An oxygen-exposed treatment system for highland domestic sewage treatment

Abstract

The present application relates to sewage treatment technical field, especially to an aerobic aeration treatment system for highland domestic sewage treatment, which comprises a pretreatment unit, a biological treatment unit, a deep treatment unit and a reuse unit connected in sequence, and further comprises a data acquisition module, an aerobic aeration intelligent control module, a biochemical environment constant temperature maintaining module, a carbon-nitrogen ratio and backflow dynamic control module, a digital twin and sludge characteristic simulation module and an energy collaborative scheduling module; compared with the prior art which adopts a standard aeration strategy and cannot adapt to the special environment of highland low air pressure and low oxygen partial pressure, the present application integrates real-time atmospheric pressure and water temperature monitoring, dynamically calculates oxygen saturation solubility and corrects the aeration control target based on the oxygen saturation solubility, so that the system can accurately adapt to the highland environment and ensure the stable supply of dissolved oxygen required by biochemical reaction, thereby fundamentally overcoming the technical bottleneck of low efficiency of traditional sewage treatment in highland areas.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, and in particular to an aerobic aeration treatment system for treating domestic wastewater in high-altitude areas. Background Technology

[0002] Wastewater treatment in high-altitude areas presents a unique challenge for my country's environmental engineering field. These regions typically feature high altitude, low atmospheric pressure, consistently low temperatures, and large diurnal temperature variations. Traditional activated sludge aerobic aeration systems face severe challenges in these challenging environments. Low oxygen partial pressure significantly reduces oxygen mass transfer efficiency in water, while low temperatures severely inhibit the metabolic activity of microorganisms, particularly nitrifying bacteria. This makes it difficult to consistently achieve the required nitrogen and carbon removal efficiencies, and results in persistently high energy consumption.

[0003] Existing technologies for treating wastewater from high-altitude areas have numerous limitations. Conventional aeration control systems mostly rely on simple feedback control based on dissolved oxygen setpoints, failing to detect and compensate for the fundamental impact of low air pressure on oxygen saturation solubility, leading to low aeration efficiency and energy waste. Regarding temperature control, effective insulation and heating linkage strategies are often lacking, or simple on / off control is used, making it difficult to maintain stable biochemical reaction temperatures in low-temperature environments. For the denitrification process, the nitrification liquor recirculation ratio and carbon source addition typically depend on fixed empirical values ​​or single parameter feedback, failing to intelligently respond to fluctuations in influent load, easily resulting in incomplete denitrification or carbon source waste. Furthermore, existing systems generally lack the ability to optimize key parameters such as sludge age and microbial activity in real time, exhibit poor coordination between process units, rely heavily on manual experience for overall operation, have low levels of intelligence, and struggle to guarantee long-term stable and efficient operation in the harsh environment of high-altitude areas.

[0004] To address the above problems, this invention proposes an aerobic aeration treatment system for treating domestic sewage in high-altitude areas. Summary of the Invention

[0005] In order to overcome the problems mentioned in the background art, the present invention proposes an aerobic aeration treatment system for treating domestic sewage in plateau areas.

[0006] The technical solution of this invention is: an aerobic aeration treatment system for treating domestic sewage in high-altitude areas, comprising a pretreatment unit, a biological treatment unit, an advanced treatment unit, and a reuse unit connected in sequence; the biological treatment unit includes a pre-denitrification tank, a hydrolysis acidification tank, a first anoxic tank, a first aerobic tank, a second anoxic tank, and a second aerobic tank; the advanced treatment unit includes a vertical flow sedimentation tank and an MBBR suspended media filter; the system further includes:

[0007] The data acquisition module is used to collect time-series data of each process unit in the plateau domestic sewage treatment system during operation;

[0008] The aerobic aeration intelligent control module is used to calculate and output control signals for the variable frequency fan speed and the opening of the aeration regulating valve in real time through a feedforward-feedback composite control algorithm.

[0009] The biochemical environment constant temperature maintenance module is used to maintain the biochemical reaction water temperature within a preset range;

[0010] The carbon-nitrogen ratio and recirculation dynamic control module is used to dynamically adjust the nitrification liquor recirculation ratio based on the effluent ammonia nitrogen and total nitrogen concentrations; dynamically adjust the sludge recirculation ratio based on the sludge concentration and sludge settling ratio; and precisely control the amount of external carbon source added based on the effluent nitrate concentrations of the first and second anoxic tanks.

[0011] The digital twin and sludge characteristic simulation module is used to build a virtual model of the system, calculate the real-time feed-to-microbe ratio based on the influent load and sludge concentration, and simulate the denitrification efficiency and sludge activity of the system under different sludge ages. It recommends and maintains optimized sludge age and sludge concentration under high-altitude and low-temperature conditions.

[0012] The energy collaborative dispatch module is used to integrate wind and solar power generation data with grid electricity price data to formulate optimal energy use strategies.

[0013] The time-series data collected by the data acquisition module includes: instantaneous flow rate and COD concentration of the inlet pipeline, dissolved oxygen concentration in the aerobic tank, real-time atmospheric pressure, water temperature of the biological treatment unit, sludge concentration, sludge settling ratio, ammonia nitrogen and total nitrogen concentrations of the effluent, and nitrate concentrations of the effluent from the first and second anoxic tanks.

[0014] Preferably, the aerobic aeration intelligent control module, when calculating and outputting control signals for the variable frequency fan speed and aeration regulating valve opening in real time through a feedforward-feedback composite control algorithm, includes:

[0015] S11: Data acquisition and input, real-time acquisition of data including influent flow rate, influent COD concentration, measured dissolved oxygen value in aerobic tank, real-time atmospheric pressure, and aerobic tank water temperature;

[0016] S12: Oxygen saturation solubility calculation. Based on real-time atmospheric pressure and water temperature, the oxygen saturation solubility under the current environment is calculated using the following formula:

[0017] ;

[0018] in, The oxygen saturation solubility under the current environment. This represents the saturated solubility of oxygen in water at standard atmospheric pressure and 20°C. This refers to atmospheric pressure collected in real time. Standard atmospheric pressure This is the saturated vapor pressure of water. This is the temperature correction factor. The water temperature in the aerobic tank;

[0019] S13: Feedforward control quantity calculation: Based on the influent flow rate and influent COD concentration, the feedforward aeration quantity requirement is calculated using the feedforward controller. The calculation formula is as follows:

[0020] ;

[0021] in, To meet the feedforward aeration requirements, The feedforward control gain coefficient is... This refers to the instantaneous inflow rate. The influent COD concentration, The oxygen demand coefficient for COD degradation;

[0022] S14: Feedback control quantity calculation. The deviation between the dissolved oxygen setpoint and the measured dissolved oxygen value is used as input. The feedback controller calculates the feedback aeration compensation quantity, employing a PID algorithm. The principle calculation formula is as follows:

[0023] ;

[0024] in, This refers to the real-time deviation between the setpoint and the measured dissolved oxygen value. , Set the dissolved oxygen value. This is the measured value of dissolved oxygen. This is the proportional gain coefficient. This is the integral gain coefficient. The differential gain coefficient;

[0025] S15: Calculate the total aeration demand, integrate the feedforward aeration demand and the feedback aeration compensation to obtain the total aeration demand signal of the system.

[0026] S16: Control signal output and execution, converting the total aeration demand signal into a control signal to adjust the speed of the variable frequency blower and the opening of the regulating valve on the aeration pipeline in real time.

[0027] Preferably, the biochemical environment constant temperature maintenance module includes the following steps during operation:

[0028] S21: Temperature data acquisition, through temperature sensors installed in the biological treatment unit, to collect the measured value of water temperature in the biological treatment unit in real time;

[0029] S22: Temperature difference calculation and judgment, calculate the temperature difference between the set water temperature and the measured value, and judge whether the temperature difference is greater than the preset start threshold;

[0030] S23: Heating power calculation. When the temperature difference is greater than the preset start-up threshold, the required real-time heating power is calculated based on the temperature difference, the effective volume of the biological treatment unit, and the overall heat loss coefficient of the system.

[0031] S24: Heating control signal output, which converts real-time heating power into a control signal to drive the heating resistor and keep the pool body warm;

[0032] S25: Maintenance and hibernation. When the temperature difference is less than the preset stop threshold, it is determined that the water temperature has entered the comfortable range, heating is stopped, and the system enters a low-power monitoring state.

[0033] Preferably, the carbon-nitrogen ratio and reflux dynamic control module, when dynamically adjusting the nitrification liquor reflux ratio based on the effluent ammonia nitrogen and total nitrogen concentrations, includes:

[0034] S31: Water quality data acquisition, real-time acquisition of ammonia nitrogen concentration and total nitrogen concentration in the effluent of the first aerobic tank and nitrate nitrogen concentration in the effluent of the second anoxic tank;

[0035] S32: Nitrification effect assessment and primary control, determining whether the ammonia nitrogen concentration exceeds the set ammonia nitrogen threshold; if it does, an instruction to increase the nitrification liquor reflux ratio is generated to reduce the nitrification load of the first aerobic tank;

[0036] S33: Denitrification demand assessment and secondary control. Under the premise that the ammonia nitrogen concentration meets the standard, determine whether the total nitrogen concentration exceeds the set total nitrogen threshold. If it does, combine the nitrate nitrogen concentration in the effluent of the second anoxic tank to assess the denitrification potential of the system and further adjust the reflux ratio.

[0037] S34: Reflux ratio synthesis calculation, which integrates primary and secondary control instructions and calculates the final target nitrification liquor reflux ratio through a control algorithm;

[0038] S35: Control signal output and execution, converting the target nitrification liquor reflux ratio into a control signal to drive the nitrification liquor reflux pump and adjust the reflux flow rate.

[0039] Preferably, the carbon-nitrogen ratio and reflux dynamic control module, when dynamically adjusting the sludge reflux ratio based on sludge concentration and sludge settling ratio, includes:

[0040] S41: Sludge characteristic data acquisition, real-time acquisition of sludge concentration in sedimentation tank effluent, return sludge concentration, mixed liquor sludge concentration in biological treatment unit, and sludge settling ratio.

[0041] S42: Sludge mass balance calculation, based on sludge concentration data, calculates the theoretical sludge recirculation ratio required to maintain the target sludge concentration in the reactor through the mass balance equation;

[0042] S43: Sludge settling performance assessment, calculate sludge volume index based on sludge settling ratio, and determine sludge settling status;

[0043] S44: Reflux ratio correction and decision-making: Based on the settling performance evaluation results, the theoretical reflux ratio is corrected to determine the final target sludge reflux ratio;

[0044] S45: Control signal output and execution, converting the target sludge return ratio into a control signal to drive the sludge return pump and adjust the return sludge flow rate.

[0045] Preferably, the carbon-nitrogen ratio and reflux dynamic control module, when precisely controlling the dosage of the external carbon source based on the nitrate concentration in the effluent from the first and second anoxic tanks, includes:

[0046] S51: Nitrate data acquisition, real-time acquisition of nitrate concentration in the effluent from the first anoxic tank and the nitrate concentration in the effluent from the second anoxic tank;

[0047] S52: Carbon source demand judgment, compare the nitrate concentration in the effluent of the second anoxic tank with the denitrification target threshold; when the nitrate concentration in the effluent of the second anoxic tank is greater than the denitrification target threshold, it is determined that the system denitrification carbon source is insufficient, and a carbon source addition instruction is generated.

[0048] S53: Carbon source dosage calculation: The basic carbon source dosage is calculated based on the theoretical nitrate removal requirements, and the distribution is optimized according to the nitrate concentration in the effluent of the first anoxic tank to calculate the accurate carbon source dosage rate.

[0049] S54: Output the dosing control signal to convert the carbon source dosing acceleration rate into a control signal to drive the carbon source dosing metering pump for precise dosing.

[0050] S55: Effect verification and adaptive correction. Continuously monitor the changing trend of nitrate concentration in the effluent of the second anoxic tank after addition, and adaptively correct the parameters of the carbon source addition model.

[0051] Preferably, the carbon-nitrogen ratio and reflux dynamic control module employs a feedforward compensation mechanism during operation. This mechanism adjusts operating parameters in advance before the influent load impacts the effluent water quality, specifically including:

[0052] S61: Impact load monitoring, which monitors the influent flow rate, influent ammonia nitrogen concentration, influent total nitrogen concentration and influent COD concentration in real time through the flow meter on the influent pipe and the online water quality analyzer;

[0053] S62: Load change rate calculation, calculates the rate of change of influent flow rate and water quality concentration per unit time, including flow rate change rate, ammonia nitrogen load change rate, total nitrogen load change rate and COD load change rate;

[0054] S63: Feedforward compensation calculation: Based on the load change rate, the feedforward compensation amount of the nitrification liquid return ratio, the feedforward compensation amount of the sludge return ratio, and the feedforward compensation amount of the external carbon source addition amount are calculated respectively through the preset feedforward compensation model.

[0055] S64: Compensation signal synthesis and output, which superimposes the feedforward compensation amount with the feedback control amount based on the effluent water quality to generate the final control signal, thereby driving the corresponding actuator in advance.

[0056] As a preferred embodiment, the digital twin and sludge characteristic simulation module enables optimized decision-making for sludge system parameters by constructing a virtual mapping model of the system, specifically including:

[0057] A11: Data interface unit, used to acquire in real time the system's influent flow rate, influent chemical oxygen demand, mixed liquor suspended solids concentration in the biological treatment tank, mixed liquor volatile suspended solids concentration, excess sludge discharge, and online monitoring data reflecting microbial activity;

[0058] A12: Virtual model unit, based on the mathematical model of activated sludge, constructs a dynamic digital twin model that operates synchronously with the physical system;

[0059] A13: Optimization Decision Unit, used to calculate key operating parameters based on real-time data; simulate system response under different control strategies through digital twin models; and output recommended values ​​for optimized sludge age and sludge concentration.

[0060] Preferably, the digital twin and sludge characteristic simulation module includes the following components during operation:

[0061] S71: Real-time data acquisition and synchronization, continuously acquiring the timing operation data of the physical system through the data interface unit;

[0062] S72: Real-time calculation and evaluation of key parameters. The optimization decision-making unit calculates the real-time food-to-microbe ratio based on real-time data and compares it with the preset optimal range to evaluate the current sludge load status.

[0063] S73: Multi-scenario simulation and optimization decision-making. The optimization decision-making unit presets different sludge age candidate values ​​in the digital twin model. By simulating the effluent ammonia nitrogen concentration and sludge activity under each sludge age, and using the comprehensive objective function to perform multi-objective optimization calculations, the optimal sludge age that minimizes the objective function is selected.

[0064] S74: Optimize command output and execution, output the optimal sludge age and corresponding recommended sludge concentration to the control system as the set value for sludge discharge and sludge return;

[0065] S75: Microbial activity early warning and intervention simulation. When the received real-time microbial activity data shows a downward trend, it automatically simulates the intervention effects of adjusting nutrient addition, water temperature, and adding vitality enhancers in the digital twin model, and generates recommended intervention strategies.

[0066] S76: Model parameter self-calibration. The predicted effluent water quality of the digital twin model is compared with the actual effluent water quality at regular intervals. If the deviation exceeds the allowable threshold, the key dynamic parameters in the model are automatically corrected.

[0067] Preferably, the reuse unit includes a reuse tank and an ultraviolet disinfection device installed inside the reuse tank. The effective volume of the reuse tank is 0.5 times the daily processing capacity, and the reuse unit adopts a tiered reuse system. The tiered reuse system, when implemented, includes:

[0068] S81: Multi-dimensional water quality data collection, real-time collection of water quality indicators of effluent after ultraviolet disinfection;

[0069] S82: Comprehensive evaluation of effluent water quality. This involves comparing real-time water quality data with preset reclaimed water quality standards, calculating the comprehensive quality index of the current effluent using a grading algorithm, and classifying the effluent into different grades accordingly.

[0070] S83: Dynamic perception of reuse demand, integrating seasonal factors, real-time meteorological data and real-time demand signals from water use units;

[0071] S84: Intelligent allocation decision-making, based on the obtained water quality level and perceived reuse demand, dynamically generates the optimal allocation scheme through decision-making algorithms, including the amount of water allocated to different pathways and their priorities;

[0072] S85: Control command execution and scheduling, converts the optimal allocation scheme into control commands, drives the recycled water pumps and valves, delivers the corresponding quality of effluent to the designated discharge outlet, and records the reuse rate of various types of effluent in real time.

[0073] The beneficial effects of this invention are:

[0074] 1. Compared with the existing technology that adopts standard aeration strategies, which cannot adapt to the special environment of low air pressure and low oxygen partial pressure in high-altitude areas, this invention integrates real-time atmospheric pressure and water temperature monitoring, dynamically calculates oxygen saturation solubility and uses it to correct aeration control targets, so that the system can accurately adapt to the high-altitude environment, ensuring a stable supply of dissolved oxygen required for biochemical reactions, and fundamentally overcoming the technical bottleneck of low efficiency in traditional wastewater treatment in high-altitude areas.

[0075] 2. Compared with existing technologies that mostly use single feedback control, which has the disadvantages of slow response and serious energy waste, this invention adopts a composite control algorithm that combines feedforward and feedback. It predicts the aeration demand in advance based on changes in influent load and makes precise adjustments, realizing on-demand oxygen supply, significantly improving the system's stability against fluctuations in water quality and quantity, and at the same time achieving the optimization of aeration energy consumption.

[0076] 3. Compared with existing technologies that use fixed temperature settings or simple on / off control, which cannot cope with temperature fluctuations and have high energy consumption, this invention adopts a dynamic power control strategy based on a thermal balance model and combines digital twin suggestions to dynamically optimize the water temperature setpoint, thereby achieving precise and efficient maintenance of the biochemical environment temperature, effectively overcoming the inhibition of nitrifying bacteria and other microorganisms by the low temperature of the plateau, and ensuring stable and efficient operation throughout the year.

[0077] 4. Compared with existing technologies that often simply couple nitrification and denitrification processes and make crude adjustments to the reflux ratio, this invention adopts a stratified intelligent decision-making strategy, using effluent ammonia nitrogen and total nitrogen as graded control targets, and introducing key point nitrate concentration as a criterion for denitrification potential. This achieves precise decoupling and synergistic control of nitrification and denitrification processes, and significantly saves carbon source dosage and reflux energy consumption while ensuring deep denitrification.

[0078] 5. Compared with existing technologies that adjust the return ratio based solely on a single sludge concentration and ignore changes in sludge settling performance, this invention combines sludge mass balance with settling performance indicators. By calculating the sludge volume index in real time and dynamically correcting the return ratio, it can effectively prevent and address sludge bulking or aging problems, and significantly improve the stability and treatment efficiency of the sludge system.

[0079] 6. Compared with existing technologies that typically add carbon sources based on a single effluent point index with a lag, this invention adopts a progressive optimization dosing strategy. It calculates the total demand based on a complete denitrification demand model and prioritizes the allocation of carbon sources to the pre-anoxic tank. This achieves precise on-demand addition of carbon sources in both space and time, significantly improving carbon source utilization and denitrification efficiency, and reducing operating chemical consumption.

[0080] 7. Compared with existing technologies that rely heavily on human experience and lack forward-looking optimization capabilities, this invention constructs a digital twin system based on an activated sludge model, which can simulate the treatment effect under different working conditions and make multi-objective optimization recommendations for key parameters such as sludge age. This realizes the transformation from experience-driven to model prediction-driven, and greatly improves the level of refinement and intelligence of operation.

[0081] 8. Compared with existing technologies that simply discharge or reuse effluent, this invention establishes an intelligent reuse system based on water quality classification and dynamic demand perception, which realizes the precise matching of different quality effluents with diversified reuse pathways, and maximizes the value of water resource recycling. Attached Figure Description

[0082] Figure 1 The diagram shown is a schematic representation of the biological structure of the aerobic aeration treatment system for treating domestic sewage in high-altitude areas according to the present invention.

[0083] Figure 2 The diagram shows the working process of the intelligent aerobic aeration control module in the aerobic aeration treatment system for treating domestic sewage in high-altitude areas according to the present invention. Detailed Implementation

[0084] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0085] Please see Figure 1 - Figure 2 This invention provides an embodiment of an aerobic aeration treatment system for treating domestic sewage in high-altitude areas, comprising a pretreatment unit, a biological treatment unit, an advanced treatment unit, and a reuse unit connected in sequence; the biological treatment unit includes a pre-denitrification tank, a hydrolysis acidification tank, a first anoxic tank, a first aerobic tank, a second anoxic tank, and a second aerobic tank; the advanced treatment unit includes a vertical flow sedimentation tank and an MBBR suspended media filter; the system further includes:

[0086] 1. Data Acquisition Module

[0087] This data is used to collect time-series data of each process unit in the plateau domestic sewage treatment system during operation, including: instantaneous flow rate and COD concentration of the influent pipeline, dissolved oxygen concentration in the aerobic tank, real-time atmospheric pressure, water temperature of the biological treatment unit, sludge concentration, sludge settling ratio, ammonia nitrogen and total nitrogen concentrations of the effluent, and nitrate concentrations of the effluent from the first and second anoxic tanks.

[0088] As described above, this system is an innovative and highly efficient aerobic aeration treatment solution for domestic sewage treatment in high-altitude and cold regions. Its core process adopts a combined flow of "pre-denitrification + hydrolysis acidification + two-stage A / O + sedimentation + MBBR deep treatment". Through the synergistic effect of the pre-denitrification tank, hydrolysis acidification tank, two-stage anoxic / aerobic tank, vertical flow sedimentation tank and MBBR filter, the denitrification and carbon removal effects are enhanced. The data acquisition module monitors key parameters of the entire process from influent to effluent in real time, including influent flow rate and COD concentration, dissolved oxygen in the aerobic tank, local real-time atmospheric pressure, biological tank water temperature, sludge concentration and settling ratio, as well as indicators such as ammonia nitrogen, total nitrogen and nitrate in the effluent of each process unit (such as the anoxic tank and the aerobic tank). This provides a comprehensive and real-time data foundation for subsequent precise aeration, intelligent recirculation, carbon source addition, constant temperature control and digital twin optimization, thereby ensuring stable operation and high-standard reuse of effluent in the special environment of high altitude, low pressure and low temperature.

[0089] 2. Intelligent control module for aerobic aeration

[0090] This is used to calculate and output control signals for the variable frequency fan speed and aeration regulating valve opening in real time through a feedforward-feedback composite control algorithm, including:

[0091] Data acquisition and input: Real-time acquisition of data including influent flow rate, influent COD concentration, measured dissolved oxygen value in aerobic tank, real-time atmospheric pressure, and aerobic tank water temperature;

[0092] Oxygen saturation solubility is calculated based on real-time atmospheric pressure and water temperature. The formula is as follows:

[0093] ;

[0094] in, The oxygen saturation solubility under the current environment. This represents the saturated solubility of oxygen in water at standard atmospheric pressure and 20°C. This refers to atmospheric pressure collected in real time. Standard atmospheric pressure This is the saturated vapor pressure of water. This is the temperature correction factor. The water temperature in the aerobic tank;

[0095] The feedforward control quantity is calculated based on the influent flow rate and influent COD concentration. The feedforward controller calculates the required feedforward aeration volume using the following formula:

[0096] ;

[0097] in, To meet the feedforward aeration requirements, The feedforward control gain coefficient is... This refers to the instantaneous inflow rate. The influent COD concentration, The oxygen demand coefficient for COD degradation;

[0098] The feedback control quantity is calculated using the deviation between the dissolved oxygen setpoint and the measured dissolved oxygen value as input. The feedback controller calculates the feedback aeration compensation quantity, employing a PID algorithm. The principle calculation formula is as follows:

[0099] ;

[0100] in, This refers to the real-time deviation between the setpoint and the measured dissolved oxygen value. , Set the dissolved oxygen value. This is the measured value of dissolved oxygen. This is the proportional gain coefficient. This is the integral gain coefficient. The differential gain coefficient;

[0101] The total aeration demand is calculated by integrating the feedforward aeration demand and the feedback aeration compensation, resulting in the total aeration demand signal of the system. The total aeration demand is a weighted sum of the feedforward and feedback quantities, and the underlying formula is as follows:

[0102] ;

[0103] in, and These are the weighting coefficients, and ;

[0104] The control signal output and execution converts the total aeration demand signal into a control signal, and adjusts the speed of the variable frequency blower and the opening of the regulating valve on the aeration pipeline in real time.

[0105] In this embodiment, the aerobic aeration intelligent control module adopts a feedforward-feedback composite control strategy. Its core features are: first, the feedforward controller monitors the influent flow rate and COD concentration in real time, predicting pollutant load changes and calculating the basic aeration demand; simultaneously, the feedback controller dynamically fine-tunes the aeration volume using a PID algorithm based on the deviation between the setpoint and measured dissolved oxygen values ​​in the aerobic tank; crucially, the module also integrates a high-altitude environment adaptive function, accurately calculating the oxygen saturation solubility under the current environment based on real-time atmospheric pressure and water temperature, thereby correcting the control target; finally, the system weighted and fused the feedforward and feedback quantities, outputting a total aeration demand signal, and precisely adjusting the variable frequency fan speed and aeration valve opening accordingly. This achieves precise on-demand oxygen supply during the aeration process, significantly enhancing the system's stability against influent load shocks, effectively overcoming the problem of low oxygen transfer efficiency caused by low pressure and low temperature at high altitudes, ensuring stable effluent quality while optimizing aeration energy consumption, resulting in significant energy savings.

[0106] 3. Biochemical Environment Constant Temperature Maintenance Module

[0107] Used to maintain the water temperature for biochemical reactions within a preset range, the working principle is as follows:

[0108] Temperature data acquisition is achieved by using temperature sensors installed within the biological treatment unit to collect real-time measurements of the water temperature inside the unit.

[0109] Temperature difference calculation and judgment: Calculate the temperature difference between the set water temperature and the measured value, and determine whether the temperature difference is greater than the preset start threshold.

[0110] Heating power calculation: When the temperature difference exceeds the preset start-up threshold, the required real-time heating power is calculated based on the temperature difference, the effective volume of the biological treatment unit, and the system's overall heat loss coefficient. The calculation of real-time heating power is based on the heat balance equation, and the principle formula is as follows:

[0111] ;

[0112] in, For the required real-time heating power, The specific heat capacity of water, The density of water, The effective volume of the biological treatment unit. The temperature difference between the setpoint and the measured value. For the set control cycle, For the comprehensive heat loss coefficient, For heat dissipation surface area, This is the measured value of the water temperature. Real-time ambient temperature;

[0113] The heating control signal output converts the real-time heating power into a control signal, drives the heating resistor to work, and keeps the pool body warm.

[0114] During maintenance and hibernation, when the temperature difference is less than the preset stop threshold, it is determined that the water temperature has entered the comfortable range, heating is stopped, and the system enters a low-power monitoring state.

[0115] The water temperature setpoint is not a fixed value, but a range that is dynamically optimized based on the sludge age target. When the digital twin and sludge characteristic simulation module suggests that the sludge age needs to be extended to promote the growth of nitrifying bacteria, the water temperature setpoint will be appropriately increased to the upper limit of the range. When the system load is low, the water temperature setpoint will be appropriately reduced to the lower limit of the range to save energy.

[0116] In this embodiment, the biochemical environment constant temperature maintenance module adopts a dynamic power control strategy based on the heat balance equation. The core of this strategy is to monitor the water temperature of the biochemical reaction unit in real time using temperature sensors. When the temperature difference between the measured value and the dynamically optimized setpoint (provided by the digital twin module based on the sludge age target, rather than a fixed value) exceeds a threshold, the module will accurately calculate the required real-time heating power based on a complete thermodynamic model that includes the sensible heat rise requirement of the water body and compensation for continuous heat loss of the system, and drive the heating element to work. This achieves precise and efficient maintenance of the water temperature. In particular, through dynamic optimization of the setpoint, it moderately raises the temperature to ensure treatment efficiency when nitrification needs to be enhanced, and cools down the system under low load to significantly reduce energy consumption. This effectively overcomes the inhibition of microbial activity caused by the low temperature at high altitudes, ensuring stable, efficient, and low-cost operation of the biochemical system under all-weather conditions.

[0117] 4. Carbon-nitrogen ratio and reflux dynamic control module

[0118] It is used to dynamically adjust the nitrification liquor recirculation ratio based on the effluent ammonia nitrogen and total nitrogen concentrations; dynamically adjust the sludge recirculation ratio based on the sludge concentration and sludge settling ratio; and precisely control the amount of external carbon source added based on the effluent nitrate concentrations of the first and second anoxic tanks.

[0119] The carbon-nitrogen ratio and reflux dynamic control module, when dynamically adjusting the nitrification liquor reflux ratio based on the effluent ammonia nitrogen and total nitrogen concentrations, includes:

[0120] Water quality data collection: Real-time collection of ammonia nitrogen and total nitrogen concentrations in the effluent from the first aerobic tank and nitrate nitrogen concentrations in the effluent from the second anoxic tank;

[0121] The nitrification effect is evaluated and initially controlled to determine whether the ammonia nitrogen concentration exceeds the set ammonia nitrogen threshold. If it does, an instruction is generated to increase the nitrification liquor reflux ratio to reduce the nitrification load of the first aerobic tank.

[0122] The denitrification demand assessment and secondary control determine whether the total nitrogen concentration exceeds the set total nitrogen threshold, provided that the ammonia nitrogen concentration meets the standard. If it does, the system's denitrification potential is assessed in conjunction with the nitrate nitrogen concentration in the effluent from the second anoxic tank, and the reflux ratio is further adjusted.

[0123] The reflux ratio calculation integrates primary and secondary control commands, and uses a control algorithm to calculate the final target nitrification liquor reflux ratio. The target nitrification liquor reflux ratio is calculated using a fuzzy rule-based method, with the following formula:

[0124] ;

[0125] in, The target nitrification liquor reflux ratio, The system's base nitrification liquor reflux ratio, This is the reflux ratio correction amount based on ammonia nitrogen deviation. This is the reflux ratio correction based on the total nitrogen deviation, where:

[0126] ;

[0127] in, The first proportionality coefficient, This refers to the ammonia nitrogen concentration. To set the ammonia nitrogen threshold;

[0128] ;

[0129] in, This is the second proportionality coefficient. This represents the total nitrogen concentration. To set the total nitrogen concentration;

[0130] The control signal output and execution converts the target nitrification liquor reflux ratio into a control signal, drives the nitrification liquor reflux pump, and adjusts the reflux flow rate.

[0131] The logic for assessing denitrification potential in the denitrification demand assessment and secondary regulation is as follows: If the nitrate nitrogen concentration in the effluent of the second anoxic tank remains low, it indicates that the denitrification carbon source is sufficient and the denitrification potential is large. In this case, the strategy of increasing the reflux ratio should be prioritized to reduce total nitrogen. If the nitrate nitrogen concentration in the effluent of the second anoxic tank remains high, it indicates that the denitrification process is hindered. In this case, the addition of external carbon sources should be prioritized, and the excessive increase of the reflux ratio should be limited to avoid the accumulation of nitrate in the system.

[0132] In this embodiment, the carbon-nitrogen ratio and reflux dynamic control module employs a hierarchical intelligent decision-making strategy to dynamically optimize the nitrification liquor reflux ratio. The core of this strategy is as follows: First, ammonia nitrogen is prioritized in primary control. If the ammonia nitrogen in the aerobic tank effluent exceeds the standard, the reflux ratio is immediately increased to reduce the nitrification load. Once the ammonia nitrogen meets the standard, secondary control targeting total nitrogen is implemented. Innovatively, the nitrate concentration in the second anoxic tank effluent is introduced as a criterion for "denitrification potential"—if the concentration is low, the reflux ratio is increased to enhance denitrification; if the concentration is high, carbon source addition is triggered, and the increase in the reflux ratio is limited to prevent nitrate accumulation. Finally, the final reflux ratio command is synthesized through a weighted formula. This achieves precise decoupling and synergistic control of the nitrification and denitrification processes. Intelligent judgment of denitrification potential avoids blind adjustments to the reflux ratio. While ensuring that both ammonia nitrogen and total nitrogen in the effluent meet the standards, it significantly improves denitrification efficiency and effectively saves carbon source consumption and reflux energy consumption.

[0133] The carbon-nitrogen ratio and reflux dynamic control module, when dynamically adjusting the sludge reflux ratio based on sludge concentration and sludge settling ratio, includes:

[0134] Sludge characteristic data acquisition: Real-time collection of sludge concentration in sedimentation tank effluent, return sludge concentration, mixed liquor sludge concentration in biological treatment unit, and sludge settling ratio.

[0135] The sludge mass balance calculation, based on sludge concentration data, uses a mass balance equation to calculate the theoretical sludge return ratio required to maintain the target sludge concentration in the reactor. This theoretical sludge return ratio is based on the sludge mass balance principle of the reactor-sedimentation tank system, and the formula is as follows:

[0136] ;

[0137] in, The theoretical sludge return ratio, The target mixed liquor sludge concentration for the biological treatment unit. The concentration of suspended solids carried by the effluent from the sedimentation tank. The concentration of the secondary return sludge;

[0138] Sludge settling performance assessment: sludge volume index is calculated based on sludge settling ratio, and sludge settling status is determined.

[0139] The return ratio correction and decision-making process involves correcting the theoretical return ratio based on the settling performance evaluation results to determine the final target sludge return ratio.

[0140] The control signal output and execution converts the target sludge return ratio into a control signal, drives the sludge return pump, and adjusts the return sludge flow rate.

[0141] In the assessment of sludge settling performance, the formula for calculating the sludge volume index is as follows:

[0142] (Unit: mL / g);

[0143] The settling performance was evaluated based on the sludge volume index, specifically:

[0144] When the sludge volume index is less than 100 mL / g, it indicates that the sludge has good settling performance;

[0145] When the sludge volume index is greater than or equal to 100 mL / g and less than or equal to 150 mL / g, it indicates that the sludge settling performance is normal.

[0146] When the sludge volume index is greater than 150 mL / g, it indicates that the sludge has a tendency to deteriorate in settling properties.

[0147] Among them, the reflux ratio correction and the reflux ratio correction strategy in decision-making are as follows:

[0148] When the sludge volume index is greater than 150 mL / g, the sludge return ratio should be appropriately reduced based on the calculated theoretical sludge return ratio.

[0149] When the sludge volume index is less than 80 mL / g, and the sludge is judged to be aging due to a low food-to-microorganism ratio, the sludge return ratio should be appropriately increased based on the theoretically calculated sludge return ratio.

[0150] In this embodiment, the carbon-nitrogen ratio and reflux dynamic control module adopts a strategy based on dual evaluation of sludge mass balance and settling performance when dynamically adjusting the sludge reflux ratio: First, by collecting data such as the effluent sludge concentration of the sedimentation tank, the reflux sludge concentration of the mixed liquor in the reaction tank, and the sludge settling ratio in real time, the theoretical reflux ratio required to maintain the target sludge concentration is calculated using the mass balance equation; then, the sludge settling performance is evaluated by calculating the sludge volume index, and the theoretical reflux ratio is dynamically corrected accordingly—when the sludge volume index is greater than 150 mL / g, indicating deterioration of settling, the reflux ratio is appropriately reduced to alleviate the load on the sedimentation tank; when the sludge volume index is less than 80 mL / g and the sludge is judged to be aging based on the low food-to-micronutrient ratio, the reflux ratio is appropriately increased to enhance the system activity. It achieves precise optimization and control of the sludge return ratio, which can effectively maintain a stable microbial concentration in the reaction tank, respond promptly to changes in sludge settling performance, and prevent the decline in treatment efficiency caused by sludge bulking or aging. This significantly improves the stability and treatment efficiency of the system while reducing energy consumption.

[0151] The carbon-nitrogen ratio and reflux dynamic control module, when precisely controlling the dosage of external carbon sources based on the nitrate concentrations in the effluents of the first and second anoxic tanks, includes:

[0152] Nitrate data acquisition: Real-time acquisition of nitrate concentrations in the effluent from the first anoxic tank and the effluent from the second anoxic tank.

[0153] Carbon source demand is determined by comparing the nitrate concentration in the effluent of the second anoxic tank with the denitrification target threshold. When the nitrate concentration in the effluent of the second anoxic tank is greater than the denitrification target threshold, it is determined that the system's denitrification carbon source is insufficient, and a carbon source addition instruction is generated.

[0154] The carbon source dosage calculation is based on the theoretical nitrate removal requirement, which determines the basic carbon source dosage. The dosage is then optimized according to the nitrate concentration in the effluent from the first anoxic tank, resulting in a precise carbon source addition acceleration rate. The formula for calculating the carbon source addition acceleration rate is as follows:

[0155] ;

[0156] in, Add a safety factor to the carbon source. This refers to the system's inlet water flow rate. This refers to the nitrate concentration in the effluent from the first anoxic tank. The target value for nitrate concentration. This is the nitration liquor reflux ratio. The nitrate concentration before entering the anoxic tank. COD equivalent of external carbon source. This is the denitrification efficiency coefficient;

[0157] The dosing control signal output converts the carbon source dosing acceleration rate into a control signal, driving the carbon source dosing metering pump to perform precise dosing.

[0158] Effect verification and adaptive correction: The trend of nitrate concentration in the effluent of the second anoxic tank after addition was continuously monitored, and the parameters of the carbon source addition model were adaptively corrected.

[0159] The carbon-nitrogen ratio and reflux dynamic control module adopts a progressive carbon source allocation strategy, prioritizing the addition of carbon sources to the first anoxic tank. The allocation ratio is determined by the following formula:

[0160] ;

[0161] ;

[0162] in, The carbon source allocation coefficient is dynamically adjusted based on the denitrification potential of the first anoxic tank. The carbon source to be allocated to the first anoxic tank. This is the carbon source allocated to the second anoxic tank. To accelerate the carbon source input rate.

[0163] In this embodiment, the carbon-nitrogen ratio and reflux dynamic control module employs a progressive optimization strategy in the precise control of external carbon source addition. This strategy uses effluent nitrate concentration as the criterion and combines it with denitrification potential assessment. By monitoring the nitrate concentration in the effluent from the first and second anoxic tanks in real time, when the effluent indicators from the second anoxic tank exceed the standard, the system first accurately calculates the total carbon source demand based on a complete material balance formula that includes the nitrate load from the nitrification liquid reflux. Then, it innovatively adopts a progressive allocation strategy, prioritizing the allocation of the calculated total carbon source to the first anoxic tank according to a dynamic coefficient to ensure sufficient pre-denitrification, with the second anoxic tank serving as a supplementary guarantee. The beneficial effects of this scheme are that it achieves precise on-demand addition and spatially optimized allocation of carbon sources, effectively ensuring the system's deep denitrification efficiency and significantly improving carbon source utilization by prioritizing the use of pre-denitrification capacity. This avoids the problems of carbon source waste and excessive load on the second anoxic tank in traditional methods, and significantly reduces operating chemical costs.

[0164] 5. Digital Twin and Sludge Characteristic Simulation Module

[0165] Used to construct a virtual model of the system, calculate the real-time food-to-microbe ratio based on influent load and sludge concentration, and simulate the denitrification efficiency and sludge activity of the system under different sludge ages. Recommends and maintains optimized sludge age and sludge concentration under high-altitude and low-temperature conditions.

[0166] Specifically, the digital twin and sludge characteristic simulation module achieves optimized decision-making for sludge system parameters by constructing a virtual mapping model of the system. The digital twin and sludge characteristic simulation module includes:

[0167] The data interface unit is used to acquire in real time the system's influent flow rate, influent chemical oxygen demand, mixed liquor suspended solids concentration in the biological treatment tank, mixed liquor volatile suspended solids concentration, excess sludge discharge, and online monitoring data reflecting microbial activity.

[0168] The virtual model unit is based on the mathematical model of activated sludge and constructs a dynamic digital twin model that operates synchronously with the physical system.

[0169] The optimization decision-making unit is used to calculate key operating parameters based on real-time data; simulate system response under different control strategies through a digital twin model; and output recommended values ​​for optimized sludge age and sludge concentration.

[0170] Among them, the optimization decision-making unit simulates the impact of sludge age on the system's nitrogen removal efficiency through a digital twin model. Its core simulation equation is based on the definition of sludge age and material balance:

[0171] ;

[0172] in, For sludge age, The total effective volume of the biological treatment unit. The concentration of suspended solids in the mixed liquor of the biological treatment tank. This represents the amount of residual sludge discharged in the virtual model.

[0173] The virtual model unit, when in operation, includes:

[0174] Model initialization and parameter setting: The activated sludge mathematical model is initialized based on the process parameters of the actual wastewater treatment system, and the basic kinetic parameters of the model are set.

[0175] Real-time data synchronization and driving: The real-time influent data obtained by the data interface unit is used as the input boundary conditions of the activated sludge mathematical model to drive the digital twin model to perform dynamic simulation.

[0176] The biochemical process dynamic simulation uses the mass balance equation and reaction kinetic equation in the activated sludge mathematical model to simulate the biochemical reaction processes in the system in real time, including carbon oxidation, nitrification and denitrification, and predicts the concentration changes of each component in the virtual system, including the spatiotemporal distribution of organic matter, ammonia nitrogen, nitrate and active microorganisms.

[0177] The simulation of sludge age and sludge concentration is based on the growth and decay dynamics of the microbial community in the mathematical model of activated sludge, combined with the virtual excess sludge discharge, to calculate the sludge age of the virtual system and predict the sludge concentration under different virtual sludge ages.

[0178] Denitrification efficiency and microbial activity prediction: The biomass ratio and activity of autotrophic bacteria under different virtual sludge ages were simulated using a mathematical model of activated sludge to predict the effluent ammonia nitrogen concentration, and the sludge activity trend was evaluated by combining a microbial decay model.

[0179] The optimization decision-making and parameter feedback process involves inputting the simulation results of the activated sludge mathematical model into the optimization decision-making unit, calculating the optimal sludge age through the objective function, and feeding the results back to the control system of the actual system.

[0180] Online calibration of model parameters involves periodically comparing the effluent quality predicted by the activated sludge mathematical model with the actual measured values. If the deviation exceeds a threshold, a parameter estimation algorithm is used to automatically correct the key kinetic parameters in the activated sludge mathematical model, ensuring the prediction accuracy of the digital twin.

[0181] In summary, the digital twin and sludge characteristic simulation module, when in operation, includes:

[0182] Real-time data acquisition and synchronization: continuously acquires the time-series operational data of the physical system through the data interface unit;

[0183] Key parameters are calculated and evaluated in real time. The optimization decision-making unit calculates the real-time food-to-microbe ratio based on real-time data and compares it with the preset optimal range to assess the current sludge load status. The formula for calculating the real-time food-to-microbe ratio is as follows:

[0184] ;

[0185] in, Let be the ratio of food to micronutrients at time t. Let be the influent COD concentration at time t. The total effective volume of the biological treatment unit. Let be the concentration of volatile suspended solids in the mixture at time t;

[0186] Multi-scenario simulation and optimization decision-making: The optimization decision-making unit presets different candidate sludge ages in the digital twin model. By simulating the effluent ammonia nitrogen concentration and sludge activity at each sludge age, and using a comprehensive objective function for multi-objective optimization calculations, the optimal sludge age that minimizes the objective function is selected. The comprehensive objective function used is as follows:

[0187] ;

[0188] in, As a comprehensive performance evaluation index for different sludge ages, The ammonia nitrogen concentration in the effluent under the corresponding SRT, as simulated by the digital twin model. The target value for ammonia nitrogen in the effluent. To maintain the minimum sludge age required for nitrification, The activity of simulated sludge is characterized by adenosine triphosphate content or specific oxygen consumption rate. , and These are the weighting coefficients;

[0189] Optimize command output and execution, outputting the optimal sludge age and corresponding recommended sludge concentration to the control system as the setpoints for sludge discharge and sludge return;

[0190] Microbial activity early warning and intervention simulation: When the received real-time microbial activity data shows a downward trend, it automatically simulates the intervention effects of adjusting nutrient addition, water temperature and adding vitality enhancers in the digital twin model, and generates recommended intervention strategies.

[0191] The model parameters are self-calibrated. The predicted effluent water quality of the digital twin model is compared with the actual effluent water quality at regular intervals. If the deviation exceeds the allowable threshold, the key dynamic parameters in the model are automatically corrected.

[0192] In this embodiment, the digital twin and sludge characteristic simulation module of this system achieves real-time optimized control of sludge age and concentration by constructing a virtual system based on an activated sludge mathematical model. Its core technology lies in the following: First, the module uses real-time influent data and sludge concentration to calculate the current food-to-microbe ratio and assess the system load status. Then, it drives the digital twin model to simulate operating scenarios under different SRT candidate values ​​and uses a comprehensive objective function for multi-objective optimization calculations. This function simultaneously weighs effluent quality, operating costs, and sludge activity to select the optimal SRT. Simultaneously, the module has self-correction and early warning functions, automatically calibrating model parameters and recommending intervention measures in advance based on microbial activity trends. This achieves a shift from "experience-driven" to "model prediction-driven" operation, actively maintaining an optimized SRT conducive to nitrifying bacteria growth in high-altitude, low-temperature environments, significantly improving the stability and reliability of denitrification efficiency. Furthermore, precise control of sludge concentration avoids energy waste, and the early warning mechanism prevents problems before they occur, ensuring the long-term stable and efficient operation of the system.

[0193] 6. Energy Coordinated Dispatch Module

[0194] The energy collaborative dispatch module is used to integrate wind and solar power generation data with grid electricity prices to formulate optimal energy use strategies. Its execution strategies include:

[0195] When there is sufficient sunlight, solar power should be used first, and the excess power should be used to heat the thermal storage tank.

[0196] At night or when there is sufficient wind, the stored thermal energy and wind power generation should be used first.

[0197] Meteorological forecast data is used as input to enable predictive scheduling of energy supply and demand for the next 24 to 48 hours.

[0198] In this embodiment, the energy collaborative scheduling module integrates real-time data on wind and solar power generation, grid electricity prices, and weather forecasts to construct an intelligent energy management strategy: prioritizing solar power and utilizing redundant electrical energy for thermal storage during periods of ample sunshine; prioritizing wind power and stored thermal energy at night or when winds are strong; and proactively allocating energy based on weather forecasts for the next 24-48 hours. The beneficial effects of this scheme are: maximizing the local consumption of renewable energy, significantly reducing dependence on external grid electricity and operating costs; and mitigating energy supply and demand fluctuations through thermal energy storage and release, ensuring the continuous, stable, and low-carbon operation of the wastewater treatment system in plateau areas under conditions of unstable energy supply.

[0199] In this embodiment, the carbon-nitrogen ratio and reflux dynamic control module employs a feedforward compensation mechanism during operation. This mechanism adjusts operating parameters in advance before the influent load impacts the effluent water quality, specifically including:

[0200] Impact load monitoring is conducted in real time using flow meters on the inlet pipe and online water quality analyzers to monitor inlet flow rate, inlet ammonia nitrogen concentration, inlet total nitrogen concentration, and inlet COD concentration.

[0201] Load change rate calculation: Calculate the rate of change of influent flow rate and water quality concentration per unit time, including flow rate change rate, ammonia nitrogen load change rate, total nitrogen load change rate, and COD load change rate;

[0202] The feedforward compensation is calculated based on the load change rate and using a preset feedforward compensation model. The feedforward compensation amounts for the nitrification liquid return ratio, sludge return ratio, and external carbon source addition are calculated respectively.

[0203] The compensation signal synthesis and output superimposes the feedforward compensation amount with the feedback control amount based on the effluent water quality to generate the final control signal, which drives the corresponding actuator in advance.

[0204] The formula for calculating the feedforward compensation amount of the nitrification liquid reflux ratio in the feedforward compensation amount calculation is as follows:

[0205] ;

[0206] in, This is the feedforward compensation amount for the nitration liquor reflux ratio. The feedforward gain coefficient for nitrification liquid reflux was determined through system debugging. This refers to the inlet water flow rate. The influent ammonia nitrogen concentration, To calculate the time interval, The change rate of influent ammonia nitrogen load.

[0207] The formula for calculating the feedforward compensation amount of the sludge return ratio in the feedforward compensation amount calculation is as follows:

[0208] ;

[0209] in, This is the feedforward compensation amount for the sludge return ratio. The feedforward gain coefficient for sludge recirculation was determined through system debugging. The influent COD concentration, The rate of change of COD load in water.

[0210] The formula for calculating the feedforward compensation amount of the external carbon source addition in the feedforward compensation amount calculation is as follows:

[0211] ;

[0212] in, This is the feedforward compensation amount for the external carbon source input. Add a feedforward gain factor to the carbon source. This is an empirical value for the carbon-to-nitrogen ratio required for denitrification. This represents the total nitrogen concentration in the influent.

[0213] The feedforward compensation mechanism and feedback control work together: the feedforward compensation is instantaneous and decays rapidly, primarily used to offset the initial effects of shock loads; while the feedback control is calculated based on the effluent quality deviation and is used to eliminate steady-state errors. The combined formula is:

[0214] ;

[0215] in, It is a natural constant. For time variables, is the time decay constant of the feedforward effect.

[0216] In this embodiment, the carbon-nitrogen ratio and reflux dynamic control module employs a feedforward-feedback composite control mechanism incorporating a decay function. Its core technology lies in: pre-calculating the feedforward compensation amounts for the nitrification liquor reflux ratio, sludge reflux ratio, and external carbon source dosage by real-time monitoring of the influent flow rate, ammonia nitrogen, total nitrogen, and COD concentration changes; this feedforward compensation amount is then combined with the feedback control amount based on the effluent quality according to a "feedforward-feedback" control mechanism. The formula is superimposed, with the exponential decay term ensuring a smooth exit of the feedforward effect. The beneficial effect of this scheme is that it endows the system with the ability to adjust the operating parameters in advance before the water quality shock wave reaches the core biochemical unit, completely overcoming the lag of traditional feedback control, greatly enhancing the system's stability against load shocks, effectively avoiding fluctuations in effluent water quality, and at the same time avoiding subsequent drastic adjustments through precise advance action, thus achieving energy saving and consumption reduction.

[0217] In this embodiment, the reuse unit includes a reuse tank and an ultraviolet disinfection device installed inside the reuse tank. The effective volume of the reuse tank is 0.5 times the daily processing capacity, and the reuse unit adopts a tiered reuse system. The tiered reuse system, when implemented, includes:

[0218] Multi-dimensional water quality data collection, real-time collection of water quality indicators of the effluent after ultraviolet disinfection;

[0219] The comprehensive effluent quality assessment compares real-time water quality data with preset reclaimed water quality standards. A grading algorithm is used to calculate the comprehensive quality index of the current effluent, classifying it into different grades. The comprehensive quality index is calculated using a weighted scoring method, with the following formula:

[0220] ;

[0221] in, For the comprehensive quality index, These are the weighting coefficients. This is a scoring function for water quality indicators;

[0222] Dynamic perception of reuse demand, integrating seasonal factors, real-time meteorological data, and real-time demand signals from water use units;

[0223] Intelligent allocation decision-making, based on the obtained water quality level and perceived reuse demand, dynamically generates the optimal allocation scheme through decision-making algorithms, including the amount of water allocated to different pathways and their priorities;

[0224] The control command execution and scheduling system converts the optimal allocation scheme into control commands, drives the recycled water pumps and valves, delivers the corresponding quality of effluent to the designated discharge outlet, and records the reuse rate of various types of effluent in real time.

[0225] In this embodiment, the reuse unit achieves optimized water resource allocation by constructing an intelligent hierarchical reuse system. Its core technology lies in: setting up a reuse tank with an effective volume of 0.5 times the daily treatment capacity and ultraviolet disinfection equipment; firstly, calculating the comprehensive quality index of the effluent in real time and classifying it through multi-dimensional water quality monitoring and a weighted scoring algorithm; then, integrating seasonal factors, meteorological data, and real-time water demand to dynamically generate the optimal allocation decision; finally, driving water pumps and valves to precisely dispatch effluent of different qualities to the corresponding reuse points. This achieves precise matching between effluent quality and reuse pathways, significantly improving the water resource recycling rate, effectively saving fresh water resources through on-demand allocation, and ensuring the stability of reused water quality through its buffer volume design. It is particularly suitable for the characteristics of large water consumption fluctuations in plateau regions, upgrading the wastewater treatment system from end-of-pipe treatment to a sustainable water resource supply unit.

[0226] Example 1: Intelligent Upgrade Project of Wastewater Treatment Plant in Plateau County

[0227] Implementation Scenario: This embodiment is applied to a wastewater treatment plant in a county in the Qinghai-Tibet Plateau region of my country, with a designed daily treatment capacity of 10,000 tons. The plant's original traditional process faced problems such as unstable nitrogen removal efficiency, excessive energy and chemical consumption, and difficulties in winter operation due to the low temperature and low pressure environment of the plateau. The system described in this invention is now being used for a comprehensive intelligent upgrade.

[0228] Process Unit Modification and Deployment: Some existing structures were retained. The biological treatment unit was modified into a series process of "pre-denitrification tank + hydrolysis acidification tank + two-stage A / O". The advanced treatment unit added an MBBR suspended media filter to enhance denitrification. All tanks and pipes were fitted with a 60mm thick polyurethane insulation layer and carbon steel protective panels. The inner wall of the biological treatment tank was lined with armored heating resistors with a total power of 8kW.

[0229] Full-process data acquisition: Electromagnetic flow meters and online COD, ammonia nitrogen, and total nitrogen analyzers are installed on the inlet main pipe. Online nitrate and ammonia nitrogen analyzers are installed at the outlets of the pre-denitrification tank, the first aerobic tank, the second aerobic tank, and the second anoxic tank, respectively. Dissolved oxygen, temperature, and MLSS sensors are installed in all biological treatment tanks. Digital barometers and weather stations are installed throughout the plant area. All data is transmitted in real time to the SCADA system in the central control room.

[0230] Intelligent modules work together in unison:

[0231] Intelligent aerobic aeration control: The system monitors influent flow rate and COD concentration in real time. When the morning peak water usage causes a sudden increase in influent load, the feedforward controller immediately calculates the basic aeration requirement and instructs the variable frequency blower to increase its speed in advance. At the same time, the feedback controller makes fine adjustments based on the measured values ​​of dissolved oxygen sensors in the aerobic tank. The control system also automatically corrects the dissolved oxygen setpoint from the commonly used 2.0 mg / L to 2.3 mg / L based on real-time low pressure data (approximately 70 kPa) to compensate for the decrease in oxygen mass transfer efficiency and ensure sufficient nitrification.

[0232] Biochemical environment constant temperature maintenance: During winter nights, when the temperature sensor detects that the water temperature in the pool has dropped to 14℃, the constant temperature maintenance module automatically starts the heating resistor and calculates the required power according to the heat balance model to keep the water temperature stable above 18℃, creating a suitable living environment for nitrifying bacteria.

[0233] Carbon-nitrogen ratio and dynamic control of reflux:

[0234] Nitrification liquor recirculation control: The system detected an increase in ammonia nitrogen concentration in the effluent of the first aerobic tank and immediately increased the nitrification liquor recirculation ratio proportionally, recirculating more nitrate to the pre-denitrification tank. Simultaneously, it was found that the nitrate concentration in the effluent of the second anoxic tank was extremely low, indicating sufficient denitrification potential. Therefore, the decision was made to further increase the recirculation ratio instead of immediately adding a carbon source, prioritizing the use of internal carbon sources for denitrification.

[0235] Sludge return control: Calculations using MLSS and SV30 data revealed a sudden increase in the sludge volume index (SVI) to 180 mL / g, indicating a tendency for sludge bulking. The system automatically reduced the sludge return ratio based on theoretical calculations, alleviating the solids load in the secondary sedimentation tank and preventing sludge loss.

[0236] Precise carbon source addition: When the influent carbon-nitrogen ratio is too low and the nitrate concentration in the effluent of the second anoxic tank remains high, the system accurately calculates the amount of sodium acetate to be added based on the total nitrate model, and through a progressive distribution strategy, prioritizes the addition of most carbon sources to the first anoxic tank, ensuring denitrification efficiency and economy.

[0237] Digital Twin and Sludge Characteristic Simulation: The digital twin module constructed a virtual mirror of the plant using the ASM1 model. When influent water quality changed, the module simulated the operational effects at different sludge ages, recommending that the SRT (Sludge Retention Time) be extended from 15 days to 22 days to address the impact of low temperatures on nitrifying bacteria growth rates. The control system adopted the recommendation, adjusting the sludge discharge rate, ultimately resulting in a significant improvement in the stability of ammonia nitrogen in the effluent.

[0238] Energy Coordination and Dispatch: The plant is equipped with solar photovoltaic panels and small wind turbines. During sunny days, the system prioritizes using solar energy to power all equipment and converts surplus electricity into thermal energy for storage. At night, the stored thermal energy is used to keep the water tank warm and to start wind power generation, greatly reducing grid electricity consumption.

[0239] Wastewater is graded and reused: After ultraviolet disinfection, the effluent is automatically graded by the system based on real-time water quality. Grade I water, which has excellent quality, is pumped to the county's greening network and municipal sprinkler truck intake points; Grade II water, which meets the standards, is used to replenish the wetland parks surrounding the county. This achieves the resource utilization of wastewater.

[0240] Results: After the upgrade, the plant achieved stable operation throughout the year in the harsh environment of the plateau, with effluent quality better than the Class A standard, energy consumption per ton of water reduced by about 25%, carbon source dosage reduced by about 30%, and reuse rate reaching 40%, making it a benchmark for wastewater treatment plants in plateau areas.

[0241] Example 2: Distributed Processing Station in a Remote Highland Tourist Town

[0242] Implementation Scenario: This example is applied to a tourist town at an altitude of 3600 meters. The local power grid is unstable, and the permanent population is small, but the population surges during the peak tourist season. A new distributed intelligent treatment station with a designed daily processing capacity of 500 tons is to be built, requiring high automation, energy efficiency, and the ability to adapt to drastic fluctuations in water volume.

[0243] Compact process design: Utilizing an integrated unit, pre-denitrification, hydrolysis acidification, two-stage A / O, sedimentation, and MBBR filter are highly integrated into a single modular device, significantly reducing floor space and heat loss. The insulation layer and heating system are integrated into the equipment's interlayer.

[0244] Enhanced data awareness and edge computing: Due to its remote location, the system employs an edge computing gateway to perform all intelligent algorithm calculations locally, reducing reliance on cloud communication. It is equipped with essential online sensors (flow rate, DO, COD, ammonia nitrogen, temperature, MLSS) to ensure real-time acquisition of core data.

[0245] Adaptive applications of intelligent modules:

[0246] Intelligent aerobic aeration control: Given the surge in water volume during peak tourist season weekends, the system's feedforward function is particularly important. When a large number of tourists check in on Friday evening, causing a sharp increase in wastewater volume and concentration, the system proactively increases aeration, successfully preventing dissolved oxygen collapse and water quality deterioration caused by the load shock.

[0247] Biochemical Environment Temperature Maintenance: Addressing the unstable voltage of the town's power grid, the temperature control module is closely integrated with energy dispatch. When the grid voltage is normal, it provides insulation and heating; when the grid fluctuates or is interrupted, it automatically switches to power from the wind, solar, and energy storage systems, prioritizing the operation of the heating and insulation system to maintain microbial activity.

[0248] Carbon-nitrogen ratio and sludge return dynamic control: The system is set with different operating modes based on the changing patterns of tourist numbers. During the off-season and low load, it automatically reduces the sludge return ratio and sludge age to prevent sludge aging. During the peak season and high load, it automatically increases various operating parameters to ensure processing capacity.

[0249] Digital twin and sludge characteristic simulation: Given that online instrumentation is not as sophisticated as in large-scale plants, the digital twin module plays a more important predictive role. Based on limited influent and water temperature data, it simulates and predicts the system's operating status for the next 24 hours, providing advance suggestions for adjusting process parameters, compensating for the lack of monitoring data, and enabling preventative maintenance.

[0250] Energy Coordinated Dispatch: The station aims to achieve energy self-sufficiency. The capacity of the wind and solar power generation system has been carefully designed. Combined with weather forecasts, the intelligent dispatch system processes wastewater as much as possible during sunny days and stores the energy for use at night and on cloudy days, essentially eliminating dependence on unstable mains power.

[0251] Wastewater is reused in stages: The treated wastewater is mainly used for greening irrigation in the town, flushing of public toilets in tourist areas, and ecological water replenishment for the scenic lake. The system intelligently allocates water according to the season and water demand, prioritizing greening during the dry season and strengthening ecological water replenishment during the rainy season.

[0252] Results: This distributed treatment station successfully solved the problems of unstable power grids, weak operation and maintenance capabilities, and large fluctuations in water volume in remote areas. It achieved unattended operation and highly self-sufficient intelligent operation, providing a replicable model for the treatment of decentralized sewage in plateau areas.

[0253] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. An aerobic aeration treatment system for treating domestic sewage in high-altitude areas, characterized in that: The system comprises a pretreatment unit, a biological treatment unit, an advanced treatment unit, and a reuse unit connected in sequence. The biological treatment unit includes a pre-denitrification tank, a hydrolysis acidification tank, a first anoxic tank, a first aerobic tank, a second anoxic tank, and a second aerobic tank. The advanced treatment unit includes a vertical flow sedimentation tank and an MBBR suspended media filter. The system also includes: The data acquisition module is used to collect time-series data of each process unit in the plateau domestic sewage treatment system during operation; The aerobic aeration intelligent control module is used to calculate and output control signals for the variable frequency fan speed and the opening of the aeration regulating valve in real time through a feedforward-feedback composite control algorithm. The biochemical environment constant temperature maintenance module is used to maintain the biochemical reaction water temperature within a preset range; The carbon-nitrogen ratio and recirculation dynamic control module is used to dynamically adjust the nitrification liquor recirculation ratio based on the effluent ammonia nitrogen and total nitrogen concentrations; dynamically adjust the sludge recirculation ratio based on the sludge concentration and sludge settling ratio; and precisely control the amount of external carbon source added based on the effluent nitrate concentrations of the first and second anoxic tanks. The digital twin and sludge characteristic simulation module is used to build a virtual model of the system, calculate the real-time feed-to-microbe ratio based on the influent load and sludge concentration, and simulate the denitrification efficiency and sludge activity of the system under different sludge ages. It recommends and maintains optimized sludge age and sludge concentration under high-altitude and low-temperature conditions. The energy collaborative dispatch module is used to integrate wind and solar power generation data with grid electricity price data to formulate the optimal energy use strategy; The carbon-nitrogen ratio and reflux dynamic control module, when dynamically adjusting the nitrification liquor reflux ratio based on the effluent ammonia nitrogen and total nitrogen concentrations, includes: S31: Water quality data acquisition, real-time acquisition of ammonia nitrogen concentration and total nitrogen concentration in the effluent of the first aerobic tank and nitrate nitrogen concentration in the effluent of the second anoxic tank; S32: Nitrification effect assessment and primary control, determining whether the ammonia nitrogen concentration exceeds the set ammonia nitrogen threshold; if it does, an instruction to increase the nitrification liquid reflux ratio is generated; S33: Denitrification demand assessment and secondary control. Under the premise that the ammonia nitrogen concentration meets the standard, determine whether the total nitrogen concentration exceeds the set total nitrogen threshold. If it does, combine the nitrate nitrogen concentration in the effluent of the second anoxic tank to assess the denitrification potential of the system and further adjust the reflux ratio. S34: Reflux ratio synthesis calculation, which integrates primary and secondary control instructions and calculates the final target nitrification liquor reflux ratio through a control algorithm; S35: Control signal output and execution, converting the target nitrification liquor reflux ratio into a control signal to drive the nitrification liquor reflux pump and adjust the reflux flow rate; Among them, the aerobic aeration intelligent control module, when calculating and outputting control signals for the variable frequency fan speed and aeration regulating valve opening in real time through a feedforward-feedback composite control algorithm, includes: S11: Data acquisition and input, real-time acquisition of data including influent flow rate, influent COD concentration, measured dissolved oxygen value in aerobic tank, real-time atmospheric pressure, and aerobic tank water temperature; S12: Oxygen saturation solubility calculation, based on real-time atmospheric pressure and water temperature, calculates the oxygen saturation solubility under the current environment; S13: Feedforward control quantity calculation: Based on the influent flow rate and influent COD concentration, the feedforward aeration quantity requirement is calculated through the feedforward controller. S14: Feedback control quantity calculation, using the deviation between the dissolved oxygen setpoint and the measured dissolved oxygen value as input, the feedback controller calculates the feedback aeration compensation quantity; S15: Calculate the total aeration demand, integrate the feedforward aeration demand and the feedback aeration compensation to obtain the total aeration demand signal of the system. S16: Control signal output and execution, converting the total aeration demand signal into a control signal to adjust the speed of the variable frequency blower and the opening of the regulating valve on the aeration pipeline in real time.

2. The aerobic aeration treatment system for treating domestic sewage in high-altitude areas according to claim 1, characterized in that: The biochemical environment constant temperature maintenance module includes the following steps during operation: S21: Temperature data acquisition, through temperature sensors installed in the biological treatment unit, to collect the measured value of water temperature in the biological treatment unit in real time; S22: Temperature difference calculation and judgment, calculate the temperature difference between the set water temperature and the measured value, and judge whether the temperature difference is greater than the preset start threshold; S23: Heating power calculation. When the temperature difference is greater than the preset start-up threshold, the required real-time heating power is calculated based on the temperature difference, the effective volume of the biological treatment unit, and the overall heat loss coefficient of the system. S24: Heating control signal output, which converts real-time heating power into a control signal to drive the heating resistor and keep the pool body warm; S25: Maintenance and hibernation. When the temperature difference is less than the preset stop threshold, it is determined that the water temperature has entered the comfortable range, heating is stopped, and the system enters a low-power monitoring state.

3. The aerobic aeration treatment system for treating domestic sewage in high-altitude areas according to claim 2, characterized in that: The carbon-nitrogen ratio and reflux dynamic control module, when dynamically adjusting the sludge reflux ratio based on sludge concentration and sludge settling ratio, includes: S41: Sludge characteristic data acquisition, real-time acquisition of sludge concentration in sedimentation tank effluent, return sludge concentration, mixed liquor sludge concentration in biological treatment unit, and sludge settling ratio. S42: Sludge mass balance calculation, based on sludge concentration data, calculates the theoretical sludge recirculation ratio required to maintain the target sludge concentration in the reactor through the mass balance equation; S43: Sludge settling performance assessment, calculate sludge volume index based on sludge settling ratio, and determine sludge settling status; S44: Reflux ratio correction and decision-making: Based on the settling performance evaluation results, the theoretical reflux ratio is corrected to determine the final target sludge reflux ratio; S45: Control signal output and execution, converting the target sludge return ratio into a control signal to drive the sludge return pump and adjust the return sludge flow rate.

4. The aerobic aeration treatment system for treating domestic sewage in high-altitude areas according to claim 3, characterized in that: The carbon-nitrogen ratio and reflux dynamic control module, when precisely controlling the dosage of external carbon sources based on the nitrate concentrations in the effluents of the first and second anoxic tanks, includes: S51: Nitrate data acquisition, real-time acquisition of nitrate concentration in the effluent from the first anoxic tank and the nitrate concentration in the effluent from the second anoxic tank; S52: Carbon source demand judgment, compare the nitrate concentration in the effluent of the second anoxic tank with the denitrification target threshold; when the nitrate concentration in the effluent of the second anoxic tank is greater than the denitrification target threshold, it is determined that the system denitrification carbon source is insufficient, and a carbon source addition instruction is generated. S53: Carbon source dosage calculation: The basic carbon source dosage is calculated based on the theoretical nitrate removal requirements, and the distribution is optimized according to the nitrate concentration in the effluent of the first anoxic tank to calculate the accurate carbon source dosage rate. S54: Output the dosing control signal to convert the carbon source dosing acceleration rate into a control signal to drive the carbon source dosing metering pump for precise dosing. S55: Effect verification and adaptive correction. Continuously monitor the changing trend of nitrate concentration in the effluent of the second anoxic tank after addition, and adaptively correct the parameters of the carbon source addition model.

5. An aerobic aeration treatment system for treating domestic sewage in high-altitude areas according to claim 4, characterized in that: The carbon-nitrogen ratio and reflux dynamic control module employs a feedforward compensation mechanism during operation. This mechanism adjusts operating parameters in advance before the influent load impacts the effluent water quality. Specifically, it includes: S61: Impact load monitoring, which monitors the influent flow rate, influent ammonia nitrogen concentration, influent total nitrogen concentration and influent COD concentration in real time through the flow meter on the influent pipe and the online water quality analyzer; S62: Load change rate calculation, calculates the rate of change of influent flow rate and water quality concentration per unit time, including flow rate change rate, ammonia nitrogen load change rate, total nitrogen load change rate and COD load change rate; S63: Feedforward compensation calculation: Based on the load change rate, the feedforward compensation amount of the nitrification liquid return ratio, the feedforward compensation amount of the sludge return ratio, and the feedforward compensation amount of the external carbon source addition amount are calculated respectively through the preset feedforward compensation model. S64: Compensation signal synthesis and output, which superimposes the feedforward compensation amount with the feedback control amount based on the effluent water quality to generate the final control signal, thereby driving the corresponding actuator in advance.

6. The aerobic aeration treatment system for treating domestic sewage in high-altitude areas according to claim 5, characterized in that: The digital twin and sludge characteristic simulation module enables optimized decision-making for sludge system parameters by constructing a virtual mapping model of the system, specifically including: A11: Data interface unit, used to acquire in real time the system's influent flow rate, influent chemical oxygen demand, mixed liquor suspended solids concentration in the biological treatment tank, mixed liquor volatile suspended solids concentration, excess sludge discharge, and online monitoring data reflecting microbial activity; A12: Virtual model unit, based on the mathematical model of activated sludge, constructs a dynamic digital twin model that operates synchronously with the physical system; A13: Optimization Decision Unit, used to calculate key operating parameters based on real-time data; simulate system response under different control strategies through digital twin models; and output recommended values ​​for optimized sludge age and sludge concentration.

7. An aerobic aeration treatment system for treating domestic sewage in high-altitude areas according to claim 6, characterized in that: The digital twin and sludge characteristic simulation module includes the following components during operation: S71: Real-time data acquisition and synchronization, continuously acquiring the timing operation data of the physical system through the data interface unit; S72: Real-time calculation and evaluation of key parameters. The optimization decision-making unit calculates the real-time food-to-microbe ratio based on real-time data and compares it with the preset optimal range to evaluate the current sludge load status. S73: Multi-scenario simulation and optimization decision-making. The optimization decision-making unit presets different sludge age candidate values ​​in the digital twin model. By simulating the effluent ammonia nitrogen concentration and sludge activity under each sludge age, and using the comprehensive objective function to perform multi-objective optimization calculations, the optimal sludge age that minimizes the objective function is selected. S74: Optimize command output and execution, output the optimal sludge age and corresponding recommended sludge concentration to the control system as the set value for sludge discharge and sludge return; S75: Microbial activity early warning and intervention simulation. When the received real-time microbial activity data shows a downward trend, it automatically simulates the intervention effects of adjusting nutrient addition, water temperature, and adding vitality enhancers in the digital twin model, and generates recommended intervention strategies. S76: Model parameter self-calibration. The predicted effluent water quality of the digital twin model is compared with the actual effluent water quality at regular intervals. If the deviation exceeds the allowable threshold, the key dynamic parameters in the model are automatically corrected.

8. The aerobic aeration treatment system for treating domestic sewage in high-altitude areas according to claim 7, characterized in that: The reuse unit includes a reuse tank and ultraviolet disinfection equipment installed inside the reuse tank. The effective volume of the reuse tank is 0.5 times the daily processing capacity. The reuse unit adopts a tiered reuse system, which includes the following when implemented: S81: Multi-dimensional water quality data collection, real-time collection of water quality indicators of effluent after ultraviolet disinfection; S82: Comprehensive evaluation of effluent water quality. This involves comparing real-time water quality data with preset reclaimed water quality standards, calculating the comprehensive quality index of the current effluent using a grading algorithm, and classifying the effluent into different grades accordingly. S83: Dynamic perception of reuse demand, integrating seasonal factors, real-time meteorological data and real-time demand signals from water use units; S84: Intelligent allocation decision-making, based on the obtained water quality level and perceived reuse demand, dynamically generates the optimal allocation scheme through decision-making algorithms, including the amount of water allocated to different pathways and their priorities; S85: Control command execution and scheduling, converts the optimal allocation scheme into control commands, drives the recycled water pumps and valves, delivers the corresponding quality of effluent to the designated discharge outlet, and records the reuse rate of various types of effluent in real time.

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