The development of industrial operations has led to a significant rise in human contact with environmental toxins, especially for individuals who live close to steel industries and petroleum refineries. Public health is seriously threatened by the potential for genetic impairment from continued exposure to these emissions. Chromosomal aberration (CA) and micronucleus (MN) assays were used in this study as reliable biological markers to assess DNA damage in people who lived close to a steel factory and an oil refinery in Iraq's Erbil Province. Blood and buccal samples were collected from 50 exposed males and 15 control participants who resided in an area free from industrial impact. Micronuclei were counted in hematoxylin-eosin-stained buccal smears, while chromosomal alterations were examined in Giemsa-stained lymphocyte cultures. The statistical analysis was performed with GraphPad Prism (version 10). According to the findings, the exposed group had a considerably higher frequency of micronucleated cells and total chromosomal alterations (p < 0.001) than the controls. Individuals who lived near industrial areas exhibited much higher levels of specific structural damage, including pulverization, exchange fragments, and dicentric and ring chromosomes, indicating ongoing genotoxic stress from extended exposure to contaminated air. In summary, the results provide the first cytogenetic evidence of measurable DNA damage associated with extended exposure to pollutants from the steel and petroleum industries in the province of Erbil. These genetic changes may increase the risk of cancer, highlighting the critical need for better environmental management, ongoing health surveillance, and preventive measures in communities near industrial complexes.

Isapa leaf extract in tri-blends acid solution for corrosion control of carbon steel (API: 5LX70) in oil and gas industries and its process optimization

Formulating decarbonization strategies for the European steel industry is crucial for meeting EU climate goals while preserving competitiveness. The present study develops an integrated modeling framework linking GCAM, a technology-rich global integrated assessment model, with the MARIO multiregional input-output framework to trace EU steel production, trade, and its direct and embedded greenhouse-gas emissions. Four scenarios are examined: a baseline aligned with current pledges and three pathways resembling major EU policy packages for 2025-2050, notably the Carbon Border Adjustment Mechanism (CBAM) and green-steel subsidy schemes. Results indicate that CBAM promotes low-emissions steel technologies, reduces total embedded emissions in EU steel consumption (up to -46% in 2030 and -23% in 2050), and helps stabilize the sector during the transition. Complementary green-steel support accelerates deployment of low-carbon production capacity and strengthens long-run competitiveness. Across scenarios, emission intensities of steel production fall (up to -80% in 2050), and consumption-based footprints associated with EU steel demand decline. The GCAM-MARIO coupling provides a transparent link between scenario design and economy-wide footprint accounting, illustrating how border measures and targeted subsidies interact. Overall, combining CBAM with sustained support for green-steel technologies offers an effective pathway to decarbonize the European steel industry while maintaining its international competitiveness.

Against the background of blast furnace burden optimization and the low-carbon transition of the steel industry, the development of high-quality Mg-bearing fluxed pellets is of great significance for the efficient utilization of medium-high silica iron ore concentrates. In this study, Mg-bearing medium-high silica fluxed pellets with a fixed SiO2 content of 5.5% were prepared, and the effect of basicity in the range of R = 1.0–1.4 on compressive strength, liquid phase behavior, slag phase composition, and pore structure evolution was systematically investigated. The results showed that the compressive strength of the pellets decreased from 2527 N/pellet to 2079 N/pellet as the basicity increased from 1.0 to 1.4. At 1250 °C, the liquid phase content first decreased from 2.66% to 1.30% and then increased to 7.38%, while the liquid phase viscosity decreased continuously. Meanwhile, the liquid phase composition evolved from a SiO2-rich calcium–iron silicate system to a Fe2O3− and CaO-rich system. XRD results indicated that Fe2O3 was the dominant crystalline phase in the pellets, accompanied by a small amount of Fe3O4, whereas no distinct highly crystalline slag phase was detected. The slag phase was mainly a Fe-Ca-Si composite slag, in which the Fe2O3 content increased and the SiO2 content decreased with increasing basicity. At higher basicity, the number and size of pores increased, and the pore morphology evolved from dispersed fine pores to irregular large pores and locally connected pores. Meanwhile, the slag phase became more widely distributed and locally enriched, weakening the continuity of the iron oxide load-bearing skeleton, which was the main reason for the decrease in compressive strength. This study provides a theoretical basis for preparing high-quality Mg-bearing fluxed pellets from medium-high silica iron ore concentrates.

Occupational environments with dust, noise, and high vocal demands can adversely impact voice production. Iron and steel workers encounter several environmental risk factors that may impair vocal function, nevertheless, objective acoustic and self-reported voice outcomes in this population have not been thoroughly investigated. This study aimed to evaluate the acoustic and self-reported voice characteristics of iron and steel manufacturing workers and compare these findings with those of a normophonic control group. This cross-sectional comparative research comprised 128 male participants: 64 steel and iron factory workers and 64 normophonic controls. Voice evaluation consisted of F0, jitter, shimmer, noise-to-harmonics ratio, smoothed cepstral peak prominence, Acoustic Voice Quality Index, maximum phonation time, Voice Handicap Index-10 and Voice-Related Quality of Life. Mann-Whitney U tests or Welch's t-tests were used for between-group comparisons, as appropriate. Iron and steel workers had significantly lower F0 and smoothed cepstral peak prominence values and higher jitter, shimmer, noise-to-harmonics ratio, and Acoustic Voice Quality Index scores than controls (all p < 0.01). The maximum phonation time was significantly shorter in workers (p < 0.001). The mean Voice Handicap Index-10 score among workers exceeded the Turkish threshold, and the Voice-Related Quality of Life scores were significantly lower than those of controls (p < 0.001). Iron and steel industry workers had lower acoustic and self-reported markers of voice quality than normophonic individuals. These findings indicate that exposure to industrial noise and airborne irritants may be associated with subclinical changes in voice characteristics. In occupational health, a comprehensive voice assessment can serve as a time-saving, non-invasive screening tool to support early diagnosis and prevention of work-related voice problems.

China's steel sector is pivotal to national and global climate goals, yet decarbonization is constrained by entrenched coal-based processes and regional disparities. Top-down and bottom-up models often neglect firm-level heterogeneity and behavioral dynamics. Here, we develop an agent-based model linking policy signals-carbon caps, pollution standards, and resource limits-with firm-level investment decisions. We evaluate four scenarios combining current and strengthened climate and air targets with neutral or accelerated technology progress. All scenarios achieve deep CO2 reductions (90 to 95% by 2060), but only the integrated policy-plus- technology pathway eliminates blast furnaces before midcentury, achieving the steepest declines. Policy- or technology-only pathways entail trade-offs, including lock-in to carbon capture and storage or delayed reductions. Our results show that synchronizing stringent climate policy with rapid technology cost declines is essential to steer China's steel industry toward a carbon-neutral trajectory.

Circular transformation of the European steel industry renders scrap metal a strategic resource

A five-indicator methodology for early-stage sustainable selection of metal scraps and raw materials: Application in the steel industry

Enhancing proactive real-time decision making in manufacturing: A methodological-functional framework

Characterization of magnetic nanoparticles (Fe3O4) obtained from industrial Iron and steel waste (mill scale)

Synthesis of magnetite nanoparticles from steel iron oxide waste as a resource recovery strategy: An optimization and characterization study

Revisiting steel slag as a modified catalyst in advanced oxidation processes for the degradation of emerging pollutants in water: A review.

Interpretable data-driven framework with multi-scale residual correction and SHAP analysis for strip profile prediction in hot rolling steel industry

Integrated evaluation of five coagulants and precipitants for fluoride and turbidity removal from steel industry wastewater: Removal performance, sedimentation, and life cycle assessment

• This study addresses resource-related scheduling challenges in steel manufacturing. • Fluctuations in oxygen consumption and scrap steel usage are the main focus. • An improved multi-objective algorithm is proposed to solve the problem. • Experiments on actual production data produce feasible and effective schedules. • Oxygen fluctuation is reduced by 19.8% and scrap additions are increased by 3.1%. In steel plants, oxygen and scrap steel are both indispensable resources in the steelmaking process. The consumption of oxygen and the addition of scrap steel are critical for ensuring production stability and achieving energy efficiency and emission reduction goals. Given the sequential and stage-based nature of steelmaking operations, this study models the scheduling process as a hybrid flow shop problem considering oxygen consumption and scrap additions (HFSP-OCSA). To support decision-making under varying production conditions and conflicting objectives, an improved multi-objective genetic algorithm (IMGA) is designed to solve HFSP-OCSA and provide a diverse set of production schemes. The IMGA integrates a problem-specific heuristic decoding method for determining the sequence and timing of steel charges across multiple stages. It also adopts an enhanced diversity preservation strategy based on penalised angular distance to improve the convergence and distribution of the Pareto front. An adaptive dual-population neighbourhood search mechanism is incorporated to further refine solution quality. The algorithm is evaluated using real data from an industrial steel plant in China. Comprehensive experimental results show that the proposed IMGA achieves up to a 19.8% reduction in oxygen consumption fluctuations, 3.1% increase in scrap additions, and a reduction of over 3,300 t in CO₂ emissions, along with direct economic savings of approximately $8.8K under the market conditions in August 2025. These results confirm the effectiveness of IMGA in improving scheduling efficiency and resource utilisation in converter steelmaking.

The growing demand for sustainable materials has stimulated the development of bio-based composites, yet the combination of natural fibers and industrial waste fillers in polymer matrices has not been exploited synergistically. In line with Sustainable Development Goal 12 (SDG-12), the paper focuses on reusing the steel industry by-product Linz-Donawitz (LD) sludge to enhance the mechanical properties of epoxy composites when used with the jute fiber. Six composite specimens with constant jute fiber loading (20 wt%) and a range of LD sludge content (0-25 wt%) were prepared using hand lay-up technique. The best composition (60 wt% epoxy, 20 wt% jute, 20 wt% LD sludge) resulted in tensile strength of 61.84MPa (28.8% better than neat epoxy), flexural strength of 31.81MPa (41.8% better) and impact strength of 18.026 kJ/m2. Interfacial defects and agglomeration of particles led to a decrease in mechanical properties beyound 20 wt% sludge. This experimental data was used to train four machine learning models to forecast mechanical properties given compositional inputs. On training data, XGBoost achieved R2 = 1.0000 with near-zero errors (MAE = 0.0005MPa, RMSE = 0.0008MPa). However, when trained on a small dataset of six specimens, this perfect fit is mostly due to memorization of the training data, as opposed to predictive power. The findings suggest the risk of overfitting, mainly in the cases of Decision Tree and Gradient Boosting models. More realistic estimates of model performance are given by cross-validation (R2 = 0.94 ± 0.04 in the case of XGBoost). The ML models can thus be used to analyze exploratory composition-property trend analysis in this particular composition space, as opposed to extrapolative prediction. These results both validate the possibility of hybrid composites that use industrial waste to obtain mechanical performance equivalent to standard natural fiber composites and indicate that waste can be valorized, although any assertion of ML predictive capacity should be carefully hedged due to limitations in the datasets.

In the steel industry, coil reallocation is commonly adopted to ensure on-time delivery by reallocating coils to orders approaching their delivery dates, thereby satisfying customer requirements in terms of quantity and specifications. In real-world operations, coils belonging to the same order should be delivered together to the customer. To save logistics costs, it is important to shorten the total distance among the storage positions of the coils belonging to the same order and to reduce the hoisting operations. Different from existing studies that primarily focus on minimising the mismatching cost, this paper investigates a new steel coil reallocation problem with consideration of logistics costs (CRPL). For the problem, we formulate an integer programming model and develop an exact branch-and-price (B&P) algorithm to obtain optimal allocation schemes. An effective labelling method is designed to optimally solve the pricing problem, and a learning-based branching strategy is introduced to accelerate the solution process. We validate the performance of the proposed algorithm through computational experiments on real-world problem instances and randomly generated instances. The experiment results show that our algorithm is effective in solving large-scale instances with up to 600 coils and that the proposed acceleration strategies substantially improve computational efficiency.

Steel surface quality critically determines the service safety and structural reliability of industrial products. Defects such as cracks, inclusions, patches, pitting, rolled-in scale, and scratches severely compromise product safety, making accurate and efficient detection a key step in quality control. However, the native A2C2f module in YOLOv13 exhibits insufficient multi-scale feature extraction for tiny defects and weak robustness under complex industrial backgrounds, hindering the detection of these six defect types. To address these gaps, we propose a multi-scale denoising enhanced module, A2C2f-MSDE, which constructs a multi-scale multi-kernel fusion branch (MSKF) with learnable adaptive weights, integrates a lightweight SEL channel attention and a DE denoising module, and employs a dual learnable residual scaling structure, while preserving the original multi-scale fusion architecture. We embed A2C2f-MSDE into the YOLOv13 backbone, perform ablation studies to verify each component’s contribution, compare it with mainstream advanced detectors on the public NEU-DET dataset, and conduct generalization tests on the GC10-DET dataset. Experiments on NEU-DET show that the improved YOLOv13n achieves mAP50-95 of 0.454 (9.4% relative gain over baseline, absolute gain 0.039), with mAP50 and mAP75 reaching 0.774 and 0.466, at an inference speed of 555 FPS, respectively, outperforming the compared mainstream models. On GC10-DET, mAP50 reaches 0.704, comparable to the baseline, maintaining stable overall detection capability, while mAP75 and mAP50-95 improve by 0.033 and 0.019, verifying the module’s performance advantages under high localization accuracy requirements and its cross-dataset generalization ability. The proposed module effectively balances detection accuracy and lightweight characteristics, providing a high-performance solution for industrial steel defect detection.

Accurate detection of blast furnace tuyere leaks is critical for operational safety and energy efficiency in the steel industry. However, significant challenges arise from the scarcity of real-world datasets and the subtle, ambiguous nature of leak-related features. Here, we propose a cross-domain detection framework guided by a sparse set of target samples to effectively bridge the sim-to-real gap. The framework incorporates a multi-dynamic attention network designed as a feature enhancement module within the detector's backbone. By employing a progressive serial fusion strategy, this module amplifies the discriminative representation of faint, multi-scale leak patterns. Furthermore, we introduce a region-refined domain adaptation strategy that utilizes a spatially selective adversarial focal mechanism. Unlike conventional global alignment approaches, this method leverages specific positive and negative samples through RoI-based alignment to achieve precise, region-focused domain adaptation. Extensive experiments conducted on both synthetic and real-world industrial datasets demonstrate that the proposed method significantly outperforms state-of-the-art approaches in terms of detection accuracy and cross-domain robustness.

Introduction: Climate change is a major global challenge, strongly influencing the Wet Bulb Globe Temperature (WBGT) index and heat stress among steel industry workers. This study evaluates the impact of geographical location and climate change on occupational heat stress exposure in Iran’s steel sector. Material and Methods: This qualitative-analytical study used data from the SABA system and the Iranian Occupational Heat Stress Atlas. Information on steel industries, their distribution, and production capacities across eight climate zones was extracted. WBGT measurements were collected in collaboration with industrial units in different zones. Data analysis was performed using ArcGIS and SPSS. The effects of climate change on heat stress were assessed for three future horizons: 2040, 2060, and 2080. Results: The findings revealed that climate zones G1 (eastern, southeastern, and desert regions) and G4 (Persian Gulf coastal provinces including Hormozgan, Bushehr, Fars, and Khuzestan), which host the highest steel production capacities, are exposed to the highest levels of heat stress (WBGT index) and water resource scarcity. WBGT values in zones G4 and G6 (Gilan province) exceeded permissible limits, whereas zones G2 (including North Khorasan, Razavi Khorasan, Tehran, Alborz, Qazvin, Hamedan, Markazi, and Chaharmahal-Bakhtiari), G5 (Kurdistan, Kermanshah, Lorestan), and G7 (Ilam, Kohgiluyeh and Boyer- Ahmad) showed the lowest WBGT levels. Considering projected temperature increases in the three future horizons and the acceptable correlation coefficient (0.40) between annual daytime temperature and WBGT index per climate zone, predicted temperature changes may lead to increased WBGT levels, particularly in zones G3, G6, and G8. Conclusion: Given climate projections and the spatial distribution of steel industries, it is essential to develop climate-responsive policies, implement sustainable water resource management, and reconsider the siting of steel production units. These measures can enhance the resilience of Iran’s steel industry against future climate change and mitigate occupational health and environmental risks