The GoAigua software solution is helping the City of Houston optimize the resources allocated to predictive cleaning of the sewer network and reduce overflows (SSOs) caused by grease and wipe blockages by over 70%.
Due to its topology, rapid population growth and demand peaks, the City of Houston has historically experienced a large number of Sanitary Sewer Overflows (SSOs) each year, caused primarily by blockages in its sewers. In addition to planning structural improvements, such as the replacement of over 100 pump stations and over 50km of sewer mains, the city also aimed to address issues involving the most common cloggers (oil, grease and wipes) based on predictive operation and maintenance.
Houston Public Works (HPW) operates one of the largest sewer networks in the United States, with 39 WWTPs, 385 pump stations and over 9,000 km of sewer mains.
The City collects millions of real-time data points from a large array of systems and technologies, including SCADAs, work order systems, GIS, rain, and manhole monitors. With GoAigua, Houston has not only integrated all of these different data sources and systems into one environment, but has also provided the necessary analytics to solve key operational challenges.
GoAigua has integrated real-time and historical data from different systems throughout the city into its cloud platform; sourced from over 385 pump stations, 600 level and flow sensors, work orders, GIS, rainfall, hydraulic models, and other information streams. This has provided Houston with a unique cloud environment and three main functions.
1. The creation of risk-based preventive maintenance plans for each asset, saving millions of dollars a year
The operator has moved from using time-based cleaning strategies to a risk-based methodology. In addition, it has been able to calculate the best cleaning schedules by integrating multiple data sources into the GoAigua platform (historical SSOs, latest preventive and reactive work orders, Geographic Information Systems and the network’s mathematical model).
2. Efficient location of water level sensors
GoAigua’s algorithms have helped optimize decisions to estimate the number of sensors needed to prevent SSOs and define the best sensor locations.
3. Use of predictive analytics for early detection of SSOs
The GoAigua technology solution identifies the risk of SSOs based on the anomalies in sensor data at various levels: drains, assets, and manholes.
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