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GenAI Powered Steel Development

Reference number
Coordinator Ferritico AB
Funding from Vinnova SEK 835 998
Project duration September 2023 - June 2024
Status Completed
Venture Emerging technology solutions
Call Emerging technology solutions stage 1 2023

Important results from the project

The project aimed to revolutionize steel development by designing a GenAI-driven platform that integrates AI and Quantum Mechanics Modelling (QMM) data. The goals were to optimize the steel production process, reduce reliance on costly and time-consuming physical experiments, and enhance sustainability. The platform was designed to provide holistic optimization, merging sub-process simulation models with cost and environmental footprint functions to meet industry needs.

Expected long term effects

The project successfully aligned with its goals to innovate steel development through digitalization and AI. Industry partners emphasized the need for simulation-driven development to reduce the inefficiencies and costs of physical experiments. The project identified critical functional requirements for a GenAI-driven platform, confirmed AI technology maturity, and validated the integration of QMM data, which enhanced simulation precision. The modular software architecture maintained high accuracy, proving effective for efficient, and sustainable steel production.

Approach and implementation

The project was structured around developing a GenAI-driven steel development platform. Key activities included identifying functional requirements, conducting a feasibility analysis, and designing a modular software architecture. The integration of sub-process simulation models with cost and environmental footprint functions was tested. Quantum Mechanics Modelling (QMM) data was incorporated to enhance precision. The project involved collaboration with industry partners to ensure the platform met real-world needs.

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 12 July 2024

Reference number 2023-01375