Prisma Photonics will initiate its innovative solution enabling a new level of monitoring, to dramatically improve INGL’s safety and security abilities
Prisma Photonics, a leading provider of smart monitoring solutions for physical infrastructures, announced today it has won the Israeli Government Companies Authority (GCA) Challenge for detecting unauthorized activities by monitoring the 700 kilometers of high-pressured natural gas lines of the Israeli Natural Gas Lines (INGL), the leader in natural gas distribution in Israel.
Prisma Photonics was chosen after participating in an extensive technical competition organized by the Israeli GCA at the end of January 2019, where the participating teams were asked to develop a system that would detect unauthorized activities at any point along the 700 kilometers of gas lines spread across the country. Participants were judged on four criteria: feasibility regarding implementation of the solution, cost-effectiveness, ability of the team to run a pilot within the INGL, and innovation in the form of a new concept or perspective.
“As Prisma Photonics, we are proud to have won the challenge. Our solution uses optical fibers that are already deployed along the gas lines and uses those for instant detection of unauthorized activities,” said Dr. Eran Inbar, CEO of Prisma Photonics.
“This win follows the recognition by the European Commission granted Prisma Photonics with the Horizon 2020 Seal-of-Excellence. Both wins show the potential and market acceptance of our solution.”
About Prisma Photonics
Founded in 2017, Prisma Photonics provides the next-generation fiber sensors for smart infrastructure, enabling a new level of monitoring sensitivity that generates unparalleled data quality for better detection and target classifications capabilities with low false alarm rates. The platform is suitable for a wide range of sectors, including smart roads, railways, powerlines, optical-networks and pipelines. With its proprietary approach, Prisma Photonics provides ultra-sensitive detection and intelligent learning detection using the pre-existing optical communication fibers as sensors.
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