01
One is used in the field of sewage treatment
AI agents
The product of the deep integration of artificial intelligence technology and water treatment, it goes beyond a single prediction model and is a possessive Perception, analysis, decision-making, execution, and learning evolutionary abilities can be regarded as the "virtual operation expert" or "autonomous decision-making brain" of the sewage treatment plant.
1. Global perception and data fusion
Multi-source data access Real-time integration SCADA , water quality online meters (COD, ammonia nitrogen, DO, pH, etc.), flow meters, sludge concentration meters, weather stations, pipe network pressure sensors, equipment status monitoring data.
IoT Edge Computing Preprocess data on the device side to reduce transmission latency and achieve millisecond response to key indicators such as abnormal water quality.
2. Intelligent cognition and diagnosis
Real-time water quality/quantity forecasting Based on models such as LSTM and Transformer, it predicts the changes in key indicators in the next few hours on a rolling basis.
Process Condition Assessment Identify the activated sludge activity, nitrification/denitrification efficiency, and sedimentation status of the sedimentation tank.
3. Independent decision-making and optimization control
Real-time control policy generation
Aeration Optimization Dynamically adjusts the blower frequency based on inlet load, DO, and ammonia nitrogen predictions.
Intelligent dosing based on phosphorus load prediction, MLSS Precise control of phosphorus remover/carbon source dosing in the state.
Reflow ratio adjustment combined with nitrate nitrogen concentration, Sludge sedimentation ratio Optimize inside/outside reflow.
Emergency plan automatic triggering The water level of the biochemical pool is pre-lowered before the rainstorm, and the standby aeration unit is activated when the high load is impacted.
A set of water quality and quantity
Predictive models
The sewage treatment water quality and quantity prediction model is not only a technical tool, but also a bridge connecting "data-decision-execution"
By mining the value of data, it can not only improve the efficiency and stability of the sewage treatment system, but also provide scientific support for environmental management, resource optimization and intelligent transformation. In the future, with the improvement of model accuracy and real-time, its role in low-carbon and resource-based sewage treatment scenarios will be more significant.
4. Support the planning and design of sewage treatment plants
03
One set of energy consumption
Manage and optimize your system
Pass Intelligent technology and management strategy Real-time monitoring, analysis and optimization and precise regulation of the energy consumption of the whole process of sewage treatment (pretreatment, biological treatment, deep treatment, sludge disposal, etc.).
1. Equipment Operation Efficiency Assessment and Optimization
Monitor the operating status, load rate, and efficiency of critical equipment (blowers, lift pumps, return pumps, agitators, dehydrators).
Identify inefficient operating equipment (such as "big horse-drawn carts", equipment aging, deviation from optimal working conditions).
Provide advice on equipment maintenance, replacement, or retrofit.
2. Intelligent Optimization Control
Dosing optimization control
Based on influent water quality (TP, SS), process parameters and outlet targets, Optimize the dosage of coagulants, flocculants, carbon sources, etc。
Objective Under the premise of ensuring the phosphorus removal/denitrification effect and sludge sedimentation, Minimizing the cost of the agent and Dosing pump Energy consumption。
Pumping station optimization scheduling
Based on inlet flow prediction, electricity price time-sharing signal, pipe network level, Optimize the start-stop combination and operation frequency of lift pump, return pump, and remaining sludge pump。
Objective Under the premise of meeting the process requirements (flow, level) and avoiding hydraulic shock, Achieve peak shaving and valley filling (using valley electricity), balanced operation, and reduce pumping energy consumption。
Aeration System Optimization Control
Based on real-time influent load, DO, ammonia nitrogen/nitrate nitrogen online data, combined with prediction models (such as ML-based load prediction), Dynamically adjust blower volume/pressure and aeration valve opening。
Objective Under the premise of ensuring the nitrification effect (effluent ammonia nitrogen meets the standard) and the hypoxic environment required for denitrification, Minimizes aeration energy consumption。
04
A highly intelligent set
Dosing system
The Fyhone intelligent dosing system is through real-time sensor monitoring, intelligent algorithm operation and automatic equipment control , to realize the integrated system of precise dosing of water treatment agents (such as coagulants, flocculants, disinfectants, etc.).
