An intelligent control method and operation and maintenance system applied to a low-carbon deep denitrification device for sewage

Abstract

The application relates to the technical field of sewage treatment, and particularly discloses an intelligent control method and operation and maintenance system applied to a sewage low-carbon deep denitrification device, which can objectively represent the health risk degree of a microbial community by collecting characteristic data reflecting the state of microorganisms and processing the characteristic data to obtain a quantitative index under the premise that the effluent quality meets the standard. When the health risk of the microorganisms reaches a certain threshold, the system can timely switch from an operation mode with energy consumption optimization as the first priority to an operation mode with guaranteeing the health of the microbial system as the first priority, so that the robustness and impact resistance of the microbial system are actively improved. The intelligent regulation and control mechanism based on the health state of the microorganisms avoids the disadvantages of the traditional control system, that is, excessively pursuing energy saving and ignoring the long-term health of the microbial system, effectively prevents system collapse caused by the recessive recession of the microorganisms, guarantees the stable and standard discharge of the sewage treatment, and balances the low-carbon operation and the robustness of the system.

Description

Technical Field

[0001] This application relates to the field of wastewater treatment technology, and more specifically, to an intelligent control method and operation and maintenance system for a wastewater low-carbon deep denitrification device. Background Technology

[0002] Wastewater treatment plants typically rely on intelligent control systems to precisely regulate aeration intensity and carbon source dosage in pursuit of low-carbon operation and advanced nitrogen removal. However, traditional control methods often depend excessively on human experience, leading to unstable operating efficiency and high energy consumption.

[0003] Furthermore, in the scenario of intelligent control for deep nitrogen removal in wastewater, when trace amounts of chronic inhibitors, difficult to detect by conventional sensors, are continuously introduced, and the system operates at its maximum energy efficiency for an extended period, the intelligent control system struggles to promptly and accurately identify the latent decline in the microbial community (especially nitrifying bacteria) and the decreasing robustness of the system. For example, in a wastewater treatment plant, trace amounts of specific organic compounds discharged from a newly built fine chemical industrial park upstream have a long-term, chronic inhibitory effect on the nitrifying bacterial community. Because the intelligent control system prioritizes low-carbon and energy-saving optimization, it tends to maintain aeration and carbon source dosage at the minimum required to meet effluent standards, leaving the microbial system in a relatively fragile equilibrium state, with its buffering capacity against external disturbances compressed to the extreme.

[0004] In this situation, the slow decline in microbial activity is difficult to directly identify in the conventional monitoring and control logic of the intelligent control system. The system continues to adjust aeration and carbon source based on its predictive model and real-time sensor data, further reinforcing this extreme energy-saving operating mode. Under the dual pressure of chronic inhibition and extreme operation, the biodiversity and functional redundancy of the microbial system gradually decrease.

[0005] When the system faces routine seasonal water temperature drops or slight increases in load, such as a drop in water temperature and a slight increase in influent ammonia nitrogen load during winter, the intelligent control system adjusts according to its established logic. However, at this time, the nitrifying bacteria community, due to long-term exposure to chronic inhibitors, has severely depleted its activity and quantity, becoming extremely sluggish in its response to low temperatures and increased load. Even if the system increases aeration, it is difficult to effectively improve the nitrification rate, leading to ammonia nitrogen accumulation and a vicious cycle. Ultimately, the entire biological denitrification system collapses due to the latent decline of the microbial community.

[0006] Existing intelligent systems, which are based on low carbon and energy conservation, have lost their inherent stability due to undetected chronic inhibition and extreme operating strategies, ultimately leading to accidents that are more unpredictable and unrecoverable than those caused by traditional manual operation.

[0007] There is currently no effective technical solution to the above problems. Summary of the Invention

[0008] The purpose of this application is to provide an intelligent control method and operation and maintenance system for wastewater low-carbon deep denitrification devices. It aims to solve the problem that existing wastewater treatment plants, in their pursuit of low-carbon operation and deep denitrification, have difficulty in identifying the hidden decline of microbial communities and the decreasing trend of system robustness, leading to system collapse and excessive effluent indicators when facing normal environmental changes.

[0009] To solve the above-mentioned technical problems, the solution proposed in this application is as follows: As one aspect of this application, an intelligent control method for a wastewater low-carbon deep denitrification device is provided, the wastewater low-carbon deep denitrification device comprising a biological reactor, including: Step S1: Obtain effluent water quality data from the wastewater low-carbon deep denitrification device. The effluent water quality data includes ammonia nitrogen concentration data and total nitrogen concentration data detected at the outlet of the biological reactor. Step S2: Determine whether the effluent water quality data exceeds the set target value to determine whether the water quality in the biological reactor meets the standard. Step S3: When it is determined that the water quality in the bioreactor meets the standards, collect characteristic data reflecting the state of microorganisms in the bioreactor, and process the characteristic data to obtain a quantitative index characterizing the degree of health risk of the microbial community. Step S4: Based on the quantitative index, determine the operating mode of the bioreactor; The bioreactor operates in two modes: a first operating mode prioritizing energy consumption optimization and a second operating mode prioritizing the health of the microbial system. When the bioreactor is operating in the first mode, if the quantitative index decreases from above a preset second risk threshold to below a preset second risk threshold, the bioreactor switches from the first operating mode to the second operating mode. Conversely, when the bioreactor is operating in the second mode, if the quantitative index increases from below a preset first risk threshold to above a preset first risk threshold, the bioreactor switches back to the first operating mode. The preset first risk threshold is greater than the preset second risk threshold. Step S5: Based on the determined operating mode of the bioreactor, adjust the operating parameters of the bioreactor.

[0010] Furthermore, step S1 includes: Step S11: Confirm the influent flow rate of the biological reactor. When the influent flow rate exceeds the preset flow rate threshold, collect the effluent water quality data of the wastewater low-carbon deep denitrification device in real time. When the influent flow rate does not exceed the preset flow rate threshold, collect the effluent water quality data of the wastewater low-carbon deep denitrification device at a set period. Step S12: Filter the collected effluent water quality data to obtain the processed effluent water quality data.

[0011] Furthermore, the control strategy corresponding to the first operating mode is as follows: The dissolved oxygen concentration in the aerobic zone of the bioreactor is controlled within the first preset range, and the carbon source dosage in the bioreactor is added according to the set demand value. The control strategy corresponding to the second operating mode is: The dissolved oxygen concentration in the aerobic zone of the bioreactor is increased from the first preset range to the second preset range, and the carbon source dosage in the bioreactor is increased by a preset ratio according to the set demand value.

[0012] Furthermore, step S5 includes: Step S51: When the bioreactor switches from the first operating mode to the second operating mode, the initial redundancy range of aeration rate and carbon source dosage is set based on the current effluent water quality data and the characteristic data reflecting the current state of microorganisms in the bioreactor. Step S52: Gradually reduce the aeration rate and carbon source dosage with a set step size, and monitor the changing trends of effluent water quality data and characteristic data reflecting the state of microorganisms in the bioreactor. Step S53: Based on the changing trend, determine the minimum redundancy of aeration volume and carbon source dosage.

[0013] Furthermore, step S3 includes: Step S31a: Collect concentration data of microbial metabolites in the mixed liquid of the bioreactor; Step S32a: Compare the concentration data of the microbial metabolites with the preset health reference concentration data to obtain the deviation. Step S33a: Map the deviation to the quantization index using a piecewise function mapping with a preset threshold.

[0014] Furthermore, step S32a is as follows: The Euclidean distance between the obtained concentration vector of microbial metabolites and the preset health reference concentration vector is calculated and used as the deviation.

[0015] Furthermore, the interval between step S32a and step S33a includes: Step A1: Obtain the self-test parameters of the online analysis device used to collect concentration data of microbial metabolites. The self-test parameters include the microchannel pressure and optical transmittance of the device. Step A2: When the microchannel pressure is higher than the first pressure threshold or the optical path transmittance is lower than the first transmittance threshold, it is determined that there is interference in the data acquisition of the online analysis device, and the currently obtained deviation is determined to be invalid. Step A3: Trigger the self-cleaning program of the online analysis device and return to execute step S31a.

[0016] Furthermore, the interval between step S32a and step S33a includes: Step B1: Obtain the influent water quality data of the wastewater low-carbon deep denitrification device, wherein the influent water quality data includes the influent suspended solids concentration and the influent chemical oxygen demand concentration; Step B2: When the influent suspended solids concentration is continuously higher than the set historical influent suspended solids concentration threshold or the influent chemical oxygen demand (COD) concentration is continuously higher than the set historical influent COD concentration threshold within the set time window, it is determined that there is a shock change in the influent water quality of the wastewater low-carbon deep denitrification device, and the currently obtained deviation is deemed invalid. Step B3: Trigger the concentration data delay acquisition program. After the concentration data delay acquisition program is completed, return to execute step S31a.

[0017] Furthermore, step S3 includes: Step S31b: Apply controlled dissolved oxygen disturbance to the bioreactor and collect dynamic response data during the disturbance process, wherein the dynamic response data includes one or more of dissolved oxygen concentration and ammonia nitrogen concentration; Step S32b: Based on the dynamic response data, calculate the characteristic parameters characterizing the microbial response and recovery ability, wherein the characteristic parameters include at least one or more of dissolved oxygen recovery time and ammonia nitrogen concentration fluctuation range; Step S33b: Calculate the vitality index based on the feature parameters and using a preset vitality index calculation function; Step S34b: Map the vitality index to the quantitative index using a piecewise function mapping with a preset threshold.

[0018] As a second aspect of this application, an operation and maintenance system for a wastewater low-carbon deep denitrification device is provided. This system is equipped with an intelligent control system applied to the wastewater low-carbon deep denitrification device. The intelligent control system employs the intelligent control method described above. The wastewater low-carbon deep denitrification device includes a biological reactor. The intelligent control system includes: The data acquisition module is used to acquire the effluent water quality data of the wastewater low-carbon deep denitrification device. The effluent water quality data includes ammonia nitrogen concentration data and total nitrogen concentration data detected at the outlet of the biological reactor. A water quality compliance judgment module is used to determine whether the effluent water quality data exceeds the set target value and to determine whether the water quality in the biological reactor meets the standard. The instruction judgment and output module is used to collect characteristic data reflecting the state of microorganisms in the bioreactor when it is determined that the water quality in the bioreactor meets the standards, and to process the characteristic data to obtain a quantitative index characterizing the degree of health risk of the microbial community. An operation mode determination module is used to determine the operation mode of the bioreactor based on the quantitative index. The operating parameter adjustment module is used to adjust the operating parameters of the bioreactor based on the determined operating mode of the bioreactor.

[0019] As can be seen from the above, the intelligent control method and operation and maintenance system for a low-carbon deep denitrification device for wastewater provided in this application solves the problem in existing technologies where intelligent control systems struggle to identify latent decline in the microbial community under long-term low-carbon operation, leading to decreased system robustness and eventual system collapse and effluent exceeding standards when facing normal environmental changes. Specifically, under the premise that the effluent quality meets standards, this method collects and processes characteristic data reflecting the microbial state to obtain a quantitative index, which objectively characterizes the degree of health risk of the microbial community. When the microbial health risk reaches a certain threshold, the system can promptly switch from an operation mode prioritizing energy consumption optimization to one prioritizing the health of the microbial system, thereby proactively improving the robustness and shock resistance of the microbial system. This intelligent control mechanism based on the microbial health status avoids the drawbacks of traditional control systems that excessively pursue energy conservation while neglecting the long-term health of the microbial system, effectively preventing system collapse caused by latent microbial decline, ensuring stable and compliant discharge of wastewater, and achieving a balance between low-carbon operation and system robustness. Attached Figure Description

[0020] Figure 1 A flowchart illustrating an intelligent control method for a wastewater low-carbon deep denitrification device, provided in this application embodiment; Figure 2 A system structure block diagram of an operation and maintenance system for a wastewater low-carbon deep denitrification device provided in this application embodiment; Figure reference numerals: 100, Intelligent control system; 101, Data acquisition module; 102, Water quality compliance judgment module; 103, Command judgment and output module; 104, Operating mode determination module; 105, Operating parameter adjustment module. Detailed Implementation

[0021] To better illustrate the present invention, the invention will now be described in further detail with reference to the accompanying drawings.

[0022] It should be understood that, in order to make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. The components of the embodiments of this disclosure 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 disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed disclosure, but merely represents selected embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0023] The following description uses at least one specific embodiment as an example. In this embodiment: Firstly, such as Figure 1 As shown, an intelligent control method for a wastewater low-carbon deep denitrification device is provided. The wastewater low-carbon deep denitrification device includes a biological reactor, comprising: Step S1: Obtain effluent water quality data from the wastewater low-carbon deep denitrification device. The effluent water quality data includes ammonia nitrogen concentration data and total nitrogen concentration data detected at the outlet of the biological reactor. Step S2: Determine whether the effluent water quality data exceeds the set target value to determine whether the water quality in the biological reactor meets the standard. Step S3: When it is determined that the water quality in the bioreactor meets the standards, collect characteristic data reflecting the state of microorganisms in the bioreactor, and process the characteristic data to obtain a quantitative index characterizing the degree of health risk of the microbial community. Step S4: Based on the quantitative index, determine the operating mode of the bioreactor; The bioreactor operates in two modes: a first operating mode prioritizing energy consumption optimization and a second operating mode prioritizing the health of the microbial system. When the bioreactor is operating in the first mode, if the quantitative index decreases from above a preset second risk threshold to below a preset second risk threshold, the bioreactor switches from the first operating mode to the second operating mode. Conversely, when the bioreactor is operating in the second mode, if the quantitative index increases from below a preset first risk threshold to above a preset first risk threshold, the bioreactor switches back to the first operating mode. The preset first risk threshold is greater than the preset second risk threshold. Step S5: Based on the determined operating mode of the bioreactor, adjust the operating parameters of the bioreactor.

[0024] This application introduces a quantitative index of the health risk level of the microbial community, enabling the intelligent control system to promptly identify the hidden decline of the microbial system while ensuring that the effluent water quality meets the standards. Based on this, the system can adjust its operating mode, thereby optimizing energy consumption while effectively ensuring the robustness and long-term stability of the microbial system and preventing system collapse.

[0025] In the specific implementation process, the first step is to obtain the effluent water quality data from the wastewater low-carbon deep denitrification device. This data can be obtained by installing an online water quality analyzer at the effluent outlet of the biological reactor to monitor ammonia nitrogen and total nitrogen concentrations in real time. For example, ion-selective electrode or colorimetric sensors can be used to continuously monitor ammonia nitrogen concentration, and ultraviolet spectrophotometry or chemiluminescence sensors can be used to monitor total nitrogen concentration. These sensors transmit real-time data to the control system.

[0026] Next, the acquired effluent water quality data is used to determine whether it exceeds the set target values, thus confirming whether the water quality in the biological reactor meets the standards. For example, the target value for ammonia nitrogen concentration can be set at 1 mg / L, and the target value for total nitrogen concentration at 10 mg / L. The control system compares the real-time detected ammonia nitrogen concentration data and total nitrogen concentration data with these target values. If either indicator exceeds the set target value, the water quality is determined to be substandard; otherwise, the water quality is determined to be compliant.

[0027] When it is determined that the water quality in the bioreactor meets the standards, it is necessary to collect characteristic data reflecting the state of microorganisms in the bioreactor and process the characteristic data to obtain a quantitative index characterizing the degree of health risk of the microbial community.

[0028] Based on the quantified index, the operating mode of the bioreactor is determined. The bioreactor's operating modes include a first operating mode prioritizing energy consumption optimization and a second operating mode prioritizing the health of the microbial system. When the bioreactor is currently operating in the first operating mode, if the quantified index decreases from above a preset second risk threshold to below a preset second risk threshold, the bioreactor is controlled to switch from the first operating mode to the second operating mode. For example, the second risk threshold can be set to 0.6. When the system is operating in the first operating mode, if the quantified index decreases from 0.7 to 0.5, a mode switch is triggered. Conversely, when the bioreactor is operating in the second operating mode, if the quantified index increases from below a preset first risk threshold to above a preset first risk threshold, the bioreactor is controlled to switch from the second operating mode to the first operating mode. For example, the first risk threshold can be set to 0.3. When the system is operating in the second operating mode, if the quantified index increases from 0.2 to 0.4, a mode switch is triggered. It should be noted that the preset first risk threshold is greater than the preset second risk threshold. For example, the first risk threshold is 0.3 and the second risk threshold is 0.6. This design is to avoid the system switching frequently between the two operating modes and to provide a certain lag effect.

[0029] Finally, based on the determined operating mode of the bioreactor, the operating parameters of the bioreactor are adjusted. For example, when the system switches from the first operating mode to the second operating mode, the aeration rate and carbon source dosage can be increased. Specifically, in the first operating mode, the aeration rate may be controlled at a low level to save energy, and the carbon source dosage is only sufficient to meet the minimum nitrogen removal requirements. When switching to the second operating mode, the aeration rate can be increased to ensure that the dissolved oxygen concentration in the aerobic zone is within an optimal range, while the carbon source dosage is increased to enhance denitrification capacity, thereby providing more sufficient growth and recovery conditions for the microbial community.

[0030] By introducing a quantitative index of the health risk level of the microbial community, refined and intelligent control of the low-carbon deep denitrification device for wastewater is achieved. This method first acquires effluent water quality data in real time to ensure that the denitrification effect meets the set standards. Based on this, when the water quality meets the standards, further characteristic data reflecting the microbial state are collected and processed into a quantitative index. This index can sensitively reflect the health risk level of the microbial community, and can provide early warning of potential decline in the microbial system even before the effluent water quality deteriorates.

[0031] In some of the embodiments described above in this application, a method for obtaining effluent water quality data from a wastewater low-carbon deep denitrification device is proposed. However, in its implementation, if only a single acquisition strategy is adopted, the data acquisition efficiency may be low or the acquired data may be subject to noise interference, thereby affecting the accuracy of subsequent water quality judgment and operation mode determination.

[0032] In this regard, step S1 includes: Step S11: Confirm the influent flow rate of the biological reactor. When the influent flow rate exceeds the preset flow rate threshold, collect the effluent water quality data of the wastewater low-carbon deep denitrification device in real time. When the influent flow rate does not exceed the preset flow rate threshold, collect the effluent water quality data of the wastewater low-carbon deep denitrification device at a set period. Step S12: Filter the collected effluent water quality data to obtain the processed effluent water quality data.

[0033] Specifically, step S11 aims to dynamically adjust the frequency of effluent water quality data collection based on the actual operating load of the bioreactor. The influent flow rate is an indicator reflecting changes in the bioreactor's load. When the influent flow rate exceeds a preset flow rate threshold, it indicates that the bioreactor's operating status may be fluctuating significantly. In this case, real-time collection of effluent water quality data is necessary to ensure timely detection of water quality changes and provide accurate information for subsequent judgment and control. Conversely, when the influent flow rate does not exceed the preset flow rate threshold, the bioreactor's operating status is relatively stable. In this case, data can be collected at a set period, such as once every certain time interval, to save sensor lifespan, reduce energy consumption, and alleviate data processing burden. The preset flow rate threshold can be set based on the wastewater treatment plant's design scale, historical operating data, and empirical values ​​to balance data real-time performance with resource consumption.

[0034] Step S12 aims to improve the reliability and accuracy of the collected effluent water quality data. In practical applications, the data collected by water quality sensors is often affected by environmental interference, equipment drift, or instantaneous fluctuations, resulting in noise. Filtering the collected effluent water quality data can effectively remove this noise, smooth the data curve, and make the data more accurately reflect the actual water quality conditions of the bioreactor.

[0035] Filtering methods can include, but are not limited to, moving average filtering, Kalman filtering, and median filtering. Filtering can yield more stable effluent quality data, providing a solid foundation for subsequent assessments.

[0036] This application's solution addresses the inefficiencies and data noise issues that can arise from traditional single-data acquisition strategies by introducing influent flow rate assessment and data filtering during the data acquisition phase. Specifically, in step S11, the system intelligently adjusts the data acquisition frequency based on the actual load changes in the bioreactor. Real-time acquisition ensures rapid response to water quality changes during high loads, while periodic acquisition optimizes resource utilization during low loads. Furthermore, step S12 filters the acquired data, effectively eliminating random noise and outliers, resulting in more stable and representative water quality data. This dynamic adjustment and data optimization mechanism enables subsequent water quality compliance assessments, microbial status evaluations, and operational mode determinations to be based on more accurate data, thereby enhancing the effectiveness of the entire intelligent control method.

[0037] In this embodiment, preferably, the control strategy corresponding to the first operating mode is to control the dissolved oxygen concentration in the aerobic zone of the bioreactor within a first preset range, and to add carbon source to the bioreactor according to the set demand value. The control strategy corresponding to the second operating mode is to increase the dissolved oxygen concentration in the aerobic zone of the bioreactor from the first preset range to the second preset range, and to increase the carbon source dosage in the bioreactor by a preset ratio according to the set demand value.

[0038] Specifically, the first operating mode aims to optimize energy consumption. Its control strategy involves maintaining the dissolved oxygen concentration in the aerobic zone of the bioreactor within a first preset range to reduce unnecessary aeration energy consumption. Simultaneously, the carbon source dosage is adjusted according to the set demand value to ensure the carbon source supply required for the denitrification process. The first preset range can be understood as the lowest possible dissolved oxygen concentration range while meeting denitrification efficiency requirements, for example, it can be set to 0.5 mg / L to 1.5 mg / L. The set demand value refers to the theoretical carbon source requirement calculated based on factors such as influent water quality and treatment load, with the aim of minimizing carbon source consumption while ensuring denitrification effectiveness.

[0039] The second operating mode aims to ensure the health of the microbial system. Its control strategy involves increasing the dissolved oxygen concentration in the aerobic zone of the bioreactor from a first preset range to a second preset range, thereby providing a more abundant oxygen supply and promoting microbial activity and recovery. Simultaneously, the carbon source dosage is increased by a preset proportion according to the set demand value. The second preset range is typically higher than the first preset range, for example, it can be set to 1.5 mg / L to 2.5 mg / L, with the aim of providing a more favorable growth environment for the microorganisms. Increasing the preset proportion means adding an additional certain percentage of carbon source on top of the original demand value, for example, increasing it by 10% to 30%. Its purpose is to accelerate the recovery of the microbial system by strengthening nutrient supply when it faces health risks.

[0040] In this embodiment, a further step of adjusting the operating parameters of the bioreactor, namely step S5, is proposed, which includes: Step S51: When the bioreactor switches from the first operating mode to the second operating mode, the initial redundancy range of aeration rate and carbon source dosage is set based on the current effluent water quality data and the characteristic data reflecting the current state of microorganisms in the bioreactor. Step S52: Gradually reduce the aeration rate and carbon source dosage with a set step size, and monitor the changing trends of effluent water quality data and characteristic data reflecting the state of microorganisms in the bioreactor. Step S53: Based on the changing trend, determine the minimum redundancy of aeration volume and carbon source dosage.

[0041] Specifically, in step S51 above, when the system determines that it needs to switch from the first operating mode to the second operating mode, it first acquires the current effluent water quality data of the wastewater low-carbon deep denitrification device, such as ammonia nitrogen concentration data and total nitrogen concentration data, as well as characteristic data reflecting the state of microorganisms in the bioreactor, such as the concentration data of microbial metabolites or characteristic parameters of microbial response and recovery capabilities. Based on this real-time data, the system sets an initial redundancy range for aeration rate and carbon source dosage. This initial redundancy range aims to provide a safe starting point to ensure that the microbial system can be adequately protected in the initial stage of mode switching, while leaving room for subsequent optimization and adjustment. Among them, the aeration rate refers to the amount of air supplied to the aerobic zone in the bioreactor, which directly affects the dissolved oxygen concentration; the carbon source dosage refers to the amount of external carbon source added to the bioreactor to support the denitrification process of microorganisms.

[0042] Furthermore, in step S52 above, the system gradually reduces the aeration rate and carbon source dosage at preset step intervals, such as at regular intervals or under specific conditions. After each reduction, the system continuously monitors and acquires the latest effluent water quality data and characteristic data reflecting the state of microorganisms in the bioreactor, and analyzes the trends of these data. For example, it monitors whether the ammonia nitrogen concentration begins to rise, whether the total nitrogen concentration deviates from the target value, or whether the quantitative index of the health risk level of the microbial community shows signs of deterioration.

[0043] Therefore, in step S53 above, the system determines the minimum redundancy of aeration rate and carbon source dosage based on the changing trends of monitored effluent water quality data and microbial state characteristic data. Minimum redundancy refers to the lowest effective level that aeration rate and carbon source dosage can achieve while ensuring effluent water quality meets standards and the health of the microbial system. For example, if the effluent water quality or microbial state begins to show an unfavorable trend when the aeration rate or carbon source dosage decreases to a certain level, the system will determine that the parameter levels before that point are the required minimum redundancy.

[0044] By introducing a dynamic parameter optimization process when switching the bioreactor from the first operating mode to the second, the resource waste that may result from traditional fixed-ratio adjustments is resolved. Specifically, an initial redundancy range is first set based on real-time water quality and microbial status data to ensure the stability of the microbial system during the initial mode switch. Subsequently, the aeration rate and carbon source dosage are gradually reduced in set increments, and their impact on effluent quality and microbial status is monitored in real time. This allows the system to accurately capture the response boundaries of the microbial system to parameter changes. Finally, based on these dynamic response data, the minimum aeration rate and carbon source dosage required to maintain the health of the microbial system and achieve effluent quality standards are determined, thereby avoiding unnecessary resource input.

[0045] Furthermore, step S3 includes: Step S31a: Collect concentration data of microbial metabolites in the mixed liquid of the bioreactor; Step S32a: Compare the concentration data of the microbial metabolites with the preset health reference concentration data to obtain the deviation. Step S33a: Map the deviation to the quantization index using a piecewise function mapping with a preset threshold.

[0046] Specifically, in step S31a, microbial metabolites refer to various organic or inorganic substances produced by microorganisms during their life activities, and their concentration data can directly reflect the physiological state and activity of microorganisms. For example, concentration data of extracellular polymers and soluble microbial products can be collected. These data can be obtained through online sensors, spectrometers, or laboratory analysis. In step S32a, the preset health reference concentration data is a baseline for microbial metabolite concentration established through long-term monitoring and data analysis of the bioreactor under stable operating conditions. By comparing the real-time collected microbial metabolite concentration data with this health reference concentration data, the difference between the current microbial community state and the ideal health state can be quantified, thus obtaining the deviation. The larger the deviation, the further the health status of the microbial community deviates from the ideal state. Further, in step S33a, piecewise function mapping is a method to convert continuous deviation values ​​into discrete quantitative indices. By setting different threshold ranges, the deviation can be divided into different risk levels, such as low risk, medium risk, and high risk, and assigned corresponding quantitative indices. For example, when the deviation is below a certain threshold, the quantitative index is low, indicating that the microbial community is in good health; when the deviation is above another threshold, the quantitative index is high, indicating that the microbial community has a high health risk. The aim is to simplify complex microbial state information into easily understood and decision-making quantitative indicators, so as to facilitate the determination of subsequent operating methods.

[0047] By directly monitoring the concentration of microbial metabolites in the mixed liquor of a bioreactor, the physiological activity and health status of the microbial community can be reflected in real time. Comparing this real-time data with preset health reference concentrations allows for accurate quantification of the deviation between the current microbial state and the ideal health state. Subsequently, a piecewise function mapping is used to transform this deviation into a quantitative index characterizing the degree of health risk to the microbial community. This provides a microbial health assessment indicator for the intelligent control system, avoiding the lag in microbial state assessments in traditional methods and making the health risk assessment of the microbial system more timely and accurate.

[0048] Preferably, step S32a is as follows: The Euclidean distance between the obtained concentration vector of microbial metabolites and the preset health reference concentration vector is calculated and used as the deviation.

[0049] In some of the above embodiments, the concentration data of microbial metabolites in the mixed liquor of the bioreactor is collected and compared with preset health reference concentration data to obtain the deviation degree, which is then mapped into a quantitative index characterizing the degree of health risk of the microbial community. However, in practical applications, the online analysis equipment used to collect microbial metabolite concentration data may be affected by various factors, such as microchannel blockage or optical path contamination inside the equipment, resulting in inaccurate data. If the deviation degree is calculated based on these inaccurate data, it may lead to an incorrect assessment of the health status of the microbial system, thereby affecting the decision-making accuracy of the intelligent control method, and even adversely affecting the stable operation of the bioreactor.

[0050] Furthermore, the interval between step S32a and step S33a includes: Step A1: Obtain the self-test parameters of the online analysis device used to collect concentration data of microbial metabolites. The self-test parameters include the microchannel pressure and optical transmittance of the device. Step A2: When the microchannel pressure is higher than the first pressure threshold or the optical path transmittance is lower than the first transmittance threshold, it is determined that there is interference in the data acquisition of the online analysis device, and the currently obtained deviation is determined to be invalid. Step A3: Trigger the self-cleaning program of the online analysis device and return to execute step S31a.

[0051] Specifically, after performing step S32a to compare the concentration data of microbial metabolites with preset health reference concentration data to obtain the deviation, but before performing step S33a to map the deviation into a quantification index, this application introduces the detection of the status of the online analysis device. The online analysis device is a key device for collecting concentration data of microbial metabolites in the mixed liquid of the bioreactor. To ensure the accuracy of its data acquisition, self-test parameters of the device are obtained. These self-test parameters specifically include the device's microchannel pressure and optical transmittance. Microchannel pressure reflects the smoothness of liquid flow within the device, while optical transmittance reflects the clarity of light signal transmission; both are important indicators for evaluating the device's operating status and data reliability.

[0052] Furthermore, the system will make judgments based on the acquired self-test parameters. When the detected microchannel pressure is higher than a preset first pressure threshold, it indicates that the microchannel of the device may be blocked, affecting the accurate transmission and detection of samples; or, when the optical path transmittance is lower than a preset first transmittance threshold, it indicates that the optical path of the device may be contaminated, affecting the strength and accuracy of the detection signal. In either of these situations, it can be determined that there is interference in the data acquisition of the online analysis device. At this time, the concentration data of microbial metabolites acquired by the device may be inaccurate. Therefore, the currently calculated deviation will be deemed invalid.

[0053] Therefore, when the deviation is deemed invalid, the system triggers the self-cleaning procedure of the online analyzer to correct the equipment malfunction and restore its normal function. This self-cleaning procedure aims to remove blockages or contaminants from inside the equipment. After the self-cleaning procedure is completed, the system returns to step S31a, which involves re-collecting the concentration data of microbial metabolites to ensure that subsequent data processing and risk assessment are based on reliable raw data.

[0054] As another method, the interval between step S32a and step S33a includes: Step B1: Obtain the influent water quality data of the wastewater low-carbon deep denitrification device, wherein the influent water quality data includes the influent suspended solids concentration and the influent chemical oxygen demand concentration; Step B2: When the influent suspended solids concentration is continuously higher than the set historical influent suspended solids concentration threshold or the influent chemical oxygen demand (COD) concentration is continuously higher than the set historical influent COD concentration threshold within the set time window, it is determined that there is a shock change in the influent water quality of the wastewater low-carbon deep denitrification device, and the currently obtained deviation is deemed invalid. Step B3: Trigger the concentration data delay acquisition program. After the concentration data delay acquisition program is completed, return to execute step S31a.

[0055] Specifically, influent water quality data refers to the various water quality indicators of the wastewater entering the low-carbon deep denitrification unit, which can be acquired in real time by online monitoring equipment installed at the unit's inlet. The set historical influent suspended solids concentration threshold and the set historical influent chemical oxygen demand (COD) concentration threshold are determined based on long-term operational data or design standards, and are used to define the upper limit of the normal influent water quality range. When the actual influent water quality data continuously exceeds these thresholds, it is determined that there is a shock change in influent water quality. Deeming the current deviation invalid means that during a shock change in influent water quality, the deviation calculated from the microbial metabolite concentration data is not adopted to prevent incorrect judgments based on inaccurate data. Triggering the concentration data delay acquisition procedure means that the system suspends the processing of microbial metabolite concentration data and deviation calculation, waiting for the influent water quality to stabilize or the shock effect to weaken. After this procedure is completed, the system will return to step S31a, that is, re-collect the microbial metabolite concentration data in the mixed liquor of the bioreactor, to ensure that subsequent deviation calculations and quantitative index assessments are based on stable and representative data.

[0056] In this embodiment, step S3 includes: Step S31b: Apply controlled dissolved oxygen disturbance to the bioreactor and collect dynamic response data during the disturbance process, wherein the dynamic response data includes one or more of dissolved oxygen concentration and ammonia nitrogen concentration; Step S32b: Based on the dynamic response data, calculate the characteristic parameters characterizing the microbial response and recovery ability, wherein the characteristic parameters include at least one or more of dissolved oxygen recovery time and ammonia nitrogen concentration fluctuation range; Step S33b: Calculate the vitality index based on the feature parameters and using a preset vitality index calculation function; Step S34b: Map the vitality index to the quantitative index using a piecewise function mapping with a preset threshold.

[0057] Specifically, applying controlled dissolved oxygen disturbance to a bioreactor involves precisely controlling the aeration equipment to temporarily alter the dissolved oxygen concentration in the aerobic zone of the bioreactor, such as by temporarily increasing or decreasing the dissolved oxygen concentration, to observe the microbial community's response to this environmental change. The dynamic response data generated during this process can include curves showing the changes in dissolved oxygen concentration over time, as well as the changes in ammonia nitrogen concentration over time. These data can reflect the metabolic activity and nitrification / denitrification capacity of the microorganisms in real time when disturbed.

[0058] Among these, the characteristic parameters characterizing the response and recovery capabilities of microorganisms can be understood as key indicators extracted through the analysis of dynamic response data. For example, dissolved oxygen recovery time refers to the time required for the dissolved oxygen concentration in the bioreactor to recover to its initial or stable level after a dissolved oxygen disturbance. A shorter recovery time usually indicates that the microbial community has a strong buffering capacity and recovery vitality. Ammonia nitrogen concentration fluctuation range refers to the range of changes in ammonia nitrogen concentration during dissolved oxygen disturbance. A smaller fluctuation range may indicate that the microorganisms are well adapted to environmental changes and that nitrification function is stable. In practical applications, one or more of dissolved oxygen recovery time and ammonia nitrogen concentration fluctuation range can be selected as characteristic parameters according to specific needs.

[0059] Furthermore, based on the aforementioned characteristic parameters, a pre-defined vitality index calculation function can be used to calculate the vitality index. This vitality index is a comprehensive indicator used to quantify the overall health level and activity of the microbial community. For example, the function can be a multivariate function, weighting parameters such as dissolved oxygen recovery time and ammonia nitrogen concentration fluctuation range to obtain a value between 0 and 100, with higher values ​​indicating stronger microbial activity.

[0060] Finally, a piecewise function with a preset threshold is used to map the vitality index to the quantitative index. This piecewise function can set different risk levels according to different ranges of the vitality index, thereby transforming the continuous vitality index into a discrete quantitative index. For example, the vitality index can be divided into several levels such as "healthy," "mild risk," "moderate risk," and "severe risk," each corresponding to a different quantitative index value. The purpose is to simplify the complex physiological state of microorganisms into risk indicators that are easy to understand and use for decision-making.

[0061] In some preferred embodiments, a specific example is illustrated below. Assuming the bioreactor is operating in a first operating mode, to assess the health of the microbial community, the control system periodically applies controlled dissolved oxygen disturbances to the bioreactor. Specifically, the dissolved oxygen concentration in the aerobic zone can be briefly reduced from the normal operating level of 2.0 mg / L to 0.5 mg / L, maintained for 5 minutes, and then restored to 2.0 mg / L. During this process, the system collects dissolved oxygen and ammonia nitrogen concentration data in real time.

[0062] For example, in a disturbance experiment, if the dissolved oxygen concentration rapidly recovers to 2.0 mg / L within 10 minutes after re-aeration, and the ammonia nitrogen concentration fluctuates within 0.2 mg / L, it indicates strong microbial community activity and good response and recovery capabilities. In this case, based on a preset activity index calculation function, a higher activity index, such as 85, may be calculated. This activity index, mapped through a piecewise function with a preset threshold, may be mapped to a lower quantitative index, indicating that the microbial system is in a "healthy" or "low-risk" state, thus maintaining the primary operating mode.

[0063] However, if in another perturbation experiment, the dissolved oxygen concentration only slowly recovers to 2.0 mg / L 25 minutes after aeration is restored, and the ammonia nitrogen concentration fluctuates by 0.8 mg / L, it indicates a decline in microbial community activity and a weakened response and recovery capacity. In this case, the calculated activity index may be low, for example, 40. This activity index, through piecewise function mapping, may be mapped to a higher quantitative index, indicating that the microbial system is in a "moderate risk" state. If this quantitative index drops from above the preset second risk threshold to below the preset second risk threshold, the control system will determine that it needs to switch from the first operating mode to the second operating mode, prioritizing the health of the microbial system. Measures such as increasing dissolved oxygen concentration and increasing carbon source dosage will be used to help the microbial community recover its activity, thereby preventing further system deterioration.

[0064] As a second aspect of this application, such as Figure 2 As shown, an operation and maintenance system for a wastewater low-carbon deep denitrification device is provided. This system includes an intelligent control system 100 configured for the wastewater low-carbon deep denitrification device. The intelligent control system 100 employs the intelligent control method described above. The wastewater low-carbon deep denitrification device includes a biological reactor. The intelligent control system 100 includes: The data acquisition module 101 is used to acquire the effluent water quality data of the wastewater low-carbon deep denitrification device. The effluent water quality data includes ammonia nitrogen concentration data and total nitrogen concentration data detected at the outlet of the biological reactor. Water quality compliance judgment module 102 is used to determine whether the water quality in the biological reactor meets the standard when the effluent water quality data exceeds the set target value. The instruction judgment output module 103 is used to collect characteristic data reflecting the state of microorganisms in the bioreactor when it is determined that the water quality in the bioreactor meets the standards, and to process the characteristic data to obtain a quantitative index characterizing the degree of health risk of the microbial community. The operation mode determination module 104 is used to determine the operation mode of the bioreactor based on the quantitative index. The operating parameter adjustment module 105 is used to adjust the operating parameters of the bioreactor based on the determined operating mode of the bioreactor.

[0065] As can be seen from the above, the intelligent control method and operation and maintenance system for a low-carbon deep denitrification device for wastewater provided in this application solves the problem in existing technologies where intelligent control systems struggle to identify latent decline in the microbial community under long-term low-carbon operation, leading to decreased system robustness and eventual system collapse and effluent exceeding standards when facing normal environmental changes. Specifically, under the premise that the effluent quality meets standards, this method collects and processes characteristic data reflecting the microbial state to obtain a quantitative index, which objectively characterizes the degree of health risk of the microbial community. When the microbial health risk reaches a certain threshold, the system can promptly switch from an operation mode prioritizing energy consumption optimization to one prioritizing the health of the microbial system, thereby proactively improving the robustness and shock resistance of the microbial system. This intelligent control mechanism based on the microbial health status avoids the drawbacks of traditional control systems that excessively pursue energy conservation while neglecting the long-term health of the microbial system, effectively preventing system collapse caused by latent microbial decline, ensuring stable and compliant discharge of wastewater, and achieving a balance between low-carbon operation and system robustness.

[0066] Finally, it should be noted that the above-described embodiments are merely specific implementations of this disclosure, used to illustrate the technical solutions of this disclosure, and not to limit them. The protection scope of this disclosure is not limited thereto. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features within the scope of the technology disclosed in this disclosure. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be covered within the protection scope of this disclosure.

Claims

1. An intelligent control method for a wastewater low-carbon deep denitrification device, the wastewater low-carbon deep denitrification device comprising a biological reactor, characterized in that, include: Step S1: Obtain effluent water quality data from the wastewater low-carbon deep denitrification device. The effluent water quality data includes ammonia nitrogen concentration data and total nitrogen concentration data detected at the outlet of the biological reactor. Step S2: Determine whether the effluent water quality data exceeds the set target value to determine whether the water quality in the biological reactor meets the standard. Step S3: When it is determined that the water quality in the bioreactor meets the standards, collect characteristic data reflecting the state of microorganisms in the bioreactor, and process the characteristic data to obtain a quantitative index characterizing the degree of health risk of the microbial community. Step S4: Based on the quantitative index, determine the operating mode of the bioreactor; The bioreactor operates in two modes: a first operating mode prioritizing energy consumption optimization and a second operating mode prioritizing the health of the microbial system. When the bioreactor is operating in the first mode, if the quantitative index decreases from above a preset second risk threshold to below a preset second risk threshold, the bioreactor switches from the first operating mode to the second operating mode. Conversely, when the bioreactor is operating in the second mode, if the quantitative index increases from below a preset first risk threshold to above a preset first risk threshold, the bioreactor switches back to the first operating mode. The preset first risk threshold is greater than the preset second risk threshold. Step S5: Based on the determined operating mode of the bioreactor, adjust the operating parameters of the bioreactor.

2. The intelligent control method for a wastewater low-carbon deep denitrification device according to claim 1, characterized in that, Step S1 includes: Step S11: Confirm the influent flow rate of the biological reactor. When the influent flow rate exceeds the preset flow rate threshold, collect the effluent water quality data of the wastewater low-carbon deep denitrification device in real time. When the influent flow rate does not exceed the preset flow rate threshold, collect the effluent water quality data of the wastewater low-carbon deep denitrification device at a set period. Step S12: Filter the collected effluent water quality data to obtain the processed effluent water quality data.

3. The intelligent control method for a wastewater low-carbon deep denitrification device according to claim 1, characterized in that: The control strategy corresponding to the first operating mode is: The dissolved oxygen concentration in the aerobic zone of the bioreactor is controlled within the first preset range, and the carbon source dosage in the bioreactor is added according to the set demand value. The control strategy corresponding to the second operating mode is: The dissolved oxygen concentration in the aerobic zone of the bioreactor is increased from the first preset range to the second preset range, and the carbon source dosage in the bioreactor is increased by a preset ratio according to the set demand value.

4. The intelligent control method for a wastewater low-carbon deep denitrification device according to claim 3, characterized in that, Step S5 includes: Step S51: When the bioreactor switches from the first operating mode to the second operating mode, the initial redundancy range of aeration rate and carbon source dosage is set based on the current effluent water quality data and the characteristic data reflecting the current state of microorganisms in the bioreactor. Step S52: Gradually reduce the aeration rate and carbon source dosage with a set step size, and monitor the changing trends of effluent water quality data and characteristic data reflecting the state of microorganisms in the bioreactor. Step S53: Based on the changing trend, determine the minimum redundancy of aeration volume and carbon source dosage.

5. The intelligent control method for a wastewater low-carbon deep denitrification device according to claim 1, characterized in that, Step S3 includes: Step S31a: Collect concentration data of microbial metabolites in the mixed liquid of the bioreactor; Step S32a: Compare the concentration data of the microbial metabolites with the preset health reference concentration data to obtain the deviation. Step S33a: Map the deviation to the quantization index using a piecewise function mapping with a preset threshold.

6. The intelligent control method for a wastewater low-carbon deep denitrification device according to claim 5, characterized in that, Step S32a is as follows: The Euclidean distance between the obtained concentration vector of microbial metabolites and the preset health reference concentration vector is calculated and used as the deviation.

7. The intelligent control method for a wastewater low-carbon deep denitrification device according to claim 5, characterized in that, Between step S32a and step S33a, the following is included: Step A1: Obtain the self-test parameters of the online analysis device used to collect concentration data of microbial metabolites. The self-test parameters include the microchannel pressure and optical transmittance of the device. Step A2: When the microchannel pressure is higher than the first pressure threshold or the optical path transmittance is lower than the first transmittance threshold, it is determined that there is interference in the data acquisition of the online analysis device, and the currently obtained deviation is determined to be invalid. Step A3: Trigger the self-cleaning program of the online analysis device and return to execute step S31a.

8. The intelligent control method for a wastewater low-carbon deep denitrification device according to claim 5, characterized in that, Between step S32a and step S33a, the following is included: Step B1: Obtain the influent water quality data of the wastewater low-carbon deep denitrification device, wherein the influent water quality data includes the influent suspended solids concentration and the influent chemical oxygen demand concentration; Step B2: When the influent suspended solids concentration is continuously higher than the set historical influent suspended solids concentration threshold or the influent chemical oxygen demand (COD) concentration is continuously higher than the set historical influent COD concentration threshold within the set time window, it is determined that there is a shock change in the influent water quality of the wastewater low-carbon deep denitrification device, and the currently obtained deviation is deemed invalid. Step B3: Trigger the concentration data delay acquisition program. After the concentration data delay acquisition program is completed, return to execute step S31a.

9. The intelligent control method for a wastewater low-carbon deep denitrification device according to claim 1, characterized in that, Step S3 includes: Step S31b: Apply controlled dissolved oxygen disturbance to the bioreactor and collect dynamic response data during the disturbance process, wherein the dynamic response data includes one or more of dissolved oxygen concentration and ammonia nitrogen concentration; Step S32b: Based on the dynamic response data, calculate the characteristic parameters characterizing the microbial response and recovery ability, wherein the characteristic parameters include at least one or more of dissolved oxygen recovery time and ammonia nitrogen concentration fluctuation range; Step S33b: Calculate the vitality index based on the feature parameters and using a preset vitality index calculation function; Step S34b: Map the vitality index to the quantification index using a piecewise function mapping with a preset threshold.

10. An operation and maintenance system for a wastewater low-carbon deep denitrification device, wherein the operation and maintenance system is configured with an intelligent control system applied to the wastewater low-carbon deep denitrification device, the intelligent control system employing the intelligent control method as described in any one of claims 1 to 9, wherein... The wastewater low-carbon deep denitrification device includes a biological reactor, characterized in that the intelligent control system includes: The data acquisition module is used to acquire the effluent water quality data of the wastewater low-carbon deep denitrification device. The effluent water quality data includes ammonia nitrogen concentration data and total nitrogen concentration data detected at the outlet of the biological reactor. A water quality compliance judgment module is used to determine whether the effluent water quality data exceeds the set target value and to determine whether the water quality in the biological reactor meets the standard. The instruction judgment and output module is used to collect characteristic data reflecting the state of microorganisms in the bioreactor when it is determined that the water quality in the bioreactor meets the standards, and to process the characteristic data to obtain a quantitative index characterizing the degree of health risk of the microbial community. An operation mode determination module is used to determine the operation mode of the bioreactor based on the quantitative index. The operating parameter adjustment module is used to adjust the operating parameters of the bioreactor based on the determined operating mode of the bioreactor.

Images