CENTAUR

 Energy Efficiency / Posted 4 months ago by eeres4water

Cost Effective Neural Technique for Alleviation of Urban Flood Risk

The project will develop a radically new market ready approach to RTC of sewer networks with the aim of reducing local flood risk in urban areas. Existing RTC pilot projects (e.g. Vienna, Dresden, Aarhus) are characterised by complex sensor networks, linked to centralised control systems governed by calibrated hydrodynamic modelling tools and fed by radar rainfall technology. Such systems are expensive and complex to install and operate, requiring a high investment in new infrastructure, communication equipment and control systems. In contrast, this proposal will develop a novel low cost de-centralised, autonomous RTC system. It will be installed, tested and demonstrated in a number of pilot study catchments.

This RTC system will utilise data driven distributed intelligence combined with local, low cost monitoring systems installed at key points within existing sewer infrastructure. The system will utilise mechanically simple, robust devices to control flow in order to reduce flood risk at vulnerable sites. This system will be informed and governed directly by sensors distributed within the local network, without the need for an expensive hydrodynamic model or real time rainfall measurements. This system will deliver many of the benefits of RTC systems, whilst avoiding the high costs and complex nature of extensive sensor networks, centralised control systems, communications systems and infrastructure modifications. It is anticipated that such a system will be of significant benefit to operators of small to medium sized sewer networks.

Start date: 1/9/2015
End date: 31/8/2018

Coordinator: The University of Sheffield

Funding scheme: H2020 Programme

Budget: € 3.532.121,25

Website: https://cordis.europa.eu/project/id/641931/en


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  • Listing ID: 2287
  • Select the type: Energy Efficiency
  • Select the country: United Kingdom
Contact details

Firth Court Western Bank S10 2TN SheffieldUnited Kingdom,S10 2TN https://cordis.europa.eu/project/id/641931/en