Text & Image by Sandvik

Over the coming three years Artificial Intelligence, Big Data and machine learning will be used by researchers at the University of Skövde, Sandvik and SSAB to analyze large amounts of data. Today, there are already comprehensive measuring processes in place in the steelmaking industry, but the basis for this project is to find out what information can be analyzed if you include all data available for a specific manufacturing process.
“Internet-based retail and economic analysis are examples of sectors that have used advanced data analysis for many years. There is a tremendous potential in the manufacturing industry, and by using machine learning, we hope to find correlations that have not yet been discovered. Correlations that can contribute to solving some of the challenges the steelmaking industry is facing,” says Gunnar Mathiason, Lecturer in Computer Science at the University of Skövde.

Recycled Steel – a matter for the future

Within Sandvik, the department handling raw material optimization and calculations is participating in the project. A major challenge from their side is how to combine an optimized steel quality and at the same time apply a cost effective and sustainable production process. With a more accurate analysis and calculation of production data, the goal is to reduce the amount of pure alloys used in the steelmaking process and use even more recycled products (currently more than 80% recycled product is utilized).
“During the project, we will analyze large amounts of data through machine learning together with researchers from the University of Skövde. We hope that this project will give us a better understanding of our recycled steel categories and our residue. Then we can improve our optimizing calculations and be able to reduce the amount of pure alloys used. If we can achieve that, the effects will benefit our finances as well as the environment,” stated Magnus Josefsson, Head of Raw Materials Optimization at Product Unit Primary Products.
At SSAB, the steel making process is analyzed to optimize time and temperature in their LD-converter with the main goal to lower both emissions and energy consumption.

Analysis of Big Data

The University of Skövde also hopes to develop new knowledge that will contribute to improved algorithms for complex process analyses.
“Through our efforts in the Swedish Metal project, we will be able to develop the field of data analysis in general and specifically machine learning,” says Gunnar Mathiasson.
The project is financed by the Knowledge Foundation with support from the iron and steel industry organization Jernkontoret.

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