Successful EU Horizon 2020 funded trials demonstrate BuntPlanet’s innovative Big Data ‘AI’ Water Losses solutions, across four diverse water utility networks
DONOSTIA-SAN SEBASTIáN, SPAIN, December 16, 2020 /EINPresswire.com/ — BuntPlanet, an innovative high-tech start-up based in Donostia-San Sebastián, Spain, has, with funding from the EU Horizon 2020 programme, successfully deployed its innovative ‘big data’ Artificial Intelligence solutions for Water Losses, or Non-Revenue Water (NRW) in four water utility networks with widely varying conditions, throughout Europe and Latin America.
The trials, which formed part of the ACCUWATER project, took place in the networks of Servicios de Txingudi/Txingudiko Zerbitzuak (STTZ) in Hondarribi, a city in the Basque Autonomous Region of Spain; Netzgesselschaft Düsseldorf GmhH (DUSS) in Düsseldorf, Germany; Empresa de Servicios Públicos de Heredia SA (ESPH), in Heredia, Costa Rica; and Empresa Local de Agua Potable y Alcantarillado de Sucre (ELAPAS), in Sucre, Bolivia.
The ‘big data’ software solutions installed during the EU funded project were BuntBrain WaterMeters and BuntBrain LeakFinder. Both are low-cost products which collect and process large volumes of data to monitor and deploy algorithms to analyse the performance within a water distribution network. They are modules within a suite of dynamic monitoring and decision support software systems developed by BuntPlanet to detect and locate NRW within water distribution networks.
NRW represents a major economic challenge for water utilities, costing c. €14 billion annually according to the World Bank. NRW includes both Physical Losses, or Leakages (water extracted and treated, but not consumed) – which is addressed by BuntBrain LeakFinder; and Apparent Losses (water consumed but not properly measured and not paid for) – addressed by BuntBrain WaterMeters.
BuntBrain WaterMeters was successfully trialled in the networks of ELAPAS and ESPH. BuntBrain LeakFinder was deployed in the networks of STTZ, DUSS and ESPH.
A key objective of the ACCUWATER project was to demonstrate the efficiency of the software modules in different network conditions. In addition to the varied geographical locations chosen for the demonstrations, the chosen networks varied widely in respect of the levels of Water Losses, ranging from low to high levels; and in respect of the degrees of water stress.
BuntBrain LeakFinder combines ‘big data’ and AI with a knowledge of the hydraulic model of the water network to detect leaks within the network and to locate the leaks to within a small radius. It performed well in each of the networks where it was installed. The software’s self-learning algorithms proved capable of detecting and locating leaks with very high levels of accuracy, resulting in significant economic benefits to all of the participating water utility companies. It achieved the most significant savings – as might be anticipated – in the water networks with highest levels of Water Losses.
BuntBrain WaterMeters uniquely takes into consideration metrology knowledge i.e. water meter technical data (water meter type, size or measurement error). It combines an analysis of consumption anomalies with water consumption forecasts and uses complex algorithms to indicate the optimal replacement date, the optimal size and most appropriate technology for each water meter. During the trials, BuntBrain WaterMeters proved highly effective in the networks where it was deployed – including the ELAPAS network which combines high levels of Water Losses with a high degree of water stress.
Each of the trial sites will now serve as a regional reference installation for BuntPlanet as it pursues further expansion. BuntPlanet CEO, Ainhoa Lete, commented that:
“We are very grateful to have had access to EU Horizon 2020 funding for the ACCUWATER project, which has enabled us to dramatically accelerate the roll out of BuntPlanet’s solutions globally. The funding contributed to the cost of installing and testing the BuntBrain software for NRW. The project has allowed us to adapt the software’s intelligent algorithms to cope with a diverse range of circumstances, including networks with high levels of water losses and water stress; and different levels of digitalisation.
We were able to improve the technology during the trials, meaning it can in future be more rapidly rolled out in the water networks of new clients. Each of the EU funded projects has become a valuable reference site for us to demonstrate the capabilities of our solutions, supporting BuntPlanet’s marketing efforts as we roll out the next stage of our business plan.”
Based in Donostia-San Sebastián, Spain, BuntPlanet has developed a suite of dynamic monitoring and decision support software systems to detect and locate NRW within water distribution networks.
BuntBrain LeakFinder is a smart, inexpensive, data analytics solution which combines artificial intelligence, to detect if an irregularity is happening in the network, and hydraulic simulation, for leak positioning. Self-learning algorithms analyse the calibrated hydraulic model and data collected from flow meters, pressure sensors and customer water meters, if available. Its ‘big data’ analysis detects irregularities in flow within pipe networks and locates the position of the irregularities.
BuntBrain LeakFinder is a transparent and accurate tool for a utility to view leakages, at all levels, within the pipe network and prioritise actions. Even very small leaks can be detected rapidly before they can escalate, and it can locate the leaks with high accuracy. Water utilities which have deployed BuntBrain LeakFinder are able to reduce the time during which leaks remained open by between two and three times on average, compared to the situation before the software was deployed – leading to significant economic savings.
BuntBrain LeakFinder enables water networks to be designed around big DMAs (>15 km pipe length), without losing accuracy – although it also detects leaks within smaller DMAs. The ability to work within a large DMA, unique amongst competing solutions, provides water utilities with the advantage of lower maintenance costs and reduces exposure to risks relating to water quality or supply.
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