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New data analytics service optimises water industry meter accuracy

TÜV SÜD National Engineering Laboratory has launched a data analytics service to help the water industry improve leakage detection rates and significantly lower operating costs.

Regulatory authorities and global environmental pressures are demanding better water management and a reduction in leakage rates. The water industry must therefore improve measurement accuracy of water flows passing through distribution networks, to control over abstraction rates and advance leakage rate estimation.

The new data analytics service will use statistical modelling techniques to predict meter performance based on live and historical data. In addition, diagnostics information can be used to understand what may be negatively impacting a meter by analysing hidden patterns to identify specific fault conditions, such as electromagnetic or acoustical interference, mechanical damage or leakage.

Gordon Lindsay, head of the digital R&D group at TÜV SÜD National Engineering Laboratory, said: “Thanks to advances in technology and increased connectivity through the Internet of Things, vast amounts of data exist but only a fraction of its potential benefit is realised.

“Our new data analytics service uses data in real-time to detect when a meter is not performing to specification and identify the cause of this failure. This means that the water industry can more accurately detect meter problems, as well as estimate the level of leakage in the system, to deliver increased measurement confidence, reduced downtime and cost savings.”

Validation of models created by the data analytics service can be conducted in TÜV SÜD National Engineering Laboratory’s accredited calibration facility. This increases end-user confidence in results by reducing any levels of error, assuring them that the problem-solving method suggested by the data analytics service will be effective in the real-world.

Topic: Drinking water quality , Leaks & bursts , Pipes & Pipelines , Water resources
Tags: leakage , water management , abstraction , Data , digital

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