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Future Directions of Production Planning and Optimized Energy- and Process Industries

Project description

DEENESFRITPL

Expanding the use of machine learning for energy efficiency

In the battle against climate change, it is essential that we decrease resource and energy consumption while enhancing overall efficiency. Machine learning proves to be a promising tool in attaining this objective and has already made significant contributions across various sectors and industries, resulting in vital advancements in automation and efficiency. The EU-funded FUDIPO project aims to expand the implementation of machine learning practices throughout European industries with the purpose of drastically enhancing energy and resource efficiency. To achieve this, the project will develop an optimisation platform, three extensive site-wide system demonstrators and two small-scale technology demonstrators. As a result, substantial improvements can be realised through data collection and simulations.

Objective

Machine learning have revolutionized the way we use computers and is a key technology in the analysis of large data sets. The FUDIPO project will integrate machine learning functions on a wide scale into several critical process industries, showcasing radical improvements in energy and resource efficiency and increasing the competitiveness of European industry. The project will develop three larger site-wide system demonstrators as well as two small-scale technology demonstrators. For this aim, FUDIPO brings together five end-user industries within the pulp and paper, refinery and power production sectors, one automation industry (LE), two research institutes and one university. A direct output is a set of tools for diagnostics, data reconciliation, and decision support, production planning and process optimization including model-based control. The approach is to construct physical process models, which then are continuously adapted using “good data” while “bad data” is used for fault diagnostics. After learning, classification of data can be automated. Further, statistical models are built from measurements with several new types of sensors combined with standard process sensors. Operators and process engineers are interacting with the system to both learn and to improve the system performance. There are three new sensors included (TOM, FOM and RF) and new functionality of one (NIR). The platform will have an open platform as the base functionality, as well as more advanced functions as add-ons. The base platform can be linked to major automation platforms and data bases. The model library also is used to evaluate impact of process modifications. By using well proven simulation models with new components and connect to the process optimization system developed we can get a good picture of the actual operations of the modified plant, and hereby get concurrent engineering – process design together with development of process automation.

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Programme(s)

Topic(s)

Call for proposal

H2020-IND-CE-2016-17

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Sub call

H2020-SPIRE-2016

Funding Scheme

RIA - Research and Innovation action

Coordinator

MALARDALENS UNIVERSITET
Net EU contribution
€ 1 135 158,75
Address
HOGSKOLEPLAN 1
721 23 VASTERAAS
Sweden

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Region
Östra Sverige Östra Mellansverige Västmanlands län
Activity type
Higher or Secondary Education Establishments
Links
Total cost
€ 1 135 158,75

Participants (12)

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Last update: 13 September 2023

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Permalink: https://cordis.europa.eu/project/id/723523

European Union, 2024