Ismael Matino

The global community has become aware of the importance of reducing the consumption of primary resources to safeguard our planet and ensure the long-term economic sustainability of our society

Although electric arc furnace (EAF)‐based steelworks produce steel from recycled ferrous scrap and inherently implement the concept of circularity, they are challenged to reduce their overall environmental impact, reduce CO ₂ emissions, and maximize energy and resource efficiency. The paper exemplary shows how advanced digital technologies, including artificial intelligence‐based techniques, can support decarbonization and sustainability improvement of electric steelmaking. The paper presents computationally efficient machine learning models estimating sterile content in different types of scrap reaching the scrap yard as well as steel chemical composition and temperature at the exit of the Ladle furnace. The models are designed to be included in an innovative software platform based on federated learning (FL) helping industrial staff in decision‐making by estimating energy consumption and other parameters affecting the environmental impact according to the material mix fed to the EAF. The paper describes the rationale behind models’ design, the approach for selecting their hyperparameters, and the results achieved on data gathered from two different steelworks, the first one exploited as reference for models’ first setup, the second one used to assess models’ usability in the considered FL context. The performances are satisfactory in both cases, and key issues for implementation and further improvement are discussed.

Bio-CO₂ is part of the natural carbon cycle and represents a sustainable carbon source for the production of Renewable Fuels of Non-Biological Origin (RFNBOs), such as synthetic methanol. This study addresses the critical knowledge gap in aligning diverse biogenic CO₂ sources with e-methanol requirements in the EU by providing harmonized mapping, based on datasets, literature sources, and reported industrial statistics at the sectoral and country level. Bio-CO₂ streams from biogas and biogas upgrading, biomass combustion, pulp and paper, bioethanol production, and the food and beverage sector are evaluated for total emissions, CO₂ concentrations and purity, the geographical distribution, seasonality, and impurity profiles. Results show that approximately 350 Mtpa of bio-CO₂ are emitted across the EU, with highly heterogeneous characteristics. Biogas upgrading and fermentation-based processes generate highly pure CO₂ streams (>98–99%), yet their small and dispersed nature complicates logistics. In contrast, biomass-combustion and pulp and paper sectors provide large volumes (around 214.6–298.2 Mtpa and 73.9 Mtpa CO₂, respectively), but in diluted streams (typically 3–15% and 10–20%). Replacing just 10% of the EU maritime fuel demand with e-methanol would require 53.6 Mtpa of bio-CO₂ and 58 GW of electrolyzer capacity, a stark contrast to the current operational 385 MW. The findings highlight the need for infrastructure planning and aggregation hubs to enable the large-scale deployment of RFNBO methanol in the maritime sector.

Electric steelmaking is pivotal for the transition toward carbon‐lean processes. Replacing fossil carbon and fuels with alternative nonfossil materials can contribute to enhancing the sustainability of this route. The investigations reported in this article explore the use of alternative carbon sources for slag foaming in the Electric Arc Furnace and the utilization of hydrogen in related burners. The effects of using alternative carbon sources are investigated via industrial trials and complementary simulations employing a flowsheet model of the entire electric route. The investigations demonstrate that, although alternative carbon materials can generally lead to fossil CO ₂ reduction of up to 15% without negatively affecting the product or most process aspects, high ratios of certain materials, such as 30% tires, can compromise operational safety and result in poor slag foaming. Concerning hydrogen use in burners, simulations show that CO ₂ reduction of up to 48% can be achieved in off‐gases before post‐combustion, accompanied by water vapor increases of up to 31%. Simulations also estimate an increase in hydrogen content in tapped metal of up to twice the reference value; however, this increase can be mitigated by standard vacuum degassing procedures.

The current, continuous increase in attention toward preservation of the environment and natural resources is forcing resource-intensive industries like steelworks to investigate new solutions to improve resource efficiency and promote the growth of a circular economy. In this context, electric steelworks, which inherently implement circularity principles, are spending efforts to enhance valorization of their main by-product, namely slags. A reliable characterization of the slag’s composition is crucial for the identification of the best valorization pathway, but, currently, slag monitoring is often discontinuous. Furthermore, in the current period of transformation of steel production, preliminary knowledge of the effect of modifications of operating practices on slags composition is crucial to assessing the viability of these modifications. In this paper, a stationary flowsheet model of the electric steelmaking route is presented; this model enables joint monitoring of key variables related to process, steel and slags. For the estimation of the content of most compounds in slags, the average relative percentage error is below 20% for most of the considered steel families. Thus, the tool can be considered suitable for scenario analyses supporting slag valorization. Higher performance is achievable by exploiting more reliable data for model tuning. These data can be obtained via novel devices that gather more numerous and representative data on the amount and composition of slags.

Matériaux & Techniques, la revue des matériaux industriels et de leurs techniques de mise en oeuvre

In the European Green Deal background, electric steelworks are highly committed to decrease their environmental impact and greenhouse gas emissions. Among the different solutions, the use of alternative non-fossil carbon sources is promising for reducing both the environmental impact and the dependency from fossil energy and C-sources markets. The paper fits into this context and focuses on the application of an advanced model to support the investigations for the adoption of alternative non-fossil carbon sources in electric steelworks. The model is an update of a previous version allowing the simulation of a standard electric scrap route; model adaptations, validations and tests are described and showed. In addition, the first simulation results depict the possibility of obtaining a decrease between 3% and 20% of EAF CO₂ emissions by substituting fossil carbon. In parallel, a survey on the perception of the use of these kinds of tools from the European steel sector stakeholders was conducted. From the survey outcomes, it emerges an evident interest but also the need of models simple to be adapted, used, transferred and that can provide significant benefits. The adapted and used model try to fit with stakeholder requirements and to be a facilitator in the application of novel environmental friendly solutions in electric steelworks.

The possibility to estimate the Jominy profile of steel based on its chemical composition is of utmost importance and high practical relevance for industries, which enables a preliminary assessment of the suitability of a specific steel grade to a particular application or to the requirements of a customer, by saving time and resources as the Jominy end‐quench test is costly and time‐consuming. More importantly, an estimator can be used in steel grade design, by supporting the investigation of the most suitable chemistry to meet some given specifications. The article proposes a novel approach to estimate the hardenability profile of medium‐carbon quench hardenable steels, which exploits the potential of deep learning to correlate the steel metallurgy to the entire shape of the curve rather than to its single points, by thus being adaptable to a wide range of steel grades while providing very accurate estimates. Moreover, the proposed approach is suitable to implement a transfer learning paradigm, as it can exploit the knowledge acquired by training on a specific dataset to adapt the model to different steel grades for which less data or data holding different features are available.

Water is a fundamental steelworks additional resource; its efficient management is crucial for process reliability, product quality and environmental sustainability. Within steelworks, water is exploited mainly for direct or indirect cooling and is usually reused and recycled after cooling and treatments to eliminate contaminants. However, bottlenecks often exist, limiting water management efficiency and increasing water consumption. These issues are mainly related to water treatments efficiency, lack of water parameters monitoring and the manual/semi-manual management of water networks. Furthermore, these aspects are generally associated with the plant’s service life; brownfield sites are mostly affected. In these cases, improving sensor circuits coupled with decision support tools can support human decisions and lead to significant advantages. The paper discusses a potential application of such tools after new sensors installation in a use case concerning the minimization of the use of high-quality make-up-water for the indirect cooling system of a wire-rod mill in electric steelworks. The effectiveness of the described tool is shown, and the advantages are highlighted in terms of potential savings that can reach 95% and 4% of the current consumption of well and osmotic water in the considered circuit, respectively, corresponding to a saving of about 9400 m³/year of high-quality water.

Integrated steelworks off-gases are generally exploited to produce heat and electricity. However, further valorization can be achieved by using them as feedstock for the synthesis of valuable products, such as methane and methanol, with the addition of renewable hydrogen. This was the aim of the recently concluded project entitled “Intelligent and integrated upgrade of carbon sources in steel industries through hydrogen intensified synthesis processes (i³upgrade)”. Within this project, several activities were carried out: from laboratory analyses to simulation investigations, from design, development and tests of innovative reactor concepts and of advanced process control to detailed economic analyses, business models and investigation of implementation cases. The final developed methane production reactors are, respectively, an additively manufactured structured fixed-bed reactor and a reactor setup using wash-coated honeycomb monoliths as catalyst; both reactors reached almost full CO_x conversion under slightly over-stoichiometric conditions. A new multi-stage concept of methanol reactor was designed, commissioned, and extensively tested at pilot-scale; it shows very effective conversion rates near to 100% for CO and slightly lower for CO₂ at one-through operation for the methanol synthesis. Online tests proved that developed dispatch controller implements a smooth control strategy in real time with a temporal resolution of 1 min and a forecasting horizon of 2 h. Furthermore, both offline simulations and cost analyses highlighted the fundamental role of hydrogen availability and costs for the feasibility of i³upgrade solutions, and showed that the industrial implementation of the i³upgrade solutions can lead to significant environmental and economic benefits for steelworks, especially in case green electricity is available at an affordable price.