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Network Monitoring

Network Monitoring helps utilities ensure that water of the correct quality flows continuously and smoothly to consumers. It also provides network engineers with the functionality they need to manage the performance of the distribution network, enabling improved customer service whilst reducing the cost of network operations.

 

ATi’s Portable Network Monitoring

ATi’s NephNet and ChlorNet portable network monitors are proven to provide accurate reliable real time data, potentially weeks or months in advance of any customer awareness, of events within the network. They alert utilities to anomalies in network behaviour and allow them to classify them. When used in conjunction with a modem and a web portal, the monitors can then geo-locate an event, indicating in which part of the network it is occurring.

ATi took the decision not to compromise the measurement in pursuit of the “portability goal”. Full specification sensors with the same performance as the fixed wall mounted process monitors have been modified, upgraded and adapted to allow low power operation. This in turn allows operation in a battery powered mode.

ATi’s monitors take online information that is already collected by supervisory control and data acquisition systems, as well as data generated by other systems. It processes the data to show real time predictions and views of the network and its behavior. When anomalies are detected, an alert is issued.

Timely warning and analysis of network anomalies means that the utility's operational staff can react before a visible and costly failure develops, energy is wasted or quality degrades.

 

How Water Network Monitoring Works

Typically utilities use data from sensors measuring flow, pressure for network analysis. Water quality indicators are less commonly deployed.

ATi’s water network monitors allow detailed accurate water quality information to be input into the models. The network data is analysed as a whole, not just as a collection of unrelated measurements. Repeating patterns and ongoing long-term changes can be discovered in the data; actual or potential issues can be detected and their effects mitigated.