Intelligent carbon source adding control system applied to sewage treatment denitrification

Abstract

The utility model relates to the technical field of sewage treatment, in particular to an intelligent carbon source feeding control system applied to denitrification in sewage treatment. The system comprises a dosing amount on-line calculation module, a dosing pump (group) control module, a dosing amount distribution module, a data processing module and the like, a plurality of detection devices such as an inlet water flow detection device, an inlet water COD detection device, an inlet water ammonia nitrogen / total KVM detection device and the like, and parts such as an electromagnetic flowmeter, a dosing pipeline, a valve and the like. The system adopts a control mode of'feedforward + model + feedback ', can adjust the adding amount of a carbon source in real time according to the change of inlet water quality, realizes accurate control, analyzes and processes system operation data, and provides a basis for optimized operation; automatic AI artificial intelligence operation is also realized, intelligent operation is realized, and the intelligent level and the operation efficiency of the system are improved; reliability is improved, and continuous and stable operation of the sewage treatment process is ensured. The method has remarkable advantages and wide application prospects.

Description

Technical Field

[0001] The utility model relates to the technical field of sewage treatment, in particular to an intelligent carbon source dosing control system applied to sewage treatment and denitrification. Background Art

[0002] Existing technology:

[0003] Water is an indispensable part of human life, and the quality of domestic drinking water directly impacts human health. With socioeconomic development and the continuous improvement of people's living standards, the demand for water quality is becoming increasingly stringent. One of the key production indicators of sewage treatment plants is the turbidity of discharged water. Coagulation and sedimentation is a crucial step in sewage treatment, affecting not only the quality of discharged water but also the effectiveness of subsequent process steps and the cost of water production.

[0004] In the wastewater treatment process, carbon source addition is a key step in nitrogen removal. Currently, most domestic water plant dosing automation systems utilize a single flow current control system and a flow proportional feedforward control system. The single flow current control system employs post-feedback control, adjusting its effects only when the controlled parameter, the flow current (SCD value), deviates. However, in practice, raw water flow changes quickly and dramatically, significantly impacting the coagulation and dosing control system. The controlled parameter takes a long time to reach a new stable state, significantly impacting water quality. The flow proportional feedforward control system integrates raw water flow proportional control with the single flow current control system. The primary dosing device is a metering pump with dual frequency and stroke control. The pump motor frequency is controlled by the flow current, while the pump stroke is controlled by the raw water flow rate. This dual frequency and stroke control approach mitigates the hysteresis inherent in the single flow current system, allowing for timely adjustment of the dosing dosage based on raw water flow changes and preventing undesirable water quality issues caused by sudden changes in raw water flow. However, when the raw water turbidity varied significantly, the flow rate proportional feedforward control system failed to adaptively adjust the dosage, requiring readjustment of the system's proportional coefficient, which was difficult for operators. Furthermore, the metering pump's stroke controller used a gear drive, which wore out over time, reducing accuracy and causing deviations between the actual dosage and the calculated dosage. This resulted in suboptimal sedimentation results, and the resulting water did not meet requirements.

[0005] With the continuous advancement of science and technology, the application of intelligent control technology in the field of sewage treatment has gradually attracted attention. Intelligent control technologies include fuzzy logic control, neural network control, and fuzzy neural network control. Fuzzy logic control is based on traditional computer digital control and uses the concepts and theories of fuzzy set theory, fuzzy linguistic variables, and fuzzy logic reasoning to simulate human thinking, judgment, and reasoning processes. Neural network technology uses neuron models to simulate the organizational structure and operating mechanism of the human brain. From the perspective of imitating human brain intelligence, it uses new computing and processing structures, new information representation, storage, and processing methods, and uses computers to simulate human thinking to create intelligent information processing systems. Fuzzy neural networks combine fuzzy logic and neural networks, which can not only give full play to the reasoning ability of fuzzy systems, but also utilize the learning ability of neural networks to improve system performance.

[0006] In the area of ​​coagulation dosing control, researchers are attempting to apply intelligent control technology to the coagulation dosing process to improve the accuracy and stability of coagulant addition. For example, fuzzy control is used to achieve optimal coagulant addition, and neural networks are used to verify the dosage calculated by the fuzzy controller. Alternatively, adaptive fuzzy neural networks are used to design a controller for the coagulation dosing process. By offline learning from historical beaker test data that reflects the coagulation process, the controller is adjusted and optimized, and an inverse dynamic model of the coagulation process is established. When online, the controller predicts the dosage in a timely manner based on water quality parameter data collected by the computer system.

[0007] Traditional coagulation dosing control methods have many drawbacks. For example, the coagulation process is complex and influenced by numerous factors, including raw water turbidity, flow rate, water temperature, and pH value. These factors exhibit nonlinear relationships, making it difficult to establish an accurate mathematical model. The process from coagulant addition to flocculation, sedimentation, and filtration takes more than two hours, resulting in nonlinearity and large hysteresis. Traditional control methods struggle to respond promptly to changes in source water quality parameters, limiting control effectiveness. Furthermore, traditional methods often fail to adjust dosing dosage in response to changes in source water quality, making dosing accuracy difficult to ensure. Both insufficient and excessive dosing can affect coagulation effectiveness. The application of intelligent control technology in wastewater treatment offers several advantages. Fuzzy control does not require a precise mathematical model; instead, it relies on the experience of the control operator, allowing for easy incorporation of human control experience and approximating human control behavior. It also exhibits strong adaptability and learning capabilities for control objects with uncertainty or high nonlinearity. Neural networks have powerful self-learning and nonlinear approximation capabilities, which can improve control accuracy and stability. Fuzzy neural networks combine the advantages of fuzzy logic and neural networks, offering enhanced robustness and nonlinear modeling capabilities, making them better able to cope with system uncertainty and interference. However, intelligent control technology also has some shortcomings in its application, such as the long training time of neural networks, which affects the real-time performance of the system; the fuzzy neural network has many parameters and is difficult to adjust, requiring experience and skills; intelligent control technology has high hardware requirements, which increases system costs.

[0008] Beneficial effects:

[0009] This utility model patent combines the advantages of related technologies and aims to provide a more efficient and energy-saving system that can adjust the carbon source dosage in real time according to the changes in the raw water quality to ensure that the total nitrogen in the effluent meets the standard, thereby effectively improving the effluent quality; through precise carbon source dosage control, excessive dosage can be avoided, which can reduce drug consumption and save costs; the intelligent control system can quickly respond to water quality changes, reduce water quality fluctuations, and improve the stability of the system; the system realizes automated control, reduces manual intervention, and reduces the labor intensity of workers; it has good adaptability to different water sources, water quality and treatment processes, which can be achieved by adjusting parameters and models; in addition, the intelligent control system has self-diagnosis and self-learning functions, which makes it easy for managers to detect problems and perform maintenance in a timely manner, thereby improving the reliability of the system. In summary, this intelligent carbon source dosage control system can effectively solve the problems of traditional coagulation dosing control methods, improve the operating efficiency and effluent quality of sewage treatment plants, and has important practical application value. Summary of the Invention

[0010] The utility model relates to the technical field of sewage treatment, and in particular to an intelligent carbon source dosing control system for sewage treatment and denitrification, which solves the problems of inaccurate carbon source dosing, high chemical consumption, and unstable effluent quality in traditional sewage treatment.

[0011] The intelligent carbon source dosing control system mainly includes functional modules such as dosing amount online calculation module, dosing pump (group) control module, dosing amount distribution module, data processing module, etc. It also includes inlet flow detection device, inlet COD detection device, inlet ammonia nitrogen / total nitrogen detection device, nitrate / total nitrogen ORP detection device, MISS detection device, DO detection device, liquid level detection device, water temperature detection device, electromagnetic flowmeter, dosing pipeline and valve and other components.

[0012] The system uses various detection devices to collect real-time signals such as influent flow rate, influent COD, influent ammonia nitrogen / total nitrogen, nitrate complex / total nitrogen ORP, MISS, DO, liquid level, and water temperature. The online dosage calculation module uses these signals to accurately calculate the dosage required for each dosing process. The dosing pump (group) control module precisely adjusts the operation of the dosing pump based on the calculation results to ensure accurate dosing. The dosage distribution module rationally allocates the dosage according to the needs of each structure to improve the utilization efficiency of the carbon source.

[0013] The data processing module rapidly processes and analyzes the detected data, identifying problems and making corresponding adjustments to ensure stable system operation and compliance with wastewater treatment standards. An electromagnetic flowmeter is used to detect the actual dosage. The dosing line connects the dosing pump to the treatment structure, and a valve is installed on the dosing line to control the distribution of the dosage.

[0014] The system adopts a "feedforward + model + feedback" control mode, in which the feedforward part refers to signals such as influent flow, influent COD, and influent ammonia nitrogen / total nitrogen; the model part performs calculations and predictions based on the detected signals; and the feedback part refers to signals such as nitrate / total nitrogen ORP, MISS, and DO. This control mode can save drug consumption on the basis of meeting the total nitrogen standard in the effluent. Specific effects include precise dosing, ensuring stable operation of the process section, reducing drug consumption, real-time monitoring, viewing dynamic data on the mobile terminal at any time, saving labor, supporting remote control, achieving unmanned operation, improving the ability to resist influent shock loads, achieving precise control of carbon source addition, ensuring stable and standard discharge of total nitrogen in the effluent, and achieving automatic closed-loop operation of the dosing system.

[0015] The system also features IoT connectivity, enabling remote monitoring and management. It also supports mobile control, allowing users to view system operating status and data at any time via mobile devices. The system also includes statistical analysis capabilities, enabling the collection and analysis of system operating data to inform operational optimization. It also implements automated AI-powered operation, enhancing the system's intelligence and operational efficiency.

[0016] The carbon source dosing control system has the following significant advantages:

[0017] 1. Improved effluent quality stability: By precisely controlling the amount of carbon source added, the system ensures that effluent total nitrogen meets standards and reduces water quality fluctuations. The system can monitor and adjust carbon source addition in real time, making the sewage treatment process more stable and effectively improving the stability of effluent quality, ensuring that discharged water meets strict environmental standards and reducing environmental impact.

[0018] 2. Reduced chemical consumption costs: Precise dosing avoids waste of carbon sources. The system accurately calculates and delivers carbon sources based on actual needs, minimizing unnecessary chemical consumption. This not only reduces wastewater treatment costs but also improves resource utilization efficiency, bringing economic benefits to the company.

[0019] 3. Enhanced system adaptability: The system can adjust the carbon source dosage in real time based on changes in influent water quality, adapting to different water quality conditions and treatment requirements. Whether it is a sudden change in water quality or seasonal fluctuations, the system can quickly respond and adjust the dosing strategy to ensure stable sewage treatment results.

[0020] 4. Improved management efficiency: IoT and mobile management capabilities enable managers to monitor system status anytime, anywhere, identifying and resolving issues promptly. This significantly reduces the need for on-site inspections, improves management convenience and efficiency, and enables managers to make decisions more quickly to ensure the normal operation of the system.

[0021] 5. Intelligent Operation: Automatic AI-powered operation enables the system to automatically learn and optimize control strategies, reducing manual intervention. The system continuously refines dosing strategies based on historical data and real-time feedback, enhancing its intelligence and enabling it to more accurately respond to complex situations.

[0022] 6. Improved reliability: The coordinated operation and precise control of all system components reduce the possibility of failure and improve the reliability of the system. Even under harsh operating conditions, it can maintain stable performance and ensure the continuous progress of the sewage treatment process.

[0023] 7. Optimize resource allocation: The reasonable allocation function of the dosing distribution module enables the carbon source to be optimally configured in each treatment unit of the sewage treatment plant, improving the overall treatment efficiency and achieving maximum resource utilization.

[0024] 8. Promote environmental protection and sustainable development: By reducing drug consumption and ensuring that the effluent meets the standards, the system helps reduce the negative impact of sewage treatment on the environment, meets the requirements of environmental protection and sustainable development, and brings positive benefits to society and the environment.

[0025] In practical applications, this intelligent carbon source dosing control system can adjust the carbon source dosage in real time based on changes in influent water quality, ensuring that effluent total nitrogen levels meet standards while also reducing chemical consumption and improving system stability and reliability. For example, in a sewage treatment plant, the system accurately detects subtle changes in influent water quality and promptly adjusts the carbon source dosage. When the influent ammonia nitrogen content suddenly increases, the system responds quickly by increasing the carbon source dosage to ensure effective denitrification. Furthermore, the chemical dosing distribution module precisely allocates carbon source based on the actual needs of each treatment unit in the sewage treatment plant, avoiding resource waste. The data processing module's rapid processing and analysis capabilities enable operators to understand system operational status in a timely manner and make accurate decisions. The implementation of the IoT module and mobile control capabilities allows managers to monitor system operating status anytime, anywhere, identifying and resolving issues promptly. The statistical analysis function provides strong support for optimized system operation. Through in-depth data analysis, methods to further reduce chemical consumption have been identified. The automatic AI operation function enables the system to automatically learn and adapt to changes in influent water quality, continuously optimizing control strategies and improving system intelligence and operational efficiency.

[0026] Furthermore, the system collects signals such as inlet flow, inlet COD, inlet ammonia nitrogen / total nitrogen, nitrate / total nitrogen ORP, MISS, DO, liquid level, and water temperature in real time through the inlet flow detection device, inlet COD detection device, inlet ammonia nitrogen / total nitrogen detection device, nitrate / total nitrogen ORP detection device, MISS detection device, DO detection device, liquid level detection device, and water temperature detection device, and transmits these signals to the data processing module.

[0027] The online dosage calculation module uses precise algorithms to calculate the dosage required for each dosing process based on the signals provided by the data processing module. For example, based on changes in parameters such as influent flow rate, influent COD, and influent ammonia nitrogen / total nitrate, combined with pre-set models and algorithms, it accurately determines the amount of carbon source required to achieve the desired treatment effect.

[0028] The dosing pump (3) is precisely regulated by the dosing pump (group) control module based on the calculation result of the dosing amount online calculation module to ensure the accuracy of dosing. The operation state of the dosing pump (3) is controlled by the main control cabinet. The main control cabinet adjusts the frequency, stroke and other parameters of the dosing pump (3) based on the calculated required drug amount signal to achieve precise control of the carbon source dosage.

[0029] The dosing distribution module rationally allocates dosing amounts based on the needs of each structure. It distributes the calculated carbon source dosage to different locations based on the actual conditions and process requirements of each treatment unit in the sewage treatment plant to ensure that the carbon source is fully and effectively utilized during the sewage treatment process.

[0030] The data processing module rapidly processes and analyzes detected data, promptly identifying anomalies and issuing alerts. It processes large amounts of real-time data, analyzing trends and changes to determine whether the system is operating normally. If an anomaly is detected, an alert is immediately issued, prompting operators to take appropriate action.

[0031] The electromagnetic flow meter is used to detect the actual dosage, the dosage pipeline is connected to the dosage pump (3) and the treatment structure, and the valve is installed on the dosage pipeline to regulate the distribution of the dosage.

[0032] The system uses a "feedforward + model + feedback" control model. The feedforward component uses signals such as influent flow, influent COD, and influent ammonia nitrogen / total nitrogen to predict carbon source addition needs in advance and provide a preliminary control strategy for the system. For example, if the influent flow suddenly increases or the influent ammonia nitrogen content rises, the feedforward component will adjust the carbon source dosage setpoint in advance based on these changes.

[0033] The modeling component calculates and predicts based on the detected signals, building a mathematical model to simulate and predict the wastewater treatment process, providing more precise control parameters for the system. The modeling component considers various factors, such as water quality parameters and reactor characteristics, to calculate the optimal carbon source dosage.

[0034] The feedback part uses signals such as nitrate / total nitrogen ORP, MISS, and DO to provide feedback on the actual operating results of the system and adjust the control strategy in a timely manner to ensure that the total nitrogen in the effluent meets the standard. For example, if the nitrate / total nitrogen ORP test value shows that the total nitrogen in the effluent is too high, the feedback part will adjust the operation of the dosing pump (3) and increase the amount of carbon source added until the total nitrogen in the effluent meets the standard.

[0035] In addition, the IoT module connects the system to the Internet of Things, allowing operators to monitor the system's operating status in real time through a remote monitoring center, enabling remote management and control. The mobile management and control function allows users to view the system's operating status and data at any time via mobile devices, allowing them to understand the system's status anytime, anywhere. The statistical analysis function collects and analyzes system operating data, providing a basis for optimized operation and helping users identify potential system issues and areas for improvement. The automatic AI operation function enables the system to automatically learn and adapt to changes in influent water quality, continuously optimizing control strategies and improving the system's intelligence and operational efficiency.

[0036] In summary, the intelligent carbon source addition control system of the utility model realizes the precise control and intelligent management of carbon source addition in the process of sewage treatment and denitrification through the limitation and synergy of each claim, and has significant advantages and broad application prospects.

[0037] The intelligent carbon source dosing control system realizes the precise control of carbon source dosing in the denitrification process of sewage treatment through the coordinated work of various components, thereby improving sewage treatment effects and reducing drug consumption and operating costs.

[0038] In summary, the intelligent carbon source addition control system of the utility model has significant advantages in the denitrification process of sewage treatment. It can achieve precise addition, reduce drug consumption, and improve the stability of effluent water quality, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a schematic diagram of the intelligent carbon source dosing control system in the embodiment of the present application;

[0040] Figure 2 Schematic diagram of the feedforward function in the embodiment of the present application;

[0041] Figure 3 This is a schematic diagram of the model function in the embodiment of the present application;

[0042] Figure 4 Schematic diagram of the feedback function in the embodiment of the present application;

[0043] Figure 5 This is a schematic diagram of the "feedforward + model + feedback" mode used in the embodiment of the present application;

[0044] Figure 6 This is a schematic diagram of an embodiment of the present application having automatic fault tolerance capability;

[0045] Figure 7 This is a functional diagram of the Internet of Things in an embodiment of the present application;

[0046] Figure 8 This is a schematic diagram of the mobile control function in an embodiment of the present application;

[0047] Figure 9 This is a functional diagram of the statistical analysis function in the embodiment of the present application;

[0048] Figure 10 This is a schematic diagram of the AI ​​artificial intelligence function in the embodiment of this application. Specific embodiments

[0049] Figure 1 The schematic diagram of the intelligent carbon source dosing control system in the embodiment of the present application describes the technical solution of this embodiment;

[0050] This intelligent carbon source dosing control system is applied in the field of wastewater treatment and denitrification, aiming to solve the problems of inaccurate carbon source dosing, high drug consumption, and unstable effluent quality in traditional wastewater treatment.

[0051] The system mainly includes functional modules such as dosing amount online calculation module, dosing pump (group) control module, dosing amount distribution module, data processing module, as well as inlet flow detection device, inlet COD detection device, inlet ammonia nitrogen / total nitrogen detection device, nitrate / total nitrogen ORP detection device, M ISS detection device, DO detection device, liquid level detection device, water temperature detection device, electromagnetic flowmeter, dosing pipeline and valve and other components.

[0052] like Figure 1 As shown, the system uses various detection devices to collect real-time signals such as influent flow rate, influent COD, influent ammonia nitrogen / total Kjeldahl nitrogen, nitrate / total nitrogen ORP, MIS, DO, liquid level, and water temperature, and transmits these signals to the data processing module. The influent flow detection device is used to measure the influent flow rate, the influent COD detection device is used to measure the influent chemical oxygen demand, the influent ammonia nitrogen / total Kjeldahl nitrogen detection device is used to measure the influent ammonia nitrogen or total Kjeldahl nitrogen content, the nitrate / total nitrogen ORP detection device is used to measure the redox potential of nitrate nitrogen or total nitrogen, the MIS detection device is used to measure the suspended solids concentration of the mixed liquor, the DO detection device is used to measure the dissolved oxygen concentration, the liquid level detection device is used to measure the liquid level of the anoxic tank, and the water temperature detection device is used to measure the water temperature. These detection devices provide the system with comprehensive information on influent and treatment process parameters.

[0053] The online dosage calculation module uses precise algorithms based on signals provided by the data processing module to calculate the required dosage for each dosing process. For example, based on changes in parameters such as influent flow rate, influent COD, and influent ammonia nitrogen / total nitrate, combined with pre-set models and algorithms, it accurately determines the carbon source dosage required to achieve the desired treatment effect. This module's precise calculation capability is crucial for achieving accurate carbon source dosing, avoiding wasteful dosage or substandard effluent quality caused by calculation errors.

[0054] The dosing pump (3) is precisely regulated by the dosing pump (group) control module based on the calculation result of the dosing amount online calculation module. The dosing pump (3) is connected to the main control cabinet, which receives the required drug amount signal and controls the operation of the dosing pump. The main control cabinet adjusts the frequency, stroke and other parameters of the dosing pump (3) based on the calculated required drug amount signal to achieve precise control of the carbon source dosage and ensure the accuracy of drug dosing.

[0055] The Dosage Distribution Module rationally allocates dosages based on the needs of each structure. During the sewage treatment process, different structures may have different carbon source requirements. The Dosage Distribution Module allocates the calculated carbon source dosage to different locations based on these needs, ensuring that the carbon source is fully and effectively utilized during the sewage treatment process. This prevents excessive or insufficient dosage in certain areas, thereby improving carbon source utilization efficiency, optimizing sewage treatment results, and ensuring the smooth progress of the entire treatment process.

[0056] The data processing module rapidly processes and analyzes detected data, promptly identifying anomalies and issuing alerts. It processes large amounts of real-time data, analyzes trends and changes, and determines whether the system is operating normally. If an anomaly is detected, an alert is immediately issued, prompting operators to take appropriate measures. This module plays a key role in ensuring stable system operation and achieving acceptable sewage treatment results. Efficient data processing and analysis capabilities are essential for achieving intelligent control in the system.

[0057] The electromagnetic flowmeter is used to detect the actual dosage. The dosing pipeline connects the dosing pump and the treatment structure. The valve is installed on the dosing pipeline to regulate the distribution of the dosage.

[0058] The system adopts the control mode of "feedforward + model + feedback", such as Figure 5 The feedforward component uses signals such as influent flow, influent COD, and influent ammonia nitrogen / total nitrogen to predict carbon source addition requirements in advance and provide a preliminary control strategy for the system. For example, if the influent flow suddenly increases or the influent ammonia nitrogen content rises, the feedforward component will adjust the carbon source dosage setpoint in advance based on these changes.

[0059] The modeling component calculates and predicts based on the detected signals, building a mathematical model to simulate and predict the wastewater treatment process, providing more precise control parameters for the system. The modeling component considers various factors, such as water quality parameters and reactor characteristics, to calculate the optimal carbon source dosage.

[0060] The feedback part uses signals such as nitrate / total nitrogen ORP, M ISS, and DO to provide feedback on the actual operating effect of the system and adjust the control strategy in a timely manner to ensure that the total nitrogen in the effluent meets the standard. For example, if the nitrate / total nitrogen ORP test value shows that the total nitrogen in the effluent is too high, the feedback part will adjust the operation of the dosing pump (3) and increase the amount of carbon source added until the total nitrogen in the effluent meets the standard.

[0061] In summary, the intelligent carbon source addition control system realizes the precise control and intelligent management of carbon source addition in the process of sewage treatment and denitrification through the coordinated work of various components and advanced control mode, improves the sewage treatment effect, reduces drug consumption and operating costs, and has significant advantages and broad application prospects.

[0062] Combine Figure 2 The schematic diagram of the feedforward function in the embodiment of the present application describes the technical solution of this embodiment;

[0063] The feed forward function (f feed forward) processes data. The feed forward function predicts the system dosage based on the water flow, influent COD, influent ammonia nitrogen / total Kai feed forward signal parameters and algorithm model.

[0064] like Figure 2 As shown in Figure 1, the feedforward function predicts the biochemical system's dosing requirements based on the influent flow rate, influent CODcr, influent ammonia nitrogen / total nitrogen feedforward signal parameters, and the algorithm model. Influent flow rate is a critical parameter that directly impacts the load on the wastewater treatment system. A larger influent flow rate requires more carbon source to meet the biochemical reaction's needs.

[0065] The influent COD (chemical oxygen demand) reflects the content of organic matter in the water. A higher influent COD means that the water contains more biodegradable organic matter, so the amount of carbon source added needs to be increased accordingly to ensure that the organic matter can be fully degraded.

[0066] Influent ammonia nitrogen / total Kjeldahl nitrogen is another key parameter, reflecting the nitrogen content and form in the water. Ammonia nitrogen is a common form of nitrogen in wastewater, while total Kjeldahl nitrogen includes both organic nitrogen and ammonia nitrogen. Higher influent ammonia nitrogen / total Kjeldahl nitrogen levels require more carbon source to support the denitrification process, as the denitrification process consumes carbon source as an electron donor.

[0067] The feedforward function comprehensively considers these parameters and uses an algorithmic model to make predictions. This algorithmic model, likely based on a deep understanding of the wastewater treatment process and analysis of extensive experimental data, establishes a mathematical relationship between influent parameters and dosage. By inputting real-time influent flow rate, influent COD, influent ammonia nitrogen / total Kjeldahl nitrogen, and other parameters, the feedforward function can quickly calculate the predicted dosage.

[0068] This predictive capability is crucial for intelligent carbon source dosing control systems. Changes in influent quality during wastewater treatment are often sudden and unpredictable. Relying solely on feedback control can lead to substandard water quality due to system lag. A feedforward function, however, can predict dosing requirements in advance based on changes in influent parameters, enabling the system to make adjustments more quickly and ensure a stable and efficient wastewater treatment process.

[0069] For example, when the inlet flow rate suddenly increases, the feedforward function will predict the amount of carbon source that needs to be increased based on the increased flow rate and the corresponding inlet COD, inlet ammonia nitrogen / total Kjeldahl nitrogen and other parameters, and promptly transmit this information to the dosing pump (group) control module to adjust the operation of the dosing pump and increase the addition of the carbon source.

[0070] In summary, the feedforward function plays an important predictive and guiding role in the intelligent carbon source dosing control system. It can predict the dosage demand in advance according to the changes in the influent parameters, enabling the system to make adjustments more quickly and improve the efficiency and stability of sewage treatment.

[0071] Combine Figure 3The model function diagram in the embodiment of the present application describes the technical solution of this embodiment;

[0072] The model function (fmode l) processes the data. The model function predicts the dosage requirements of the biochemical system under different conditions based on the biochemical reaction model parameters.

[0073] like Figure 3 As shown, the model function establishes a mathematical model to analyze and calculate data from the biochemical reaction process, such as microbial metabolic rate, substrate concentration, temperature, and pH, to accurately predict the aeration rate required by the biochemical system under different conditions. Here, aeration rate can be understood as an important parameter related to the dosage, as the dosage directly affects the metabolic activity and oxygen demand of the microorganisms in the biochemical reaction. The biochemical reaction model parameters form the foundation of the model function. These parameters include, but are not limited to, the type, number, and activity of the microorganisms; the type and concentration of the substrate; and the temperature and pH of the reaction environment. These parameters interact to determine the progress and effectiveness of the biochemical reaction.

[0074] By comprehensively analyzing these parameters, the model function can predict the dosage requirements of the biochemical system under different conditions. For example, when the temperature rises, the metabolic rate of microorganisms may increase, requiring more carbon source to support their metabolic activities. In this case, the model function will predict the corresponding increase in dosage based on the temperature change.

[0075] Similarly, when substrate concentration changes, the model function will also adjust its prediction of the dosage based on the type and concentration of the substrate. If the substrate concentration is high, more carbon source may be needed to promote microbial degradation of the substrate; conversely, if the substrate concentration is low, the dosage may need to be appropriately reduced to avoid waste.

[0076] In addition, changes in pH also affect microbial activity and the progress of biochemical reactions. The model function takes the influence of pH into account and predicts the appropriate dosage under different pH conditions to ensure that the biochemical reaction can proceed smoothly.

[0077] By predicting biochemical reaction model parameters, the model function can provide a more precise dosing control strategy for the intelligent carbon source dosing control system. Combined with the feedforward function, the model function can better respond to changes in influent water quality and other conditions, achieving a more stable and efficient wastewater treatment process.

[0078] For example, in actual applications, if the inlet water quality changes significantly, the model function can quickly calculate the corresponding dosing adjustment plan based on the detected parameter changes, and pass this plan to the dosing pump (group) control module, thereby achieving precise control of the dosing amount.

[0079] In summary, the model function plays a vital role in the intelligent carbon source dosing control system. It can accurately predict the biochemical system's demand for dosing based on the biochemical reaction model parameters, provide the system with precise control strategies, and improve the effect and efficiency of sewage treatment.

[0080] Combine Figure 4 The feedback function diagram in the embodiment of the present application describes the technical solution of this embodiment;

[0081] Feedback function (ffeedback) The feedback function monitors the operating status of the biochemical system in real time according to the nitrate / total nitrogen ORP, M ISS, and DO parameters. The feedback function will adjust the operating status of the switch valve in time according to the changes in the pipe pressure to ensure the stable operation of the system.

[0082] like Figure 4 As shown in Figure 1, the feedback function monitors the operating status of the biochemical system in real time based on parameters such as nitrate / total nitrogen ORP, MIS, and DO. The nitrate / total nitrogen ORP (oxidation-reduction potential) reflects the redox state of nitrogen in the water. By monitoring this parameter, we can understand the progress of the denitrification reaction in the biochemical system. If the nitrate / total nitrogen ORP value indicates unsatisfactory denitrification, the feedback function will make timely adjustments to increase or decrease the amount of carbon source added to optimize denitrification.

[0083] Mixed Liquor Suspended Solids (MISS) reflects the number and activity of microorganisms in a biochemical system. A suitable MISS concentration is crucial for the proper functioning of biochemical reactions. A feedback function adjusts the amount of carbon source added based on changes in the MISS to ensure that the microorganisms have sufficient nutrients for their metabolic activities.

[0084] Dissolved oxygen (DO) is another crucial parameter, directly influencing the rates of microbial respiration and biochemical reactions. A feedback function monitors DO concentrations in real time and adjusts aeration equipment or carbon source dosage as needed to maintain appropriate DO levels and ensure efficient biochemical reactions.

[0085] Furthermore, the feedback function adjusts the on-off valve's operating state in real time based on changes in line pressure. These changes can affect the flow and stability of dosing. Adjusting the on-off valve's opening ensures accurate and stable dosing, ensuring stable system operation.

[0086] For example, when the ORP value of nitrate / total nitrogen shows that the total nitrogen in the water is too high, the feedback function will determine that the current carbon source dosage is insufficient and issue a command to increase the carbon source dosage. At the same time, the feedback function will adjust the specific value of the dosage based on the parameters of M ISS and DO to ensure that the microorganisms can fully utilize the carbon source for denitrification reaction while maintaining a good growth environment.

[0087] In actual operation, the feedback function works in conjunction with the feedforward function and the model function to form a complete closed-loop control system. The feedforward function predicts the dosage based on influent parameters, the model function calculates the dosage based on the biochemical reaction model, and the feedback function adjusts and optimizes the dosage based on real-time monitored parameters. In this way, the intelligent carbon source dosing control system achieves precise control of carbon source dosage, improving the effectiveness and stability of wastewater treatment.

[0088] Furthermore, the feedback function can promptly detect anomalies in the system and take appropriate measures to address them. For example, if the MIS concentration rises or falls abnormally, the feedback function will determine that abnormal microbial growth or other problems may exist and issue an alarm, prompting operators to inspect and address them. In short, the feedback function plays a vital role in the intelligent carbon source dosing control system. By real-time monitoring of parameters such as nitrate / total nitrogen ORP, MIS, and DO, and adjusting the operating status of the on-off valve according to changes in pipe pressure, it ensures stable system operation and compliance with wastewater treatment standards.

[0089] Figure 5 In the embodiment of the present application, a schematic diagram of a “feedforward + model + feedback” mode is used to describe the technical solution of this embodiment;

[0090] The intelligent carbon source addition control system adopts the "feedforward + model + feedback" mode. When some instruments are damaged, the system can automatically detect and identify the damaged instruments through redundant design or intelligent diagnostic functions, and perform automatic closed-loop operation based on the data of other normal instruments and preset algorithms to ensure that the system can still operate stably and reduce the intensity of manual operation.

[0091] like Figure 5 As shown in the figure, the "feedforward + model + feedback" model is the core control mode of this intelligent carbon source addition control system. The feedforward part predicts the demand for carbon source addition in advance based on signals such as influent flow rate, influent COD, and influent ammonia nitrogen / total nitrogen, providing a preliminary control strategy for the system. The model part calculates and predicts the detected signals by establishing a mathematical model, providing more precise control parameters for the system. The feedback part provides feedback on the actual operation of the system based on signals such as nitrate / total nitrogen ORP, M ISS, and DO, and promptly adjusts the control strategy to ensure that the total nitrogen in the effluent meets the standard.

[0092] In actual operation, this control mode can achieve chemical savings while meeting effluent total nitrogen standards. It offers advantages such as precise dosing, stable process operation, reduced chemical consumption, and real-time monitoring. However, during the wastewater treatment process, some instruments may become damaged. This is where the system's redundancy and intelligent diagnostic capabilities come into play.

[0093] Redundancy design means having backup instruments or devices in the system. If the primary instrument fails, the backup instrument automatically takes over, ensuring the system can continue to obtain the required monitoring data. For example, a water flow detection device may have multiple sensors. If one sensor fails, the others can continue to provide water flow data, ensuring the normal operation of the system.

[0094] The intelligent diagnostic function automatically detects and identifies damaged instruments. The system monitors the operating status of each instrument in real time and determines whether it is damaged by comparing its measured data with the preset normal range. If a damaged instrument is detected, the system immediately issues an alarm and provides feedback to the operator.

[0095] At the same time, the system automatically operates in a closed loop based on data from other functioning instruments and pre-set algorithms. Even if some instruments are damaged, the system can still utilize the remaining functioning instrument data, combined with the algorithm model, to calculate the appropriate carbon source dosage and control strategy. For example, if the influent COD detection device is damaged, the system can estimate the approximate range of influent COD based on other parameters such as influent flow rate, influent ammonia nitrogen / total nitrate, as well as historical data and model predictions, and thus adjust the carbon source dosage.

[0096] This automatic closed-loop operation ensures the system can continue to operate stably even if some instruments are damaged, significantly reducing the intensity of manual operation. Operators no longer need to constantly monitor the status of instruments; the system automatically handles any faults and ensures the continued operation of the sewage treatment process.

[0097] For example, in a sewage treatment plant, when an inlet ammonia nitrogen / total nitrogen detector malfunctioned, the system's intelligent diagnostics promptly detected the problem and automatically switched to a redundant backup detector. Simultaneously, based on data from other functioning instruments and algorithmic models, the system adjusted the carbon source dosage to ensure that the effluent total nitrogen met standards. Throughout this process, operators only needed to repair or replace the damaged instrument upon receiving a system alert, without having to manually intervene in system operations. This significantly reduced manual effort and improved work efficiency.

[0098] In summary, the "feedforward + model + feedback" mode adopted by the intelligent carbon source dosing control system, combined with redundant design and intelligent diagnostic functions, can automatically operate in a closed loop when some instruments are damaged, ensuring stable operation of the system, reducing the intensity of manual operation, and improving the reliability and stability of the system, which has important practical application value.

[0099] Figure 6 The schematic diagram of the embodiment of the present application with automatic fault tolerance capability describes the technical solution of this implementation;

[0100] The intelligent carbon source dosing control system has automatic fault tolerance capability. When some instruments are damaged, it can automatically adjust the control strategy to adapt to the impact of instrument damage and ensure the normal operation of the sewage treatment process. Figure 6 As shown, the system's automatic fault tolerance is crucial for ensuring stable operation of wastewater treatment. Various instruments and equipment play a critical role in monitoring and controlling the wastewater treatment process. However, due to various reasons, some instruments may become damaged. If the system lacks automatic fault tolerance, instrument damage could lead to system control failure, compromising wastewater treatment effectiveness and even causing serious consequences such as substandard emissions. When the system detects instrument damage, it immediately initiates an automatic control strategy adjustment mechanism. First, the system uses intelligent diagnostics to quickly determine the type and location of the damaged instrument and assess its impact on overall system operation. Then, based on pre-set algorithms and data from other functioning instruments, the system automatically adjusts the control strategy to compensate for the information loss caused by the damaged instrument. For example, if the inlet flow rate sensor is damaged, the system may estimate the approximate inlet flow rate range based on historical data and other relevant parameters, such as inlet COD and inlet ammonia nitrogen / total nitrate. Simultaneously, the system adjusts the control strategy of the dosing pump to determine the appropriate dosing amount based on the estimated inlet flow rate and other parameters to ensure the normal operation of the wastewater treatment process. The system also monitors various indicators during the wastewater treatment process in real time, such as nitrate / total nitrogen (ORP), MISs, and DO, to verify the effectiveness of the automatically adjusted control strategy. If any issues are identified, the system will further optimize and adjust to ensure that the effluent total nitrogen meets standards and guarantee treatment effectiveness. Automatic fault tolerance significantly reduces the need for manual intervention, reducing system downtime and repair costs due to instrument failure. It also improves system reliability and stability, enabling more continuous and stable wastewater treatment. For example, during actual operation at a wastewater treatment plant, an influent COD monitoring device suddenly failed. The system promptly detected this and automatically adjusted the control strategy. Based on historical data and other relevant parameters, the system estimated the approximate range of the influent COD and adjusted the carbon source dosage accordingly. Throughout this process, the wastewater treatment plant's operations were not significantly impacted, and the effluent total nitrogen remained within standards. Subsequently, following the system's prompts, staff promptly repaired and replaced the damaged instrument, restoring normal operation. In short, the intelligent carbon source dosing control system's automatic fault tolerance allows it to automatically adjust its control strategy even when some instrumentation fails, ensuring the normal operation of the sewage treatment process. This capability improves system reliability and stability, reduces operating costs, and is crucial for ensuring the stable operation of sewage treatment plants.

[0101] Figure 7This is a functional diagram of the Internet of Things in the embodiment of the present application to describe the technical solution of this implementation;

[0102] The system has the Internet of Things function, which can realize the interconnection and remote monitoring between devices. Figure 7As shown, the Internet of Things (IoT) is a key feature of this intelligent carbon source dosing control system. Through IoT technology, various devices in the system can be interconnected, forming a tightly coordinated whole. Sensors such as the inlet flow rate detector, inlet COD detector, inlet ammonia nitrogen / total nitrogen detector, nitrate / total nitrogen ORP detector, MIS detector, DO detector, liquid level detector, and water temperature detector collect various data in real time and transmit this data to the data processing module and other related devices via the IoT. Furthermore, control devices such as the dosing pump (group) control module and the dosing amount distribution module can also communicate with other devices via the IoT, enabling collaborative operation. For example, the online dosing amount calculation module calculates the required dosing amount based on sensor data and then sends instructions to the dosing pump (group) control module via the IoT, precisely adjusting the dosing pump's operation and ensuring accurate dosing. The IoT also enables remote monitoring of the system. Managers can access the system's operating status, various monitoring data, and equipment performance in real time through remote terminals such as computers and mobile phones. Regardless of their location, managers can monitor and manage the system as long as they have an internet connection. This remote monitoring feature offers many advantages and benefits. First, it significantly improves management efficiency. Managers can comprehensively monitor and manage the system without having to be physically present, identifying problems and taking appropriate measures. Second, it enables real-time monitoring to ensure stable system operation. Managers can view system operating data at any time, promptly identify anomalies, and address them before they escalate. Furthermore, remote monitoring enables centralized management of multiple sewage treatment sites, reducing management costs. For example, at a sewage treatment plant, managers can use a remote terminal to view real-time data from each treatment process, including influent flow, water quality parameters, and dosage. If a sudden increase in influent flow or abnormal water quality parameters is detected, managers can promptly adjust the operating parameters of the dosing pump to ensure that sewage treatment meets standards. Managers can also remotely monitor the operating status of equipment such as the dosing pump, promptly identify equipment failures, and arrange for maintenance personnel to ensure normal system operation. Furthermore, the Internet of Things (IoT) feature supports data analysis and optimization. The vast amount of data collected by the system can be transmitted to the cloud via the IoT for storage and analysis. Through in-depth analysis of this data, managers can understand the system's operating patterns, identify potential problems, and optimize the system's operational strategies, improving wastewater treatment efficiency and reducing operating costs. In short, the IoT functionality of this intelligent carbon source dosing control system enables interconnection and remote monitoring between devices, improving management efficiency, ensuring stable system operation, and providing strong support for intelligent management of wastewater treatment plants.

[0103] Figure 8The mobile control diagram in the embodiment of this application describes the technical solution of this implementation;

[0104] The mobile management and control feature provides users with significant convenience. Through mobile devices, users can gain real-time insights into system operations, breaking the constraints of time and space. Regardless of location, as long as their mobile device is connected to the internet, they can easily access the system and view real-time data on parameters such as influent flow, influent COD, influent ammonia nitrogen / total nitrogen, nitrate complex nitrogen / total nitrogen ORP, miss, and DO, as well as the operating status of the dosing pump and dosing distribution. For example, even when users are away from home, they can still monitor system status at any time via their mobile device. If any parameter is found to be abnormal, users can contact relevant personnel immediately to prevent the problem from escalating. This real-time monitoring capability enables users to make more timely decisions, improving system responsiveness and management efficiency. Furthermore, the mobile management feature allows users to query historical data, allowing users to view system operating trends over time for better analysis and evaluation of system performance. Furthermore, the mobile device can also receive system alerts, ensuring users are promptly notified of system anomalies and able to take appropriate measures. In short, the mobile management and control feature allows users to more conveniently manage and monitor the intelligent carbon source dosing control system, improving system operability and management efficiency.

[0105] Figure 9 This is a functional diagram of the statistical analysis function in the embodiment of the present application to describe the technical solution of this embodiment;

[0106] Statistical analysis is a crucial component of this system. The system collects and stores extensive operational data, including real-time data on parameters such as influent flow, influent COD, influent ammonia nitrogen / total nitrogen, nitrate / total nitrogen ORP, MISs, and DO, as well as information on dosing pump operation and dosing distribution. By analyzing and processing this data, the system can draw valuable conclusions. For example, it can analyze trends in influent water quality and understand fluctuations over time, thereby predicting potential future changes and adjusting carbon source dosing strategies in advance to ensure that effluent total nitrogen meets standards. Furthermore, the statistical analysis function can assess dosing pump efficiency and identify potential issues in the dosing process, such as uneven dosing distribution and inappropriate dosing timing. It can also provide improvement recommendations to improve carbon source utilization and reduce chemical consumption. Furthermore, based on the statistical analysis results, the system can optimize operating parameters and adjust control strategies to better adapt to the actual operating environment, enhancing wastewater treatment effectiveness and stability. For example, if historical data analysis reveals elevated ammonia and nitrogen levels in the influent during a specific time period, the system can automatically adjust the dosing pump's operating parameters and increase the carbon source dosage to account for the change in water quality. Overall, statistical analysis provides a crucial basis for optimizing the intelligent carbon source dosing control system, helping to improve system efficiency and wastewater treatment effectiveness.

[0107] Figure 10 The AI ​​artificial intelligence function diagram in the embodiment of this application describes the technical solution of this implementation;

[0108] Automatic AI operation is a key feature of this system. The system utilizes AI technology to learn and analyze large amounts of historical data, extracting useful insights and patterns. This historical data includes changes in parameters such as influent flow rate, influent COD, influent ammonia nitrogen / total nitrogen, nitrate complex nitrogen / total nitrogen ORP, MISs, and DO, as well as corresponding dosing pump operating parameters and effluent water quality data. By learning from this historical data, the AI ​​can establish a complex relationship model between influent water quality, system operating parameters, and effluent water quality. When the system receives real-time influent water quality data, the AI ​​automatically predicts appropriate operating parameters based on this model and promptly adjusts dosing pump operation to ensure precise carbon source dosing. For example, if the influent ammonia nitrogen content suddenly increases, the AI ​​can quickly identify this change and, based on historical experience, automatically increase the carbon source dosage to ensure that the effluent total nitrogen content meets the standard. Furthermore, the AI ​​monitors the system's operating status in real time and continuously optimizes and adjusts operating parameters based on feedback to enhance system stability and reliability. Furthermore, the AI's automatic adjustment function can adapt to varying influent water quality and treatment requirements, achieving optimized system operation without manual intervention. This not only enhances the system's intelligence level but also reduces manual operation, improving the efficiency and quality of wastewater treatment. In short, automatic AI operation enables the intelligent carbon source dosing control system to operate more intelligently and efficiently, providing a more precise and reliable solution for wastewater treatment and denitrification.

Claims

1. An intelligent carbon source dosing control system for wastewater treatment and denitrification, characterized in that: include: Dosage online calculation module, dosing pump group control module, dosing amount distribution module, data processing module, dosing pump (3), connected to the main control cabinet, used to perform dosing operations according to the instructions of the main control cabinet, main control cabinet, receives the required dosage signal and controls the operation of the dosing pump, inlet water flow detection device, used to detect the inlet water flow, inlet water COD detection device, used to detect the inlet water COD, inlet water ammonia nitrogen / total nitrogen detection device, used to detect the inlet water ammonia nitrogen / total nitrogen, nitrate complex / total nitrogen ORP detection device, used to detect nitrate complex / total nitrogen ORP, MISS detection device, used to detect MISS, DO detection device, used to detect DO, liquid level detection device, used to detect the liquid level of the anoxic tank (5), water temperature detection device, used to detect water temperature, electromagnetic flowmeter, used to detect the actual dosing amount, dosing pipeline, connected to the dosing pump and the treatment structure, valve, installed on the dosing pipeline, used to control the distribution of dosing amount, The signals received by the system include influent flow, influent COD, influent ammonia nitrogen / total nitrogen, nitrate complex / total nitrogen ORP, MISS, DO, liquid level and water temperature signals. The instructions issued by the system include drug dosage instructions and valve opening control instructions. The intelligent carbon source dosing control system for wastewater treatment and denitrification is based on the multi-parameter control mode of "feedforward + model + feedback", and the mathematical formula is expressed as follows: y=f_feedforward(X_feedforward)+f_model(X_model)+f_feedback(X_feedback), Wherein, y represents the output, f_feedforward represents the feedforward function, X_feedforward represents the feedforward signal-related parameters, including the inlet flow rate, inlet COD, and inlet ammonia nitrogen / total nitrogen signal, f_model represents the model function, X_model represents the model-related parameters, including the calculation and prediction of the detected signal, f_feedback represents the feedback function, and X_feedback represents the feedback signal-related parameters, including nitrate / total nitrogen ORP, MISS, and DO. The feedforward function predicts the biochemical system's demand for dosing based on the inlet flow rate, inlet COD, inlet ammonia nitrogen / total nitrogen feedforward signal parameters and the algorithm model; the model function predicts the biochemical system's demand for dosing under different conditions based on the biochemical reaction model parameters; the feedback function monitors the biochemical system's operating status in real time based on the nitrate / total nitrogen ORP, MISS, and DO parameters, and promptly adjusts the operating status of the on-off valve based on changes in pipe pressure to ensure stable system operation and achieve chemical savings while ensuring that the effluent total nitrogen meets the standard.

2. The intelligent carbon source dosing control system according to claim 1, characterized in that: The f_feedforward feedforward function processes data and predicts the system dosage based on water flow, influent COD, influent ammonia nitrogen / total nitrogen feedforward signal parameters and an algorithm model.

3. The intelligent carbon source dosing control system according to claim 1, characterized in that: The f_model model function processes data and predicts the demand of the biochemical system for dosage under different conditions based on biochemical reaction model parameters.

4. The intelligent carbon source dosing control system according to claim 1, characterized in that: The f_feedback feedback function monitors the operating status of the biochemical system in real time according to the nitrate / total nitrogen ORP, MISS, and DO parameters, and adjusts the operating status of the switch valve in time according to the change of the pipe pressure to ensure stable operation of the system.

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