of microorganisms in the sewage and the concentration of substrates that can be degraded by the microorganisms exhibit a nonlinear relationship. Another example is the exponential relationship between the sludge settling rate and sludge concentration in the sedimentation tank, which is also a nonlinear relationship. Furthermore, the oxygen transfer efficiency of a sewage treatment plant's aeration system is nonlinearly related to bubble size and water depth. Furthermore, the aeration valve opening and air flow have dead zones and saturation zones, which also exhibit nonlinear characteristics.

[0045] In addition, the sewage treatment process may experience different operating conditions, such as fluctuations in sewage inflow load, such as the significant difference between the daily sewage inflow peak (morning / evening) and the sewage inflow low peak (noon), and the dilution effect caused by the sudden increase in flow during heavy rain; for example, changes in process mode, such as the alternating operation of anaerobic treatment stages and aerobic treatment stages during denitrification and phosphorus removal, and seasonal temperature changes; and the impact of abnormal events, such as the illegal discharge of industrial wastewater and equipment failures. The different operating conditions and nonlinear characteristics of the sewage treatment process will make it difficult for traditional models to be accurately described, thereby reducing the stability and reliability of the model and the sewage treatment process prediction.

[0046] Therefore, the present invention installs multiple sensors to monitor key parameters in real time during different treatment stages, and enables a controller to obtain relevant parameters of sewage treatment. The controller uses a mixed integer piecewise affine system (MI-PWA) to construct a piecewise affine exogenous autoregressive model in the sewage treatment process. The piecewise affine exogenous autoregressive model can divide the sewage treatment system into multiple linear submodels according to the mixed integer partitioning rule. Each linear subsystem represents a typical operating condition of the sewage treatment system, and a linear subsystem corresponds to a submodel in the piecewise affine exogenous autoregressive model. That is, the multiple submodels can accurately describe the multiple subsystems in a one-to-one correspondence. Based on the relevant parameters of sewage treatment detected in real time, the operating condition of the sewage treatment system is determined, thereby determining the linear subsystem corresponding to the operating condition, and then selectively and dynamically activating the corresponding submodel to predict the effluent pollutant concentration and energy consumption. For example, when the sewage treatment system is under low influent load conditions, the sewage treatment subsystem corresponds to a submodel in the piecewise affine exogenous autoregressive model. The controller selectively and dynamically activates the submodel to more accurately describe the current sewage treatment subsystem, thereby accurately and reliably predicting the current effluent pollutant concentration and energy consumption.

[0047] On this basis, predictive control based on the economic model adjusts the balance between pollutant removal, energy consumption cost and emission cost, generates the optimal control input at the current moment, and adjusts the operating parameters of the sewage treatment system in real time. This not only ensures the stable operation of the sewage treatment system under different working conditions, but also optimizes costs while improving the decontamination accuracy of the sewage treatment system.

[0048] Furthermore, after the sewage treatment system adjusts the operating parameters of the sewage treatment plant according to the optimal control input at the previous moment, the relevant data detected by multiple sensors are changed according to the optimal control input at the previous moment, and the above steps are repeated according to the changed data. In this way, the sewage treatment system can always have high decontamination accuracy and low economic cost.

[0049] Figure 2 It is shown that after the sewage is treated by the sewage treatment method of the present invention, the predicted value of the effluent water quality is compared with the actual value, and the error is small, which meets the requirements.

[0050] In some embodiments of the present invention, the step of selecting the optimal control input at the current moment and adjusting relevant parameters of sewage treatment further includes: the relevant parameters of sewage treatment include but are not limited to aeration volume and sludge return ratio.

[0051] Adjusting the aeration rate can optimize the dissolved oxygen (DO) level. The aeration rate provides oxygen for microorganisms, promotes the degradation of organic matter (COD / BOD) and ammonia nitrogen, and sludge return reintroduces the activated sludge from the secondary sedimentation tank into the activated sludge reaction tank to maintain the microbial concentration.

[0052] Increasing the aeration rate can improve pollutant removal rates but increases energy consumption. Reducing the aeration rate can reduce energy consumption, but it can lead to insufficient dissolved oxygen, leading to sludge bulking or incomplete nitrification (excessive ammonia nitrogen). Increasing the return ratio allows more microorganisms to participate in degradation, enhancing treatment capacity, improving sedimentation and reducing floating sludge in the secondary sedimentation tank, but it increases energy consumption. Reducing the return ratio can save energy but also carries the risk of system collapse due to sludge loss. Therefore, it is necessary to use economic model predictive control to adjust the balance between pollutant removal, energy consumption, and emission costs, and generate optimal control inputs based on demand. For example, when environmental protection requirements are stricter, the aeration rate and sludge return ratio can be increased to improve pollutant removal, with less consideration given to energy consumption and emission costs.

[0053] In some embodiments of the present invention, the piecewise affine exogenous autoregressive model has the form: ;in, is the output (outlet pollutant concentration); is the regression vector; is the state vector, and , including water inlet flow , influent pollutant concentration and control inputs ; is noise (can be regarded as disturbance); is a piecewise affine (PWA) function, Inlet flow The historical data order, Influent pollutant concentration The historical data order, For control input The historical data order determines how many past input values ​​the model uses to predict the current output.

[0054] In some embodiments of the present invention, the piecewise affine function is in the form of:

[0055] ;in, are the parameters of each sub-model, and the sub-model is in the form of: ; Defines the polyhedral region of each submodel; each region Constitute a polyhedral partition, The activation area of ​​the j-th sub-model is defined by the linear inequality The state space is divided to realize dynamic modeling of multiple working conditions. The region is a convex polyhedron in the state space. When the state vector φ falls into this region, the jth sub-model is activated.

[0056] In some specific embodiments of the present invention, if is an m×n matrix, then represents m linear inequalities, which together form a polyhedron.

[0057] In some embodiments of the present invention, Figure 3 As shown, a piecewise affine exogenous autoregressive model is constructed in a sewage treatment process using a mixed integer piecewise affine system (MI-PWA). The steps of identifying the nonlinear dynamic characteristics of multiple working conditions may include: using a "hinging hyperplane model (HH)" to approximate the piecewise affine exogenous autoregressive model, wherein the hinged hyperplane model is composed of a set of hinge functions, each hinge function is composed of two half-hyperplanes, and a nonlinear approximation is formed by taking the maximum value, wherein the basic form of the hinged hyperplane model is: ;in, and are the parameters of the two hinge hyperplanes; The symbol is used to represent a convex or nonconvex function. By taking the max operation, the articulated hyperplane model can adapt to nonlinear systems with broken line characteristics. It should be noted that the max operation is the operation of taking the maximum value in a set of data.

[0058] Combine Figure 1 As shown, the articulated hyperplane model approximates the piecewise affine exogenous autoregressive model. The articulated hyperplane model can be extended to a piecewise affine exogenous autoregressive model. The piecewise affine exogenous autoregressive model is in the form of:

[0059] ; M represents the number of sub-models of the piecewise affine exogenous autoregressive model; is the weight parameter that determines which sub-models are activated. are the global linear basis parameters of the model, Provides the basic linear response of the model, even if no submodel is activated, i.e. all =0, the output is still Decide, and are all state vectors The weight parameter, This is the global linear part and is always effective. is the nonlinear correction parameter of the ith sub-model, which is effective only when the corresponding region is activated. The two together constitute the complete model dynamics, that is, Capturing commonalities, Capture operating condition specificity.

[0060] The structure ensures that the piecewise affine exogenous autoregressive model can handle nonlinear systems; let ; Binary variables: , represents the activation state of the piecewise affine exogenous autoregressive model; continuous variable definition: , which is used to implement piecewise affine exogenous autoregressive models.

[0061] Specifically, the sensor will monitor and detect the relevant parameters of sewage treatment in real time. For example, the sensor will detect the inlet flow rate and the inlet pollutant concentration. When the inlet flow rate is less than the preset value or the inlet pollutant concentration is less than the preset value, the sewage treatment system is in a low-load condition, that is, When the inlet flow rate is greater than the preset value or the inlet pollutant concentration is greater than the preset value, the high load condition is triggered, i.e. Learning parameters of piecewise affine exogenous autoregressive models via mixed integer programming When the sewage treatment system is in different working conditions, the corresponding sub-model in the piecewise affine exogenous autoregressive model corresponds to the sewage treatment subsystem under the current working conditions, avoiding the prediction failure of a single model under sudden working conditions.

[0062] Provides the basic linear response of the model, even if no submodel is activated, i.e. all =0, the output is still Decide, and are all state vectors The weight parameter, This is the global linear part and is always effective. is the nonlinear correction parameter of the ith sub-model, which is effective only when the corresponding region is activated. The two together constitute the complete model dynamics, that is, Capturing commonalities, Capture operating condition specificity.

[0063] In some embodiments of the present invention, the parameters of the piecewise affine exogenous autoregressive model are learned by mixed integer programming (MILP) , the mixed integer programming (MILP) is of the form: , where the error definition: ,error The constraints are: , and They are The upper and lower boundaries of and The constraints are: , due to the redundancy of the max operation, the redundancy constraint of the piecewise affine exogenous autoregressive model is: , is any non-zero vector.

[0064] Specifically, a piecewise affine exogenous autoregressive model is learned via mixed integer programming (MILP), which can improve computational efficiency and control accuracy.

[0065] It should be noted that when the state vector When located near the partition boundary of the two sub-models, multiple sub-models in the piecewise affine exogenous autoregressive model that predict different operating conditions of the sewage treatment system may be activated simultaneously, resulting in the same input corresponding to multiple possible output values, resulting in multiple local solutions to the optimization problem, and the piecewise affine exogenous autoregressive model is disordered. By limiting the redundant constraints of the piecewise affine exogenous autoregressive model, one control input can only obtain one output value, thereby improving the stability and reliability of the piecewise affine exogenous autoregressive model.

[0066] In some embodiments of the present invention, the objective function of the economic model predictive control is in the form of: ;in, is the target pollutant concentration, is the deviation between the effluent pollutant concentration and the target pollutant concentration; Energy consumption cost of sewage treatment process, such as aeration volume Corresponding energy consumption costs; The cost of pollutant emissions; is a weight coefficient used to reflect priority, such as when environmental protection is strict By increasing the weight coefficient, it is suitable for adjusting the balance between pollutant removal, energy consumption cost and emission cost.

[0067] It should be noted that in a multi-objective optimization problem, multiple objective functions need to be considered simultaneously. In this case, for example, pollutant emission costs, energy consumption costs, and target pollutant concentrations, these objectives may conflict with each other. To achieve unified optimization, a weight is assigned to each objective, converting the multi-objective problem into a single-objective optimization problem.

[0068] Therefore, in the economic model predictive control (EMPC) of wastewater treatment, the standard of "optimal control input" is defined by the objective function and the constraints. Under the premise of meeting all process constraints, the comprehensive economic efficiency of water quality, energy consumption cost and emission cost is balanced, and the control input sequence that minimizes the multi-objective weighted cost is selected. .

[0069] In some embodiments of the present invention, the water flow rate The constraints are: ; and / or influent pollutant concentration The constraints are: ; and / or effluent pollutant concentrations Constraints: ; and / or control input The constraints are: , in order to prevent the control variable from mutating, the control input Use a smoothness constraint: .

[0070] According to the sewage treatment system of the present invention, the above-mentioned sewage treatment method is applicable. The sewage treatment system using the above-mentioned sewage treatment method can not only improve the accuracy of sewage treatment, but also optimize economic costs.

[0071] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0072] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "illustrative embodiments," "example," "specific example," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example.

[0073] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

Claims

1. A method for treating sewage, characterized in that: The following steps are involved: Obtain relevant parameters of sewage treatment; Using mixed integer piecewise affine system to construct piecewise affine exogenous autoregressive model in sewage treatment process, the nonlinear dynamic characteristics of multiple working conditions are identified; At each moment, the piecewise affine exogenous autoregressive model predicts effluent pollutant concentrations and energy consumption based on past sewage treatment parameters; According to the predicted effluent pollutant concentration and energy consumption, the balance between pollutant removal, energy consumption cost and emission cost is adjusted based on economic model predictive control; Select the optimal control input at the current moment and adjust the relevant parameters of sewage treatment; Enter the next moment and repeat the above steps; The piecewise affine exogenous autoregressive model is of the form: ;in, is the output, that is, the effluent pollutant concentration; is the regression vector; is the state vector, and , including water inlet flow , influent pollutant concentration and control inputs ; is noise or disturbance; is a piecewise affine function, Inlet flow The historical data order, Influent pollutant concentration The historical data order, For control input The historical data order determines how many past input values ​​the model uses to predict the current output; The piecewise affine function is in the form: ;in, are the parameters of each sub-model, and the sub-model is in the form of: ; Defines the polyhedral region of each submodel; each region Constitute a polyhedral partition, The activation area of ​​the j-th sub-model is defined by the linear inequality Divide the state space to realize dynamic modeling of multiple working conditions. The region is a convex polyhedron in the state space. When the state vector φ falls into this region, the j-th sub-model is activated. The step of using a mixed integer piecewise affine system to construct a piecewise affine exogenous autoregressive model in the sewage treatment process to identify the nonlinear dynamic characteristics of multiple working conditions may include: Use the "articulated hyperplane model" to approximate the piecewise affine exogenous autoregressive model, where the articulated hyperplane model consists of a set of articulated functions, each of which consists of two half hyperplanes, and forms a nonlinear approximation by taking the maximum value, where The basic form of the articulated hyperplane model is: ； in, and are the parameters of the two hinge hyperplanes; The symbol is used to represent convex or non-convex functions; by taking the max operation, the articulated hyperplane model can adapt to nonlinear systems with broken line characteristics.

2. The method for treating sewage according to claim 1, wherein: The step of selecting the optimal control input at the current moment and adjusting the relevant parameters of the sewage treatment further includes: The relevant parameters of the sewage treatment include but are not limited to aeration volume and sludge return ratio.

3. The method for treating sewage according to claim 1, wherein: The articulated hyperplane model approximates a piecewise affine exogenous autoregressive model, which can be extended to a piecewise affine exogenous autoregressive model. The piecewise affine exogenous autoregressive model has the form: ； M represents the number of sub-models of the piecewise affine exogenous autoregressive model; is the weight parameter that determines which sub-models are activated. are the global linear basis parameters of the model, Provides the base linear response of the model, even if no submodels are activated (i.e. all =0), the output is still Decide, and are all state vectors The weight parameter, It is the global linear part and is always effective; is the nonlinear correction parameter of the i-th sub-model, which is effective only when the corresponding area is activated; Together, they form the complete model dynamics, i.e. Capturing commonalities, Capture operating condition specificity; The structure ensures that the piecewise affine exogenous autoregressive model can handle nonlinear systems; make ; Binary variables: , represents the activation state of the piecewise affine exogenous autoregressive model; Continuous variable definition: , used to implement the piecewise affine exogenous autoregressive model.

4. The method for treating sewage according to claim 3, wherein: Learning parameters of piecewise affine exogenous autoregressive models via mixed integer programming , the mixed integer programming is in the form of: ; Among them, the error definition: ,error The constraints are: ; and They are The upper and lower boundaries of and The constraints are: ; Due to the redundancy of the max operation, the redundancy constraint of the piecewise affine exogenous autoregressive model is: , is any non-zero vector.

5. The method for treating sewage according to claim 4, characterized in that: The objective function of the economic model predictive control is in the form of: ； in, is the target pollutant concentration, is the deviation between the effluent pollutant concentration and the target pollutant concentration; Energy consumption cost of sewage treatment process; The cost of pollutant emissions; is a weight coefficient suitable for adjusting the balance between pollutant removal, energy consumption cost, and emission cost, k=t,t+1,...,t+N−1, k represents each time step in the prediction time domain, y k represents the effluent pollutant concentration at time k.

6. The method for treating sewage according to claim 5, characterized in that: The water inlet flow The constraints are: and / or The influent pollutant concentration The constraints are: and / or The effluent pollutant concentration Constraints: and / or The control input The constraints are: make , in order to prevent the control variable from mutating, the control input Use a smoothness constraint: .

7. A sewage treatment system, characterized in that: The sewage treatment method according to any one of claims 1 to 6 is applicable.

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