Steel is the backbone of our modern civilization, but the production of one ton of steel emits an average of almost two tons of CO2. Therefore, the steel industry is responsible for about 8% of anthropogenic CO2 emissions.
Due to rising average global temperatures the resulting urgency to reduce greenhouse gas emissions is increasing rapidly. Especially the steel industry has understood the challenge of a fundamental and necessary transformation.
Customer requirements have changed and the demand for carbon-friendly steel products is growing. Consuming industries such as automotive and aerospace are pushing themselves and their supply chains to reduce their carbon footprint. Growing investor and public interest in sustainability further forster the transformation towards a CO2 friendly steel production.
The pressure to reduce CO2 emissions and energy consumption in steel production is therefore rapidly and legitemalty increasing. Most of the direct emissions (Scope 1) in integrated steel manufacturing originate from coking, the sinter process and the blast furnace process. Additional direct and indirect emissions (Scope 1 and Scope 2) can be associated with downstream processes like reheating and rolling.The big lever in CO2 reduction in the steel industry lays undoubtedly in alternative steel making equipment, for example direct reduction plants based on hydrogen technology followed by EAF routes also based on green energy. However, it will take several years before the new plants and alternative process routes make a significant contribution to steel production. Artificial Intelligence and Machine Learning - assisted production has the potential to lower energy consumption, increase yield and lower the carbon footprint already now, for existing plants, and for newly constructed steel mills.
Smart Steel Technologies' AI-based process control software supports the transition of the steel industry already now. In 24/7 production use, the software helps to minimize inefficiencies across various production routes. Each reduction of quality deviations, energy inefficiencies and CO2 inefficiencies minimizes the CO2 footprint of steel products.
Software solutions based on artificial intelligence and machine learning increase energy efficiency and thus reduce CO2 emissions along the entire process chain from iron ore reduction and liquid steel to the finished long or flat product. For example, they make it possible to reduce temperatures in the liquid phase through optimized processes, thus minimizing energy requirements. They also increase output, so that more semi finished products of prime quality can be sold with the same energy input, fewer coils are devalued and less scrap has to be remelted. They improve the metallurgical properties of the steel and the quality of the surface. And finally, based on current process data, they precisely calculate the energy input and CO2 emissions for each ton of liquid steel and for each product.