6456 results about "Aeration" patented technology

The invention discloses a sewage plant total nitrogen concentration real-time prediction and process regulation and control method based on an optimization integration algorithm, and belongs to the technical field of environment monitoring and treatment. According to the method, sewage plant data are monitored and collected, a sliding window and a time sequence are combined to analyze and clean the data and reconstruct features, total nitrogen concentration strong correlation variables are screened, data quality is standardized and optimized, a plurality of machine learning algorithms are adopted to construct a prediction model, and an optimal model is optimized through cross validation and performance evaluation. The robustness is improved by global parameter adjustment in combination with optimization algorithms such as a particle swarm, process schemes such as aeration intensity and carbon source adding are generated through multi-objective optimization after containerization deployment, and a whole-process intelligent management and control system is constructed by integrating virtual verification, graded early warning and a self-adaptive feedback mechanism. According to the method, the problems of detection lag, insufficient model generalization ability, regulation response delay and the like of a traditional method are solved, and the operation energy consumption and the medicament cost are remarkably reduced while it is guaranteed that the effluent quality stably reaches the standard.

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 food material cleaning tank comprises an outer tank and a draining tank detachably arranged in the outer tank, the bottom of the draining tank is provided with an aeration channel capable of being communicated with an external air source, and the inner bottom wall of the draining tank is provided with aeration holes which are communicated with the aeration channel and used for blowing air into the draining tank. An air guide pipe with an upward air outlet end is arranged below the draining groove, and an assembly opening for the upper end of the air guide pipe to be detachably inserted into so as to be communicated with the aeration channel is formed in the outer bottom wall of the draining groove. The aeration holes in the inner bottom wall of the draining tank directly blow air into the draining tank, and the blown air cannot be blocked by the bottom wall of the draining tank, so that boiling turbulent flow is formed in the draining tank, pesticide residues and dirt on the surface of an object to be cleaned are powerfully stripped, and the cleaning effect is improved; the aeration channel at the bottom of the draining tank is detachably connected with the air guide pipe at the bottom of the outer tank, so that whether the air channel is communicated or not can be selected according to needs.

The invention provides a sewage treatment aeration control system and method based on multi-parameter collaborative optimization. The method comprises the following steps: acquiring multi-sensor data of COD (Chemical Oxygen Demand), BOD5 (Biochemical Oxygen Demand), DO (Dissolved Oxygen Demand), ammonia nitrogen, ORP (Oxidation Reduction Potential), pH (Potential of Hydrogen), water inlet flow and sludge concentration of wastewater in an aeration tank by utilizing a multi-parameter sensor module; according to the dynamic correction oxygen mass transfer coefficient and the feedforward parameter, establishing a relationship between the water inlet load and aeration; calculating the air volume of an air blower and the fine adjustment value of the opening degree of a butterfly valve through a PID control algorithm and feedback parameters, and correcting the aeration quantity to obtain a total air volume target value; and adjusting the opening degree and the rotating speed of a guide vane of the air blower according to the self-adaptive PID control parameters and the corrected total air volume target value by utilizing a self-adaptive PID controller, and adjusting the opening degree of a butterfly valve according to a gradient butterfly valve adjusting strategy. The output of the air blower is optimized by adopting self-adaptive PID, and the opening degree of the butterfly valve of each biological tank is dynamically adjusted through a gradient valve adjusting strategy, so that the accurate control of the aeration rate is realized; the sewage treatment effect is ensured.

The invention relates to the technical field of sewage plant-oriented carbon neutralization regulation and control scheme design, in particular to a sewage plant-oriented carbon neutralization regulation and control method and a sewage plant-oriented carbon neutralization regulation and control system. The method comprises the steps of collecting water quality, energy consumption and gas emission data in real time through a multi-source sensor, inputting the data into a space-time diagram convolutional network to predict a carbon emission trend after edge computing node cleaning, and dynamically optimizing an aeration rate and a sludge reflux ratio based on a fuzzy PID controller. A whole-process carbon footprint is tracked by adopting a life cycle evaluation method, a multi-target compensation strategy is generated in combination with an improved NSGA-III algorithm, and photovoltaic, biogas and green power resources are scheduled preferentially. A digital twin platform and a block chain evidence storage module are innovatively integrated, the former calibrates model parameters in real time, and the latter generates a non-tampering carbon neutralization voucher. According to the method, the problems of one-sided data, regulation and control lag and insufficient credibility of a traditional method are solved, and minute-level sensing, prediction and offset closed-loop control of carbon emission in the sewage treatment process is realized.

The invention provides a sewage AI intelligent management and control system based on a neural network algorithm, which comprises multi-source data acquisition units, an algorithm processing unit and an execution feedback unit, and is characterized in that the multi-source data acquisition units are deployed at a water inlet and a water outlet of a sewage treatment assembly line and in a biological reaction tank; comprising a fast parameter detection array composed of a pH sensor, a conductivity sensor, a dissolved oxygen sensor, a turbidity sensor, a temperature sensor and an ORP sensor. The process state monitoring group consists of an aeration equipment rotating speed sensor, a reflux pump flowmeter and a sludge concentration meter; the fast parameter detection array and the process state detection group are connected with the edge computing node through an industrial bus, perform sliding window mean filtering preprocessing on original data, generate regulation and control instructions of aeration rate, reflux ratio and sludge discharge frequency through model calculation, and send the regulation and control instructions to the execution unit; the complete refined AI intelligent control sewage treatment system based on the neural network algorithm is realized.

The invention discloses an integrated water quality monitoring and processing system for aquaculture, and the system comprises a data collection module which is used for obtaining dissolved oxygen, ammonia nitrogen, pH value, temperature and flow data from a plurality of spatial distributed sensing nodes; the data preprocessing module is used for completing noise removal, correction and time sequence synchronization; the water quality state estimation module is used for outputting real-time multi-point water quality states based on a distributed unscented Kalman filtering algorithm; the space-time fluid modeling module is used for constructing a Lagrange model to extract a flow velocity field and a diffusion coefficient; the scheduling optimization module is used for generating an aeration and dosing scheme by adopting an improved multi-target Pareto optimization algorithm based on a space-time sensitive boundary; the execution module is used for controlling an air blower, an aeration pipe and a dosing pump to complete oxygenation and dosing operations; and the self-adaptive updating module is used for collecting feedback data to adjust filtering gain and optimizing parameters so as to realize closed-loop control of the system.

The invention discloses a rapid culture method of aerobic granular sludge, and belongs to the technical field of sewage treatment. According to the invention, pharmaceutical factory sludge is used as a raw material to prepare pharmaceutical factory sludge biochar; an SBR reactor is constructed and operates in a water inlet-anoxic-aeration-precipitation-drainage mode, and pharmaceutical factory sludge biochar is added in the sludge inoculation process to promote rapid formation of system particles and stable operation. According to the method disclosed by the invention, the mature aerobic granular sludge with the capabilities of removing organic matters and synchronously denitrifying can be obtained by culturing for 30 days on average, so that the granulation process of the sludge is effectively improved, the forming time of the aerobic granular sludge is remarkably shortened, and the aerobic granular sludge has higher long-term operation stability.

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 shallow lake water quality pollution treatment system and method, and relates to the technical field of water pollution, and the method comprises the steps: S1, building a disturbance intensity index DIX and ammonia nitrogen concentration change curve, achieving the quantitative recognition of bottom mud disturbance and pollution release behaviors, training a rebound risk prediction model according to the quantitative recognition, and outputting a rebound risk prediction coefficient Ft; and the capability of pre-judging the rebound trend of the ammonia nitrogen pollution is effectively improved. In the step S2, the adjusted aeration response frequency BAF is dynamically generated based on the rebound risk prediction coefficient Ft value, so that the aeration behavior is converted into a risk-driven system from a traditional timing system, the energy consumption and disturbance intensity are further reduced, and the treatment efficiency is improved. And S3, further introducing a governance residual coefficient GRC, constructing a complete feedback closed loop, and realizing accurate evaluation and secondary strategy optimization of the governance effect of each round, thereby avoiding the problem of effect back-off or excessive disturbance after governance.

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 discloses a dynamic optimization setting method and system for process parameters of a biological reaction tank of a sewage treatment plant based on machine learning. The method comprises the following steps: S1, data acquisition; s2, data integration and preprocessing; s3, establishing an effluent quality and concentration simulation prediction matrix; s4, carrying out model fusion; and S5, carrying out dynamic optimization setting on the technological parameters of the biological reaction tank based on the effluent quality simulation prediction model matrix established in the steps S1 to S4. The method comprises the following steps: firstly, establishing an outlet water quality high-precision simulation prediction model matrix, performing setting optimization on process parameters of a green pool and a reverse pool by adopting a particle swarm algorithm according to the model matrix, and finally obtaining process parameter set values of a future operation period; the advanced data analysis technology is fully utilized, the operation efficiency and effect of the sewage treatment plant are improved through the steps of collecting, processing, predicting, optimizing and the like, and energy conservation and consumption reduction are achieved on the premise that it is guaranteed that the effluent of the sewage treatment plant stably reaches the standard.

The invention discloses an ultra-aeration air flotation system and an implementation method thereof, and belongs to the technical field of wet-process phosphoric acid production, and the ultra-aeration air flotation system comprises a reaction tank, an inclined guide plate arranged on the inner wall of the reaction tank, a dish-shaped tank which is arranged at the bottom of the reaction tank and is annularly arranged around the center of the reaction tank, and a central island which is formed by protruding the inner ring part of the dish-shaped tank at the bottom of the reaction tank, the aeration mechanism is arranged on the central island and is communicated with the outside through a pipeline; the transmission mechanism is arranged above the reaction tank; the central transmission rod is connected with the output end of the transmission mechanism and is arranged along the axis of the reaction tank; the dynamic settler is arranged at the upper part in the reaction tank and is connected with the central transmission rod; and the overflow port is matched with the dynamic settler. The inclined guide plate, the dish-shaped aeration structure and the dynamic settler are integrally arranged in the reaction tank, so that the super-aeration uniformity, the foam separation capacity, the phosphoric acid purification and the liquid vortex intensity in the wet-process phosphoric acid production process are remarkably enhanced, and the reaction efficiency and the product quality are improved.

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 an intelligent aeration control system and method for a sewage treatment plant, and relates to the technical field of sewage treatment control. Comprising a data acquisition module, an aeration test module, a demand prediction module, a normal state analysis module and an aeration control module, wherein the data acquisition module is used for acquiring real-time working data of a primary sedimentation tank, test data of an aeration tank, historical working data and real-time working data of the aeration tank and preprocessing the data; the technical key points are as follows: an aeration tank is scientifically divided into multiple sections, professional monitoring equipment is deployed in each section, multi-dimensional data is acquired, the data of each section is independently acquired and tested, the operation condition and the processing effect of the aeration equipment in each section can be accurately mastered, and on the basis of the accurate data and in combination with an aeration efficiency evaluation model and a regulation and control strategy, the aeration efficiency of the aeration tank is evaluated. And fine adjustment can be performed according to actual requirements of each section, so that resource waste or poor treatment effect caused by one-step regulation and control is avoided, efficient and accurate operation of the aeration equipment is realized, and the aeration equipment has a good use prospect.

The invention discloses a biological nitrogen and phosphorus removal MBR sewage treatment system and a treatment process thereof. The process comprises the following steps: 1) pretreatment; 2) treatment in an anaerobic tank; (3) treating in an anoxic tank; 4) treatment in an aerobic tank; 5) MBR membrane pool treatment; sewage enters the box body through the water inlet, the moving mechanism drives the aeration mechanism and the scum cleaning mechanism to move, the aeration mechanism performs aeration treatment on the sewage in the box body, and when the aeration mechanism blows gas into the sewage through the branch gas pipe, bubbles can be scattered by spiral blades, so that the contact area between the gas and liquid is further increased, and the aeration effect is enhanced; when the sewage is subjected to aeration treatment, the scum after aeration treatment rises, the scum cleaning mechanism scrapes the scum floating after aeration for multiple times, collects the scraped scum and sends the scum out of the box body, impurities suspended in the middle layer of the sewage are fished, and the sewage after scum cleaning enters the next treatment procedure through the water outlet.

The invention relates to a method for treating nitrogen and phosphorus-containing cleaning wastewater, which comprises the following steps: adding residual activated sludge, activated carbon and a nitrogen and phosphorus removal bacterial agent into a treatment system, carrying out braising exposure treatment, and regularly adding an organic carbon source; the nitrogen and phosphorus removal bacterial agent at least contains microbacterium kitamiense (PR) and aeromicrobe (Aeromicrobe tamlense) (PW), and is characterized in that the nitrogen and phosphorus removal bacterial agent at least contains the microbacterium kitamiense (PR) and the aeromicrobe tamlense (PW); the preservation numbers of the strain are respectively CGMCC (China General Microbiological Culture Collection Center) No. 25182 and CGMCC No. Starting water feeding after braising exposure is finished, and continuously operating to treat the cleaning wastewater. According to the method, total nitrogen, total phosphorus and COD in the wastewater can be efficiently removed at the same time in one structure, the treatment burden of a subsequent biochemical unit is avoided, the process is simple, and the cost is low.

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 relates to the technical field of sewage treatment and automatic control thereof, in particular to an automatic monitoring system and method for sewage treatment of a vibrating membrane bioreactor. The method comprises the following steps: monitoring key indexes in real time, and carrying out standardization processing and interpolation on sensor data to ensure the accuracy of the data; an information code generation and verification mechanism is adopted to ensure data integrity and security; a physical model and a deep learning model are combined to predict sewage treatment efficiency, and space-time relevance is analyzed through a space-time convolutional network; operating parameters of the system are optimized through a deep reinforcement learning algorithm, and the frequency, the amplitude, the aeration power and the medicament dosage of a vibrating membrane are adjusted to achieve an optimal treatment effect; through real-time adjustment of the vibrating membrane and the aeration equipment, the sewage treatment efficiency is improved; the real-time treatment state is displayed, the sewage removal rate is monitored, and an alarm mechanism is triggered under the abnormal condition. According to the method, the data security and the system optimization are considered while the processing effect is ensured.

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 discloses an oxidation leaching system and method for argillaceous sandstone uranium ore. The system comprises a uranium leaching pool, a recovery pool, a liquid preparation-dosing unit pool and an intermediate pool. Wherein the uranium leaching tank is provided with a water dispersing layer, a uranium ore layer and a liquid collecting layer from top to bottom, and efficient oxidation of uranium ore is achieved through coupling of oxygen and ferric iron; the recovery pool is sequentially provided with a resin adsorption layer and a water inlet layer from top to bottom, and efficient recovery of uranium in the leachate is achieved; the liquid preparation-dosing unit pool is provided with an acid-resistant submersible sewage pump used for circularly conveying the ore leaching liquid to the uranium leaching pool and adjusting the pH value of the ore leaching liquid and the initial concentration of Fe < 3 + >. And the middle tank drives the ore leaching liquid to form periodic tidal flow circulation in the uranium leaching tank through an acid-resistant submersible sewage pump. According to the system and the method, tidal flow regulation and control, aeration oxygen supply enhancement and iron ion circulation oxidation technologies are coupled, and the uranium leaching rate gt of the argillaceous sandstone uranium ore is achieved by establishing a multi-stage oxidation field domain, optimizing a fluid migration path and establishing an iron element regeneration mechanism; 92%.

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 a maltodextrin waste residue treatment device and method.The maltodextrin waste residue treatment device comprises a fermentation tank, a stirring shaft, a stirring paddle, a first driving mechanism and an air guide bin, the interior of the stirring shaft is hollow to form a first air channel, the interior of the stirring paddle is hollow to form a second air channel, the first air channel is communicated with the second air channel, and a plurality of aeration holes are formed in the stirring paddle; a connecting rod, a spring and a plurality of blocking blocks provided with air passing holes are arranged in the second air channel, the blocking blocks are connected in series through the connecting rod, the spring pulls the connecting rod to block the aeration holes through the blocking blocks, a pull rope is arranged in the first air channel, one end of the pull rope is fixedly connected with the connecting rod, the pull rope pulls the connecting rod to move horizontally, and the air guide bin is installed at the top of the stirring shaft. The air guide bin is rotationally connected with the stirring shaft, an air inlet pipe is arranged on the air guide bin, and an air inlet hole is formed in the stirring shaft. According to the method, the maltodextrin waste residues are subjected to pulse feed fermentation, the ineffective oxygen supply proportion is reduced, the problem of waste of a large amount of energy is solved, the environment is protected, and enterprise benefits are improved.

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 belongs to the field of atmospheric environment particulate matter monitoring, and particularly relates to an atmospheric environment salt mist concentration real-time monitoring device and method. The monitoring device comprises a computer control module, a container and a buoy, a conductivity electrode, a temperature sensor, a displacement sensor, an air inlet pipe and an air outlet pipe are integrated on the top of the container, and the air outlet pipe is connected with a reciprocating cylinder with a piston and a limit switch. The monitoring method comprises the following steps: calibrating the initial volume V < initial > of liquid and the volume V < initial > of a reciprocating cylinder; circularly collecting the conductivity k0 and k1, the temperature T0 and T1 and the displacement D and Dtotal of the buoy before and after ventilation; correcting the salt mass increment delta M through the dynamic volume, and finally outputting the salt mist concentration C; according to the invention, the defects of inaccurate ventilation volume metering, solution density change and interference of moisture in high-humidity air on concentration monitoring in the prior art are overcome, full-automatic continuous monitoring of the salt mist concentration is realized, the measurement error is effectively reduced, the measurement precision and the monitoring capability are remarkably improved, and the method has relatively good environmental adaptability.

The invention provides a control system and a control method for a suspension aeration fan, and relates to the technical field of intelligent control, and the system comprises an intelligent control module which is used for carrying out the automatic analysis of the operation state of the suspension aeration fan through a fuzzy control algorithm according to the real-time operation parameters of the suspension aeration fan and the environment parameters of an aeration tank, generating a fan adjusting instruction; the execution module is used for adjusting the rotating speed and air flow operating parameters of the fan according to the fan adjusting instruction so as to control aeration; and the coupling module is used for constructing a multiphase flow coupling model, realizing real-time solution of the oxygen transfer coefficient and performing reverse optimization adjustment on the operating parameters according to a simulation result of the multiphase flow coupling model. Through the synergistic effect of the intelligent control module, the execution adjustment module, the multiphase flow coupling optimization module and the fault early warning module, intelligent, efficient and stable operation of the aeration process is achieved, and the reliability and safety of the system are improved.

The invention discloses a high-activity short-cut nitrification quick-start long-term stable operation process, which relates to the technical field of biological sewage treatment, and comprises the following steps: inoculating sludge, carrying out anaerobic fermentation pretreatment, and removing expanded sludge and sludge foam in combination with quick sludge discharge and manual removal; controlling the number of days of sludge age, reproliferating the sludge, enriching ammonia oxidizing bacteria, and inhibiting and elutriating nitrite oxidizing bacteria; long-term operation is carried out under the condition of no sludge discharge, the abundance and activity of ammonia oxidizing bacteria are maintained, and meanwhile, the abundance of nitrite oxidizing bacteria is controlled; the proliferation of nitrite oxidizing bacteria is controlled by using the predation effect of protozoa and metazoan, and the accumulation rate of nitrite in the reactor is controlled; monitoring the dissolved oxygen, the pH value and the concentrations of ammonia nitrogen, nitrite nitrogen and nitrate nitrogen in the reactor in real time, and regulating and controlling the influent ammonia nitrogen load and the free ammonia concentration; dissolved oxygen distribution is adjusted in an intermittent aeration or microporous aeration mode, and the operation conditions of the short-cut nitrification reactor are optimized. The method is simple to operate, economical and effective.