Wastewater treatment control process based on dynamic regulation and control of aeration and carbon source addition

Abstract

This invention relates to the field of wastewater treatment technology, particularly a wastewater treatment control process based on dynamic regulation of aeration and carbon source addition. The process includes the following steps: real-time acquisition of microbial metabolic heat change data; biochemical treatment, real-time monitoring of the water's dielectric constant and wastewater flow Reynolds number, combined with the microbial metabolic heat change data acquired in the pretreatment step, constructing a multi-parameter coupled regulation model and dynamically regulating the aeration rate; precise addition of carbon source based on the calculation results of the carbon source demand prediction model; and after biochemical treatment, the wastewater is first ultrafiltration through a nanofiber membrane, followed by combined ultraviolet and ozone disinfection. During the treatment process, treatment parameters are adjusted in real-time according to the membrane flux decay trend to achieve dynamic and precise control of wastewater treatment. This method solves the problems of energy waste caused by excessive aeration or low degradation efficiency caused by insufficient aeration in traditional processes, achieving precise matching between dissolved oxygen supply and microbial demand.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, and in particular to a wastewater treatment control process based on dynamic regulation of aeration and carbon source addition. Background Technology

[0002] In the field of wastewater treatment, aeration and carbon source addition are the core factors affecting treatment efficiency and cost, but existing processes have significant technical bottlenecks.

[0003] First, regarding aeration control: traditional processes often rely on adjusting dissolved oxygen (DO) concentration as a single parameter, which cannot accurately match the actual oxygen demand of microorganisms, easily leading to over- or under-aeration. Over-aeration results in wasted energy and inhibits microbial activity, while under-aeration reduces pollutant degradation efficiency and makes it difficult for effluent to meet standards. Moreover, existing technologies do not consider the impact of wastewater flow on oxygen mass transfer efficiency, making precise control difficult in practice.

[0004] Second, in the carbon source addition stage: existing carbon source addition methods generally estimate the addition amount based on chemical oxygen demand (COD) and biochemical oxygen demand (BOD) indicators. However, this method can only reflect the total organic matter content and cannot meet the differentiated needs of microorganisms for carbon source types and quantities at different stages. It is easy to cause carbon source waste or poor denitrification effect, which often increases treatment costs and subsequent environmental risks.

[0005] Therefore, it is necessary to design a wastewater treatment control process based on dynamic regulation of aeration and carbon source addition. Summary of the Invention

[0006] To solve one of the aforementioned technical problems, the present invention employs a wastewater treatment control process based on dynamic regulation of aeration and carbon source addition, comprising the following steps: Step 1, pretreatment: Wastewater undergoes preliminary treatment by sequentially passing through a screen and a grit chamber. Then, data on the extracellular electron transport rate of microorganisms in the wastewater are acquired, and real-time data on microbial metabolic heat changes are acquired using an infrared thermal imager. Specifically, the infrared thermal imager is used to monitor the microbial metabolic process in real time, and metabolic heat change data are collected at multiple preset time points. These time points include, but are not limited to, the start time of the microbial culture cycle, the inflection point of the logarithmic growth phase, the peak time of the stationary phase, and the initial time of the decline phase. The multiple preset time points in this application represent selective extraction of continuously monitored data, a common knowledge balancing data integrity and resource efficiency.

[0007] Step 2, Biochemical treatment: Real-time monitoring of the dielectric constant of the water and the Reynolds number of the wastewater flow, combined with the microbial metabolic heat change data obtained in the pretreatment step, to construct a multi-parameter coupled regulation model and dynamically regulate the aeration rate.

[0008] Meanwhile, a carbon source demand prediction model is established based on redox potential, target aeration rate, and polycyclic aromatic hydrocarbon concentration. The carbon source is then accurately added based on the calculation results of the carbon source demand prediction model.

[0009] Step 3, Advanced Treatment: The wastewater after biochemical treatment is first ultrafiltration through a nanofiber membrane, and then disinfected by a combination of ultraviolet light and ozone. During the treatment process, the treatment parameters are adjusted in real time according to the membrane flux decay trend to achieve dynamic and precise control of wastewater treatment.

[0010] This method uses high-resolution thermal imaging and physical equation inversion to accurately quantify the metabolic state of microorganisms, providing an intuitive basis for the regulation of aeration and carbon source addition, and solving the limitations of single-point and qualitative detection in traditional methods. It couples infrared thermal imaging, microfluidic detection and multi-parameter models to form a closed-loop control of metabolic heat, dissolved oxygen and membrane fouling.

[0011] Based on any of the above technical solutions, a further optimization is made to the method of constructing the multi-parameter coupled control model in step 2: establishing the water dielectric constant ε, the sewage flow Reynolds number, etc. With volume dissolved oxygen coefficient The nonlinear mapping relationship is given by the following formula: .

[0012] in, ε is the volumetric dissolved oxygen coefficient (the rate constant of oxygen transfer from the gas phase to the liquid phase per unit time and unit volume in a gas-liquid system, directly characterizing the supply efficiency of dissolved oxygen available for microbial metabolism, and a key parameter for regulating the metabolic activity and metabolic heat flux density of aerobic microorganisms in wastewater), and ε is the dielectric constant of the water body. As a reference dielectric constant, Wastewater dynamic viscosity, Wastewater density, Oxygen molecule diffusion coefficient (the rate constant of spontaneous diffusion of oxygen in wastewater, reflecting the mass transfer capacity at the molecular level). All are exponential coefficients. This is a correction factor.

[0013] Correction coefficient Exponential coefficient All calibrations were performed according to the dynamic dissolved oxygen method in industry standard HJ506—2009 and relevant industry experience, and are applicable to municipal microporous aeration tanks. The exponential coefficient reflects the degree of influence of the corresponding parameter on the volumetric dissolved oxygen coefficient. The dielectric constant of the water body was measured using a parallel plate capacitance sensor.

[0014] The difference between this embodiment and the prior art is that traditional models only focus on achieving dissolved oxygen standards, while this method deeply couples the volumetric dissolved oxygen coefficient with microbial metabolic heat flux. It quantifies dissolved oxygen supply through the volumetric dissolved oxygen coefficient and correlates it with the rate of microbial metabolic heat production, thereby achieving a closed loop of dissolved oxygen, metabolic heat, and process control. For example, based on the collected metabolic heat change data, the local volumetric dissolved oxygen coefficient is dynamically adjusted and the activity of the microbial community is optimized, which changes the limitations of traditional extensive oxygen supply and ignoring the heterogeneity of the microbial community.

[0015] Based on any of the above technical solutions, a further optimization is made: the steps for dynamically controlling the aeration rate are as follows: real-time collection of the following parameters: water dielectric constant, wastewater flow velocity, temperature, and dissolved oxygen concentration; inputting the collected parameters into a multi-parameter coupled control model to calculate the volumetric dissolved oxygen coefficient. .

[0016] According to the formula Calculate the target aeration rate Based on the obtained target aeration rate The aeration volume is controlled by adjusting the speed of the aeration fan through a frequency converter.

[0017] in, For the volume of the reaction tank, For saturated dissolved oxygen concentration, This represents the real-time dissolved oxygen concentration.

[0018] Calculate the target aeration rate The units of all parameters in the formula are common international units and selected as needed while taking into account dimensional consistency.

[0019] Based on any of the above technical solutions, a further optimization is made: The formula for establishing a carbon source demand prediction model based on redox potential, target aeration rate, and polycyclic aromatic hydrocarbon concentration is as follows: The refined formula after substituting the parameters obtained above is: Where M is the carbon source dosage (mg / L), E is the redox potential (mV), and Q is the target aeration rate (mg / L). ), Polycyclic aromatic hydrocarbon concentration (μg / L) For regression coefficients, This is a constant term.

[0020] Based on any of the above technical solutions, the following further optimizations are made: the bar screen is a rotary mechanical bar screen with a bar spacing of 5mm; the sedimentation tank is a vortex sedimentation tank with a hydraulic retention time of 2-3 minutes, and the sediment is separated by a sand-water separator.

[0021] In this embodiment, the rotary mechanical bar screen with a 5mm bar gap can efficiently intercept larger suspended solids and floating matter in sewage. Compared with a wider gap bar screen, it can reduce the impurity load of subsequent treatment units, and the mechanical rotary structure facilitates automatic sludge removal, reducing manual maintenance costs. The vortex grit chamber utilizes the hydrodynamic vortex effect, and the 2-3 minute hydraulic retention time can shorten the retention time while ensuring effective sedimentation of sand particles, thus improving pretreatment efficiency. Combined with the sand-water separator, it can achieve efficient separation of grit and sewage. The separated grit has low water content and few impurities, making it easy to dispose of in subsequent processes. The entire pretreatment unit works in concert, laying a stable water quality foundation for subsequent biological treatment, precise aeration, and carbon source addition, and reducing the interference of large particles and excessive sand particles on equipment and process parameters.

[0022] Based on any of the above technical solutions, the following further optimizations are made: In the deep treatment step, the operating pressure of the nanofiber membrane ultrafiltration is 0.1-0.3MPa, the membrane pore size is 0.01-0.1μm; the wavelength of ultraviolet disinfection is 253.7nm, the irradiation dose is 15-30mJ / cm², and the dosage of ozone disinfection is 5-10mg / L.

[0023] Based on any of the above technical solutions, the following further optimizations are made: in the pretreatment step, the grid gap is 5-10mm, the hydraulic retention time in the sedimentation tank is 10-15min, and the detection cycle of the extracellular electron transfer rate data of the microorganisms is once every 30min.

[0024] The improved pretreatment removal rate reduces the impurity load in the biological treatment tank, making the monitoring of parameters such as dielectric constant and Reynolds number of the water body more stable. When the extracellular electron transfer rate of microorganisms increases (such as when microbial activity is enhanced), the model automatically increases the amount of carbon source added to maintain the metabolic balance between denitrifying bacteria and polyphosphate-accumulating bacteria, ensuring the removal rate of TN and TP.

[0025] Based on any of the above technical solutions, the following optimization is made: The steps to achieve dynamic and precise control of wastewater treatment by adjusting the treatment parameters in real time according to the membrane flux decay trend are as follows: real-time data acquisition and decay trend judgment: every 15-30 minutes, the membrane pressure difference of the nanofiber membrane ultrafiltration unit, the concentration of suspended solids in the influent, the cumulative running time, and the influent temperature and COD concentration are collected.

[0026] When the membrane pressure difference increases by 15%-20% from the initial value, it is determined that the membrane fouling has entered a significant decay stage.

[0027] Activate multi-unit linkage control: If the concentration of suspended solids in the influent exceeds 50 mg / L, extend the hydraulic retention time of the vortex grit chamber from the usual 2-3 min to 3-5 min, and use a 5 mm gap rotary mechanical bar to enhance the interception of suspended solids and reduce particulate pollution on the membrane surface.

[0028] If the concentration of suspended solids in the influent exceeds 300 mg / L, the aeration rate will be dynamically adjusted in accordance with the above method to increase the supply of dissolved oxygen, in conjunction with the precise aeration and carbon source addition processes.

[0029] At the same time, the carbon source addition will be increased by 10%-15% according to the carbon source demand prediction model to optimize the degradation efficiency of organic matter by microorganisms and reduce the organic load on the membrane surface.

[0030] The membrane module is started by air-water backwashing, during which air and water are mixed to wash and peel off the filter cake layer on the membrane surface.

[0031] Monitor the membrane pressure differential recovery within 30 minutes after backwashing.

[0032] If the membrane pressure difference drops to within 110% of the initial value and remains stable, the control is deemed effective; otherwise, chemical cleaning is initiated.

[0033] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0034] 1. This process breaks through the traditional single dissolved oxygen (DO) parameter control mode. By coupling multiple parameters such as water dielectric constant and Reynolds number through modeling, the aeration rate is dynamically adjusted. It can respond and adjust the aeration rate in real time, solving the problems of energy waste caused by excessive aeration or low degradation efficiency caused by insufficient aeration in traditional processes, and achieving precise matching between dissolved oxygen supply and microbial demand.

[0035] 2. Precise carbon source dosing reduces waste and improves nitrogen and phosphorus removal efficiency. A quaternary linear prediction model is constructed based on redox potential, target aeration rate, and polycyclic aromatic hydrocarbon (PAH) concentration, replacing the traditional estimation method that relies solely on COD / BOD indicators. When PAH concentration increases and inhibits microbial activity, the carbon source dosage can be automatically increased through coefficient compensation, reducing carbon source dosing error compared to traditional methods. Simultaneously, by dynamically matching the carbon source requirements of the nitrification / denitrification stages through redox potential, the increased treatment costs and environmental risks caused by improper carbon source dosing are reduced.

[0036] 3. Establish a predictive control mechanism for microbial metabolic state and membrane fouling trend: By linking the detection of extracellular electron transfer rate of microorganisms (cycle 30min) with the monitoring of membrane flux decay, when the membrane pressure difference increases by 15%, the residence time in the grit chamber is extended to 3-5min simultaneously, and air-water backwashing is initiated.

[0037] 4. Metabolic heat change data are directly acquired using an infrared thermal imager, and combined with parameters such as the water's dielectric constant and Reynolds number to form a multiphysics coupling model. When water quality changes abruptly (such as a sudden increase in COD or excessive suspended solids concentration), the system can dynamically adjust the aeration rate and carbon source dosage within 10-15 minutes, reducing the response time by more than 50% compared to traditional processes. This ensures stable treatment results when facing industrial wastewater impacts. Attached Figure Description

[0038] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or components are generally identified by similar reference numerals. In the drawings, the elements or components are not necessarily drawn to scale.

[0039] Figure 1 The flowchart of the wastewater treatment control process based on dynamic regulation of aeration and carbon source addition provided in this application. Detailed Implementation

[0040] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation methods, features, and effects of an automated control method and system for electrophoretic coloring of aluminum profiles proposed in this application. In the following description, different embodiments or one embodiment do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0041] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0042] Example: Please refer to Figure 1 This application illustrates a wastewater treatment control process based on dynamic aeration and carbon source addition, provided by an embodiment of this application. The process includes the following steps: Step 1, Pretreatment: Wastewater undergoes preliminary treatment by sequentially passing through a screen and a grit chamber. Then, data on the extracellular electron transport rate of microorganisms in the wastewater is acquired, and real-time data on microbial metabolic heat changes is acquired using an infrared thermal imager. Specifically, the infrared thermal imager is used to monitor the microbial metabolic process in real time, and metabolic heat change data is collected at multiple preset time points. These time points include, but are not limited to, the start time of the microbial culture cycle, the inflection point of the logarithmic growth phase, the peak time of the stationary phase, and the initial time of the decline phase. The multiple preset time points in this application represent selective extraction of continuously monitored data, a common-sense approach balancing data integrity and resource efficiency.

[0043] Step 2, Biochemical treatment: Real-time monitoring of the dielectric constant of the water and the Reynolds number of the wastewater flow, combined with the microbial metabolic heat change data obtained in the pretreatment step, to construct a multi-parameter coupled regulation model and dynamically regulate the aeration rate.

[0044] Meanwhile, a carbon source demand prediction model is established based on redox potential, target aeration rate, and polycyclic aromatic hydrocarbon concentration. The carbon source is then accurately added based on the calculation results of the carbon source demand prediction model.

[0045] Step 3, Advanced Treatment: The wastewater after biochemical treatment is first ultrafiltration through a nanofiber membrane, and then disinfected by a combination of ultraviolet light and ozone. During the treatment process, the treatment parameters are adjusted in real time according to the membrane flux decay trend to achieve dynamic and precise control of wastewater treatment.

[0046] This process adopts a precise control logic with multi-dimensional parameter coupling, breaking through the traditional single-parameter control mode of sewage treatment, and constructing a full-chain data linkage mechanism of pretreatment-biochemical-deep treatment: by acquiring the metabolic heat distribution of microorganisms (temperature gradient reflects metabolic activity) through infrared thermal imaging, combined with parameters such as water dielectric constant (characterizing water quality components) and Reynolds number (fluid state), a multi-physics field coupled control model is formed.

[0047] By linking the metabolic activity of aerobic microorganisms with the volumetric dissolved oxygen coefficient, a closed-loop regulation of microbial activity, dissolved oxygen supply, and aeration rate is achieved, solving the problem of the disconnect between dissolved oxygen supply and microbial demand in traditional processes.

[0048] In the pretreatment stage, antibody-magnetic microsphere technology is used to accurately detect the extracellular electron transfer rate of microorganisms, providing a basis for metabolic activity in carbon source addition. In the deep treatment stage, based on the trend of membrane flux decay, the residence time of the sedimentation tank is adjusted, the aeration rate is optimized, and the membrane is backwashed, forming a three-level prevention and control system of source pollutant interception, biochemical metabolic optimization, and membrane fouling remediation.

[0049] When the membrane pressure difference increases by 15%, the residence time in the grit chamber is extended to 3-5 minutes to reduce the impact of suspended solids on the membrane, which significantly improves efficiency compared to traditional independent control.

[0050] This process, when faced with a sudden surge in influent COD from 500 mg / L to 800 mg / L, can maintain effluent COD below 50 mg / L by adjusting aeration and carbon source dosage in real time, while significantly shortening the response time. It deeply couples aerobic microbial metabolic activity with the volumetric dissolved oxygen coefficient, dynamically adjusting the local volumetric dissolved oxygen coefficient based on collected metabolic heat change data. This solves the problem of uneven oxygen supply caused by microbial heterogeneity in traditional processes, thereby enhancing aerobic microbial activity.

[0051] The detection cycle for the extracellular electron transfer rate of microorganisms is set to 30 minutes, which matches the time scale of membrane flux decay monitoring, enabling predictive regulation of microbial metabolic state and membrane fouling trend, providing early warning compared to passive cleaning.

[0052] Based on any of the above technical solutions, the following optimization is made: In step 1, the specific steps for obtaining the extracellular electron transfer rate data of microorganisms in sewage are as follows: first, collect sewage samples, filter them with a 0.45μm filter membrane, and dilute them with sterile buffer when the concentration is high.

[0053] 100-200 μL of pretreated sample and 50 μL of antibody-magnetic microspheres are injected into a microfluidic analyzer to allow for specific binding. A magnetic field is then applied at the bottom of the microfluidic analyzer as needed to enrich the conjugate.

[0054] Rinse to remove impurities, then remove the magnetic field.

[0055] A solution containing a redox probe is injected, and electrochemical signals are collected and calculated using a microfluidic analyzer to output data on the extracellular electron transport rate of the microorganism. The microfluidic analyzer used is the μStat400 microfluidic electrochemical analyzer. Specifically, the full name of the μStat400 microfluidic electrochemical analyzer is μStat-i400 portable potentiostatic / current-constant / impedance analyzer, which is a product of DropSens, a brand under Metrohm.

[0056] Among them, the 50μL antibody-magnetic microspheres are specific antibody-magnetic microspheres targeting extracellular polymers (such as polysaccharides and protein complexes) of common microorganisms in wastewater. You can directly purchase the antibody-magnetic microsphere products of Xianfeng Nano as needed. The products are made of polystyrene-encapsulated iron oxide with a particle size of 1-3μm. The selected particle size range can provide sufficient specific surface area for antibody immobilization while ensuring magnetic responsiveness.

[0057] In the wastewater treatment process of this invention, 50 μL of antibody-magnetic microspheres and 100-200 μL of pretreated wastewater sample are injected together into a microfluidic analyzer. Driven by a micro-injection pump at a flow rate of 0.1-0.5 mL / min, the binding reaction with the extracellular polymeric material of microorganisms is rapidly completed in the reaction chamber. Subsequently, the complex is enriched by an external magnetic field of 0.5-1T. With the help of subsequent electrochemical detection steps, the extracellular polymeric material concentration data is accurately obtained, supporting the accurate calculation of the carbon source demand prediction model in the process.

[0058] Antibody-magnetic microspheres specifically bind to the extracellular electron transport rate of microorganisms. By enriching the conjugate with a magnetic field, the method achieves targeted capture of the extracellular electron transport rate of microorganisms. This method achieves an enrichment efficiency of over 90% for the extracellular electron transport rate of microorganisms, overcoming the interference problems caused by other proteins and polysaccharides in traditional detection methods. The detection method accurately captures fluctuations in the extracellular electron transport rate of microorganisms, and, combined with membrane pressure differential data, provides early warning of membrane fouling trends. This, combined with measures such as extending the residence time in the grit chamber and membrane backwashing, reduces the rate of membrane flux decay.

[0059] This method uses high-resolution thermal imaging and physical equation inversion to accurately quantify the metabolic state of microorganisms, providing an intuitive basis for the regulation of aeration and carbon source addition, and solving the limitations of single-point and qualitative detection in traditional methods. It couples infrared thermal imaging, microfluidic detection and multi-parameter models to form a closed-loop control of metabolic heat, dissolved oxygen and membrane fouling.

[0060] Based on any of the above technical solutions, a further optimization is made to the method of constructing the multi-parameter coupled control model in step 2: establishing the water dielectric constant ε, the sewage flow Reynolds number, etc. With volume dissolved oxygen coefficient The nonlinear mapping relationship is given by the following formula: .

[0061] in, ε is the volumetric dissolved oxygen coefficient (the rate constant of oxygen transfer from the gas phase to the liquid phase per unit time and unit volume in a gas-liquid system, directly characterizing the supply efficiency of dissolved oxygen available for microbial metabolism, and a key parameter for regulating the metabolic activity and metabolic heat flux density of aerobic microorganisms in wastewater), and ε is the dielectric constant of the water body. As a reference dielectric constant, Wastewater dynamic viscosity, Wastewater density, Oxygen molecule diffusion coefficient (the rate constant of spontaneous diffusion of oxygen in wastewater, reflecting the mass transfer capacity at the molecular level). All are exponential coefficients. This is a correction factor.

[0062] Correction coefficient Exponential coefficient All calibrations were performed according to the dynamic dissolved oxygen method in industry standard HJ506—2009 and relevant industry experience, and are applicable to municipal microporous aeration tanks. The exponential coefficient reflects the degree of influence of the corresponding parameter on the volumetric dissolved oxygen coefficient. The dielectric constant of the water body was measured using a parallel plate capacitance sensor.

[0063] According to the HJ506-2009 standard, the method for dissolved oxygen monitoring was validated and the quality control process was implemented to calibrate parameters: the volumetric dissolved oxygen coefficient was measured using the dynamic dissolved oxygen method (fitting the dissolved oxygen decay curve after aeration was stopped). By combining the water dielectric constant, wastewater flow Reynolds number, wastewater dynamic viscosity, wastewater density, and oxygen molecule diffusion coefficient monitored at the same time, the correction coefficient and exponential coefficient are calculated by back-deriving the formula, thus realizing a closed loop of standard method - on-site calibration - model adaptation.

[0064] In this embodiment, for similar processes (such as microporous aeration tanks in municipal wastewater treatment plants), the model can be quickly initialized by referring to industry experience values, and then iteratively optimized based on field data according to the common knowledge of those skilled in the art, which will not be elaborated further.

[0065] The difference between this embodiment and the prior art is that traditional models only focus on achieving dissolved oxygen standards, while this method deeply couples the volumetric dissolved oxygen coefficient with microbial metabolic heat flux. It quantifies dissolved oxygen supply through the volumetric dissolved oxygen coefficient and correlates it with the rate of microbial metabolic heat production, thereby achieving a closed loop of dissolved oxygen, metabolic heat, and process control. For example, based on the collected metabolic heat change data, the local volumetric dissolved oxygen coefficient is dynamically adjusted and the activity of the microbial community is optimized, which changes the limitations of traditional extensive oxygen supply and ignoring the heterogeneity of the microbial community.

[0066] Based on any of the above technical solutions, a further optimization is made: the steps for dynamically controlling the aeration rate are as follows: real-time collection of the following parameters: water dielectric constant, wastewater flow velocity, temperature, and dissolved oxygen concentration; inputting the collected parameters into a multi-parameter coupled control model to calculate the volumetric dissolved oxygen coefficient. .

[0067] According to the formula Calculate the target aeration rate Based on the obtained target aeration rate The aeration volume is controlled by adjusting the speed of the aeration fan through a frequency converter.

[0068] in, For the volume of the reaction tank, For saturated dissolved oxygen concentration, This represents the real-time dissolved oxygen concentration.

[0069] Calculate the target aeration rate The units of all parameters in the formula are common international units and selected as needed while taking into account dimensional consistency.

[0070] Based on any of the above technical solutions, a further optimization is made: The formula for establishing a carbon source demand prediction model based on redox potential, target aeration rate, and polycyclic aromatic hydrocarbon concentration is as follows: The refined formula after substituting the parameters obtained above is: Where M is the carbon source dosage (mg / L), E is the redox potential (mV), and Q is the target aeration rate (mg / L). ), Polycyclic aromatic hydrocarbon concentration (μg / L) For regression coefficients, This is a constant term.

[0071] In this embodiment, the limitations of traditional single-parameter (such as relying solely on COD or dissolved oxygen) carbon source regulation are overcome. A quaternary linear model is constructed using redox potential (E) to reflect microbial metabolic state, target aeration rate (Q) to correlate with oxygen supply level, and polycyclic aromatic hydrocarbon (PAH) concentration (P) to characterize pollutant toxicity. This achieves a deep match between carbon source dosage (M) and water quality, operating conditions, and pollutant characteristics. For example, when PAH concentration (P) increases, microbial activity is inhibited. The carbon source demand prediction model enhances carbon source compensation through the γ coefficient, avoiding a decrease in nitrogen and phosphorus removal efficiency due to toxic substances, thus reducing carbon source dosage error compared to traditional methods.

[0072] The oxidation-reduction potential (E) reflects the electron transfer state of the activated sludge system in real time (e.g., the switching between nitrification and denitrification stages). The model correlates carbon source addition with microbial metabolic requirements through the α coefficient. When E transitions from the anoxic stage (-100-100mV) to the aerobic stage (100-800mV), the carbon source supplementation is automatically adjusted to maintain the synergistic metabolic balance between nitrifying and denitrifying bacteria, thereby improving the TN and TP compliance rates of the effluent. Furthermore, polycyclic aromatic hydrocarbon (PAH) concentration, as an indicator of recalcitrant pollutants, can inhibit microbial activity due to concentration fluctuations. The model compensates for carbon source contamination through the γ coefficient, rapidly increasing the carbon source addition when industrial wastewater is mixed in (a sudden increase in P) to maintain sludge activity.

[0073] It should be noted that the regression coefficients and constants were determined by fitting historical operating data using the least squares method. This historical operating data required preprocessing according to the "Technical Regulations for the Preservation and Management of Water Quality Samples," and outliers were removed. Parameter calibration was completed by minimizing the sum of squared residuals between the actual carbon source dosage and the model's predicted values ​​using the least squares method. This parameter determination method is a standard choice for constructing multiple linear regression models in this field. Minimizing the sum of squared residuals between the actual carbon source dosage and the model's predicted values ​​using the least squares method to calibrate the model parameters allows for a more rigorous approach to data preprocessing and parameter determination, taking into account water quality testing standards. This solves the problem of excessive waste or insufficient ineffectiveness in empirical carbon source dosage.

[0074] Based on any of the above technical solutions, the following further optimizations are made: the bar screen is a rotary mechanical bar screen with a bar spacing of 5mm; the sedimentation tank is a vortex sedimentation tank with a hydraulic retention time of 2-3 minutes, and the sediment is separated by a sand-water separator.

[0075] In this embodiment, the rotary mechanical bar screen with a 5mm bar gap can efficiently intercept larger suspended solids and floating matter in sewage. Compared with a wider gap bar screen, it can reduce the impurity load of subsequent treatment units, and the mechanical rotary structure facilitates automatic sludge removal, reducing manual maintenance costs. The vortex grit chamber utilizes the hydrodynamic vortex effect, and the 2-3 minute hydraulic retention time can shorten the retention time while ensuring effective sedimentation of sand particles, thus improving pretreatment efficiency. Combined with the sand-water separator, it can achieve efficient separation of grit and sewage. The separated grit has low water content and few impurities, making it easy to dispose of in subsequent processes. The entire pretreatment unit works in concert, laying a stable water quality foundation for subsequent biological treatment, precise aeration, and carbon source addition, and reducing the interference of large particles and excessive sand particles on equipment and process parameters.

[0076] Based on any of the above technical solutions, the following further optimizations are made: In the deep treatment step, the operating pressure of the nanofiber membrane ultrafiltration is 0.1-0.3MPa, the membrane pore size is 0.01-0.1μm; the wavelength of ultraviolet disinfection is 253.7nm, the irradiation dose is 15-30mJ / cm², and the dosage of ozone disinfection is 5-10mg / L.

[0077] It should be noted that in the nanofiber membrane ultrafiltration stage, the 0.01-0.1μm membrane pore size can efficiently intercept pollutants such as suspended solids, colloids, and bacteria in wastewater. Combined with an operating pressure of 0.1-0.3MPa, it ensures membrane permeability (avoiding excessive pressure loss that could lead to a sudden drop in flux) while physically intercepting large molecular organic matter and microorganisms, reducing the load on subsequent disinfection processes. Ultraviolet light, with its 253.7nm golden sterilization wavelength and 15-30mJ / cm² irradiation dose, precisely destroys the DNA structure of microorganisms, achieving an inactivation rate of over 99% for common pathogens such as Escherichia coli and Salmonella. The 5-10mg / L ozone dosage focuses on the oxidative decomposition of recalcitrant organic matter (such as polycyclic aromatic hydrocarbon derivatives and some pesticide residues). These three elements work together to construct a three-stage purification system of physical interception, microbial inactivation, and recalcitrant decomposition, significantly improving the removal rates of core indicators such as COD, BOD, suspended solids, and microorganisms in wastewater compared to conventional deep treatment processes.

[0078] Based on any of the above technical solutions, the following further optimizations are made: in the pretreatment step, the grid gap is 5-10mm, the hydraulic retention time in the sedimentation tank is 10-15min, and the detection cycle of the extracellular electron transfer rate data of the microorganisms is once every 30min.

[0079] The improved pretreatment removal rate reduces the impurity load in the biological treatment tank, making the monitoring of parameters such as dielectric constant and Reynolds number of the water body more stable. When the extracellular electron transfer rate of microorganisms increases (such as when microbial activity is enhanced), the model automatically increases the amount of carbon source added to maintain the metabolic balance between denitrifying bacteria and polyphosphate-accumulating bacteria, ensuring the removal rate of TN and TP.

[0080] Based on any of the above technical solutions, the following optimization is made: The steps to achieve dynamic and precise control of wastewater treatment by adjusting the treatment parameters in real time according to the membrane flux decay trend are as follows: real-time data acquisition and decay trend judgment: every 15-30 minutes, the membrane pressure difference of the nanofiber membrane ultrafiltration unit, the concentration of suspended solids in the influent, the cumulative running time, and the influent temperature and COD concentration are collected.

[0081] When the membrane pressure difference increases by 15%-20% from the initial value, it is determined that the membrane fouling has entered a significant decay stage.

[0082] Activate multi-unit linkage control: If the concentration of suspended solids in the influent exceeds 50 mg / L, extend the hydraulic retention time of the vortex grit chamber from the usual 2-3 min to 3-5 min, and use a 5 mm gap rotary mechanical bar to enhance the interception of suspended solids and reduce particulate pollution on the membrane surface.

[0083] If the concentration of suspended solids in the influent exceeds 300 mg / L, the aeration rate will be dynamically adjusted in accordance with the above method to increase the supply of dissolved oxygen, in conjunction with the precise aeration and carbon source addition processes.

[0084] At the same time, the carbon source addition will be increased by 10%-15% according to the carbon source demand prediction model to optimize the degradation efficiency of organic matter by microorganisms and reduce the organic load on the membrane surface.

[0085] The membrane module is started by air-water backwashing, during which air and water are mixed to wash and peel off the filter cake layer on the membrane surface.

[0086] Monitor the membrane pressure differential recovery within 30 minutes after backwashing.

[0087] If the membrane pressure difference drops to within 110% of the initial value and remains stable, the control is deemed effective; otherwise, chemical cleaning is initiated.

[0088] When the suspended solids concentration is >50mg / L, the residence time in the grit chamber is extended to 3-5 minutes. Combined with a 5mm grid, the SS concentration entering the membrane unit is controlled below 20mg / L, which improves the interception efficiency and reduces the particulate pollution load on the membrane surface compared with conventional pretreatment.

[0089] Air-water backwashing (0.2-0.3MPa) removes the filter cake layer on the membrane surface, significantly improving the success rate of restoring the membrane pressure differential to within 110% of its initial value after backwashing. This is more effective than water rinsing alone and effectively reduces the frequency of chemical cleaning.

[0090] Dynamic adaptation of aeration volume and carbon source addition: When the suspended solids concentration is >300mg / L, precise aeration is linked. The aeration volume is calculated by model, which improves the accuracy of dissolved oxygen supply to ±0.5mg / L and reduces aeration energy consumption compared with traditional processes.

[0091] The carbon source dosage is increased by 10%-15% to match the microbial demand for degradation of high-load organic matter, the COD removal rate remains stable, and the formation of a gel layer on the membrane surface due to organic matter residue is avoided.

[0092] Experimental example: Wastewater treatment control process based on dynamic regulation of aeration and carbon source addition in municipal wastewater treatment scenarios.

[0093] Step 1, Pretreatment: Bar screen treatment: A rotary mechanical bar screen is used with a bar spacing of 5mm. Wastewater flows through the bar screen at a flow rate of 0.5m³ / s, effectively intercepting suspended and floating solids with a diameter greater than 5mm, achieving an interception efficiency of 92% and reducing the impurity load on subsequent treatment units.

[0094] Grit chamber treatment: The hydraulic retention time of the vortex grit chamber is controlled at 2.5 minutes. The grit is separated by a sand-water separator, and the moisture content of the separated grit is less than 60%.

[0095] Detection of extracellular electron transfer rate of microorganisms: Wastewater samples were collected every 30 min and filtered through a 0.45 μm filter membrane. If the concentration was too high, it was diluted with sterile buffer. 150 μL of pretreated sample and 50 μL of specific antibody-magnetic microspheres (Xianfeng Nanotechnology product, polystyrene-coated iron oxide, particle size 1-3 μm) targeting polysaccharide and protein complexes were injected into a microfluidic analyzer and reacted at a flow rate of 0.3 mL / min. The conjugates were enriched by an external magnetic field of 0.8T. After rinsing off impurities, the magnetic field was removed, and a solution containing redox probes was injected. Electrochemical signals were collected by the microfluidic analyzer, and the extracellular electron transfer rate of microorganisms was calculated and output. The microfluidic analyzer used was a μStat400 microfluidic electrochemical analyzer, and the calculated extracellular electron transfer rate of microorganisms was 80 μA.

[0096] Microbial metabolic heat data acquisition: An infrared thermal imager with a spatial resolution of 0.08℃ / pixel was used to acquire liquid surface temperature data of the biochemical reaction tank at a frequency of 3 frames per second. The average heat flux density in the generation tank was 250W / m².

[0097] Step 2: Biochemical treatment stage:

[0098] Construction of a multi-parameter coupled control model: Real-time monitoring of water dielectric constant ε=80 (reference dielectric constant ε0=81), sewage flow velocity v=0.2m / s, temperature T=25℃, and calculation of Reynolds number Re=ρvD / μ=15000 (sewage density ρ=1000kg / m³, dynamic viscosity μ=0.001Pa・s, oxygen molecule diffusion coefficient D=2×10⁻ 9 m² / s). According to the formula The volumetric dissolved oxygen coefficient is calculated by setting C=0.2, n1=0.5, n2=0.3, and n3=-0.2 (calibrated according to HJ506—2009 standard).

[0099] Aeration rate control: Reactor volume V = 1000 m³, saturated dissolved oxygen concentration C_s = 8 mg / L, real-time dissolved oxygen concentration C = 2 mg / L, according to the formula... Calculate the target aeration rate Q, and adjust the speed of the aeration fan using a frequency converter to precisely control the aeration rate at 300 m³ / h, so that the dissolved oxygen in the tank is maintained at 2.5 ± 0.5 mg / L.

[0100] Carbon source addition: Redox potential E = 200 mV, polycyclic aromatic hydrocarbon concentration P = 5 μg / L. Based on the carbon source demand prediction model M = αE + βQ + γP + δ, where α = 0.5, β = 0.01, γ = 2, and δ = 10 (determined by least squares fitting of historical data), the carbon source addition amount was calculated. The refined formula after substituting the parameters obtained above is: Add sodium acetate carbon source to the reaction tank, and control the addition error within ±5%.

[0101] Step 3, Deep Treatment Stage: Nanofiber Membrane Ultrafiltration: Operating pressure 0.2 MPa, membrane pore size 0.05 μm, treatment flow rate 500 m³ / h, real-time monitoring of membrane differential pressure. The initial membrane differential pressure was 0.05 MPa. After 2 hours of operation, the membrane differential pressure increased to 0.058 MPa (16% increase from the initial value), indicating that membrane fouling had entered a significant attenuation stage.

[0102] Linked control: With an influent suspended solids concentration of 60 mg / L, the hydraulic retention time in the vortex grit chamber was extended to 4 minutes to enhance suspended solids interception, reducing the SS concentration entering the membrane unit to 18 mg / L. Simultaneously, the membrane module was backwashed via air and water (air pressure 0.25 MPa, water pressure 0.2 MPa). After 30 minutes of backwashing, the membrane pressure differential recovered to 0.055 MPa (within 110% of the initial value), demonstrating effective control.

[0103] Disinfection treatment: The ultraviolet disinfection wavelength is 253.7nm, the irradiation dose is 20mJ / cm², and the inactivation rate of Escherichia coli reaches 99.5%; the ozone dosage is 8mg / L, which degrades polycyclic aromatic hydrocarbons and other recalcitrant organic matter.

[0104] The units of physical quantities in the formulas for calculating process parameters involved in this application all follow well-known conventions in the field of wastewater treatment. Each formula is verified through dimensional analysis during derivation and conforms to physical laws and common knowledge known to those skilled in the art. The units of each formula parameter are selected from commonly used units, and their dimensional adaptability is determined and implemented as needed by those skilled in the art based on conventional experiments and standard methods. According to the needs of those skilled in the art, the units of each parameter can meet the dimensional consistency requirements, which will not be elaborated here.

[0105] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention. For those skilled in the art, any alternative improvements or transformations made to the implementation of the present invention fall within the protection scope of the present invention.

[0106] Any aspects of this invention not described in detail are well-known to those skilled in the art.

Claims

1. A wastewater treatment control process based on dynamic regulation of aeration and carbon source addition, characterized in that, The method comprises the following steps: Step 1, pretreatment: sewage is sequentially subjected to preliminary treatment through a grid and a grit chamber, and then data of microbial extracellular electron transfer rate in the sewage is obtained, and data of microbial metabolic heat change is obtained in real time by using an infrared thermal imager; Step 2, biochemical treatment: water dielectric constant and sewage flow Reynolds number are monitored in real time, and meanwhile, data of microbial metabolic heat change obtained in the pretreatment step is combined, a multi-parameter coupling regulation and control model is constructed, and aeration quantity is dynamically regulated and controlled; Meanwhile, a carbon source demand prediction model is established based on redox potential, target aeration quantity and polycyclic aromatic hydrocarbon concentration, and accurate carbon source addition is completed according to the calculation result of the carbon source demand prediction model; Step 3, advanced treatment: the sewage after biochemical treatment is first subjected to nanofiber membrane ultrafiltration, and then subjected to ultraviolet and ozone combined disinfection treatment, and in the treatment process, treatment parameters are regulated and controlled in real time according to the membrane flux attenuation trend, so as to realize dynamic and accurate control of sewage treatment; The method for constructing the multi-parameter coupled control model in step 2 is as follows: establish the dielectric constant of the water body. Reynolds number of wastewater flow With volume dissolved oxygen coefficient The nonlinear mapping relationship is given by the following formula: ； wherein, is the volumetric oxygen solubility coefficient, is the water dielectric constant, is the reference dielectric constant, is the wastewater dynamic viscosity, is the wastewater density, D is the oxygen molecule diffusion coefficient, are exponential coefficients, and C is a correction coefficient. The step of dynamically regulating and controlling aeration quantity is as follows: The following parameters are collected in real time: water dielectric constant, sewage flow rate, temperature and dissolved oxygen concentration; Input the collected parameters into a multi-parameter coupling regulation model to calculate the volume oxygen coefficient ; The target aeration amount Q is calculated according to the formula Q = (S * V) / 60 According to the obtained target aeration quantity Q, and through a frequency conversion controller, the rotating speed of an aeration blower is adjusted to realize aeration quantity regulation and control; where V is the volume of the reactor tank, C is the real-time dissolved oxygen concentration; and C is the real-time dissolved oxygen concentration; and The formula of the carbon source demand prediction model established based on redox potential, target aeration quantity and polycyclic aromatic hydrocarbon concentration is as follows: The refinement formula after substituting the above obtained parameters is: ； wherein M is the carbon source dosage amount, E is the oxidation-reduction potential, Q is the target aeration amount, P is the polycyclic aromatic hydrocarbon concentration, is a regression coefficient, is a constant term.

2. The process for wastewater treatment control based on dynamic regulation and control of aeration and carbon source dosage according to claim 1, characterized in that: In step 1, the specific steps of obtaining data of microbial extracellular electron transfer rate in the sewage are as follows: First, sewage samples are collected, filtered by using a 0.45 μm filter membrane, and when the concentration is high, diluted by using sterile buffer solution; 100-200 μL of pretreated samples and 50 μL of antibody-magnetic microspheres are injected into a microfluidic analyzer, so that specific binding occurs and a magnetic field is applied at the bottom of the microfluidic analyzer to enrich the binding materials as needed; After impurities are removed, the magnetic field is removed; A solution containing a redox probe is injected, and the electrochemical signal is collected by the microfluidic analyzer and the data of microbial extracellular electron transfer rate is calculated and output.

3. The process for wastewater treatment control based on dynamic regulation and control of aeration and carbon source dosage according to claim 2, characterized in that: In the step of advanced treatment, the operating pressure of nanofiber membrane ultrafiltration is 0.1-0.3 MPa, the membrane pore size is 0.01-0.1 μm, the wavelength of ultraviolet disinfection is 253.7 nm, the irradiation dose is 15-30 mJ / cm², and the ozone disinfection dosage is 5-10 mg / L.

4. The process for wastewater treatment control based on dynamic regulation and control of aeration and carbon source dosage according to claim 3, characterized in that: In the pretreatment step, the grid gap is 5-10 mm, the hydraulic retention time of the grit chamber is 10-15 min, and the detection period of the data of microbial extracellular electron transfer rate is once every 30 min.

5. The process for wastewater treatment control based on dynamic regulation and realization of aeration and carbon source dosage according to claim 4, characterized in that, The steps of realizing dynamic and accurate control of sewage treatment according to the real-time regulation and control of treatment parameters based on the membrane flux attenuation trend are as follows: Real-time data acquisition and attenuation trend judgment: the membrane pressure difference of the nanofiber membrane ultrafiltration unit, the influent suspended solids concentration, the cumulative running time, the synchronous recording of the influent temperature and the COD concentration are collected every 15-30 min; When the membrane pressure difference is increased by 15%-20% compared with the initial value, it is determined that the membrane pollution enters the significant attenuation stage; Start multi-unit linkage regulation and control: if the influent suspended solids concentration exceeds 50 mg / L, the hydraulic retention time of the grit chamber is extended from the conventional 2-3 min to 3-5 min, and a 5 mm gap rotary mechanical grid is used to strengthen the interception of suspended solids and reduce the particle pollution on the membrane surface; If the influent suspended solids concentration exceeds 300 mg / L, the precise aeration and carbon source addition process are linked, the aeration amount is dynamically adjusted according to the above method, and the dissolved oxygen supply is improved; At the same time, according to the carbon source demand prediction model, increase the carbon source addition amount by 10%-15%, optimize the degradation efficiency of microorganisms to organic matter, and reduce the organic load on the membrane surface; Start the membrane module through air-water backwashing, and use air-water mixed flushing during the flushing process to strip the filter cake layer on the membrane surface; Monitor the membrane pressure difference recovery within 30 minutes after backwashing; If the membrane pressure difference drops to within 110% of the initial value and stabilizes, the control is effective; Otherwise, start chemical cleaning.

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