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BuntBrain software implementation brings “first world” water distribution network standards to Latin America

BuntBrain Water Meters and BuntBrain LeakFinder have been successfully demonstrated in Latin America, with support from the EU Horizon 2020 programme, through the ACCUWATER project. Trials of both software modules were rolled out in the water distribution network of Empresa de Servicios Públicos de Heredia SA (ESPH), in Heredia, Costa Rica. BuntBrain Water Meters was also successfully trialled in the network of Empresa Local de Agua Potable y Alcantarillado de Sucre (ELAPAS) in Bolivia.

The modules have now been analysing the performance of both networks for over two years and have demonstrated enormous potential to reduce Water Losses, or Non-Revenue Water (NRW) for the Latin American utilities. BuntBrain LeakFinder reduced the period during which leaks in the ESPH network remained open by between two and three times on average, leading to significant economic savings.

In Latin America, Apparent Losses, addressed by BuntBrain Water Meters, represent over two thirds of total NRW and often represent the most acute challenge for water utilities. Analysis by BuntBrain Water Meters has demonstrated that implementation of an optimal meter replacement programme would generate significant benefits to Latin American utilities, by reducing the levels of erroneous measurements from meters leading to higher levels of billing and associated revenues.

The Latin American utility partners have praised the ACCUWATER project and thanked the EU Horizon 2020 fund and Bunt Planet. ELAPAS pointed to the “transfer of modern technology” to the benefit of the City of Sucre, which suffers from high levels of water stress. ESPH Engineer, Dwight Sáenz, observed that the software “encourages us to maintain [our water distribution network] at the standards of first world countries”.

The ACCUWATER project has been a great success in Latin America, according to BuntPlanet CEO, Ainhoa Lete, demonstrating that the BuntBrain software modules can adapt to challenging environments and that the self-learning algorithms can deal with large volumes of diverse data inputs to build upon their AI functionality.