A device and method for detecting abnormal operating conditions in a wastewater treatment plant

Abstract

This invention belongs to the field of wastewater treatment control technology, specifically providing a device and method for detecting abnormal operating conditions in wastewater treatment plants. The method includes: acquiring influent flow data from the bioreactor and calculating the local fluid mixing time; determining the excitation signal frequency and generating a gas volume control command corresponding to the excitation signal frequency; collecting and cross-correlating the actual gas volume feedback sequence of the aeration execution module and the dissolved oxygen concentration response sequence in the bioreactor to determine the physical transport lag time; extracting the net phase lag angle, converting the net phase lag angle into the oxygen mass transfer coefficient, and calculating the oxygen consumption rate in conjunction with the dissolved oxygen linear concentration; comparing the changing trends of the oxygen mass transfer coefficient and oxygen consumption rate with pre-stored benchmark parameters to determine the type of operational anomaly and outputting diagnostic results. This invention solves the problem of existing methods failing to accurately distinguish between physical faults in aeration equipment and abnormal microbial biochemical activity. It effectively separates physical hysteresis interference, improving the accuracy of diagnosing different types of operational faults.

Description

Technical Field

[0001] This invention belongs to the field of wastewater treatment control technology, specifically relating to a device and method for detecting abnormal operating status of a wastewater treatment plant. Background Technology

[0002] In the activated sludge process of modern wastewater treatment plants, the aeration system of the bioreactor is the core component for ensuring the metabolic activity of microorganisms. Due to the complexity of wastewater quality and the wear and tear of equipment from long-term operation, bioreactors frequently experience abnormal operating conditions. For example, blockage of the bottom aeration micropores can lead to a decrease in physical mass transfer efficiency, or the presence of toxic substances in the influent can inhibit the biochemical oxygen consumption of microorganisms. Accurately identifying these abnormal conditions is crucial for ensuring effluent quality and controlling process energy consumption.

[0003] Existing anomaly detection methods typically rely on monitoring a single threshold for dissolved oxygen concentration in the reaction tank, or assessing sludge activity through periodic offline sampling and analysis. However, changes in dissolved oxygen concentration are jointly determined by the physical mass transfer processes of the aeration equipment and the biochemical oxygen consumption processes of microorganisms. Under actual dynamic operating conditions, fluctuations in influent load and hydraulic retention time lead to changes in gas-liquid mixing time. Traditional detection methods, lacking effective decoupling from the dynamic characteristics of the equipment, often treat aeration pipeline transport delays, fluid mixing delays, and microbial response delays together, failing to separate physical transport lags from biochemical reaction characteristics from the overall response to dissolved oxygen fluctuations.

[0004] This parameter coupling phenomenon makes it difficult for existing systems to independently obtain accurate oxygen mass transfer coefficients and oxygen consumption rates. When dissolved oxygen data deviates from the normal range, operators cannot accurately distinguish whether the root cause of the fault is the degradation of the physical performance of the aeration equipment, or abnormal rheological properties of the sludge mixture and inhibition of microorganisms. A single static threshold determination is prone to misjudgment when faced with varying operating conditions, affecting the timeliness of process control and resulting in low system diagnostic accuracy.

[0005] Chinese patent document CN121348999A discloses a wastewater treatment information intelligent management method, system, and equipment based on the Internet of Things (IoT), relating to the field of wastewater treatment information management technology. The method includes: Step 1, collecting multi-source water quality data, initializing sensor equipment, and performing preliminary data processing and caching; Step 2, performing edge intelligent prediction of pollutant concentration and sudden increases in pollution load; Step 3, establishing a prediction and control response model, introducing a control strategy generation mechanism based on a rule engine and priority trade-offs, and calculating the dynamic sensitivity value for chemical dosing; Step 4, adjusting the blower frequency converter, dosing module, and return pump, and collecting and uploading feedback in real time; Step 5, performing artificial intelligence strategy center verification and digital twin simulation strategies, analyzing multi-plant load scheduling and implementing optimization measures, and providing real-time visual interaction. This solves the problem of energy and chemical waste caused by the lack of a feedforward prediction mechanism. However, it does not solve the problem that when dissolved oxygen data deviates from the normal range, operators cannot accurately distinguish whether the root cause of the fault is the physical performance degradation of the aeration equipment, or the abnormal rheological properties of the sludge mixture and the inhibition of microorganisms. Summary of the Invention

[0006] The present invention provides a device and method for detecting abnormal operating conditions in a wastewater treatment plant. The purpose is to overcome the problem that existing abnormal detection technologies are unable to effectively decouple physical mass transfer lag from biochemical oxygen consumption characteristics under dynamic hydraulic conditions, resulting in the inability to accurately distinguish between physical faults in aeration equipment and abnormal biochemical activity of microorganisms.

[0007] To address this, the present invention provides a device for detecting abnormal operating conditions in a wastewater treatment plant, comprising a flow acquisition module, an aeration execution module, a dissolved oxygen detection module, a calculation and control module, a water quality parameter acquisition module, and a status diagnosis module. The signal output terminals of the flow acquisition module and the dissolved oxygen detection module are electrically connected to the signal input terminal of the calculation and control module. The signal output terminal of the calculation and control module is electrically connected to the signal input terminal of the aeration execution module and the signal input terminal of the status diagnosis module, respectively. The signal output terminal of the water quality parameter acquisition module is electrically connected to the signal input terminal of the status diagnosis module.

[0008] Preferably, the flow acquisition module is used to acquire the influent flow rate data of the bioreactor and transmit the acquired influent flow rate data to the calculation and control module; The aeration execution module is used to receive the gas volume control command from the calculation and control module, and supply gas to the biological reactor according to the gas volume control command. The dissolved oxygen detection module is used to collect the dissolved oxygen concentration signal of the mixed liquor in the bioreactor in real time and transmit the collected dissolved oxygen concentration signal of the mixed liquor to the computing control module. The calculation and control module is used to generate control commands from the received influent flow data, send the control commands to the aeration execution module, demodulate and separate the oxygen mass transfer coefficient and oxygen consumption rate according to the received mixed liquor dissolved oxygen concentration signal, and transmit the demodulated and separated oxygen mass transfer coefficient and oxygen consumption rate to the status diagnosis module. The water quality parameter acquisition module is used to acquire the concentration of suspended solids in the mixed liquor and its real-time water temperature data in the bioreactor, and transmit the acquired concentration of suspended solids in the mixed liquor and its real-time water temperature data to the status diagnosis module. The status diagnosis module is used to receive the oxygen mass transfer coefficient and oxygen consumption rate output by the calculation control module, as well as the mixed liquor suspended solids concentration and real-time water temperature data output by the water quality parameter acquisition module. The status diagnosis module determines the abnormality type based on the received data and outputs processing instructions.

[0009] A method for detecting abnormal operating conditions in a wastewater treatment plant includes the following steps: S1. Obtain the influent flow rate data of the bioreactor and calculate the local fluid mixing time by combining the spatial geometric volume parameters of the bioreactor. S2. Determine the excitation signal frequency based on the local fluid mixing time, generate the gas volume control command corresponding to the excitation signal frequency, and the aeration execution module supplies gas to the biological reactor according to the gas volume control command. The gas supply is a periodic fluctuating gas volume. S3. Collect the actual air volume feedback sequence of the aeration execution module and simultaneously collect the dissolved oxygen concentration response sequence in the bioreactor. S4. Perform cross-correlation calculation on the actual gas volume feedback sequence and dissolved oxygen concentration response sequence in step S3 to determine the pure lag time of physical transport. S5. Use the physical transport pure lag time to perform time alignment correction on the dissolved oxygen concentration response sequence, perform orthogonal demodulation on the corrected dissolved oxygen concentration response sequence, extract the net phase lag angle, convert the net phase lag angle into the oxygen mass transfer coefficient, and calculate the oxygen consumption rate in combination with the dissolved oxygen linear concentration. S6. Compare the changing trends of oxygen mass transfer coefficient and oxygen consumption rate with the pre-stored baseline parameters, determine the type of operational anomaly, and output the diagnostic results.

[0010] Preferably, step S1 includes the following steps: S1.1 Obtain the influent flow rate data of the bioreactor, the total effective volume of each cell of the bioreactor, and the volume coefficient of the local mixing unit; S1.2 When the influent flow rate data of the bioreactor is greater than or equal to the pre-stored minimum flow rate threshold, the product of the local mixing unit volume factor of the bioreactor and the total effective volume of the unit is divided by the influent flow rate data to obtain the local fluid mixing time. When the influent flow rate of the bioreactor is less than the pre-stored minimum flow rate threshold, the pre-stored maximum mixing time constant is used as the local fluid mixing time.

[0011] Preferably, step S2, which determines the excitation signal frequency based on the local fluid mixing time and generates a gas volume control command corresponding to the excitation signal frequency, includes the following steps: S2.1. Divide the frequency tuning coefficient by the local fluid mixing time to obtain the first angular frequency; S2.2 Obtain the mechanical response cutoff frequency of the aeration execution module; S2.3. The minimum value between the first angular frequency and the mechanical response cutoff frequency of the aeration execution module is determined as the excitation signal frequency; S2.4. The reference aeration volume setpoint is superimposed with the product of the micro-disturbance amplitude coefficient and the sine wave function to generate a discrete aeration volume control command; the frequency of the sine wave function is the frequency of the excitation signal.

[0012] Preferably, step S4 includes the following steps: S4.1 Construct a sliding observation window, and perform DC preprocessing on the actual gas volume feedback sequence and dissolved oxygen concentration response sequence within the sliding observation window to generate the corresponding AC fluctuation sequence; S4.2 Calculate the cross-correlation values ​​of the AC wave sequence under different discrete lag steps, and determine the target discrete lag step number corresponding to the global maximum cross-correlation value; S4.3 When the global maximum cross-correlation value is greater than the pre-stored correlation threshold, divide the target discrete lag step corresponding to the global maximum cross-correlation value by the signal sampling frequency to obtain the physical transmission pure lag time.

[0013] Preferably, step S5 involves using the physical transport time lag to perform time alignment correction on the dissolved oxygen concentration response sequence, performing orthogonal demodulation on the corrected dissolved oxygen concentration response sequence, and extracting the net phase lag angle, including the following steps: S5.1 Based on the signal sampling period, the physical transmission pure delay time is decomposed into integer part steps and fractional part steps; S5.2 Perform linear interpolation on the dissolved oxygen concentration response sequence using the integer part number of steps and the fractional part number of steps to obtain the corrected dissolved oxygen concentration response sequence; S5.3 Generate an in-phase reference signal and a quadrature reference signal with the same frequency as the excitation signal; S5.4. Perform multiplication and accumulation operations on the corrected dissolved oxygen concentration response sequence with the in-phase reference signal and the orthogonal reference signal from step S5.3, respectively, and extract the in-phase component and the orthogonal component. S5.5 Calculate the arctangent of the ratio of the quadrature component to the in-phase component to obtain the net phase lag angle.

[0014] Preferably, the step S5 of converting the net phase lag angle into the oxygen mass transfer coefficient includes the following steps: dividing the excitation signal frequency by the tangent value corresponding to the opposite of the net phase lag angle to obtain the oxygen mass transfer coefficient.

[0015] Preferably, the calculation of oxygen consumption rate in step S5, which combines the dissolved oxygen concentration, includes the following steps: S5.6 Obtain the current water temperature, atmospheric pressure, and corresponding salinity correction coefficient of the mixed liquor in the bioreactor; S5.7. Based on the current water temperature and atmospheric pressure obtained in step S5.6, obtain the standard saturated dissolved oxygen concentration; S5.8 Multiply the standard saturated dissolved oxygen concentration by the salinity correction coefficient obtained in step S5.6, and then subtract the dissolved oxygen concentration to obtain the concentration difference. S5.9 Multiply the oxygen mass transfer coefficient by the concentration difference to obtain the oxygen consumption rate.

[0016] Preferably, the pre-stored reference parameters in step S6 include a reference oxygen mass transfer coefficient and a reference oxygen consumption rate; the comparison of the changing trends of the oxygen mass transfer coefficient and oxygen consumption rate with the pre-stored reference parameters to determine the type of operational anomaly includes the following steps: S6.1 Obtain the real-time water temperature, suspended solids concentration, and current set gas volume of the mixed liquor in the bioreactor; S6.2 Calculate the baseline oxygen mass transfer coefficient based on the acquired real-time water temperature and the current set gas volume; calculate the baseline oxygen consumption rate based on the acquired real-time water temperature and the concentration of suspended solids in the mixed liquor. S6.3 Calculate the physical performance degradation index based on the oxygen mass transfer coefficient and the baseline oxygen mass transfer coefficient; calculate the biochemical activity inhibition index based on the oxygen consumption rate and the baseline oxygen consumption rate. S6.4 Determine the type of operational anomaly based on the physical efficacy decay index and the biochemical activity inhibition index.

[0017] The beneficial effects of this invention are: This invention provides a device and method for detecting abnormal operating conditions in wastewater treatment plants. It generates a gas flow control command corresponding to the excitation signal frequency based on the local fluid mixing time, allowing the excitation signal frequency to be dynamically adjusted according to fluctuations in the influent flow rate of the biological reactor. This avoids measurement distortion caused by using a fixed frequency under different hydraulic loads and improves the accuracy of extracting fluid dynamic response characteristics under varying operating conditions. This invention utilizes cross-correlation calculations to determine the pure physical transport delay time, uses this pure physical transport delay time to perform time alignment correction on the dissolved oxygen concentration response sequence, and performs orthogonal demodulation on the corrected dissolved oxygen concentration response sequence. This eliminates the physical hysteresis interference caused by mechanical transport from the total response time, achieving independent decoupling calculation of the oxygen mass transfer coefficient and oxygen consumption rate, thus improving the accuracy of core parameter identification. This invention compares the changing trends of oxygen mass transfer coefficient and oxygen consumption rate with pre-stored benchmark parameters to determine the type of operational anomaly and output diagnostic results. It eliminates the interference of ambient temperature and influent load fluctuations on static threshold settings and can clearly distinguish the type of operational anomaly, including accurately distinguishing physical faults of aeration equipment, inhibition of influent biochemical toxicity, and abnormal rheological properties of sludge mixed liquor, thereby reducing the false alarm rate of system diagnosis. Attached Figure Description

[0018] The present invention will now be described in further detail with reference to the accompanying drawings.

[0019] Figure 1 This is a diagram of the architecture of a wastewater treatment plant's abnormal operation detection device. Figure 2 This is a flowchart of the method for detecting abnormal operating conditions in wastewater treatment plants. Detailed Implementation

[0020] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0021] Example 1: like Figure 1 As shown, a wastewater treatment plant operation status anomaly detection device includes a flow acquisition module, an aeration execution module, a dissolved oxygen detection module, a calculation and control module, a water quality parameter acquisition module, and a status diagnosis module. The signal output terminals of the flow acquisition module and the dissolved oxygen detection module are electrically connected to the signal input terminal of the calculation and control module. The signal output terminal of the calculation and control module is electrically connected to the signal input terminal of the aeration execution module and the signal input terminal of the status diagnosis module, respectively. The signal output terminal of the water quality parameter acquisition module is electrically connected to the signal input terminal of the status diagnosis module.

[0022] The detection device of this invention has a simple structure and is convenient for detecting abnormalities in the operating status of sewage treatment plants.

[0023] Preferably, the flow acquisition module is used to acquire the influent flow rate data of the bioreactor and transmit the acquired influent flow rate data to the calculation and control module; Specifically, the flow acquisition module is located at the inlet of the bioreactor and uses an electromagnetic flow meter or an ultrasonic flow meter to acquire the instantaneous inlet flow signal. The flow acquisition module establishes a communication connection with the calculation and control module and transmits the inlet flow data to the calculation and control module as the basic parameter for calculating the fluid state.

[0024] The aeration execution module is used to receive the gas volume control command from the calculation and control module, and supply gas to the biological reactor according to the gas volume control command. Specifically, the aeration execution module includes a blower unit, a frequency converter, and an air piping system. The aeration execution module is electrically connected to the operational control module. The signal input terminal of the frequency converter receives air volume control commands sent by the operational control module, and the signal output terminal of the frequency converter supplies air to the bioreactor through the blower unit and the air piping system. The aeration execution module receives the air volume control commands and adjusts the blower speed according to these commands using existing technology. By superimposing a sinusoidal fluctuating air volume onto a baseline air supply, a periodically fluctuating aeration air volume is formed, and this air volume change is output to the aeration heads at the bottom of the bioreactor to achieve periodic fluctuating aeration.

[0025] The dissolved oxygen detection module is used to collect the dissolved oxygen concentration signal of the mixed liquor in the bioreactor in real time and transmit the collected dissolved oxygen concentration signal of the mixed liquor to the computing control module. Specifically, the bioreactor includes at least one existing biochemical functional zone, within which a dissolved oxygen detection module is located. This module utilizes existing electrochemical or optical sensors to collect dissolved oxygen concentration signals from the mixed solution within the bioreactor. The dissolved oxygen detection module then feeds back the real-time collected dissolved oxygen concentration signals to the computational control module.

[0026] The calculation and control module is used to generate control commands from the received influent flow data, send the control commands to the aeration execution module, demodulate and separate the oxygen mass transfer coefficient and oxygen consumption rate according to the received mixed liquor dissolved oxygen concentration signal, and transmit the demodulated and separated oxygen mass transfer coefficient and oxygen consumption rate to the status diagnosis module. Specifically, the computational control module, as the core processing module of the detection device, integrates a time delay compensation algorithm and a phase demodulation algorithm. Based on the influent flow rate, the computational control module calculates the local fluid residence time, generates a sinusoidal excitation signal of the corresponding frequency, and sends it to the aeration execution module. Simultaneously, it receives the dissolved oxygen concentration signal from the dissolved oxygen detection module, performs time delay compensation through cross-correlation calculations to eliminate physical transmission lag, and then obtains the oxygen mass transfer coefficient and oxygen consumption rate through orthogonal demodulation separation.

[0027] The water quality parameter acquisition module is used to acquire the concentration of suspended solids in the mixed liquor and its real-time water temperature data in the bioreactor, and transmit the acquired concentration of suspended solids in the mixed liquor and its real-time water temperature data to the status diagnosis module. Specifically, the water quality parameter acquisition module is used to obtain the concentration of suspended solids in the mixed liquor within the bioreactor. MLSS ) and real-time water temperature ( T The water quality parameter acquisition module receives and transmits the data to the status diagnosis module, serving as the basic data source for dynamic baseline parameter calculation. The water quality parameter acquisition module can be an existing online sensor directly installed in the bioreactor; alternatively, it can be a data interface that communicates with an existing laboratory host computer to receive the entered water quality parameter data.

[0028] The status diagnosis module is used to receive the oxygen mass transfer coefficient and oxygen consumption rate output by the calculation control module, as well as the mixed liquor suspended solids concentration and real-time water temperature data output by the water quality parameter acquisition module. The status diagnosis module determines the abnormality type based on the received data and outputs processing instructions.

[0029] Specifically, the status diagnosis module has pre-stored normal operating condition baseline parameters. The status diagnosis module receives the oxygen mass transfer coefficient and oxygen consumption rate, mixed liquor suspended solids concentration and real-time water temperature data output by the calculation and control module. By comparing the parameter change trends, it determines whether the aeration equipment is blocked, microbial poisoning or abnormal sludge properties, and outputs an alarm signal.

[0030] Example 2: Based on Example 1, such as Figure 2 As shown, a method for detecting abnormal operating conditions in a wastewater treatment plant includes the following steps: S1. Obtain the influent flow rate data of the bioreactor and calculate the local fluid mixing time by combining the spatial geometric volume parameters of the bioreactor. Specifically, the computational control module reads the real-time influent flow data uploaded by the flow acquisition module, and combines this data with the geometric volume parameters of the bioreactor and the local mixing unit volume coefficient of the flow acquisition module. The computational control module then calculates the local fluid mixing time in the area where the flow acquisition module sensor is located under the current operating conditions. This local fluid mixing time reflects the current hydraulic load status.

[0031] S2. Determine the excitation signal frequency based on the local fluid mixing time, generate the gas volume control command corresponding to the excitation signal frequency, and the aeration execution module supplies gas to the biological reactor according to the gas volume control command. The gas supply is a periodic fluctuating gas volume. Specifically, the frequency of the sinusoidal excitation signal is determined based on the local fluid mixing time, and a corresponding gas volume control command is generated. The calculation and control module superimposes the sinusoidal command onto the current production baseline gas volume setting value, and the synthesized gas volume control command is sent to the aeration execution module to drive the blower to generate periodic sinusoidal gas volume fluctuations with the same frequency as the excitation signal and supply gas to the biological reactor.

[0032] S3. Collect the actual air volume feedback sequence of the aeration execution module and simultaneously collect the dissolved oxygen concentration response sequence in the bioreactor. Specifically, the dissolved oxygen detection module records the response curve (dissolved oxygen concentration response sequence) generated by the fluctuation of dissolved oxygen concentration in the bioreactor as the gas volume fluctuates, and the operation and control module synchronously obtains the actual gas volume feedback sequence (dissolved oxygen concentration response sequence in the bioreactor) from the aeration execution module.

[0033] S4. Perform cross-correlation calculation on the actual gas volume feedback sequence and dissolved oxygen concentration response sequence in step S3 to determine the pure lag time of physical transport. Specifically, the computational control module performs cross-correlation calculations on the actual gas volume feedback sequence and the dissolved oxygen concentration response sequence, searches for the lag time corresponding to the peak value of the cross-correlation function, and determines the physical time delay caused by gas phase and liquid phase transport.

[0034] S5. Use the physical transport pure lag time to perform time alignment correction on the dissolved oxygen concentration response sequence, perform orthogonal demodulation on the corrected dissolved oxygen concentration response sequence, extract the net phase lag angle, convert the net phase lag angle into the oxygen mass transfer coefficient, and calculate the oxygen consumption rate in combination with the dissolved oxygen linear concentration. Specifically, the computational control module uses physical time delay to perform time alignment correction on the dissolved oxygen concentration response sequence. The computational control module performs digital phase-locked loop operation on the corrected dissolved oxygen concentration response sequence to extract the in-phase and quadrature components of the fundamental component. The computational control module calculates the net phase lag angle, uses a first-order kinetic model to convert the net phase lag angle into the oxygen mass transfer coefficient, and combines it with the dissolved oxygen linear concentration data to back-calculate the oxygen consumption rate.

[0035] S6. Compare the changing trends of oxygen mass transfer coefficient and oxygen consumption rate with the pre-stored baseline parameters, determine the type of operational anomaly, and output the diagnostic results.

[0036] The status diagnosis module monitors changes in oxygen mass transfer coefficient and oxygen consumption rate, mixed liquor suspended solids concentration, and real-time water temperature data in real time. When the oxygen mass transfer coefficient decreases monotonically and the oxygen consumption rate remains stable, the status diagnosis module determines that the aerator head is physically blocked. When the oxygen consumption rate decreases significantly and the oxygen mass transfer coefficient does not change significantly, the status diagnosis module determines that the organism is inhibited. When the oxygen mass transfer coefficient and oxygen consumption rate show a correlation, the status diagnosis module determines the risk of sludge bulking based on historical trends.

[0037] This invention generates gas flow control commands corresponding to the excitation signal frequency based on the local fluid mixing time, enabling the excitation signal frequency to be dynamically adjusted according to fluctuations in the influent flow rate of the biological reactor. This avoids measurement distortion caused by using a fixed frequency under different hydraulic loads and improves the accuracy of extracting fluid dynamic response characteristics under varying operating conditions. This invention utilizes cross-correlation calculations to determine the pure physical transport delay time, and uses this pure physical transport delay time to perform time alignment correction on the dissolved oxygen concentration response sequence. Orthogonal demodulation is then performed on the corrected dissolved oxygen concentration response sequence, eliminating the physical hysteresis interference caused by mechanical transport from the total response time. This achieves independent decoupling calculation of the oxygen mass transfer coefficient and oxygen consumption rate, improving the accuracy of core parameter identification. This invention compares the changing trends of the oxygen mass transfer coefficient and oxygen consumption rate with pre-stored benchmark parameters to determine the type of operational anomaly and output diagnostic results. This eliminates the interference of ambient temperature and influent load fluctuations on static threshold settings, and can clearly distinguish the type of operational anomaly, including accurately differentiating physical faults in aeration equipment, influent biochemical toxicity inhibition, and abnormal rheological properties of sludge mixed liquor, reducing the false alarm rate of system diagnosis.

[0038] Example 3: Based on Example 2, step S1 includes the following steps: S1.1 Obtain the influent flow rate data of the bioreactor, the total effective volume of each cell of the bioreactor, and the volume coefficient of the local mixing unit; Includes the following steps: S1.1.1 Obtain the instantaneous influent flow rate data of the biological reactor.

[0039] Specifically, the acquisition of the instantaneous influent flow rate data of the bioreactor involves the computational control module reading the real-time influent flow rate value uploaded by the flow acquisition module via a communication interface at a pre-stored sampling frequency. This real-time influent flow rate value reflects the current hydraulic load status of the bioreactor. The acquisition and digital filtering of the instantaneous influent flow rate value are signal processing techniques well-known to those skilled in the art and will not be elaborated upon here.

[0040] S1.1.2 Constructing local mixing units for the bioreactor; Specifically, to address the issue of the excessively large overall hydraulic retention time scale of the bioreactor, which fails to meet the requirements for rapid biochemical reaction detection, this invention logically defines a local mixing unit surrounding the dissolved oxygen detection module. This local mixing unit is considered an ideal completely mixed reactor (CSTR). The computational control module pre-stores the total effective volume of each cell in the bioreactor. This pretreatment step prevents drastic oscillations in the subsequently calculated mixing time due to instantaneous changes in the flow signal, thereby ensuring the stability of the control module's control.

[0041] S1.1.3 Determine the volume factor of the local mixed unit; Specifically, to overcome the time-scale mismatch problem caused by treating the entire bioreactor as a single homogeneous body in traditional fully mixed-flow models, this embodiment introduces a local mixing unit volume factor. Locally mixed element volume factor It is a dimensionless physical quantity that characterizes the effective fluid region around the dissolved oxygen monitoring probe in the dissolved oxygen detection module, which can be considered as instantaneously completely mixed, as a percentage of the total effective volume of a single cell in the bioreactor. The proportion.

[0042] In practical engineering applications, the volume factor of locally mixed units The value range is typically set between 0.05 and 0.20. This is the volume factor for the local mixing unit. The specific values ​​can be determined based on existing tracer residence time distribution experiments, or by simulating the dead zone distribution of the flow field under aeration and stirring using existing computational fluid dynamics simulations. The computational control module pre-stores the total effective volume of each cell in the bioreactor. and the volume factor of the local mixing unit determined by the above method .

[0043] S1.2 When the influent flow rate data of the bioreactor is greater than or equal to the pre-stored minimum flow rate threshold, the product of the local mixing unit volume factor of the bioreactor and the total effective volume of the unit is divided by the influent flow rate data to obtain the local fluid mixing time. When the influent flow rate of the bioreactor is less than the pre-stored minimum flow rate threshold, the pre-stored maximum mixing time constant is used as the local fluid mixing time.

[0044] Specifically, the computational control module calculates the dynamic time constant based on the mass conservation principle of continuum mechanics, utilizing the displacement effect of the influent flow rate on the local mixing unit. Considering that actual wastewater treatment processes may involve pump stoppages or extremely low flow conditions, direct division could lead to numerical overflow or calculation results approaching infinity. Therefore, the computational control module pre-stores a minimum flow threshold. (For example, 10% of the design traffic).

[0045] The specific calculation logic follows the piecewise function as follows: ; In the formula: Indicates the discrete sampling time; express The local fluid mixing time, calculated at any moment, is in seconds (s). Its physical meaning is the hydraulic residence time for the fluid in the local mixing unit to be completely renewed once. This represents the volume factor of the locally mixed element, with a value ranging from 0.05 to 0.20. This represents the total effective volume of a single cell in a biological reactor, expressed in cubic meters (m³). 3 ); express The instantaneous flow rate of the influent after filtering is expressed in cubic meters per second (m²). 3 / s); A lower limit threshold for flow rate is set to prevent the denominator from approaching zero; This is the pre-stored maximum mixing time constant, corresponding to the system's default response period under extremely low flow conditions.

[0046] The calculation control module will obtain This serves as the reference system time constant under current operating conditions. From the perspective of signal and detection devices, the bioreactor acts as a low-pass filter to external disturbances. This directly determines the cutoff frequency of the filter.

[0047] When the influent flow rate increases A decrease in frequency means faster local fluid turnover, enabling it to respond to higher frequency disturbances; conversely, a decrease in frequency means it can only respond to low frequency disturbances.

[0048] Through the above calculations, this invention transforms the original experience-based frequency setting into a hydraulic state adaptive process based on a physical model, providing a physical theoretical support for subsequent steps to generate an excitation signal that can effectively penetrate the fluid medium and be captured by the sensor.

[0049] Example 4: Based on Example 3, step S2, which determines the excitation signal frequency based on the local fluid mixing time and generates a gas volume control command corresponding to the excitation signal frequency, includes the following steps: S2.1. Divide the frequency tuning coefficient by the local fluid mixing time to obtain the first angular frequency; S2.2 Obtain the mechanical response cutoff frequency of the aeration execution module; S2.3. The minimum value between the first angular frequency and the mechanical response cutoff frequency of the aeration execution module is determined as the excitation signal frequency; Specifically, step S2 aims to synthesize a gas volume control command that can effectively penetrate the physical medium and stimulate a biochemical response, based on step S1. The technique utilizes the frequency domain response principle to lock the excitation signal frequency within the phase-sensitive region of the detection device, while simultaneously ensuring the biochemical reaction remains in a pseudo-linear range (i.e., under small-amplitude excitation, the dissolved oxygen response is approximately linear with the gas volume excitation). The computational control module specifically executes the following sub-steps: The operation control module reads the local fluid mixing time determined in step S1. Based on linear system theory, the phase lag of a first-order lag system changes most significantly near the cutoff frequency; therefore, this frequency is the optimal detection frequency. To avoid the calculated frequency exceeding the mechanical equipment's response capability, the operation and control module pre-stores the blower's mechanical response cutoff frequency. (Usually determined by the equipment's inertia, such as 0.5 rad / s).

[0050] The operation and control module pre-stores the local fluid mixing time. Upper and lower limits (e.g.) T min ≤ ≤ T max ): lower limit T min : Ensure that the excitation frequency does not exceed the mechanical response cutoff frequency ωmax of the blower; upper limit T max Avoid excessively low excitation frequency, which can cause the signal to be overwhelmed by noise or the detection cycle to be too long.

[0051] After confirmation Once the target is within the effective range (upper and lower limits), the target excitation angular frequency is calculated based on the reciprocal relationship between the time constant and the angular frequency. : ; in: This represents the angular frequency of the final output excitation signal, expressed in radians per second (rad / s). The local fluid mixing time calculated in step S1.2 is expressed in seconds (s). This is the frequency tuning factor, preferably ranging from 0.8 to 1.2. The physical meaning of this factor is to lock the excitation frequency near the -3dB cutoff frequency of the system's Bode plot, thereby obtaining the maximum signal-to-noise ratio and phase sensitivity. This represents the minimum value function, used to prevent the calculation frequency from exceeding the physical dynamic response limit of the blower.

[0052] To ensure that the nonlinear Michaelis-Menten biochemical kinetic equations satisfy the small-signal linear approximation condition near the operating point, the computational control module must strictly limit the amplitude of the excitation signal. The computational control module pre-stores the amplitude coefficients of the micro-perturbation. Its value is preferably in the range of 0.05 to 0.10.

[0053] The selection of the micro-perturbation amplitude coefficient α needs to balance the signal-to-noise ratio of the dissolved oxygen response signal and the flow path noise (sensor noise, flow fluctuations, etc.), as well as the linearity of the gas volume excitation and biochemical reaction: if it is too small, the dissolved oxygen response signal will be submerged in the flow path white noise; if it is too large, it will introduce high-order harmonic distortion. Furthermore, to prevent gas volume command overflow, the computational control module performs the following boundary checks:

[0054] in: The effective amplitude coefficient of the micro-perturbation; The baseline aeration rate setpoint is the value required for the current process operation; it is output by the upper-level process control module (such as PID control based on effluent quality and MLSS concentration) and is used to maintain the basic oxygen demand for the biochemical reaction. and These represent the maximum and minimum safe operating air volume allowed by the blower; this logic ensures that the final superimposed waveform will not trigger the inverter's overload or surge protection.

[0055] S2.4. The reference aeration volume setpoint is superimposed with the product of the micro-disturbance amplitude coefficient and the sine wave function to generate a discrete aeration volume control command; the frequency of the sine wave function is the frequency of the excitation signal.

[0056] Specifically, the calculation and control module obtains the baseline aeration setpoint required for the current process operation in real time. Considering the discrete nature of digital control systems, the computational control module operates in the discrete time domain. ( To control the cycle, k is the discrete time step number (k=0,1,2,...), a discrete gas volume control command sequence is constructed.

[0057] The composition logic follows the formula below: ; in: express The air volume control command value sent to the blower actuator at all times, in standard cubic meters per minute (Nm³). 3 / min); This indicates the baseline aeration rate setting. Indicates the discrete sampling time; This represents the operation of the sine function.

[0058] The arithmetic control module calculates the discrete sequence generated in the digital domain. Converted into analog signals suitable for industrial field actuators.

[0059] In this embodiment, the arithmetic control module uses a built-in high-precision digital-to-analog converter to... The signal is linearly converted to a 4-20mA current signal corresponding to the blower's air volume range (4mA corresponds to the minimum safe air volume, and 20mA corresponds to the maximum safe air volume). To eliminate digital quantization noise, an RC low-pass filter circuit is connected in parallel at the output.

[0060] The analog signal is transmitted via shielded twisted-pair cable to the inverter control terminal in the aeration execution module. The inverter linearly adjusts the operating frequency of the blower motor based on the received analog signal, thereby driving the blower to output a sinusoidally varying physical airflow. This airflow is then transported to the biological reactor via the existing air duct system. Impedance matching and electromagnetic interference suppression measures in the signal transmission line, as well as the design of the air duct system, can be implemented by those skilled in the art according to industrial automation wiring and wastewater treatment engineering standards, and will not be elaborated upon here.

[0061] Example 5: Based on Example 4, step S3 involves collecting the actual air volume feedback sequence of the aeration execution module and simultaneously collecting the dissolved oxygen concentration response sequence in the bioreactor.

[0062] Specifically, step S3 aims to use statistical signal processing to accurately isolate the purely physical lag caused by physical pipeline transmission and gas-liquid mixing, thereby solving the technical problem of parameter oscillation caused by timing misalignment in traditional PID control.

[0063] The operation control module specifically executes the following sub-steps: The arithmetic control module uses an internal unified clock source to trigger data sampling, ensuring strict alignment of input and output signals on the time axis.

[0064] The operation and control module simultaneously collects two sets of time series data: the first set is the actual air volume feedback sequence from the aeration execution module. The second group consists of dissolved oxygen concentration response sequences from the dissolved oxygen detection module. Sampling frequency The frequency is set to at least 10 times the excitation signal frequency (e.g., 1Hz to 10Hz) to satisfy the Nyquist sampling theorem and ensure the resolution of subsequent time-domain analysis. Before storing the data in the buffer, abnormal data from the two types of sensors are pre-checked, including situations such as sensor disconnection (value is 0 or full scale) and long-term unchanged signal (variance approaches 0). If an anomaly is detected, the effective time delay parameter from the previous moment is maintained to prevent subsequent algorithms from diverging due to invalid input data.

[0065] Example 6: Based on Example 5, step S4 includes the following steps: S4.1 Construct a sliding observation window, and perform DC preprocessing on the actual gas volume feedback sequence and dissolved oxygen concentration response sequence within the sliding observation window to generate the corresponding AC fluctuation sequence; Specifically, in order to eliminate dissolved oxygen line drift caused by slow changes in influent load and thus extract pure dynamic frequency response characteristics, the computational control module constructs a memory structure with a length of [length missing]. The sliding data window. Sliding observation window length. It needs to cover at least 3 to 5 complete excitation signal cycles. (Right now ); Excitation signal angular frequency This is to ensure that the statistical sample has sufficient ergodicity.

[0066] Within the sliding observation window, the operation control module... and Perform deDCization preprocessing to generate a zero-mean sequence containing only AC ripple components. and This step is achieved by subtracting the arithmetic mean within the sliding observation window, calculated using the following formula:

[0067] in, This represents the total number of sampling points within the sliding observation window; i The sampling point number within the window ( i =0,1,...,N 1); This process effectively eliminates the interference of DC bias on the correlation calculation amplitude, ensuring that the analysis focuses on the dynamic fluctuation pattern.

[0068] S4.2 Calculate the cross-correlation values ​​of the AC wave sequence under different discrete lag steps, and determine the target discrete lag step number corresponding to the global maximum cross-correlation value; Specifically, based on linear system theory, the input signal and its resulting output response have waveform similarity in the time domain, and the maximum similarity occurs at the lag time point.

[0069] The arithmetic control module uses digital signal processing algorithms to calculate the cross-correlation function between input fluctuations and output dissolved oxygen fluctuations. To avoid excessive computation, a lag step index is used. The search range is limited to the maximum physically feasible lag interval. Inside( ).

[0070] Discrete cross-correlation is calculated based on the following formula:

[0071] in: Indicates that the time displacement is The cross-relationship values ​​at each sampling point; This indicates the number of data points corresponding to the length of the sliding observation window. ; Window length;

[0072] This represents the discrete lag step count (i.e., the lag step index), and its value range is... ,in It is calculated from the maximum hydraulic retention time of the biological tank; Indicates a discrete-time index; and These are the pretreated gas volume and dissolved oxygen sequences, respectively.

[0073] traversal of the operation control module ∈[0, Calculate the cross-correlation coefficient and find the global maximum cross-correlation value. R uy,max Its corresponding m That is, the number of discrete lag steps of the target. m It is used for calculating the pure delay time of subsequent physical transmission.

[0074] S4.3 When the global maximum cross-correlation value is greater than the pre-stored correlation threshold, divide the target discrete lag step corresponding to the global maximum cross-correlation value by the signal sampling frequency to obtain the physical transmission pure lag time.

[0075] Specifically, relying solely on the maximum peak value may lead to misjudgment due to environmental noise. Therefore, this embodiment adopts a peak significance verification strategy.

[0076] The operation control module first searches for cross-correlation sequences. Global maximum value in and the corresponding normalized correlation coefficient magnitude The operation control module pre-stores correlation thresholds. (e.g., 0.6), only when the normalized correlation coefficient Greater than the pre-stored correlation threshold hour, Only then was it recognized as an effective physical lag; Otherwise, the operation and control module determines that the signal-to-noise ratio is too low under the current operating conditions, and keeps the lag time parameter of the previous week unchanged or uses an empirical formula based on the real-time water flow rate for estimation.

[0077] After passing the validity verification, the operation control module determines the physical transmission pure delay time according to the following formula. : ; in: This represents the final determined physical transport delay time, expressed in seconds (s). This time parameter physically characterizes the transport delay of air in the pipeline, the release delay of bubbles from the aerator, and the mass transfer and mixing delay of oxygen molecules at the gas-liquid interface. This represents the number of discrete steps corresponding to the maximum correlation obtained through statistical optimization. This is the signal sampling frequency.

[0078] This value The decoupling module, which will be passed to the subsequent calculation and control module, is used to perform time axis translation correction on the original dissolved oxygen signal, thereby ensuring that the phase difference calculated later only reflects the biochemical oxygen consumption characteristics of the activated sludge, fundamentally eliminating the interference of the geometric dimensions of the physical facility on the identification of biochemical parameters.

[0079] Example 7: Based on Example 6, step S5 utilizes the pure physical transport time lag to perform time alignment correction on the dissolved oxygen concentration response sequence, performs orthogonal demodulation on the corrected dissolved oxygen concentration response sequence, and extracts the net phase lag angle, including the following steps: S5.1 Based on the signal sampling period, the physical transmission pure delay time is decomposed into integer part steps and fractional part steps; Specifically, the operation control module reads the physical transmission pure delay time determined by cross-correlation analysis in step S4. Due to the pure time delay in physical transmission. Often non-sampling period

[0080] ( Integer multiples of ) will introduce quantization phase error if direct integer shift is used.

[0081] Therefore, this embodiment uses linear interpolation or cubic spline interpolation algorithms to construct a fractional time delay filter. The computation control module generates the aligned dissolved oxygen sequence according to the following logic. : ; in: The number of steps for the lagging integer part (rounded down); The lagging decimal part ( ); The original dissolved oxygen response sequence, and They are the same physical quantity; t k This represents the discrete sampling time.

[0082] S5.2 Perform linear interpolation (or cubic spline interpolation using existing techniques) on the dissolved oxygen concentration response sequence using the integer part number and the fractional part number to obtain the corrected dissolved oxygen concentration response sequence; Through this step, the virtually corrected dissolved oxygen signal is precisely shifted to the gas-liquid interface of the aeration head on the time axis, eliminating the interference of purely physical lag caused by the length of the duct (the duct refers to the air delivery pipeline system between the blower outlet and the aeration head in the aeration tank) and the upward movement of bubbles on the biochemical phase analysis.

[0083] S5.3 Generate an in-phase reference signal and a quadrature reference signal with the same frequency as the excitation signal; Specifically, in order to extract weak single-frequency responses under operating conditions with background noise (such as random noise from bubble bursting and turbulent hydraulic stirring), this embodiment employs digital lock-in amplification technology. This technology utilizes the orthogonality principle of sine functions, equivalent to a high-quality factor bandpass filter with an extremely narrow center frequency.

[0084] The operation control module generates the frequency in memory and the excitation frequency in step S2. Completely consistent in-phase reference signal Orthogonal reference signal .

[0085] S5.4. Perform multiplication and accumulation operations on the corrected dissolved oxygen concentration response sequence with the in-phase reference signal and the orthogonal reference signal from step S5.3, respectively, and extract the in-phase component and the orthogonal component. Specifically, the in-phase reference signal and the quadrature reference signal are multiplied and accumulated to calculate the in-phase component. Orthogonal components : ; ; in: The in-phase component represents the real part of the energy in the response signal that is in phase with the excitation signal. These are orthogonal components, representing the imaginary part of the energy in the response signal that lags behind the excitation signal by 90 degrees. The dissolved oxygen response sequence is after fractional time delay correction and elimination of physical hysteresis. This represents the total number of samples within the integration window (i.e., the sliding observation window), and its value must be an integer multiple of the excitation signal period to eliminate spectral leakage.

[0086] S5.5 Calculate the arctangent of the ratio of the quadrature component to the in-phase component to obtain the net phase lag angle.

[0087] Specifically, the operation control module is based on in-phase components. Orthogonal components Calculate the biochemical net phase hysteresis angle of dissolved oxygen response relative to the excitation signal. Considering the in-phase components Orthogonal components The value may approach 0, causing overflow in the conventional arctangent function calculation, and the phase may cross quadrants. Therefore, the operation control module uses the four-quadrant arctangent function. Perform robustness calculations: ; Simultaneously, the arithmetic control module performs a phase validity boundary check: if the calculated... If the decoupling angle is less than 5° (system response is too fast, approaching pure resistivity) or greater than 85° (system response is too slow, approaching pure integral), the operation and control module will mark the current decoupling result as having low confidence and trigger step S2 to adaptively adjust the excitation frequency. This causes it to fall back into the phase-sensitive region (5°). 85°).

[0088] Preferably, the step S5 of converting the net phase lag angle into the oxygen mass transfer coefficient includes the following steps: dividing the excitation signal frequency by the tangent value corresponding to the opposite of the net phase lag angle to obtain the oxygen mass transfer coefficient.

[0089] Specifically, based on the dual-membrane theory and the biochemical reaction kinetics of activated sludge, under small-signal perturbation conditions, the oxygen transfer dynamic process in the aeration tank approximately satisfies a first-order linear ordinary differential equation. Its frequency domain characteristics resemble a first-order low-pass filter, with the cutoff frequency being the oxygen mass transfer coefficient. The computational control module uses the following formula to solve for the oxygen mass transfer coefficient. : ; in: The oxygen mass transfer coefficient is the rate of oxygen volumetric mass transfer per unit time, expressed in hours (h). -1 ; The current excitation angular frequency; This is the net phase lag angle (usually a negative value); A minimal regularization constant (such as 10) is introduced to prevent the denominator from being zero. -6 ).

[0090] Preferably, the calculation of oxygen consumption rate in step S5, which combines the dissolved oxygen concentration, includes the following steps: S5.6 Obtain the current water temperature, atmospheric pressure, and corresponding salinity correction coefficient of the mixed liquor in the bioreactor; S5.7. Based on the current water temperature and atmospheric pressure obtained in step S5.6, obtain the standard saturated dissolved oxygen concentration; S5.8 Multiply the standard saturated dissolved oxygen concentration by the salinity correction coefficient obtained in step S5.6, and then subtract the dissolved oxygen concentration to obtain the concentration difference. S5.9 Multiply the oxygen mass transfer coefficient by the concentration difference to obtain the oxygen consumption rate.

[0091] Specifically, after obtaining the oxygen mass transfer coefficient Then, the operation control module combines the current dissolved oxygen concentration... The oxygen consumption rate was derived based on the dynamic mass balance equation of the activated sludge system.

[0092] The original equation for the dynamic mass balance of dissolved oxygen in the activated sludge system is:

[0093] Assuming that within the micro-perturbation observation window (i.e., the sliding observation window), the rate of change of the dissolved oxygen line is much smaller than the perturbation frequency (i.e., the quasi-steady-state assumption holds), the dynamic mass balance equation simplifies to algebraic form: ; in: This represents the oxygen consumption rate of activated sludge, in units of... ; The average value of the dissolved oxygen line calculated before the DC removal process in step S3; This is the standard saturated dissolved oxygen concentration, a value determined by the calculation and control module based on the real-time collected current water temperature. With atmospheric pressure The value can be obtained by interpolation from the built-in Henry's Law constant table; This is the salinity correction factor (usually taken as 0.95–0.98), used to compensate for the reduction effect of total dissolved solids in wastewater on saturated solubility.

[0094] Through the above steps, this embodiment achieves complete decoupling of physical transport characteristics and biochemical reaction characteristics, solving the problem that traditional methods cannot simultaneously and independently identify these characteristics during online operation. and The technical challenges will characterize the equipment's performance. With characterization of microbial activity Conduct independent evaluations to accurately pinpoint the source of the fault.

[0095] Example 8: Based on Example 7, the pre-stored reference parameters in step S6 include the reference oxygen mass transfer coefficient and the reference oxygen consumption rate; the comparison of the changing trends of the oxygen mass transfer coefficient and oxygen consumption rate with the pre-stored reference parameters to determine the type of operational anomaly includes the following steps: S6.1 Obtain the real-time water temperature, suspended solids concentration, and current set gas volume of the mixed liquor in the bioreactor; Specifically, in order to eliminate the interference of ambient temperature and influent load fluctuations on the determination of a single threshold, this example adopts a dynamic benchmark generation mechanism based on multivariate regression.

[0096] The status diagnosis module reads the real-time water temperature of the mixed liquor in the bioreactor through the water quality parameter acquisition module. ) and the concentration of suspended solids in the mixture ( Based on historical normal operating data of the mixed liquor in the bioreactor, a baseline sensing coefficient was established. Compared with the baseline oxygen consumption rate The prediction model. Reference sensing coefficient. Compared with the baseline oxygen consumption rate The prediction model uses the real-time water temperature of the mixed liquor in the current bioreactor ( ), concentration of suspended solids in the mixture ( ) and the current set gas volume ( Using as the input variable, calculate the theoretical expected value under the current operating conditions: ; ; in: The fitting constants related to the aeration pore structure and water depth ( Typically, it is taken as 0.8 to 1.0; (On-site calibration required) This is the temperature correction factor (usually taken as 1.024). The baseline value for specific oxygen consumption activity per unit biomass; This is the temperature sensitivity coefficient for biochemical reactions.

[0097] The aforementioned prediction model ensures that the judgment logic is based on the expected performance under the current operating conditions rather than fixed static values.

[0098] S6.2 Calculate the baseline oxygen mass transfer coefficient based on the acquired real-time water temperature and the current set gas volume; calculate the baseline oxygen consumption rate based on the acquired real-time water temperature and the concentration of suspended solids in the mixed liquor. Specifically, the state diagnosis module calculates the oxygen mass transfer coefficient decoupled in real time. Sensing coefficient relative to reference value Physical performance degradation index To prevent the reference value sensing coefficient A value of zero causes calculation overflow, and the denominator introduces a very small positive number. (e.g., 10) 6).

[0099] ; Based on physical cause-and-effect logic, if the aeration head is blocked or the pipeline is damaged, the gas-liquid contact area or pressure will decrease significantly, directly affecting the oxygen mass transfer coefficient. The gas supply level decreases, but microbial activity remains unaffected in the short term. Therefore, the status diagnosis module uses the following combined logic to determine physical gas supply failures: ; in: The physical blockage alarm threshold ranges from 0.25 to 0.35, indicating a mass transfer efficiency decrease of more than 65%-75%. This represents the normal fluctuation tolerance range, ranging from 0.1 to 0.15, indicating... O O ； Prevent overflow of extremely small positive numbers.

[0100] When this condition is met, the fault source is determined to be at the hardware level of the aeration execution module, such as scaling of the microporous aerator, aging and cracking of the rubber membrane, or leakage of the air supply pipeline.

[0101] S6.3 Calculate the physical performance degradation index based on the oxygen mass transfer coefficient and the baseline oxygen mass transfer coefficient; calculate the biochemical activity inhibition index based on the oxygen consumption rate and the baseline oxygen consumption rate. Specifically, the status diagnosis module calculates the real-time oxygen consumption rate of activated sludge. Relative to the baseline oxygen consumption rate Biochemical activity inhibition index : ; The biochemical activity inhibition index This characterizes the degree of metabolic damage to the activated sludge under the current environment. If the following logic is detected: ; in: The toxic shock alarm threshold is set to 0.4, which means that the microbial respiration rate decreases by more than 40%. This refers to the warning threshold for physical parameters.

[0102] This method eliminates the possibility of insufficient oxygen supply affecting the oxygen consumption rate of activated sludge. The apparent passive reduction clearly points to the presence of heavy metals, antibiotics, or toxic organic solvents in the influent, which inhibits the activity of microbial enzymes.

[0103] S6.4 Determine the type of operational anomaly based on the physical efficacy decay index and the biochemical activity inhibition index.

[0104] Specifically, for abnormal viscosity conditions of sludge bulking or mixed liquor (activated sludge mixed liquor) in the aeration tank, simple single-parameter verification often fails because the change in the rheological properties of the mixed liquor at this time will simultaneously lead to bubble coalescence. (Decrease) and increase in mass transfer resistance.

[0105] The state diagnosis module constructs state vectors in a two-dimensional feature space. And calculate its modulus. and deflection angle The calculation formula is:

[0106] When both the physical efficacy decay index and the biochemical activity inhibition index show a certain proportion of decay, the following dual-weight coupling criterion is satisfied: ; like and The mixture was determined to have abnormal rheological properties.

[0107] The weighting coefficients are determined based on principal component analysis (PCA) of historical operating conditions, and are preferred. ; The comprehensive threshold for viscosity anomalies is 0.3-0.4.

[0108] This determination corresponds to sludge bulking caused by excessive proliferation of filamentous bacteria or high-viscosity influent shock.

[0109] Preferably, the status diagnosis module will run anomaly type coding and hierarchical control output.

[0110] Specifically, the status diagnosis module maps the fault types obtained from the above logical operations to standard hexadecimal fault codes, such as physical gas supply fault code 0x01, biochemical activity abnormality code 0x02, and mixed liquid rheology abnormality code 0x03. The diagnostic information is not only used for alarm display on the host computer, but also directly triggers the fault-tolerant control strategy of the detection device.

[0111] For physical faults: The status diagnosis module sends a command to the aeration execution module to request an increase in the blower outlet pressure setpoint to compensate for pipeline resistance loss and ensure that the basic dissolved oxygen level meets the standard. For biochemical inhibition: The status diagnosis module sends a command to the inlet pumping station to request a reduction in the inlet flow rate or the opening of the bypass emergency pool, while simultaneously sending an advanced alarm to the central control room to investigate upstream pollution sources. For rheological anomalies: The status diagnosis module triggers the sludge pump start command, improving settling performance by adjusting sludge age or adding coagulants. The sludge pump is the sludge discharge device in the activated sludge system, used to discharge expanded sludge and adjust sludge age.

[0112] In the description of this invention, it should be understood that any terms or positions indicated are based on the positions or positions shown in the drawings, and do not indicate or imply that the device or element referred to must have a specific position, or be constructed and operated in a specific position. Therefore, the terms used to describe positional relationships in the drawings are for illustrative purposes only and should not be construed as limiting the invention.

[0113] The above examples are merely illustrative of the present invention and do not constitute a limitation on the scope of protection of the present invention. All designs that are the same as or similar to the present invention are within the scope of protection of the present invention.

Claims

1. A device for detecting abnormal operating status of a wastewater treatment plant, characterized in that: It includes a flow acquisition module, an aeration execution module, a dissolved oxygen detection module, a calculation and control module, a water quality parameter acquisition module, and a status diagnosis module. The signal output terminals of the flow acquisition module and the dissolved oxygen detection module are electrically connected to the signal input terminal of the calculation and control module. The signal output terminal of the calculation and control module is electrically connected to the signal input terminal of the aeration execution module and the signal input terminal of the status diagnosis module, respectively. The signal output terminal of the water quality parameter acquisition module is electrically connected to the signal input terminal of the status diagnosis module.

2. The wastewater treatment plant operation status anomaly detection device as described in claim 1, characterized in that: The flow acquisition module is used to acquire the influent flow data of the bioreactor and transmit the acquired influent flow data to the calculation and control module. The aeration execution module is used to receive the gas volume control command from the calculation and control module, and supply gas to the biological reactor according to the gas volume control command. The dissolved oxygen detection module is used to collect the dissolved oxygen concentration signal of the mixed liquor in the bioreactor in real time and transmit the collected dissolved oxygen concentration signal of the mixed liquor to the computing control module. The calculation and control module is used to generate control commands from the received influent flow data, send the control commands to the aeration execution module, demodulate and separate the oxygen mass transfer coefficient and oxygen consumption rate according to the received mixed liquor dissolved oxygen concentration signal, and transmit the demodulated and separated oxygen mass transfer coefficient and oxygen consumption rate to the status diagnosis module. The water quality parameter acquisition module is used to acquire the concentration of suspended solids in the mixed liquor and its real-time water temperature data in the bioreactor, and transmit the acquired concentration of suspended solids in the mixed liquor and its real-time water temperature data to the status diagnosis module. The status diagnosis module is used to receive the oxygen mass transfer coefficient and oxygen consumption rate output by the calculation control module, as well as the mixed liquor suspended solids concentration and real-time water temperature data output by the water quality parameter acquisition module. The status diagnosis module determines the abnormality type based on the received data and outputs processing instructions.

3. A method for detecting abnormal operating conditions in a wastewater treatment plant, characterized in that: Includes the following steps: S1. Obtain the influent flow rate data of the bioreactor and calculate the local fluid mixing time by combining the spatial geometric volume parameters of the bioreactor. S2. Determine the excitation signal frequency based on the local fluid mixing time, generate the gas volume control command corresponding to the excitation signal frequency, and the aeration execution module supplies gas to the biological reactor according to the gas volume control command. The gas supply is a periodic fluctuating gas volume. S3. Collect the actual air volume feedback sequence of the aeration execution module and simultaneously collect the dissolved oxygen concentration response sequence in the bioreactor. S4. Perform cross-correlation calculation on the actual gas volume feedback sequence and dissolved oxygen concentration response sequence in step S3 to determine the pure lag time of physical transport. S5. Use the physical transport pure lag time to perform time alignment correction on the dissolved oxygen concentration response sequence, perform orthogonal demodulation on the corrected dissolved oxygen concentration response sequence, extract the net phase lag angle, convert the net phase lag angle into the oxygen mass transfer coefficient, and calculate the oxygen consumption rate in combination with the dissolved oxygen linear concentration. S6. Compare the changing trends of oxygen mass transfer coefficient and oxygen consumption rate with the pre-stored baseline parameters, determine the type of operational anomaly, and output the diagnostic results.

4. The method for detecting abnormal operating status of a wastewater treatment plant as described in claim 3, characterized in that: Step S1 includes the following steps: S1.1 Obtain the influent flow rate data of the bioreactor, the total effective volume of each cell of the bioreactor, and the volume coefficient of the local mixing unit; S1.2 When the influent flow rate data of the bioreactor is greater than or equal to the pre-stored minimum flow rate threshold, the product of the local mixing unit volume factor of the bioreactor and the total effective volume of the unit is divided by the influent flow rate data to obtain the local fluid mixing time. When the influent flow rate of the bioreactor is less than the pre-stored minimum flow rate threshold, the pre-stored maximum mixing time constant is used as the local fluid mixing time.

5. The method for detecting abnormal operating status of a wastewater treatment plant as described in claim 3, characterized in that: Step S2, which determines the excitation signal frequency based on the local fluid mixing time and generates a gas volume control command corresponding to the excitation signal frequency, includes the following steps: S2.

1. Divide the frequency tuning coefficient by the local fluid mixing time to obtain the first angular frequency; S2.2 Obtain the mechanical response cutoff frequency of the aeration execution module; S2.

3. The minimum value between the first angular frequency and the mechanical response cutoff frequency of the aeration execution module is determined as the excitation signal frequency; S2.

4. The reference aeration volume setpoint is superimposed with the product of the micro-disturbance amplitude coefficient and the sine wave function to generate a discrete aeration volume control command; the frequency of the sine wave function is the frequency of the excitation signal.

6. The method for detecting abnormal operating status of a wastewater treatment plant as described in claim 1, characterized in that: Step S4 includes the following steps: S4.1 Construct a sliding observation window, and perform DC preprocessing on the actual gas volume feedback sequence and dissolved oxygen concentration response sequence within the sliding observation window to generate the corresponding AC fluctuation sequence; S4.2 Calculate the cross-correlation values ​​of the AC wave sequence under different discrete lag steps, and determine the target discrete lag step number corresponding to the global maximum cross-correlation value; S4.3 When the global maximum cross-correlation value is greater than the pre-stored correlation threshold, divide the target discrete lag step corresponding to the global maximum cross-correlation value by the signal sampling frequency to obtain the physical transmission pure lag time.

7. The method for detecting abnormal operating status of a wastewater treatment plant as described in claim 3, characterized in that: In step S5, the dissolved oxygen concentration response sequence is time-aligned and corrected using the pure physical transport time delay. The corrected dissolved oxygen concentration response sequence is then subjected to orthogonal demodulation to extract the net phase lag angle. This includes the following steps: S5.1 Based on the signal sampling period, the physical transmission pure delay time is decomposed into integer part steps and fractional part steps; S5.2 Perform linear interpolation on the dissolved oxygen concentration response sequence using the integer part number of steps and the fractional part number of steps to obtain the corrected dissolved oxygen concentration response sequence; S5.3 Generate an in-phase reference signal and a quadrature reference signal with the same frequency as the excitation signal; S5.

4. Perform multiplication and accumulation operations on the corrected dissolved oxygen concentration response sequence with the in-phase reference signal and the orthogonal reference signal from step S5.3, respectively, and extract the in-phase component and the orthogonal component. S5.5 Calculate the arctangent of the ratio of the quadrature component to the in-phase component to obtain the net phase lag angle.

8. The method for detecting abnormal operating status of a wastewater treatment plant as described in claim 3, characterized in that: The step S5 of converting the net phase lag angle into the oxygen mass transfer coefficient includes the following steps: dividing the excitation signal frequency by the tangent corresponding to the negative number of the net phase lag angle to obtain the oxygen mass transfer coefficient.

9. The method for detecting abnormal operating status of a wastewater treatment plant as described in claim 3, characterized in that: The calculation of oxygen consumption rate in step S5, which combines the dissolved oxygen concentration, includes the following steps: S5.6 Obtain the current water temperature, atmospheric pressure, and corresponding salinity correction coefficient of the mixed liquor in the bioreactor; S5.

7. Based on the current water temperature and atmospheric pressure obtained in step S5.6, obtain the standard saturated dissolved oxygen concentration; S5.8 Multiply the standard saturated dissolved oxygen concentration by the salinity correction coefficient obtained in step S5.6, and then subtract the dissolved oxygen concentration to obtain the concentration difference. S5.9 Multiply the oxygen mass transfer coefficient by the concentration difference to obtain the oxygen consumption rate.

10. The method for detecting abnormal operating status of a wastewater treatment plant as described in claim 3, characterized in that: The pre-stored reference parameters in step S6 include the reference oxygen mass transfer coefficient and the reference oxygen consumption rate; comparing the changing trends of the oxygen mass transfer coefficient and oxygen consumption rate with the pre-stored reference parameters to determine the type of operational anomaly includes the following steps: S6.1 Obtain the real-time water temperature, suspended solids concentration, and current set gas volume of the mixed liquor in the bioreactor; S6.2 Calculate the baseline oxygen mass transfer coefficient based on the acquired real-time water temperature and the current set gas volume; calculate the baseline oxygen consumption rate based on the acquired real-time water temperature and the concentration of suspended solids in the mixed liquor. S6.3 Calculate the physical performance degradation index based on the oxygen mass transfer coefficient and the reference oxygen mass transfer coefficient; The biochemical activity inhibition index was calculated based on the oxygen consumption rate and the baseline oxygen consumption rate. S6.4 Determine the type of operational anomaly based on the physical efficacy decay index and the biochemical activity inhibition index.

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