Staged regulation and control type anaerobic ammonification coupling resource recovery treatment method

Abstract

The invention discloses a staged regulation type anaerobic ammonification coupling resource recovery treatment method, and relates to the technical field of sewage treatment and resource recovery, and the method comprises the following steps: designing and operating a multi-stage series reactor based on optimal operation parameters and treatment requirements, treating specific pollutants, and generating a purified water body and by-products; resource recovery and energy recovery are performed on the basis of the purified water body and byproducts; the whole anaerobic ammonification coupling resource recovery performance is evaluated regularly, the optimal operation parameters are dynamically adjusted according to the evaluation result, and the multi-stage series reactor is optimized. According to the method, the optimal operation parameters required for realizing the optimal treatment effect and the resource recovery rate are predicted through big data analysis and a machine learning algorithm, so that the problem that the traditional sewage treatment technology is lack of real-time monitoring and dynamic adjustment is solved, and effective recovery and reutilization of resources are greatly promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage treatment and resource recovery, and in particular to a stage-controlled anaerobic ammoniation coupled resource recovery treatment method. Background Art

[0002] Anaerobic ammoniation is an effective biological treatment technology. This technology uses the metabolic process of microorganisms under anaerobic conditions to convert ammonia nitrogen into nitrogen gas, thereby achieving effective removal of ammonia nitrogen in water bodies. Traditional anaerobic ammoniation processes have gradually shown their limitations, especially in terms of efficiency, stability and resource recovery. Traditional methods often rely on fixed operating parameters and are difficult to adapt to treatment requirements under different water quality conditions, resulting in unstable treatment effects and low resource utilization. In addition, the existing anaerobic ammoniation system does not provide sufficient support for real-time monitoring and dynamic adjustment, which limits its ability to cope with complex and changing water quality conditions.

[0003] One of the main deficiencies of existing technologies in practical applications is the lack of efficient real-time monitoring and control mechanisms. Most traditional anaerobic ammoniation systems rely on manual sampling and analysis to obtain water quality information. This method is not only time-consuming and labor-intensive, but also difficult to achieve timely feedback and adjustments to the treatment process. Another significant problem is the low efficiency of resource recovery. Although some advanced systems can recover by-products such as methane from the treatment process, the effective recovery of other valuable by-products such as phosphates and hydrogen is still challenging. The existence of these problems has greatly restricted the development and promotion of anaerobic ammoniation technology. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a staged regulated anaerobic ammoniation coupled resource recovery treatment method to solve the problems of insufficient real-time monitoring and control mechanism and low resource recovery efficiency in the existing technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In the first aspect, the present invention provides a staged regulated anaerobic ammoniation coupled resource recovery treatment method, which includes installing sensors at each key node of the anaerobic treatment, and collecting multivariate parameter data and transmitting it to a central control center for pretreatment; predicting the optimal operating parameters of the recovery method based on the multivariate parameter data and in combination with historical operation data based on big data and machine learning algorithms; designing and operating a multi-stage series reactor based on the optimal operating parameters and treatment requirements to treat specific pollutants and generate purified water and by-products; recovering resources and energy based on the purified water and by-products; regularly evaluating the performance of the entire anaerobic ammoniation coupled resource recovery, dynamically adjusting the optimal operating parameters based on the evaluation results, and optimizing the multi-stage series reactor.

[0007] As a preferred solution of the staged regulated anaerobic ammoniation coupled resource recovery treatment method described in the present invention, the sensors include a pH sensor, a temperature sensor, an ammonia nitrogen sensor, a nitrate sensor, and an oxidation-reduction potential sensor.

[0008] As a preferred solution of the staged regulated anaerobic ammoniation coupled resource recovery treatment method of the present invention, wherein: the collected multivariate parameter data is transmitted to the central control center for pre-processing to generate real-time water quality data, the specific steps are as follows: pH sensors are based on solid-state ion-selective electrodes and determine pH by detecting the concentration of hydrogen ions in aqueous solutions; Temperature sensors use thermal resistors to measure temperature using the property that the resistance of a material changes with temperature. The ammonia nitrogen sensor uses the ion selective electrode method to determine the ammonia nitrogen content in water; The nitrate sensor measures nitrate concentration by means of ion selective electrodes and ultraviolet absorption spectroscopy; Redox potential sensors react to redox states by measuring the voltage difference between two electrodes; The preprocessing includes data cleaning, format conversion and anomaly detection.

[0009] As a preferred solution of the staged regulated anaerobic ammoniation coupled resource recovery and treatment method of the present invention, wherein: the optimal operating parameters of the recovery method are predicted based on multivariate parameter data and historical operation data based on big data and machine learning algorithms, and the specific steps are as follows: Train LSTM based on a large amount of historical data; Analyze multivariate parameter data through big data and machine learning algorithms, and dynamically identify water quality and operating parameters using adaptive feature selection functions; The water quality and operating parameters are input into the optimized trained LSTM, and the machine learning model predicts the optimal operating parameters to achieve the best treatment effect and resource recovery rate under current conditions based on the adaptive features.

[0010] As a preferred solution of the staged regulated anaerobic ammoniation coupled resource recovery treatment method of the present invention, wherein: based on the optimal operating parameters and treatment requirements, a multi-stage series reactor is designed and operated to treat specific pollutants and generate purified water and by-products. The specific steps are as follows: Define treatment requirements based on water quality characteristics and treatment objectives; Select the reactor type according to the processing requirements, set the operating instructions for each reactor using the optimal operating parameters, and arrange the reactors; Operate multiple stages of series reactors to introduce pollutants into the reactors for purification, generating purified water and by-products; The byproducts include methane, phosphates, hydrogen, and microbial biomass.

[0011] As a preferred solution of the staged regulated anaerobic ammoniation coupled resource recovery treatment method of the present invention, wherein: the resource recovery and energy recovery are carried out based on the purified water and by-products, and the specific steps are as follows: Resource recovery extracts phosphates from purification byproducts and converts them into phosphate fertilizers through chemical conversion, using the microbial biomass produced during the conversion process for processing to produce soil conditioners and animal feed additives; Energy recovery captures methane produced during the process and feeds it into the CHP, where it is burned to produce electricity and heat, while the produced hydrogen is converted into electricity via a fuel cell.

[0012] As a preferred solution of the staged regulated anaerobic ammoniation coupled resource recovery treatment method of the present invention, wherein: the performance of the entire anaerobic ammoniation coupled resource recovery is regularly evaluated, and the specific steps are as follows: Based on the core elements of resource recovery and energy recovery, the performance of the entire anaerobic ammoniation coupled resource recovery is evaluated and the comprehensive performance index is used to evaluate the performance of the entire anaerobic ammoniation coupled resource recovery. To indicate overall efficiency; The core elements include phosphate recovery efficiency, microbial utilization, methane energy conversion efficiency, and hydrogen energy conversion efficiency; As a preferred solution of the staged regulated anaerobic ammoniation coupled resource recovery treatment method of the present invention, wherein: the optimal operating parameters are dynamically adjusted according to the evaluation results, and the multi-stage series reactor is optimized; Set execution effect thresholds based on statistical analysis of historical comprehensive performance indexes ; when ≥ When the anaerobic ammoniation coupled resource recovery treatment method with phased regulation is found to have achieved the expected effect; when < When the optimal operating parameters are adjusted, it is considered that the effect of the staged regulated anaerobic ammoniation coupled resource recovery treatment method has not met expectations.

[0013] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the staged regulated anaerobic ammoniation coupled resource recovery treatment method as described in the first aspect of the present invention is implemented.

[0014] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the staged-regulated anaerobic ammoniation coupled resource recovery treatment method as described in the first aspect of the present invention is implemented.

[0015] The beneficial effects of the present invention are as follows: by installing a variety of sensors at key nodes of anaerobic treatment and collecting multivariate parameter data of these nodes and transmitting them to a central control center for pre-processing, accurate monitoring of the sewage treatment process is achieved. Furthermore, by utilizing historical operating data in combination with currently collected multivariate parameters, big data analysis and machine learning algorithms are used to predict the optimal operating parameters required to achieve the best treatment effect and resource recovery rate. This not only solves the problem of lack of real-time monitoring and dynamic adjustment in traditional sewage treatment technology, but also greatly promotes the effective recovery and reuse of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0017] Figure 1 This is a flow chart of the staged regulated anaerobic ammoniation coupled resource recovery treatment method in Example 1.

[0018] Figure 2 This is a flow chart for collecting multivariate parameter data in Example 1. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0020] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a staged regulated anaerobic ammoniation coupled resource recovery treatment method, comprising the following steps: S1: Install sensors at each key node of the anaerobic treatment and collect multivariate parameter data to transmit to the central control center for pre-processing.

[0021] S1.1: The sensors include pH sensor, temperature sensor, ammonia nitrogen sensor, nitrate sensor, and redox potential sensor.

[0022] It should be noted that by installing pH sensors, temperature sensors, ammonia nitrogen sensors, nitrate sensors and redox potential sensors at various key nodes in the anaerobic treatment process, real-time monitoring of multiple key parameters in the sewage treatment process is achieved. Through this comprehensive water quality monitoring, it can not only ensure that the treatment process is carried out under optimal conditions, but also promptly discover and correct possible problems, thereby improving the stability and treatment efficiency of the entire treatment process.

[0023] S1.2: pH sensors are based on solid-state ion-selective electrodes and determine pH by detecting the concentration of hydrogen ions in aqueous solutions.

[0024] Furthermore, the pH sensor immerses its sensitive element in the solution to be tested and indirectly measures the pH value of the solution by measuring the potential difference between the electrode and the reference electrode. This electrical signal is then converted into a digital signal and transmitted to the central control center for further data processing and analysis. In this way, the central control center can obtain the pH information of the water quality in real time, providing data support for subsequent adjustment of operating parameters.

[0025] S1.3: Temperature sensors use the property of a material's resistance changing with temperature to measure temperature through a thermistor.

[0026] Furthermore, when the temperature sensor is placed in the aqueous solution or environment to be measured, the resistance of the material changes as the ambient temperature changes. The sensor determines the resistance value by applying a known small current to the thermal resistor material and measuring the voltage drop across the material. Since there is a clear relationship between resistance and temperature, the current temperature can be accurately calculated from the measured resistance value. This electrical signal is then converted into a digital signal and transmitted to the central control center for processing and analysis.

[0027] S1.4: The nitrate sensor measures nitrate concentration using ion selective electrodes and ultraviolet absorption spectroscopy.

[0028] Furthermore, the nitrate sensor uses an ion selective electrode (ISE) method to measure the nitrate concentration in the aqueous solution. The nitrate ISE is used together with a reference electrode. When both are placed in the solution to be tested, a potential difference is generated between the ISE and the reference electrode due to the activity of the nitrate ions. This potential difference is measured by a high impedance circuit and converted into an electrical signal. Because the potential change of the ISE directly reflects the change in the nitrate concentration in the solution, this electrical signal can be converted into a specific nitrate concentration value by means of a calibration curve or direct reading. Subsequently, these data are transmitted to the central control center for further data processing and analysis.

[0029] S1.5: The redox potential sensor reflects the redox state by measuring the voltage difference between two electrodes.

[0030] Furthermore, the redox potential sensor measures the voltage difference between the two electrodes through a high impedance circuit. This voltage difference directly reflects the redox state of the solution, that is, the relative concentrations of the oxidant and the reductant in the solution. A higher positive potential indicates a stronger oxidation condition in the solution, while a lower negative potential indicates a stronger reduction condition. The electrical signal is then converted into a digital signal and transmitted to a central control center for processing and analysis.

[0031] S1.6: Preprocessing includes data cleaning, format conversion, and anomaly detection.

[0032] Furthermore, data cleaning aims to remove or correct inaccurate, incomplete or irrelevant data, such as filling missing values, deleting duplicate records, and correcting obvious measurement errors. Next is format conversion, which ensures that all data is converted into a unified standard format for subsequent analysis and processing, such as unifying timestamps from different sources into the same date and time format, or standardizing the units of various physical quantities. Finally, anomaly detection is used to identify and process data points that deviate significantly from the normal range. These outliers may be caused by sensor failures or other external interference.

[0033] S2: Based on the multivariate parameter data and combined with historical operation data, the optimal operating parameters of the recovery method treatment are predicted based on big data and machine learning algorithms.

[0034] S2.1: Train LSTM based on a large amount of historical data.

[0035] The specific process of training LSTM based on a large amount of historical data is as follows: First, prepare and organize the data set for training. This includes collecting various water quality parameters involved in the sewage treatment process (such as pH value, temperature, ammonia nitrogen concentration, etc.) and the corresponding operating conditions and historical records of treatment effects. Preprocessing steps are required, including data cleaning, format conversion, and anomaly detection to ensure that the data input into the model is accurate and consistent.

[0036] Next, the preprocessed data is divided into training set, validation set and test set. Usually, most of the data will be used as training set to train the model; part of it will be used as validation set to adjust model hyperparameters and prevent overfitting; finally, an independent test set is used to evaluate the generalization ability of the model, that is, the performance of the model on unseen data.

[0037] Then, build the LSTM model architecture. This involves choosing the appropriate number of layers, number of neurons per layer, activation function, loss function, and optimization algorithm. LSTM is particularly suitable for processing time series data because it can capture long-term dependencies, which is particularly important for understanding the changes in water quality parameters over time.

[0038] When the training process begins, the training set is input into the LSTM model. In each training iteration, the LSTM calculates the predicted value through forward propagation and compares it with the actual target value to calculate the loss. After that, the model weights are updated using the backpropagation algorithm to minimize the loss function. In this process, the selection of hyperparameters such as the learning rate is crucial to the training effect and may require multiple experiments to optimize.

[0039] At the same time, use the validation set to monitor model performance to prevent overfitting. If you find that the model performs well on the training set but poorly on the validation set, you may need to adjust the model complexity or apply regularization techniques. In addition, you can use the early stopping strategy to end training early when the validation set performance no longer improves.

[0040] Finally, when training is complete, the final performance of the model is evaluated using the test set.

[0041] S2.2: Analyze multivariate parameter data through big data and machine learning algorithms, and use adaptive feature selection functions to dynamically identify water quality and operating parameters.

[0042] Furthermore, multivariate parameter data are analyzed through big data and machine learning algorithms. First, a large amount of historical operation data collected from various sensors is integrated, and then these data are processed using adaptive feature selection functions. This process aims to dynamically identify the water quality and operating parameters that are most relevant to sewage treatment effects and resource recovery rates. The adaptive feature selection function can automatically adjust its selection criteria according to the characteristics of the input data to ensure that the most influential features can be accurately selected under different operating conditions. Next, the screened data is input into the optimized and trained LSTM model. Based on previous learning, the model can understand the complex relationship between water quality parameters and their impact on treatment effects. In this way, not only can the current water quality conditions be analyzed in real time, but also the optimal operating parameter settings can be predicted and recommended, thereby achieving precise control and optimization of the sewage treatment process, and ultimately achieving the purpose of improving pollutant removal efficiency and resource recovery rate.

[0043] It should be noted that the operating parameters include temperature control, pH adjustment, dissolved oxygen concentration, sludge return ratio and redox potential.

[0044] S2.3: The water quality and operating parameters are input into the optimized and trained LSTM. The machine learning model predicts the optimal operating parameters to achieve the best treatment effect and resource recovery rate under the current conditions based on the adaptive features. The specific expression is: ; in, express The best processing effect and resource recovery rate at all times, represents the output weight matrix, Indicates that at time step The output gate value of Indicates that at time step The forget gate value of express The cell state at a moment in time, Indicates that at time step The input gate value is Indicates that at time step The water quality parameter input vector, Indicates that at time step The input vector of operation parameters, Indications and water quality parameters The associated weight matrix, Representation and Operation Parameters The associated weight matrix, Indicates that the cell state is biased to the top, indicates that the output layer is biased to the top, Indicates the current moment, Represents element-wise multiplication of two matrices of the same shape.

[0045] S3: Design and operate multi-stage series reactors to treat specific pollutants and generate purified water and by-products based on optimal operating parameters and treatment requirements.

[0046] S3.1: Define treatment requirements based on water quality characteristics and treatment objectives.

[0047] Furthermore, in order to define the treatment requirements based on the water quality characteristics and treatment objectives, it is necessary to first conduct a comprehensive analysis of the influent water quality, including the detection and evaluation of key parameters such as pH value, temperature, ammonia nitrogen concentration, nitrate concentration and redox potential. Through these data, the specific composition of the sewage and its degree of pollution can be understood, and then combined with the expected treatment objectives such as the removal rate of specific pollutants, resource recovery efficiency or effluent quality standards, etc., to comprehensively consider factors and determine specific operating requirements and technical routes. For example, if the goal is to efficiently remove ammonia nitrogen and maximize methane production, it is necessary to set operating conditions that are conducive to the activity of anaerobic ammonia-oxidizing bacteria, and optimize the design and operation strategy of the reactor to promote biochemical reactions. At the same time, the feasibility and economic cost in actual operation should also be considered to ensure that the defined treatment requirements can meet the requirements of environmental protection and maximize the utilization of resources. On this basis, a detailed operation plan and control strategy are formulated to guide the subsequent design and operation of multi-stage series reactors to ensure that the entire sewage treatment process can operate under optimal conditions.

[0048] S3.2: Select the reactor type according to the processing requirements, set the operating instructions for each reactor using the optimal operating parameters, and layout the reactors.

[0049] Furthermore, the reactor type is selected according to the treatment requirements, and the operating instructions are set for each reactor using the optimal operating parameters. At the same time, the reactors are laid out. First, the specific goals of sewage treatment, such as removing specific pollutants or recycling resources, need to be clarified. Based on these goals and water quality characteristics, the appropriate reactor type is determined. For example, an anaerobic digestion reactor can be selected for organic matter degradation and methane production, or a nitrification and denitrification reactor can be selected to achieve effective nitrogen removal. After determining the reactor type, specific operating instructions are set for each selected reactor based on the optimal operating parameters obtained through big data analysis and machine learning algorithms, including controlling key parameters such as temperature, pH value, and dissolved oxygen concentration to ensure that each reactor can operate under optimal conditions. At the same time, the layout design between reactors needs to be considered. For example, the use of a multi-stage series connection can gradually improve the treatment efficiency and optimize the resource recovery process. The order of reactors with different functions can be reasonably arranged so that the effluent of the previous stage can be used as the influent of the next stage, thereby forming an efficient treatment process. In this process, the balance of material flow and energy flow must also be considered to ensure the stability and sustainability of the entire process.

[0050] S3.3: Operate multiple series reactors to introduce pollutants into the arranged reactors for purification, generating purified water and by-products.

[0051] Furthermore, a multi-stage series reactor is operated to introduce pollutants into the reactors after the layout for purification. First, the operating conditions of each reactor are set according to the reactor type and optimal operating parameters determined by the treatment requirements and water quality characteristics. Then the sewage to be treated is introduced into these reactors arranged in a specific order in turn. Each stage of the reactor treats specific pollutants in the sewage according to its design purpose. For example, the first stage may be used to adjust the pH value and temperature to create an environment suitable for microbial growth. The next few stages may focus on different types of biochemical reactions such as anaerobic ammonia oxidation, nitrification or denitrification processes to remove nitrogen and other organic pollutants. In each stage of the reactor, due to the precise control of key factors such as dissolved oxygen, nutrient supply and residence time, efficient pollutant degradation and transformation are ensured. As the sewage flows through each reactor step by step, the pollutant concentration gradually decreases until it meets the emission standard or reuse requirements. At the same time, valuable by-products such as methane, phosphate, hydrogen and microbial biomass are generated in this process. These by-products not only reduce the amount of waste but also provide additional resource recovery channels. Finally, after the treatment of the entire multi-stage series reaction, purified water and useful by-products are obtained, achieving the dual goals of sewage treatment and resource recovery.

[0052] S4: Resource recovery and energy recovery based on purified water and by-products.

[0053] S4.1: Resource Recovery Phosphates are extracted from purification byproducts and converted to phosphate fertilizers through chemical conversion, and the microbial biomass produced during the conversion process is processed to produce soil conditioners and animal feed additives.

[0054] Furthermore, resource recovery extracts phosphates from purified by-products and converts them into phosphate fertilizers through chemical conversion. First, the phosphate-rich by-products accumulated during the sewage treatment process are collected. These phosphates can be separated and purified from the wastewater through chemical methods such as precipitation. The extracted phosphates are then further chemically treated and converted into a form of phosphate fertilizer suitable for agricultural applications. This not only solves the problem of phosphate as a potential pollutant, but also achieves effective reuse of resources. While converting phosphates, the microbial biomass produced during the treatment process is not wasted. This part of biomass can be further processed due to its high nutritional value and organic matter content. For example, it can be converted into a soil conditioner through specific biotechnology means. This conditioner can improve soil structure and increase soil fertility. At the same time, microbial biomass can also be used as an animal feed additive to provide necessary nutrients to promote the healthy growth of animals. Such resource recovery and conversion strategies not only reduce waste emissions and reduce environmental pollution risks, but also create economic value and social benefits, reflecting the concept of sustainable development.

[0055] S4.2: Energy Recovery Methane generated from the treatment process is captured and fed into the CHP, where it is burned to generate electricity and heat. The generated hydrogen is converted into electricity via a fuel cell.

[0056] Furthermore, energy recovery captures the methane generated during the treatment process and sends it to the CHP, where it generates electricity and heat through combustion. First, in the anaerobic digestion stage of sewage treatment, organic matter is decomposed and converted into gases such as methane. These energy-rich methane are efficiently collected and directly transported to the CHP, where methane is burned as fuel. This process not only generates electricity to meet the facility's own electricity needs, but also provides additional heat energy for heating water or supporting other processes that require heat energy, further improving energy efficiency. At the same time, hydrogen is also generated when treating certain types of sewage. This part of hydrogen is not directly burned like methane, but its chemical energy is converted into electricity through fuel cell technology. Fuel cells work through electrochemical reactions rather than traditional combustion methods, so they have higher energy conversion efficiency and cleaner emissions. The entire energy recovery process not only significantly reduces dependence on external energy, but also achieves the goal of energy conservation and emission reduction through the effective reuse of resources.

[0057] S5: Regularly evaluate the performance of the entire anaerobic ammoniation coupled resource recovery process, dynamically adjust the optimal operating parameters based on the evaluation results, and optimize the multi-stage series reactors.

[0058] S5.1: Based on the core elements of resource recovery and energy recovery, the performance of the entire anaerobic ammoniation coupled resource recovery is evaluated and the performance index is used to evaluate the performance of the entire anaerobic ammoniation coupled resource recovery. To express the overall efficiency, the specific expression is: ; in, represents the comprehensive performance index, Indicates the number of different resource recovery core elements, represents the total number of energy recovery core elements, Indicates the index of the energy recovery element, is an index variable, It means the The core elements of resource recycling, Indicates The weight coefficients of the core elements of resource recovery are Indicates The weight coefficients of the energy recovery factors, Indicates The core element of energy recovery.

[0059] S5.2: Comprehensively evaluate the efficiency and benefits of the entire anaerobic ammoniation coupled resource recovery process through a comprehensive performance index.

[0060] Furthermore, through the comprehensive performance index, the efficiency and benefits of the entire anaerobic ammoniation coupled resource recovery are comprehensively evaluated. First, it is necessary to establish an evaluation system containing multiple core elements. These elements cover various aspects from pollutant removal effect to resource recovery and energy conversion efficiency, such as phosphate recovery efficiency, microbial utilization rate, methane energy conversion efficiency and hydrogen energy conversion efficiency. Based on these core elements, the corresponding weight coefficients are set and compared with the preset target values ​​or industry standards. The index provides a quantitative tool to measure the operating status of the entire process. It can not only intuitively show which aspects perform well and which aspects are deficient, but also provide a scientific basis for subsequent optimization and adjustment to ensure that the sewage treatment process is not only efficient but also sustainable, and ultimately maximize the environmental, economic and social benefits.

[0061] S5.3: Set execution effect thresholds based on statistical analysis of historical comprehensive performance indexes ; when ≥ When the anaerobic ammoniation coupled resource recovery treatment method with phased regulation is found to have achieved the expected effect; when < When the optimal operating parameters are adjusted, it is considered that the effect of the staged regulated anaerobic ammoniation coupled resource recovery treatment method has not met expectations.

[0062] Furthermore, when the comprehensive performance index is less than the set threshold, it means that the staged regulated anaerobic ammoniation coupled resource recovery treatment method has failed to achieve the expected results. At this time, the diagnostic program should be started immediately to identify possible sources of problems, such as sensor failure, improper operating parameter settings, or changes in the external environment. Factors such as these may have led to performance degradation. For the identified problems, the optimal operating parameters should be adjusted in a timely manner, which may involve recalibrating sensors, optimizing reactor layout, and changing certain key parameters such as the control range of temperature or pH. At the same time, it is also necessary to strengthen the monitoring frequency of the operating status and collect more real-time data to more accurately evaluate the effects of the adjustments, ensure that the ideal operating state is restored as soon as possible, and prevent similar problems from occurring again. Through this dynamic adjustment mechanism, adaptability and stability can be continuously improved to ensure long-term maintenance of efficient sewage treatment and resource recovery efficiency.

[0063] This embodiment also provides a computer device, which is suitable for the case of a stage-by-stage regulated anaerobic ammoniation coupled resource recovery treatment method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the stage-by-stage regulated anaerobic ammoniation coupled resource recovery treatment method proposed in the above embodiment.

[0064] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0065] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for realizing the staged regulated anaerobic ammoniation coupled resource recovery treatment proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0066] In summary, the present invention achieves accurate monitoring of the sewage treatment process by installing multiple sensors at key nodes of anaerobic treatment, and collecting multivariate parameter data of these nodes and transmitting them to the central control center for pre-processing. Furthermore, historical operating data is combined with currently collected multivariate parameters to predict the optimal operating parameters required to achieve the best treatment effect and resource recovery rate through big data analysis and machine learning algorithms. This not only solves the problem of lack of real-time monitoring and dynamic adjustment in traditional sewage treatment technology, but also greatly promotes the effective recovery and reuse of resources.

[0067] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A staged regulated anaerobic ammoniation coupled resource recovery treatment method, characterized in that: include, Install sensors at each key node of the anaerobic treatment and collect multivariate parameter data to transmit to the central control center for pre-processing; Based on multivariate parameter data and combined with historical operation data, the optimal operating parameters of the recovery method treatment are predicted based on big data and machine learning algorithms; Design and operate multi-stage series reactors to treat specific pollutants and generate purified water and by-products based on optimal operating parameters and treatment requirements; Resource recovery and energy recovery based on purified water and by-products; The performance of the entire anaerobic ammoniation coupled resource recovery system is regularly evaluated. Based on the evaluation results, the optimal operating parameters are dynamically adjusted and the multi-stage series reactors are optimized.

2. The staged regulated anaerobic ammoniation coupled resource recovery method according to claim 1, characterized in that: The sensors include a pH sensor, a temperature sensor, an ammonia nitrogen sensor, a nitrate sensor, and an oxidation-reduction potential sensor.

3. The staged regulated anaerobic ammoniation coupled resource recovery method according to claim 2, characterized in that: The collected multivariate parameter data is transmitted to the central control center for preprocessing to generate real-time water quality data. The specific steps are as follows: pH sensors are based on solid-state ion-selective electrodes and determine pH by detecting the concentration of hydrogen ions in aqueous solutions; Temperature sensors use thermal resistors to measure temperature using the property that the resistance of a material changes with temperature. The ammonia nitrogen sensor uses the ion selective electrode method to determine the ammonia nitrogen content in water; The nitrate sensor measures nitrate concentration by means of ion selective electrodes and ultraviolet absorption spectroscopy; Redox potential sensors react to redox states by measuring the voltage difference between two electrodes; The preprocessing includes data cleaning, format conversion and anomaly detection.

4. The staged regulated anaerobic ammoniation coupled resource recovery method according to claim 3, characterized in that: The method of predicting the optimal operating parameters of the recovery method based on the multivariate parameter data and the historical operation data based on big data and machine learning algorithms is as follows: Train LSTM based on a large amount of historical data; Analyze multivariate parameter data through big data and machine learning algorithms, and dynamically identify water quality and operating parameters using adaptive feature selection functions; The water quality and operating parameters are input into the optimized trained LSTM, and the machine learning model predicts the optimal operating parameters to achieve the best treatment effect and resource recovery rate under current conditions based on the adaptive features.

5. The staged regulated anaerobic ammoniation coupled resource recovery method according to claim 4, characterized in that: Based on the optimal operating parameters and treatment requirements, a multi-stage series reactor is designed and operated to treat specific pollutants and generate purified water and by-products. The specific steps are as follows: Define treatment requirements based on water quality characteristics and treatment objectives; Select the reactor type according to the processing requirements, set the operating instructions for each reactor using the optimal operating parameters, and arrange the reactors; Operate multiple stages of series reactors to introduce pollutants into the reactors for purification, generating purified water and by-products; The byproducts include methane, phosphates, hydrogen, and microbial biomass.

6. The staged regulated anaerobic ammoniation coupled resource recovery method according to claim 5, characterized in that: The resource recovery and energy recovery based on the purified water and by-products are as follows: Resource recovery extracts phosphates from purification byproducts and converts them into phosphate fertilizers through chemical conversion, using the microbial biomass produced during the conversion process for processing to produce soil conditioners and animal feed additives; Energy recovery captures methane produced during the process and feeds it into the CHP, where it is burned to produce electricity and heat, while the produced hydrogen is converted into electricity via a fuel cell.

7. The staged regulated anaerobic ammoniation coupled resource recovery method according to claim 6, characterized in that: The performance of the entire anaerobic ammoniation coupled resource recovery is regularly evaluated, and the specific steps are as follows: Based on the core elements of resource recovery and energy recovery, the performance of the entire anaerobic ammoniation coupled resource recovery is evaluated and the comprehensive performance index is used to evaluate the performance of the entire anaerobic ammoniation coupled resource recovery. To express the overall efficiency; The core elements include phosphate recovery efficiency, microbial utilization, methane energy conversion efficiency, and hydrogen energy conversion efficiency; Through the comprehensive performance index, the efficiency and benefits of the entire anaerobic ammoniation coupled resource recovery are comprehensively evaluated.

8. The staged regulated anaerobic ammoniation coupled resource recovery method according to claim 7, characterized in that: According to the evaluation results, the optimal operating parameters are dynamically adjusted, and the multi-stage series reactor is optimized; Set execution effect thresholds based on statistical analysis of historical comprehensive performance indexes ; when ≥ When the anaerobic ammoniation coupled resource recovery treatment method with phased regulation is found to have achieved the expected effect; when < When the optimal operating parameters are adjusted, it is considered that the effect of the staged regulated anaerobic ammoniation coupled resource recovery treatment method has not met expectations.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the staged regulated anaerobic ammoniation coupled resource recovery treatment method described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the staged regulated anaerobic ammoniation coupled resource recovery treatment method described in any one of claims 1 to 8 are implemented.

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