| projects-411 |
101070080 |
DARROW |
DRIVING THE FUTURE OF WATER RESOURCE RECOVERY FACILITIES THROUGH DATA INTELLIGENCE |
HORIZON |
HORIZON-CL4-2021-DIGITAL-EMERGING-01 |
HORIZON-CL4-2021-DIGITAL-EMERGING-01-09 |
2022-09-01 |
2026-02-28 |
On going |
€ 003 531 446.25 |
The wastewater sector is going through a profound transformation with energy efficiency and resource recovery as key priorities in wastewater treatment plants (WWTP) and these installations started to be perceived as Water Resource Recovery Facilities (WRRF). Under this context, the exploitation of data through artificial intelligence tools with the objective of accelerating the transition of WWTP to WRRF has not been fully addressed yet. When compared to treatment technologies, the deployment of AI-powered tools in production is much faster and, therefore, provides immediate benefits. In that sense, three main barriers have been identified in this domain: i) Mechanistic mathematical models involve complex formulations and specific terminology that are difficult to understand for plant operators; ii) WRRF are harsh environments with strong impact on the quality of data; iii) Essential information in WRRFs is limited and not continuously available.In particular, to overcome these challenges, DARROW will build and demonstrate into an operational environment, an innovative, optimised, modular, and flexible data-driven AI solution to make existing WWTP more autonomous, more energy efficient and better prepared for their transformation into WRRF. DARROW will take advantage of existing AI & Data analysis techniques with the final objective of contributing to a greener planet by: i) Reducing energy consumption of WRRF; ii) Reducing Greenhouse Gas Emissions of WRRF; iii) Increasing Resource Recovery iv) Improving water quality.To do so, DARROW gathers the necessary experience, knowledge and resources through a multi-stakeholder approach that covers the whole value chain of the project. It consists of a multidisciplinary team of 8 entities from 4 different EU countries (Spain, Belgium, Germany and Netherlands), among which, 3 RTOs, 1 university,1 NPO, and 3 SMEs to ensure market exploitation (2 industrial companies and 1 water company). |
https://cordis.europa.eu/project/id/101070080 |
Urban water' |
| projects-412 |
101151781 |
DyNeMo |
Statistical Machine Learning for Dynamic Network Modelling |
HORIZON |
HORIZON-MSCA-2023-PF-01 |
HORIZON-MSCA-2023-PF-01-01 |
2024-06-01 |
2026-05-31 |
On going |
No data |
Networks are ubiquitous in the society, technology, biology and economy. Being able to perform accurate inference and prediction on such data is highly non-trivial due to their large size and complex dynamic behaviour. In many networks such as social ones, connectivity patterns and network structurechange dynamically. On the other hand, technological networks such as water systems have fixed structure but dynamic processes might take place on them appear, such as failures. DyNeMo is a cross-disciplinary project that aims to provide a novel, explainable yet scalable framework using statistical machine learning to model dynamic behavior on networks, with the goal to answer crucial scientific questions with social and environmental impact, influencing public policy. The project combines statistical tools that ensure flexibility, data adaptivity and interpretable parameters with Deep Learning tools ensuring efficiency and scalability. The methods will be used to answer two highly impactful scientific questions given access on unique datasets. The first is how companies worldwide can maximize their socio-environmental impact aligned with UN sustainable goal strategies. The second question is how to quantify and predict behaviour of cascading pipe failures on the water distribution systems in Cyprus. The results will detect patterns and perform prediction, thus influencing public policy in alignment to the EU strategic goals on monitoring water systems. The fellow and the supervisor have complementary expertise which in combination with the plan, the arrangements and environment provided by the host institution ensure a successful implementation of this novel and timely project as well as an effective dissemination and utilisation of the expected outcomes. The results of the proposed action are expected to have important conclusions on socio-environmental impact and can shape decision making and policy making in the EU and around the world. |
https://cordis.europa.eu/project/id/101151781 |
Urban water' |
| projects-413 |
101045646 |
Partres |
Particle Resolving Fluid-Sediment Interaction |
HORIZON |
ERC-2021-COG |
ERC-2021-COG |
2023-01-01 |
2027-12-31 |
On going |
€ 002 000 000.00 |
Climate change leads to increased frequency and magnitude of flash flood events in rivers and of storm surges in coastal areas. Flash floods are associated with larger discharges and water levels, whereas storm surges are characterized by higher wave heights and water levels. These events have significant consequences for both rivers and coastlines triggering a morphodynamic response. Resulting erosion and soil mechanical failures can result in severe damage to civil infrastructure and buildings. There is a knowledge gap that connects the hydraulic, hydrodynamic and geotechnical aspects of environmental loading due to current, wave action as well as sediment and soil response respectively. With the increased likelihood of extreme weather events, there is an urgent need to study coastal morphology and mitigation approaches from a multi-disciplinary physics-based perspective.Representing the interconnected processes of current, waves, sediment transport and soil deformation constitutes an interdisciplinary challenge. In the current project, particle based sediment transport models are created that take a significant step towards a realistic representation of these processes. The missing link between the individual modules will be developed, bridging the confinements of the disciplines of hydraulic, coastal and geotechnical engineering with heavy use of advanced computational fluid and solid mechanics. The multi-scale nature of extreme hydrodynamic events and their interaction with sediment and soil particle physics will be solved through a holistic multi-scale numerical framework. The proposed research lays the foundation for taking a significant step in sediment transport research that is required for dealing with current and future challenges arising from climate change. Innovative solutions to extreme weather event impact in the coastal, estuarine and riverine environments can be rapidly proposed and verified using the current numerical modeling strategy |
https://cordis.europa.eu/project/id/101045646 |
Rivers and estuaries', 'Coastal waters' |
| projects-414 |
101084362 |
H-HOPE |
Hidden Hydro Oscillating Power for Europe |
HORIZON |
HORIZON-CL5-2021-D3-03 |
HORIZON-CL5-2021-D3-03-11 |
2022-11-01 |
2026-10-31 |
On going |
€ 004 854 229.65 |
The H-HOPE project addresses the development and demonstration of innovative and sustainable energy harvesting systems capable of recovering hidden hydro energy from existing piping systems, open streams and open channels. This new technology is based on both the use of piezoelectric materials attached to submerged bodies with deforming walls and of electromagnetic regulators absorbing the transverse motion of oscillating bodies inside flows. The power density of the proposed energy harvesters will be significantly improved thanks to the multi-physics design approach and to the innovative adaptive power take-off (PTO) allowing to tune the resonance frequency of the coupled fluid-structure-electrical system and thus increase the flow induced vibrations under lock-in conditions. Eight (8) different case studies representative of actual industrial water facilities and free-flowing streams located across Europe will be used to experimentally test and validate the effectiveness of the technology in adequate and real operating conditions reproduced in laboratories. In parallel, numerical models will be developed and included in a multi-physics design strategy so as to optimize their design whereas an adaptive PTO will be developed and customize on the energy harvesting system so as to maximize the performance even in variable operating conditions. The assessment of the environmental and socio-economic impacts will be used to demonstrate the value of the selected case studies and the sustainability of the proposed technology aimed also at increasing the resilience of the water facilities. In order to extend this knowledge and promote the applications of the H-HOPE technologies to potential prosumers, an open-access and open-source do-it-yourself platform will be set up. As a result, the H-HOPE platform will certainly contribute to reduce the negative effects of the climate change and to reduce the CO2 emissions while increasing the energy independence of the EU. |
https://cordis.europa.eu/project/id/101084362 |
Rivers and estuaries', 'Urban water' |
| projects-415 |
101064994 |
NETOPT |
Modeling and forecasting supply networks using functional time series and mathematical programming |
HORIZON |
HORIZON-MSCA-2021-PF-01 |
HORIZON-MSCA-2021-PF-01-01 |
2022-09-01 |
2024-08-31 |
Completed |
No data |
In NETOPT, we will develop an innovative high-frequency forecasting model that analyses complex spatial and temporal dynamics in large-scale networks under demand and supply constraints. Using supply networks as an example, NETOPT aims to model and forecast large-scale network flows using a Functional Time Series (TS) and Mathematical Programming (MP) approach. The network model proposed in NETOPT can optimize decision planning and efficient scheduling by reducing financial and technical risks in transmission and distribution networks, such as gas and water networks. To demonstrate the practical effectiveness of our new approach, we will implement models to describe and predict hourly gas flows for several days ahead in the German high-pressure gas transmission network using real-world data. Efficient prediction and interpretation of the complex dynamics in transport and distribution networks is a crucial component of an intelligent decision support system that helps to reach the climate targets of the European Green Deal in a fair, cost-effective, and competitive way. |
https://cordis.europa.eu/project/id/101064994 |
Urban water' |
| projects-416 |
101081276 |
PREVENT |
IMPROVED PREDICTABILITY OF EXTREMES OVER THE MEDITERRANEAN FROM SEASONAL TO DECADAL TIMESCALES |
HORIZON |
HORIZON-CL5-2022-D1-02 |
HORIZON-CL5-2022-D1-02-04 |
2023-10-01 |
2026-09-30 |
On going |
€ 002 997 875.00 |
It is well-established that the Mediterranean is a climate change hot spot that warms faster than the global mean rates and the frequency of extremes increases. Moreover, climate models often fail to provide sufficient skill in predicting extremes. Extremes are of utmost importance for many socio-economic sectors and activities, including human health, agriculture and water resource management, ecology, and tourism. Most of these activities are dominant drivers of the Mediterranean macro- and micro-economy.The overall objective of PREVENT is to improve the predictability of impact-relevant extremes in the Mediterranean region on timescales from seasonal to decadal using state-of-the-art dynamical, statistical, and machine learning methods. Additionally, PREVENT brings together experts in different disciplines and geographical regions for a comprehensive study of impact-relevant climate extremes in the Mediterranean with the goal to improve their seasonal and decadal predictions in a changing climate. PREVENT intent to1.Define for local climate extreme hotspot regions, including major urban centers in the Mediterranean.2.Provide new management tools that can be used in many domains, to guide and direct processes, support monitoring activities, and increase organizational efficiency3.Develop awareness and competencies by enabling policymakers, industry, farmers, and other producers to understand, promote and practice the inclusion of seasonal and decadal data in their project management.PREVENT has a small flexible consortium consisting of colleagues with great experience in the Mediterranean climate and especially the analysis of extreme climate events and the use of impact models. One of the main PREVENT ambitions is to enhance gender balance in the coordination of EU- funded research programs and give young researchers the opportunity to participate as leaders in the work packages by providing fresh new ideas for research. |
https://cordis.europa.eu/project/id/101081276 |
Coastal waters', 'Urban water' |
| projects-417 |
101066651 |
SKYNET |
Estimating the ice volume of Earth's glaciers via Artificial Intelligence and remote sensing |
HORIZON |
HORIZON-MSCA-2021-PF-01 |
HORIZON-MSCA-2021-PF-01-01 |
2023-10-01 |
2026-09-30 |
On going |
No data |
Estimating the ice volume of Earth's glaciers is a grand challenge of Earth System science. Besides being a critical parameter to model glacier evolution, knowledge of glacier volume is fundamental to quantify global sea level rise and available freshwater resources. Under current global warming glaciers are losing mass, making improved glacier ice volume estimates a top-priority to constrain future climate scenarios. Direct glacier ice volume estimates are limited by difficulty in directly measuring the ice thickness. As a result, estimates rely on models, many of which depend on explicit physical laws but require parameters often poorly constrained. Today, the amount of satellite data is increasing at such a rate that it cannot be efficiently exploited by traditional processing pipelines. At the same time, Artificial Intelligence techniques are becoming increasingly dominant problem-solving techniques. In particular, deep learning models have recently shown the ability to surpass human accuracy in many scientific tasks. The goal of the SKYNET project is to develop an innovative deep learning-based model capable of exploiting the huge amount of available satellite data to improve the current estimates of ice volumes of all Earth’s glaciers, from continental alpine glaciers to polar glaciers, including those in the periphery of Greenland and Antarctica. The proposed methodology makes use of state-of-the art image inpainting architectures fed with satellite-based digital elevation models (TanDEM-X,REMA), altimetry (NASA’s ICESat-2), gravity and ice surface velocity data to infer subglacial topographies hence ice volumes. Modelled topographies will be constrained towards realistic solutions using glacier ice thickness measurements (GlaThiDa repository) from in-situ and remotely sensed observations. SKYNET will be jointly developed by two leading institutions in glaciology and remote sensing: the University of Venice and the University of California Irvine. |
https://cordis.europa.eu/project/id/101066651 |
Snow and ice' |
| projects-418 |
101152927 |
WATER-RENEQ |
Water Distribution Network Efficiency Optimisation with Resilience and Equity Quantification |
HORIZON |
HORIZON-MSCA-2023-PF-01 |
HORIZON-MSCA-2023-PF-01-01 |
2024-12-21 |
2027-06-20 |
On going |
No data |
The proposal focuses on addressing critical challenges in water distribution networks (WDN), recognizing its fundamental role as an essential service for society. The WATER-RENEQ approach is a comprehensive methodology that optimizes WDNs by ensuring their response capacity to adverse events (resilience), guaranteeing equity for all users, and maximizing energy savings. The first objective (O1) aims to establish a set of quantifiable indicators and performance indices that measure the operation of WDNs. These indicators will encompass key aspects of resilience and equity while focusing on energy efficiency as a quantifiable metric. Objective two (O2) focuses on the development of an innovative mathematical model. This optimization model will calculate the indices defined in O1 within a WDN. It will provide a practical methodology to evaluate and analyze WDN resilience, addressing both operational aspects and overall system performance. The third objective (O3) targets advancements in the computational efficiency of optimization algorithms. This involves reducing the number of discrete variables and optimizing the overall search space. Improving computational efficiency is crucial for solving complex real-world optimization problems effectively. Objective four (O4) involves the development of an optimization tool that proposes investment strategies for WDNs. The tool's primary goal is to maximize resilience and equity indicators within the network, even when budget constraints are present. This approach aims to provide a robust mathematical framework that considers resilience and equity criteria. In summary, the proposal addresses fundamental challenges facing WDN by introducing an innovative approach to measure and improve resilience, equity, and energy efficiency, developing a computational tool that can be applied to real-world scenarios. Once the tool will be validated in a benchmarking network, this will be applied on a real WDN in a wáter supply company. |
https://cordis.europa.eu/project/id/101152927 |
Urban water' |
| projects-419 |
101056782 |
RESTORE4Cs |
Modelling RESTORation of wEtlands for Carbon pathways, Climate Change mitigation and adaptation, ecosystem services, and biodiversity, Co-benefits |
HORIZON |
HORIZON-CL5-2021-D1-01 |
HORIZON-CL5-2021-D1-01-08 |
2023-01-01 |
2025-12-31 |
On going |
€ 006 644 842.50 |
RESTORE4Cs aims to assess the role of restoration action on wetlands climate change mitigation capacity and a wide range of ecosystem services using an integrative socio-ecological systems approach. Focusing on coastal wetlands across Europe, RESTORE4Cs will deliver standardised methodologies and approaches for the prioritisation of restoration promoting carbon-storage and greenhouse gasses (GHG) emissions abatement, while improving the ecological status and the provision of additional ecosystem services such as flood regulation and coastal erosion protection. Project results will support the implementation of Climate and Biodiversity policies in the context of the European Green Deal. Effectiveness data on restoration and land use management actions on climate services and other ecosystem and socio-economic services will be gathered both from six Case Pilot sites across European coastal areas, including well-preserved, altered, and restored wetlands, and from meta-analysis. Models and integrative assessment tools will be upscaled to wider geographical (European) and ecological (other wetland types, including floodplains and peatlands) contexts using remote sensing and machine learning methods to develop an integrated status assessment of European wetlands. The results will be integrated into a digital platform to serve as a Decision Support System (DSS) for stakeholders that will steer project efforts as part of a newly created Community of Practice around wetland restoration. |
https://cordis.europa.eu/project/id/101056782 |
Wetlands', 'Coastal waters' |
| projects-420 |
101065751 |
Hydro-ALPS |
Hydrological changes and chemical weathering through time in the southwestern Alps using isotopes from siliceous microalgae |
HORIZON |
HORIZON-MSCA-2021-PF-01 |
HORIZON-MSCA-2021-PF-01-01 |
2022-07-01 |
2024-06-30 |
Completed |
No data |
Across middle Europe, the Alps cover four sub-climatic regions and play a major role in accumulating and supplying water to the continent. Famously called as the “water towers of Europe”, they host most of the headwaters of the rivers Danube, Rhine, Po and Rhone. Located in the Mediterranean basin, the southwestern Alps are known to be particularly vulnerable to flood, landslide events and summer droughts. Precipitation is expected to decrease by 20-30 % in the Mediterranean basin by 2100 threatening water resources. However, large uncertainties remain on future precipitation regimes at a local scale. Climate models spatial resolution do not integrate regional climate heterogeneity and models are calibrated on a short temporal “observations” window (the last century mainly). The Hydro-Alps project aims at providing new perspectives on the history of past local hydrological changes that needs to be considered in order to improve climate projections and strategies to water resources issues. Hydrological changes and chemical weathering will be studied in the southwestern Alps for the pre-industrial period and over several millennia. Valuable information can be provided by microalgae called diatoms which are accumulating through time in the sediments of alpine lakes. They build a cell wall in silica (SiO2) which records lake water chemistry during the shell formation. The project will rely on measurements of oxygen and silicon isotopes from diatoms extracted from lacustrine sediments used as tracers of local hydrological conditions and chemical weathering of the crystalline bedrock. This innovative approach will provide new information on mountainous past climate variability by combining isotopic tracers on same sample and new perspective about long-term changes in the Mediterranean climate. Altogether, the Hydro-Alps project will improve our understanding of climate changes at local scale and will help stakeholders to adapt water resource strategies. |
https://cordis.europa.eu/project/id/101065751 |
Rivers and estuaries', 'Snow and ice', 'Lake' |
