Low energy consumption, stable compliance with standards
The fluctuations of COD and ammonia nitrogen were monitored in real time, and the linkage dosing system dynamically adjusted the PAC dosing amount, reducing the drug consumption by 20%.
The aeration volume fuzzy control model dynamically adjusts the fan speed, reducing power consumption per ton of water by 18%~25%. The dosing prediction algorithm reduces drug waste and reduces drug consumption costs by 20%.
Through the characteristics of high speed, low latency, and wide coverage, the bottleneck of traditional communication methods in remote site coverage, real-time control, and access to massive equipment is solved.
Integrate parameters such as water quality (COD/ammonia nitrogen/pH), equipment status (vibration/temperature), and pipe network pressure collected in real time by the sensing layer, and combine external data such as meteorology and water demand to form a global data pool
The AI algorithm engine promotes the transformation of sewage treatment from empirical operation and maintenance to data intelligence through a "prediction-optimization-self-healing" closed loop. The core value is to improve the effluent compliance rate (>98%) while reducing the comprehensive cost by 20-30%.
Through virtual mapping and real-time data driving, refined management and intelligent decision-making of the entire sewage treatment process are achieved.
It can help city management departments make emergency preparations before waterlogging occurs and reduce losses caused by waterlogging.
Conduct real-time monitoring and intelligent analysis of the operating status of the sewage plant to achieve prediction and early warning of potential risks.
The water pollution early warning traceability instrument has three functions: early warning of pollution, accurate traceability of pollution and automatic retention of pollution certificates.
Pressure sensors (±0.1% accuracy) monitor pressure fluctuations in the pipe network in real time and locate risk points of pipe burst.
It can reduce data transmission time, improve response speed and real-time performance, and effectively improve device response rate.
Its core function is to collect, store, manage, analyze and visualize geospatial data.
Hydrodynamic models can simulate the flow of rainwater in the pipe network.
Monitoring and management operations of sewage treatment plants are carried out through mobile devices (such as mobile phones or tablets).
Simulate the emergency response process when a fault occurs to ensure that problems can be solved quickly and effectively under real conditions and reduce losses.
It can simultaneously measure multiple basic water quality parameters such as pH value, dissolved oxygen, conductivity, temperature, and turbidity, making it easy to quickly understand the overall water quality of the water source.
It has the characteristics of long maintenance-free time and good adaptability, and can be widely used in automatic rainfall monitoring in harsh environments. Optical rainfall sensors play a key role in the automatic monitoring and early warning of severe precipitation weather such as heavy rains, flash floods, and mudslides.
Electromagnetic flowmeters, ultrasonic flowmeters, etc. can be installed on water intakes or water pipelines to accurately measure the water intake and water flow speed of the water source.
Feedback liquid level data to the control system to realize automated feeding, drainage and other processes to improve production efficiency.
Observe the operation status of sewage treatment facilities in real time, including the operating status of the equipment, treatment effects, etc. Through cameras and image recognition technology, equipment abnormalities, leaks and other problems can be discovered in a timely manner to ensure the normal operation of processing facilities.
The fully automatic electrical control system and equipment fault alarm system have good impact load resistance, stable effluent water quality, simple and worry-free operation, stable operation and easy maintenance.
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Project Value:
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