Sandvik on board to solve challenges in the steel industry by Artificial Intelligence
A new research project named Swedish Metal is about to kick off, where Sandvik is participating together with SSAB and the University of Skövde. By using Artificial Intelligence, Big Data and machine learning unique and comprehensive production data analyses will be made. These analyses will hopefully show new cause-and-effect relationships – which can lead to more efficient and sustainable steelmaking processes.
It’s by using Artificial Intelligence, Big Data and machine learning that the researchers at the University of Skövde, Sandvik and SSAB over three years will 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
From Sandvik, it’s the department handling raw material optimization and calculations that 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 of our recycled products. (currently at more than 80%).
"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 and use more of our recycled products. If we can achieve that, the effects will benefit our finances as well as the environment," says 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.
Contributions to Developing the 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 project Swedish Metal, 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.