Reduction metallurgy

The group’s scientific mission is rooted in the development of sustainable metallurgical processes capable of replacing conventional carbon‑intensive routes, thereby contributing to the global transition toward low‑emission manufacturing.

The research activities primarily focus on the solid‑state reduction of metal oxides through controlled atmospheres containing hydrogen, ammonia, and other environmentally benign reducing agents. Particular attention is devoted to the steel industry, where the substitution of carbon‑based reductants represents one of the most impactful strategies for reducing overall emissions. The group investigates both the fundamental reaction mechanisms and the process engineering aspects associated with these alternative reduction pathways.

A central line of research concerns the direct reduction of agglomerated iron‑bearing minerals, supported by combined numerical and experimental approaches. These studies aim to reproduce and analyze the thermochemical and kinetic conditions typical of industrial reactors, such as shaft furnaces and fluidized bed systems, in order to optimize reduction efficiency and product quality. Building on these results, the group also examines the behaviour of reduced minerals during subsequent smelting and melting stages, with a focus on slag formation, melting dynamics, and the generation of secondary products.

In recent years, the group has expanded its expertise toward emerging technologies based on hydrogen plasma smelting reduction (HPSR). This research direction explores the use of hydrogen‑rich plasma environments to achieve rapid, carbon‑free reduction and melting of iron ores. The group investigates plasma–material interactions, heat and mass transfer phenomena, and the potential of HPSR to serve as a next‑generation route for fully carbon‑neutral ironmaking. These studies position the group at the forefront of research on plasma‑assisted metallurgical processes, an area of growing strategic relevance for the decarbonization of primary metals production.

Alongside experimental and process‑engineering activities, the group is also highly active in the application of artificial intelligence and machine learning techniques to process metallurgy. These tools are employed to model complex thermochemical systems, predict reduction kinetics, optimize reactor operating conditions, and analyze large datasets generated from high‑temperature experiments. By integrating data‑driven approaches with fundamental metallurgical knowledge, the group aims to accelerate process understanding, enhance predictive capabilities, and support the design of next‑generation low‑emission metallurgical technologies.

The group currently consists of three researchers who are continuously engaged in securing competitive funding at both national and international levels. Their collaborative efforts contribute to the advancement of metallurgical science and to the development of sustainable technologies aligned with the evolving needs of modern industry.