FLEXIndustries 

Project Title Digitally-enabled FLEXible Industries for reliable energy grids under high penetration of Variable Renewable Energy Sources (VRES)
Project Duration 48 months
Support Type HORIZON EUROPE-TWIN-TRANSITION IA
Consortium

9 countries & 36 partners

Aim of the Project

The main goal of FLEXIndustries project is to build upon a holistic multi-disciplinary and multi-scale approach fostering its 7 multi-sector (automotive, biofuels, polymers, steel, pulp & paper, pharmaceuticals, cement) energy intensive industries, designing and deploying the most suitable Energy Efficiency Measures and Process Flexibility Methods for their industrial environments along with a positive impact onto their interconnection with the electrical & heating networks.

FLEXIndustries develops a Dynamic Energy & Process Management Platform to monitor, analyse and optimize the most energy-intensive industrial processes, by managing properly emerging demand response mechanisms and providing plant and process flexibility as well as offering grid services.

FLEXIndustries in which TÜBİTAK BİLGEM is involved as a stakeholder with Ford Otosan, Sakarya Elektrik Dağıtım AŞ, Mutlu Akü and Türkiye Petrol Rafineleri AŞ, aim to overcome technical and non-technical barriers to prepare EU industries for an energy flexibility transition enabled by the market uptake of the overall project concept and digital platform.

Pilot Ford Otosan Use Case

As TÜBİTAK BİLGEM Cloud Computing and Big Data Research Laboratory (B3LAB) and the Robotics and Autonomous Systems Laboratory, with our industry partner Ford Otosan, we develop AI-based models supporting energy pattern identification, surveillance and forecasting that run on the cloud and communicate with the digital twin at edge level using big data technologies.

It is aimed to improve monitoring infrastructure and implement FLEXIndustries’ platform focusing on the development of  (i) paintshop digital twin and (ii) advanced energy management and optimal Ford Otosan's Demo Site process planning algorithms.

Targets of the offered actions are described as follows:
•    4% reduction in electricity consumption
•    400% increase in RES production in terms of primary energy
•    3.058 tCO2eq reduction
•    3.8% energy cost reduction