Marina Díaz Piloñeta

Industrial waste valorization in asphalt mixtures can reduce natural resource use and climate impacts, although benefits depend on the substituted material, conditioning needs, and avoided waste management route. This study evaluates the cradle-to-gate environmental performance of asphalt mixtures incorporating welding slag and paint sludge from wind-tower manufacturing. A comparative LCA was conducted for 1 t of asphalt mixture produced at the plant. Four scenarios were assessed: a conventional reference mixture, full replacement of limestone filler by welding slag, incorporation of conditioned paint sludge with reduced virgin bitumen content, and a combined formulation. The model was implemented in SimaPro using ecoinvent v3.8 under the cut-off system model, applying IPCC 2021 GWP 100a and ReCiPe 2016 midpoint and endpoint methods. The reference mixture presented a global warming potential of 95.20 kg CO₂ eq/t. Under the baseline avoided treatment crediting approach, welding slag reduced GWP by 9.67%, paint sludge by 28.28%, and the combined scenario by 37.95%. Endpoint results generally indicated lower impacts for the modified scenarios, although rankings differed among damage categories, particularly for human health due to the avoided landfill credit. Overall, these valorization routes showed environmental benefits, especially when hazardous paint sludge reduced virgin bitumen use and avoided conventional incineration.

Graph – based learning underpins a wide range of machine learning applications, including chemistry, recommendation, program analysis, and engineered systems. Despite substantial progress in graph ...

Mine tailings are an unavoidable waste generated during iron ore mining operations, of which millions of tonnes are generated worldwide. Given the importance of steel, and therefore, iron ore mining, solutions are needed to recover this waste. Despite global efforts, the current proposed solutions struggle to reach the market due to cost-effectiveness issues. This study explores a potential solution, presenting iron tailings as a viable, economical, and sustainable material for thermal energy storage systems. Thermal characterization showed a specific heat capacity of 780–990 J/kg·K up to 590 °C. The material remained thermally stable without melting or decomposition up to 1000 °C, and the resulting storage density was estimated up to 450 kWh/m³. The material stands up safety, minimal environmental impact, and favourable thermophysical properties at a low investment cost. This innovative application not only addresses energy challenges but also contributes to resolving the waste management crisis in the iron mining industry.

Atmospheric corrosion, especially in coastal environments, presents a major challenge for the long-term durability of metallic and concrete infrastructure due to chloride deposition from marine aerosols. With a significant portion of the global population residing in coastal zones—often associated with intense industrial activity—there is growing demand for accurate and early corrosion prediction methods. Traditional standards for assessing atmospheric corrosivity depend on long-term empirical data, limiting their usefulness during the design stage of infrastructure projects. To address this limitation, this study develops predictive models using machine-learning techniques, namely gradient boosting, support vector machine, and neural networks, to estimate chloride deposition levels based on easily accessible climatic and geographical parameters. Our models were trained on a comprehensive dataset that included variables such as land coverage, wind speed, and orientation. Among the models tested, tree-based algorithms, particularly gradient boosting, provided the highest prediction accuracy (F1 score: 0.8673). This approach not only highlights the most influential environmental variables driving chloride deposition but also offers a scalable and cost-effective solution to support corrosion monitoring and structural life assessment in coastal infrastructure.

Biogas, generated through the anaerobic digestion of organic matter, is an alternative energy source to fossil fuels that is increasingly gaining importance in the effort to reduce CO2 emissions. Prior to its use, it must undergo purification processes to remove the H2S present in its composition, usually through chemical oxidation in scrubbing towers, which involves the need for large quantities of reagents and high economic costs. This study evaluates the feasibility of biogas desulfurization using residues from the steel industry, specifically high-purity iron oxides (Fe2O3) derived from spent pickling acid regeneration plants. Laboratory-scale experiments were conducted using a fixed-bed reactor, varying gas flow rates, iron oxide dosages (bed mass), and residence times, resulting in adsorption ranges of 1.15 to 1.97 g H2S/kgbed. Thus, not only would the costs associated with biogas treatment be reduced, but a second life would also be given to a waste product, providing added value to the generated biogas.