The steel industry is crucial for the economy but significantly contributes to energy consumption and carbon emissions, impacting global warming. To reduce its carbon footprint and achieve carbon neutrality, nations and manufacturers are developing low-carbon steelmaking technologies. Natural gas injection in blast furnaces can reduce the steelmaking in-dustry’s carbon footprint. However, excessive injection can freeze the furnace and lower flame temperatures. This study explores how externally superheated reducing gas, generated by plasma torches or chemical processes such as pyrolysis, can overcome these limitations by providing more sensible heat to the furnace using computational fluid dynamics. The goal is to increase natural gas flow and achieve decarbonization through electrification.

The refractory wear of the runner in blast furnaces impacts process efficiency, operational continuity and maintenance planning. Traditional monitoring methods rely on periodic inspections, limiting predictive accuracy. This study presents a machine learning–based approach using a Random Forest Regressor to predict refractory wear heat by heat, enabling proactive maintenance and process optimization. The model was trained on data from the last eight campaigns, incorpo-rating hot metal and slag composition, thermal conditions, and production metrics. Predictions were made for eight critical points of the runner (four per side), with individually optimized hyperparameters. An advanced data expansion strategy, combining probabilistic modeling and scenario simulation, enhanced prediction granularity. Validated through Mean Ab-solute Percentage Error, the model demonstrated high accuracy over traditional interpolation methods. Industrial imple-mentation resulted in optimized maintenance scheduling, fewer unplanned stoppages and improved efficiency. 

Hydrogen is one pathway to decarbonize the iron and steel sector. Given the investment costs for both hydrogen produc-tion and integration, and the operating costs associated with hydrogen, there are trade-offs between most efficient use of hydrogen against the overall system costs. Hydrogen integration in blast furnace ironmaking was studied to assess hydro-gen usage efficiency; the implications for the plantwide energy balance were compared with the potential abatement costs. This article considers various configurations of direct and indirect hydrogen usage. The authors will describe conditions where hydrogen use is viable to reduce integrated steel mill CO2 emissions from a technoeconomic perspective.

Most standard tests for blast furnace (BF) iron ores consider only CO as the reductant gas. Nevertheless, all BFs have dif-ferent ranges of hydrogen in the reducing gases, which vary according to the auxiliary fuels used. A fundamental step in laboratory characterization is to reduce the gap between lab results and industrial data. This article shows the development of nonstandard metallurgical tests applied to pellets, lumps and sinter, considering H2 content as the reducing gas of a blast furnace with high natural gas injection rates. In addition, the nonstandard tests results are compared with industrial data of a real blast furnace.