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Knowledge capture for advanced supervision of water distribution networks

Objectif



Water distribution networks are geographically distributed systems, with greater heterogeneity in terms of control structures, management strategies, and with varying geometry (continuous expansion and changes in demand) during their life. Because of these characteristics, water distribution companies face the problem of data and knowledge integration related with control and optimal exploitation. An important step in the course of current control systems is the integration of machine learning capabilities enabling the knowledge capture from large amounts of data collected during system exploitation as well as integrating captured knowledge in a decision support subsystem.

WATERNET aims at designing and developing an evolutionary knowledge capture and management system towards the control, optimal operation and decision support of drinking water distribution networks, to minimise the costs of exploitation, guarantee the continuous supply of water through better quality monitoring, save energy consumption and minimise the waste of natural resources.

In order to accomplish its goals the WATERNET project will develop an open reference architecture for water distribution networks and a supervision system integrating:

- a distributed information management subsystem,
- a machine learning subsystem,
- an optimisation subsystem and,
- a water quality monitoring subsystem.

The reference architecture, contributes to the definition of best codes of practice/standardisation of the sector and will help the development and rapid spread over Europe of better water distribution systems. Similarly the supervision system, will offer to the European water distribution companies the possibility to control, manage and decision support as well as minimisation of energy and water waste, integrated into a single system. Besides the European market, where most existing systems show a very low level of automation and integration, the resulting technology also has a high potential for export, especially to countries where water is a scarce resource.

The expected results of WATERNET will provide the basis background for the application of the current state of the art in machine learning to water distribution control and management systems for applying completed basic research to a particular industry field, as well as for modelling water distribution networks addressing a standardisation in the sector. Such an approach will enable drinking water distribution industries to increase the quality of their services, as well as the implement advanced management and exploitation strategies based on proper decision support tools.

The WATERNET partners intend to disseminate the results of the project within the established channels of existing Esprit Projects as well as within well-known water distribution conferences, seminars and forums.

The consortium includes end-users (water distribution companies) with strong needs to install the planned functionalities in their systems, to provide specifications; system developers, who have been involved in the design, development and implantation of state of the art networks, and are committed to exploiting the results; and research institutions, providing background in machine learning, optimisation, systems integration, modelling, forecasting and information management.

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Coordinateur

Estec - Estudos E Tecnologias Da Informação
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Adresse
Rua Dos Lusíadas 118-2°ESQ.
1300 Lisboa
Portugal

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