An Adaptive Control Method for Micro-Aeration Oxidation Ditches Driven by Process Simulation Models

Abstract

This invention discloses an adaptive control method for micro-aeration oxidation ditches driven by a process simulation model. A process simulation model is constructed using process parameters, and continuous operating data is used for model calibration and verification. Steady-state and dynamic simulations are performed using the process simulation model. The optimal steady-state mode is selected using a nitrogen and phosphorus removal potential index analysis method, and the optimal dynamic mode is selected using an additional reagent cost index analysis method. An adaptive control system is designed, and by setting instrument components, field control components, backtracking time, and scenario boundaries, the optimal operating mode is automatically matched and switched. This safe and efficient adaptive control method can dynamically monitor water quality changes, rationally judge and match the optimal operating mode, and continuously optimize wastewater treatment effects through precise control while ensuring system stability. This not only reduces the need for human intervention but also significantly improves the system's operating efficiency and stability.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, and in particular to an adaptive control method for micro-aeration oxidation ditches driven by a process simulation model. Background Technology

[0002] The influent water quality of urban wastewater treatment systems is often complex and variable, especially the fluctuations in key water quality indicators such as COD, TN, and TP, which have a complex impact on the operating performance and operating costs of the wastewater treatment system.

[0003] This complexity manifests itself in two ways. First, the concentrations of COD, TN, and TP, as key control indicators in wastewater treatment, directly impact whether effluent quality meets standards. Furthermore, these indicators are not isolated but interconnected through parameters such as the carbon-to-nitrogen ratio (C / N) and the carbon-to-phosphorus ratio (C / P), collectively affecting the biodegradability of wastewater. Imbalances in the C / N and C / P ratios can lead to nutrient deficiencies or excesses in microorganisms, thus affecting the efficiency of biological nitrogen and phosphorus removal. In addition, biological nitrogen and phosphorus removal processes compete at the microbial metabolic level, such as through carbon source competition and nitrate inhibition, making it difficult to achieve efficient nitrogen and phosphorus removal simultaneously within the same treatment system, often resulting in compromises. Second, the complexity also stems from the dynamic, sequential interference between preceding and subsequent processes. Wastewater treatment is a continuous, dynamic process whose effectiveness is influenced not only by current water quality conditions but also by preceding operating conditions, further exacerbating the complexity of the impact.

[0004] To address the aforementioned challenges, existing biological nitrogen and phosphorus removal technologies, such as switchable micro-aeration oxidation ditches, are equipped with switchable components, allowing for adjustment of operating modes based on nitrogen and phosphorus removal requirements. While this technology has achieved some success, it still has several shortcomings. First, it relies heavily on human operational experience. Maintenance personnel need to accumulate extensive experience dealing with various influent conditions under multiple operating modes before making reasonable operational control decisions, resulting in long processing times, high labor costs, and high trial-and-error risks. Second, due to the complexity of the impact of influent water quality on biological nitrogen and phosphorus removal, experience-based operational control methods have low accuracy and are difficult to adapt to the complex effects of various water quality concentration combinations on treatment effectiveness and costs. Third, the effects of operational control can only be reported through online effluent monitoring, which has a time lag and is susceptible to external interference, including chemical dosing and advanced treatment, rendering it meaningless. If the control effects could be predicted in advance, the amount of chemicals needed could be reduced, saving operating costs.

[0005] Meanwhile, with the rapid development of big data and artificial intelligence technologies, intelligent management and control technologies for wastewater treatment are gradually emerging. However, in practical applications, this technology still faces some limitations that cannot be ignored. First, big data model-driven intelligent management and control technologies are often accompanied by the "black box" problem, meaning that the internal logic, basis, calculation, and decision-making processes are often opaque or difficult to explain intuitively, weakening the reliability of the technology and easily leading to a decrease in user trust. Second, the complex and ever-changing wastewater treatment problems force intelligent management and control systems to rely on large and complex model algorithms to ensure high-precision operation. Although this refined data processing and calculation significantly improves the system's sensitivity to water quality changes and the accuracy of prediction, and saves processing energy consumption, it also increases hardware investment costs and software maintenance costs, resulting in low cost-effectiveness. Third, when the influent water quality fluctuates only within a small range, the wastewater treatment facility itself has a certain ability to withstand shock loads, and in reality, such high-frequency and high-precision real-time control is not necessary. Otherwise, it may not only lead to frequent equipment failures due to frequent start-ups and shutdowns or overload operation, but may also accelerate equipment aging and shorten its service life. There is a clear contradiction between this overemphasis on refined management and control and the practical and stable goals pursued in actual operation. Summary of the Invention

[0006] In view of the shortcomings of the prior art, the purpose of this invention is to provide an adaptive control method for micro-aeration oxidation ditches based on process simulation model, which solves the problems of long decision-making time, low accuracy and low cost-effectiveness of switchable micro-aeration oxidation ditch control in the prior art.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: an adaptive control method for micro-aeration oxidation ditches driven by process simulation models, characterized by the following steps: Data collection: collecting process design parameters, historical influent and effluent concentration data, and continuous operation data; Constructing a Biowin process simulation model: based on the collected process design parameters, establishing anaerobic zone model units, anoxic zone model units, aerobic zone model units, and secondary sedimentation tank model units in the BioWin process simulation software, and connecting them through model pipelines, setting up diversion pipes to construct multiple process simulation models; Model calibration and verification: ensuring the accuracy of the model through initial steady-state simulation verification, influent component parameter calibration, determination of adjustable microbial parameters, adjustable microbial parameter calibration, and repeated steady-state simulation verification; Steady-state scenario preset and simulation: preset steady-state scenarios as input values ​​for the steady-state model, running all steady-state models in the BioWin process simulation software to obtain a steady-state effluent concentration dataset, and using the Potential of Nutrient Removal (PNR) index analysis method. The system employs a process of removal (removal) to select the steady-state optimal mode; dynamic scenario pre-setting and simulation: multiple dynamic scenarios are pre-set as input values ​​for the dynamic model. All dynamic models are run in the BioWin process simulation software to obtain a dynamic effluent concentration dataset. The Additional Chemical Cost (ACC) index analysis method is used to select the dynamic optimal mode under each dynamic scenario; adaptive control system design: instrument components, field control components, backtracking time, and scenario boundaries are set. Multiple control scenarios are divided according to the scenario boundaries. The steady-state optimal mode and the dynamic optimal mode are matched according to the control scenarios, thereby matching control commands, setting flow calibration and protection time, and realizing adaptive control of the micro-aeration oxidation ditch system.

[0008] Furthermore, in the step of constructing the Biowin process simulation model, the setting of the shunt pipes includes setting shunt pipe one on the model pipeline and connecting shunt pipe one to the anoxic zone; setting shunt pipe two on the model pipeline and connecting shunt pipe two to the anaerobic zone; setting shunt pipe three on the model pipeline and connecting shunt pipe three to the anoxic zone; and setting shunt pipe four and shunt pipe five on the model pipeline, with shunt pipe four connected to the anaerobic zone and shunt pipe five connected to the anoxic zone.

[0009] Furthermore, in the model calibration and verification steps, the initial steady-state simulation verification uses the average value of the collected influent concentration data as the model input value. The average value of the collected effluent concentration data and the model output value are compared with the relative error. If the relative error is ≥10%, the model parameters are calibrated. The influent component parameters are calibrated by taking on-site water samples and changing the measured values ​​to the measured values ​​for parameters that differ from the default values. Adjustable microbial parameters are determined and calibrated. Otherwise, the steady-state verification is considered qualified, and the model calibration and verification are completed.

[0010] Furthermore, in the steady-state scenario pre-setting and simulation, the calculation formula for the Potential of Nutrient Removal (PNR) index using the nitrogen and phosphorus removal potential index analysis method is as follows:

[0011] ;

[0012] Where Y is the true yield coefficient of denitrifying bacteria; k dn SRT is the endogenous metabolic constant of denitrifying bacteria; TN is the sludge age; SRT is the sludge age; TN is the total nitrogen (TN) of the sludge. i Input value for the model: TN concentration; TN o The model output values ​​are TN concentration and TP concentration. i Input value for the model: TP concentration; TP o The model output values ​​are TP concentration and COD concentration. i The input value for the model is COD concentration; B / C is the influent BOD5 / COD ratio.

[0013] Furthermore, in the dynamic scenario pre-setting and simulation, the calculation formula for the Additional Chemical Cost (ACC) index in the analysis method is as follows:

[0014] ;

[0015] ,like Then uniformly follow ;

[0016] ,like Then uniformly follow ;

[0017] Among them, ACC N For additional nitrogen removal reagent cost indicators; ACC P For additional phosphorus removal agents, the cost indicator is K. N1 COD coefficient of the external carbon source required for nitrate nitrogen denitrification; C N1 For the COD equivalent of the added carbon source; C N2 The effective content of the added carbon source; P N The unit price of the added carbon source; TN O The TN concentration in the effluent output by the model; TN S For effluent TN standard; K P1 The molar ratio of aluminum to phosphorus; C P1 P represents the effective content of Al2O3 in PAC. P11 The unit price of PAC; TP O The effluent TP concentration output by the model; TP S For effluent TP standard; KP2 C is the dosage rate of PAM. P2 The effective content in PAM; P P2 This is the unit price of PAM.

[0018] Furthermore, in the design steps of the adaptive control system, when determining the current influent concentration, a backtracking time is set, and the average value of the online monitoring instrument within a preset fixed backtracking time is taken as the influent concentration.

[0019] Furthermore, in the adaptive control system design steps, when determining what scenario the current influent concentration belongs to, a scenario boundary is set, and the scenario boundary and the area it divides are used as the judgment condition.

[0020] Furthermore, in the design steps of the adaptive control system, after mode switching, flow calibration is set. The influent flow rate and external return sludge flow rate after mode switching are detected by an electromagnetic flow meter and compared with the standard flow rate of the corresponding mode. If the deviation is large, the system is calibrated by fine-tuning the opening of the field control components.

[0021] Furthermore, in the design steps of the adaptive control system, a protection time is set after mode switching. After completing one mode switching and flow calibration, the system enters the protection time and does not perform any mode switching during the protection time.

[0022] Furthermore, a special boundary is set within the protection time period. The line segment of the scenario boundary that was previously reached or crossed is pushed by a fixed amount in the opposite direction of the crossing trend. The pushed line segment serves as the special boundary within the protection time period. Within the protection time period, if the current influent concentration reaches or crosses the special boundary, the mode switch is directly triggered.

[0023] As described above, the adaptive control method for micro-aeration oxidation ditches based on process simulation models of the present invention has the following beneficial effects:

[0024] 1. This invention is an adaptive control method for micro-aeration oxidation ditches driven by process simulation models. The model calculation equations are closely based on the physical, chemical and biological reaction mechanisms of the wastewater treatment process, ensuring the scientific nature and interpretability of the model.

[0025] 2. This invention employs multiple calibration techniques to finely adjust model parameters. Simultaneously, it effectively combines steady-state and dynamic simulations, capturing not only the system's steady-state characteristics but also accurately simulating the dynamic response to water quality fluctuations, providing comprehensive and precise data support for the formulation of control strategies.

[0026] 3. This invention fully considers the complexity and variability in actual operation. By pre-setting multiple scenarios and modes, it adapts to various water quality conditions and operational needs, thereby ensuring the accuracy and effectiveness of regulation while reasonably balancing the convenience and economy of calculation and operation.

[0027] 4. This invention innovatively introduces the PNR index analysis method and the ACC index analysis method to evaluate and screen the model results. It not only considers the contradictions and complexities of biological nitrogen and phosphorus removal at the microscopic level, but also combines objective factors such as influent conditions, effluent standards and reagent costs. Through comprehensive evaluation, the optimal operating mode is screened out to achieve the best overall level of treatment effect and operating cost, and promote the synergistic effect of wastewater treatment in reducing pollution and carbon emissions.

[0028] 5. This invention establishes a safe and efficient adaptive control system capable of dynamically monitoring water quality changes, rationally determining and matching the optimal operating mode, and continuously optimizing wastewater treatment effects through precise control while ensuring system stability. This adaptive mechanism not only reduces the need for human intervention but also significantly improves the system's operating efficiency and stability, providing strong support for the intelligent transformation of wastewater treatment technology. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a schematic diagram of the overall process of the present invention;

[0031] Figure 2 This is a schematic diagram of the planar structure provided by the present invention;

[0032] Figure 3 A schematic diagram of the model flow in the Biowin process simulation software provided by this invention;

[0033] Figure 4 This is a schematic diagram of the model calibration and verification process provided by the present invention;

[0034] Figure 5 This is a schematic diagram of the design process of the adaptive control system provided by the present invention;

[0035] Figure 6 This is a schematic diagram of the backtracking time provided by the present invention;

[0036] Figure 7 A schematic diagram of the scenario boundary provided by the present invention;

[0037] Figure 8 This is a schematic diagram of a special boundary provided for the present invention.

[0038] Component designation explanation:

[0039] Preceding Structures 001, Anaerobic Zone 002, Anoxic Zone 003, Aerobic Zone 004, Secondary Settling Tank 005, COD Online Monitor 101, TN Online Monitor 102, TP Online Monitor 103, Anaerobic Zone Inlet Electromagnetic Flow Meter 104, Anoxic Zone Inlet Electromagnetic Flow Meter 105, Anaerobic Zone External Return Sludge Electromagnetic Flow Meter 106, Anoxic Zone External Return Sludge Electromagnetic Flow Meter 107, Anaerobic Zone Inlet Branch Pipe Electric Regulating Valve 201, Anoxic Zone Inlet Branch Pipe Electric Regulating Valve 202, Anaerobic Zone External Return Branch Pipe Electric Regulating Valve 203, Anoxic Zone External Return Branch Pipe Electric Regulating Valve 204, Anoxic Zone to... 205, 206, 205, 206, 301, 302, 303, 304, 305, 305, 401, 402, 303, 304, 305, 401, 402, 403, 404, 305, 405, 401, 402, 403, 404, 505, 501, 502, 503, 504, 505.

[0040] Explanation of proper nouns:

[0041] PNR: Potential of Nutrient Removal;

[0042] ACC: Additional Chemical Cost.

[0043] COD: Chemical Oxygen Demand, is a chemically measured amount of reducing substances in a water sample that require oxidation.

[0044] TN: Total nitrogen, is the total amount of inorganic and organic nitrogen in water.

[0045] TP: Total phosphorus, the sum of phosphorus in wastewater in both inorganic and organic forms;

[0046] SS: Suspended solids, refers to solid substances suspended in water, including inorganic and organic matter that are insoluble in water, as well as mud, sand, clay, microorganisms, etc.

[0047] BOD5: 5-day biological oxygen demand, refers to the amount of dissolved oxygen consumed by microorganisms in decomposing certain oxidizable substances, especially organic matter, in a certain volume of water within a certain period.

[0048] PAC: Polyaluminum chloride, commonly used as a chemical phosphorus removal coagulant in wastewater;

[0049] PAM: Polyacrylamide, often used as a chemical phosphorus removal flocculant in wastewater, in combination with PAC. Detailed Implementation

[0050] This invention provides an adaptive control method for micro-aeration oxidation ditches driven by a process simulation model. To make the objectives, technical solutions, and effects of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0051] In the description of this invention, it should be understood that the terms "up, down, left, right," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this invention and simplifying the description, and should not be construed as limiting this invention; in addition, the terms "installation," "connection," etc. should be interpreted broadly, and those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0052] Please see Figures 1 to 8This invention provides an adaptive control method for micro-aeration oxidation ditches driven by a process simulation model, comprising the following steps: Data collection: collecting process design parameters, historical influent and effluent concentration data, and continuous operation data; Constructing a BioWin process simulation model: based on the collected process design parameters, establishing anaerobic zone model units, anoxic zone model units, aerobic zone model units, and secondary sedimentation tank model units in the BioWin process simulation software, and connecting them through model pipelines, setting up diversion pipes to construct multiple process simulation models; Model calibration and verification: ensuring the accuracy of the model through initial steady-state simulation verification, influent component parameter calibration, determination of adjustable microbial parameters, adjustable microbial parameter calibration, and repeated steady-state simulation verification; Steady-state scenario preset and simulation: preset steady-state scenarios as input values ​​for the steady-state model, running all steady-state models in the BioWin process simulation software to obtain a steady-state effluent concentration dataset, and using the Potential of Nutrient Removal Index (PONIRS) analysis method. (Removal) Screening out the steady-state optimal mode; Dynamic scenario pre-setting and simulation: Pre-setting multiple dynamic scenarios as input values ​​for the dynamic model, running all dynamic models in BioWin process simulation software to obtain dynamic effluent concentration datasets, and using the additional reagent cost index analysis method to screen out the dynamic optimal mode under each dynamic scenario; Adaptive control system design: Setting instrument components, field control components, backtracking time, and scenario boundaries, dividing multiple control scenarios according to scenario boundaries, matching the steady-state optimal mode and the dynamic optimal mode according to the control scenario, thereby matching control commands, setting flow calibration and protection time, and realizing adaptive control of the micro-aeration oxidation ditch system.

[0053] The process design parameters include design flow rate, effluent standard, effective volume of the anaerobic zone, effective water depth of the anaerobic zone, corridor width of the anaerobic zone, dissolved oxygen concentration of the anaerobic zone, effective volume of the anoxic zone, effective water depth of the anoxic zone, corridor width of the anoxic zone, dissolved oxygen concentration of the anoxic zone, effective volume of the aerobic zone, effective water depth of the aerobic zone, corridor width of the aerobic zone, dissolved oxygen concentration of the aerobic zone, effective volume of the secondary sedimentation tank, and excess sludge flow rate of the secondary sedimentation tank. Continuous operating data includes COD, TN, TP, BOD5, NH3-N, SS, pH, and water temperature. All data are continuously collected for more than one year, with a data collection and recording cycle of 24 hours. Continuous operating data also includes the unit price and effective content of the added carbon source and chemical phosphorus removal agents.

[0054] Please see Figure 2The planar structure of this invention includes: a preliminary structure 001, an anaerobic zone 002, an anoxic zone 003, an aerobic zone 004, and a secondary sedimentation tank 005. The instrumentation components in the planar structure include: an online COD monitor 101, an online TN monitor 102, an online TP monitor 103, an electromagnetic flow meter for anaerobic zone influent 104, an electromagnetic flow meter for anoxic zone influent 105, an electromagnetic flow meter for anaerobic zone external return sludge 106, and an electromagnetic flow meter for anoxic zone external return sludge 107. The online COD monitor 101, online TN monitor 102, and online TP monitor 103 are all located at the end of the preliminary structure 001 and are used to monitor the influent COD, TN, and TP concentrations in real time. The monitoring results serve as conditions for triggering the adaptive control system. The electromagnetic flow meter for anaerobic zone influent 104 is located at the anaerobic zone influent branch pipe 301 and is used to detect and provide feedback on the raw water flow rate entering the anaerobic zone. The electromagnetic flow meter 105 for the anoxic zone inlet is installed at the anoxic zone inlet branch pipe 302 and is used to detect and provide feedback on the raw water flow rate entering the anoxic zone. The electromagnetic flow meter 106 for the anaerobic zone external return sludge is installed at the anaerobic zone external return branch pipe 303 and is used to detect and provide feedback on the external return sludge flow rate entering the anaerobic zone. The electromagnetic flow meter 107 for the anoxic zone external return sludge is installed at the anoxic zone external return branch pipe 304 and is used to detect and provide feedback on the external return sludge flow rate entering the anoxic zone.

[0055] Please see Figure 2 The field control components in the planar structure of this invention include: an electrically adjustable valve 201 for the anaerobic zone inlet branch pipe, an electrically adjustable valve 202 for the anoxic zone inlet branch pipe, an electrically adjustable valve 203 for the anaerobic zone external return branch pipe, an electrically adjustable valve 204 for the anoxic zone external return branch pipe, a rotary gate 205 for internal return from the anoxic zone to the anaerobic zone, and a rotary gate 206 for internal return from the aerobic zone to the anoxic zone. The electrically adjustable valve 201 for the anaerobic zone inlet branch pipe is located at the anaerobic zone inlet branch pipe 301 and is used to regulate the flow rate of raw water entering the anaerobic zone. The electrically adjustable valve 202 for the anoxic zone inlet branch pipe is located at the anoxic zone inlet branch pipe 302 and is used to regulate the flow rate of raw water entering the anoxic zone. The electrically adjustable valve 203 for the anaerobic zone external return branch pipe is located at the anaerobic zone external return branch pipe 303 and is used to regulate the flow rate of externally returned sludge entering the anaerobic zone. The electrically operated regulating valve 204 for the external return branch pipe of the anoxic zone is installed at the external return branch pipe 304 of the anoxic zone and is used to regulate the flow rate of externally returned sludge entering the anoxic zone. The internal return rotary gate 205 from the anoxic zone to the anaerobic zone is installed at the inlet of the return corridor of the anoxic zone and is used to regulate the internal return flow rate from the anoxic zone to the anaerobic zone. The internal return rotary gate 206 from the aerobic zone to the anoxic zone is installed at the inlet of the return corridor of the aerobic zone and is used to regulate the internal return flow rate from the aerobic zone to the anoxic zone.

[0056] Please see Figure 2The pipes in the planar structure of this invention include: anaerobic zone inlet branch pipe 301, anoxic tank inlet branch pipe 302, anaerobic tank external return branch pipe 303, anoxic tank external return branch pipe 304, and aerobic tank outlet pipe 305.

[0057] Please see Figure 2 The process equipment in the planar structure of the present invention includes: anaerobic zone flow promoter 401, anoxic tank flow promoter 402, aerobic zone flow promoter 403, and microporous aerator 404.

[0058] Please see Figure 2 In the BioWin process simulation software, based on the collected process design parameters, anaerobic zone model units, anoxic zone model units, aerobic zone model units, and secondary sedimentation tank model units were established. In the BioWin process simulation software, each model unit was connected by model pipelines. The sequence of connection for the wastewater model pipelines was "influent - anaerobic zone - anoxic zone - aerobic zone - secondary sedimentation tank - effluent," and the sequence of connection for the sludge model pipelines was "secondary sedimentation tank - excess sludge."

[0059] Please see Figure 3 In the BioWin process simulation software, branch pipes are set up on model pipelines at specific locations: Branch pipe 1 (501) is set up on the wastewater model pipeline of the "influent-anaerobic zone," and branch pipe 1 (501) is connected to the anoxic zone; Branch pipe 2 (502) is set up on the wastewater model pipeline of the "anoxic zone-aerobic zone," and branch pipe 2 (502) is connected to the anaerobic zone; Branch pipe 3 (503) is set up on the wastewater model pipeline of the "aerobic zone-secondary sedimentation tank," and branch pipe 3 (503) is connected to the anoxic zone; Branch pipe 4 (504) and branch pipe 5 (505) are set up on the sludge model pipeline of the "secondary sedimentation tank-residue sludge" zone, with branch pipe 4 (504) connected to the anaerobic zone and branch pipe 5 (505) connected to the anoxic zone. By adjusting the flow ratio of each branch pipe, various process simulation models can be constructed, each corresponding to a preset mode.

[0060] Please see Figure 3 Regarding the construction of Biowin process simulation models, the typical operating mode is as follows:

[0061] Preset Mode X1: Branch pipe 1 501 is set to 0% design flow rate, branch pipe 2 502 is set to 0% design flow rate, branch pipe 3 503 is set to 200% design flow rate, branch pipe 4 504 is set to 100% design flow rate, and branch pipe 5 505 is set to 0% design flow rate.

[0062] Preset Mode X2: Branch pipe 1 501 is set to 100% design flow rate, branch pipe 2 502 is set to 200% design flow rate, branch pipe 3 503 is set to 200% design flow rate, branch pipe 4 504 is set to 100% design flow rate, and branch pipe 5 505 is set to 0% design flow rate.

[0063] Preset Mode X3: Branch pipe 1 501 is set to 0% design flow rate, branch pipe 2 502 is set to 200% design flow rate, branch pipe 3 503 is set to 200% design flow rate, branch pipe 4 504 is set to 0% design flow rate, and branch pipe 5 505 is set to 100% design flow rate.

[0064] Preset Mode X4: Branch pipe 1 501 is set to 25% of the design flow rate, branch pipe 2 502 is set to 200% of the design flow rate, branch pipe 3 503 is set to 200% of the design flow rate, branch pipe 4 504 is set to 75% of the design flow rate, and branch pipe 5 505 is set to 25% of the design flow rate.

[0065] Please see Figure 4 Initial steady-state simulation validation: The average value of the collected influent concentration data (including COD, TN, and TP) is used as the model input. An uncalibrated model is used to run a steady-state simulation, and the model output is the effluent concentration data (including COD, TN, and TP). A relative error analysis is performed between the average value of the collected effluent concentration data and the model output. If the relative error is ≥10%, the steady-state validation is considered unqualified, and model parameter calibration is required. If the relative error is <10%, the steady-state validation is considered qualified, and model calibration and validation are completed.

[0066] Please see Figure 4 Influent component parameter calibration: Take on-site water samples for measurement, input the influent components into the BioWin software's built-in influent component analysis tool, which can automatically calculate the measured values ​​and default values ​​of the project's influent component parameters. Parameters with discrepancies between measured and default values ​​are then corrected to use the measured values. Common influent component parameters with discrepancies between measured and default values ​​are listed below. Regarding model influent component parameter calibration, the following are common types of influent component parameters with discrepancies between measured and default values:

[0067] Fbs - COD content in readily biodegradable substances (including acetic acid);

[0068] COD content in Fac-acetic acid;

[0069] Fxsp - content of granular, slow-degrading COD;

[0070] Fus - Non-biodegradable soluble COD content;

[0071] Fup - COD content of non-biodegradable particles;

[0072] Fcel - Lignin COD content of non-biodegradable particles;

[0073] Fpo4-phosphate content.

[0074] Please see Figure 4 To determine adjustable microbial parameters: Sensitivity analysis was performed on all microbial parameters in the model. Based on the sensitivity analysis results, laboratory conditions, model adjustability, and relevant domestic and international literature examples, adjustable microbial parameters were determined. Common types of adjustable microorganisms are listed below. Regarding the determination and calibration of adjustable microbial parameters in the model, common types of adjustable microorganisms and their recommended adjustment ranges are as follows:

[0075] Conventional hydrolysis rate (1 / d): 2.1~4.8;

[0076] Conventional anaerobic hydrolysis factor (-): 0.04~0.05;

[0077] Transition state - DO half-saturation coefficient (mgO2 / L): 0.15~0.25

[0078] Ammonia-oxidizing bacteria (AOB) - Maximum growth rate (1 / d): 0.9~1.2;

[0079] Ammonia-oxidizing bacteria (AOB) - aerobic decay rate (1 / d): 0.14~0.17;

[0080] Ammonia-oxidizing bacteria (AOB) - anaerobic / anoxic decay rate (1 / d): 0.06~0.08;

[0081] Common heterotrophic bacteria (OHO) - maximum growth rate (1 / d): 3.20~5.49;

[0082] The proportion of ordinary heterotrophic bacteria (OHO) in denitrification (NO3 or NO2 to N2) (-): 0.2~0.5;

[0083] Common heterotrophic bacteria (OHO) aerobic decay rate (1 / d): 0.62~0.66;

[0084] Polyphosphate-accumulating bacteria (PAO) - Maximum growth rate (1 / d): 0.95~1.80;

[0085] Polyphosphate-accumulating bacteria (PAO) - aerobic / anaerobic decay rate (1 / d): 0.01~0.10;

[0086] Polyphosphate-accumulating organisms (PAO) - storage rate (1 / d): 4.5~9.0;

[0087] Polyphosphate-accumulating bacteria (PAO) - hypoxia growth inhibitor (-): 0.20~0.33.

[0088] Please see Figure 4 Adjustable microbial parameter calibration: The determined adjustable microbial parameters are adjusted in accordance with the "Guidelines for the Application of Activated Sludge Models" published by the International Water Association (IWA) or the specific implementation of this invention.

[0089] Please see Figure 4 Repeated steady-state simulation validation: The average value of the collected influent concentration data (including COD, TN, and TP) is used as the model input value. The model is calibrated with parameters and a steady-state simulation is run. The model output value is the effluent concentration data (including COD, TN, and TP). A relative error analysis is performed between the average value of the collected effluent concentration data and the model output value. If the relative error is ≥10%, the steady-state validation is considered unqualified. Based on the trend of the model output value, the determined adjustable microbial parameters are adjusted until the steady-state validation is qualified. If the relative error is <10%, the steady-state validation is considered qualified, and the model calibration and validation are completed.

[0090] Steady-state simulation preset: Before conducting steady-state simulation, a steady-state scenario must be preset as the input value for the steady-state model. The steady-state scenario represents the situation where the influent concentration of the current project remains normal and stable over a relatively long continuous period. A steady-state scenario is preset for each project. When presetting the steady-state scenario, the influent concentration is a set of constant values ​​that do not change with time, called the steady-state concentration. The steady-state concentration (including COD, TN, and TP) is set by taking the average value of the collected influent concentration data.

[0091] Steady-state simulation: In BioWin process simulation software, using the steady-state scenario as input, all n steady-state models are run, for a total of n steady-state simulations. The output of the steady-state model is n sets of steady-state effluent concentration datasets. Each set of steady-state effluent datasets includes at least three indicators: COD, TN, and TP, with one effluent concentration data point for each indicator.

[0092] Steady-state result evaluation and screening: The method for evaluating and screening steady-state simulation results adopts the Potential of Nutrient Removal (PNR) analysis method. The calculation formula for the PNR index is as follows:

[0093] Among them, the higher the PNR index, the greater the biological nitrogen and phosphorus removal potential of the system, and the better the overall evaluation.

[0094] Regarding the calculation formula for the PNR index, 2.86 represents the oxygen equivalent of nitrate nitrogen reduced to nitrogen gas; Y is the true yield coefficient of denitrifying bacteria, with a default value of 0.57 gCOD / gCOD; k dn is the endogenous metabolic constant of denitrifying bacteria, with a default value of 0.15 d⁻¹; SRT is the sludge age in days; TN i Input value for the model: TN concentration, in mg / L; TN o TN concentration is the model output value, in mg / L; 10.5 is the carbon source demand coefficient based on the bio-storage phosphorus removal mechanism; TP iInput value for the model: TP concentration, in mg / L; TP o The model output values ​​are TP concentration, in mg / L; COD i The input value for the model is COD concentration, in mg / L; B / C is the influent BOD5 / COD ratio of the current project or similar projects, which is determined by averaging the collected influent concentration data, or by averaging the influent components measured from on-site water samples.

[0095] During evaluation and selection, for the output results of a model under steady-state scenarios, a Probability of Noise (PNR) index is calculated. The model with the highest PNR index is selected, and its corresponding preset mode is taken as the steady-state optimal mode. One steady-state optimal mode is selected for each project.

[0096] Dynamic Scenario Preset: Before conducting dynamic simulations, dynamic scenarios must be preset as input values ​​for the dynamic model. A dynamic scenario represents a situation where the influent concentration of the current project may change significantly within a short period. Multiple dynamic scenarios are preset for each project, each representing a specific change characteristic. When presetting dynamic scenarios, the influent concentration is a set of variables that change over time. The influent concentration representing the change characteristic of that dynamic scenario is called the shock concentration, and the duration of the shock concentration is called the shock duration.

[0097] Regarding dynamic scenario pre-setting, typical dynamic scenarios are as follows:

[0098]

[0099] The duration should be set based on the characteristics of the collected influent concentration data. The higher the frequency of influent concentration fluctuations or the higher the required control precision, the shorter the duration should be set, and vice versa. All dynamic scenarios for the same project should use the same duration and steady-state concentration.

[0100] The setting of shock concentrations (including COD, TN, and TP) can be based on the collected influent concentration data and variation characteristics. The values ​​are determined by multiplying the steady-state concentration by a variation coefficient and adjusting the C / N ratio. Typical shock concentrations are shown below. Regarding dynamic scenario presets, typical shock concentrations are as follows:

[0101]

[0102] The setting of the shock concentration can be based on the characteristics of the collected influent concentration data. The smaller the fluctuation range of the influent concentration or the higher the required control precision, the smaller the difference between different shock concentrations set, and vice versa. Through extensive simulation tests, with COD and C / N (or TN) remaining constant, changing C / P (or TP) has a very small effect on the results, and the difference can be ignored.

[0103] Dynamic simulation: In the BioWin process simulation software, all m dynamic scenarios were used as input values, and all n dynamic models were run for a total of m×n dynamic simulations. The output of the dynamic model is m×n sets of dynamic effluent concentration datasets. Each set of dynamic effluent concentration datasets contains at least three indicators: COD, TN, and TP. For each indicator, there is an effluent concentration data point every 1 hour in chronological order.

[0104] Dynamic Result Evaluation and Screening: The method for evaluating and screening dynamic simulation results employs the Additional Chemical Cost (ACC) analysis. The formula for calculating the ACC index is as follows:

[0105]

[0106] in,

[0107]

[0108] like Then uniformly follow .

[0109]

[0110] like Then uniformly follow .

[0111] A lower ACC (Advanced Cost of Action) index indicates lower additional drug and consumable costs required to achieve operational targets, resulting in a better overall evaluation. The formula for calculating the ACC index is explained below:

[0112] ACC N For additional nitrogen removal reagent cost indicators; ACC P For additional phosphorus removal agents, the cost indicator is K. N1 The COD coefficient of the external carbon source required for nitrate nitrogen denitrification is taken as 5 kg COD / kg NO3-N; C N1 For the added carbon source COD equivalent, methanol is taken as 1.5 kg COD / kg, acetic acid as 1.07 kg COD / kg, sodium acetate as 0.68 kg COD / kg, and glucose as 0.6 kg COD / kg; C N2 The effective content of the external carbon source procured for the current project, in %; P N The unit price of the external carbon source procured for the current project is expressed in yuan / kg; TN O TN is the effluent TN concentration output by the model, in mg / L; S The current project's effluent TN standard is expressed in mg / L; K P1When using polyaluminum chloride (PAC) as a coagulant for chemical phosphorus removal, the molar ratio of aluminum to phosphorus is taken as 0.87; 0.53 represents the Al content in Al₂O₃; C P1 The effective Al2O3 content in the PAC procured for the current project, in units of % . P P1 The unit price of PAC procured for this project is expressed in yuan / kg; TP O The effluent TP concentration output by the model, in mg / L; TP S The current project's effluent TP standard is expressed in mg / L; K P2 The dosage of polyacrylamide (PAM) as a flocculant for chemical phosphorus removal is 1-3 ppm; C P2 The effective content of PAM procured for the current project, in units of % . P2 The unit price of PAM procured for the current project is expressed in yuan / kg.

[0113] During evaluation and selection, each dynamic scenario is conducted independently. For the output results of a model under a given dynamic scenario, the ACC index is calculated every hour from the start to the end of the dynamic scenario, based on the effluent concentration data. The average value is then taken after batch calculations as the average ACC index for that model under that dynamic scenario. All n average ACC indices under that dynamic scenario are then compared, and the model with the lowest average ACC index is selected. Its corresponding preset mode is taken as the dynamic optimal mode for that dynamic scenario. One dynamic optimal mode is selected for each dynamic scenario in a project, for a total of m dynamic optimal modes.

[0114] Instrumentation components: including online monitoring instruments and electromagnetic flow meters. Specific installation locations and functions of the instrumentation components.

[0115] Set up field control components, including electric regulating valves and rotary gates. Specify the installation location and function of each field control component.

[0116] Setting the backtracking time: When determining the current influent concentration, the adaptive control system calculates the average value of the online monitoring instruments over the specified backtracking time period from the current point in time. The length of the backtracking time is fixed, but its range dynamically shifts with the current point in time.

[0117] The main function of setting the backtracking time is to reduce the following situations: When the adaptive control system determines the current influent concentration, if it uses the instantaneous value of the online monitoring instrument, the adaptive control system may become too sensitive due to the large amplitude or high frequency of instantaneous value changes, which may easily lead to misjudgment and unnecessary mode switching.

[0118] Recommended values ​​for the backtracking time: 4~8h, and not exceeding 10% of the impact duration in the dynamic simulation. It is recommended to take into account the characteristics of the collected influent concentration data changes and set a reasonable value. The greater the data fluctuation amplitude or the higher the fluctuation frequency, the longer the backtracking time should be set, and vice versa.

[0119] The function and specific settings for time rewind: An example of time rewind is as follows: (See reference) Figure 6 In the embodiment described above, the backtracking time is 4 hours. That is, the current influent concentration at each time point is the average value of the online monitoring instrument over the past 4 hours. For example, the influent concentration at the 5th hour is the average value over the 2nd to 5th hours, and the influent concentration at the 20th hour is the average value over the 17th to 20th hours. Please refer to [link / reference]. Figure 6 After setting the backtracking time, the current influent concentration changes more gradually compared to the instantaneous influent concentration, which helps the adaptive control system maintain stability. Especially when there are sudden and significant changes, such as in the 5th to 6th hour and the 20th to 21st hour, the system can avoid being too sensitive and initiating unnecessary mode switching.

[0120] Setting scenario boundaries: When determining which scenario the current influent concentration belongs to, the adaptive control system uses the scenario boundaries and their defined areas as judgment conditions. When the influent concentration reaches or exceeds the corresponding scenario boundary, it will switch from one scenario to another.

[0121] Please see Figure 7 The specific method for setting the scenario boundary is as follows: The adaptive control system can set three-dimensional scenario boundaries for three dimensions: COD, C / N (or TN), and C / P (or TP). Extensive simulation tests have shown that when COD and C / N (or TN) remain constant, changes in the C / P (or TP) dimension have a very small impact on the results; the difference is negligible. Therefore, adaptive control systems generally use two-dimensional scenario boundaries for COD and C / N (or TN).

[0122] Reference Appendix Figure 7 In this embodiment, a two-dimensional scenario boundary is set for two dimensions: COD and C / N. First, the steady-state concentration (COD=100mg / L, C / N=5) is taken as the center point of the steady-state scenario. Then, the other eight typical impact concentrations in the dynamic scenario preset are taken as the dividing points. The boundary formed by connecting the eight dividing points and its extension lines to the surrounding areas are taken as the scenario boundary, and a complete two-dimensional scenario boundary map is drawn. The different regions divided represent different scenarios.

[0123] When the influent concentration reaches or exceeds the corresponding scenario boundary, the scenario will transition from one to another. Specific examples are as follows: (See attached document) Figure 7When the influent concentration changes from COD=100mg / L, C / N=5 to COD=100mg / L, C / N=6.68, crossing the boundary between scenario 1 and scenario 3, the system determines that it is changing from scenario 1 to scenario 3. When the influent concentration changes from COD=100mg / L, C / N=5 to COD=151mg / L, C / N=5, crossing the boundary between scenario 1 and scenario 7, the system determines that it is changing from scenario 1 to scenario 7.

[0124] Matching scenarios and patterns: In an adaptive control system, each scenario is matched one by one with the optimal pattern selected through simulation experiments.

[0125] Matching Modes and Control Commands: In an adaptive control system, each mode is matched one-to-one with the control commands of its corresponding field control components. The system automatically adjusts the field control components to achieve mode switching. Typical modes and their corresponding field control component control commands are as follows: The control commands for a typical operating mode are as follows:

[0126] Preset Mode X1:

[0127] Electric regulating valve 201 for the anaerobic zone inlet branch pipe: 100% open; Electric regulating valve 202 for the anoxic zone inlet branch pipe: closed; Electric regulating valve 203 for the anaerobic zone external return branch pipe: 100% open; Electric regulating valve 204 for the anoxic zone external return branch pipe: closed; Electric recirculation rotary gate 205 for the anoxic zone to the anaerobic zone internal return: closed; Electric recirculation rotary gate 206 for the aerobic zone to the anoxic zone internal return: 100% open.

[0128] Preset Mode X2:

[0129] 201 Electric regulating valve for anaerobic zone inlet branch pipe: Closed; 202 Electric regulating valve for anoxic zone inlet branch pipe: 100% open; 203 Electric regulating valve for anaerobic zone external return branch pipe: 100% open; 204 Electric regulating valve for anoxic zone external return branch pipe: Closed; 205 Internal return rotary gate for anoxic zone to anaerobic zone: 100% open; 206 Internal return rotary gate for aerobic zone to anoxic zone: 100% open.

[0130] Preset Mode X3:

[0131] Electric regulating valve 201 for the anaerobic zone inlet branch pipe: 100% open; Electric regulating valve 202 for the anoxic zone inlet branch pipe: closed;

[0132] Electric regulating valve 203 for the anaerobic zone external return branch: closed; electric regulating valve 204 for the anoxic zone external return branch: 100% open; rotary gate 205 for the return from the anoxic zone to the anaerobic zone: 100% open; rotary gate 206 for the return from the aerobic zone to the anoxic zone: 100% open.

[0133] Preset Mode X4:

[0134] Electric regulating valve 201 for the anaerobic zone inlet branch pipe: 75% open; Electric regulating valve 202 for the anoxic zone inlet branch pipe: 25% open;

[0135] Electric regulating valve 203 for the external return branch pipe in the anaerobic zone: 75% open; Electric regulating valve 204 for the external return branch pipe in the anoxic zone: 25% open;

[0136] The recirculation rotary gate 205 between the anoxic zone and the anaerobic zone is 100% open; the recirculation rotary gate 206 between the aerobic zone and the anoxic zone is 100% open.

[0137] Flow calibration setting: After completing a mode switch, the adaptive control system uses an electromagnetic flow meter to detect the influent flow rate and external return sludge flow rate after the mode switch, and compares them with the standard flow rate of the corresponding mode. If the deviation is large, the system will be calibrated by fine-tuning the opening of the field control components.

[0138] Setting a protection time: After completing a mode switch and flow calibration, the adaptive control system will enter a fixed protection time. During this protection time, except in special circumstances, the system will maintain its current operating mode without any mode switching, prioritizing system stability. The function, specific setting method, and special circumstances of the protection time are explained. The main function of setting a protection time is to reduce the following situations: After completing a mode switch and flow calibration, the current influent concentration may fluctuate slightly near the scenario boundary, causing the system to frequently switch modes and disrupt system stability.

[0139] Recommended protection time setting: 24~48h. It is recommended to choose a reasonable value based on the current project's requirements for operational stability. The higher the requirements for operational stability, the longer the protection time should be set, and vice versa.

[0140] During the protection period, even if the influent concentration changes again and crosses the scenario boundary, the system will not switch modes. Specific examples are as follows: (See reference) Figure 7The system's current influent concentration changes from COD=100mg / L, C / N=5 to COD=100mg / L, C / N=6.68, crossing the scenario boundary between scenario 1 and scenario 3. The system determines that it has changed from scenario 1 to scenario 3, and switches the operating mode from the optimal mode X1 of scenario 1 to the optimal mode X4 of scenario 3. In this embodiment, the protection time is 24 hours. In the first hour after entering the protection time, the system's current influent concentration becomes COD=100mg / L, C / N=6.66. If the original scenario boundary rules are followed, the system will determine that it has changed from scenario 3 to scenario 1 and trigger the mode switch again. However, since it is within the protection time, the system will not trigger the mode switch and will continue to operate under X4 until the 24th hour, after which it will re-determine based on the current influent concentration.

[0141] During the protection period, the following special circumstances may occur: The current influent concentration changes significantly, and the trend shows a clear shift towards the steady-state concentration. If no mode switching is performed during this time, the control may lag or even miss the effect of the optimal mode, causing the adaptive control system to fail. To reduce the occurrence of these special circumstances, a special boundary can be set within the protection period.

[0142] If the current influent concentration reaches or exceeds the special boundary within the protection period, mode switching can still be triggered. The special boundary is set as follows: the line segment of the previously reached or exceeded scenario boundary is shifted by a fixed amount in the opposite direction of the crossing trend; the shifted line segment serves as the special boundary for this protection period. It is recommended that the fixed shift amount for the COD special boundary be 50 mg / L, and the fixed shift amount for the C / N special boundary be 1.67.

[0143] Specific examples of special boundaries are as follows: First, refer to... Figure 7 The system's current influent concentration changed from COD=100mg / L, C / N=5 to COD=100mg / L, C / N=6.68, crossing the scenario boundary between Scenario 1 and Scenario 3. The system determined a shift from Scenario 1 to Scenario 3 and switched its operating mode from the optimal mode X1 of Scenario 1 to the optimal mode X4 of Scenario 3. After completing flow calibration, the system entered protection mode. (The last sentence appears to be incomplete and possibly refers to a different context.) Figure 8, within the protection time, the scenario boundary line segment between the original scenario 1 and scenario 3 is shifted 1.67 in the opposite direction of the trend of the last crossing, that is, in the direction of decreasing C / N, from C / N = 6.67 to C / N = 5, and the shifted line segment is used as the special boundary within this protection time; within the protection time, the range of scenario 3 is expanded to 50 mg / L < COD < 150 mg / L, 5 < C / N. If the influent concentration of the system changes from C / N = 6.68 to C / N = 4.9 and crosses the special boundary between scenario 3 and scenario 1, the system will judge the transition from scenario 3 to scenario 1, and the system will switch the operation mode from the optimal mode X4 of scenario 3 to the optimal mode X1 of scenario 1. The special boundary is only valid within the protection time. If no special situation occurs within the protection time, after the end of the protection time, the special boundary becomes invalid and reverts to the originally set scenario boundary.

[0144] In summary, the adaptive regulation method for micro-aerated oxidation ditch driven by a process simulation model of the present invention establishes a safe and efficient adaptive regulation system, which can dynamically monitor water quality changes, reasonably judge and match the optimal operation mode. On the premise of ensuring the stability of the system, through precise regulation, the continuous optimization of the sewage treatment effect is achieved. It not only reduces the need for human intervention, but also significantly improves the operation efficiency and stability of the system, providing strong support for the intelligent transformation of the sewage treatment technology field. Therefore, the present invention effectively overcomes various shortcomings in the prior art and has high industrial utilization value.

[0145] It can be understood that for those of ordinary skill in the art, equivalent substitutions or changes can be made according to the technical solution of the present invention and its inventive concept, and all such changes or substitutions should fall within the protection scope of the present invention.

Claims

1. An adaptive control method for micro-aeration oxidation ditches driven by process simulation models, characterized in that, Includes the following steps: Data collection: Collect process design parameters, historical influent and effluent concentration data, and continuous operation data; Constructing BioWin process simulation models: Based on the collected process design parameters, anaerobic zone model units, anoxic zone model units, aerobic zone model units, and secondary sedimentation tank model units are established in BioWin process simulation software, and connected through model pipelines and set up diversion pipes to construct various process simulation models; Model calibration and validation: The accuracy of the model is ensured through initial steady-state simulation validation, influent component parameter calibration, determination of adjustable microbial parameters, adjustable microbial parameter calibration, and repeated steady-state simulation validation. Steady-state scenario pre-setting and simulation: The steady-state scenario is pre-set as the input value of the steady-state model. All steady-state models are run in BioWin process simulation software to obtain the steady-state effluent concentration dataset. The optimal steady-state mode is screened out by the Potential of Nutrient Removal (PNR) index analysis method. Dynamic scenario pre-setting and simulation: Multiple dynamic scenarios are pre-set as input values ​​for the dynamic model. All dynamic models are run in BioWin process simulation software to obtain dynamic effluent concentration datasets. The Additional Chemical Cost (ACC) index analysis method is used to screen out the dynamic optimal mode under each dynamic scenario. Adaptive control system design: setting instrument components, field control components, backtracking time, and scenario boundaries; dividing multiple control scenarios according to scenario boundaries; matching steady-state optimal mode and dynamic optimal mode according to control scenarios; matching control commands accordingly; setting flow calibration and protection time; and realizing adaptive control of the micro-aeration oxidation ditch system. In the aforementioned steady-state scenario pre-setting and simulation, the calculation formula for the Potential of Nutrient Removal (PNR) index, analyzed by the nitrogen and phosphorus removal potential index method, is as follows: Where Y is the true yield coefficient of denitrifying bacteria; k dn SRT is the endogenous metabolic constant of denitrifying bacteria; TN is the sludge age; SRT is the sludge age; TN is the total nitrogen (TN) of the sludge. i Input value for the model: TN concentration; TN o The model output values ​​are TN concentration and TP concentration. i Input value for the model: TP concentration; TP o The model output values ​​are TP concentration and COD concentration. i The input value for the model is COD concentration; B / C is the influent BOD5 / COD ratio. In the aforementioned dynamic scenario pre-setting and simulation, the calculation formula for the Additional Chemical Cost (ACC) index using the analysis method is as follows: ACC=ACC N +ACC P ; If (TN) O -TN S If )≤0, then follow ACC. N =0; If (TP) O -TP S If )≤0, then follow ACC. P =0; Among them, ACC N For additional nitrogen removal reagent cost indicators; ACC P For additional phosphorus removal agents, the cost indicator is K. N1 COD coefficient of the external carbon source required for nitrate nitrogen denitrification; C N1 For the COD equivalent of the added carbon source; C N2 The effective content of the added carbon source; P N The unit price of the added carbon source; TN O The TN concentration in the effluent output by the model; TN S For effluent TN standard; K P1 The molar ratio of aluminum to phosphorus; C P1 P represents the effective content of Al2O3 in PAC. P1 The unit price of PAC; TP O The effluent TP concentration output by the model; TP S For effluent TP standard; K P2 C is the dosage rate of PAM. P2 The effective content in PAM; P P2 This is the unit price of PAM.

2. The adaptive control method for micro-aeration oxidation ditches based on process simulation model driven according to claim 1, characterized in that, In the steps of constructing the Biowin process simulation model, the setting of the shunt pipes includes setting shunt pipe one on the model pipeline and connecting shunt pipe one to the anoxic zone; setting shunt pipe two on the model pipeline and connecting shunt pipe two to the anaerobic zone; setting shunt pipe three on the model pipeline and connecting shunt pipe three to the anoxic zone; and setting shunt pipe four and shunt pipe five on the model pipeline, with shunt pipe four connected to the anaerobic zone and shunt pipe five connected to the anoxic zone.

3. The adaptive control method for micro-aeration oxidation ditches based on process simulation model driven according to claim 1, characterized in that, In the model calibration and verification steps, the initial steady-state simulation verification uses the average value of the collected influent concentration data as the model input value. The average value of the collected effluent concentration data and the model output value are compared with the relative error. If the relative error is ≥10%, the model parameters are calibrated. The influent component parameters are calibrated by taking on-site water samples and measuring them. Parameters with differences between the measured values ​​and the default values ​​are changed to use the measured values. Adjustable microbial parameters are determined and calibrated. Otherwise, the steady-state verification is considered qualified, and the model calibration and verification are completed.

4. The adaptive control method for micro-aeration oxidation ditches based on process simulation model driven according to claim 1, characterized in that, The adaptive control system design steps involve determining the current influent concentration, setting a backtracking time, and using the average value of the online monitoring instrument within a preset fixed backtracking time period from the current time point as the influent concentration.

5. The adaptive control method for micro-aeration oxidation ditches based on process simulation model driven according to claim 1, characterized in that, The adaptive control system design steps involve determining the current influent concentration and setting scenario boundaries, using these boundaries and their defined areas as the judgment criteria.

6. The adaptive control method for micro-aeration oxidation ditches based on process simulation model driven according to claim 1, characterized in that, The adaptive control system design steps include setting flow calibration after mode switching, detecting the influent flow and external return sludge flow after mode switching using an electromagnetic flow meter, and comparing them with the standard flow of the corresponding mode. If the deviation is large, the system is calibrated by fine-tuning the opening of the field control components.

7. The adaptive control method for micro-aeration oxidation ditches based on process simulation model driven according to claim 1, characterized in that, The adaptive control system design steps include setting a protection time after mode switching, entering the protection time after completing one mode switch and flow calibration, and maintaining no mode switching during the protection time.

8. The adaptive control method for micro-aeration oxidation ditches based on process simulation model driven according to claim 7, characterized in that, A special boundary is set within the protection period. The boundary line segment that was previously reached or crossed is shifted by a fixed amount in the opposite direction of the crossing trend. The shifted line segment serves as the special boundary within the protection period. If the current influent concentration reaches or crosses the special boundary within the protection period, the mode switch is directly triggered.

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