The commitment to limit global warming to a maximum of two degrees Celsius requires a significant reduction in greenhouse gas emissions in all sectors. The steel industry is responsible for around 7% of global greenhouse gas emissions from the energy system. Several projects have therefore been initiated to minimize greenhouse gas emissions in steel production.
Currently, the predominant technology for primary steelmaking is the carbon-based blast furnace-basic oxygen furnace (BF-BOF) route. In the short and medium term, carbon capture and storage (CCS) technologies, among others, offer an opportunity to reduce the emission of greenhouse gases from these production processes into the atmosphere. In the long term, hydrogen-based direct reduction (H-DR) is seen as a promising solution for almost completely avoiding greenhouse gas emissions. However, in addition to the environmental benefits of this technology, economic expenses must also be considered, which lead to an increase in the specific costs of steel production. In addition, an uncertain regulatory environment makes long-term decision-making difficult. This requires supporting strategic decisions about the design of the transformation path to low-carbon steelmaking by quantitative techno-economic planning approaches.
Within the scope of a bachelor or student thesis, an existing model for capacity reduction planning in production networks is to be extended. Besides already included decisions for the reduction and modification of production capacities, additional decisions for capacity expansion planning are to be added. This will allow the design of the ramp-up of production capacities for low-CO2 steel production in parallel with the design of the dismantling of the blast furnace-converter route. The objective is thus to design the transformation of steelmaking production networks from the conventional blast furnace-converter route to the hydrogen-based direct reduction route, considering bridging technologies (e.g., CCS).
For the preparation of a bachelor or student thesis on this topic, knowledge of mathematical optimization (lecture Operations Management) as well as knowledge of software solutions for the implementation of optimization models are helpful (Python, AIMMS, Gurobi, ...).
If you are interested, please contact Yannik Graupner.