4059 results about "Aeration" patented technology

The invention provides a multi-source data fusion modeling method and system in an aeration process, and is applied to the field of intelligent aeration control in sewage treatment. The method comprises the steps that multi-source time sequence data such as dissolved oxygen, turbidity, flow, temperature, power and pool bottom pressure pulsation signals are collected, dissolved oxygen response lag is calculated through cross-correlation analysis with power change as the reference, time sequence alignment is carried out, and a dissolved oxygen reference interval is predicted by utilizing calibration data in combination with a physical constraint LSTM model; performing spectral analysis on the pressure pulsation signal to extract a gas-liquid coupling characteristic value, and generating a cooperative regulation instruction of the frequency of the blower and the rotating speed of the stirrer based on the information; by means of the scheme, control oscillation caused by lag of the dissolved oxygen sensor can be effectively overcome, online monitoring of bubble form distribution is achieved, the gas-liquid mass transfer efficiency is improved, invalid aeration is avoided, and system energy consumption is remarkably reduced on the premise that stable effluent quality is guaranteed.

The invention belongs to the technical field of crossing of environmental engineering and ocean dynamics, and particularly relates to a tidal dynamics prediction method and control device for a seawater desulfurization system, and the method comprises the steps: constructing a time-space coupling prediction model fusing multi-source hydrological observation data, and introducing a nonlinear dynamic weight distribution mechanism; the influence of terrain constraint, wind stress disturbance and upstream runoff on tidal propagation is quantified in real time, and a rolling prediction sequence of tide level phase, flow velocity gradient and salinity disturbance in the next three hours is output; the prediction result drives the scheduling of a desulfurization pump set, the adjustment of spraying density and the matching of aeration intensity, and the model weight is corrected on line based on the actually measured feedback of desulfurization efficiency to form closed-loop control. By means of the technical scheme, accurate cooperation of the operation parameters of the desulfurization system and tidal dynamics is achieved, meaningless energy consumption is reduced while the desulfurization efficiency is guaranteed, and the utilization rate of the desulfurization agent and the stability of the system are improved.

The invention relates to an aeration fan predictive maintenance method, system and equipment based on multi-modal perception and adaptive learning and a medium. The method comprises the following steps: generating a time sequence data set through synchronous acquisition and combined noise reduction processing of a sensor group; generating a multi-dimensional feature vector through time-frequency feature spectrum characterization and interpretability contribution analysis in combination with dynamic weight distribution coupled by environmental factors; on the basis of the multi-dimensional feature vectors, real-time anomaly detection is carried out at the edge end through a lightweight model, and abnormal data fragments are uploaded to the cloud end; and performing cross-sensor bidirectional reasoning on abnormal data fragments through a reasoning model deployed at the cloud, reconstructing a sensor topological graph, intelligently triggering elastic incremental learning, cooperatively processing equipment degradation trend analysis, multi-source evidence fusion and space calibration, and outputting a life prediction result and a fault thermodynamic diagram. According to the method, the core pain points of high early fault omission ratio, insufficient model robustness and the like are solved, and cost reduction, efficiency improvement and equipment life prolonging are realized while the diagnosis precision is maintained.

The invention discloses a sewage treatment plant greenhouse gas optimization control method and device and a storage medium, and relates to the technical field of environmental protection, and the method comprises the steps: discretizing a sewage treatment process of an aeration tank into a plurality of complete mixing reactors connected in series, and constructing a sewage treatment process mechanism model based on an activated sludge model; optimizing parameters of the sewage treatment process mechanism model according to the target greenhouse gas concentration spatial distribution data, the target greenhouse gas emission flux and the water quality spatial distribution data to obtain a digital twinborn model; based on a digital twinborn model, a dissolved oxygen set value of each complete mixing reactor partition in an aeration tank is used as a decision variable, a multi-objective evolutionary algorithm is adopted for solving, a Pareto optimal control strategy is generated, and the technical problem that in the prior art, an optimal control strategy of greenhouse gas is not accurate is solved. And precise quantification and collaborative optimization control of greenhouse gas emission are realized.

The invention relates to the technical field of sewage treatment, in particular to an aerobic aeration treatment system for plateau domestic sewage treatment, which comprises a pretreatment unit, a biological treatment unit, an advanced treatment unit and a recycling unit which are connected in sequence, the system further comprises a data acquisition module, an aerobic aeration intelligent regulation and control module, a biochemical environment constant-temperature maintenance module, a carbon-nitrogen proportion and backflow dynamic regulation and control module, a digital twinning and sludge characteristic simulation module and an energy collaborative scheduling module. Compared with the prior art which adopts a standard aeration strategy and cannot adapt to the special environment of low air pressure and low oxygen partial pressure of the plateau, the system can accurately adapt to the plateau environment by integrating real-time atmospheric pressure and water temperature monitoring, dynamically calculating the oxygen saturation solubility and correcting the aeration control target according to the oxygen saturation solubility, so that the aeration control target is optimized. Stable supply of dissolved oxygen required by biochemical reaction is ensured, and the technical bottleneck of low efficiency of traditional sewage treatment in plateau regions is fundamentally overcome.

The invention discloses a precise sewage aeration method, and relates to the field of water, wastewater, sewage or sludge treatment, the method comprises a feed-forward model and a feedback model, the feed-forward model collects historical data of aeration tank operation parameters, environmental parameters and oxygen demand, and a decision tree is iteratively generated to correct errors and determine a target dissolved oxygen value; the feedback model corrects the target dissolved oxygen value according to the aeration tank dissolved oxygen concentration and the mixed liquid suspended solid concentration; and controlling the power of the aeration equipment according to the target dissolved oxygen value. Through common coordination of the feedforward model and the feedback model, accurate aeration of sewage treatment is realized.

The invention relates to the technical field of rural sewage treatment, and discloses a rural sewage intelligent monitoring and aeration regulation and control system based on Internet of Things. The system comprises a pollution parameter sensing layer, a pollution index analysis layer, an aeration path generation layer, a process parameter matching layer, a dynamic monitoring focusing layer, a cross-modal mapping layer, an equipment scheduling decision-making layer and an aeration parameter generation layer. Wherein the pollution parameter sensing layer collects operation parameters in real time by means of a multi-source sensing terminal; the pollution index analysis layer divides pollution parameter intervals and calculates a distribution coefficient; the aeration path generation layer generates an aeration intensity grading path in sequence; the process parameter matching layer outputs a process deviation parameter set; dynamically monitoring a focusing layer to adjust the density of monitoring points; the cross-modal mapping layer establishes space-time correlation mapping of water quality parameters and aeration intensity; the equipment scheduling decision-making layer sorts the calling sequence of the aeration equipment; and the aeration parameter generation layer converts the parameters into executable regulation and control parameters. The system realizes intelligent monitoring of rural sewage and accurate aeration regulation and control.

The invention discloses a sewage treatment whole-process operation regulation and control calculation system based on deep learning, and relates to the technical field of intelligent control, and the system comprises a strategy optimization module which optimizes a future pollution trend prediction sequence and a regulation and control factor weight table by using a reinforcement learning strategy network in combination with a genetic algorithm optimizer, dynamically generating an aeration, dosing and backflow operation parameter combination aiming at a regulation target to form a dynamic regulation instruction set; the instruction execution module is used for issuing a dynamic regulation and control instruction set to the programmable logic controller by utilizing an industrial control interface, and collecting response information and real-time effluent quality data as execution feedback information; and the model updating module carries out error comparison on execution feedback information and a future pollution trend prediction sequence, constructs a weighted error sequence, and dynamically corrects deep network parameters through an online fine tuning strategy to form a prediction control model. According to the method, the generalization ability and robustness of the predictive control model are remarkably improved.

The invention provides a bioreactor aeration self-adaptive control method and system based on machine learning, and the method comprises the steps: collecting the water inlet flow, ammonia nitrogen concentration, dissolved oxygen concentration, water temperature and chemical oxygen demand data of a bioreactor in real time through a sensor, and carrying out the water balance verification and the Kalman filtering pretreatment containing a water temperature compensation item; generating an aeration instruction by adopting multi-model collaboration; outputting a reference aeration rate by a deep reinforcement learning DRL model; the self-adaptive PID controller dynamically adjusts the proportionality coefficient according to the dissolved oxygen deviation and calculates the correction amount; and the LSTM network generates a fusion weight, and combines the seasonal compensation factor and the safety coefficient to synthesize the final aeration rate. The system passes mutation rate constraint and nitrification efficiency verification, an ASM1 model is triggered to recheck when the deviation exceeds the standard, and after passing, an air blower with the precision of + / -2% executes an instruction and performs closed-loop feedback in a period smaller than or equal to 10 seconds. After the method is implemented, the aeration energy consumption is reduced to 0.38 kWh / m, the effluent ammonia nitrogen standard reaching rate is improved to 98.5%, the dissolved oxygen fluctuation range is reduced to + / -0.3 mg / L, and the impact load response time is shortened to be within 15 minutes.

The invention relates to the technical field of environmental engineering water treatment automatic control, and discloses a landfill leachate treatment control method based on cooperative control. The method comprises the following steps: acquiring real-time monitored water quality parameter data streams including a pretreatment stage pH value, a main treatment stage COD concentration, a deep treatment stage membrane pressure difference and the like in a landfill leachate treatment system, inputting the data streams into a cooperative control framework, and dynamically correcting PID controller parameters through a nonlinear gain function to generate a primary adjustment instruction; evaluating and generating a strategy optimization instruction based on the reinforcement learning strategy network; inputting the two into a grey prediction model and outputting a cooperative control signal containing a feedforward compensation amount; and finally, dynamically adjusting the coagulant dosage, the aeration intensity and the working pressure of the membrane component according to the signal to form a closed-loop control loop. The method can master the water quality change in real time, accurately regulate and control the treatment process, improve the stability and reliability of the treatment system, and effectively improve the landfill leachate treatment effect.

The invention discloses an urban ventilation corridor construction method based on local climate region classification and remote sensing inversion, and belongs to the technical field of urban climate regulation and space optimization. According to the method, a multi-scale urban thermal environment analysis framework is constructed, a coupling mechanism of an urban form and a thermal effect is systematically identified from macroscopic, mesoscopic and microscopic levels, and fine modeling and regulation and control path identification of the thermal environment are realized. Meanwhile, in combination with machine learning and interpretability analysis methods, scientificity and transparency of thermal environment driving factor identification are enhanced. Through combination of remote sensing inversion, morphological factor construction and three-dimensional analogue simulation, a complete technical path of cold source identification, resistance modeling, path optimization and microclimate response evaluation is provided, and the aeration-cooling potential identification capability of blue-green infrastructures is effectively improved. The method is suitable for wind gallery planning and climate adaptation type update design of various urban spaces, and has wide popularization and application prospects.

The invention discloses a low-temperature sewage deep denitrification treatment method and system based on an AOA process, and relates to the technical field of sewage treatment.The method comprises the steps that multiple types of sensing units are arranged in an anoxic zone and an aerobic zone to obtain multi-zone characteristics, and a low-temperature denitrification collaborative state tensor is constructed through space-time decoupling and correlation analysis; and inputting a denitrification task target, a state tensor and an equipment state into a collaborative decision model, outputting a combined control instruction of gas-liquid distribution, a reflux ratio, aeration intensity and the like through attention fusion, and issuing the combined control instruction to a related unit so as to realize deep denitrification control under a low-temperature condition. The method solves the technical problems of low denitrification efficiency and poor stability caused by the fact that the existing AOA process cannot effectively identify and regulate the coupling denitrification dynamic relationship of multiple reaction intervals under the low-temperature condition, realizes accurate regulation and control of multi-zone denitrification power under the low-temperature condition through multi-zone cooperative control and refined parameter optimization, and improves the denitrification efficiency. The denitrification efficiency and the stability are improved.

The invention belongs to the technical field of wastewater treatment, and provides a wastewater treatment equipment remote control system based on the Internet of Things, which aims at solving the problems that the traditional fixed DO concentration control cannot adapt to water inlet load fluctuation, the effluent is easy to be substandard or the aeration energy consumption is high, the monitoring data noise is large, and the time sequence is misplaced. According to the scheme, a multi-dimensional real-time sensing network is constructed, flow, COD, BOD, ammonia nitrogen, water temperature, distributed DO and sludge activity sensors are deployed, discrete wavelet transform is adopted for noise reduction, and a data time sequence is aligned; a three-layer load-DO-energy consumption dynamic correlation model is designed, a basic layer predicts a load trend through LSTM, a middle layer quantifies a DO demand through a microbial metabolism model, and an optimization layer outputs an optimal DO set value through a PPO algorithm; and developing a dynamic decision-making system, adaptively generating a DO interval according to a load state, and adjusting fan parameters and number in a linkage manner. According to the invention, load dynamic adaptation is realized, effluent ammonia nitrogen is guaranteed to reach the standard, aeration energy consumption is reduced, and remote control precision and equipment operation efficiency are improved.

The invention discloses latex skeleton aerobic granular sludge and a preparation method thereof, and belongs to the technical field of sewage treatment. The aerobic granular sludge takes a porous natural latex carrier as a core skeleton, anaerobic sludge is fixed on the carrier, and after being mixed and wrapped with a sodium alginate solution, the mixture is dropwise added into a calcium chloride solution to be cured to form gel particles. The preparation method comprises the following steps: crushing the natural latex, screening to obtain particles with the particle size of 0.4-1.2 mm, and soaking the particles in alkali liquor with the pH value of 10-12; the method comprises the following steps: centrifugally concentrating anaerobic sludge to 10000 to 20000 mg / L; mixing the treated latex carrier, anaerobic sludge and a 1%-5% sodium alginate solution according to a volume ratio of 5: (2-5): (2-5); dropwise adding the mixture into a calcium chloride solution with the concentration of 3-7%, and curing to form gel particles; and putting the gel particles into a sewage treatment aerobic tank, injecting domestic sewage, and carrying out aeration domestication for 3-6 days until the particles become earthy yellow. The granular sludge prepared by the invention has the advantages of stable structure, good settling performance and high pollutant removal efficiency.

The application discloses an aeration tank for sewage treatment and relates to the technical field of sewage treatment.The aeration tank comprises a tank body, a horizontal reciprocating moving mechanism and an aeration mechanism.The tank body is connected with a guide rail through a depth adjusting mechanism at the top.The guide rail is connected with a sliding seat through sliding connection.The bottom outer wall of the sliding seat is fixedly connected with a box.The aeration mechanism comprises a rotary driving assembly, a pipeline switching assembly, a water and air dual-purpose pump, a rotary pipe, an aeration head, an agitating impeller and a cleaning and anti-blocking assembly.The cleaning and anti-blocking assembly can automatically clean the inner wall of the aeration head in a continuous rotating state, thereby effectively avoiding the serious blocking of the air holes of the aeration head.The horizontal reciprocating moving mechanism can expand the aeration range of the aeration head and can uniformly distribute the aeration air into the sewage, thereby effectively improving the oxygenation effect of the sewage.

The present application relates to the field of municipal wastewater treatment, and particularly relates to a highway service area wastewater treatment device and method based on AOOA-MBBR process. The device comprises a first anoxic tank, a first aerobic tank, a second aerobic tank and a second anoxic tank connected in sequence, and is filled with MBBR filler respectively, and the volume of the filled MBBR filler is taken as the effective volume; the first aerobic tank and the second aerobic tank are respectively provided with an aeration device; the first anoxic tank is used for inoculating hydrolytic bacteria and denitrifying bacteria; the first aerobic tank is used for inoculating hydrolytic bacteria, nitrifying bacteria and denitrifying bacteria; the second aerobic tank is used for inoculating nitrifying bacteria, and the second anoxic tank is used for inoculating hydrolytic bacteria and denitrifying bacteria; the second aerobic tank is connected with the first anoxic tank through a reflux pipe, and the reflux pipe is provided with a reflux power assembly. The present application solves the problems of existing highway service wastewater treatment with extremely low carbon-nitrogen ratio, such as the need for additional carbon source / electron donor, high operation cost, poor denitrification effect and high sludge yield.

The invention discloses a sewage treatment aeration air volume control method, device and equipment and a storage medium, and relates to the technical field of sewage treatment.The sewage treatment aeration air volume control method comprises the steps that historical and latest real-time data are obtained and preprocessed to form historical effective sample data and latest real-time sample data, an LSTM network is used for encoding to form a feature encoding vector, and the feature encoding vector is used for encoding the historical effective sample data and the latest real-time sample data; and performing fusion with latest real-time sample data, generating and optimizing by utilizing an Actor-Critic model to form an optimized valve opening adjustment amount and an optimized adjustment dead zone threshold value of each branch, and further determining a valve adjustment action and executing the valve adjustment action through an actuator. The aeration air volume demand of the system is dynamically predicted through the LSTM network and the Actor-Critic model, a control strategy is generated and continuously optimized, meanwhile, a dead zone is determined and adjusted by optimizing an adjustment dead zone threshold value, the dead zone range is continuously and dynamically optimized, the action frequency and mechanical abrasion are reduced, and the operation and maintenance cost can be reduced under long-period use.

The utility model provides a low-carbon high-efficiency biochemical system control system. The low-carbon high-efficiency biochemical system control system comprises an internal reflux control module, a carbon source adding module and a precise aeration module, the internal reflux control module is used for refluxing nitrate nitrogen required to be denitrified to the anoxic tank for denitrification; the carbon source adding module is used for giving the current required dosage through online nitrate nitrogen instruments arranged in an anoxic tank and an aeration tank according to dynamic nitrate nitrogen value comparison and variation trend comparison at the tail end of the anoxic tank; and the accurate aeration module reads the real-time numerical value of ammonia nitrogen at the tail end of the aeration tank, performs mean value calculation on the numerical value in each period, performs difference value calculation on the average value of ammonia nitrogen in the current period and the average value of ammonia nitrogen in the last period, and reaches the numerical value of dissolved oxygen through frequency adjustment of an aeration fan to run in a required operation interval. The control method of the low-carbon high-efficiency biochemical system aims to realize low-carbon high-efficiency accurate control of the biochemical system, aeration as required, addition of an external carbon source and control of internal reflux.

The invention belongs to the technical field of sewage treatment, and discloses a two-stage MABR-based efficient denitrification system, method and application, the system comprises an MABR-PN / DN reactor, an MABR-PN / A reactor, a peristaltic pump, an aeration hollow fiber membrane module, a carrier, an aeration pump, a gas flowmeter, a pressure gauge and a liquid flowmeter, and the MABR-PN / DN reactor and the MABR-PN / A reactor are arranged along the vertical direction. According to the system, the series design of the MABR-PN / DN and the MABR-PN / A is utilized, the adverse effect of organic matter in sewage on anaerobic ammonia oxidation treatment of high-concentration ammonia nitrogen wastewater is reduced, nitrate nitrogen generated by the MABR-PN / A flows back to the first-stage MABR-PN / DN to achieve denitrification, the denitrification limitation of a single reactor and a single denitrification path is broken through, and the overall total nitrogen removal rate of the system is higher than 90%.

Embodiments are directed to an advanced, modular, and scalable system integrating a Dissolved Gas Infusion (DGI) system and a Programmable Logic Controller (PLC) to automate and optimize processes such as water filtration, oxygenation, and waste management. The DGI system infuses high-purity oxygen directly into the water, maintaining optimal dissolved oxygen (DO) levels from the post-larvae stage to harvest, and significantly reducing oxygen wastage compared to traditional bubble aeration processes. The system includes modular raceways with load-bearing support beams, twist locks for secure stacking, and observation ports for easy access. This innovative approach ensures efficient, cost-effective, and reliable shrimp production, leading to higher survival rates, faster growth, and improved overall health of the shrimp.

The direct drinking water system comprises a pretreatment device, a membrane treatment device, a finished product water tank, a cleaning device, a water supply branch and a water return branch, the cleaning device comprises a concentrated water tank, a gas-liquid mixing branch, a bypass pipeline, a pulse water supply branch, a cleaning main pipe, a forward washing branch, a backwashing branch and a lower opening sewage branch. According to the utility model, quick switching in the cleaning process of the hollow fiber membrane is realized, the highest and lowest pressurization values in each period are accurately controlled, and meanwhile, adverse effects on membrane water production caused by water production of a water hammer and an air hammer are avoided; the pulse type positive and back washing is combined, so that the membrane pollutants are removed more thoroughly; according to the device, ozone microbubbles of a cleaning medium are exploded, so that the pollutant removal capacity is also improved; and when the filtered water in the finished water tank is stored for a long time, the ozone aeration device can also be used for sterilizing the filtered water, so that the application range of the device is expanded, and the safety of direct drinking water is guaranteed.

The invention relates to a coal chemical wastewater treatment device with nano-catalysis coupled with bioelectrochemistry. The invention belongs to the technical field of coal chemical wastewater treatment. The device comprises a pretreatment area, a bioelectrochemical treatment area, a catalyst recovery area and a clear water area which are connected in sequence, a nano Fe3O4 / TiO2 composite catalyst filling layer and a microporous aeration device are arranged in the pretreatment area; a biological anode region of the biological electrochemical treatment region adopts a graphene modified carbon brush, and a biological cathode region of the biological electrochemical treatment region adopts a sulfur-doped carbon felt; and the catalyst recovery area adopts a high-gradient permanent magnetic roller and is matched with a pulse backwashing system. The treatment cost of each ton of water is reduced to 25-35 yuan, and the cycle life of the catalyst is gt; the time is 200 times; the sulfur can be reused in a desulfurization process of a sewage treatment plant, so that the medicament cost is reduced; the carbon emission is reduced by 60%; the COD removal rate is not less than 95%, the total phenol removal rate is not less than 98%, and the total nitrogen removal rate is not less than 90%; energy consumption is reduced by 40% compared with a traditional electrochemical method, and the catalyst recovery rate is gt; 95%.

The invention discloses a real-time sewage treatment method based on sewage data monitoring, particularly relates to the technical field of sewage treatment, and is used for solving the problem of how to realize real-time sewage treatment under the condition that quality problems such as sewage blockage, drifting and missing exist in online monitoring data under complex and fluctuating working conditions. The problem that real-time, stable and energy-saving self-adaptive regulation and control can still be conducted on the sewage treatment process based on sewage monitoring data is solved, and the problems that due to dirty data and sudden change of working conditions, outlet water exceeds the standard, and energy consumption / chemical consumption is abnormal are avoided. Key water quality soft measurement and multi-source difference checking are implemented under the constraint of working condition numbers by constructing a sewage working condition map and a data quality evaluation rule base and introducing sliding time window feature extraction, monitoring credibility grading and sewage state fingerprints. The control strategy can still generate reliable aeration control instructions, backflow control instructions and dosing control instructions when monitoring data distortion and working condition fluctuation, so that the effluent is ensured to stably reach the standard, and the risk of control deviation is reduced.

The invention relates to a kitchen waste sewage accurate aeration control method, system and device based on fuzzy PID and deep learning and a medium, and the method comprises the steps: collecting parameters, and processing the parameters to obtain preprocessed data; predicting the oxygen demand through a deep learning time sequence prediction model, and generating a dissolved oxygen set value interval in combination with multi-target reinforcement learning; based on the deviation and the deviation change rate of a dissolved oxygen set value interval and a real-time measured value, an evolutionary fuzzy reasoning system is used for adjusting gain parameters of an aeration controller, PID control quantity is output and converted into an aeration equipment driving signal, and multi-area aeration intensity dynamic distribution is implemented in combination with a hydrodynamic mapping relation and preprocessed data. Through the synergistic effect of the technologies, the core defects of single sensing dimension, prediction lag, control target conflict, extensive execution and the like of a traditional method are systematically solved, and collaborative optimization of degradation efficiency, energy consumption economy and equipment operation life in the kitchen waste sewage treatment process is realized.

The invention discloses a culture tail water resource treatment system based on pearl mussels, which belongs to the technical field of aquaculture and water environment protection and comprises a culture pond, a plurality of first treatment ponds, at least one second treatment pond, a third pipeline and a control unit. Each first treatment pond is connected with the culture pond through a first pipeline, activated sludge is laid in each first treatment pond, and an aeration device and an adsorption filter with the height capable of being adjusted through a lifting mechanism are arranged on each first treatment pond. The second treatment pond is connected with the first treatment pond through a second pipeline, the interior of the second treatment pond is divided into a phycomycete symbiotic area and a water quality purification area, the water quality purification area sequentially comprises a supporting layer, an adsorption layer and a substrate layer from bottom to top, and various aquatic plants are planted in the substrate layer. The third pipeline is connected with the second treatment pond and the culture pond to form a closed water treatment circulation loop. The control unit is responsible for monitoring and regulating the operation state of the system. According to the system, efficient purification and resource recovery of aquaculture tail water are realized through multi-stage treatment and cyclic utilization.

The invention discloses a method and system for removing algae pollutants in water, and relates to the technical field of water treatment and environmental protection, and the method comprises the following steps: obtaining algae floc characteristic parameters of target raw water, and determining an adaptive interval of floc and bubble attachment; coupling the adaptive interval with the real-time hydrolysis state of the flocculating agent, constructing a nonlinear mapping relation based on phase matching of hydrolysis hysteresis characteristics and floc bubble capturing gain response, and generating an adjustable control instruction; the dissolved air pressure and the micropore aeration intensity are dynamically adjusted according to the adjustable control instruction, so that the bubble particle size is matched with the floc structure; and mixing the matched micro-nano bubbles with the floc to generate a stable scum layer and complete solid-liquid separation. According to the method, the floc characteristics are obtained in real time, and the parameters of the air flotation system are dynamically adjusted, so that bubbles are matched with the floc to form a stable scum layer, solid-liquid separation is efficiently completed, and the problems of unstable scum and low separation efficiency of a traditional air flotation method are solved.

The invention discloses a method and system for accurately controlling the concentration of dissolved oxygen (DO) of a sewage treatment aeration unit, and the method comprises the steps: constructing a prediction model, and obtaining a model prediction value of the DO concentration at a current moment or a future moment according to the prediction model; calculating a current confidence coefficient; taking the current confidence coefficient as a weight coefficient, and carrying out weighted fusion calculation on a DO concentration measured value at the current moment and a DO concentration model prediction value to obtain a DO concentration fusion variable; the aeration air volume is adjusted or controlled with the purpose of minimizing the error between the DO concentration fusion variable and the DO concentration target value; the problem of overshoot oscillation caused by DO response lag in traditional control is solved, and a dynamic and accurate control solution is provided for the DO concentration of the aeration unit of the sewage plant.

The invention belongs to the crossing field of artificial intelligence and industrial automation, particularly relates to an aeration fan frequency conversion group control energy-saving method based on an intelligent optimization algorithm, and aims to solve the problems of high energy consumption, response lag and large equipment loss of a traditional control mode. According to the method, parameters such as dissolved oxygen, flow and pressure are collected in real time, a nonlinear optimization model with minimum energy consumption, accurate oxygen supply and minimum start and stop as multiple targets is constructed, an optimal rotating speed distribution scheme is solved online by adopting an improved particle swarm algorithm, and a smooth slope instruction output and closed-loop feedback correction mechanism is combined, so that the optimal rotating speed distribution scheme is obtained. And dynamic self-adaptive adjustment of fan group control is realized. The energy-saving efficiency is remarkably improved by 18%-25%, the annual electricity is saved by more than 800000 kilowatt-hour, meanwhile, the load unevenness is reduced to below 8%, fan start and stop is reduced by 60%, the service life of equipment is prolonged by 2-3 years, hardware does not need to be newly added, the deployment period is short, investment recovery is fast, and the method is successfully applied to multiple sewage plants.

The invention provides an efficiency prediction method and system for a rural sewage resourceful treatment system. According to the method, the fluctuation amplitude and the duration are extracted through a flow fluctuation waveform and a retention duration deviation value, and a flow interference identifier is generated; triggering segmented deformation of the adjusting tank based on the fluctuation amplitude, adjusting the volume of the tank body according to the pressure gradient to dynamically compensate the flow fluctuation, and generating a volume compensation signal in combination with the retention duration difference and the fluctuation duration; the retention time length deviation is matched with a pollutant concentration time sequence, an aeration and precipitation equipment efficiency early warning track is generated based on correlation between the concentration change rate in the lag time period and a volume compensation signal, and a critical failure node is calibrated through a track trend turning point; and if the node is located in the preset discharge time period, updating the regulating reservoir deformation pressure gradient threshold value, synchronizing the dynamic compensation instruction, and executing the instruction to complete closed-loop correction. The closed-loop optimization of the treatment efficiency of the sewage system is realized.

The utility model discloses a sewage treatment aeration mechanism which comprises a bottom plate, an aeration tank and a plurality of groups of aeration heads, a cleaning scraper is rotatably arranged in the aeration tank, the lower surface of the cleaning scraper is fixedly connected with a cleaning brush plate, two groups of mounting top plates are symmetrically arranged on the upper surface of the aeration tank, and the upper surfaces of the two groups of mounting top plates are jointly clamped with a support plate; a rotating assembly used for driving the cleaning scraper and the cleaning brush plate to rotate is arranged on the upper surface of the supporting plate. The upper surface of the supporting plate is provided with an adjusting assembly used for adjusting the height of the cleaning scraper and the cleaning brush plate. The cleaning scraping plate is driven by the rotating assembly to clean the inner wall of the aeration tank, then the surface of the aeration head is cleaned by the cleaning brush plate, and after the aeration heads with different heights are replaced, the cleaning scraping plate and the cleaning brush plate are driven by the adjusting assembly to move up and down, so that the cleaning brush plate can be suitable for the aeration heads with different heights; therefore, manual cleaning of workers is avoided, and meanwhile, the cleaning quality of the aeration head is ensured.