鲁尔物联-物联网数字底座

Scene Introduction

As cities expand and industries grow, the challenges of managing urban wastewater become more complex, requiring advanced technologies and sustainable practices to minimize environmental and societal impacts.

Enabled by advanced technologies such as the Internet of Things (IoT), cloud computing, and hydrodynamic analysis, the Ruhr IoT Sewage Network Sensing System integrates water quality monitoring system, sewage network monitoring system, early warning platform.

Demand Analysis

Combined Sewer System/Overflow of Wastewater

During heavy rainfall, the combined sewer system experienced an overflow, leading to untreated wastewater spilling into nearby rivers and causing environmental pollution.

Black and Odorous Water Bodies

When Wastewater is discharged into rivers, it often causes the water to emit foul odors, develop unnatural colors, and form foam or oil slicks, making it unsafe for human use and damaging aquatic ecosystems.

Blocked Drainage Pipes

Clogged sewer pipes can easily cause sewage to overflow onto the road through manholes, leading to pollution issues and creating significant difficulties for the living environment of nearby residents.

Program Highlights

Pollutant Source Monitoring

Water quality monitoring devices are installed at the discharge outlets of major totrack parameters such as chemical COD, NH3-N ,TN, TP etc., analyze data to classify pollution sources (e.g., industrial, domestic, agricultural), and quantify the pollution contribution rate of each major discharger.
Rainwater and Sewage Diversion

By implementing a comprehensive stormwater and sewage separation monitoring , was able to detect of combined sewer overflows.
Black and Odorous Water Bodies

Through water quality monitoring devices, westwater discharge points can be identified and alerted.
Risk Assessment of Sewage Network

Static Risk Assessment Model: focuses on historical data and fixed attributes (e.g., pipeline material, age, and past failure records); Provides a baseline understanding of potential risks; Dynamic Risk Assessment Model: incorporates real-time data (e.g., water level, flow rates and water quality) and environmental factors (e.g., weather, soil conditions).