A SND-ED intelligent wastewater treatment deep denitrification system and its control method

Abstract

The invention discloses a SND-ED intelligent wastewater treatment deep denitrification system and a control method thereof, and specifically relates to the technical field of wastewater treatment. A reaction environment for nitrification and denitrification is provided by a reactor, and a data monitoring module is used to collect dissolved oxygen, redox potential and temperature data in real time, and the data is transmitted to a control module. The control module dynamically adjusts aeration intensity, aeration time and carbon source dosage based on an ORP deviation index and a temperature change rate abnormality index through fuzzy logic to ensure the synchronization of nitrification and denitrification, adjusts aeration and carbon source dosage according to a DO dynamic equilibrium state, optimizes the operating frequency and intensity of an aeration device by using a prediction model based on feedback control, and adjusts the carbon source dosage to reduce energy consumption and optimize denitrification efficiency, thereby achieving an intelligent and stable deep denitrification wastewater treatment effect.

Description

Technical Field

[0001] The present invention relates to the technical field of wastewater treatment, and in particular to a SND-ED intelligent wastewater treatment deep denitrification system and a control method thereof. Background Art

[0002] With the increase in the discharge of industrial and domestic wastewater, the problem of nitrogen pollution in wastewater has become increasingly serious. Nitrogen pollution, mainly in the form of ammonia nitrogen and nitrate nitrogen, exists in wastewater, which will have serious impacts on the water environment such as eutrophication. To address this problem, traditional denitrification processes usually include staged nitrification and denitrification processes, but these methods have defects such as large space, high energy consumption, and long reaction time. In addition, nitrification and denitrification need to be completed separately in different oxygen environments (nitrification requires oxygen, and denitrification requires anoxic conditions), which brings complexity to process design.

[0003] The SND-ED intelligent deep denitrification system uses simultaneous nitrification and denitrification technology to complete nitrification and denitrification in the same reactor. The system precisely controls the dissolved oxygen concentration, redox potential and carbon source addition, so that the nitrification and denitrification processes can be carried out simultaneously under appropriate conditions, greatly improving the denitrification efficiency. In addition, the SND-ED process combines enhanced denitrification technology and uses specific carbon sources or high-efficiency bacteria to further promote the denitrification process and effectively reduce the total nitrogen content in the effluent.

[0004] To ensure the efficient operation of the system, intelligent control technology has become the key to the SND-ED process. This type of control system combines sensors, automated controllers and advanced algorithms to ensure that the system operates in the best denitrification state through real-time monitoring and dynamic adjustment of treatment parameters. At the same time, the intelligent control system can adapt to different water quality changes and provide a stable denitrification effect. This intelligent deep denitrification system achieves efficient and stable wastewater denitrification with a smaller footprint and lower energy consumption, meeting modern environmental protection needs.

[0005] The prior art has the following deficiencies:

[0006] In the SND-ED system, the nitrification and denitrification processes rely on extremely precise dissolved oxygen (DO) control. However, when the wastewater quality or water volume changes dramatically, the system's dissolved oxygen dynamic balance is easily out of control, resulting in nitrification and denitrification not being able to proceed effectively at the same time. This imbalance may lead to a significant reduction in the removal efficiency of ammonia nitrogen and nitrate nitrogen, ultimately affecting the effluent water quality. At the same time, if the dissolved oxygen dynamic balance cannot be controlled in a timely and effective manner, the system may experience frequent shutdowns and excessive emissions, increasing operating costs. Summary of the invention

[0007] The purpose of the present invention is to provide a SND-ED intelligent wastewater treatment deep denitrification system and a control method thereof to solve the deficiencies in the background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solutions: a SND-ED intelligent wastewater treatment deep denitrification system, including a reactor, a data monitoring module, a control module, an aeration device and a carbon source dosing device. The system realizes simultaneous nitrification and denitrification and enhanced denitrification in the same reactor through intelligent control, specifically including:

[0009] Reactor, used to provide a reaction environment for nitrification and denitrification of wastewater;

[0010] The data monitoring module includes a dissolved oxygen sensor, an oxidation-reduction potential sensor and a temperature sensor, which respectively monitor the dissolved oxygen, oxidation-reduction potential and temperature in the wastewater in real time and transmit the data to the control module;

[0011] The control module receives and analyzes the data transmitted by the data monitoring module, and dynamically adjusts the working state of the aeration device and the carbon source dosing device by using fuzzy logic according to the imbalance state of the redox potential and the abnormal degree of the temperature fluctuation rate during the synchronous reaction of nitrification and denitrification;

[0012] The aeration device is connected to the reactor, and the control module automatically adjusts the aeration intensity and time, and adjusts the dissolved oxygen concentration in real time to match the changes in wastewater quality and water volume;

[0013] The carbon source dosing device automatically adds carbon source into the reactor according to the instructions of the control module, controls the carbon source concentration to optimize the denitrification rate and prevent dissolved oxygen imbalance.

[0014] Preferably, in the control module, the ORP deviation index is generated after analyzing the dynamic fluctuation of ORP and the degree of deviation from the optimal range. The method for obtaining the ORP deviation index is:

[0015] The collected ORP signal is set to be the time series ORP(v), where Represents time, determines the mother wavelet function ψ(v) and the number of decomposition layers J, and uses wavelet transform to decompose the ORP signal into low-frequency and high-frequency components of different scales: ; In the formula, Represents the approximate component of the jth layer, reflecting the low-frequency component, Represents the detail component of the jth layer, reflecting the high-frequency component, for each layer of detail components Calculate its RMS value: ; In the formula, represents the deviation amplitude of the jth layer, It represents the number of sampling points of the signal in the decomposition of the jth layer. The ORP deviation index is defined as the weighted average of the deviation amplitudes of each layer to reflect the overall deviation degree. The expression is: ; Where MK is the ORP deviation index, is the weight coefficient, which indicates the contribution of each layer to the deviation index.

[0016] Preferably, in the control module, the abnormal temperature change rate during the nitrification and denitrification reaction is analyzed to generate a temperature change rate abnormality index, and the method for obtaining the temperature change rate abnormality index is:

[0017] Assume the temperature time series is T(t), where, represents time, and the temperature change rate V(t) is the temperature difference between the current moment and the previous moment: ; In the formula, V(t) represents the temperature change rate at time t, T(t) and T(t−1) are the temperature values ​​at time t and t−1 respectively, and the window size is determined to be w. In each window, there are w continuous temperature change rate data , calculate the mean and standard deviation within the sliding window; use the current temperature change rate V(t) and the mean value within the window and standard deviation Calculate the Z-score: ; In the formula, Z(t) is the Z score of the temperature change rate at time t, which is used to measure the degree to which the current change rate deviates from the average rate in the window. Based on the Z score at each moment, the temperature change rate anomaly index is calculated: ; is the temperature change rate anomaly index, and N represents the total number of moments in the observation period.

[0018] Preferably, according to the imbalance state of the redox potential and the abnormal degree of the temperature fluctuation rate during the synchronous reaction of nitrification and denitrification, the working state of the aeration device and the carbon source dosing device is dynamically regulated by using fuzzy logic, specifically:

[0019] The ORP deviation index MK and the temperature change rate abnormal index DF are used as the input items of fuzzy logic, and the aeration intensity and time of the aeration device and the carbon source dosage of the carbon source dosing device are used as the output items of fuzzy logic;

[0020] The ORP deviation index MK and the temperature change rate abnormal index DF are fuzzified and divided into different fuzzy sets. The output variables of the fuzzy logic are set as the aeration intensity and time of the aeration device and the carbon source dosage of the carbon source dosing device.

[0021] Based on different combinations of MK and DF, fuzzy control rules are established;

[0022] Enter the current fuzzy set of MK and DF into the rule table and match it with all eligible fuzzy logic rules;

[0023] Generate the corresponding fuzzy output according to the rule table, convert the fuzzified output result into actual control instruction, and obtain the specific numerical control output.

[0024] The present invention also provides a SND-ED intelligent wastewater treatment deep denitrification control method, comprising:

[0025] S1: Dissolved oxygen sensor, redox potential sensor and temperature sensor are used to monitor dissolved oxygen, redox potential and temperature in wastewater in real time respectively;

[0026] S2: Analyze the dissolved oxygen, redox potential and temperature in the wastewater, and dynamically adjust the working status of the aeration device and the carbon source dosing device using fuzzy logic according to the imbalance of the redox potential and the abnormal degree of the temperature fluctuation rate during the synchronous reaction of nitrification and denitrification;

[0027] S3: According to the dynamic equilibrium state of dissolved oxygen concentration, the aeration intensity and time are adjusted accordingly to match the changes in wastewater quality and water volume, and carbon source is automatically added to the reactor to control the carbon source concentration to optimize the denitrification rate and prevent dissolved oxygen imbalance;

[0028] S4: Evaluate and predict the dynamic balance control effect of dissolved oxygen concentration, and automatically optimize the operating frequency and carbon source dosage of the aeration device according to the prediction results to reduce energy consumption.

[0029] Preferably, in S4, a prediction model based on feedback control is used, and the model predicts the deviation of DO concentration and optimizes the system. The specific formula is: ; In the formula, is the predicted DO concentration at the next moment, is the DO concentration at the current moment, indicating the actual value of DO in the current system state, α is the feedback gain coefficient, is the DO target concentration, is the current DO concentration measured in real time, β is the change rate coefficient, is the rate of change of DO concentration, defined as the difference between the current and previous DO concentrations;

[0030] According to the predicted DO concentration value , automatically optimize the operating frequency of the aeration device and strength , so that the DO concentration tends to be stable and the energy consumption is minimized, the optimization formula is: ; ; In the formula, Aeration operation frequency, indicating the frequency of opening the aeration device to adjust the oxygen supply frequency. is the maximum value of aeration frequency, indicating the upper limit of the maximum allowable aeration frequency of the system. γ is the frequency adjustment coefficient, which is used to control the sensitivity of frequency adjustment. is the aeration intensity, indicating the amount of aeration. is the maximum value of aeration intensity, indicating the upper limit of aeration volume in the system, and δ is the intensity adjustment coefficient.

[0031] Preferably, the carbon source dosage is automatically adjusted according to the predicted DO concentration change. To optimize the denitrification rate and reduce the waste of carbon sources, the adjustment formula is: ; In the formula, is the carbon source dosage, is the basic dosage of carbon source, and κ is the carbon source adjustment coefficient.

[0032] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0033] 1. The present invention monitors dissolved oxygen, redox potential and temperature in real time, and combines the ORP deviation index and the temperature change rate abnormal index. The system uses fuzzy logic to dynamically control the aeration intensity, time and carbon source dosage, so that the nitrification and denitrification processes can be carried out stably and synchronously. Compared with traditional technologies, it effectively reduces the problem of reduced removal efficiency of ammonia nitrogen and nitrate nitrogen caused by dissolved oxygen imbalance, thereby improving the effluent water quality and reducing the risk of system shutdown and excessive emissions.

[0034] 2. The present invention evaluates and predicts the DO concentration deviation through a prediction model based on feedback control, thereby automatically optimizing the operating frequency and intensity of the aeration device and dynamically adjusting the carbon source dosage, achieving precise control and reducing energy and carbon source consumption. This intelligent control method not only effectively reduces the operating cost of the system, but also ensures efficient and continuous denitrification effects, providing a reliable solution for energy saving and efficiency improvement of wastewater treatment systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0036] Figure 1 The figure is a flow chart of the method of the present invention.

[0037] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] Example 1, please refer to Figure 1 and Figure 2 As shown, the SND-ED intelligent wastewater treatment deep denitrification system described in this embodiment includes a reactor, a data monitoring module, a control module, an aeration device and a carbon source dosing device. The system realizes simultaneous nitrification and denitrification and enhanced denitrification in the same reactor through intelligent control, specifically including:

[0040] Reactor, used to provide a reaction environment for nitrification and denitrification of wastewater;

[0041] The data monitoring module includes a dissolved oxygen sensor, an oxidation-reduction potential sensor and a temperature sensor, which respectively monitor the dissolved oxygen, oxidation-reduction potential and temperature in the wastewater in real time and transmit the data to the control module;

[0042] The control module receives and analyzes the data transmitted by the data monitoring module, and dynamically adjusts the working state of the aeration device and the carbon source dosing device by using fuzzy logic according to the imbalance state of the redox potential and the abnormal degree of the temperature fluctuation rate during the synchronous reaction of nitrification and denitrification;

[0043] The aeration device is connected to the reactor, and the control module automatically adjusts the aeration intensity and time, and adjusts the dissolved oxygen concentration in real time to match the changes in wastewater quality and water volume;

[0044] The carbon source dosing device automatically adds carbon source into the reactor according to the instructions of the control module, controls the carbon source concentration to optimize the denitrification rate and prevent dissolved oxygen imbalance.

[0045] It should be noted that the reactor in the SND-ED (Simultaneous Nitrification, Denitrification and Enhanced Nitrogen Removal) system is a specially designed biological treatment device used to provide a suitable physical and chemical environment for the nitrification and denitrification reactions of wastewater. The design of the reactor takes into account the different conditions required for the nitrification and denitrification processes to achieve efficient nitrogen removal in a single space. The following is the detailed composition and working principle of the reactor:

[0046] The physical structure of the reactor usually includes multiple compartments or layers to accommodate the different needs of nitrification and denitrification. These zones may be designed in the form of oxygen concentration gradients, thereby forming oxidized zones (for nitrification) and anoxic zones (for denitrification) within the reactor. This structure can support different microbial reactions in the same reactor at the same time. Common designs include integrated sequencing batch reactors (SBRs) and multi-stage flow reactors.

[0047] Fillers or biofilm materials suitable for microbial growth are arranged in the reactor to increase biomass and specific surface area. Nitrifying and denitrifying bacteria attach to these fillers and carry out corresponding reactions. Nitrifying bacteria are aerobic bacteria that use fillers to attach to the oxidation zone, while denitrifying bacteria carry out denitrification reactions in the anoxic zone. The selection and arrangement of fillers can make the microbial population in the reactor more stable and provide higher nitrogen removal efficiency.

[0048] Since nitrification and denitrification reactions have different oxygen requirements, reactors are usually equipped with dissolved oxygen control systems. In different areas of the reactor, a gradient distribution of dissolved oxygen (DO) is achieved by adjusting the aeration device. For example, aeration is increased in the nitrification area to increase the DO concentration, while aeration is reduced or not aerated in the denitrification area to maintain anoxic conditions. This DO control system helps keep nitrification and denitrification going simultaneously, reducing the need for multiple reactors.

[0049] Nitrifying and denitrifying bacteria have specific requirements for temperature and pH, so the reactor is also equipped with a temperature control system and a pH adjustment system to ensure that the wastewater temperature and pH are always in the optimal range. For example, the temperature is usually maintained at 20°C-35°C to maintain the activity of nitrifying bacteria. The pH is usually maintained at a neutral to alkaline level to allow the nitrification reaction to proceed smoothly.

[0050] In order to keep the wastewater in contact with the microorganisms, the reactor may be equipped with a stirring device or a water flow diversion system. These devices can prevent excessive accumulation of microorganisms on the surface of the filler and make the DO concentration and nutrients in the reactor more evenly distributed. Control of the stirring speed and water flow direction can improve the reaction efficiency and ensure the stability of the system.

[0051] In the SND-ED system, the main function of the data monitoring module is to monitor key parameters in wastewater in real time through high-precision sensors, including dissolved oxygen (DO), oxidation-reduction potential (ORP) and temperature. The module continuously collects this data and transmits it to the control module, enabling the system to dynamically adjust various treatment conditions according to actual operating conditions to ensure efficient and synchronous nitrification and denitrification processes.

[0052] Dissolved oxygen (DO) sensors are used to detect the dissolved oxygen concentration in wastewater in real time, which is an important indicator for evaluating whether nitrification and denitrification reactions are in optimal conditions. Since the nitrification process requires a higher DO and the denitrification process requires an anoxic environment, precise control of dissolved oxygen is essential for simultaneous denitrification. The working principle of DO sensors is generally based on electrochemical or optical detection methods:

[0053] The electrochemical DO sensor detects dissolved oxygen through electrode reaction. The electrolyte on the sensor surface reacts chemically with the dissolved oxygen in the wastewater, and the electrode current represents the DO concentration. The optical DO sensor uses the principle of fluorescence quenching. The fluorescence emitted by the sensor changes under the influence of dissolved oxygen. The DO concentration in the reactor is determined by detecting the fluorescence change. The sensor transmits the detected DO data to the control module in real time. The control module automatically adjusts the intensity of the aeration device according to the target DO value to ensure that the DO level in the reactor is within the optimal range.

[0054] Oxidation reduction potential (ORP) sensors are used to monitor the redox environment of wastewater. The ORP value reflects the relative concentrations of reducing and oxidizing substances in the wastewater. Nitrification usually occurs at higher ORP values, while denitrification is more active at lower ORP values. By real-time monitoring of ORP, the balance of oxygen and electron donors in the reactor can be evaluated to ensure the synchronization of nitrification and denitrification processes.

[0055] ORP sensors usually use the electrode detection principle: the sensor consists of a reference electrode and a measuring electrode. When placed in wastewater, the measuring electrode reacts with the redox substances in the wastewater, and the resulting potential difference is the ORP value. ORP sensors are very sensitive and react quickly to different chemical substances in the water, so they can reflect the redox state of the wastewater in real time. When the ORP value deviates from the preset range, the sensor transmits data to the control module, triggering the system to adjust aeration or carbon source addition to maintain the appropriate ORP value in the reactor, thereby stabilizing the nitrification and denitrification process.

[0056] Temperature sensors are used to detect the temperature of wastewater in real time, because the nitrification and denitrification processes are sensitive to temperature, and microbial activity can only reach optimal levels within a specific temperature range. Typically, the optimal operating temperature of the SND-ED system is 20°C to 35°C.

[0057] Temperature sensors mostly use the principle of thermal resistors or thermistors: thermal resistor sensors measure temperature by changes in resistance. The higher the temperature, the more resistance changes, and the water temperature can be measured through a calibrated circuit. Thermistor sensors are very sensitive to temperature changes and are suitable for detecting temperature fluctuations and providing timely feedback on changes in microbial adaptability. Temperature data is transmitted to the control module in real time. If the temperature deviates from the optimal range, the control module will adjust the heating or cooling device to keep the wastewater temperature stable and ensure that the activity of nitrifying and denitrifying microorganisms is not affected.

[0058] In the SND-ED intelligent wastewater treatment system, the control module receives data such as oxidation-reduction potential (ORP) and temperature transmitted by the data monitoring module, and uses fuzzy logic algorithm to process these data, dynamically adjusting the aeration device and carbon source dosing device to keep the nitrification and denitrification processes going on synchronously.

[0059] The redox potential (ORP) can reflect the redox environment in the system and indirectly reveal whether the dissolved oxygen in the reactor is at an appropriate level. Nitrification tends to proceed at higher ORP values, while denitrification occurs significantly at lower ORP values. In the SND-ED system, by monitoring the dynamic changes in ORP, it can be determined whether the DO concentration is in a suitable equilibrium state. If the ORP value fluctuates frequently or deviates from the optimal range, this indicates that the DO balance may be out of control.

[0060] The ORP deviation index is generated by analyzing the dynamic fluctuation of ORP and the degree of deviation from the optimal range. The method for obtaining the ORP deviation index is:

[0061] The collected ORP signal is set to be the time series ORP(v), where Represents time. Select a suitable mother wavelet function ψ(v), such as Daubechies wavelet (db4) or Symlet wavelet (sym5), which show good balance when analyzing fluctuation signals. Select an appropriate decomposition layer number J, which is determined according to the data length and the required frequency resolution. Usually, the number of decomposition layers can be between 3 and 5 to ensure that fluctuation information of different time scales is covered.

[0062] The ORP signal is decomposed into low-frequency and high-frequency components of different scales using wavelet transform: ; In the formula, represents the approximate component of the jth layer, reflecting the low-frequency component and indicating a long-term deviation from the trend. Represents the detail component of the jth layer, reflects the high-frequency component, and indicates short-term deviation fluctuation. Calculate its root mean square (RMS) value to quantify the deviation amplitude of the layer: ; In the formula, represents the deviation amplitude of the jth layer, It represents the number of sampling points of the signal in the decomposition of the jth layer. The ORP deviation index is defined as the weighted average of the deviation amplitudes of each layer to reflect the overall deviation degree. The expression is: ; Where MK is the ORP deviation index, is the weight coefficient, which indicates the contribution of each layer to the deviation index. Generally speaking, the lower frequency components (such as ) represents the long-term trend and can be given a higher weight; higher frequency components (such as the high-frequency layer ) represents short-term fluctuations and can be given a lower weight. It can be set to decrease layer by layer based on experience (for example ).

[0063] The larger the ORP deviation index, the greater the volatility and deviation of the ORP value, indicating that the dynamic equilibrium state of the DO concentration is unstable. At this time, the high-frequency fluctuation of the ORP signal or the significant deviation from the optimal range indicates that the redox environment in the system may not be suitable for the balance of simultaneous nitrification and denitrification, and aeration or carbon source addition may need to be adjusted.

[0064] The smaller the ORP deviation index, the smaller the volatility and deviation of the ORP value, indicating that the dynamic equilibrium state of the DO concentration is more stable. At this time, the ORP signal fluctuates less around the optimal range, indicating that the DO concentration in the system is relatively stable, and the nitrification and denitrification processes can proceed simultaneously under good redox conditions.

[0065] Temperature changes can significantly affect microbial activity and reaction rates. If the temperature fluctuates abnormally, the efficiency of nitrification and denitrification will also fluctuate. Abnormal temperature fluctuation rates may indicate instability in system operating conditions.

[0066] The abnormal temperature change rate index is generated by analyzing the abnormal temperature change rate during the nitrification and denitrification reactions. The method for obtaining the abnormal temperature change rate index is as follows:

[0067] Assume the temperature time series is T(t), where, represents time, and the temperature change rate V(t) is the temperature difference between the current moment and the previous moment: ; In the formula, V(t) represents the temperature change rate at time t, T(t) and T(t−1) are the temperature values ​​at time t and t−1 respectively, and the window size is determined to be w, which is usually based on the time characteristics of the system response (for example, a 1 minute or 5 minute window can be selected). In each window, there are w continuous temperature change rate data , calculate the mean and standard deviation within the sliding window; use the current temperature change rate V(t) and the mean value within the window and standard deviation Calculate the Z-score: ; In the formula, Z(t) is the Z score of the temperature change rate at time t, which is used to measure the degree to which the current change rate deviates from the average rate in the window. Based on the Z score at each moment, the temperature change rate anomaly index is calculated: ; It is the temperature change rate anomaly index, which indicates the abnormal degree of temperature change rate during the observation period, and N indicates the total number of moments in the observation period. Ensure that any abnormalities above or below the mean within the window are recorded.

[0068] The larger the temperature change rate anomaly index is, the higher the temperature change rate in the system is, that is, the temperature fluctuates violently or frequently. Such drastic temperature changes will significantly affect the activity of nitrifying and denitrifying microorganisms, because microorganisms are very sensitive to temperature changes, and temperature fluctuations will lead to unstable reaction rates, which in turn cause abnormal changes in the system's dissolved oxygen (DO) demand. Since nitrification and denitrification reactions depend on different oxygen concentration conditions, when the temperature fluctuates frequently, the system's DO demand fluctuates violently in a short period of time, and it is difficult for the control module to accurately adjust the aeration and carbon source dosing devices, making the dynamic equilibrium state of the DO concentration unstable. In short, a higher temperature change rate anomaly index means that the dynamic equilibrium state of the DO concentration is poor, and the system needs to be adjusted frequently to maintain stability.

[0069] When the temperature change rate abnormal index is small, it means that the temperature change rate in the system is relatively stable and there is no significant fluctuation. This stable temperature state is conducive to maintaining the activity of microorganisms at a stable level. Nitrification and denitrification reactions can be carried out in a relatively constant temperature environment, and the DO demand remains stable. Therefore, the control module can more easily adjust the DO concentration so that the system maintains a better dynamic equilibrium state. A lower temperature change rate abnormal index usually indicates that the DO concentration in the system is in a good state of balance, and the frequency and amplitude of DO concentration regulation are small, which helps to maintain the denitrification efficiency and stable operation of the system.

[0070] The ORP deviation index MK and the temperature change rate abnormal index DF are used as the input items of fuzzy logic, and the aeration intensity and time of the aeration device and the carbon source dosage of the carbon source dosing device are used as the output items of fuzzy logic;

[0071] The ORP deviation index MK and the temperature change rate abnormality index DF are fuzzyized and divided into different fuzzy sets, for example:

[0072] Fuzzy set of MK: low (L), medium (M), high (H).

[0073] Fuzzy sets of DF: low (L), medium (M), high (H).

[0074] Fuzzification rules: According to the current MK and DF values, the specific values ​​are fuzzified. For example:

[0075] If MK is within a small range, it is classified as "low", if it is within a medium range, it is classified as "medium", and if it is above a certain value, it is classified as "high". If DF fluctuates less, it is classified as "low", and if the frequency of fluctuation is high, it is classified as "high".

[0076] Determine fuzzy output variables: Set the output variables of fuzzy logic to be the aeration intensity and time of the aeration device, and the carbon source dosage of the carbon source dosing device. Aeration intensity (AI): low (L), medium (M), high (H). Aeration time (AT): short (S), medium (M), long (L). Carbon source dosage (C): small amount (S), medium amount (M), large amount (L).

[0077] Based on different combinations of MK and DF, fuzzy control rules are established, including:

[0078] Rule 1: If MK is "high" and DF is "high", the aeration intensity is set to "high", the aeration time is set to "long", and the carbon source dosage is set to "small".

[0079] Explanation: When both ORP deviation and temperature anomaly are high, the simultaneous demands for nitrification and denitrification are unbalanced. It is prioritized to increase aeration to increase DO concentration, while reducing carbon source addition to avoid excess carbon source caused by temperature fluctuations.

[0080] Rule 2: If MK is "high" and DF is "low", the aeration intensity is set to "medium", the aeration time is set to "medium", and the carbon source dosage is set to "medium".

[0081] Explanation: When ORP deviates to a high level and temperature fluctuations are low, the system mainly has redox imbalance. The DO concentration is adjusted by moderate aeration intensity and time, and an appropriate amount of carbon source is added to promote denitrification.

[0082] Rule 3: If MK is "low" and DF is "high", the aeration intensity is set to "medium", the aeration time is set to "short", and the carbon source dosage is set to "medium".

[0083] Explanation: When ORP deviation is small but temperature fluctuation is large, the system DO is relatively balanced but temperature fluctuation may affect DO demand. Appropriate aeration and moderate carbon source addition can help stabilize the reaction.

[0084] Rule 4: If MK is "low" and DF is "low", the aeration intensity is set to "low", the aeration time is set to "short", and the carbon source dosage is set to "small".

[0085] When ORP deviation and temperature fluctuation are small, it means that the system is stable, and low-intensity, short-time aeration and a small amount of carbon source addition can maintain DO balance.

[0086] The fuzzy sets of the current MK and DF are input into the rule table and matched with all the fuzzy logic rules that meet the conditions.

[0087] Calculate fuzzy output: Generate corresponding fuzzy output according to the rule table. For example, if MK is "high" and DF is "medium", the rule recommends aeration intensity as "medium", aeration time as "long", and carbon source dosage as "medium".

[0088] The fuzzified output is converted into actual control instructions. Defuzzification methods such as the centroid method or the maximum membership method can be used to obtain specific numerical control outputs.

[0089] Generate control instructions: Aeration intensity: Control the air volume or pressure of the aeration device according to the defuzzified numerical output. Aeration time: Adjust the aeration duration according to the output time parameter. Carbon source dosage: Accurately adjust the carbon source dosage according to the defuzzified result.

[0090] Execute control and feedback adjustment: Command execution: send the defuzzified aeration intensity, time and carbon source dosage instructions to the aeration device and carbon source dosage device. Real-time feedback: continuously monitor ORP and temperature data, analyze the changes of MK and DF in real time, and judge the control effect. Feedback adjustment: if the DO or ORP deviation is still not stable within the predetermined time, automatically fine-tune the aeration and carbon source dosage until the system returns to the target equilibrium state.

[0091] The aeration device is connected to the reactor. It is an important part of the control system. It provides oxygen for the nitrification reaction through the connection with the reactor to maintain an appropriate dissolved oxygen (DO) concentration. The control methods of the aeration device are divided into the following two aspects:

[0092] Automatically adjust aeration intensity: Aeration intensity directly determines the dissolved oxygen level in the reactor. The control module monitors ORP, DO and water quality parameters in real time. When the nitrification reaction requires more oxygen, the control module will increase the aeration intensity and increase the DO concentration; when the DO concentration is too high and may inhibit denitrification, the control module will reduce the aeration intensity to maintain a suitable oxygen environment.

[0093] Automatically control aeration time: Aeration time determines the continuous level of DO concentration. The control module automatically adjusts the duration of aeration according to the amount of wastewater and the concentration of pollutants. For wastewater with poor water quality, the aeration time will be extended, and vice versa, the aeration time will be shortened to avoid energy waste. This can not only maintain the balance of nitrification and denitrification, but also effectively reduce operating costs.

[0094] The carbon source dosing device automatically adjusts the carbon source concentration. The denitrification process requires an appropriate amount of carbon source to promote the conversion of nitrate nitrogen into nitrogen gas, thereby reducing the total nitrogen content in the wastewater. The carbon source dosing device automatically adjusts the amount of carbon source added in the reactor according to the instructions of the control module in order to achieve the ideal denitrification rate. The specific control method is as follows:

[0095] Precisely control the amount of carbon source added: The control module will automatically set the amount of carbon source added according to parameters such as ORP, DO and ammonia nitrogen concentration to meet the needs of the denitrification process. If the denitrification rate is insufficient, the control module will increase the amount of carbon source added; if the denitrification process is strong or DO is high, the system will reduce the amount of carbon source added to prevent an imbalance in oxygen demand caused by excessive carbon source.

[0096] Avoid dissolved oxygen imbalance: Excessive addition of carbon source may consume too much oxygen, causing the DO concentration to drop and affecting the nitrification reaction. Therefore, the carbon source addition device will accurately control the carbon source concentration on the premise of meeting the denitrification demand to avoid DO imbalance and ensure the coordinated progress of nitrification and denitrification reactions.

[0097] The control module monitors and adjusts the working status of the aeration device and carbon source dosing device in real time: the control module analyzes the real-time changes of parameters such as ORP, DO and temperature to determine whether it is necessary to adjust the aeration intensity, aeration time or carbon source dosage. Through closed-loop feedback regulation, it ensures that when the wastewater quality and water volume fluctuate, the DO concentration is always at an appropriate level, the denitrification rate is optimized, and the system operates stably and efficiently.

[0098] In this embodiment, the reactor provides the necessary reaction environment for nitrification and denitrification. The data monitoring module monitors the key parameters in the wastewater in real time through dissolved oxygen, oxidation-reduction potential (ORP) and temperature sensors, and transmits the data to the control module. The control module receives and analyzes these data, and dynamically adjusts the working status of the aeration device and the carbon source dosing device by detecting the imbalance state of ORP and the abnormality of the temperature fluctuation rate using fuzzy logic. The aeration device is connected to the reactor, and the control module automatically adjusts the intensity and time of aeration to adjust the dissolved oxygen concentration in real time to match the changes in the water quality and water volume of the wastewater. The carbon source dosing device accurately adds carbon source according to the instructions of the control module to optimize the denitrification rate and avoid dissolved oxygen imbalance, thereby ensuring efficient and stable operation of the system and achieving synchronous denitrification treatment.

[0099] Example 2: A SND-ED intelligent wastewater treatment deep denitrification control method described in this example comprises the following steps:

[0100] S1: Dissolved oxygen sensor, redox potential sensor and temperature sensor are used to monitor dissolved oxygen, redox potential and temperature in wastewater in real time respectively;

[0101] S2: Analyze the dissolved oxygen, redox potential and temperature in the wastewater, and dynamically adjust the working status of the aeration device and the carbon source dosing device using fuzzy logic according to the imbalance of the redox potential and the abnormal degree of the temperature fluctuation rate during the synchronous reaction of nitrification and denitrification;

[0102] S3: According to the dynamic equilibrium state of dissolved oxygen concentration, the aeration intensity and time are adjusted accordingly to match the changes in wastewater quality and water volume, and carbon source is automatically added to the reactor to control the carbon source concentration to optimize the denitrification rate and prevent dissolved oxygen imbalance;

[0103] S4: Evaluate and predict the dynamic balance control effect of dissolved oxygen concentration, and automatically optimize the operating frequency and carbon source dosage of the aeration device according to the prediction results to reduce energy consumption.

[0104] A prediction model based on feedback control is used, which predicts the deviation of DO concentration and optimizes the system. The specific formula is: ; In the formula, It is the predicted DO concentration at the next moment, which is used to evaluate the future DO state of the system. is the DO concentration at the current moment, indicating the actual value of DO under the current system state. α is the feedback gain coefficient, which is used to adjust the impact of the deviation between the target and actual DO concentration on the prediction, reflecting the sensitivity of the system to errors. is the DO target concentration, usually the median of the optimal DO concentration range set by the system to maintain the balance between nitrification and denitrification. is the current DO concentration measured in real time, β is the change rate coefficient, which indicates the influence of DO change rate on the predicted value, is the rate of change of DO concentration, defined as the difference between the current and previous DO concentrations;

[0105] According to the predicted DO concentration value , automatically optimize the operating frequency of the aeration device and strength , making the DO concentration stable and the energy consumption minimal. ; ; In the formula, Aeration operation frequency, indicating the frequency of opening the aeration device to adjust the oxygen supply frequency. is the maximum value of the aeration frequency, indicating the upper limit of the maximum allowable aeration frequency of the system. γ is the frequency adjustment coefficient, which is used to control the sensitivity of the frequency adjustment and is usually set according to the response speed of the actual system. Aeration intensity indicates the amount of aeration, which directly affects the rate of increase of DO concentration in the system. is the maximum value of aeration intensity, indicating the upper limit of aeration volume in the system; δ is the intensity adjustment coefficient, which is used to control the sensitivity of intensity adjustment and is usually set according to the energy consumption requirements of the system.

[0106] Automatically adjust the carbon source dosage according to the predicted DO concentration changes To optimize the denitrification rate and reduce the waste of carbon sources: ; In the formula, It is the amount of carbon source added, used to maintain the stable progress of denitrification. is the basic dosage of carbon source, which is the minimum carbon source requirement set by the system. κ is the carbon source adjustment coefficient, which reflects the effect of DO deviation on the carbon source dosage, and is usually adjusted according to the demand for denitrification rate.

[0107] In this embodiment, the system collects real-time DO data, predicts the DO concentration at the next moment according to the feedback formula, and adjusts the aeration frequency and intensity according to the predicted DO value to keep the DO concentration within the target range. The carbon source dosage is adjusted according to the DO deviation to ensure that the denitrification rate is optimized while preventing DO imbalance. The DO prediction value is continuously updated according to the actual monitoring data, and the aeration device and the carbon source addition device are dynamically adjusted to ensure that the DO concentration is within the optimal range and achieve stable operation with low energy consumption.

[0108] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0109] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0110] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0111] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A SND-ED intelligent wastewater treatment deep denitrification system, characterized by: The system comprises a reactor, a data monitoring module, a control module, an aeration device and a carbon source dosing device. The system realizes simultaneous nitrification and denitrification and enhanced denitrification in the same reactor through intelligent control, specifically including: Reactor, used to provide a reaction environment for nitrification and denitrification of wastewater; The data monitoring module includes a dissolved oxygen sensor, an oxidation-reduction potential sensor and a temperature sensor, which respectively monitor the dissolved oxygen, oxidation-reduction potential and temperature in the wastewater in real time and transmit the data to the control module; The control module receives and analyzes the data transmitted by the data monitoring module, and dynamically adjusts the working state of the aeration device and the carbon source dosing device by using fuzzy logic according to the imbalance state of the redox potential and the abnormal degree of the temperature fluctuation rate during the synchronous reaction of nitrification and denitrification; According to the imbalance of redox potential and the abnormal degree of temperature fluctuation rate in the synchronous reaction of nitrification and denitrification, the working state of the aeration device and the carbon source dosing device are dynamically regulated by using fuzzy logic, specifically: The ORP deviation index MK and the temperature change rate abnormal index DF are used as the input items of fuzzy logic, and the aeration intensity and time of the aeration device and the carbon source dosage of the carbon source dosing device are used as the output items of fuzzy logic; The ORP deviation index MK and the temperature change rate abnormal index DF are fuzzified and divided into different fuzzy sets. The output variables of the fuzzy logic are set as the aeration intensity and time of the aeration device and the carbon source dosage of the carbon source dosing device. Based on different combinations of MK and DF, fuzzy control rules are established; Enter the current fuzzy set of MK and DF into the rule table and match it with all eligible fuzzy logic rules; Generate corresponding fuzzy output according to the rule table, convert the fuzzified output result into actual control instruction, and obtain specific numerical control output; The aeration device is connected to the reactor, and the control module automatically adjusts the aeration intensity and time, and adjusts the dissolved oxygen concentration in real time to match the changes in wastewater quality and water volume; The carbon source dosing device automatically adds carbon source into the reactor according to the instructions of the control module, controls the carbon source concentration to optimize the denitrification rate and prevent dissolved oxygen imbalance.

2. The SND-ED intelligent wastewater treatment deep denitrification system according to claim 1 is characterized by: In the control module, the ORP deviation index is generated after analyzing the dynamic fluctuation of ORP and the degree of deviation from the optimal range. The method for obtaining the ORP deviation index is: The collected ORP signal is set to be the time series ORP(v), where Represents time, determines the mother wavelet function ψ(v) and the number of decomposition layers J, and uses wavelet transform to decompose the ORP signal into low-frequency and high-frequency components of different scales: ; In the formula, Represents the approximate component of the jth layer, reflecting the low-frequency component, Represents the detail component of the jth layer, reflecting the high-frequency component, for each layer of detail components Calculate its RMS value: ; In the formula, represents the deviation amplitude of the jth layer, It represents the number of sampling points of the signal in the decomposition of the jth layer. The ORP deviation index is defined as the weighted average of the deviation amplitudes of each layer to reflect the overall deviation degree. The expression is: ; Where MK is the ORP deviation index, is the weight coefficient, which indicates the contribution of each layer to the deviation index.

3. The SND-ED intelligent wastewater treatment deep denitrification system according to claim 2 is characterized by: In the control module, the abnormal temperature change rate during the nitrification and denitrification reaction is analyzed to generate the abnormal temperature change rate index. The method for obtaining the abnormal temperature change rate index is as follows: Assume the temperature time series is T(t), where, represents time, and the temperature change rate V(t) is the temperature difference between the current moment and the previous moment: ; In the formula, V(t) represents the temperature change rate at time t, T(t) and T(t−1) are the temperature values ​​at time t and t−1 respectively, and the window size is determined to be w. In each window, there are w continuous temperature change rate data , calculate the mean and standard deviation within the sliding window; use the current temperature change rate V(t) and the mean value within the window and standard deviation Calculate the Z-score: ; In the formula, Z(t) is the Z score of the temperature change rate at time t, which is used to measure the degree to which the current change rate deviates from the average rate in the window. Based on the Z score at each moment, the temperature change rate anomaly index is calculated: ; is the temperature change rate anomaly index, and N represents the total number of moments in the observation period.

4. A SND-ED intelligent wastewater treatment deep denitrification control method, used to implement a SND-ED intelligent wastewater treatment deep denitrification system according to any one of claims 1 to 3, characterized in that: S1: Dissolved oxygen sensor, redox potential sensor and temperature sensor are used to monitor dissolved oxygen, redox potential and temperature in wastewater in real time respectively; S2: Analyze the dissolved oxygen, redox potential and temperature in the wastewater, and dynamically adjust the working status of the aeration device and the carbon source dosing device using fuzzy logic according to the imbalance of the redox potential and the abnormal degree of the temperature fluctuation rate during the synchronous reaction of nitrification and denitrification; S3: According to the dynamic equilibrium state of dissolved oxygen concentration, the aeration intensity and time are adjusted accordingly to match the changes in wastewater quality and water volume, and carbon source is automatically added to the reactor to control the carbon source concentration to optimize the denitrification rate and prevent dissolved oxygen imbalance; S4: Evaluate and predict the dynamic balance control effect of dissolved oxygen concentration, and automatically optimize the operating frequency and carbon source dosage of the aeration device according to the prediction results to reduce energy consumption.

5. A SND-ED intelligent wastewater treatment deep denitrification control method according to claim 4, characterized in that: In S4, a prediction model based on feedback control is used. The model predicts the deviation of DO concentration and optimizes the system. The specific formula is: ; In the formula, is the predicted DO concentration at the next moment, is the DO concentration at the current moment, indicating the actual value of DO in the current system state, α is the feedback gain coefficient, is the DO target concentration, is the current DO concentration measured in real time, β is the change rate coefficient, is the rate of change of DO concentration, defined as the difference between the current and previous DO concentrations; According to the predicted DO concentration value , automatically optimize the operating frequency of the aeration device and strength , so that the DO concentration tends to be stable and the energy consumption is minimized, the optimization formula is: ; ; In the formula, The aeration operation frequency indicates the frequency of opening the aeration device to adjust the oxygen supply frequency. is the maximum value of aeration frequency, indicating the upper limit of the maximum allowable aeration frequency of the system. γ is the frequency adjustment coefficient, which is used to control the sensitivity of frequency adjustment. is the aeration intensity, indicating the amount of aeration. is the maximum value of aeration intensity, indicating the upper limit of aeration volume in the system, and δ is the intensity adjustment coefficient.

6. A SND-ED intelligent wastewater treatment deep denitrification control method according to claim 5, characterized in that: Automatically adjust the carbon source dosage according to the predicted DO concentration changes To optimize the denitrification rate and reduce the waste of carbon sources, the adjustment formula is: ; In the formula, is the carbon source dosage, is the basic dosage of carbon source, and κ is the carbon source adjustment coefficient.

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