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TRIMIS

Robust and data-Efficient Learning for Industrial Control

PROJECTS
Funding
European
European Union
Duration
-
Geo-spatial type
Other
Total project cost
€0
EU Contribution
€210 911
Project Acronym
RELIC
STRIA Roadmaps
Connected and automated transport (CAT)
Transport mode
Multimodal icon
Transport policies
Digitalisation
Transport sectors
Passenger transport,
Freight transport

Overview

Call for proposal
HORIZON-MSCA-2021-PF-01
Link to CORDIS
Background & Policy context

Our life depends on heat, power and gas networks. The greening of these networks is crucial to Europe’s energy and resource efficiency targets. In this context, the EU-funded RELIC project will explore a holistic approach to how resources and energy are delivered to the industry via distribution networks. It will explore how incorporating data-driven learning in the design of control algorithms leads to improved environmental performance. Currently, timescales ranging from milliseconds to ensure the safe operation of pumps or generators to days or months make operation complicated. There is uncertainty in terms of the operating conditions and incomplete information. The project will develop new operating strategies for distribution networks.

Objectives

"Increasing energy and resource efficiency in industrial systems is key to decrease harmful emissions by 90% by 2050. Reaching the environmental targets requires a holistic approach to how resources and energy are delivered to the industry by means of distribution networks, such as heat networks, electricity networks, or gas transport networks. I will devise new control strategies that ensure robust operation of distribution networks while ensuring safety and satisfaction of environmental objectives.

The environmental performance of the whole system hinges on the performance of distribution networks. Optimal control of such networks is complex due to timescales, from milliseconds to ensure safe operation of pumps or generators, to days or months to include environmental goals, spatial complexity, uncertainty related to varying operating conditions, incomplete information available, and limited computational power. Existing control frameworks are usually application specific and have limited use in large-scale systems. In the project, I will advance theory in data analytics and optimisation, and build on my industrial experience to develop operating strategies for distribution networks that will enable safe implementation and reaching the environmental targets.

There is a potential in integrating machine learning in control design to overcome the complexity while satisfying safety constraints, as shown in robotics and automotive industry. However, IPCC indicated that ""The key challenge for making an assessment of the industry sector is the diversity in practices, which results in uncertainty, lack of comparability, incompleteness, and quality of data available in the public domain on process and technology specific energy use and costs"". The research question I will address in this project is if and how incorporating data-driven learning in design of control algorithms leads to improved environmental performance and safe operation of large-scale industrial networks."

Funding

Specific funding programme
HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
Other Programme
HORIZON-MSCA-2021-PF-01-01 MSCA Postdoctoral Fellowships 2021

Partners

Lead Organisation
Organisation
NORGES TEKNISK-NATURVITENSKAPELIGE UNIVERSITET NTNU
Address
HOGSKOLERINGEN 1, 7491 TRONDHEIM, Norway
Organisation website
EU Contribution
€210 911

Technologies

Technology Theme
Energy efficiency
Technology
Energy efficient information systems
Development phase
Demonstration/prototyping/Pilot Production

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