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|Trykkforvaltning og maskinlæring i vanndistribusjonsnettet
|NMBU/ Vann og avløpsetaten i Oslo
|Bruk av teknikker som stordataanalyse og maskinlæring til optimalisering av drift på vanndistribusjonsnettet, herunder trykkoptimalisering
|Interpretable Models with Graph Neural Networks to support the Green Transition of Critical Infrastructures
|The aim of the project is to develop data-driven models of critical infrastructure networks based on Graph Neural Networks (GNNs). GNNs are inherently connected to physical objects in the graphs (roads, crossings, pipes, sensors, manholes, consumers, etc.) which improves learning ability and usage of data. Developed models will support infrastructure planners in identifying which parts are most vulnerable to climate change, revealing where most efficiency is lost, and suggesting structural alternatives to circumvent systematic flaws with respect to the coming green transition.
|Prasanna Mohan Doss
|Modeling, Analysis, and Tools for improving water-smartness in Water and Wastewater Networks: A case study.
|Globally, water utilities lose about 30% of treated water on an average in their Water Distribution Networks (WDNs) during supply. In addition to the amount of water lost, leaky WDNs increase the risk of contamination, consumes additional energy, and infiltration into Wastewater Collection Systems (WCS). The project aims to develop a digital twin of WDNs and WCSs for leakage and infiltration detection. Methods based on sensor fusion and fault detection using data-driven and hybrid modeling will be validated using benchmark networks and implemented on a real-world network in Bodø Municipality in Norway. https://www.ntnu.edu/employees/prasanna.m.doss
|Charuka Saamantha Meegoda
|Legionella Management in non-chlorinated drinking water
|Legionella are pervasive environmental bacteria that can incidentally cause severe and sometimes fatal infections upon inhalation. Because Legionella inhabit engineered environments and proliferate in warm, stagnant premise water systems, the majority of outbreaks are associated with preventable water system maintenance deficiencies. This project will assess the use of flushing as a corrective action and ongoing control strategy to reduce Legionella levels in service lines and premise water systems. Using a combination of laboratory experiments and real-world case studies to generate novel, hypothesis-driven data, evidence-based guidance will be provided for a broad audience of potential stakeholders regarding the efficacy of flushing for Legionella control. The guidance documents will provide practical recommendations, including measurable goals and potential costs, based on evidence produced in the laboratory and in the field. https://www.waterrf.org/research/projects/demonstrating-effectiveness-flushing-reducing-levels-legionella-service-lines-and
|Framework development for control of quality and health risks of recovered products from wastewater
|WIDER UPTAKE is a H2020 project that seeks to facilitate industrial symbiosis as a mean to increase resource recovery, limit emissions and develop sustainable business models based on water-smart solutions. The project is built around a set of innovative Circular Economy case studies to recover water, energy, fertilizer and other products from municipal wastewater treatment plants. The goal of this PhD project is to develop a general framework for monitoring, and control of quality and health risks associated with the utilization of wastewater reuse and recovered products from wastewater treatment that can account for the multi-actor contributions to the risks. The framework will facilitate circular economy actors to evaluate any unintended consequences of their product choices and provide the end-users with a more transparent and verifiable monitoring and quality assurance method. https://www.sintef.no/projectweb/wider-uptake/
|Climate change and future of green infrastructure from roof to city scale.
|Developing design tools for green stormwater infrastructures in cold climates.
|Kristin Jenssen Sola
|Fremmedvann i avløpsnettet. Analyser av påvirkningsfaktorer, konsekvenser og mulige tiltak
|Håndtering av overvann
|Økonomisk og robust overvannshåndtering for en by i vekst og en by i endring
|NMBU/ Vann og avløpsetaten i Oslo
|Målet med prosjektet er å utvikle et beslutningsstøttesystem for en økonomisk og robust overvannshåndtering i Oslo med utgangspunkt i lokale forhold, teknisk funksjonalitet og samfunnsøkonomisk lønnsomhet. I prosjektet utvikles metodikk og verktøy for ulike beslutningsnivå for planlegging av overvanns- og klimatilpasningstiltak.
Kostnadseffektiv håndtering av overvann i byer krever helhetstenkning | NMBU