Micro-nano bubble coupling oxidation wastewater treatment system and method

Abstract

The application relates to the technical field of wastewater treatment, and provides a micro-nano bubble coupling oxidation wastewater treatment system and method, which comprises the following steps: increasing a target opening degree increment of a gas flow control valve, and obtaining a sensing pressure value of a first pressure sensor and a sensing pressure value of a second pressure sensor in the opening degree increasing process; determining whether the abnormal cause of a micro-nano bubble generator is a pollutant deposition abnormality according to the sensing pressure value of the first pressure sensor and the sensing pressure value of the second pressure sensor; when the abnormal cause of the micro-nano bubble generator is not the pollutant deposition abnormality, controlling a shaker to shake, determining whether the abnormal cause of the micro-nano bubble generator is a sensor abnormality according to ORP data before shaking and ORP data after shaking; and determining a wastewater treatment strategy of the micro-nano bubble generator according to the abnormal cause of the micro-nano bubble generator. Through the scheme, the efficiency of wastewater treatment can be improved.

Description

Technical Field

[0001] This application relates to the field of wastewater treatment technology, and in particular to a micro / nano bubble coupled oxidation wastewater treatment system and method. Background Technology

[0002] Currently, the micro / nano bubble coupled oxidation process is widely used in wastewater treatment to treat complex industrial wastewater. As the core equipment of this process, the micro / nano bubble generator's nozzle orifice gradually forms a hard layer of inorganic salt scale mixed with organic matter on its inner wall during long-term operation due to the supersaturation precipitation of hardness ions such as calcium and magnesium in the wastewater, as well as the adsorption and aggregation of organic colloids such as humic acid and proteins. This deposit significantly alters the internal hydrodynamic conditions of the nozzle, reducing the effective orifice size and increasing surface roughness, leading to a continuous decline in micro / nano bubble generation efficiency, a shift in bubble size distribution towards larger sizes, and a decrease in mass transfer efficiency.

[0003] Faced with declining treatment efficiency, existing control systems typically rely on preset correlations between water quality detection values ​​and power output for adaptive adjustments. This often mistakenly attributes the efficiency drop to insufficient bubble generator power, leading to a continuous increase in operating power and catalyst dosage. However, this control logic, based on a single water quality parameter, fails to identify the root cause of nozzle physical blockage. This results in a surge in energy consumption, catalyst waste, and the formation of a complex fouling layer on sensor surfaces by deposits and excess catalyst particles. This causes sluggish responses and reading drift in critical sensors such as redox potentials. When influent water quality fluctuates, the system's adjustments based on this inaccurate data are often delayed or excessive, ultimately trapping wastewater treatment in a vicious cycle of inaccurate detection and erroneous adjustments, severely impacting treatment stability and economic efficiency. Summary of the Invention

[0004] This application provides a micro / nano bubble coupled oxidation wastewater treatment system and method, which aims to solve the problem that the existing technology cannot perform real-time non-invasive diagnosis of the internal physical state of the micro / nano bubble generator, resulting in a surge in energy consumption and catalyst waste due to misjudgment and blockage.

[0005] The technical solution of this application is as follows: In a first aspect, this application discloses a method for treating wastewater using micro / nano bubble coupled oxidation, comprising the following steps: when an abnormality is detected in the micro / nano bubble generator, the current opening degree of the gas flow control valve included in the micro / nano bubble generator is obtained, and a target opening degree increment of the gas flow control valve is determined based on the current opening degree; the micro / nano bubble generator also includes a nozzle, a first pressure sensor is disposed in front of the nozzle, and a second pressure sensor is disposed behind the nozzle; the micro / nano bubble generator also includes an oxidation-reduction potential (ORP) sensor; an oscillator is disposed on the ORP sensor; the opening degree of the gas flow control valve is controlled to increase by the target opening degree increment, and the sensing pressure values ​​of the first pressure sensor and the second pressure sensor are obtained during the opening degree increase process. The sensor pressure values ​​of the device are used to determine whether the abnormality of the micro-nano bubble generator is due to abnormal pollutant deposition. If the abnormality is not due to pollutant deposition, the oscillator is controlled to oscillate, and the pre-oscillation ORP data of the ORP sensor and the post-oscillation ORP data are obtained. Based on the pre-oscillation and post-oscillation ORP data, the abnormality of the micro-nano bubble generator is determined to be due to sensor malfunction. The wastewater treatment strategy of the micro-nano bubble generator is determined based on the cause of the abnormality.

[0006] Further, determining the target opening increment of the gas flow control valve based on the current opening degree includes: obtaining the pollution hazard index of the wastewater currently being treated by the micro-nano bubble generator and a first correspondence relationship; the first correspondence relationship includes a one-to-one correspondence between multiple pollution hazard index ranges and multiple first coefficients; taking the first coefficient corresponding to the pollution hazard index range in which the wastewater's pollution hazard index is located as the target first coefficient; and taking the product of the original opening increment and the target first coefficient as the target opening increment of the gas flow control valve.

[0007] Furthermore, based on the sensing pressure values ​​of the first and second pressure sensors, it is determined whether the abnormality of the micro / nano bubble generator is due to abnormal contaminant deposition. This includes: using the difference between the sensing pressure values ​​of the first and second pressure sensors at the same moment as the pressure difference before and after the nozzle; determining the actual pressure difference change curve before and after the nozzle based on multiple pressure difference values ​​before and after the nozzle opening increases; and determining whether the abnormality of the micro / nano bubble generator is due to abnormal contaminant deposition based on the actual pressure difference change curve before and after the nozzle opening.

[0008] Furthermore, the inner wall of the micro-nano bubble generator is also equipped with an acoustic sensor. Based on the actual pressure difference change curve before and after the nozzle, the sensor determines whether the abnormality of the micro-nano bubble generator is due to abnormal contaminant deposition. This includes: taking the time between the moment corresponding to the peak pressure difference and the moment when the opening of the gas flow control valve begins to increase as the pressure difference rise time; taking the time between the moment corresponding to the peak pressure difference and the moment when the opening of the gas flow control valve stops increasing as the pressure difference fall time; determining whether both the pressure difference rise time and the pressure difference fall time are greater than a preset time threshold; if so, acquiring and determining whether the abnormality of the micro-nano bubble generator is due to abnormal contaminant deposition based on the acoustic data from the acoustic sensor; otherwise, determining that the abnormality of the micro-nano bubble generator is not due to abnormal contaminant deposition.

[0009] Furthermore, determining whether the abnormality of the micro / nano bubble generator is due to contaminant deposition anomalies based on the acoustic data from the acoustic sensor includes: performing a fast Fourier transform on the acoustic data from the acoustic sensor to obtain the frequency characteristics of the acoustic data; determining whether the energy of a preset frequency component in the frequency characteristics of the acoustic data is less than a preset energy threshold; if the energy of the preset frequency component in the frequency characteristics of the acoustic data is greater than or equal to the preset energy threshold, determining that the abnormality of the micro / nano bubble generator is not due to contaminant deposition anomalies; if the energy of the preset frequency component in the frequency characteristics of the acoustic data is less than the preset energy threshold, determining that the abnormality of the micro / nano bubble generator is due to contaminant deposition anomalies.

[0010] Furthermore, the micro-nano bubble generator also includes a water outlet end, which is equipped with a turbidity sensor. The sensor detects any abnormalities in the micro-nano bubble generator by: acquiring the treatment cleanliness of the current wastewater treatment task and a second correspondence; the second correspondence includes a one-to-one correspondence between multiple treatment cleanliness ranges and multiple second coefficients; using the second coefficient corresponding to the treatment cleanliness range of the current wastewater treatment task in the second correspondence as a target second coefficient; multiplying the target second coefficient by a preset turbidity threshold as a target turbidity threshold; acquiring turbidity data from the turbidity sensor and determining whether the turbidity data is greater than the target turbidity threshold; determining that the micro-nano bubble generator is abnormal when the turbidity data is greater than the target turbidity threshold; and determining that the micro-nano bubble generator is not abnormal when the turbidity data is less than or equal to the target turbidity threshold.

[0011] Furthermore, based on the ORP data before and after oscillation, it is determined whether the abnormality of the micro-nano bubble generator is due to sensor malfunction, including: taking the difference between the ORP data after oscillation and the ORP data before oscillation as the ORP deviation value; determining whether the ORP deviation value is greater than a preset ORP deviation threshold; if the ORP deviation value is greater than the preset ORP deviation threshold, determining that the abnormality of the micro-nano bubble generator is due to sensor malfunction; if the ORP deviation value is less than or equal to the preset ORP deviation threshold, determining that the abnormality of the micro-nano bubble generator is not due to sensor malfunction.

[0012] Furthermore, the micro-nano bubble generator also includes a stirrer. The wastewater treatment strategy for the micro-nano bubble generator is determined based on the cause of the abnormality of the micro-nano bubble generator, including: determining whether the cause of the abnormality of the micro-nano bubble generator is abnormal pollutant deposition; when the cause of the abnormality of the micro-nano bubble generator is abnormal pollutant deposition, the wastewater treatment strategy for the micro-nano bubble generator is to increase the stirring speed of the stirrer of the micro-nano bubble generator by a preset stirring speed step.

[0013] Furthermore, when the cause of the malfunction of the micro-nano bubble generator is not due to abnormal pollutant deposition, the wastewater treatment strategy for the micro-nano bubble generator is determined based on the cause of the malfunction, including: determining whether the cause of the malfunction is a sensor malfunction; when the cause of the malfunction is a sensor malfunction, determining the wastewater treatment strategy for the micro-nano bubble generator to generate and send a first alarm message to the equipment maintenance personnel; the first alarm message is used to instruct the equipment maintenance personnel to repair the oxidation-reduction potential (ORP) sensor; when the cause of the malfunction is not a sensor malfunction, generating and sending a second alarm message to the wastewater treatment personnel; the second alarm message is used to indicate the deterioration of the water quality of the wastewater currently being treated by the micro-nano bubble generator, and to instruct the wastewater treatment personnel to increase the dosage of the wastewater treatment agent in the micro-nano bubble generator by a preset dosage step.

[0014] Secondly, this application also discloses a micro / nano bubble coupled oxidation wastewater treatment system, comprising: an acquisition device and a processing device; the acquisition device is used to acquire the current opening degree of the gas flow control valve included in the micro / nano bubble generator when an abnormality is detected in the micro / nano bubble generator, and determine the target opening degree increment of the gas flow control valve based on the current opening degree; the micro / nano bubble generator also includes a nozzle, a first pressure sensor is disposed in front of the nozzle, and a second pressure sensor is disposed behind the nozzle; the micro / nano bubble generator also includes an oxidation-reduction potential (ORP) sensor; an oscillator is disposed on the ORP sensor; the processing device is used to control the opening degree of the gas flow control valve to increase the target opening degree increment, and acquire the sensing pressure value of the first pressure sensor and the sensing pressure value of the second pressure sensor during the opening degree increase process. The device is configured to: determine whether the abnormality of the micro / nano bubble generator is due to abnormal pollutant deposition based on the pressure values ​​sensed by the first pressure sensor and the second pressure sensor; control the oscillator to oscillate when the abnormality is not due to abnormal pollutant deposition, and acquire the pre-oscillation ORP data of the oxidation-reduction potential (ORP) sensor before oscillation and the post-oscillation ORP data of the ORP sensor after oscillation; determine whether the abnormality of the micro / nano bubble generator is due to sensor malfunction based on the pre-oscillation and post-oscillation ORP data; and determine the wastewater treatment strategy for the micro / nano bubble generator based on the cause of the abnormality. Beneficial effects

[0015] This application breaks through the traditional passive control mode that relies on feedback from a single water quality parameter by constructing a dual diagnostic system based on dynamic pressure difference curves and active excitation from an ORP sensor. Pressure sensors are placed before and after the nozzle. By analyzing the pressure difference change curves during nozzle opening adjustment, changes in flow channel resistance characteristics caused by contaminant deposition can be identified non-invasively. Simultaneously, active disturbance testing of the ORP sensor using an oscillator effectively distinguishes between sensor surface contamination and actual water quality changes. This layered, progressive diagnostic logic fundamentally solves the problems of power misjudgment and excessive catalyst addition caused by the inability to identify physical blockages in existing technologies. It achieves precise control of energy consumption and reagent consumption, significantly improving the system's adaptability to complex water quality fluctuations and its long-term operational stability. Attached Figure Description

[0016] Figure 1 A schematic diagram of a micro / nano bubble coupled oxidation wastewater treatment method provided in this application; Figure 2 A schematic diagram of a micro / nano bubble coupled oxidation wastewater treatment method provided in this application; Figure 3 This is a schematic diagram of the architecture of a micro / nano bubble coupled oxidation wastewater treatment system provided in this application. Detailed Implementation

[0017] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0018] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0019] In the field of industrial wastewater treatment, especially for wastewater containing high concentrations of hardness ions and complex organic colloids, micro / nano bubble coupled oxidation technology is widely used due to its high mass transfer efficiency. This type of wastewater typically originates from the chemical, dyeing, or food processing industries, and its composition is complex and fluctuates frequently, containing substances such as calcium and magnesium ions, humic acid, proteins, and polysaccharides. During long-term continuous operation, a composite deposit layer composed of inorganic salt scale and organic matter gradually accumulates on the inner wall of the nozzle orifice, the core component of the micro / nano bubble generator. The formation of this deposit layer alters the hydrodynamic characteristics inside the nozzle, leading to a decrease in the generation efficiency of micro / nano bubbles and a shift in bubble size distribution towards larger sizes.

[0020] Meanwhile, existing control systems often rely on feedback from a single water quality parameter for adaptive adjustment. When the redox potential or chemical oxygen demand deviates from expectations, the system misinterprets this as insufficient bubble generator power, leading to a continuous increase in operating power and catalyst dosage. This misinterpretation not only wastes energy and reagents but also causes excessive catalyst and deposits to form a complex fouling layer on the surface of the redox potential sensor, resulting in sluggish sensor response and drifting readings. When influent water quality fluctuates, the system, based on inaccurate sensor data, cannot promptly and accurately perceive the actual water quality changes, thus falling into a vicious cycle of inaccurate detection and incorrect adjustment. To address these technical challenges, there is an urgent need for a method that can diagnose the internal physical state of micro / nano bubble generators in real time, non-invasively, and accurately distinguish between abnormal pollutant deposition and sensor malfunctions.

[0021] In this regard, such as Figure 1 As shown, this application provides a micro / nano bubble coupled oxidation wastewater treatment method, including the following steps: S101. When an abnormality is detected in the micro-nano bubble generator, the current opening degree of the gas flow control valve included in the micro-nano bubble generator is obtained, and the target opening degree increment of the gas flow control valve is determined based on the current opening degree.

[0022] The micro-nano bubble generator also includes a nozzle, with a first pressure sensor in front of the nozzle and a second pressure sensor behind the nozzle; the micro-nano bubble generator also includes an oxidation-reduction potential (ORP) sensor; and an oscillator is installed on the ORP sensor.

[0023] As one possible implementation, when an anomaly is detected in the micro / nano bubble generator, the current opening degree of the gas flow control valve included in the micro / nano bubble generator is obtained, and the pollution hazard index of the wastewater currently being treated by the micro / nano bubble generator and a first correspondence are obtained; the first correspondence includes a one-to-one correspondence between multiple pollution hazard index ranges and multiple first coefficients; the first coefficient corresponding to the pollution hazard index range in which the pollution hazard index of the wastewater is located is taken as the target first coefficient; the product of the original opening increment and the target first coefficient is taken as the target opening increment of the gas flow control valve.

[0024] It should be noted that a detailed description of this possible implementation method can be found in the following sections of the specific implementation method of this application.

[0025] S102, control the gas flow control valve to increase the target opening increment, and obtain the sensing pressure value of the first pressure sensor and the sensing pressure value of the second pressure sensor during the opening increase process.

[0026] As one possible implementation, a command is sent to the gas flow control valve to control the opening of the gas flow control valve to increase the target opening increment, and the sensing pressure values ​​of the first pressure sensor and the second pressure sensor are obtained during the opening increase process.

[0027] S103. Based on the pressure values ​​sensed by the first pressure sensor and the second pressure sensor, determine whether the abnormality of the micro / nano bubble generator is due to abnormal pollutant deposition.

[0028] As one possible implementation, the difference between the pressure values ​​sensed by the first pressure sensor and the second pressure sensor at the same moment is taken as the pressure difference between the nozzle and the nozzle. Based on the multiple pressure differences between the nozzle and the nozzle during the increase of the opening, the actual pressure difference change curve of the nozzle is determined. Based on the actual pressure difference change curve of the nozzle, it is determined whether the abnormality of the micro-nano bubble generator is due to abnormal pollutant deposition.

[0029] It should be noted that a detailed description of this possible implementation method can be found in the following sections of the specific implementation method of this application.

[0030] S104. When the cause of the abnormality in the micro / nano bubble generator is not due to abnormal pollutant deposition, control the oscillator to oscillate, and acquire the pre-oscillation ORP data of the ORP sensor before the oscillator oscillates, and acquire the post-oscillation ORP data of the ORP sensor after the oscillator oscillates.

[0031] As one possible implementation, when the cause of the abnormality in the micro / nano bubble generator is not due to abnormal pollutant deposition, a command is sent to the oscillator to control the oscillator to oscillate, and the pre-oscillation ORP data of the ORP sensor before the oscillator oscillates, and the post-oscillation ORP data of the ORP sensor after the oscillator oscillates, are obtained.

[0032] S105. Based on the ORP data before and after oscillation, determine whether the abnormality of the micro-nano bubble generator is due to sensor malfunction.

[0033] As one possible implementation, the difference between the ORP data after oscillation and the ORP data before oscillation is used as the ORP deviation value; it is determined whether the ORP deviation value is greater than the preset ORP deviation threshold; when the ORP deviation value is greater than the preset ORP deviation threshold, the abnormality of the micro-nano bubble generator is determined to be due to sensor abnormality; when the ORP deviation value is less than or equal to the preset ORP deviation threshold, the abnormality of the micro-nano bubble generator is determined not to be due to sensor abnormality.

[0034] It should be noted that a detailed description of this possible implementation method can be found in the following sections of the specific implementation method of this application.

[0035] S106. Determine the wastewater treatment strategy for the micro-nano bubble generator based on the causes of its abnormality.

[0036] As one possible implementation, when the micro-nano bubble generator also includes a stirrer, it is determined whether the abnormality of the micro-nano bubble generator is due to abnormal pollutant deposition; if the abnormality of the micro-nano bubble generator is due to abnormal pollutant deposition, the wastewater treatment strategy of the micro-nano bubble generator is determined to increase the stirring speed of the stirrer of the micro-nano bubble generator by a preset stirring speed step.

[0037] It should be noted that a detailed description of this possible implementation method can be found in the following sections of the specific implementation method of this application.

[0038] To enable those skilled in the art to accurately understand the technical solution of this application, the basic technical concepts involved are first explained. A micro / nano bubble generator is a device that disperses gas into micron or nano-sized bubbles through a specific physical method. Its core component, the nozzle, utilizes high-speed shearing or pressure release principles to achieve bubble nucleation and breakup. A gas flow control valve is an actuator installed on a gas delivery pipeline. It regulates the amount of gas entering the generator per unit time by changing the valve core opening; its opening is usually quantified as a percentage or angle value. The target opening increment refers to the absolute amount of valve opening increased by the control system based on the current opening during the diagnostic process, used to induce a specific fluid dynamic response. A first pressure sensor and a second pressure sensor are respectively installed at the inlet and outlet ends of the nozzle to monitor the pressure state of the fluid before and after the nozzle in real time. The difference between the two reflects the flow resistance characteristics inside the nozzle. A redox potential sensor is an electrochemical sensor that characterizes the redox state of water by measuring the relative proportion of oxidized and reduced substances in the water. Its measurement accuracy is crucial for the chemical metrological control of wastewater treatment processes. An oscillator is a device capable of generating mechanical vibration. In this application, it is installed on the body of a redox potential sensor to detect the contamination status of the sensor surface through active excitation. Abnormal contaminant deposition specifically refers to the phenomenon of reduced effective flow cross-sectional area and increased surface roughness within the nozzle orifice due to inorganic salt crystallization, organic adsorption, or catalyst particle accumulation. Sensor abnormalities specifically refer to the response hysteresis, decreased sensitivity, or reading drift of the redox potential sensor caused by surface contamination.

[0039] The aforementioned technical solution constructs a hierarchical diagnostic logic. First, it uses the opening adjustment of the gas flow control valve to induce dynamic changes in the pressure field before and after the nozzle, identifying abnormal flow resistance caused by pollutant deposition based on fluid dynamics principles. Then, it applies active mechanical excitation to the oxidation-reduction potential sensor via an oscillator, distinguishing between surface contamination and actual water quality changes based on the sensor's vibration response characteristics. This composite diagnostic mechanism overcomes the limitations of traditional single water quality parameter feedback, achieving real-time, non-invasive diagnosis of the internal physical state of the micro / nano bubble generator, effectively avoiding energy consumption spikes and catalyst waste caused by misdiagnosis and blockage.

[0040] like Figure 2 As shown, this application further proposes a method for dynamically determining the target opening increment based on the wastewater pollution hazard index. Determining the target opening increment of the gas flow control valve based on the current opening includes: S201. Obtain the pollution hazard index and first correspondence of the wastewater currently being treated by the micro-nano bubble generator; the first correspondence includes a one-to-one correspondence between multiple pollution hazard index ranges and multiple first coefficients.

[0041] S202. The first coefficient corresponding to the pollution hazard index range in which the pollution hazard index of the wastewater is located shall be used as the target first coefficient.

[0042] S203. The product of the original opening increment and the target first coefficient is used as the target opening increment of the gas flow control valve.

[0043] Here, the pollution hazard index is obtained through an online monitoring instrument group deployed at the inlet of the micro-nano bubble generator. Specifically, chemical oxygen demand (COD) is collected in real time using an online COD analyzer based on ultraviolet light absorption at a specific wavelength. This instrument outputs a COD concentration value every 5 minutes, with a range of 0 to 2000 mg / L and a resolution of 1 mg / L. Total suspended solids (TSS) content is indirectly calculated using a laser scattering turbidity sensor. This sensor is installed on the straight section of the inlet pipe, at a distance of no less than 5 times the pipe diameter from bends or valves to ensure flow stability. The output signal is converted into TSS concentration using a calibration curve, with units in mg / L. Heavy metal factors are obtained through an electrochemical sensor array or ion-selective electrode method, detecting key heavy metal ions such as copper, zinc, and nickel separately, and taking the weighted average of the concentrations of each ion. The above three parameters are transmitted to a programmable logic controller (PLC) via an industrial Ethernet network. The PLC calculates the pollution hazard index in real time according to a preset weighting formula: Pollution Hazard Index = 0.5 × Measured COD + 0.3 × Measured TSS + 0.2 × Calculated Heavy Metal Factor. The calculated value of the heavy metal factor is Σ(concentration of the i-th heavy metal ion × toxicity weight coefficient i), and the toxicity weight coefficient is determined by normalizing the inverse of the limit value in GB 8978 Integrated Wastewater Discharge Standard.

[0044] The establishment of the first correspondence is based on historical operating data and experimental verification. The specific process is as follows: Operating records under different pollution load conditions over the past 12 months were collected and grouped into intervals of 100 units each according to the pollution hazard index. Diagnostic test data within each interval were analyzed. By comparing the diagnostic success rate and equipment safety under the fixed-increment strategy, the optimal correction coefficient for each interval was determined. For example, in the pollution hazard index range of 0 to 500, experiments showed that using a 1.5 times original increment ensured both the detectability of the pressure signal and prevented sediment erosion and blockage due to sudden increases in flow rate; therefore, the first coefficient was set to 1.5. In the range of 500 to 1000, a 1.0 times original increment was used to balance detection sensitivity and safety. In the range greater than 1000, to prevent high-concentration pollutants from caking and clogging under airflow impact, a 0.6 times original increment was used. This correspondence is stored in the non-volatile memory of the control system in the form of a lookup table.

[0045] With the initial aperture increment set at a base value of 10%, the target aperture increment is 6% under high-pollution conditions and 15% under low-pollution conditions. This dynamic adjustment mechanism ensures the safety of the diagnostic process in highly polluted wastewater, avoiding sediment detachment and blockage caused by airflow impact; at the same time, it ensures the adequacy of the diagnostic process in low-pollution wastewater, generating a significant pressure difference signal through larger aperture changes, thereby improving the ability to identify early minor deposits.

[0046] After determining the target opening increment, the control system sends an opening adjustment command to the gas flow control valve, which linearly increases its opening to the target position within 2 to 5 seconds. During this process, the first and second pressure sensors record pressure data at a sampling frequency of 50 to 100 times per second. The first pressure sensor is installed in the flow stabilizing pipe section at the front end of the nozzle inlet to measure the static pressure before entering the nozzle; the second pressure sensor is installed in the diffuser section at the rear end of the nozzle outlet to measure the back pressure after bubble generation. Both sensors are silicon piezoresistive pressure transmitters with a range of 0 to 1.0 MPa, an accuracy class of 0.1, and output a standard current signal of 4 to 20 mA.

[0047] After acquiring pressure data, it is necessary to determine whether the anomaly is due to abnormal contaminant deposition. Simple single-point pressure threshold comparison methods are insufficient to distinguish between sediment blockage and transient flow disturbance, as both can cause instantaneous changes in pressure differences. For example, when large particles of impurities in wastewater temporarily pass through a nozzle, a brief pressure fluctuation can occur; relying solely on a single moment's pressure difference can easily lead to misjudgment.

[0048] Therefore, this application proposes an analysis method based on pressure difference change curves. Based on the pressure values ​​sensed by the first pressure sensor and the second pressure sensor, it determines whether the anomaly in the micro / nano bubble generator is due to abnormal contaminant deposition, including: The difference between the pressure values ​​sensed by the first pressure sensor and the second pressure sensor at the same moment is taken as the pressure difference between the nozzle and the front and back. Based on the multiple pressure differences between the nozzle and the front and back during the increase of the opening, the actual pressure difference change curve of the nozzle and the front and back is determined. Based on the actual pressure difference change curve of the nozzle and the front and back, it is determined whether the abnormality of the micro-nano bubble generator is due to abnormal pollutant deposition.

[0049] In practice, the control system synchronously acquires 4-20 mA current signals from the first and second pressure sensors via an analog input module at a sampling rate of 100 Hz. After analog-to-digital conversion, digital pressure values ​​P1 and P2 are obtained. For each sampling time t, the pressure difference ΔP(t) = P1(t) - P2(t) is calculated in real time. During the process of the valve opening linearly increasing from the initial opening θ0 to the target opening θ0 + Δθ, all ΔP(t) values ​​are recorded from t0 (the time when the opening starts to increase) to t1 (the time when the opening reaches the target), forming a discrete dataset {ΔP(t0), ΔP(t0 + Δt), ..., ΔP(t1)}, where Δt is the sampling period of 10 milliseconds. The dataset is smoothed using the least squares method or moving average filtering to eliminate high-frequency noise, and then the actual pressure difference change curve of ΔP over time t is plotted.

[0050] The analysis and processing of the curve includes the following steps: First, calculate the average slope k of the rising segment of the curve by linearly fitting the first 80% of data points from t0 to t_peak (the moment when the pressure difference reaches its peak). Second, identify the delay ratio η = (t_peak - t0) / T between the peak occurrence time t_peak and the total opening adjustment time T. For clean nozzles, due to the unobstructed flow path, the flow rate increases rapidly with the increase in opening, and the pressure difference reaches its peak in a short time and then tends to stabilize, resulting in a curve that shows a rapid rise followed by a stable trend. In this case, the k value is relatively large and the η value is relatively small (usually η < 0.3). However, for nozzles with contaminant deposits, due to the obstruction effect of the deposits, the increase in flow rate lags behind the increase in opening, the rate of pressure difference increase slows down, and the irregular surface of the deposits generates additional turbulence, causing the curve to fluctuate or have a delayed peak. In this case, the k value is relatively small (usually less than 60% of the k value in the clean state) and the η value is relatively large (usually η > 0.5). This method, by analyzing the dynamic process rather than static values, effectively eliminates the interference of instantaneous disturbances and significantly improves the accuracy of diagnosis.

[0051] In another implementation, the triggering mechanism for anomaly detection also requires careful design. Traditional fixed turbidity threshold detection methods are difficult to adapt to treatment tasks with different emission standards. For example, when the treatment requirement is to meet Class A standards (turbidity must be below 10 NTU), if a fixed threshold of 20 NTU, which is applicable to Class III discharge (turbidity below 50 NTU), is used as the anomaly judgment standard, the diagnosis may not be triggered even when the effluent turbidity reaches 15 NTU, resulting in excessive discharge. Conversely, if an overly strict fixed threshold is used, false diagnoses may be frequently triggered when treating tasks with lower standards, causing unnecessary shutdowns for inspection.

[0052] To address this issue, this application provides a method for dynamically adjusting the detection threshold based on the cleanliness of the treated area. The micro / nano bubble generator also includes a water outlet equipped with a turbidity sensor. Upon detecting an anomaly in the micro / nano bubble generator, the sensor detects: The following steps are taken: First, obtain the treatment cleanliness level of the current wastewater treatment task using the micro / nano bubble generator and its corresponding second-order relationship. The second-order relationship includes a one-to-one correspondence between multiple treatment cleanliness ranges and multiple second coefficients. Second, use the second coefficient corresponding to the treatment cleanliness range of the current wastewater treatment task in the second-order relationship as the target second coefficient. Then, use the product of the target second coefficient and a preset turbidity threshold as the target turbidity threshold. Next, obtain turbidity data from the turbidity sensor and determine whether the turbidity data is greater than the target turbidity threshold. If the turbidity data is greater than the target turbidity threshold, determine that the micro / nano bubble generator is malfunctioning. If the turbidity data is less than or equal to the target turbidity threshold, determine that the micro / nano bubble generator is not malfunctioning.

[0053] In practice, the cleanliness level is set to different grades according to emission requirements. For example, Grade A corresponds to Grade 1, Grade B to Grade 2, and Grade II to Grade 3. This grade information is entered by the operator through the human-machine interface when the system starts the processing task, or automatically obtained from the production management system. The second correspondence is established based on the matching analysis of emission standards and process capabilities: the optimal turbidity control point of the system under different emission standards is determined by analyzing historical data, and a safety margin setting coefficient is considered.

[0054] The preset turbidity threshold is set to a base value of 20 NTU. When performing a Class A standard treatment task, the target turbidity threshold is 10 NTU, making the system more sensitive to changes in effluent quality and initiating a diagnostic procedure as soon as the turbidity exceeds 10 NTU. When performing a Class III discharge task, the target turbidity threshold is 24 NTU, avoiding frequent triggering of diagnostics due to slight fluctuations in water quality. Turbidity data is acquired using an infrared scattering turbidimeter installed on the effluent pipe. This instrument has a range of 0 to 100 NTU, a resolution of 0.1 NTU, and outputs a measurement value every 30 seconds. The control system compares the real-time turbidity value with the calculated target turbidity threshold. If the turbidity value exceeds the target turbidity threshold for three consecutive sampling cycles (i.e., within 90 seconds), an anomaly detection process is triggered. This dynamic threshold setting mechanism ensures precise matching between the anomaly detection standard and the treatment target, guaranteeing timely warnings for high-standard treatment while preventing oversensitivity for low-standard treatment.

[0055] When analysis of pressure difference change curves determines that the cause of the anomaly is not due to abnormal contaminant deposition, further differentiation is needed to determine whether it is a sensor malfunction. Traditional sensor fault diagnosis relies on comparing sensor readings with laboratory test results or replacing the sensor with a spare for verification. These methods are time-consuming and complex, and cannot achieve online real-time diagnosis.

[0056] This application utilizes an oscillator to actively excite a redox potential sensor, identifying the surface contamination state by analyzing the sensor's response to mechanical vibration. When the cause of the micro / nano bubble generator malfunction is not due to abnormal contaminant deposition, the oscillator is controlled to oscillate. The oscillator can be a piezoelectric ceramic transducer, with an operating frequency set between 20 kHz and 40 kHz, an amplitude controlled between 1 and 5 micrometers, and a continuous oscillation time of 3 to 5 seconds. The piezoelectric ceramic transducer is fixed to the outer wall of the probe protective cover of the redox potential sensor using epoxy resin adhesive and connected to the control system via a shielded cable. The control system applies a specific frequency AC voltage signal to the transducer through a drive circuit to excite mechanical vibration. To avoid damage to the sensor electrodes caused by vibration, the vibration amplitude is monitored and adjusted in real time through a feedback loop to ensure that the amplitude does not exceed the safety threshold A_max.

[0057] The ORP data before oscillation is obtained as follows: During the 5 seconds before oscillation begins, the output value of the redox potential sensor is collected at a frequency of 10 times per second. After removing the maximum and minimum values, the arithmetic mean is taken, denoted as ORP_pre. The ORP data after oscillation is obtained as follows: During the 5 seconds after oscillation stops, the output value is also collected at a frequency of 10 times per second. After removing the maximum and minimum values, the arithmetic mean is taken, denoted as ORP_post. The removal of extreme values ​​is implemented using a sorting algorithm: the 50 sampled values ​​are arranged in ascending order, the first 5 maximum values ​​and the last 5 minimum values ​​are removed, and the average of the remaining 40 values ​​is calculated.

[0058] For methods to determine sensor malfunctions based on data before and after oscillation, simple absolute value comparisons are insufficient to rule out the possibility that the water quality itself changes during oscillation. Therefore, this application proposes a deviation value determination method. Based on ORP data before and after oscillation, it determines whether the cause of the micro / nano bubble generator malfunction is a sensor malfunction, including: The difference between the ORP data after oscillation and the ORP data before oscillation is taken as the ORP deviation value; it is determined whether the ORP deviation value is greater than the preset ORP deviation threshold; if the ORP deviation value is greater than the preset ORP deviation threshold, the cause of the micro-nano bubble generator abnormality is determined to be sensor abnormality; if the ORP deviation value is less than or equal to the preset ORP deviation threshold, the cause of the micro-nano bubble generator abnormality is determined not to be sensor abnormality.

[0059] In practice, the ORP deviation value ΔORP is calculated using the formula ΔORP=|ORP_post - ORP_pre|. The preset ORP deviation threshold ΔORP_threshold is determined through experimental calibration: 10 groups of sensors with known clean surfaces are selected for oscillation tests, and the ΔORP values ​​are recorded. The maximum value is taken and multiplied by a safety factor of 1.5 as the threshold, typically set to 10 to 20 millivolts. The judgment logic is implemented through a comparator or software conditional statements: if ΔORP > ΔORP_threshold, the sensor abnormality flag is set; otherwise, the flag is cleared. For sensors with clean surfaces and high responsiveness, mechanical oscillation will disturb the boundary layer on the sensor probe surface, causing a temporary change in the probe's contact state with the water. However, due to the sensor's inherent good performance, the reading quickly recovers to a level close to that before the oscillation after the oscillation stops, and the ORP deviation value is usually less than 5 millivolts.

[0060] For sensors with a complex layer of fouling on their surface, this fouling layer hinders effective contact between the probe and the water being tested. Mechanical vibration can loosen or partially peel off the fouling layer, causing significant jumps in sensor readings. After the vibration stops, the readings are difficult to recover to their original levels due to the redistribution of the fouling layer, with ORP deviations typically exceeding 20 millivolts. By setting a reasonable deviation threshold, sensor anomalies caused by surface contamination can be accurately identified, excluding the influence of normal water quality fluctuations. This method, through active excitation, achieves online detection of the sensor's physical state without disassembling the sensor or interrupting the processing flow, greatly improving maintenance efficiency.

[0061] Finally, based on the identified cause of the anomaly, the system executes the corresponding wastewater treatment strategy. If the anomaly is determined to be due to abnormal pollutant deposition, the agitator is activated to accelerate cleaning or operating parameters are adjusted to suppress deposition; if the anomaly is determined to be due to sensor malfunction, a maintenance alarm is sent; if the anomaly is determined to be due to water quality degradation, the dosage of chemicals is adjusted. This classification and treatment strategy based on precise diagnosis ensures that the micro-nano bubble coupled oxidation wastewater treatment system can take effective measures against specific root causes of failures, avoiding the resource waste caused by traditional blind adjustments, and significantly improving the system's intelligence level and operational economy.

[0062] In actual operation at industrial wastewater treatment sites, although analyzing the morphological characteristics of pressure difference curves can initially identify abnormal flow resistance caused by pollutant deposition, relying solely on pressure difference curves can still lead to misjudgments under certain complex conditions. For example, when high concentrations of fibrous suspended matter or viscous colloidal flocs pass through wastewater instantaneously, these substances may form temporary agglomerations or bridging at the nozzles, causing the pressure difference curve to exhibit a similar upward hysteresis characteristic to that of sediment blockage. However, such temporary blockages often dissipate spontaneously under subsequent fluid shearing and are not true pollutant deposition. If a deposition anomaly is determined solely by changes in the morphology of the pressure difference curve, and a cleaning procedure is initiated, it not only causes unnecessary downtime and resource consumption but may also disrupt the normal treatment process due to frequent interventions. Therefore, a confirmatory mechanism is needed to further distinguish between temporary flow resistance disturbances and substantial sediment adhesion to improve the accuracy and reliability of diagnosis.

[0063] To this end, this application further proposes to add an acoustic sensor to the inner wall of the micro-nano bubble generator, and to introduce a dual duration verification mechanism of pressure difference rise time and fall time when making judgments based on pressure difference change curves, and to cross-confirm by combining acoustic characteristics.

[0064] The inner wall of the micro-nano bubble generator is also equipped with an acoustic sensor. Based on the actual pressure difference change curve before and after the nozzle, the system determines whether the abnormality of the micro-nano bubble generator is due to abnormal contaminant deposition. This includes: taking the time between the moment corresponding to the peak pressure difference and the moment when the opening of the gas flow control valve begins to increase as the pressure difference rise time; taking the time between the moment corresponding to the peak pressure difference and the moment when the opening of the gas flow control valve stops increasing as the pressure difference fall time; determining whether both the pressure difference rise time and the pressure difference fall time are greater than a preset time threshold; if so, acquiring and determining whether the abnormality of the micro-nano bubble generator is due to abnormal contaminant deposition based on the acoustic data from the acoustic sensor; otherwise, determining that the abnormality of the micro-nano bubble generator is not due to abnormal contaminant deposition.

[0065] In practical applications, piezoelectric accelerometers or broadband acoustic emission sensors are recommended for acoustic sensing. These sensors should be mounted on the metal substrate of the nozzle's outer wall, secured with a magnetic base or high-temperature adhesive (such as an epoxy-polyamide curing system) to ensure good acoustic coupling between the sensor and the wall surface. If necessary, an ultrasonic coupling agent can be applied to the contact surface to eliminate air gaps. The sensor's frequency response range is typically set to 1 kHz to 20 kHz, with a sensitivity of no less than 100 mV per gravitational acceleration, used to capture broadband acoustic signals generated by turbulence, cavitation, or bubble collapse as fluid passes through the nozzle. When hard deposits are present on the nozzle's inner wall, the acoustic impedance characteristics of the flow channel wall change, resulting in different reflection and absorption characteristics of sound waves at the wall compared to a clean metal surface. This change will be reflected in the frequency domain distribution of the acoustic signal.

[0066] In practical engineering deployments, the accurate calculation of the rise and fall times of the pressure difference relies on the automatic identification of the temporal characteristics of the pressure difference change curve. The control system records the starting moment t_start when the gas flow control valve receives the opening increase command via a high-speed data acquisition card. This moment is triggered by the rising edge of the pulse signal sent by the controller to the valve drive circuit. Simultaneously, the system monitors and calculates the difference sequence between the first and second pressure sensors in real time at a sampling frequency of 1000 times per second.

[0067] To accurately identify the peak pressure difference moment t_peak, the system employs a sliding window slope analysis method: a moving window containing 50 sampling points (i.e., a 50-millisecond time window) is set, and the average rate of change of the pressure difference sequence within the window is calculated. When the average rate of change of three consecutive windows changes from positive to negative, and the absolute value of the negative value exceeds 5% of the maximum upward slope of the previous segment, the center moment of the previous window is determined to be the peak pressure difference moment. This algorithm can effectively filter out false peaks caused by high-frequency noise. The pressure difference rise time Δt_rise is the time interval between t_peak and t_start. Similarly, determining the pressure difference fall time Δt_fall requires recording the termination moment t_end when the gas flow control valve reaches the target opening degree. This moment is confirmed when the valve position feedback signal reaches the target value and remains stable for more than 100 milliseconds. Δt_fall is defined as the time interval between t_end and t_peak. These two duration parameters together reflect the response speed of the pressure field to changes in valve opening: in a clean flow channel, the pressure difference is established quickly and reaches equilibrium rapidly, with both Δt_rise and Δt_fall being relatively short; while in a flow channel with viscoelastic deposits, the pressure establishment and dissipation processes exhibit significant viscous lag, resulting in a significant extension of both durations.

[0068] Regarding the determination of the preset duration threshold T_th, it is recommended to establish it based on statistical analysis of historical operating data in practical applications. Specifically, after confirming the nozzles are clean during system maintenance, perform 20 to 30 consecutive diagnostic tests, recording the measured values ​​of Δt_rise and Δt_fall in each test. Perform a normality test on these paired data sets, calculating the arithmetic mean μ and standard deviation σ of Δt_rise and Δt_fall under normal operating conditions. Only when both Δt_rise > T_th and Δt_fall > T_th are simultaneously true will the control system determine that the hysteresis characteristic of the pressure difference curve is statistically significant, triggering the subsequent acoustic verification process; otherwise, even if the pressure difference curve shows some fluctuation, it is judged as a temporary disturbance rather than a deposition anomaly, avoiding overreaction.

[0069] In one specific implementation, the control system triggers the acoustic data acquisition module after the dual-duration condition is met. For example, the system continuously acquires acoustic signals for 2 seconds at a sampling rate of 40 kHz, obtaining time-domain waveform data of 8192 sampling points through a 16-bit analog-to-digital converter, and storing the data in a circular buffer. At this point, judging solely based on the time-domain amplitude of the acoustic signal is easily affected by background interference such as pump vibration and pipe-conducted noise. Therefore, further frequency domain analysis of the acoustic data is needed to extract features strongly correlated with the sediment state.

[0070] Based on acoustic data from acoustic sensors, determine whether the anomaly in the micro / nano bubble generator is due to abnormal contaminant deposition, including: The acoustic data from the acoustic sensor is processed by Fast Fourier Transform to obtain the frequency characteristics of the acoustic data. It is then determined whether the energy of the preset frequency component in the frequency characteristics of the acoustic data is less than a preset energy threshold. If the energy of the preset frequency component in the frequency characteristics of the acoustic data is greater than or equal to the preset energy threshold, the abnormality of the micro / nano bubble generator is determined not to be due to abnormal contaminant deposition. If the energy of the preset frequency component in the frequency characteristics of the acoustic data is less than the preset energy threshold, the abnormality of the micro / nano bubble generator is determined to be due to abnormal contaminant deposition.

[0071] The following detailed signal processing example illustrates the specific implementation of the above process.

[0072] In practical applications, the Fast Fourier Transform (FFT) uses the radix-2 FFT algorithm to preprocess the 8192-point time-domain data: first, the DC component is removed by calculating the arithmetic mean of the entire data segment and subtracting it from each sampling point; then, the Hanning window is applied for windowing. The windowed data is then processed by FFT to obtain the complex spectrum X(k) of 4096 frequency components, from which the power spectral density is calculated. The selection of the preset frequency components is based on the physical mechanism of the sediment's effect on sound waves: the high-frequency turbulence noise generated by micro- and nano-bubbles passing through the nozzle's converging section at high speed is mainly concentrated in the 2 kHz to 6 kHz range. When the inner wall of the nozzle is covered with a sediment layer (usually composed of calcium carbonate, organic polymers, and metal oxides), this sediment layer has a porous viscoelastic structure, which strongly absorbs and scatters high-frequency sound waves, resulting in a significant attenuation of acoustic emission energy in this frequency band. Therefore, the preset frequency components are determined to be in the 3 kHz to 5 kHz band.

[0073] The energy E in this frequency band is calculated using the integrated power spectral density. The preset energy threshold E_th needs to be calibrated in a clean state after system maintenance: continuously collect 10 sets of acoustic data, calculate the energy value of each set in the 3 to 5 kHz frequency band according to the above FFT processing procedure, take the arithmetic mean E_clean, and set E_th = 0.65 × E_clean (considering normal fluctuations and measurement errors, the attenuation coefficient is usually taken between 0.6 and 0.7).

[0074] In actual diagnostics, if the calculated E ≥ E_th, it indicates that the high-frequency acoustic energy has not significantly attenuated, and the nozzle inner wall may be clean or only temporarily covered with soft deposits, thus the cause of the anomaly is not contaminant deposition. If E < E_th, it indicates that the high-frequency component energy is significantly lower than the clean baseline, confirming the presence of a substantial hard deposit layer, and the cause of the anomaly is determined to be contaminant deposition. Through dual verification using pressure difference duration characteristics and acoustic frequency domain characteristics, interference from temporary flow disturbances is effectively eliminated, significantly improving the accuracy of contaminant deposition anomaly identification. This ensures that subsequent cleaning or adjustment strategies are triggered only when intervention is truly necessary, thereby optimizing the system's maintenance cycle and operational economy.

[0075] After accurately identifying whether the anomaly in the micro / nano bubble generator was due to contaminant deposition through the aforementioned pressure difference curve analysis and acoustic characteristic verification, differentiated treatment strategies are needed for anomalies of different natures. If it is determined to be a contaminant deposition anomaly, it indicates that a substantial deposit layer has formed in the nozzle orifice or reactor inner wall. In this case, continuing to use traditional methods such as increasing power or adding reagents will not only fail to remove the deposits but may also exacerbate blockages or waste resources. Therefore, an online treatment method capable of directly and physically removing deposits is needed.

[0076] This application further proposes to configure a stirrer in the reaction chamber of the micro / nano bubble generator and dynamically adjust its operating parameters based on the diagnostic results.

[0077] The micro / nano bubble generator also includes a stirrer. Wastewater treatment strategies for the micro / nano bubble generator are determined based on the causes of malfunctions, including: Determine whether the abnormality of the micro-nano bubble generator is due to abnormal pollutant deposition; if the abnormality of the micro-nano bubble generator is due to abnormal pollutant deposition, determine the wastewater treatment strategy of the micro-nano bubble generator to increase the stirring speed of the stirrer of the micro-nano bubble generator by a preset stirring speed step.

[0078] In practice, the agitator is a paddle or turbine type driven by a variable frequency speed control motor, installed at the center of the bottom of the reactor or tangentially along the side wall. The paddle blades are made of duplex stainless steel or coated with polytetrafluoroethylene to resist corrosion. The preset stirring speed step is set based on the effective volume of the reactor, the hardness of the sediment, and the viscosity of the fluid. For example, for a reactor with an effective volume of 5 cubic meters, the single increment is set from 50 to 100 revolutions per minute. If the current stirring speed is 200 revolutions per minute, it is increased to 250 to 300 revolutions per minute. The maximum speed is limited to 80% of the rated speed to prevent equipment overload. The increase in stirring speed is achieved gradually through the frequency converter, with the ramp-up time set to 10 to 30 seconds to avoid mechanical shock caused by sudden changes in speed. The enhanced fluid shear force can effectively strip loose deposits from the outer wall of the nozzle and the inner wall of the reactor, causing them to be resuspended in the water and carried away by subsequent treatment processes. This achieves physical repair of the root cause of the failure, avoiding the additional consumption of chemical cleaning agents and the time cost of downtime disassembly and maintenance.

[0079] When diagnostic results indicate that the anomaly is not caused by abnormal pollutant deposition, it is necessary to further differentiate between sensor malfunction and water quality deterioration in order to take different countermeasures. Confusing these two fundamentally different problems—mistaking sensor malfunction for water quality deterioration and blindly increasing reagents, or mistaking water quality deterioration for sensor malfunction and delaying treatment—will both lead to reduced treatment effectiveness or wasted resources. Therefore, a tiered alarm and categorized response mechanism is needed to send targeted instructions to personnel in different roles based on the specific cause of the anomaly.

[0080] To this end, this application further proposes a hierarchical alarm and classification handling mechanism, which sends targeted instructions to personnel in different roles based on the specific cause of the anomaly.

[0081] When the cause of the malfunction in the micro / nano bubble generator is not due to abnormal pollutant deposition, the wastewater treatment strategy for the micro / nano bubble generator is determined based on the cause of the malfunction, including: Determine whether the abnormality of the micro-nano bubble generator is due to sensor malfunction. If the abnormality is due to sensor malfunction, determine the wastewater treatment strategy of the micro-nano bubble generator to generate and send a first alarm message to the equipment maintenance personnel. The first alarm message is used to instruct the equipment maintenance personnel to repair the oxidation-reduction potential (ORP) sensor. If the abnormality is not due to sensor malfunction, generate and send a second alarm message to the wastewater treatment personnel. The second alarm message is used to indicate the deterioration of the water quality of the wastewater currently being treated by the micro-nano bubble generator, and to instruct the wastewater treatment personnel to increase the dosage of the wastewater treatment agent in the micro-nano bubble generator by a preset dosage step.

[0082] In practice, the first alarm message is automatically generated and pushed to the mobile terminal of maintenance personnel or the display screen in the duty room through the work order module of the Equipment Asset Management System (EAM). The information includes the unique identification number of the equipment, the fault type code (such as "ORP-SENSOR-001" indicating sensor contamination), details of the suggested treatment measures (including disassembly steps, cleaning solution ratio, calibration procedure), and a navigation link to the equipment location; at the same time, it triggers an audible and visual alarm to alert on-site personnel. The second alarm message is sent to the operation terminal of the wastewater treatment team through the Production Execution System (MES) or Distributed Control System (DCS). The information includes the current real-time water quality parameters (such as COD, turbidity exceeding the standard multiple), the suggested reagent increment calculation formula (such as increasing the ozone dosage by 10% to 15% based on the baseline value), a diagram of the dosing point, and safety operation tips (such as wearing protective equipment). The preset dosage increment is set according to the reagent type and treatment scale, for example, increasing the ozone generator power by 5 kilowatts, or increasing the ferrous sulfate dosage in Fenton's reagent by 50 mg / L.

[0083] The above technical solution can establish a hierarchical alarm mechanism to distinguish between two different types of problems: equipment failure and water quality deterioration. This breaks through the limitations of traditional unified alarms that lead to misallocation of maintenance resources, enables precise scheduling of maintenance personnel and treatment personnel, and significantly improves operation and maintenance efficiency and the targeting of treatment.

[0084] To automate the operation of the above methods, it is necessary to build a corresponding hardware system architecture that integrates sensor networks, actuators, and control logic into a complete processing system.

[0085] like Figure 3As shown, this application also proposes a micro / nano bubble coupled oxidation wastewater treatment system, comprising: an acquisition device and a processing device; the acquisition device is used to acquire the current opening degree of the gas flow control valve included in the micro / nano bubble generator when an abnormality is detected in the micro / nano bubble generator, and determine the target opening degree increment of the gas flow control valve based on the current opening degree; the micro / nano bubble generator also includes a nozzle, a first pressure sensor is disposed in front of the nozzle, and a second pressure sensor is disposed behind the nozzle; the micro / nano bubble generator also includes an oxidation-reduction potential (ORP) sensor; an oscillator is disposed on the ORP sensor; the processing device is used to control the opening degree of the gas flow control valve to increase the target opening degree increment, and acquire the sensing pressure value of the first pressure sensor and the sensing pressure of the second pressure sensor during the opening degree increase process. The processing device is further configured to determine, based on the sensing pressure values ​​of the first pressure sensor and the second pressure sensor, whether the abnormality of the micro / nano bubble generator is due to abnormal pollutant deposition; the processing device is further configured to, when the abnormality of the micro / nano bubble generator is not due to abnormal pollutant deposition, control the oscillator to oscillate, and acquire the pre-oscillation ORP data of the oxidation-reduction potential (ORP) sensor before oscillation, and the post-oscillation ORP data of the ORP sensor after oscillation; the processing device is further configured to determine, based on the pre-oscillation ORP data and the post-oscillation ORP data, whether the abnormality of the micro / nano bubble generator is due to sensor malfunction; the processing device is further configured to determine the wastewater treatment strategy for the micro / nano bubble generator based on the abnormality of the micro / nano bubble generator.

[0086] In practical implementation, the acquisition device includes an analog input module (such as a 16-bit resolution, 8-channel isolated A / D conversion module for acquiring 4-20 mA current signals or 0-10 V voltage signals output from sensors such as pressure, turbidity, and ORP), a digital input module (for receiving encoder signals or limit switch signals from valve position feedback), and a communication interface (such as RS-485, Ethernet, or wireless 4G modules for interaction with a host computer, cloud platform, or mobile terminal). The processing device includes a programmable logic controller (PLC) such as the Siemens S7-1500 series or an industrial control computer (IPC), with built-in diagnostic algorithm programs and control logic scripts. It controls valve opening actuators and agitator frequency converters through the analog output module, controls oscillator relays through the digital output module, and sends alarm information to the management system through the network module. The system hardware adopts industrial-grade protection standards, with a protection level of no less than IP65, adapting to the humid and corrosive environment of wastewater treatment sites.

[0087] The above technical solution provides a reliable physical carrier for the implementation of the above method through the collaborative design of hardware architecture and software logic, enabling real-time perception and intelligent diagnosis of the internal state of the micro-nano bubble generator, and ensuring the accurate execution of diagnosis and control strategies and system integration.

[0088] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for treating wastewater using micro / nano bubble coupled oxidation, characterized in that, Includes the following steps: When an anomaly is detected in the micro / nano bubble generator, the current opening degree of the gas flow control valve included in the micro / nano bubble generator is obtained, and the target opening degree increment of the gas flow control valve is determined based on the current opening degree; the micro / nano bubble generator also includes a nozzle, a first pressure sensor is set in front of the nozzle, and a second pressure sensor is set behind the nozzle; the micro / nano bubble generator also includes an oxidation-reduction potential (ORP) sensor; an oscillator is set on the oxidation-reduction potential (ORP) sensor; The gas flow control valve is controlled to increase the target opening increment, and the pressure values ​​sensed by the first pressure sensor and the second pressure sensor are obtained during the opening increase process. Based on the pressure values ​​sensed by the first pressure sensor and the second pressure sensor, determine whether the abnormality of the micro-nano bubble generator is due to abnormal pollutant deposition. When the cause of the abnormality in the micro-nano bubble generator is not due to abnormal pollutant deposition, the oscillator is controlled to oscillate, and the pre-oscillation ORP data of the ORP sensor is obtained before the oscillator oscillates, and the post-oscillation ORP data of the ORP sensor is obtained after the oscillator oscillates. Based on the ORP data before and after the oscillation, determine whether the abnormality of the micro-nano bubble generator is due to sensor malfunction. Determine the wastewater treatment strategy for the micro-nano bubble generator based on the causes of its malfunctions.

2. The method for treating wastewater by micro / nano bubble coupled oxidation according to claim 1, characterized in that, Determine the target opening increment of the gas flow control valve based on the current opening degree, including: Obtain the pollution hazard index and first correspondence relationship of the wastewater currently being treated by the micro-nano bubble generator; the first correspondence relationship includes a one-to-one correspondence relationship between multiple pollution hazard index ranges and multiple first coefficients; The first coefficient corresponding to the pollution hazard index range in which the pollution hazard index of the wastewater falls is used as the target first coefficient; The product of the original opening increment and the target first coefficient is used as the target opening increment of the gas flow control valve.

3. The method for treating wastewater by micro / nano bubble coupled oxidation according to claim 1, characterized in that, Based on the pressure values ​​sensed by the first pressure sensor and the second pressure sensor, determine whether the anomaly in the micro / nano bubble generator is due to abnormal contaminant deposition, including: The difference between the pressure value sensed by the first pressure sensor and the pressure value sensed by the second pressure sensor at the same moment is taken as the pressure difference before and after the nozzle. Based on the multiple pressure difference values ​​before and after the nozzle during the increase of the opening, determine the actual pressure difference change curve before and after the nozzle; Based on the actual pressure difference change curve before and after the nozzle, determine whether the abnormality of the micro-nano bubble generator is due to abnormal pollutant deposition.

4. The method for treating wastewater by micro / nano bubble coupled oxidation according to claim 3, characterized in that, The inner wall of the micro / nano bubble generator is also equipped with acoustic sensors. Based on the actual pressure difference change curve before and after the nozzle, the system determines whether the abnormality of the micro / nano bubble generator is due to abnormal contaminant deposition, including: The time between the peak pressure difference and the start of the increase in the opening of the gas flow control valve is taken as the pressure difference rise time. The time between the peak pressure difference and the moment when the opening of the gas flow control valve stops increasing is taken as the pressure difference decrease time. Determine whether both the duration of the pressure difference increase and the duration of the pressure difference decrease are greater than a preset duration threshold; If so, obtain and determine whether the abnormality of the micro-nano bubble generator is due to abnormal pollutant deposition based on the acoustic data of the acoustic sensor; otherwise, determine that the abnormality of the micro-nano bubble generator is not due to abnormal pollutant deposition.

5. The method for treating wastewater by micro / nano bubble coupled oxidation according to claim 4, characterized in that, Based on acoustic data from acoustic sensors, determine whether the anomaly in the micro / nano bubble generator is due to abnormal contaminant deposition, including: The acoustic data from the acoustic sensor is processed by Fast Fourier Transform to obtain the frequency characteristics of the acoustic data. Determine whether the energy of a preset frequency component in the frequency characteristics of acoustic data is less than a preset energy threshold. When the energy of a preset frequency component in the frequency characteristics of acoustic data is greater than or equal to a preset energy threshold, the abnormal cause of the micro-nano bubble generator is determined not to be abnormal pollutant deposition. When the energy of a preset frequency component in the frequency characteristics of acoustic data is less than a preset energy threshold, the abnormal cause of the micro / nano bubble generator is determined to be abnormal pollutant deposition.

6. The method for treating wastewater by micro / nano bubble coupled oxidation according to claim 1, characterized in that, The micro / nano bubble generator also includes a water outlet, which is equipped with a turbidity sensor. This sensor detects any abnormalities in the micro / nano bubble generator, including: Obtain the treatment cleanliness of the current wastewater treatment task using the micro / nano bubble generator and the corresponding relationship between the second and the second relationship; the second relationship includes a one-to-one correspondence between multiple treatment cleanliness ranges and multiple second coefficients. The second coefficient corresponding to the treatment cleanliness range of the current wastewater treatment task in the second correspondence is used as the target second coefficient. The product of the target second coefficient and the preset turbidity threshold is used as the target turbidity threshold; Acquire turbidity data from the turbidity sensor and determine whether the turbidity data is greater than the target turbidity threshold; When the turbidity data is greater than the target turbidity threshold, it is determined that there is an anomaly in the micro / nano bubble generator; When the turbidity data is less than or equal to the target turbidity threshold, it is determined that there is no abnormality in the micro / nano bubble generator.

7. The method for treating wastewater by micro / nano bubble coupled oxidation according to claim 1, characterized in that, Based on the ORP data before and after the oscillation, determine whether the abnormality of the micro / nano bubble generator is due to sensor malfunction, including: The difference between the ORP data after oscillation and the ORP data before oscillation is taken as the ORP deviation value. Determine whether the ORP deviation value is greater than the preset ORP deviation threshold; When the ORP deviation value is greater than the preset ORP deviation threshold, the cause of the micro / nano bubble generator malfunction is determined to be a sensor malfunction. When the ORP deviation value is less than or equal to the preset ORP deviation threshold, it is determined that the cause of the micro / nano bubble generator malfunction is not a sensor malfunction.

8. The method for treating wastewater by micro / nano bubble coupled oxidation according to claim 1, characterized in that, The micro / nano bubble generator also includes a stirrer. Wastewater treatment strategies for the micro / nano bubble generator are determined based on the causes of malfunctions, including: Determine whether the abnormality of the micro / nano bubble generator is due to abnormal contaminant deposition; When the abnormality of the micro-nano bubble generator is due to abnormal pollutant deposition, the wastewater treatment strategy for the micro-nano bubble generator is determined to be to increase the stirring speed of the stirrer of the micro-nano bubble generator by a preset stirring speed step.

9. The method for treating wastewater by micro / nano bubble coupled oxidation according to claim 8, characterized in that, When the cause of the malfunction in the micro / nano bubble generator is not due to abnormal pollutant deposition, the wastewater treatment strategy for the micro / nano bubble generator is determined based on the cause of the malfunction, including: Determine whether the abnormality of the micro / nano bubble generator is due to sensor malfunction; When the cause of the abnormality in the micro-nano bubble generator is a sensor malfunction, the wastewater treatment strategy of the micro-nano bubble generator is determined to generate and send a first alarm message to the equipment maintenance personnel; the first alarm message is used to instruct the equipment maintenance personnel to repair the oxidation-reduction potential (ORP) sensor; When the cause of the abnormality in the micro-nano bubble generator is not a sensor malfunction, a second alarm message is generated and sent to the wastewater treatment personnel. The second alarm message is used to indicate the deterioration of the water quality of the wastewater currently being treated by the micro-nano bubble generator, and to instruct the wastewater treatment personnel to increase the amount of wastewater treatment agent added to the micro-nano bubble generator by a preset addition step.

10. A micro / nano bubble coupled oxidation wastewater treatment system, characterized in that, include: Acquisition device and processing device; The acquisition device is used to acquire the current opening degree of the gas flow control valve included in the micro-nano bubble generator when an abnormality is detected in the micro-nano bubble generator, and to determine the target opening degree increment of the gas flow control valve based on the current opening degree; the micro-nano bubble generator also includes a nozzle, a first pressure sensor is arranged in front of the nozzle, and a second pressure sensor is arranged behind the nozzle; the micro-nano bubble generator also includes an oxidation-reduction potential (ORP) sensor; an oscillator is arranged on the oxidation-reduction potential (ORP) sensor. The processing device is used to control the opening degree of the gas flow control valve to increase the target opening degree increment, and to acquire the sensing pressure value of the first pressure sensor and the sensing pressure value of the second pressure sensor during the opening degree increase process. The processing device is also used to determine whether the abnormality of the micro-nano bubble generator is due to abnormal pollutant deposition based on the sensing pressure value of the first pressure sensor and the sensing pressure value of the second pressure sensor. The processing device is also used to control the oscillator to oscillate when the cause of the abnormality of the micro-nano bubble generator is not the abnormality of pollutant deposition, and to acquire the pre-oscillation ORP data of the oxidation-reduction potential (ORP) sensor before the oscillator oscillates, and the post-oscillation ORP data of the ORP sensor after the oscillator oscillates. The processing device is also used to determine whether the abnormality of the micro-nano bubble generator is due to sensor malfunction based on the ORP data before and after oscillation. The treatment device is also used to determine the wastewater treatment strategy for the micro-nano bubble generator based on the cause of the malfunction.

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