Tomas Lundberg

Research Engineer, Dept of Metallurgy Resource Efficiency & Environment

At

Swerim AB

Biography:

Tomas Lundberg holds a PhD in theoretical physics from Linköping University, Sweden. Prior to joining Swerim he worked for Ericsson Research for 19 years, mainly with objective quality models for speech, audio, and video, or other modelling works related to user behaviour. From around 2014 this modelling work naturally led to interest in applying machine learning and big data technologies in the models. Since joining Swerim in 2018, he has been involved in several projects related to big data, data management, and advanced analytics using machine learning for the steel industry.

Abstract:

Optimizing the Use of Surplus Gases at SSAB’s Steel Plant in Luleå using Artificial Intelligence by Tomas Lundberg, Department of Metallurgy, Swerim AB Sweden.

In steel production, a number of energy-rich gases are formed as by-products. Most of the gases are collected and stored in gas holders and used internally at various process units inside the steel plant. When excess process gases exist, they are usually used externally as energy carriers. In general, the energy system at an integrated steel plant is very complex and non-linear as the process gases are generated from various process units, either continuously or discontinuously with varying energetic content, flow rate and chemical analysis. Therefore, it is quite difficult to model and optimize the energy system dynamically.

At SSAB’s integrated steel plant in Luleå, the excess process gas is delivered to a combined heat and power (CHP) plant to produce district heating for the surrounding community and electricity. However, due to the limited capacity of the gas holders and the lack of dynamic interaction of related process units, from time to time some process gases need to be flared. Due to this, external fuels such as oil or LPG might then be required at the CHP plant to compensate for the lack of process gases. An improvement of the regulation of gases could lead to an increase in the utilization of the surplus gases, which in turn would reduce oil consumption at the CHP. The benefit here is thus twofold: instead of flaring gas at SSAB, it is used to reduce oil consumption at the CHP plant; consequently it will reduce both fossil CO2 emissions and production cost.

The purpose of this work is to optimize and visualize the complex energy system by using an artificial intelligence approach. A system for energy visualization with AI, ‘EnVisA’, will be created which enables a real-time view of the current energy performance and the availability of gases. In addition, EnVisA is planned to be able to optimize and predict the supply of surplus gases for the CHP plant, thus contributing to a more climate-friendly district heating system by joint efforts from involved stakeholders, i.e. the steel plant, CHP plant, and the commune.