5324results about "Water treatment parameter control" patented technology

The invention discloses an intelligent regulation and control method and system for sewage treatment and a program product, and relates to the technical field of sewage treatment.The method comprises the steps that a multi-modal water quality sensing network is constructed, and sewage quality parameter information and overall treatment target numerical value information are obtained; according to the equipment operation state and the overall treatment target numerical value information, sewage treatment process parameters are generated, and treatment units in the sewage treatment system are distributed in a plurality of areas; triggering a process regulation and control instruction according to the sewage treatment process parameters and the overall treatment target numerical value information; obtaining partition processing effect detection information, and comparing the partition processing effect detection information with the corresponding index value to obtain a partition difference value; if the partition difference value exceeds a preset change threshold range, triggering a partition self-adaptive regulation and control updating instruction; obtaining partition adjustment parameter information according to the partition self-adaptive regulation and control updating instruction; and triggering an adjustment instruction. The invention provides an intelligent regulation and control method based on multi-dimensional perception and adaptive decision.

The present invention relates to the technical field of the control of wastewater treatment devices. Disclosed is an intelligent control method for wastewater treatment devices at a dry bulk cargo terminal, which is used for solving the problem of poor control of wastewater treatment devices at a terminal. The method comprises the following steps: installing a plurality of types of sensors at key locations of a dry bulk cargo terminal, and using edge computing nodes to perform real-time data collection and preprocessing; on the basis of historical features and temporal features, using a machine learning model to perform wastewater type classification, thereby realizing efficient dynamic adjustment of operating parameters of wastewater treatment devices; then, by means of weighted voting and confidence evaluation, integrating a plurality of classification results to ensure an optimal treatment effect; and analyzing actual wastewater treatment conditions to continuously optimize device control, thereby preventing faults, extending the service life of devices, and improving the wastewater treatment effect.

The invention provides a sewage treatment carbon source intelligent adding method, which comprises the following steps: collecting multi-parameter feed-forward and feedback signals of water inlet and an anoxic tank, constructing a dynamic model containing feed-forward compensation, model prediction control and feedback compensation, calculating the theoretical adding amount of a carbon source, inputting a predicted value and feedback parameters into an LSTM network for correction, and optimizing a network structure by a genetic algorithm. The adding amount is controlled in a closed-loop mode through a variable frequency pump, the LSTM weight is updated on the basis that the error is larger than 5%, and finally a control strategy is optimized by using an NSGA-II algorithm and integrating carbon source consumption, effluent total nitrogen and energy consumption. The dynamic self-adaptive carbon source adding method is constructed by fusing feedforward perception, LSTM prediction, feedback regulation and multi-objective optimization, so that quick response and accurate control on water quality fluctuation are realized, the denitrification efficiency and the carbon source utilization rate are improved, and the method has excellent engineering adaptability and popularization value.

The invention relates to the technical field of sewage treatment, in particular to a sewage treatment control process based on dynamic regulation and control of aeration and carbon source addition, which comprises the following steps: acquiring microbial metabolism heat change data in real time; biochemical treatment: monitoring the dielectric constant of a water body and the Reynolds number of sewage flow in real time, and constructing a multi-parameter coupling regulation and control model and dynamically regulating and controlling the aeration rate in combination with microbial metabolism heat change data obtained in the pretreatment step; accurately adding the carbon source according to a calculation result of the carbon source demand prediction model; sewage subjected to biochemical treatment is subjected to ultrafiltration through a nanofiber membrane and then subjected to combined disinfection treatment through ultraviolet rays and ozone, treatment parameters are regulated and controlled in real time according to the membrane flux attenuation trend in the treatment process, and dynamic and accurate control over sewage treatment is achieved. According to the method, the problem of energy consumption waste caused by excessive aeration or low degradation efficiency caused by insufficient aeration in a traditional process is solved, and accurate matching of dissolved oxygen supply and microbial requirements is realized.

The invention relates to the technical field of river and lake landscape water system purification, and discloses a river and lake landscape water system composite purification system, which comprises a water quality parameter acquisition module, an ecological analysis module, a microbiological treatment module, a multi-stage collaborative purification module, a purification parameter generation module, an authority association module and a dynamic regulation and control module. The water quality parameter acquisition module acquires data in multiple dimensions to quantify the pollution level and trigger a purification instruction; the ecological analysis module evaluates the ecological self-purification capability, and the microbiological treatment module predicts the degradation efficiency; the multi-stage collaborative purification module generates a regulation and control signal in combination with the evaluation value; the purification parameter generation module generates repair and activation parameters accordingly; the permission association module monitors the emission permission of the pollution source; and the dynamic regulation and control module forms a closed-loop purification process. According to the system, through multi-module cooperation and closed-loop regulation and control, accurate recognition of river and lake landscape water system pollution, ecological self-purification capability activation and microbial degradation enhancement are achieved, and the purification efficiency and ecological restoration long-term effect are improved.

The invention relates to the technical field of sewage treatment, in particular to a coupling control system and method for deep denitrification of sewage, and the system comprises a real-time water quality monitoring module, a microorganism twinborn modeling module, a real-time prediction module, an intelligent decision module, an execution mechanism module and a prediction regulation and control module. Compared with the prior art that a passive feedback control strategy based on an effluent quality index is generally adopted, the hysteresis quality is high, and violent fluctuation of an inflow load cannot be coped with; according to the method, a digital twinborn body capable of reflecting the functional state of a microbial community in real time is constructed, and a control target is improved from a traditional process parameter set point to direct optimization of microbial ecological functions; according to the invention, the method achieves the fundamental crossing from the control of technological parameters to the regulation and control of microbial ecology, can carry out intervention from the root of the reaction process, enables the system to have the active health management capability, and remarkably improves the stability of the treatment efficiency and the intelligent level of coping with complex working conditions.

The invention relates to the technical field of swimming pool cleaning equipment control, and provides a cleaning mode switching method and device for a multifunctional swimming pool cleaning robot. According to the method, water quality data corresponding to a swimming pool, stain distribution images of pool walls and pool bottoms, swimming pool structure size data and stain type data are collected in real time; dividing a cleaning area according to the swimming pool structure size data, generating a stain area marking result in the cleaning area in combination with the stain distribution image and the stain type data, and judging the pollution level of the whole swimming pool according to the water quality data so as to retrieve a corresponding cleaning mode from a preset cleaning mode library, the preset cleaning mode library comprises a global coverage mode, a key enhancement mode, a corner fitting mode and a water quality maintenance mode; and according to the called cleaning mode, a corresponding cleaning path plan and execution instruction is generated, and the driving module, the scrubbing module and the purification module are controlled to work cooperatively. According to the method, accurate analysis of the swimming pool environment is achieved, and blind cleaning of a traditional fixed mode is avoided.

The invention provides an intelligent aeration system and a control method, and the system comprises a multi-parameter monitoring module which collects parameters such as water depth, pressure, temperature, dissolved oxygen, inlet water ammonia nitrogen, chemical oxygen demand, and mixed liquid suspended solid concentration in real time; the data analysis and preprocessing module adopts a sliding window algorithm to carry out data cleaning and anomaly detection; the oxygen supply demand prediction module establishes an oxygen demand prediction model based on machine learning, and combines theoretical calculation and deep learning prediction; the multi-target intelligent control module adopts a reinforcement learning algorithm, establishes a Markov decision process model, and optimizes the processing effect and energy consumption at the same time; the execution control module is used for accurately adjusting the rotating speed of a fan, the opening degree of a valve and the running state of an aerator; and the system collaborative optimization module realizes linkage control of the aeration system and the carbon source adding system. The system has the functions of self-adaptive parameter adjustment, fault diagnosis and self-recovery, the DO control precision reaches 95% or above, the daily average electricity is saved by 9.7%, the NH4-N of effluent is reduced by 25%, and the annual comprehensive benefit exceeds 500,000 yuan.

The invention relates to the technical field of intelligent control of filters, and discloses an intelligent control method for a self-cleaning filter based on sensing of the Internet of Things. The method comprises the following steps: collecting multi-source sensing data such as inlet and outlet water pressure difference, flow fluctuation frequency, turbidity change rate and vibration spectrum characteristics when the filter runs; performing hierarchical clustering on the data according to a preset blockage level threshold value, and generating a blockage feature matrix with a timestamp; the matrix dynamic state is monitored in real time, and when the turbidity change rate and the covariance of the vibration spectrum characteristics exceed a dynamic threshold value, self-adaptive cleaning strategy switching is triggered; in combination with the time-space matching degree of a historical cleaning period and a current matrix, decomposing sub-feature sets of high-frequency blockage and low-frequency deposition modes, and correcting the weight of the high-frequency sub-feature sets through cross validation of fluid dynamics and mechanical wear parameters; and finally, a preferential cleaning area is reversely positioned along the structural topological graph of the filter, a three-dimensional cleaning path instruction is generated, and the opening degree of a blow-down valve is synchronously adjusted, so that the control and operation performance of the filter is optimized.

The invention discloses an integrated process for cooperative treatment and up-to-standard discharge of industrial wastewater and domestic wastewater of a power plant, and relates to the field of cooperative treatment of wastewater and sewage, and the integrated process comprises the following steps: configuring a raw water collection system, setting a raw water source, configuring a multi-stage filtration and heating pretreatment process, configuring an ultrafiltration membrane system, and setting a fine filtration path. A first-stage reverse osmosis system is configured, reverse osmosis parameters are set, a second-stage reverse osmosis system is configured, a low-conductivity water treatment path is set, a mixed ion exchanger is configured, a desalted water output path is set, a standby pool body is set, and the adjusting capacity is set. A raw water collecting system, a multi-stage filtering and heating pretreatment process, an ultrafiltration membrane system, a first-stage reverse osmosis system, a second-stage reverse osmosis system, a mixed ion exchanger and a standby pool body are sequentially arranged in the process. The problems of unstable effluent quality, insufficient deep desalination capability and poor emergency regulation capability in a traditional power plant wastewater treatment mode can be effectively solved.

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 modularized ultrapure water preparation and on-line monitoring system, which comprises a pretreatment module, a purification module, a monitoring module and a control module, and is characterized in that a dynamic monitoring end is arranged, and when ultrapure water preparation is monitored in real time, a multi-parameter fusion detection mechanism and self-adaptive threshold analysis are integrated; meanwhile, the abnormal state of the water quality is dynamically judged based on the conductivity, TOC and particulate matter data collected in real time, the sudden change risk of the water quality output by the purification module can be recognized in real time, error accumulation caused by traditional single-point monitoring is avoided, and therefore the purity detection reliability of the ultrapure water is improved; and the reverse osmosis unit and the electrode deionization unit of the purification module can immediately respond to the effluent parameter change of the pretreatment module, so that the hidden dangers of membrane blockage and recovery rate reduction caused by water quality fluctuation are reduced, and the energy consumption redundancy is reduced while the water quality stability is ensured.

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 present invention discloses an intelligent control method for sewage treatment equipment at dry bulk cargo terminals, relating to the technical field of sewage treatment equipment control, and is used to address the problem of poor control of sewage treatment equipment at terminals. The method comprises following steps: installing multiple types of sensors at key locations on dry bulk cargo terminals, utilizing edge computing nodes for real-time data acquisition and preprocessing, combining historical and temporal features with machine learning models to classify sewage types, achieving efficient dynamic adjustment of sewage treatment equipment operation parameters, then using weighted voting and confidence assessment to integrate multiple classification results to ensure optimal treatment results, and analyzing the actual sewage treatment situation to optimize equipment control continuously, thereby preventing failures, extending equipment life, and improving sewage treatment effectiveness.

The invention relates to the technical field of water treatment, in particular to a high-recovery-rate short-process salt-tolerant reverse osmosis system and treatment method.The system comprises a pretreatment unit, a sensor monitoring unit, a self-adaptive pressure-flow coupling control unit, an intelligent operation and maintenance unit and a gradient boosting reverse osmosis membrane set. According to the invention, ultrahigh recovery rate and desalination performance are realized, gradient boost membrane module design is combined with concentration polarization inhibition technology, membrane surface flow state distribution is optimized, osmotic pressure influence is significantly reduced, pressure-flow coupling control and concentrated water excess pressure energy recovery technology are integrated, energy dynamic cyclic utilization is realized, energy consumption is reduced, and the method is suitable for industrial production. By monitoring the membrane flux attenuation rate and the transmembrane pressure difference in real time and dynamically optimizing a pulse cleaning strategy, dynamic optimization of high-salinity wastewater treatment is realized, and the problems of low recovery rate, high energy consumption and quick membrane pollution in high-salinity wastewater treatment are solved.

The invention relates to the technical field of organic fertilizer preparation and waste recycling, in particular to a food industrial park wastewater recycling and sludge resourceful treatment method which comprises the following steps: step 1, dynamically partitioning wastewater and cooperatively separating grease, monitoring the conductivity and turbidity of wastewater at water outlets of all workshops in real time, and dynamically dividing wastewater load grades according to monitored values; step 2, coupling biological treatment and carbon source circulation, inputting the effluent in the step 1 into an anaerobic reactor according to a real-time COD load ratio, adding a sludge glycolysis solution as a carbon source supplement, and dynamically adjusting the adding amount based on the ratio of volatile fatty acid to alkalinity; step 3, catalytic oxidation-membrane coupling advanced treatment: carrying out catalytic ozonation on aerobic effluent through sludge-based activated carbon, and adjusting the filling amount of a catalyst in a stepped manner along with characteristic parameters of organic matters in the effluent; 4, directional glycolysis of the sludge and closed-loop utilization of products. Through monitoring and dynamic adjustment, the problems of high energy consumption, low treatment efficiency, resource waste and the like in a traditional wastewater treatment method are effectively solved.

A water filtration system is also disclosed. The water filtration system includes a regenerative media filter vessel, a filtrate line, a feed line, a recirculation line, a gas line, and at least one pump. A method of filtering water in a system comprising a regenerative media filter is disclosed. The method includes operating the system in a filtration mode, operating the system in a cleaning mode responsive to a differential pressure measurement across the regenerative media filter, operating the system in an aeration mode, operating the system in a pre-filtration mode after operating the system in the cleaning mode, operating the system in a drain mode, and operating the system in the filtration mode following the drain mode. A controller and non-transitory computer-readable medium having computer-readable signals stored thereon that define instructions that, as a result of being executed by the controller, instruct the controller to perform a method of operating a water filtration system are disclosed.

The invention relates to the technical field of industrial wastewater deep purification, and discloses an industrial wastewater deep purification system based on nano catalytic membrane separation, which comprises a pretreatment module, a catalytic degradation module, a membrane separation module, an intelligent control module and a regeneration maintenance module which are sequentially connected according to a treatment flow, the pretreatment module adjusts the water quality of inlet water to a range suitable for subsequent treatment through pH adjustment and suspended matter interception, that is, the pH is 2-11, and the turbidity is less than or equal to 50NTU; the catalytic degradation module is used for decomposing organic pollutants and mineralizing refractory substances under the excitation of ultraviolet / visible light by utilizing the photocatalytic characteristic of a nano catalytic membrane. The TiO2 / WO3 heterojunction nano catalytic membrane is adopted, so that deep oxidation of refractory pollutants such as phenol and sulfonamide antibiotics is realized, finally, the COD removal rate of 98.7% is achieved, and compared with a traditional photocatalytic process, the energy consumption is reduced by 54%.

The invention discloses an intelligent self-adaptive silt filtering system and method for a pumped storage power station, and belongs to the technical field of pumped storage power stations. The system comprises a multi-parameter water quality monitoring module, an intelligent self-adaptive filtering module, a hydraulic cyclone enhanced separation module, a self-cleaning and backwashing module and a central control and data processing module. According to the method, river sediment characteristics are monitored in real time, filtering parameters are dynamically adjusted through an artificial intelligence algorithm, efficient sediment interception is achieved by combining hydraulic cyclone separation and multi-stage filtering, the problem of filtering medium blockage is solved through a self-cleaning mechanism, and meanwhile the self-adaptive learning capacity is achieved so as to optimize system performance. The system can significantly improve the filtering efficiency, reduce energy consumption and operation and maintenance cost, adapt to complex water quality working conditions, and guarantee safe and stable operation of key equipment of the pumped storage power station.

The invention provides an intelligent chemical adding control method and system, equipment and a storage medium, and belongs to the technical field of intelligent control, the method is applied to a water treatment system, and the method comprises the following steps: based on raw water data, obtaining a first chemical adding amount through a chemical adding amount prediction model; generating an initial dosing amount instruction based on the first dosing amount, and sending the initial dosing amount instruction to a polyaluminum chloride dosing device and a sodium hypochlorite dosing device; obtaining the raw water ion concentration, alumen ustum form data and a floating current value after chemical adding, calculating a first difference value between the raw water ion concentration and a target ion concentration, calculating a second difference value between the alumen ustum form data and target alumen ustum form data, and calculating a third difference value between the floating current value and a target floating current value; and determining a target correction coefficient based on the first difference value, the second difference value and the third difference value, and updating the first dosage based on the target correction coefficient to obtain a second dosage. According to the invention, the dosing precision and adaptability can be improved.

The invention discloses a process and equipment for synthesizing fluorapatite from wastewater, the equipment comprises a blending tank, a dispensing tank, a crystallization fluidized bed reactor, a reaction tank, a coagulation tank, a flocculation tank, a sedimentation tank, a pH detection device, a lift pump and a control system, fluorine-containing sewage and phosphorus-containing sewage are mixed in the blending tank, so that the molar ratio of fluorine to phosphorus is 0.34-0.4; the method comprises the following steps: adding fluorapatite into a reaction tank, adjusting the pH value to 7.5-8.5, pumping the fluorapatite and lime slurry into a crystallization fluidized bed reactor according to a calcium-phosphorus molar ratio of (3-3.5): 1, carrying out a crystallization reaction, naturally airing the generated fluorapatite crystals, carrying out a reaction on crystallization treatment water and the lime slurry in the reaction tank to remove fluorine and phosphorus, carrying out coagulation, flocculation and precipitation treatment, and discharging the effluent after reaching the standard. The high-quality fluorapatite product is prepared from the fluorine-containing sewage and the phosphorus-containing sewage, so that the preparation cost is low, and resourceful treatment of the fluorine-containing sewage and the phosphorus-containing sewage is realized.

The invention discloses a regulation and control method for stably improving the water quality of water produced by a reverse osmosis system of a power plant, and relates to the field of water quality regulation and control, which comprises the following steps: configuring a water inlet flow regulation device, performing initial setting, configuring the position of an online sensor, performing data acquisition, setting judgment rules of turbidity, differential pressure and transmembrane pressure difference, and determining the water quality. And configuring a flocculant adding device and adding control logic, configuring a backwashing and adding linkage control state machine, and carrying out data smoothing, trend analysis and self-adaptive threshold adjustment. When the water quality of the raw water fluctuates for a short time or the pollution load is increased, dynamically adjusting the water inlet flow, the backwashing intensity and the flocculant adding concentration based on parameters such as turbidity, pollution index, conductivity and pressure difference which are acquired by an online sensor in real time; and meanwhile, the concentrated water reflux proportion and the high-pressure pump frequency are automatically corrected by continuously calculating the desalting rates of the first-stage and second-stage reverse osmosis devices, so that the running state of the membrane component is kept in an optimal interval.

The invention relates to the technical field of generator set mixed bed operation monitoring, and discloses a fine processing mixed bed failure situation situation sensing system and method, the sensing system comprises a sampling tube, a sampling electric valve, a sampling isolation valve, a cation resin column, a conductivity meter, a mixed bed failure monitoring device and a resin regeneration system; the sampling pipe extends into the central axis of the fine treatment mixed bed; a plurality of sampling pipes are arranged along the height direction of the fine treatment mixed bed, are sequentially connected with a sampling electric valve and a sampling isolation valve, and then are connected in series to one end of a cation resin column; the other end of the cation resin column is connected with the conductivity meter; the mixed bed failure monitoring device is electrically connected with the sampling electric valve, the sampling isolation valve and the conductivity meter; and the resin regeneration system is connected with a water inlet pipeline and a water outlet pipeline of the fine treatment mixed bed through a fine treatment mixed bed resin inlet valve and a fine treatment mixed bed resin outlet valve respectively. Situation awareness of mixed bed failure is achieved by establishing a layered monitoring system, and long-term stability of water quality of a water vapor system of a power plant is guaranteed.

The invention discloses an adsorption-extraction combined heavy metal polluted water remediation system and method, and belongs to the field of water pollution treatment. The system is provided with a sensing unit which collects data in real time through a heavy metal concentration sensor and the like; the model unit outputs key information such as adsorbent real-time saturation and the like based on the acquired data and an adsorbent saturation prediction model; the decision-making unit is used for calculating a dynamic threshold value according to the information, accurately judging regeneration time and optimizing regeneration parameters; the execution unit executes a regeneration operation according to the instruction. And the feedback unit feeds back the regenerated data to the model unit to realize dynamic optimization of the system. According to the method, closed-loop operation of data acquisition, processing, decision making and feedback is realized based on cooperation of all units of the system. According to the invention, the remediation efficiency of the heavy metal polluted water body is effectively improved, the regeneration of the adsorbent is accurately controlled, the operation energy consumption is reduced, the complex and changeable pollution working conditions can be better handled, and the effluent quality is guaranteed.

The invention discloses an intelligent sewage treatment method and system based on data fusion, and relates to the technical field of sewage treatment.The intelligent sewage treatment method and system have the advantages that compared with a traditional monitoring mode depending on a single index, key parameters such as pollutant types, sludge volume indexes and filamentous bacterium biomass are integrated, comprehensive pollution load indexes are generated in combination with a dynamic normalization algorithm, and the comprehensive pollution load indexes are calculated; the coupling effect of organic matter impact and filamentous bacterium activity can be recognized more accurately, misjudgment of a single parameter is avoided, regulation and control can be started at the initial stage of exceeding the standard of the filamentous bacterium activity coefficient through a preset threshold triggering response strategy, compared with a mode that traditional microscopic examination lags for 4-8 hours, the response speed is effectively increased, and the detection efficiency is improved. Meanwhile, floc structure recovery serves as a termination condition, carbon source waste caused by excessive adjustment can be avoided, and the system recovery period is shortened by 30%.

The invention provides a sewage treatment state monitoring method and system based on image feature analysis. The method comprises the following steps: acquiring a plurality of image frame data and a plurality of sensor time sequence data of a flocculation basin in a first time period; performing data splicing on the image frame data and the plurality of sensor time sequence data to obtain a plurality of multi-modal data at different moments; inputting each piece of multi-modal data into a preset floc detection model, so that the floc detection model performs feature fusion on each piece of data in the multi-modal data through an attention mechanism, and generates a frame-level floc feature vector corresponding to the multi-modal data according to a feature fusion result; and inputting each frame-level floc feature vector and the plurality of sensor time sequence data into a preset sewage treatment state evaluation model, so that the sewage treatment state evaluation model outputs the current sewage treatment state according to the input data, and the accuracy and real-time performance of sewage treatment state monitoring are improved.

Methods and systems for water quality assessment are disclosed. The method includes obtaining a first input data indicative of properties of a first liquid sample, the first input data including turbidity data and total suspended solids data for the first liquid sample, where the first liquid sample is acquired from a liquid source. The method further includes determining, using a computer processor and a machine learning model, a first predicted particle-size distribution of the first liquid sample based on the first input data, where particle-size distribution is controlled, at least in part, by a set of dosage parameters configurable by a water quality system. The method further includes determining, with an optimizer applied to the machine learning model, an optimal set of dosage parameters based on the first predicted particle-size distribution and adjusting the set of dosage parameters of the water quality system to the optimal set of dosage parameters.

The invention relates to the technical field of sewage treatment, and discloses a sewage chemical characteristic identification-based treatment flow intelligent control method, which comprises the following steps of: acquiring inlet water chemical fingerprint spectrum data through a spectrum sensor deployed in a sewage pipe network, and predicting the time when the inlet water reaches a treatment plant; before inflow water arrives, inputting the fingerprint data into a biochemical reaction potential analysis model to obtain a biochemical reaction potential vector, and calculating a hydraulic affinity score of each parallel biochemical treatment unit according to the biochemical reaction potential vector; in combination with sludge function portrait data acquired from the plant, a targeted gain affinity score for deploying a specific return sludge source to a specific biochemical treatment unit is calculated. According to the method, based on the two scores, the optimal flow distribution and sludge allocation strategy is cooperatively determined through optimization solution, so that water quality impact is effectively dealt with, stable operation of the system is ensured, and the impact resistance and stability of the system are remarkably improved.

The invention discloses a sewage treatment system and method based on combination of ultrasonic micro-electrolysis and catalytic oxidation, and relates to the technical field of environmental protection, and the method comprises the following steps: sewage is pretreated by generating active free radicals through ultrasonic enhanced micro-electrolysis, and then enters a catalytic oxidation unit to be deeply mineralized together with ozone; the ultrasonic power, the ozone adding amount and the reflux ratio are dynamically adjusted through the fluid circulation and intelligent control unit; and the tail water is fed back through online monitoring to reach the standard. The system integrates an ultrasonic micro-electrolysis module, a catalytic oxidation module, an intelligent control module and an automatic backwashing module, and realizes closed-loop optimized operation. The method solves the problems of low treatment efficiency of refractory organic matters, strong drug dependence, easy damage of equipment, poor adaptability and the like in the prior art, obviously improves the COD removal rate to 95% or above, reduces the energy consumption by 20%, does not need additional drugs, is suitable for high-difficulty wastewater treatment of printing and dyeing, pharmacy and the like, and has the advantages of high efficiency, stability, low consumption and engineering.

The present disclosure relates to the technical field of water treatment, and provides a dosing control system in a water treatment process. The system comprises: a programmable logic controller configured to transmit current characteristic values of water inlet parameter characteristics and water outlet parameter characteristics collected by sensors to an edge gateway, and drive a camera to capture a floc image; the camera is configured to transmit the captured floc image to the edge gateway; the edge gateway is configured to obtain a dosing amount prediction value based on a preset dosing amount prediction model according to the water inlet parameter characteristics, the water outlet parameter characteristics and the floc image, and feed back the obtained dosing amount prediction value to the programmable logic controller; and the programmable logic controller is further configured to control the dosing pump to perform a dosing operation in the water treatment process according to the dosing amount prediction value. The present scheme can improve the dosing control efficiency of the water treatment process.