Exploring hydrogen’s potential to decarbonise steel from blast furnaces

The complexity of the blast furnace process and limited data on physicochemical and mechanical processes hinder accurate prediction of technological impacts. My research explores the sensitivity of hydrogen injection modeling to uncertainties in assumptions commonly applied by researchers. In some models found in scientific literature, assumptions outnumber practical data thereby undermining the validity of predictions.

So far, hydrogen injection trials in industrial blast furnaces have been limited in scope and not yielded a full understanding of the impacts. Recent trials at an experimental blast furnace in Japan revealed insights into key factors including the water gas shift reaction equilibrium, hydrogen and carbon monoxide utilisation ratios, and vertical and radial temperature patterns.

Despite their small scale (around 1/300 the volume of an average modern blast furnace), the trials are essential for addressing modeling uncertainties and bringing hydrogen injection closer to practical deployment.

Hydrogen can cut coke usage by up to 20%, leading to lower fuel costs and improved efficiency. Although investment in hydrogen generation is necessary, the long-term savings on coal and reduced cokemaking capacity can justify this expense. Hydrogen injection also enhances blast furnace productivity, contributing to overall cost efficiency.

In addition, hydrogen injection reduces CO₂ emissions, enabling companies to earn carbon credits from cap-and-trade systems, thereby offsetting costs. For instance, using 20% less coke decreases emissions by hundreds of kilograms per tonne of steel, supporting sustainability goals and enhancing companies’ reputations.

Early adopters can gain a competitive edge amid rising decarbonisation pressures by attracting environmentally conscious clients and benefiting from regulatory incentives. This can increase market share and brand loyalty while offering substantial long-term financial benefits.

Defining optimal injection parameters - blast temperature, oxygen enrichment, and hydrogen consumption - is crucial for scaling hydrogen injection. These factors influence the raceway adiabatic flame temperature, bosh gas rate, and top gas rate that all affect heat and mass transfer patterns within the blast furnace.

Complications are exacerbated when hydrogen injection coincides with pulverised coal injection, which adversely affects coal combustion in the raceway. In addition, when hydrogen mixes with hot, oxygen-enriched air exiting the tuyere, it oxidises into water vapour and releases significant amounts of heat.

Deeper in the hearth, water vapour is converted into hydrogen by reacting with coke, which consumes heat. This could lead to overheating at the periphery and degradation of refractory materials, while cooling in the centre may deactivate the deadman (the mass of coke in the hearth centre) and thereby adversely affect stable furnace operation.

Hydrogen is highly flammable and can form explosive mixtures with air, increasing ignition risks, especially with delivery system leaks. It can cause embrittlement in metals, leading to structural failures if materials aren’t designed for hydrogen exposure. Investments in enhanced monitoring systems in the form of advanced sensors and analytics are therefore essential for detecting leaks and ensuring safety.

Technological issues may also arise. In the experimental blast furnace in Japan that I mentioned earlier, intensive hydrogen injection with very high oxygen enrichment lowered top gas temperatures below the dew point, potentially prolonging the burden drying process. This diminishes a furnace’s effective height and causes water condensation as well as wet dust particle accumulation in the gas cleaning.

Addressing safety and technological risks with thorough assessments, planning, and safety measures is crucial.

Blast furnace ironmaking is a highly complex technology, characterised by numerous feedback mechanisms that account for operator errors and raw material variability. Many critical questions about blast furnace theory and practice remain unresolved after decades of study.

Different research teams often develop their own models based on specific industrial benchmarks, leading to significant discrepancies in methodologies. In addition, industrial experiments are costly, and data on actual conditions in blast furnaces are often scarce and contradictory.

Collaboration among research teams and industrial stakeholders is therefore essential for creating a reliable database. Emerging computational techniques and data analytics, including AI, can play a pivotal role in standardising modeling parameters. This can ultimately reduce discrepancies in research findings and enhance overall efficiency in blast furnace ironmaking.

Learn more about the research in the paper published in Steel Research International