Accurate backflow method and system for nitrification liquid of sewage treatment plant and electronic equipment

Abstract

The invention relates to the technical field of sewage treatment plant nitrification liquid accurate backflow scheme design, in particular to a sewage treatment plant nitrification liquid accurate backflow method and system and electronic equipment. The method comprises the steps of multi-parameter real-time monitoring, data preprocessing, backflow ratio calculation based on a dynamic model, backflow adjustment through an intelligent control algorithm, sludge loss monitoring and reduction, mixing promotion and model and algorithm self-learning optimization. By accurately monitoring and controlling key parameters and combining dynamic adjustment and model optimization of an intelligent algorithm, the accuracy and efficiency of nitrification liquid backflow are effectively improved, the problems of inaccurate control, high energy consumption and sludge loss in the existing technical scheme are solved, and the method has the beneficial effects of energy conservation, consumption reduction, stable operation and wide applicability.

Description

Technical Field

[0001] This invention relates to the technical field of precise reflux scheme design for nitrification liquor in wastewater treatment plants, specifically to a method, system, and electronic equipment for precise reflux of nitrification liquor in wastewater treatment plants. Background Technology

[0002] In the field of wastewater treatment, precise nitrification liquor recirculation is a crucial step in ensuring the effectiveness and efficiency of wastewater treatment. While existing wastewater treatment plants have various solutions for nitrification liquor recirculation, numerous technical challenges remain. Traditional recirculation methods rely primarily on experience or simple detection techniques to adjust the recirculation ratio, making it difficult to adapt to fluctuations in influent water quality and quantity, and thus unable to achieve precise control. Regarding monitoring, existing sensors are highly susceptible to interference from the complex composition of the water, leading to inaccurate monitoring data and affecting recirculation decisions. Furthermore, most systems lack dynamic adjustment capabilities, failing to quickly adjust recirculation strategies based on actual conditions, resulting in unstable treatment effects. In addition, current recirculation equipment and systems consume high energy during operation and are prone to sludge loss, impacting the overall operating costs and effluent quality of the wastewater treatment plant. Moreover, existing nitrification liquor recirculation models are mostly based on specific operating conditions, lacking versatility and adaptability, and unable to meet the actual needs of different wastewater treatment plants. Limitations also exist in intelligent control algorithms, making it difficult to handle complex nonlinear problems, resulting in low accuracy and efficiency in recirculation control. These problems severely restrict the effective management of the nitrification liquor recirculation process in wastewater treatment plants, and new technical solutions are urgently needed to address them.

[0003] Therefore, the existing technology still needs further development. Summary of the Invention

[0004] The purpose of this invention is to overcome the above-mentioned technical deficiencies and provide a method, system, and electronic equipment for precise reflux of nitrified liquor from wastewater treatment plants, so as to solve the problems existing in the prior art.

[0005] To achieve the above-mentioned technical objectives, according to a first aspect of the present invention, the present invention provides a method for precise recirculation of nitrification liquor from a wastewater treatment plant, comprising: S1: Real-time monitoring of multiple parameters of nitrification liquid during the wastewater treatment process, including flow rate, dissolved oxygen, ammonia nitrogen concentration, suspended solids content, chemical oxygen demand, and pH. S2: The monitored multi-parameter data is transmitted to the data processing module, which preprocesses the data to remove outliers and interference signals; S3: Using the pre-processed data, the nitrification liquid recirculation ratio under the current operating conditions is calculated based on a preset dynamic model. The dynamic model comprehensively considers the changes in influent water quality, water quantity, temperature, and the operating status of the wastewater treatment plant. S4: Based on the calculated reflux ratio, the speed of the reflux pump and the opening of the reflux valve are adjusted through an intelligent control algorithm to achieve precise reflux of the nitrified liquid; S5: Real-time monitoring of the concentration and settling properties of the returned sludge; when sludge loss is detected, adjust the operating parameters of the returned sludge system to reduce sludge loss. S6: During the reflux process, the mixing area of ​​the reflux liquid and the inlet water is stirred or a flow guiding device is installed; S7: Based on the treatment effect and energy consumption of the sewage treatment plant in different time periods, the dynamic model and intelligent control algorithm are self-learned and optimized.

[0006] Specifically, in step S1, multi-sensor fusion technology is used to monitor dissolved oxygen, ammonia nitrogen concentration, suspended solids content, chemical oxygen demand, and pH.

[0007] Specifically, in step S2, the preprocessing methods used by the data processing module include filtering algorithms, data normalization processing, and wavelet transform denoising.

[0008] Specifically, in step S3, the dynamic model adopts a neural network model and is trained and optimized by combining historical data and real-time data.

[0009] Specifically, in step S4, the intelligent control algorithm adopts the fuzzy PID control algorithm.

[0010] Specifically, in step S5, when sludge loss is detected, sludge loss is reduced by decreasing the speed of the return pump, decreasing the opening of the return valve, or increasing the amount of sludge return replenishment.

[0011] Specifically, in step S6, the stirring device is a mechanical stirrer or an underwater aeration stirrer, and the flow guiding device includes a flow guide plate or a flow guide cylinder.

[0012] Specifically, in step S7, the self-learning and optimization adopts online learning and model update technology to adjust model parameters and control algorithms in real time based on new data.

[0013] According to a second aspect of the present invention, a precise reflux system for nitrification liquor in a wastewater treatment plant is provided, comprising: The acquisition module is used to perform real-time monitoring of multiple parameters of nitrification liquid in the wastewater treatment process. These multiple parameters include flow rate, dissolved oxygen, ammonia nitrogen concentration, suspended solids content, chemical oxygen demand, and pH. The control module transmits monitored multi-parameter data to the data processing module, which preprocesses the data to remove outliers and interference signals. It then uses the preprocessed data to calculate the nitrification liquid recirculation ratio under the current operating conditions based on a preset dynamic model, which comprehensively considers changes in influent water quality, quantity, temperature, and the wastewater treatment plant's operating status. Based on the calculated recirculation ratio, it adjusts the speed of the recirculation pump and the opening of the recirculation valve using an intelligent control algorithm to achieve precise recirculation of the nitrification liquid. It also monitors the concentration and settling performance of the recirculated sludge in real time, adjusting the operating parameters of the recirculation system to reduce sludge loss when sludge loss is detected. During the recirculation process, it stirs the mixing area of ​​the recirculated liquid and influent or sets up a flow guiding device. Finally, it performs self-learning and optimization of the dynamic model and intelligent control algorithm based on the wastewater treatment plant's treatment effect and energy consumption over different time periods.

[0014] According to a third aspect of the present invention, an electronic device is provided, comprising: a memory; and a processor, wherein the memory stores computer-readable instructions, which, when executed by the processor, implement the above-described method for precise reflux of nitrified liquor in a wastewater treatment plant.

[0015] Beneficial effects: The precise nitrification liquor recirculation solution for wastewater treatment plants provided by this invention offers several significant advantages. Regarding the precision of recirculation control, by utilizing real-time monitoring of multiple parameters, dynamic models, and intelligent control algorithms, the nitrification liquor recirculation ratio can be precisely adjusted according to different operating conditions, effectively avoiding errors caused by experience-based judgment or simple control methods, and significantly improving the stability of treatment results. In terms of energy conservation and consumption reduction, by comprehensively considering factors such as water quality, water quantity, and system operating status, combined with intelligent control algorithms, energy consumption can be rationally allocated, significantly reducing energy consumption during the recirculation process and lowering operating costs. Regarding sludge management, real-time monitoring of the recirculated sludge status and timely adjustment of operating parameters effectively reduce sludge loss, ensuring the quantity and activity of microorganisms in the biochemical reaction, thereby improving wastewater treatment efficiency. Regarding equipment operational stability and reliability, advanced sensor technology, intelligent control algorithms, and equipment protection mechanisms reduce equipment failure rates and ensure long-term stable system operation. In terms of model adaptability, the adopted dynamic model can self-learn and optimize, adjusting to changes in water quality and operating conditions in different wastewater treatment plants, greatly improving the model's versatility and applicability. In addition, the system organically integrates various processing units, enabling information sharing and collaborative work, which further improves the operational efficiency and management level of the entire wastewater treatment system. Attached Figure Description

[0016] Figure 1 This is a schematic flowchart of the precise reflux method for nitrification liquor in a wastewater treatment plant provided in a specific embodiment of the present invention; Figure 2 This is a schematic diagram of the system composition of the precise reflux system for nitrification liquor in a wastewater treatment plant provided in a specific embodiment of the present invention. Detailed Implementation

[0017] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Based on the embodiments in this application, other similar embodiments obtained by those skilled in the art without creative effort should all fall within the scope of protection of this application. Furthermore, the directional terms mentioned in the following embodiments, such as "up," "down," "left," and "right," are only for reference to the directions in the accompanying drawings; therefore, the directional terms used are for illustrative purposes and not for limiting the invention.

[0018] The present invention will be further described below with reference to the accompanying drawings and preferred embodiments.

[0019] Please see Figure 1 This invention provides a method for precise reflux of nitrification liquor from a wastewater treatment plant, comprising: S1: Real-time monitoring of multiple parameters of nitrification liquid during wastewater treatment, including flow rate, dissolved oxygen, ammonia nitrogen concentration, suspended solids content, chemical oxygen demand, and pH.

[0020] Specifically, in step S1, multi-sensor fusion technology is used to monitor dissolved oxygen, ammonia nitrogen concentration, suspended solids content, chemical oxygen demand, and pH.

[0021] It should be further explained that the design scheme of this invention regarding sensor selection and installation is as follows: Dissolved oxygen sensor: A fluorescence-based dissolved oxygen sensor is selected and installed in a region of the return channel where the water flow velocity is stable (preferably in the middle section of the return channel). The water flow is stable in this location, making the measured values ​​more representative. This sensor can monitor dissolved oxygen content in real time, with a measurement range of 0-20 mg / L and an accuracy of ±0.1 mg / L. This measurement range was chosen to account for the normal fluctuation range of dissolved oxygen during the nitrification process, and the required accuracy meets the needs of precise control.

[0022] Ammonia nitrogen concentration sensor: Employing Nessler's reagent colorimetric method, this sensor is installed at a key node in the nitrification liquid return pipeline. It accurately detects ammonia nitrogen concentration, with a measurement range of 0-100 mg / L and an accuracy of ±1 mg / L. Since the nitrification process primarily treats ammonia nitrogen, this range covers the ammonia nitrogen concentration conditions of nitrification liquids commonly found in wastewater treatment plants, ensuring accurate detection.

[0023] Suspended solids content sensor: Utilizing an optical scattering method, this sensor is positioned at the connection between the sedimentation tank outlet and the return channel. Its measurement range is 0-0000 mg / L, with an accuracy of ±50 mg / L. This location allows for timely reflection of suspended solids in the returned liquid after sedimentation. The measurement range is adaptable to various water quality conditions, and the accuracy meets control requirements.

[0024] Chemical oxygen demand (COD) sensor: Employs a dichromate method sensor, placed at the inlet of the reflux pump, with a measurement range of 0-2000 mg / L and high accuracy. mg / L. Since the inlet of the reflux pump reflects the overall organic matter content of the reflux liquid, this measurement range and accuracy ensure effective monitoring of organic matter content.

[0025] pH sensor: Employing a glass electrode method, this sensor is installed in the reflux liquid mixing zone. The measurement range is pH 0-14, with an accuracy of ±0.1 pH. Changes in the pH of the mixing zone reflect the overall mixing conditions. This measurement range covers common acidic and alkaline environments encountered in wastewater treatment, and its accuracy meets adjustment requirements.

[0026] Multi-sensor fusion technology: This technique uses a Kalman filter algorithm to fuse data from various sensors. Let the measured value of each sensor be... ( (These correspond to sensors for dissolved oxygen, ammonia nitrogen concentration, suspended solids content, chemical oxygen demand, and pH, respectively). The resulting measured values ​​are fused together. The calculation formula is: in, For the first The weights of the sensors are determined as follows: dissolved oxygen, ammonia nitrogen concentration, suspended solids content, chemical oxygen demand (COD), and pH are assigned weights of 0.3, 0.25, 0.2, 0.15, and 0.1, respectively. These weights were chosen because dissolved oxygen and ammonia nitrogen concentrations are key influencing factors in nitrification liquor reflux control, significantly impacting the biochemical reaction process, hence their higher weights. While pH has some influence, it is relatively smaller than the other two, thus receiving a lower weight. By rationally allocating these weights, the accuracy and reliability of the fused data can be improved.

[0027] S2: The monitored multi-parameter data is transmitted to the data processing module, which preprocesses the data to remove outliers and interference signals.

[0028] Specifically, in step S2, the preprocessing methods used by the data processing module include filtering algorithms, data normalization processing, and wavelet transform denoising.

[0029] It should be further explained that, regarding data preprocessing, the design scheme of this invention is as follows: Filtering Algorithm: A moving average filtering algorithm is used to process the data for each monitored parameter. Taking flow rate as an example, the size of the moving window is set. Let the flow data sequence be... ( (where the data sequence number is the data number), then the flow rate value after moving average filtering. for: The average of five consecutive flow rate data points is taken as the current filtered flow rate value. Other parameters, such as dissolved oxygen and ammonia nitrogen concentration, have sliding window sizes of 7 and 6 respectively, based on data fluctuation characteristics. This algorithm effectively removes short-term random noise and smooths the data curve. Data normalization: A minimum-maximum normalization method is used to map each parameter data to the [0,1] interval. Let the original value of a certain parameter be... The minimum value of this parameter in historical data is The maximum value is The normalized value for: Unifying the data to the same scale facilitates subsequent data processing and analysis, and avoids the impact of different data units on model calculations.

[0030] Wavelet transform denoising: Daubechies wavelet basis functions (preferably db4 in this invention) are used to decompose the data into three levels. Let the original data be... The wavelet coefficients after decomposition are Denoising based on wavelet coefficient thresholding, threshold The calculation formula is: in It is the standard deviation of the wavelet coefficients. This refers to the data length. The Daubechies wavelet basis function was chosen because of its excellent time-frequency localization properties. The three-level decomposition can effectively remove noise while preserving the main features of the signal. The threshold selection is based on a comprehensive consideration of noise and signal characteristics to ensure the denoising effect.

[0031] S3: Using the pre-processed data, the nitrification liquid recirculation ratio under the current operating conditions is calculated based on a preset dynamic model. The dynamic model comprehensively considers changes in influent water quality, water quantity, temperature, and the operating status of the wastewater treatment plant.

[0032] Specifically, in step S3, the dynamic model adopts a neural network model and is trained and optimized by combining historical data and real-time data.

[0033] It should be further explained that, regarding the dynamic model for calculating the reflux ratio, the scheme designed in this invention includes: Neural network model structure: A three-layer feedforward neural network is used, with the number of nodes in the input layer... These correspond to the influent water quality (chemical oxygen demand). ammonia nitrogen concentration Suspended solids content ), water volume and temperature Number of hidden layer nodes Using the Sigmoid activation function Number of output layer nodes That is, the nitrification liquid reflux ratio .

[0034] Data training: Data collection and partitioning: Collect at least one year of historical data and divide the data into training and test sets in a 7:3 ratio. The training set is used for model parameter learning, and the test set is used for evaluating model performance.

[0035] Training parameter settings: Backpropagation algorithm is used for training, and the learning rate is... Number of training sessions The learning rate is chosen to ensure fast model convergence while avoiding excessive oscillations around the optimal solution; training 1000 times allows the model to fully learn the feature patterns in the data. During training, the mean squared error (MSE) is used as the loss function. in It is the number of training samples. It is the true reflux ratio. This refers to the model's predicted backflow ratio. The MSE (Mean Sequence Equation) is minimized by continuously adjusting the model parameters (weights and biases of neurons in each layer). Model optimization: To improve the model's generalization ability under different water qualities and operating conditions, a cross-entropy loss function is used. Further optimization of the model: in, It is the sample size. It is a real label (appropriately coded). These are the model's predicted values. By adjusting and optimizing the model, we can better adapt it to the actual situation and improve prediction accuracy.

[0036] S4: Based on the calculated reflux ratio, the speed of the reflux pump and the opening of the reflux valve are adjusted through an intelligent control algorithm to achieve precise reflux of the nitrified liquid.

[0037] Specifically, in step S4, the intelligent control algorithm adopts the fuzzy PID control algorithm.

[0038] It should be noted here that the design scheme of this invention for adjusting the reflux pump and reflux valve through intelligent control algorithms is as follows: Fuzzy controller design: selecting error and error change rate As input to the fuzzy controller, and The dataset is divided into seven fuzzy subsets: negative large (NB), negative medium (NM), negative small (NS), zero (Z), positive small (PS), positive medium (PM), and positive large (PB). The proportional gain of the PID controller... Integral coefficient and differential coefficients As output. For example, when For PB and When using PS, For PB. Fuzzy rule establishment: Based on the actual operating experience of wastewater treatment plants and expert knowledge, a fuzzy rule base is established. For example, when Larger and When it is positive, in order to quickly reduce the error, it should be increased. When the error is small, to avoid system oscillation, appropriately reduce... Integral coefficient Primarily used to eliminate steady-state errors, it gradually increases as the error persists. Differential coefficients Used to improve the dynamic response of the system, increasing when the error changes rapidly. To suppress overshoot.

[0039] Parameter adjustment: based on the output of the fuzzy rule base. and The value is used to adjust the parameters of the PID controller. Let... , , , , , Then we have: actual , and Add the corresponding increment to the initial value respectively and This allows for dynamic adjustment of control parameters based on real-time error and the rate of error change, improving system control accuracy and response speed.

[0040] S5: Monitor the concentration and sedimentation performance of the return sludge in real time. When sludge loss is detected, adjust the operating parameters of the return system to reduce sludge loss.

[0041] Specifically, in step S5, when sludge loss is detected, reduce sludge loss by decreasing the rotational speed of the return pump, reducing the opening of the return valve, or increasing the sludge return supplement amount.

[0042] It should be noted here that regarding the monitoring of the return sludge concentration and sedimentation performance and parameter adjustment, the design scheme of the present invention includes: Monitoring of sludge concentration and sedimentation performance: Install optical sensors in the return sludge pipeline and the return tank to monitor the sludge concentration C in real time. The sedimentation performance is measured by regularly sampling and conducting sedimentation experiments in a specific container to measure the sludge sedimentation velocity v and the sludge volume index SVI. Set the sludge loss thresholds C_threshold and SVI_threshold. When C < C_threshold or SVI > SVI_threshold is monitored, it is determined that sludge loss has occurred.

[0043] Parameter adjustment: When sludge loss is detected, reduce sludge loss by decreasing the rotational speed n of the return pump, reducing the opening α of the return valve, or increasing the sludge return supplement amount to reduce sludge loss.

[0044] The specific adjustment method is as follows: Adjustment of the rotational speed of the return pump: Set a sludge loss threshold. When the monitored sludge loss amount exceeds this threshold, adjust it by reducing the rotational speed by 5% every 10 minutes. The rotational speed adjustment formula is: where is the initial rotational speed and k is the number of adjustments. Choosing to reduce the rotational speed by 5% every 10 minutes is because while effectively reducing sludge loss, it will not cause the return liquid flow rate to be too small due to too rapid reduction of the rotational speed, affecting the sewage treatment effect. <00​​​​​​​​​​​​​​ Choosing a concentration ratio of 2-3 times is to ensure that the sludge has a certain level of activity and settling properties while replenishing it.

[0047] S6: During the reflux process, the mixing area of ​​the reflux liquid and the incoming water is stirred or a flow guiding device is installed.

[0048] Specifically, in step S6, the stirring device is a mechanical stirrer or an underwater aeration stirrer, and the flow guiding device includes a flow guide plate or a flow guide cylinder.

[0049] It should be further explained that step S6 includes: Selection and parameter settings of the stirring device: A paddle agitator is selected as the stirring device, and the installed power is based on the return liquid flow rate. and mixing region volume Calculation. Assume the stirring power is... Power standard (Usually 1-2 is used, but 1.5 is used here), the density of the mixture is... The mixer speed is The diameter of the stirrer is Then we have: Determine the appropriate amount based on the reflux flow rate and the mixing zone volume. and To ensure the mixing power meets the mixing requirements, the agitator speed and diameter are adjusted to ensure uniform mixing. The installation and adjustment of the flow guide device: The installation position and angle of the flow guide plate or flow guide cylinder are adjusted according to the shape of the return channel and mixing zone and the water flow conditions. Flow guide plate installation angle. The angle is generally set between 30° and 45° based on experiments and experience to guide the water flow and create a suitable flow field, promoting the mixing of the return fluid and the incoming water. The diameter of the guide tube... The dimensions of the return channel and mixing zone are designed in a certain proportion to ensure that the water can flow smoothly through the guide tube and achieve effective mixing.

[0050] S7: Based on the treatment effect and energy consumption of the sewage treatment plant in different time periods, the dynamic model and intelligent control algorithm are self-learned and optimized.

[0051] Specifically, in step S7, the self-learning and optimization adopts online learning and model update technology to adjust model parameters and control algorithms in real time based on new data.

[0052] It should be further explained that step S7 includes: Online learning: An incremental learning algorithm is used. Whenever new monitoring data arrives, the new data is added to the training set. Let the new data be Xnew, and the corresponding true feedback ratio be Ynew. The learning rate is dynamically adjusted based on the characteristics of the new data and the model's convergence. The initial learning rate... When the distribution of new data differs significantly from that of historical data (by calculating a similarity index of data distribution, preferably Mahalanobis distance, and setting a threshold), When the difference is greater than (At that time), appropriately reduce the learning rate to Retrain the neural network model, updating the model's weights and thresholds, using the following formula: in, These are the model weights. It's the learning rate. It is the loss function. Through online learning, the model can adapt to new changes in water quality and operating conditions in a timely manner, maintaining high prediction accuracy.

[0053] Model Update: The model is comprehensively evaluated at regular intervals (preferably weekly). If the model's prediction error exceeds a set threshold (preferably 10%), the model is retrained and optimized, and its parameters are updated. The evaluation metric is the mean squared error (MSE), i.e.: Where m is the number of samples in the test set. It is the true reflux ratio. This refers to the model's predicted reflux ratio. Regular evaluation and updates ensure that the model's performance consistently meets the needs of real-world applications.

[0054] It is understood that the precise nitrification liquor recirculation solution for wastewater treatment plants provided by this invention has several significant advantages. Regarding the precision of recirculation control, through real-time monitoring of multiple parameters and the application of dynamic models and intelligent control algorithms, the nitrification liquor recirculation ratio can be precisely adjusted according to different operating conditions, effectively avoiding errors caused by experience-based judgment or simple control methods, and significantly improving the stability of treatment effects. In terms of energy saving and consumption reduction, by comprehensively considering factors such as water quality, water quantity, and system operating status, combined with intelligent control algorithms, energy consumption can be rationally allocated, significantly reducing energy consumption during the recirculation process and reducing operating costs. Regarding sludge management, real-time monitoring of the recirculated sludge status and timely adjustment of operating parameters effectively reduce sludge loss, ensuring the quantity and activity of microorganisms in the biochemical reaction, thereby improving wastewater treatment efficiency. Regarding equipment operational stability and reliability, advanced sensor technology, intelligent control algorithms, and equipment protection mechanisms reduce equipment failure rates and ensure long-term stable system operation. Regarding model adaptability, the adopted dynamic model can self-learn and optimize, adjusting to changes in water quality and operating conditions in different wastewater treatment plants, greatly improving the model's versatility and applicability. In addition, the system organically integrates various processing units, enabling information sharing and collaborative work, which further improves the operational efficiency and management level of the entire wastewater treatment system.

[0055] Please see Figure 2 The present invention provides another embodiment, which provides a precise reflux system for nitrified liquor in a wastewater treatment plant, the precise reflux system for nitrified liquor in a wastewater treatment plant comprising: The acquisition module 100 is used to perform real-time monitoring of multiple parameters of nitrification liquid in the wastewater treatment process. The multiple parameters include flow rate, dissolved oxygen, ammonia nitrogen concentration, suspended solids content, chemical oxygen demand, and pH. The control module 200 is used to transmit monitored multi-parameter data to the data processing module. The data processing module preprocesses the data to remove outliers and interference signals. It then uses the preprocessed data to calculate the nitrification liquid recirculation ratio under the current operating conditions based on a preset dynamic model. This dynamic model comprehensively considers changes in influent water quality, quantity, temperature, and the operating status of the wastewater treatment plant. Based on the calculated recirculation ratio, it adjusts the speed of the recirculation pump and the opening of the recirculation valve using an intelligent control algorithm to achieve precise recirculation of the nitrification liquid. It also monitors the concentration and settling performance of the recirculated sludge in real time, adjusting the operating parameters of the recirculation system to reduce sludge loss when sludge loss is detected. During the recirculation process, it stirs the mixing area of ​​the recirculated liquid and influent or sets up a flow guiding device. Finally, it performs self-learning and optimization of the dynamic model and intelligent control algorithm based on the treatment effect and energy consumption of the wastewater treatment plant over different time periods.

[0056] It should be noted that the precise nitrification liquor recirculation solution for wastewater treatment plants provided by this invention has several significant advantages. Regarding the accuracy of recirculation control, through real-time monitoring of multiple parameters and the application of dynamic models and intelligent control algorithms, the nitrification liquor recirculation ratio can be precisely adjusted according to different operating conditions, effectively avoiding errors caused by experience-based judgment or simple control methods, and significantly improving the stability of treatment results. In terms of energy saving and consumption reduction, by comprehensively considering factors such as water quality, water quantity, and system operating status, combined with intelligent control algorithms, energy consumption can be rationally allocated, significantly reducing energy consumption during the recirculation process and reducing operating costs. Regarding sludge management, real-time monitoring of the recirculated sludge status and timely adjustment of operating parameters effectively reduce sludge loss, ensuring the quantity and activity of microorganisms in the biochemical reaction, thereby improving wastewater treatment efficiency. Regarding equipment operational stability and reliability, advanced sensor technology, intelligent control algorithms, and equipment protection mechanisms reduce equipment failure rates and ensure long-term stable system operation. In terms of model adaptability, the adopted dynamic model can self-learn and optimize, adjusting to changes in water quality and operating conditions in different wastewater treatment plants, greatly improving the model's versatility and applicability. In addition, the system organically integrates various processing units, enabling information sharing and collaborative work, which further improves the operational efficiency and management level of the entire wastewater treatment system.

[0057] In a preferred embodiment, this application also provides an electronic device, the electronic device comprising: The computer device includes a memory and a processor, wherein the memory stores computer-readable instructions that, when executed by the processor, implement the precise reflux method for nitrified liquor in a wastewater treatment plant. The computer device can be broadly categorized as a server, terminal, or any other electronic device with the necessary computing and / or processing capabilities. In one embodiment, the computer device may include a processor, memory, network interface, and communication interface connected via a system bus. The processor of the computer device can be used to provide the necessary computing, processing, and / or control capabilities. The memory of the computer device may include a non-volatile storage medium and internal memory. The non-volatile storage medium may store an operating system and computer programs. The internal memory can provide an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface and communication interface of the computer device can be used to connect and communicate with external devices via a network. When the computer program is executed by the processor, it performs the steps of the method of the present invention.

[0058] This invention can be implemented as a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, causes the steps of the methods of embodiments of the invention to be performed. In one embodiment, the computer program is distributed across multiple network-coupled computer devices or processors, such that the computer program is stored, accessed, and executed in a distributed manner by one or more computer devices or processors. A single method step / operation, or two or more method steps / operations, may be executed by a single computer device or processor or by two or more computer devices or processors. One or more method steps / operations may be executed by one or more computer devices or processors, and one or more other method steps / operations may be executed by one or more other computer devices or processors. One or more computer devices or processors may execute a single method step / operation, or execute two or more method steps / operations.

[0059] It will be understood by those skilled in the art that the method steps of the present invention can be performed by a computer program instructing related hardware, such as a computer device or processor, which may be stored in a non-transitory computer-readable storage medium. When the computer program is executed, the steps of the present invention are performed. Depending on the context, any references herein to memory, storage, database, or other media may include non-volatile and / or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, and solid-state drive. Examples of volatile memory include random access memory (RAM) and external cache memory.

[0060] It is understood that the precise nitrification liquor recirculation solution for wastewater treatment plants provided by this invention has several significant advantages. Regarding the precision of recirculation control, through real-time monitoring of multiple parameters and the application of dynamic models and intelligent control algorithms, the nitrification liquor recirculation ratio can be precisely adjusted according to different operating conditions, effectively avoiding errors caused by experience-based judgment or simple control methods, and significantly improving the stability of treatment effects. In terms of energy saving and consumption reduction, by comprehensively considering factors such as water quality, water quantity, and system operating status, combined with intelligent control algorithms, energy consumption can be rationally allocated, significantly reducing energy consumption during the recirculation process and reducing operating costs. Regarding sludge management, real-time monitoring of the recirculated sludge status and timely adjustment of operating parameters effectively reduce sludge loss, ensuring the quantity and activity of microorganisms in the biochemical reaction, thereby improving wastewater treatment efficiency. Regarding equipment operational stability and reliability, advanced sensor technology, intelligent control algorithms, and equipment protection mechanisms reduce equipment failure rates and ensure long-term stable system operation. Regarding model adaptability, the adopted dynamic model can self-learn and optimize, adjusting to changes in water quality and operating conditions in different wastewater treatment plants, greatly improving the model's versatility and applicability. In addition, the system organically integrates various processing units, enabling information sharing and collaborative work, which further improves the operational efficiency and management level of the entire wastewater treatment system.

[0061] The technical features described above can be combined arbitrarily. Although not all possible combinations of these technical features are described, any combination of these technical features should be considered to be covered by this specification, provided that such combination does not contain contradictions.

[0062] The specific embodiments of the present invention described above do not constitute a limitation on the scope of protection of the present invention. Any other corresponding changes and modifications made in accordance with the technical concept of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for precise recirculation of nitrification liquor from a wastewater treatment plant, characterized in that, The method includes: S1: Real-time monitoring of multiple parameters of nitrification liquid during the wastewater treatment process, including flow rate, dissolved oxygen, ammonia nitrogen concentration, suspended solids content, chemical oxygen demand, and pH. S2: The monitored multi-parameter data is transmitted to the data processing module, which preprocesses the data to remove outliers and interference signals; S3: Using the pre-processed data, the nitrification liquid recirculation ratio under the current operating conditions is calculated based on a preset dynamic model. The dynamic model comprehensively considers the changes in influent water quality, water quantity, temperature, and the operating status of the wastewater treatment plant. S4: Based on the calculated reflux ratio, the speed of the reflux pump and the opening of the reflux valve are adjusted through an intelligent control algorithm to achieve precise reflux of the nitrified liquid; S5: Real-time monitoring of the concentration and settling properties of the returned sludge; when sludge loss is detected, adjust the operating parameters of the returned sludge system to reduce sludge loss. S6: During the reflux process, the mixing area of ​​the reflux liquid and the inlet water is stirred or a flow guiding device is installed; S7: Based on the treatment effect and energy consumption of the sewage treatment plant in different time periods, the dynamic model and intelligent control algorithm are self-learned and optimized.

2. The method for precise reflux of nitrified liquor from a wastewater treatment plant according to claim 1, characterized in that, In step S1, multi-sensor fusion technology is used to monitor dissolved oxygen, ammonia nitrogen concentration, suspended solids content, chemical oxygen demand, and pH.

3. The method for precise reflux of nitrified liquor from a wastewater treatment plant according to claim 2, characterized in that, In step S2, the preprocessing methods used by the data processing module include filtering algorithms, data normalization processing, and wavelet transform denoising.

4. The method for precise reflux of nitrified liquor from a wastewater treatment plant according to claim 1, characterized in that, In step S3, the dynamic model adopts a neural network model and is trained and optimized by combining historical data and real-time data.

5. The method for precise reflux of nitrified liquor from a wastewater treatment plant according to claim 1, characterized in that, In step S4, the intelligent control algorithm adopts the fuzzy PID control algorithm.

6. The method for precise reflux of nitrified liquor from a wastewater treatment plant according to claim 1, characterized in that, In step S5, when sludge loss is detected, sludge loss is reduced by decreasing the speed of the return pump, decreasing the opening of the return valve, or increasing the amount of sludge return replenishment.

7. The method for precise reflux of nitrified liquor from a wastewater treatment plant according to claim 1, characterized in that, In step S6, the stirring device is a mechanical stirrer or an underwater aeration stirrer, and the flow guiding device includes a flow guide plate or a flow guide cylinder.

8. The method for precise reflux of nitrified liquor from a wastewater treatment plant according to claim 1, characterized in that, In step S7, the self-learning and optimization employs online learning and model update techniques to adjust model parameters and control algorithms in real time based on new data.

9. A precise reflux system for nitrification liquor in a wastewater treatment plant, characterized in that, include: The acquisition module is used to perform real-time monitoring of multiple parameters of nitrification liquid in the wastewater treatment process. These multiple parameters include flow rate, dissolved oxygen, ammonia nitrogen concentration, suspended solids content, chemical oxygen demand, and pH. The control module is used to transmit the monitored multi-parameter data to the data processing module, which preprocesses the data to remove outliers and interference signals. This is used to calculate the nitrification liquid recirculation ratio under the current operating conditions using pre-processed data and based on a preset dynamic model. The dynamic model comprehensively considers changes in influent water quality, water quantity, temperature, and the operating status of the wastewater treatment plant. Based on the calculated reflux ratio, the system uses an intelligent control algorithm to adjust the speed of the reflux pump and the opening of the reflux valve, thereby achieving precise reflux of the nitrated liquid. It is used to monitor the concentration and settling properties of the returned sludge in real time. When sludge loss is detected, the operating parameters of the returned sludge system are adjusted to reduce sludge loss. Used to stir the mixing zone of the reflux liquid and the influent water during the reflux process or to set up a flow guiding device; It is used to self-learn and optimize dynamic models and intelligent control algorithms based on the treatment effect and energy consumption of sewage treatment plants in different time periods.

10. An electronic device, characterized in that, include: Memory; The processor, wherein the memory stores computer-readable instructions that, when executed by the processor, implement the precise reflux method for nitrified liquor in a wastewater treatment plant according to any one of claims 1 to 8.

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