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Simulation and optimisation for future industrial applications (SOFIA)

OPERA - Operational AI for Process Industry

The project outputs will pave the way for sustainable AI in industrial processes.

Start

2019-09-02

Planned completion

2021-09-30

Main financing

Vinnova Process Industrial IT and Automation (PiiA)

Collaboration partners

RISE, Mälarenergi, Outokumpu, ABB, Sigholm, Mimer

Project manager at MDH

Professor

Konstantinos Kyprianidis

+4621151765

konstantinos.kyprianidis@mdh.se

Development of real-time machine learning predictions for complex industrial processes is associated with challenges such as long time-delays and complexity of components and machinery involved. Moreover, industrial processes are continuously evolving through component replacements or expansions. Therefore, machine-learning-based correlations between cause and consequence become intricate.

OPERA is built upon the knowledge and data accrued during the previous PiiA/Vinnova project “Smarta Flöden”. In this way, OPERA capitalizes on available cleaned, streaming process data and working machine learning models in order to provide process industry with address key innovation challenges associated with the fully operational application of AI on a process industrial pilot.

OPERA investigates two distinct industrial processes district heating and cold rolling milling, to capture general process industrial solutions. These cases also include essential value chain aspects, through direct customer participation. Moreover, key technology suppliers are part of the project delivering platforms, competence, and commercialization. The project outputs will pave the way for sustainable AI in industrial processes.

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