Congratulations to the DETENTE team for winning the First Manuel Laborde Prize!
RT @NanoBioSep: Very grateful for the support from @Euskampus. Keen on getting started with our colleagues from San Sebastián and Bordeau…
Our new website is ready! We invite you to have a look!
RT @jakiunde: “Investigar no es garantía de resultados, pero los hace posibles. No investigar es perder seguro. [...] Hace falta una socied…
RT @grupospri: 👤 “El desarrollo sostenible sin los polímeros es una quimera” 👤 “Polimerorik gabe, garapen iraunkorra lortzea ameskeria da”…
RT @NanoBioSep: A snapshot about a situation that unfortunately is in slow motion, and certainly not only in ITCs. Thanks to Nedjeljka for…
SURPHASE predicts at an early-stage and in a short-term the fouling tendency of desalination membranes. This provides an opportunity for the operator to plan the cleaning cycles well ahead. The data obtained by SURPHASE enable a long-term optimization of the operation of the desalination plant.
By detecting fouling at an early-stage, SURPHASE helps keeping membrane lifetime at the level warranted by the manufacturer.
Furthermore, as an intelligent monitoring device SURPHASE offers the possibility to record – similar to a “black box” – the operating conditions the membrane is being exposed to during operation.
SURPHASE enables an optimized design of new desalination plants based on the big data obtained from its network of monitoring devices.
Until today, a significant amount of desalination plants is designed on standard concepts which can be far from optimum. In these cases, posterior adaptation of the plant can turn out costly and even economically inviable, resulting in an inefficient operation of the desalination plant.
SURPHASE enables designing a strategy toward the most sustainable use of detergents. This means a significant advantage for cleaning agent producers over their competitors.
We have learnt that our device offers producers of cleaning agents a unique tool to test their formulations in situ and in real time. This is in stark contrast to current trial&error approaches which are unable to systematically verify the actual detergent efficiency, both ecologically and economically.