We work closely with university partners and end-users to accelerate technology diffusion from idea to proof of concept, demonstration, implementation, and broad application. We embrace the learnings from taking the first steps demonstrating and implementing new technology in the water sector.
All new technologies face practical challenges when put into action. To pave the way and accelerate technology diffusion from academia to application we actively invest in R&D projects and partnerships. Our focus is to encourage a fast and agile development process from idea to prototype to solutions incorporating practical challenges and feedback.
We support research in holistic observation-driven technologies for water systems by donating part of our profit to our university partners. As a Dryp user, you help to support further research and contribution to the application of IoT, big data, machine learning, and a combination of Natural Intelligence (NI) and Artificial Intelligence.
The water sector is lacking information & knowledge about the water cycle
How to gather high-quality data that clarifies the complex water system interactions to ensure holistic, sustainable, & fact-based decision making?
Develop and commercialize a unified solution that utilizes the current advances in smart energy-efficient sensors, IoT, big-data, and ICT. This will enable cost-efficient distributed monitoring of the hydrological and hydraulic states of the urban water cycle & provide data & knowledge about the systems correlations.
DONUT (2018-2021) is a three-year project that utilizes the current advances in smart energy-efficient sensors, IoT, big data, and ICT. All these combined with the project partners’ comprehensive knowledge and experience to develop a unified cost-efficient grand solution spanning from measurement to information creation.
Lacking knowledge and sufficient data about the water cycle are in great demand to monitor both artificial and natural water systems. This project will develop and commercialize a unified solution, which will enable cost-efficient distributed monitoring of the hydrological and hydraulic states of the urban water cycle and provide data and knowledge about the system correlations.
Also, this project will help the water sector to move into the big data era as it will develop and mature the basis of the forthcoming IoT and ICT infrastructures for blue, green, and climate-adapted cities.
Read more here.
The SWI was a Strategic Danish Project, which aim was to close the knowledge gaps within the prediction and control of current and future conditions in integrated urban wastewater systems. The outputs from the project held components for an intelligent real-time decision support system, following a drop of water from the cloud, throughout the sewer–wastewater treatment system, and into the receiving waters.
Several questions concerning taking optimal action during weather events were researched: How much and where does it rain?; How many storms- and wastewater flow is currently in the drainage system and the wastewater treatment plant?; and How much will there be 2 hours from now?. The answers to these questions would allow to explore optimized ways to act when a cloudburst occurs.
However, "acting optimally" depends highly on the context of the desired outcome. For instance, when we strive to minimize general environmental impact, to protect individual health, or to save money, requires different ways to act.
The obtained knowledge was integrated into research training efforts for the project members through several pilot installations. Results and experience were shared broadly in the water sector to inspire further research and development in integrated real-time control and decisions systems for drainage systems and wastewater treatment plants. Read more here.
The goal was to conduct a series of coherent measurements of rainfall, water levels, flow, and substance concentrations in combined sewers and overflow structures. These measurements were to document the dilution of substance concentrations in overflow effluent water during rainfall. The online measurements, level gauges, upstream, overflow construction, and rainfall measurements have been collected. For selected rainy weather events, we have conducted sampling, laboratory analysis of water quality parameters as well as selected priority chemical substances and microbial parameters. The project also included initial exploration of data-driven and CFD-based software sensors using observations from simple, reliable sensor units.
The project was supported by the Danish Water Sector's Development and Demonstration Program (VUDP).
Read more here.
The water utility collaboration, VeVa, is based on a common intention to make the use of weather radar data for hydrological and hydraulic purposes easier and more transparent for "non-weather radar specialists" across the water sector industry in Denmark.
Reliable rainfall estimates from weather radars should be as available as the rain meter data is today. The data processing method from polar estimates of radar reflectivity (dBZ) to corrected and adjusted cartesian rainfall intensity estimates (mm/h) shall be transparent with clear interfaces in a well-defined data model.
This ensures increased confidence and usability of the weather radar data for hydrological and hydraulic applications in the water sector.
The mission of the OrEO project was to develop a comprehensive and easy-to-use solution for monitoring overflows and SUDS (Sustainable Urban Drainage Systems). We used simple hydraulic measurement principles and combined them with smart energy-efficient measurement units. Thus, observation is transmitted wirelessly to a cloud environment where data is combined and translated into information in real-time. Such a solution enables continuous performance monitoring and learning about the actual response for a given load of the overflow and SUDS structure and allows cost-efficient monitoring of overflow structures and SUDS.
The Danish Eco-Innovation Program (MUDP) supported the project.