Intelligent self-adaptive control method and system for sewage aeration

Abstract

The application provides a sewage aeration intelligent adaptive control method and system, which is applied to the intelligent control field of activated sludge method sewage treatment; the method comprises the following steps: collecting water quality parameters of influent water through a sensor array and identifying abnormal fluctuations by using a support vector machine, combining a neural network to compare historical patterns to calculate the probability of sudden increase of pollutant concentration, calculating a response delay index based on the influent flow and biochemical kinetics parameters when the threshold is exceeded, and generating an oxygen injection sequence to regulate the aeration equipment, and simultaneously performing closed-loop feedback adjustment according to real-time dissolved oxygen data; through the above scheme, the application can predict pollution impact in advance and compensate for system lag, realize accurate matching of oxygen supply and load in time sequence, effectively improve the water quality stability of effluent and reduce energy consumption.

Description

Technical Field

[0001] This invention relates to the fields of environmental engineering and industrial automation control technology, and in particular to an intelligent adaptive control method and system for wastewater aeration. Background Technology

[0002] In the field of wastewater treatment, activated sludge technology is widely used for the degradation of organic pollutants. This process relies on precise control of dissolved oxygen concentration in the biological treatment tank to ensure the efficient operation of microbial metabolic activities.

[0003] However, traditional aeration control methods often employ constant dissolved oxygen setpoints or proportional-integral-derivative (PID) control based on real-time dissolved oxygen feedback. These methods exhibit significant response lag when faced with sudden changes in influent water quality. The fundamental reason lies in the dual time delays inherent in the system: firstly, the hydraulic transport delay from the influent to the biochemical reaction zone, causing pollutants to arrive in the reaction zone later than the detection time; secondly, the delay in oxygen mass transfer and the biochemical reaction required for microbial response after aeration system adjustments. Because current technologies cannot quantify these combined delay effects, peak oxygen supply often fails to synchronize with the pollutant degradation requirements, easily leading to insufficient treatment or over-aeration. Furthermore, influent monitoring often relies on single-type sensors, making it difficult to simultaneously meet the demands of rapid response and high-precision measurement, and failing to promptly and accurately identify pollution characteristics during shock loads.

[0004] Therefore, conventional control strategies often encounter the problem of excessive effluent quality and increased energy consumption when dealing with sudden fluctuations in water pollution load, which restricts the stability and operational efficiency of the wastewater treatment system.

[0005] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] In view of this, the present invention provides an intelligent adaptive control method and system for wastewater aeration, which aims to solve the problems of delayed response, untimely dissolved oxygen regulation leading to excessive effluent quality, and excessive energy consumption in the existing wastewater treatment process when facing influent water pollution load shocks. By introducing a response delay index calculation mechanism based on hydraulic transmission delay and biochemical reaction response time, and combining it with a feedforward control strategy of neural network prediction and time axis translation compensation, the present invention achieves precise synchronization of oxygen supply and pollutant degradation demand in the time domain, thereby improving the system's shock resistance and reducing operating energy consumption.

[0007] This invention provides an intelligent adaptive control method for wastewater aeration, comprising:

[0008] The water quality parameters of the influent are collected by an array of sensors deployed at the inlet of the sewage treatment plant, and the water quality parameters are classified by a support vector machine model to identify abnormal fluctuation characteristics.

[0009] In response to the identified abnormal fluctuation characteristics, a neural network model is used to compare water quality parameter signals with pollution patterns in a historical water quality database to calculate the probability value of a sudden increase in the concentration of pollutants in the influent.

[0010] When the probability value of sudden increase exceeds the preset threshold, the current influent flow rate data and biochemical reaction kinetic parameters are obtained, and the system response delay index is calculated. The response delay index characterizes the physical lag time from the influent end to the biochemical reaction response end.

[0011] Based on the response delay index and the preset microbial degradation efficiency model, an oxygen injection sequence is generated for the current water pollution load. The oxygen injection sequence defines the operating trajectory of the aeration equipment over time.

[0012] The oxygen injection sequence is converted into control commands and sent to the aeration equipment for execution in order to adjust the dissolved oxygen concentration in the wastewater treatment process;

[0013] During the execution of the oxygen injection sequence, real-time dissolved oxygen data of the biological tank is continuously collected, and the operating status of the aeration equipment is adjusted in a closed-loop feedback manner based on the deviation between the real-time dissolved oxygen data and the set value of the oxygen injection sequence.

[0014] In some optional embodiments, a support vector machine model is used to classify water quality parameter signals to identify anomalous fluctuation characteristics, including:

[0015] The water quality parameter signals are standardized and converted into standard signals with a mean of 0 and a variance of 1.

[0016] The standard signal is input into the support vector machine model using a radial basis function kernel;

[0017] The standard signal is output by the support vector machine model, and the signal marked as an abnormal fluctuation category is identified as an abnormal fluctuation feature.

[0018] In some optional embodiments, a neural network model is used to calculate the probability value of a sudden increase in the concentration of pollutants in the influent, including:

[0019] Extract historical pollution pattern fragments that are similar to the characteristics of abnormal fluctuations from historical water quality databases;

[0020] A three-layer BP neural network model was constructed, which included parameters of chemical oxygen demand, ammonia nitrogen concentration, and pH value.

[0021] Water quality parameter signals are used as input vectors to a three-layer BP neural network model to calculate the probability that the input vector will evolve into a high-load water pollution event, and output a sudden increase probability value.

[0022] In some alternative embodiments, the system's response latency metrics are calculated, including:

[0023] Calculate the hydraulic transport delay time and the biochemical reaction response time respectively;

[0024] The hydraulic transmission delay time is the physical transmission time of water from the location of the sensor array through the pretreatment unit to the biological tank;

[0025] The biochemical reaction response time is the time difference between when the aeration equipment adjusts the oxygen supply and when the degradation rate of pollutants by activated sludge microorganisms reaches its peak.

[0026] The response delay index is obtained by weighted summation of the hydraulic transmission delay time and the biochemical reaction response time.

[0027] In some optional embodiments, generating an oxygen injection sequence for the current water pollution load includes:

[0028] Generate a baseline oxygen demand curve that matches the influent pollutant concentration change curve;

[0029] Based on the response delay index, the basic oxygen demand curve is shifted along the time axis so that the peak time of the shifted oxygen demand curve lags behind the peak time of the influent pollutant concentration, and the lag is equal to the response delay index, thereby forming an oxygen injection sequence to achieve time-domain synchronization between the peak oxygen supply and the peak pollutant degradation.

[0030] In some optional embodiments, generating an oxygen injection sequence for the current water pollution load further includes:

[0031] Obtain multiple alternative control schemes for response delay indicators with different values;

[0032] By using information fusion algorithms combined with microbial degradation efficiency models and energy consumption models, a comprehensive score for each alternative control scheme is calculated.

[0033] The parameter set corresponding to the candidate control scheme with the highest comprehensive score was selected to generate an oxygen injection sequence. The comprehensive score was positively correlated with the microbial degradation efficiency and negatively correlated with the system's operating energy consumption.

[0034] In some optional embodiments, the operating status of the aeration equipment is adjusted using closed-loop feedback based on the deviation between real-time dissolved oxygen data and the oxygen injection sequence setpoint, including:

[0035] Real-time dissolved oxygen data of the biochemical tank is collected at a preset feedback cycle;

[0036] Calculate the difference between real-time dissolved oxygen data and the target concentration value at the current moment in the oxygen injection sequence;

[0037] The frequency compensation amount is calculated based on the difference using a PID control algorithm.

[0038] The frequency compensation is superimposed on the reference frequency command generated based on the oxygen injection sequence to obtain the final inverter drive command, and the inverter drive command is used to control the speed of the aeration blower.

[0039] In some optional embodiments, the method further includes an adaptive backtracking step based on effluent water quality:

[0040] Collect effluent water quality data from the wastewater treatment outlet and compare it with discharge standards;

[0041] If the effluent water quality data does not meet the discharge standards, the degree of exceedance of the standards is calculated.

[0042] Based on the degree of exceedance of the effluent standard, the control sensor array switches from the first working mode to the second working mode;

[0043] The second working mode has a higher data sampling frequency and signal detection accuracy than the first working mode, and is used to capture minute changes in water pollution characteristics of subsequent influent.

[0044] In some optional embodiments, controlling the sensor array to switch from a first operating mode to a second operating mode includes:

[0045] Shorten the data acquisition interval of the sensor array, adjusting it from minute-level sampling to second-level sampling;

[0046] Alternatively, the high-precision electrochemical probes in the sensor array, which are in standby mode, can be activated to perform targeted detection of specific pollutant components in the influent.

[0047] In some optional embodiments, the method further includes a signal preprocessing step:

[0048] Before using the support vector machine model for classification, the water quality parameter signal is decomposed into sub-signals of different frequency bands using the wavelet transform algorithm;

[0049] Calculate the standard deviation of the fluctuation amplitude for each sub-signal;

[0050] Sub-signals with fluctuation amplitude standard deviations below the environmental noise threshold are removed, and only sub-signals containing significant fluctuation characteristics are retained for subsequent processing.

[0051] In some optional embodiments, the method further includes a pollution source feature fingerprint matching step:

[0052] When calculating the probability of a sudden increase, a multi-dimensional feature vector of the water quality parameter signal is extracted. The multi-dimensional feature vector includes the pH change rate, the frequency of turbidity fluctuation, and the slope of chemical oxygen demand increase.

[0053] The multidimensional feature vectors are matched with a pre-set water quality fingerprint database of typical sewage discharge enterprises based on similarity.

[0054] The probability value of sudden increase is adjusted based on the matching results. When a high-concentration emission characteristic fingerprint is matched, the weight of the probability value of sudden increase is increased.

[0055] In some alternative embodiments, the generation of the oxygen injection sequence for the current water pollution load is also constrained by environmental parameters:

[0056] Collect current water temperature and atmospheric pressure data;

[0057] The saturated dissolved oxygen concentration under current environmental conditions was calculated using a gas solubility model.

[0058] If the target dissolved oxygen concentration in the oxygen injection sequence exceeds the saturated dissolved oxygen concentration, the target value at that moment will be corrected to a preset percentage of the saturated dissolved oxygen concentration to avoid ineffective aeration.

[0059] In some optional embodiments, the method further includes a sequence smoothing step:

[0060] After generating the oxygen injection sequence, the rate of change of aeration volume at adjacent time points in the oxygen injection sequence was detected.

[0061] If the rate of change of aeration volume exceeds the mechanical response limit threshold of the aeration equipment, an interpolation algorithm is used to smooth the oxygen injection sequence, generating a final execution sequence with a smooth transition, in order to prevent the aeration equipment from being damaged due to frequent and large-scale adjustments.

[0062] In some optional embodiments, closed-loop feedback regulation further includes steady-state determination:

[0063] Monitor the changing trend of the difference over multiple consecutive feedback cycles;

[0064] If the absolute value of the difference remains less than the stable threshold within a preset time window, the system is determined to have entered a steady state, and the frequency of feedback adjustment is reduced to save computing resources.

[0065] If a sudden change in the difference is detected, high-frequency feedback regulation is restored.

[0066] In some optional embodiments, the method further includes an online model update step:

[0067] Record the effluent water quality response data and the actual observed values ​​of system response delay after each execution of the oxygen injection sequence;

[0068] The actual observed values ​​are compared with the calculated values ​​of the response delay index to obtain the prediction residuals;

[0069] When the accumulated prediction residuals exceed the error tolerance limit, the neural network model and the microbial degradation efficiency model are retrained using historical operating data from the most recent week, and the model parameters are updated.

[0070] This invention provides an intelligent adaptive control system for wastewater aeration, comprising:

[0071] The water quality sensing and acquisition module, deployed at the wastewater treatment inlet, includes a sensor array configured to monitor influent water quality parameters in real time, used to acquire raw signals reflecting the degree of water pollution.

[0072] The intelligent computing module is connected to the water quality sensing and acquisition module and is configured to run a support vector machine algorithm to identify abnormal fluctuation features in the original signal and run a neural network model to calculate the probability value of sudden increase in pollutant concentration based on historical patterns.

[0073] The timing compensation and control module is configured to calculate the response delay index based on the hydraulic and biochemical physical parameters of the system when the probability value of a sudden increase triggers the threshold, and generate an oxygen injection sequence that has been corrected by time axis translation. The oxygen injection sequence realizes the time-domain synchronization between oxygen supply action and pollutant degradation demand.

[0074] The closed-loop actuator, which includes an aeration device and a dissolved oxygen feedback probe, is configured to receive an oxygen injection sequence and convert it into mechanical operation commands to drive the aeration device to work, while performing real-time deviation correction based on the data collected by the dissolved oxygen feedback probe.

[0075] In some optional embodiments, the water quality sensing and acquisition module employs a dual-channel physical architecture:

[0076] Includes a constant monitoring unit and an enhanced verification unit;

[0077] The constant monitoring unit is configured to continuously collect basic water quality parameters at the first frequency;

[0078] The enhanced verification unit is configured to include a high-sensitivity analytical probe or an automatic sampling device, which is physically activated only when the intelligent computing module issues a high-load warning or an effluent exceeding the standard command, in order to provide high-precision verification data.

[0079] In some optional embodiments, the timing compensation control module includes:

[0080] The delay parameter calculation unit is configured to store the fluid dynamics model of the sewage treatment tank and dynamically calculate the water flow transmission lag time in combination with real-time flow.

[0081] The sequence generator is configured to store the microbial degradation kinetics model, predict the peak pollution load based on the sudden increase probability value, and synthesize the aeration control curve with feedforward characteristics by combining the transmission lag time.

[0082] In some optional embodiments, the closed-loop actuator is connected to the aeration device via a variable frequency drive interface:

[0083] The variable frequency drive interface is configured to map the target dissolved oxygen value in the oxygen injection sequence to a fan speed frequency signal;

[0084] The closed-loop actuator also has a built-in PID controller, which is configured to superimpose a fine-tuning frequency based on the real-time dissolved oxygen deviation calculation on the basis of the fan speed frequency signal, so as to smooth out transient disturbances in the system.

[0085] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention.

[0086] The intelligent adaptive control method and system for wastewater aeration of the present invention have the following beneficial effects:

[0087] This invention achieves early identification and quantification of the probability of sudden increases in influent pollution shocks by constructing a prediction mechanism that combines a dual-modal perception architecture with neural networks. By introducing a response delay index and compensating for the time-axis shift of the oxygen injection sequence, it effectively solves the temporal mismatch problem between oxygen supply and pollutant degradation in traditional control, significantly improving the system's resistance to shock loads. Combined with a feedforward-feedback coordinated control strategy, it reduces dissolved oxygen fluctuations and aeration energy consumption while ensuring stable effluent quality, achieving a balance of intelligence, stability, and energy efficiency in the wastewater treatment process. Attached Figure Description

[0088] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings.

[0089] Figure 1 This is a flowchart of an embodiment of the intelligent adaptive control method for wastewater aeration according to the present invention;

[0090] Figure 2 This is a schematic diagram of the structure of an intelligent adaptive control system for wastewater aeration according to an embodiment of the present invention. Detailed Implementation

[0091] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the invention will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0092] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0093] The flowchart shown in the attached diagram is merely an illustrative example and does not necessarily include all steps. For example, some steps may be broken down, while others may be combined or partially combined. Therefore, the actual execution order may change depending on the specific circumstances.

[0094] In wastewater treatment, fluctuations in influent quality, especially sudden surges in pollutant concentrations, can lead to an imbalance between oxygen supply and demand in the biochemical system. Due to the inherent time delays in water flow and biological reactions, traditional control strategies struggle to match oxygen supply with pollutant degradation needs in time. This invention constructs a time lag model based on physical mechanisms, quantifying the system's hydraulic retention time and biochemical response time into comprehensive time lag parameters. This allows for the prediction of pollutant impact moments before they reach the biochemical reaction zone, enabling proactive adjustments to control actions. The method provided in this invention dynamically identifies influent characteristics using multi-source sensor data, distinguishes between normal disturbances and actual shock loads using statistical learning methods, and assesses the likelihood of pollutant upward trends based on historical pattern matching, thus achieving early warning without relying on real-time high-precision analysis. Furthermore, a feedforward-based oxygen supply plan is generated to coordinate with the evolution of the pollution load, allowing changes in aeration intensity to anticipate future oxygen demand peaks and avoiding treatment lags or over-adjustments caused by the passivity of feedback control. By introducing a dynamic compensation mechanism in the time dimension, the control system can actively adapt to the time characteristics of changes in influent load, so that the dissolved oxygen level is always maintained within the range required for efficient degradation. This not only ensures the stability of effluent water quality, but also reduces energy waste caused by instantaneous over-aeration or repeated adjustments, ultimately achieving a simultaneous improvement in anti-interference capability and operational efficiency.

[0095] like Figure 1As shown in the figure, this invention provides an implementation method for an intelligent adaptive control method for wastewater aeration. The method includes the following steps:

[0096] Step S100: Collect influent water quality parameter signals and identify abnormal fluctuation characteristics.

[0097] In one embodiment, a sensor array consisting of multiple types of sensors is deployed downstream of the wastewater treatment plant's influent pumping station to acquire real-time physicochemical parameters of the influent, including pH, oxidation-reduction potential (ORP), conductivity, and optical turbidity. The collected raw signals are transmitted to a central processing unit, which runs a support vector machine model to classify the signals and determine whether current water quality fluctuations exhibit abnormal patterns consistent with historical shock load events. During this processing, the system maps the input signals to a high-dimensional feature space and outputs classification results based on the optimal segmentation hyperplane obtained through training. If fluctuations are determined to significantly deviate from normal operating trends, they are marked as abnormal fluctuation features.

[0098] In some other alternative implementations, the support vector machine model can be replaced with other machine learning models with good small-sample classification performance, such as random forest or extreme learning machine; the sensor array can also be configured with different combinations of probe types according to actual monitoring needs, such as adding temperature or dissolved organic matter fluorescence intensity detection channels.

[0099] Step S200: Calculate the probability value of a sudden increase in pollutant concentration based on abnormal characteristics.

[0100] In response to the abnormal fluctuation characteristics identified in step S100, a neural network model is activated to perform a secondary assessment. This model receives a pre-processed sequence of water quality parameters as input and, combined with typical pollution evolution patterns from a built-in historical water quality database, analyzes the current event's development trend. It assesses the probability of a sharp increase in influent pollutant concentration in the future period through a nonlinear mapping relationship and outputs a value between 0 and 1 as the probability of this surge. This process fully utilizes the model's learning ability on complex time-series data, achieving a quantitative extrapolation from characterizing indicators to pollution risk levels.

[0101] In other alternative implementations, the neural network model can employ a recurrent neural network (RNN) structure to enhance the ability to capture the dynamic characteristics of time series; the construction of historical databases can also incorporate datasets from external wastewater treatment facilities through transfer learning mechanisms to improve the model's generalization performance without relying on manual annotation.

[0102] Step S300: Calculate the system's response delay index.

[0103] When the probability of a sudden increase exceeds a preset threshold, advanced control logic is triggered. At this point, the system calls upon real-time influent flow data and locally stored biochemical reaction kinetic parameters to comprehensively assess the overall lag time between the occurrence of disturbance at the influent end and the generation of an observable response by the microbial community within the biochemical tank. This lag time is expressed as a "response delay index," the value of which is jointly determined by the system's internal fluid transport characteristics and biological reaction kinetics. The central processing unit dynamically updates this index based on the current operating conditions, ensuring that the time base of subsequent control actions remains consistent with the actual physical process.

[0104] In other alternative implementations, the response delay index can be obtained through system identification methods, such as fitting a first-order plus pure time delay model using a step response experiment; alternatively, a virtual simulation environment can be established using digital twin technology to estimate the delay parameters online.

[0105] Step S400: Generate an oxygen injection sequence for the current water pollution load.

[0106] Based on the response delay index obtained in step S300, and combined with the pre-stored microbial degradation efficiency model, an oxygen injection sequence adapted to the current load change trend is generated. This sequence defines the target operating trajectory that the aeration equipment should follow over a period of time, and its time axis is compensated and adjusted to match the expected peak oxygen supply with the time when pollutants arrive at the biochemical reaction zone. The microbial degradation efficiency model converts the predicted pollution load into a theoretical oxygen demand rate curve based on the oxygen required to remove a unit of pollutant and the system's oxygen transfer capacity, and synthesizes feedforward control commands accordingly.

[0107] In some other alternative implementations, the generation of the oxygen injection sequence can also incorporate a multi-objective optimization strategy to minimize energy consumption while satisfying the treatment effect; the optimization objective function can include multiple dimensions such as DO stability, fan power consumption, and carbon source consumption.

[0108] Step S500: Convert the oxygen injection sequence into control commands and drive the aeration equipment to execute.

[0109] The oxygen injection sequence generated in step S400 is uploaded to the distributed control system (DCS) and then sent to the variable frequency drive unit of the on-site aeration equipment via the communication interface. Control commands are issued to the blower or microporous aerator in the form of frequency setpoints or valve opening commands to adjust the air supply, thereby achieving active regulation of the dissolved oxygen concentration in the biological treatment tank. This process demonstrates the forward-looking nature of feedforward control, enabling oxygen supply preparation to be initiated before pollutants enter the main reaction zone.

[0110] In other alternative implementations, control commands can be transmitted via Industrial Ethernet, Modbus TCP, or OPCUA protocols; the actuators can also adopt a distributed node architecture to support independent control of the aeration intensity of different corridors by region.

[0111] Step S600: Perform closed-loop feedback regulation based on real-time dissolved oxygen data.

[0112] During the oxygen injection sequence, actual dissolved oxygen (DO) concentration data are continuously collected from multiple locations within the biochemical tank, fed back by probes. The central processing unit compares the measured values ​​with the target setpoints at corresponding moments in the oxygen injection sequence, calculates the deviation, and generates a dynamic correction signal based on this deviation. This correction signal is superimposed on the original feedforward command to form the final output command, used to compensate for control deviations caused by model errors, environmental disturbances, or equipment aging. The entire feedback loop operates independently within a fixed sampling period, ensuring the system's robust response to external disturbances.

[0113] In other alternative implementations, the feedback regulation algorithm may employ advanced control strategies such as fuzzy PID, adaptive gain scheduling, or model predictive control (MPC); the feedback data source may also incorporate multi-point mean or spatial gradient information to improve control representativeness.

[0114] The aforementioned technical features form a close synergy: steps S100 and S200 constitute the perception layer, enabling early warning and risk quantification of shock loads; step S300, as the decision-making center, accurately characterizes the inherent physical and biochemical hysteresis characteristics of the system; step S400 applies time compensation to control commands based on this hysteresis information, ensuring that oxygen supply behavior precisely matches pollutant degradation needs in the time domain, fundamentally overcoming the "hysteresis adjustment" defect caused by response delay in traditional control; steps S500 and S600 constitute the execution layer, completing command implementation primarily through feedforward and secondarily through feedback, leveraging the initiative of predictive control while retaining the safety redundancy of feedback correction. The entire process achieves a shift from "passive response" to "proactive pre-control," making it particularly suitable for urban wastewater treatment scenarios with frequent fluctuations in influent load.

[0115] Through the above solution, this embodiment can effectively alleviate the control inaccuracy problem caused by the lag in hydraulic transmission and biochemical reaction during the sewage treatment process, significantly improve the system's anti-interference ability and effluent water quality stability when facing sudden water pollution shocks, and avoid energy waste caused by excessive aeration, thus achieving dual optimization of environmental benefits and operational economy.

[0116] In one specific implementation, the classification process for water quality parameter signals first involves standardizing the acquired raw signals. Specifically, multi-channel signals from pH meters, ORP probes, conductivity meters, and turbidimeters are normalized to convert each signal sequence into a standard signal with a mean of 0 and a variance of 1, thus eliminating the influence of dimensional differences between different sensors and background drift on the classification results. Then, the standardized signal vectors are... The input is fed into a support vector machine model, which uses a radial basis function (RBF) as its kernel function, and its form is: ,in The kernel parameters are pre-determined through cross-validation. The support vector machine model, based on the optimal classification hyperplane learned during training, performs binary classification on the input signal and outputs the category label of the signal. Signals classified as "abnormal fluctuations" are identified as abnormal fluctuation features and used to trigger subsequent load forecasting processes. In other optional implementations, the standardization method can be replaced with min-max scaling or robust standardization (based on median and interquartile range) to accommodate situations with significant outliers; the kernel function can also be a multinomial kernel or a sigmoid kernel, adjusting the model's nonlinear mapping capability according to the actual data distribution characteristics.

[0117] Through the above scheme, this embodiment can effectively improve the accuracy of the support vector machine model in identifying abnormal fluctuations in water quality, avoid classification bias caused by differences in sensor signal scale, and capture complex water quality change patterns by utilizing the strong nonlinear fitting capability of the RBF kernel, thereby enhancing the reliability of early warning under low signal-to-noise ratio conditions.

[0118] In one specific implementation, after the support vector machine identifies abnormal fluctuation characteristics, the system first retrieves historical pollution event segments similar to the current signal pattern from a pre-stored historical water quality database. Specifically, the Dynamic Time Warping (DTW) algorithm is used to match the current denoised multi-parameter time-series signal (including pH, turbidity, ORP, and conductivity) with typical shock load waveforms recorded in the database, selecting several historical segments with the highest similarity as reference templates. Then, a three-layer backpropagation neural network (BP neural network) is constructed, consisting of an input layer, a hidden layer, and an output layer. The input layer has 9 nodes, corresponding to the current chemical oxygen demand (COD), ammonia nitrogen concentration (NH3-N), and pH value, as well as the changes in these three parameters over the past two time windows (i.e., first-order difference terms). The hidden layer contains 15 neurons, and the activation function is a sigmoid function.

[0119]

[0120] The output layer consists of a single node, representing the probability of a sudden increase in pollutant concentration, with a value ranging from [0,1]. The network training uses a mean squared error loss function and adjusts the weights using gradient descent, with a learning rate of 0.01. During the inference phase, the real-time extracted input vector is normalized and fed into the trained model, where it undergoes forward propagation to calculate the output probability value. This probability value is used to determine whether to trigger subsequent timing compensation control logic.

[0121] In other alternative implementations, the neural network architecture can be replaced with a shallow convolutional neural network (CNN) to enhance the extraction of local temporal features; or a long short-term memory (LSTM) network architecture can be used instead of a backpropagation (BP) network to better capture dependencies over long time spans. Furthermore, the input variables can be expanded to include auxiliary parameters such as temperature and dissolved oxygen rise rate to improve prediction accuracy.

[0122] Through the above-described scheme, this embodiment can achieve a quantitative assessment of the sudden increase trend of influent pollutants based on the deep correlation between actual monitoring data and historical pollution patterns, thus improving the accuracy and robustness of the probability prediction of sudden increases. Compared with existing technologies, this embodiment utilizes multi-dimensional water quality parameter joint modeling and combines it with a waveform matching mechanism of historical events. This enables the neural network to not only rely on static threshold judgments but also possess the ability to dynamically identify complex pollution evolution processes. This allows for a more reliable distinction between occasional disturbances and actual shock loads, providing high-quality feedforward decision-making basis for subsequent precise aeration control.

[0123] In one specific implementation, the system's response latency metrics are calculated, including:

[0124] In response to identified abnormal fluctuations and a sudden increase in probability exceeding a preset threshold, the system first acquires the current influent flow rate data and kinetic parameters related to the biochemical reaction to calculate the overall time delay between the influent and the actual oxygen demand of the microorganisms in the biochemical tank. Specifically, this delay index is obtained by calculating two independent time components and then performing a weighted synthesis.

[0125] The first component is the hydraulic transport delay time, which characterizes the physical transport time required for water carrying pollutants from the location of the sensor array through the pretreatment unit (including the screen, grit chamber, and primary sedimentation tank) to reach the activated sludge reaction zone of the biological treatment tank. This time is determined based on the ratio of the effective volume of the pretreatment unit to the real-time influent flow rate, where the effective volume is a known fixed geometric parameter, and the influent flow rate is continuously measured by an electromagnetic flow meter installed on the influent pipeline.

[0126] The second component is the biochemical reaction response time, defined as the time interval from when the aeration equipment receives the oxygen supply adjustment command to when the degradation rate of pollutants by the activated sludge microbial community reaches its peak. This time does not rely on theoretical model assumptions but is obtained by identifying the input-output relationship in historical operating data: the system records the time difference between the moment the fan frequency changes and the subsequent inflection point of the dissolved oxygen concentration curve, and combines this with the changing trend of effluent pollutant removal rate for statistical averaging, dynamically updating this parameter to reflect the biological activity level under the current operating conditions.

[0127] Then, the two time components are linearly weighted and summed according to the set weighting coefficients to generate the final response delay index. In some other optional implementations, the weighting coefficients can be adaptively adjusted according to seasonal factors (such as the slowdown of biological reaction due to low winter temperatures) or changes in sludge time (SRT); or, the biochemical reaction response time can also be periodically calibrated by online pulse testing, that is, artificially applying short-term high aeration disturbance and observing the time constant of the DO response curve, thereby improving the accuracy of the parameters.

[0128] Through the above scheme, this embodiment can achieve quantitative separation and comprehensive modeling of multi-source lag effects in the wastewater treatment process, enabling subsequent control commands to have a clear time compensation basis. Compared with the prior art, this embodiment improves the system's ability to analyze time mismatch problems under complex operating conditions by independently modeling and fusing the delays of two different physical mechanisms: hydraulic transmission and biochemical response. This provides a reliable technical foundation for achieving precise matching of oxygen supply actions and pollutant degradation requirements on the time axis.

[0129] In one specific implementation, when generating the oxygen injection sequence for the current water pollution load, the process first involves using the influent pollutant concentration change curve predicted by a neural network, combined with the real-time influent flow rate. Using a microbial degradation efficiency model, the theoretical oxygen demand per unit time is calculated, generating a baseline oxygen demand curve that dynamically changes over time. This curve reflects the intensity of oxygen demand at different time points after pollutants enter the system, with its peak value corresponding to the highest point of pollutant concentration.

[0130] Specifically, the system obtains the response latency index calculated by the superior solution. This value already incorporates the combined effects of hydraulic transport delay and biochemical reaction response delay. Next, a time-axis translation transformation is performed on the baseline oxygen demand curve, shifting the original function to the right along the time axis. Units were used to obtain the corrected target control sequence:

[0131]

[0132] This results in the peak time of the shifted oxygen demand curve lagging behind the peak time of the original influent pollutant concentration, with the lag duration precisely equal to the overall system response delay. The resulting... This is the final oxygen injection sequence, used to guide the aeration equipment to start the oxygen supply action in advance at the appropriate time, so as to achieve synchronous matching between the peak oxygen supply and the actual degradation needs of pollutants in the time domain.

[0133] Then, to adapt to control flexibility under different operating conditions, in some other optional implementations, the time axis translation operation can achieve high-precision alignment for non-integer hours through piecewise linear interpolation or spline interpolation methods; or a rolling optimization window mechanism can be adopted to dynamically update the translation amount based on the latest observation data in each control cycle to cope with sudden changes in flow. Fluctuations. In addition, the translation operation can also be combined with historical DO response data for residual compensation, and the least squares method can be used to fit the actual response deviation to further improve the timing matching accuracy.

[0134] Through the above-described solution, this embodiment effectively solves the problems of untimely oxygen supply or excessive aeration caused by large system lag in traditional control strategies. It achieves precise synchronization between dissolved oxygen supply and the arrival and degradation process of pollutants, significantly reducing energy consumption fluctuations while ensuring stable effluent quality. Compared with existing technologies, this embodiment achieves optimal layout of feedforward control commands in the time dimension, improving the wastewater treatment system's proactive adaptability to shock loads, while avoiding instantaneous hypoxia or over-aeration caused by control delays.

[0135] In one specific implementation, generating an oxygen injection sequence for the current water pollution load further includes:

[0136] For different value ranges of the response delay index, the system first generates multiple alternative control schemes. Each control scheme includes a set of candidate oxygen injection sequence parameters, including the target dissolved oxygen setpoint, the aeration intensity gradient change rate, and the time segment allocation weights. Specifically, the information fusion algorithm is configured to receive the predicted output from the microbial degradation efficiency model and the calculation results from the energy consumption model. The microbial degradation efficiency model is based on the dynamic relationship between the specific oxygen consumption rate (SOUR) of activated sludge and the pollutant removal rate, while the energy consumption model calculates the energy consumption per unit of oxygen transfer based on the blower power curve, valve opening resistance characteristics, and the integral of operating time. Then, the comprehensive score is calculated using a weighted function:

[0137]

[0138] in This is the normalized value of the predicted pollutant removal efficiency. This represents the electrical energy consumption per unit of oxygen demand. and The weighting coefficients are adjustable, initially set to 0.6 and 0.4. The system iterates through all candidate control schemes, calculates their comprehensive scores one by one, and selects the parameter set with the highest score as the basis for generating the final oxygen injection sequence. In some other optional implementations, the information fusion algorithm can be replaced by a fuzzy logic-based decision engine or adopt the Multi-Attribute Utility Theory (MAUT) framework; weighting coefficients It can be dynamically adjusted according to seasonal electricity pricing strategies or emission limit levels; the generation of alternative solutions can also incorporate genetic algorithms for intelligent search to improve optimization efficiency.

[0139] Through the above scheme, this embodiment can achieve multi-objective synergistic optimization of oxygen supply strategy, significantly reduce the overall energy consumption of the system while ensuring efficient degradation of pollutants, avoid energy waste caused by solely pursuing treatment effect, and improve the adaptability and robustness of control strategy under different operating conditions.

[0140] In one specific implementation, the operating status of the aeration equipment is adjusted using closed-loop feedback based on the deviation between real-time dissolved oxygen data and the oxygen injection sequence setpoint, including:

[0141] Real-time dissolved oxygen (DO) data from the dissolved oxygen probe in the biochemical tank is collected at a preset feedback period. Specifically, this feedback period is set according to the sampling capability and dynamic response requirements of the control system, typically between 15 and 60 seconds, ensuring that the DO change trend is captured while avoiding high-frequency noise introduced by over-frequency sampling. Then, the deviation between the current real-time DO value and the target DO setpoint at the corresponding time point in the oxygen injection sequence is calculated. Next, this deviation is input into the PID control algorithm module, which calculates a frequency compensation amount for correcting the aeration intensity through a linear combination of proportional, integral, and derivative terms. This frequency compensation amount is then superimposed on the reference frequency command generated by the oxygen injection sequence to form the final inverter drive command, which is sent to the inverter drive device of the aeration blower through the industrial communication interface, thereby achieving dynamic adjustment of the blower speed. In other optional implementations, the parameters of the PID control algorithm can be tuned through adaptive tuning strategies, such as adjusting the Kp, Ki, and Kd coefficients online based on fuzzy logic rules, or using Model Reference Adaptive Control (MRAC) to optimize the control gain based on the system's historical response characteristics.

[0142] Through the above scheme, this embodiment can effectively suppress dissolved oxygen deviation caused by model prediction errors, environmental disturbances or load fluctuations, and improve the robustness and accuracy of aeration control. At the same time, the composite control architecture with feedforward control as the mainstay and feedback regulation as the supplement can ensure that the effluent water quality meets the standards while avoiding the lag response and energy waste under simple feedback control, and achieve synergistic optimization of dynamic performance and operational energy efficiency.

[0143] In one specific implementation, the method further includes an adaptive backtracking step based on effluent quality: the effluent end of the wastewater treatment system is equipped with an online water quality monitoring device for continuously collecting effluent water quality data, including chemical oxygen demand (COD), ammonia nitrogen concentration, and total phosphorus content. First, the system compares the real-time acquired effluent water quality data with preset emission standard limits to determine whether the current treatment result meets environmental protection requirements. Specifically, when any pollutant index exceeds the corresponding emission threshold, the system calculates the degree of exceedance, i.e., the difference between the measured value and the standard value, and determines the response level based on the magnitude of this difference. Then, based on the determined response level, the control system sends a mode switching command to the sensor array at the influent end, switching its operating mode from a first working mode to a second working mode. The second working mode has a higher data sampling density and stronger component resolution than the first working mode, improving the ability to capture weak pollution characteristics in subsequent incoming water in terms of both temporal resolution and detection sensitivity, thereby providing high-quality feedback information for the prediction model to support parameter correction. Next, the system continues to operate in the second working mode until it confirms that the effluent meets the standards for multiple consecutive cycles or completes a full round of data learning and updating. Then, it automatically reverts to the first working mode to reduce operating energy consumption and equipment wear. In other optional implementations, the second working mode can achieve refined sensing of influent characteristics by enabling a backup high-precision sensing channel, increasing the number of samples per unit time, or combining it with spectral scanning analysis.

[0144] Through the above scheme, this embodiment can realize dynamic backtracking of control strategy and on-demand enhancement of sensing capabilities. When the effluent does not meet the standards, it can proactively improve the observation granularity of the front-end sensing system, thereby effectively identifying subtle disturbance factors that cause processing failure. Compared with the prior art, this embodiment avoids the waste of resources caused by all-weather high-precision monitoring, and can strengthen data acquisition capabilities in a timely manner after abnormal events occur, providing a reliable basis for model self-learning and control optimization, thereby improving the overall robustness and long-term stability of the system.

[0145] In one specific implementation, switching the control sensor array from a first operating mode to a second operating mode includes: when the control system determines that the effluent water quality data does not meet the discharge standards and triggers an adaptive backtracking mechanism, the operating mode of the sensor array is first dynamically adjusted. Specifically, the system sends a mode switching command to the sensor array at the inlet end through the central processing unit. This command is used to shorten the data acquisition time interval, increasing the sampling frequency from the original minute-level (e.g., once every 5 minutes) to the second-level (e.g., once every 30 seconds or less), thereby enhancing the ability to capture weak fluctuation signals in subsequent influent. This high-frequency sampling mode can promptly acquire fine temporal characteristics of influent water quality changes after a pollutant impact event, providing high-resolution input data for model correction.

[0146] The system then performs another optional mode-switching operation: activating high-precision electrochemical probes deployed in the sensor array that are in standby mode. These probes include, but are not limited to, UV-Vis spectrometers, ion-selective electrodes (such as cyanide ion and heavy metal ion electrodes), or high-sensitivity dissolved organic carbon (DOC) detectors. These probes remain in low-power standby mode under normal operating conditions and are only powered on and activated when backtracking conditions are met, for targeted identification and quantitative analysis of characteristic pollutant components that may be present in the influent. The analysis results are uploaded in real time to the intelligent computing module to update the weight parameters in the neural network model and optimize the accuracy of future surge probability predictions.

[0147] In some other alternative implementations, the data acquisition interval can be adjusted according to the degree of exceedance of the effluent: for mild exceedance, sampling can be adjusted to once every 2 minutes; for moderate exceedance, sampling can be adjusted to once every 30 seconds; and for severe exceedance, continuous scanning mode can be activated. Alternatively, the high-precision electrochemical probe can be activated using a selective wake-up mechanism, which activates only the dedicated detection channel associated with the specific type of pollution indicated by the previous fingerprint matching results, in order to reduce system energy consumption and data redundancy.

[0148] Through the above scheme, this embodiment can achieve on-demand allocation of sensor resources, ensuring data quality during events while avoiding high-cost, 24 / 7 operation. Compared with existing technologies, this embodiment proactively improves the spatiotemporal resolution and detection accuracy of the front-end sensing system after water outflow anomalies, forming a closed-loop learning path of "control failure—sensor enhancement—model correction." This not only helps to trace secondary pollution risks that were not identified in time, but also provides a reliable data foundation for online optimization of control strategies, thereby improving the overall robustness and long-term stability of the system.

[0149] In one specific implementation, the method further includes a signal preprocessing step: Addressing the environmental noise interference in the raw water quality parameter signals acquired by the sensor array, a wavelet transform algorithm is first used to perform frequency domain decomposition on the signal. Specifically, the raw signal... The signal is input to the wavelet transform module, which selects the Daubechies wavelet basis (db4) as the mother wavelet function to perform multi-scale discrete wavelet decomposition on the signal, obtaining a set of low-frequency approximate components. and several high-frequency detail components (in ), each Dynamic fluctuation components corresponding to specific frequency bands.

[0150] Then, for each layer of high-frequency detail components Calculate its standard deviation within the sliding time window. This indicator is used to quantify the fluctuation intensity of signals in this frequency band. An environmental noise threshold is set based on statistical analysis of historical operational data. (e.g., 0.05 V), if a certain layer corresponding If the noise component is determined to be mainly caused by non-polluting factors such as water flow turbulence, bubble disturbance, or electrode drift, it is set to zero to eliminate noise. (The remaining text appears to be incomplete and requires further context.) Significant fluctuation components, and low-frequency approximate components. Together they are reconstructed into a denoised net signal. .

[0151] The reconstructed signal was then fed into a subsequent support vector machine classification model for identifying anomalous fluctuation features. By transmitting only signal components with actual hydraulic or pollution significance, the classifier's sensitivity in identifying real load shocks was improved.

[0152] In other alternative implementations, the wavelet basis can be replaced with orthogonal wavelet bases such as Symlets (sym6) or Coiflets (coif3) depending on the characteristics of the field signal; the number of decomposition layers can be adaptively adjusted according to the sampling frequency, for example, using 4 layers when the sampling period is 10 seconds, and expanding to 6 layers when the sampling period is shortened to 1 second to enhance high-frequency resolution; noise threshold It can be set to a dynamic adaptive mode, which automatically updates its baseline value based on day and night or seasonal traffic changes.

[0153] Through the above scheme, this embodiment can effectively filter out meaningless high-frequency noise caused by physical disturbances in the sensor signal, retain the fluctuation characteristics that reflect the actual changes in pollutants, thereby improving the signal-to-noise ratio and classification accuracy of the anomaly detection link, avoiding unnecessary high-precision analysis or aeration control actions triggered by false fluctuations, and enhancing the robustness and resource utilization efficiency of the entire control system.

[0154] In one specific implementation, the method further includes a pollution source feature fingerprint matching step: when the system determines that abnormal fluctuation characteristics exist, it first extracts the dynamic change characteristics from the influent water quality parameter signal to construct a multidimensional feature vector. Specifically, this multidimensional feature vector includes the pH change rate. Turbidity fluctuation frequency is defined as the absolute value of the pH difference per unit time. The dominant frequency component and the slope of the chemical oxygen demand (COD) increase were obtained by performing spectral analysis on the turbidity signal over a continuous 10-minute period using Fourier transform. The incremental rate is obtained by fitting a linear trend to the three most recent valid measurement points. Then, this multidimensional feature vector is... The data is input into a pre-stored database of water quality fingerprints from typical wastewater discharge enterprises for similarity matching. The database stores emission patterns from multiple industrial sources calibrated using historical data, such as dyeing and printing plants (characteristics: high and medium). Electroplating plant (characteristics: negative high) (Associations with extremely high conductivity, etc.). The cosine similarity algorithm is used to calculate the matching degree between the current vector and each fingerprint template:

[0155]

[0156] If any If the current shock load originates from emissions from the corresponding type of enterprise, then the probability value of the sudden increase is weighted and corrected based on the matching results: for categories that are successfully matched, their original predicted probabilities are adjusted accordingly. Multiply by weighting factor ,in The warning level is determined based on the historical severity of the pollution source. For example, if pharmaceutical wastewater characteristics are identified (often accompanied by recalcitrant organic matter), a higher weight is assigned to increase the warning level.

[0157] In other alternative implementations, the multidimensional feature vector can also incorporate ORP change acceleration or temperature change gradient as additional dimensions; the similarity matching algorithm can be replaced with Mahalanobis distance or dynamic time warping (DTW) methods to accommodate the need for non-equal length sequence alignment; and the fingerprint database update mechanism can be configured to periodically receive calibration data from the plant's main discharge monitoring system to achieve automatic iterative learning.

[0158] Through the above scheme, this embodiment can identify the types of potential pollution sources based on the dynamic characteristics of influent water quality, and dynamically adjust the confidence weight of sudden increases in probability accordingly, thereby improving the pertinence and reliability of subsequent control decisions. Compared with the prior art, this embodiment can complete endogenous source inference without relying on external regulatory information. Without increasing additional hardware investment, it enhances the model's judgment ability by utilizing the temporal characteristics of existing sensor signals, significantly reducing false alarm and false negative rates, and maintaining high classification accuracy, especially under complex shock load scenarios.

[0159] In one specific implementation, the generation of the oxygen injection sequence for the current water pollution load is also constrained by environmental parameters:

[0160] The system collects the current water temperature in real time using high-precision temperature sensors and atmospheric pressure sensors deployed in the biological treatment tank. and atmospheric pressure at the scene The data is uploaded to the central processing unit every 5 minutes. Then, a gas solubility model is used to calculate the saturated dissolved oxygen concentration in the water under the current conditions. The calculation formula is as follows:

[0161]

[0162] in, This is the partial pressure of water vapor at the corresponding water temperature (which can be obtained by referring to a table or using an empirical formula). The standard atmospheric pressure is 101.325 kPa. Next, the target dissolved oxygen setpoints at each time point in the oxygen injection sequence are compared with... Compare. If the target value at a certain moment exceeds... Then it will be corrected to This serves as the final target control value at that moment, thereby avoiding inputting oxygen supply commands into the water that exceed physical limits and preventing energy waste caused by the blower running idle or over-operating.

[0163] In some other alternative implementations, the preset percentage can be dynamically adjusted according to seasonal or regional climate characteristics, and set to a specific value during the low-temperature winter period. To improve oxygen utilization, it is reduced to [a certain value] during the high-temperature period of summer. To allow for greater control margin; alternatively, the gas solubility model can be replaced with a regression model trained on historical measured data, directly establishing... , and The mapping relationship eliminates the need for explicit calculations of theoretical formulas.

[0164] Through the above-described solution, this embodiment effectively avoids ineffective aeration behavior caused by ignoring environmental factors, and improves the adaptability and energy efficiency of the control system under different meteorological conditions. Compared with the prior art, this embodiment ensures the physical feasibility of the oxygen injection sequence by introducing a saturated dissolved oxygen constraint mechanism based on measured environmental parameters, significantly reduces the power consumption caused by excessive aeration, and extends the service life of the aeration equipment.

[0165] In one specific implementation, the method further includes a sequence smoothing step. This step first discretizes the generated oxygen injection sequence over time, specifically extracting the target aeration rate values ​​at adjacent time steps with a time step of 5 minutes. Then, it calculates the rate of change of aeration rate between adjacent time points, i.e., the increase or decrease in aeration intensity per unit time. Next, it compares this rate of change with a preset mechanical response limit threshold, which is set according to the blower model and the technical parameters of the variable frequency drive device. For example, the maximum allowable frequency change rate for a certain model of single-stage high-speed centrifugal blower is 10 Hz / min. If the rate of change exceeds this threshold, an interpolation correction mechanism is triggered, using a linear or cubic spline interpolation algorithm to insert transitional data points within the abrupt change interval, reconstructing a continuous and smooth final execution sequence. This sequence ensures that the gradient of the aeration equipment's output power change is within its safe operating range. In other optional implementations, the interpolation algorithm can be replaced with low-pass filtering, using a first-order inertial element. Dynamic smoothing is performed on the original sequence, where the smoothing coefficient is... Adjust online based on the device's response characteristics.

[0166] Through the above solution, this embodiment can effectively suppress drastic adjustment commands caused by predictive fluctuations or control jumps in the oxygen injection sequence, avoid frequent start-ups and shutdowns or large speed changes in the aeration equipment, thereby reducing mechanical wear and failure risks, extending the service life of the actuators, and ensuring the stability and engineering feasibility of the control system output.

[0167] In one specific implementation, the closed-loop feedback control also includes steady-state determination, and the closed-loop feedback control module is further configured to execute steady-state determination logic. Specifically, the system continuously collects real-time dissolved oxygen data from the biochemical tank at a preset feedback cycle and calculates the deviation between this data and the target concentration value at the corresponding moment in the oxygen injection sequence. First, the processor monitors the trend of the absolute value of this deviation over multiple consecutive feedback cycles; if the absolute value is consistently less than a stable threshold (e.g., 0.2 Mg / L) within a preset time window (e.g., 10 minutes), it is determined that the current control system has entered a steady-state operation. Subsequently, the control unit automatically reduces the execution frequency of feedback control, for example, adjusting the PID parameter update cycle from once every 10 seconds to once every 60 seconds, while pausing the accumulation operation of the integral term to reduce unnecessary computational resource consumption and controller output fluctuations. Then, the system continuously monitors subsequent deviation changes; if a sudden change in deviation is detected, i.e., the absolute value jumps above the recovery threshold (e.g., 0.5 Mg / L) within a single cycle, the high-frequency feedback control mode is immediately restored, and the full-function PID operation is reactivated to ensure a rapid response to sudden disturbances.

[0168] In some other alternative implementations, the stability threshold can be dynamically adjusted according to the seasonal water quality fluctuation characteristics, for example, by learning from historical data to set a wide threshold for summer and a narrow threshold for winter; or, the length of the preset time window can be adaptively changed according to the influent flow rate, shortening the judgment time under high flow conditions to improve response sensitivity.

[0169] Through the above scheme, this embodiment can achieve dynamic energy efficiency optimization of the feedback control mechanism, effectively reduce the computing load and communication overhead of the central processing unit during the stable operation phase of the system, and extend the mechanical life of the actuator; at the same time, it retains the ability to quickly wake up to abnormal disturbances, ensuring the stability and robustness of the wastewater treatment process in the long term.

[0170] In one specific implementation, the method further includes an online model update step: the system is configured to dynamically update the parameters of the neural network model and the microbial degradation efficiency model. First, after each oxygen injection sequence is executed, the central processing unit records the effluent water quality response data corresponding to that control cycle, including the actual measured values ​​of indicators such as effluent COD, ammonia nitrogen concentration, and total phosphorus, and simultaneously collects the response delay time actually observed by the system. This time was obtained by marking the time difference between the starting point of the rise in influent pollutant concentration and the moment when the peak dissolved oxygen demand in the biological treatment tank occurred.

[0171] Specifically, the above-mentioned actual observations The response delay index calculated in the previous stage Perform periodic comparisons and calculate the single prediction residual. Then, the system continuously accumulates the prediction residuals of the most recent N consecutive control cycles (e.g., N=7) to form the cumulative error. .when Exceeding the preset error tolerance limit When the time is set to 2.5 hours, the model retraining mechanism is triggered.

[0172] Next, the control system automatically retrieves the complete operation logs from the past seven days, including historical influent flow rate, water quality parameters, aeration command sequences, actual DO response curves, and final effluent data, as a new training sample set. Using this dataset, the connection weights of the three-layer BP neural network are re-optimized using the backpropagation algorithm; simultaneously, the yield coefficient Y and oxygen utilization rate η in the microbial degradation efficiency model are regressed and corrected based on the least squares method, completing the online update and calibration of the model parameters.

[0173] In other alternative implementations, the model update strategy can be flexibly adjusted according to the plant's operating conditions: for example, a fixed periodic update mode can be set (such as once a week), or a sliding window weighting mechanism can be introduced to give higher weight to recent data in order to improve the model's adaptation speed to changes in operating conditions; the Kalman filter can also be combined to estimate the state of the response delay observations to further improve the accuracy of residual calculation.

[0174] Through the above-described scheme, this embodiment achieves closed-loop self-learning capability of the control model, effectively compensating for model mismatch caused by equipment aging, sludge activity fluctuations, or seasonal temperature changes, thereby maintaining the accuracy and robustness of aeration control over the long term. Compared with existing technologies, this embodiment avoids the problem of static model performance degradation over time, significantly extends the effective service life of the intelligent control system, and reduces the frequency of manual intervention and maintenance costs.

[0175] This invention provides an intelligent adaptive control system for wastewater aeration. For example... Figure 2 As shown, the device includes:

[0176] The water quality sensing and acquisition module M100 is deployed at the inlet of the sewage treatment system. It includes a sensor array for real-time monitoring of influent water quality parameters. It is configured to continuously acquire raw signals reflecting the degree of water pollution. The raw signals include at least pH value, oxidation-reduction potential (ORP), conductivity and turbidity. The sensor array transmits the acquired raw signals to the subsequent processing unit through wired or wireless communication.

[0177] The intelligent computing module M200 establishes a data connection with the water quality sensing and acquisition module M100 and is configured to analyze and process the raw signal. This module further integrates a signal preprocessing unit (or wavelet transform module), configured to receive the raw signal from M100 and perform wavelet transform denoising processing, transmitting the processed net signal to the subsequent support vector machine (SVM) algorithm engine. This module runs the SVM algorithm to identify abnormal fluctuation features in the raw signal that deviate from the normal fluctuation range. Upon detecting abnormal fluctuation features, it activates the neural network model, comparing the current signal pattern with known pollution event patterns in the historical database, and outputting a probability value for a sudden increase in pollutant concentration between 0 and 1. The intelligent computing module M200 also includes or is connected to a memory unit containing a historical water quality database and a water quality fingerprint database of typical polluting enterprises, which are used by the neural network model for pattern matching.

[0178] The timing compensation and control module M300 is communicatively connected to the intelligent computing module M200. It is configured to calculate the overall response delay index from the influent end to the biochemical reaction response end based on the physical structure parameters and operating status data of the wastewater treatment system when the received sudden increase probability value exceeds the preset trigger threshold. The module further combines the predicted pollution load intensity to generate an oxygen injection sequence that varies with time. The oxygen injection sequence is corrected by translation on the time axis so that the time distribution of oxygen supply action is synchronized with the time distribution of pollutants arriving at the biochemical tank and starting to degrade.

[0179] The closed-loop actuator M400, connected to the timing compensation control module M300, includes aeration equipment installed within the biological treatment tank and dissolved oxygen feedback probes installed at different depths within the tank. This mechanism is configured to receive the oxygen injection sequence and convert it into mechanical operating commands to drive the aeration equipment. Simultaneously, it continuously collects the actual dissolved oxygen concentration using the dissolved oxygen feedback probes and performs real-time adjustments based on the deviation between the actual and target values, dynamically correcting the operating state of the aeration equipment to maintain a stable dissolved oxygen concentration within the set range. The closed-loop actuator M400 also incorporates a PID control unit (or controller), configured to run a PID control algorithm module to calculate the frequency compensation amount.

[0180] In one embodiment, the intelligent computing module M200 is implemented using an embedded industrial computer or a programmable logic controller (PLC) equipped with a general-purpose machine learning inference engine, supporting the loading and inference operations of SVM and neural network models. In other optional implementations, the module can also receive local data and complete computing tasks via an industrial IoT protocol from a remote cloud server, and then transmit the results back to the field control unit. In another optional implementation, the neural network model can be replaced with a gradient boosting decision tree (GBDT) or other probabilistic prediction models suitable for time series classification, as long as it can output interpretable probability estimates based on input features.

[0181] The modules mentioned above are interconnected via a standardized industrial communication bus, forming a complete control link: the water quality sensing and acquisition module M100 provides front-end sensing capabilities, converting environmental conditions into quantifiable signals; the intelligent computing module M200 acts as the judgment center, completing a two-level assessment from "whether it is abnormal" to "how likely it is to deteriorate"; the time-series compensation and control module M300, based on the inherent physical laws of the system, transforms the predicted results into a time-forward control plan; and the closed-loop actuator M400 is responsible for implementing this plan into specific equipment actions and resisting process disturbances through feedback channels. This architecture realizes the transformation from "passive response" to "active pre-control," effectively addressing the problem of untimely response of traditional control methods caused by the dual lags of hydraulic transmission and biochemical reactions in wastewater treatment. Specifically, by introducing a response delay index and shifting the oxygen demand curve along the time axis accordingly, the peak oxygen supply and the peak pollutant degradation demand are precisely matched in the time dimension, avoiding excessive effluent due to early hypoxia and energy waste caused by later over-aeration. Meanwhile, the feedforward oxygen injection sequence provides strong predictive guidance for the main control path, while the real-time deviation correction mechanism based on dissolved oxygen feedback compensates for the errors caused by model uncertainty. The two work together to significantly improve the stability and energy efficiency of the system under shock load conditions.

[0182] Through the above-mentioned solution, this embodiment can effectively improve the anti-interference ability of the sewage treatment system when facing sudden changes in influent water quality, reduce the risk of effluent water quality exceeding standards, and optimize aeration energy consumption. Compared with the prior art, this embodiment realizes dynamic alignment of oxygen supply control and pollution load on the time axis, solves the response delay problem caused by the large lag characteristic of traditional feedback control, and enhances the overall adaptability and robustness of the system through a multi-level perception-decision-execution closed-loop structure.

[0183] In one specific implementation, the water quality sensing and acquisition module M100 adopts a dual-channel physical architecture, including a continuous monitoring unit and an enhanced verification unit. The continuous monitoring unit consists of a set of low-maintenance electrochemical and optical sensors, specifically including a pH meter, an oxidation-reduction potential (ORP) probe, a conductivity meter, and an optical turbidimeter. It is configured to continuously acquire basic water quality parameter signals of the incoming water at a sampling frequency of 10 seconds per sampling, which is used to capture water quality fluctuation trends in real time and transmit them to the intelligent computing module M200 for preliminary analysis. The enhanced verification unit integrates a high-sensitivity spectroscopic chemical oxygen demand (COD) online analyzer and an automatic water sample cold storage and retention device. It remains in standby mode and does not participate in routine data acquisition. Only when the intelligent computing module M200 outputs a high-load warning signal (i.e., a sudden increase in probability exceeding a preset threshold) or receives a feedback instruction indicating that the effluent water quality exceeds standards, does the controller issue an activation command, activating the enhanced verification unit to perform high-precision detection: the spectrometer begins scanning the water sample in the 200–800 nm band, acquiring the characteristic absorption spectra of pollutants, completing a full analysis in approximately 60 seconds; simultaneously, the automatic sample retention device collects water samples for the current time period according to a program and seals and refrigerates them for subsequent laboratory verification. This dual-channel architecture achieves working mode switching through hardware-level physical isolation, avoiding resource waste and ensuring long-term stable system operation.

[0184] In other alternative implementations, the high-sensitivity analytical probe in the enhanced verification unit can be replaced with an ion chromatograph or an ammonia nitrogen selective electrode array for precise identification of specific inorganic pollutants; alternatively, the automatic sample retention device can be equipped with a wireless communication module to synchronously upload the sampling timestamp and location information to a remote monitoring platform under trigger conditions. In other implementations, the sensor combination of the continuous monitoring unit can be adjusted according to the characteristics of the wastewater treatment plant's service area; for example, adding heavy metal ion selective electrodes in areas with a high proportion of industrial wastewater, and enhancing ammonia nitrogen and phosphate sensing configurations in areas dominated by domestic sewage.

[0185] Through the above solution, this embodiment can significantly reduce system operation and maintenance costs while ensuring the daily monitoring response speed, and effectively improve the reliability and traceability of data under abnormal operating conditions by enabling high-precision detection methods on demand. Compared with the prior art, this embodiment avoids the problems of accelerated aging and reagent consumption caused by the continuous operation of high-cost sensors, extends the service life of the equipment, and enhances the ability to identify weak pollution signals through dynamic activation mechanism, providing high-quality input basis for subsequent control decisions.

[0186] In one specific implementation, the timing compensation and control module M300 includes a delay parameter calculation unit and a sequence generator. The delay parameter calculation unit embeds a simplified hydraulic model based on the geometry and fluid dynamics characteristics of the wastewater treatment plant's pretreatment unit. This model uses real-time flow data collected at the influent pumping station. As an input variable, the effective volumes of the screen, grit chamber, and primary sedimentation tank are considered. Dynamically calculate the water transport time from the inlet to the biochemical reaction zone. The time value is then output to the sequence generator. The sequence generator incorporates a microbial degradation kinetic model, employing a simplified form of the activated sludge model (ASM), and receives pollutant surge probability values ​​from the intelligent computing module M200. and its corresponding predicted concentration peak , combined The oxygen demand response curve is time-shifted to synthesize a target aeration control curve. The oxygen supply intensity distribution on the time axis matches the expected degradation demand after the pollutants arrive, thus forming a control command sequence with feedforward characteristics.

[0187] In some other alternative implementations, the hydraulic model in the delay parameter solution unit can be replaced with an empirical regression model trained based on historical flow response data or a one-dimensional convection-diffusion equation numerical solver; the microbial degradation kinetic model in the sequence generator can adopt bioreaction kinetic expressions of different complexities, including a dynamic simulation module driven by the Monod equation or a pre-calibrated lookup table mapping relationship, whose parameters can be corrected online according to seasonal temperature changes or sludge age (SRT).

[0188] Through the above scheme, this embodiment can realize the quantitative modeling of physical transport lag and biochemical response delay in the sewage treatment process, and generate an aeration control sequence that is synchronized with the spatiotemporal evolution of pollution load, thereby improving the accuracy and adaptability of feedforward control and avoiding insufficient oxygen supply or excessive aeration caused by timing mismatch.

[0189] In one specific implementation, based on the above embodiments, the closed-loop actuator M400 establishes a communication connection with the aeration equipment via a variable frequency drive interface. This variable frequency drive interface is configured to receive an oxygen injection sequence generated by the timing compensation control module M300, and convert the target dissolved oxygen concentration value corresponding to each time point in the sequence into a blower speed frequency signal. Specifically, the conversion is based on a preset mapping table or function model, which comprehensively considers the blower characteristic curve, pipeline resistance coefficient, and oxygen transfer efficiency under the current operating conditions to ensure that the output frequency can effectively drive the aeration equipment to reach the target oxygen supply within a specified time. The closed-loop actuator M400 has a built-in PID controller, whose input is connected to a real-time dissolved oxygen monitoring probe in the biological treatment tank, and whose output is superimposed on the reference frequency signal of the variable frequency drive interface. When the actual dissolved oxygen value deviates from the target value, the PID controller calculates a fine-tuning frequency component based on the magnitude of the deviation and its rate of change, and dynamically superimposes it onto the reference frequency to compensate for transient disturbances caused by water quality fluctuations, temperature changes, or equipment aging.

[0190] In some other alternative implementations, the variable frequency drive interface uses analog output (such as 4–20mA) or digital communication protocols (such as Modbus RTU or Profinet) to interact with the blower frequency converter; the parameters of the PID controller use fixed set values, or automatically switch multiple sets of gain parameters according to the system operating status to adapt to different load conditions; the superposition method of fine-tuning frequency includes linear superposition or priority arbitration mechanism to prevent instruction conflicts from causing execution abnormalities.

[0191] Through the above scheme, this embodiment can achieve synergistic optimization between the precise execution of feedforward control commands and the rapid suppression of dynamic environmental disturbances, thereby improving the response accuracy and stability of the aeration process. Compared with the prior art, this embodiment not only ensures the accurate implementation of the oxygen supply strategy based on time-series compensation, but also effectively mitigates short-term deviations caused by measurement noise, fluctuations in biological activity, or nonlinear responses of equipment through a local feedback mechanism, avoiding the occurrence of dissolved oxygen concentration overshoot or under-adjustment. Thus, while ensuring that the effluent water quality meets the standards, it further reduces the risk of energy consumption fluctuations.

[0192] It should be noted that in the above description of the method steps, the central processing unit or control system specifically performs the corresponding calculation and decision-making tasks through the intelligent computing module M200 and / or the timing compensation and control module M300 in the system; the PID algorithm is specifically executed by the PID controller in the closed-loop actuator M400.

[0193] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A sewage aeration intelligent adaptive control method, characterized in that, The method comprises the following steps: Collecting water quality parameter signals of influent water through a sensor array arranged at an influent water inlet of a sewage treatment system, and classifying the water quality parameter signals by using a support vector machine model to identify abnormal fluctuation characteristics; In response to the identified abnormal fluctuation characteristics, comparing the water quality parameter signals with pollution patterns in a historical water quality database by using a neural network model to calculate a sudden increase probability value of influent water pollutant concentration, specifically, the calculation of the sudden increase probability value of influent water pollutant concentration by using the neural network model comprises the following steps: extracting a historical pollution pattern segment similar to the abnormal fluctuation characteristics from the historical water quality database; constructing a three-layer BP neural network model containing chemical oxygen demand parameters, ammonia nitrogen concentration parameters and pH value parameters; inputting the water quality parameter signals as an input vector into the three-layer BP neural network model to calculate the possibility of the input vector evolving into a high-load water pollution event, and outputting the sudden increase probability value; When the sudden increase probability value exceeds a preset threshold value, acquiring current influent flow data and biochemical reaction kinetics parameters, and calculating a response delay index of the system, the response delay index representing a physical lag time from the influent end to the biochemical reaction response end; According to the response delay index and a preset microbial degradation efficiency model, an oxygen injection sequence for the current water pollution load is generated, the oxygen injection sequence defining a running trajectory of an aeration device changing over time, wherein the microbial degradation efficiency model is modeled based on the dynamic relationship between specific oxygen consumption rate of activated sludge and pollutant removal rate; The generation of the oxygen injection sequence for the current water pollution load comprises the following steps: generating a basic oxygen demand curve matched with the influent water pollutant concentration change curve; performing time axis translation on the basic oxygen demand curve based on the response delay index, so that the peak time of the translated oxygen demand curve lags behind the peak time of the influent water pollutant concentration, and the lag amount is equal to the response delay index, thereby forming the oxygen injection sequence to realize time domain synchronization of oxygen supply peak and pollutant degradation peak; further comprising: obtaining multiple alternative regulation schemes for different numerical values of the response delay index; calculating a comprehensive score of each of the alternative regulation schemes by using an information fusion algorithm in combination with the microbial degradation efficiency model and an energy consumption model, wherein the energy consumption model calculates unit oxygen transfer energy consumption according to a blower power curve, a valve opening resistance characteristic and an operation time integral; selecting a parameter set corresponding to the alternative regulation scheme with the highest comprehensive score to generate the oxygen injection sequence, wherein the comprehensive score is positively correlated with the microbial degradation efficiency, and is negatively correlated with the system operation energy consumption; Converting the oxygen injection sequence into control instructions and sending the control instructions to the aeration device for execution to adjust the dissolved oxygen concentration in the sewage treatment process; During the execution of the oxygen injection sequence, real-time dissolved oxygen data of the biochemical tank are continuously collected, and a closed-loop feedback adjustment is performed on the running state of the aeration device according to the deviation between the real-time dissolved oxygen data and the set value of the oxygen injection sequence; The response delay index of the computing system comprises: calculating a hydraulic transmission delay time and a biochemical reaction response time respectively; the hydraulic transmission delay time is the physical transmission time of water flow from the position where the sensor array is located to the biochemical pool through the pretreatment unit; the biochemical reaction response time is the time difference between the time when the aeration device adjusts the oxygen supply amount and the time when the active sludge microorganism reaches the peak of the pollutant degradation rate; the hydraulic transmission delay time and the biochemical reaction response time are weighted and summed to obtain the response delay index.

2. The intelligent adaptive control method for wastewater aeration according to claim 1, wherein, The classification of the water quality parameter signal by using the support vector machine model to identify the abnormal fluctuation feature comprises: The water quality parameter signal is standardized to convert it into a standard signal with a mean value of 0 and a variance of 1; The standard signal is input into the support vector machine model using a radial basis function kernel; The class to which the standard signal belongs is output by the support vector machine model, and the signal marked as an abnormal fluctuation class is determined as the abnormal fluctuation feature.

3. The intelligent adaptive control method for wastewater aeration according to claim 1, wherein, The closed-loop feedback regulation of the operation state of the aeration device according to the deviation between the real-time dissolved oxygen data and the oxygen injection sequence set value comprises: The real-time dissolved oxygen data of the biochemical pool are collected at a preset feedback period; The difference between the real-time dissolved oxygen data and the target concentration value at the current time in the oxygen injection sequence is calculated; The frequency compensation amount is calculated according to the difference by using a PID control algorithm; The frequency compensation amount is superimposed on the reference frequency instruction generated based on the oxygen injection sequence to obtain the final frequency converter drive instruction, and the rotational speed of the aeration fan is controlled by using the frequency converter drive instruction.

4. The intelligent adaptive control method for wastewater aeration according to claim 1, wherein, The method further comprises an adaptive backtracking step based on the effluent water quality: The effluent water quality data of the sewage treatment effluent outlet are collected and compared with the discharge standard; If the effluent water quality data does not meet the discharge standard, the effluent over-standard degree is calculated; According to the effluent over-standard degree, the sensor array is controlled to switch from the first working mode to the second working mode; Wherein, the data sampling frequency and signal detection accuracy of the second working mode are higher than those of the first working mode, which are used to capture the slight water pollution characteristic changes of subsequent influent.

5. The intelligent adaptive control method for wastewater aeration according to claim 4, wherein, The control of the sensor array from the first working mode to the second working mode comprises: Shortening the data collection interval time of the sensor array, so that it is adjusted from minute-level sampling to second-level sampling; Or, activating the high-precision electrochemical probe in the standby state of the sensor array to detect specific pollutant components in the influent.

6. A sewage aeration intelligent adaptive control system, characterized in that, For performing the control method as claimed in any one of claims 1-5, comprising: A water quality perception acquisition module is deployed at the sewage treatment influent outlet and comprises a sensor array configured to monitor the influent water quality parameters in real time, which is used to obtain the original signal reflecting the water pollution degree; The intelligent computing module is in communication connection with the water quality sensing and collecting module and is configured to run a support vector machine algorithm to identify abnormal fluctuation characteristics in the original signal and run a neural network model to calculate a sudden increase probability value of a pollutant concentration based on historical patterns. Specifically, the running of the neural network model to calculate the sudden increase probability value of the pollutant concentration based on the historical patterns comprises: extracting a historical pollution pattern segment similar to the abnormal fluctuation characteristics from the historical water quality database; constructing a three-layer BP neural network model containing a chemical oxygen demand parameter, an ammonia nitrogen concentration parameter and a pH value parameter; inputting the water quality parameter signal as an input vector into the three-layer BP neural network model to calculate a possibility that the input vector evolves into a high-load water pollution event and output the sudden increase probability value; The time sequence compensation regulation module is configured to calculate a response delay index based on hydraulic and biochemical physical parameters of the system when the sudden increase probability value triggers a threshold value, and to generate an oxygen injection sequence that is corrected by time axis translation according to the response delay index, so that the oxygen injection sequence realizes time domain synchronization of oxygen supply actions and pollutant degradation requirements. The closed-loop execution mechanism comprises an aeration equipment and a dissolved oxygen feedback probe and is configured to receive the oxygen injection sequence and convert it into mechanical operation instructions to drive the aeration equipment to work, while performing real-time deviation correction based on data collected by the dissolved oxygen feedback probe.

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