Search Results (721)

Wet–dry cycles are a key factor aggravating the durability degradation of foam concrete. To address this issue, this study prepared bentonite slurry–steel slag foam concrete (with steel slag and cement as main raw materials, and bentonite slurry as admixture) using the physical foaming method. Based on 7-day unconfined compressive strength tests with different mix proportions, the optimal mix proportion was determined as follows: mass ratio of bentonite to water 1:15, steel slag content 10%, and mass fraction of bentonite slurry 5%. Based on this optimal mix proportion, dry–wet cycle tests were carried out in both water and salt solution environments to systematically analyze the improvement effect of steel slag and bentonite slurry on the durability of foam concrete. The results show the following: steel slag can act as fine aggregate to play a skeleton role; after fully mixing with cement paste, it wraps the outer wall of foam, which not only reduces foam breakage but also inhibits the formation of large pores inside the specimen; bentonite slurry can densify the interface transition zone, improve the toughness of foam concrete, and inhibit the initiation and propagation of matrix cracks during the dry–wet cycle process; the composite addition of the two can significantly enhance the water erosion resistance and salt solution erosion resistance of foam concrete. The dry–wet cycle in the salt solution environment causes more severe erosion damage to foam concrete. The main reason is that, after chloride ions invade the cement matrix, they erode hydration products and generate expansive substances, thereby aggravating the matrix damage. Scanning Electron Microscopy (SEM) analysis shows that, whether in water environment or salt solution environment, the fractal dimension of foam concrete decreased slightly with an increasing number of wet–dry cycle times. Based on fractal theory, this study established a compressive strength–porosity prediction model and a dense concrete compressive strength–dry–wet cycle times prediction model, and both models were validated against experimental data from other researchers. The research results can provide technical support for the development of durable foam concrete in harsh environments and the high-value utilization of steel slag solid waste, and are applicable to civil engineering lightweight porous material application scenarios requiring resistance to dry–wet cycle erosion, such as wall bodies and subgrade filling. Full article

This study investigates the early-age compressive strength development of concrete incorporating ground granulated blast-furnace slag (GGBFS) under varying water-to-binder (W/B) ratios (35%, 45%, and 55%) and curing temperatures (5 °C, 20 °C, and 35 °C). Concrete mixtures were prepared with 0%, 20%, and 40% GGBFS replacement levels, maintaining a constant slump of 180 mm. The influence of GGBFS on fresh properties was evident, as higher GGBFS content reduced the demand for high-performance air-entraining water-reducing admixture (AEWR) by up to 72% at 40% GGBFS and W/B of 35%. All mixtures maintained target air content within 4.5 ± 1.5%. The Nurse–Saul maturity method was applied to determine the datum temperature $T_0$ (The minimum temperature required for the degree of maturity to increase) for early-age strength prediction. The optimal $T_0$ was found to be −3 °C for both OPC and GGBFS-blended concretes, replacing the conventional −10 °C value. Compressive strength predictions were conducted using Plowman, Logistic, and Gompertz models within the 5–10 MPa range. The Plowman and Gompertz models predicted early-age compressive strength with an error of approximately 10% in the 5–10 MPa range. In the lower strength range of 3–5 MPa, the Gompertz model exhibited superior predictive performance, with prediction errors 0.5–1 MPa lower than those obtained using the Plowman model. These findings will help in enhancing the maturity method’s reliability for low-temperature or time-constrained construction and support the use of GGBFS as a sustainable cement replacement. The study offers practical insights into optimizing early-age performance in blended cementitious systems. Full article

This study investigates the environmental stability of ferronickel slag (FNS) and primary lead slags (GCS and FCS) from historical metallurgical complexes in Greece, in rainwater and seawater media. Leaching experiments revealed that nickel is the most mobile element from FNS (43.5 μg·g⁻¹ in seawater after 90 days). Chromium release, on the other hand, is very limited, not exceeding 0.04 μg·g⁻¹. In lead slags, zinc and lead exhibit significant leaching (up to 650 and 230 μg·g⁻¹, respectively), while arsenic release reaches 22.6 μg·g⁻¹. GCS contains pores primarily in the range of 50–90 Å. The majority of pore volume in FCS is centered around 30 Å. The porosity appears to have a significant effect on the element’s leachability. Pb, Zn, As, Sb, and Cd are released in significantly higher amounts from the finely porous FCS compared to GCS. Thermodynamic modeling was used to identify the pollutant speciation in water media in relation to the oxygen concentration. The release of toxic elements such as Cr from FNS and As from lead slags is enhanced under oxic (open-air) conditions. Therefore, their land disposal poses a greater environmental threat compared to sea disposal, where anoxic conditions prevail. Full article

Plastic waste and industrial residues like steel slag pose significant environmental challenges, with limited recycling solutions. This study investigates a sustainable approach by repurposing waste polystyrene and steel slag into composite membranes via electrospinning for membrane distillation applications. Steel slag incorporation enhanced membrane porosity, hydrophobicity, and thermal stability, with process optimization performed through response surface methodology by varying slag content (0–10 wt%), voltage (15–30 kV), and feed rate (0.18–10 mL·h⁻¹). Optimized membranes achieved a reduced fiber diameter (1.172 µm), high porosity (82.3%), and superior hydrophobicity (contact angle 102.2°). Mechanical performance improved with a 12% increase in tensile strength and a threefold rise in liquid entry pressure over pure polystyrene membranes, indicating greater durability and wetting resistance. In direct contact membrane distillation, water flux improved by 15% while maintaining salt rejection above 98%. Under photothermal membrane distillation, evaporation rates rose by 69% and solar-to-thermal conversion efficiency by 60% compared to standard PVDF membranes. These results demonstrate the feasibility of transforming waste materials into high-performance, durable membranes, offering a scalable and eco-friendly solution for sustainable desalination. Full article

The calciothermic reduction slag (CRS) generated in heavy rare earth metal production, is rich in rare earth elements (REE) and highly amenable to recovery. In the present study, the CRS was treated with a H₂SO₄ roasting–water leaching method for the recovery of REEs. The feasibility of this process was confirmed by thermodynamic analysis. Key roasting and leaching factors governing the leaching efficiency of REE were identified and optimized. The maximum REE extraction efficiency reached 94.65% under the optimal conditions: roasting at 150 °C for 240 min with 15 mL of H₂SO₄, followed by water leaching at 20 °C for 60 min at a liquid–solid ratio of 15:1. Results of XRD, SEM, and EDS revealed that the REEs in the CRS were transformed into water-soluble rare earth sulfates after roasting. In the leaching process, the rare earth sulfate is efficiently extracted, whereas CaSO₄ has low solubility in water. A CaSO₄ product with a 98.10% purity was obtained with a calcium recovery of 90.79%, and the removal rate of fluorine in the CRS was 99.99%. The leaching kinetics of the REEs follow a diffusion plus interfacial transfer model with an apparent activation energy of –46.45 kJ·mol⁻¹. This study demonstrates that sulfation roasting–water leaching is a viable route for the comprehensive utilization of CRS. Full article

A novel thermal conductivity prediction model was developed to address the complex influence of pore structure in porous materials. This model incorporates pore size (d) and a pore distribution parameter (t) to calculate the material’s thermal conductivity. To validate the model’s accuracy, geopolymer foamed concrete (GFC) samples with varying pore structures were fabricated. These utilized ground granulated blast furnace slag (GGBS) as the precursor, a mixed solution of sodium hydroxide (NaOH) and sodium silicate as the alkaline activator, and sodium stearate (NaSt), hydroxypropyl methylcellulose (HPMC), and sodium carboxymethyl cellulose (CMC-Na) as foam stabilizers. Conventional pore size characterization techniques exhibit limitations; consequently, this research implements a high-fidelity machine vision-driven image analysis methodology. Pore size measurement is achieved through a combined technical approach involving equivalent diameter modeling and morphological optimization. The feasibility of the proposed theory is validated by our experimental data and data from previous literature, with the error between experimental and theoretical values maintained within 5%. The value of t increases with increasing porosity and increasing disorder in pore distribution. Based on the experimental data obtained in this study and the research data from previous scholars’ studies, the t value for porous materials can be categorized according to porosity: when porosity is approximately 30%, t ≈ 0.9; when porosity is 55~65%, t ranges from 1.2 to 1.3; and when porosity is approximately 80%, t ranges from 1.9 to 2.2. Full article

As metal resource extraction increases, heavy metal ion pollution in the saturated zone intensifies. Hence, research on the migration of heavy metal ions in aquifers and the efficacy of protective measures is essential to inform pollution prevention and control engineering. This study focuses on the slag pond and its surrounding area of a smelting plant. Utilizing field hydrological surveys and experiments, and data from previous studies, we employed FEFLOW7.0 simulation software to model the groundwater system of the boulder aquifer in this region. The model divides the domain based on natural topography: the eastern river serves as a constant-head boundary, while other areas are set as specified-flux boundaries. The impermeable layer at the bottom is treated as a no-flow boundary, with a maximum simulation period of 2500 days. The simulation examines the natural movement of zinc ions and how the construction of the wall impacts their migration, as well as the wall’s effectiveness in preventing seepage. Findings indicate that the movement of zinc ions is significantly influenced by the reaction coefficient. When the reaction coefficient exceeds 10⁻⁸ s⁻¹, zinc ions decrease rapidly in the area. After the construction of the cutoff wall, the maximum migration distance of zinc ions within 2500 days decreased from 220 m to 77 m, demonstrating its effectiveness in controlling zinc transport in groundwater. Full article

This article focuses on the problem of processing slag waste from non-ferrous metallurgy, in particular, the loss of copper, gold and silver with slag during autogenous smelting in the Vanyukov furnace at the Balkhash Copper Smelter (BMZ). An analysis of factors affecting metal losses, including electrochemical and mechanical components, is presented. This paper offers a comprehensive study of the distribution of Cu, Pb, As, Au and Ag between matte and slag, taking into account the unique characteristics of the raw material and the technological conditions of the copper smelter, which distinguishes it from previous studies. This paper establishes numerical values of dissolved and mechanical losses of valuable metals. It has been established that the most important quantitative result of smelting polymetallic raw materials in a Vanyukov furnace is the proportion of mechanical copper losses in the slag, which is approximately 75–80% of the total copper content in the slag. Mathematical models are proposed to predict the distribution of metals in the process of smelting and loss of copper, gold and silver with slag. It is proposed to integrate model representations into the technology control loop, which will optimize the process of metal recovery. This will lead to an increase in profitability and a reduction in the negative impact on the environment during copper production. Full article

In this paper, an intelligent simulation method for chloride ion diffusion behavior in marine concrete is established based on a physical information neural network. The dimensionless constraint equation is constructed to solve the influence of different physical parameter dimensions on the generalization ability of the model. The performance of the simulation method is verified by field measured data. The influence of different exposure ages and chloride ion diffusion coefficients on chloride ion diffusion behavior is quantified. The temporal and spatial distribution characteristics of chlorine ion (C) in concrete under a multi-dimensional diffusion state are analyzed, and the reliability model is further constructed to evaluate the degradation law of the service performance of marine concrete. The results show that the dimensionless physical information neural network model can effectively simulate the diffusion behavior and spatial–temporal distribution of C in marine concrete. The maximum error between the predicted value and the experimental value obtained by the method proposed in this paper is less than 15%. The dimension problem of high-order nonlinear equations can be solved by Non-PINN, with the maximum error value less than 5%. The spatial–temporal distributions of C on different exposed surfaces under a multi-dimensional diffusion state are independent of each other. The service performance of marine concrete will increase with an increase in slag content and protective layer thickness, and decrease with an increase in surface chloride ion concentration. Full article

Electric arc furnace slag is a major by-product of steelmaking, yet its industrial utilization remains limited due to its complex chemical and mineralogical composition. This study presents a hydrogen-based approach to recover metallic components from EAF slag for potential reuse in steelmaking. Laboratory experiments were conducted by melting 50 g of industrial slag samples at 1600 °C and injecting hydrogen gas through a ceramic tube into the liquid slag. After cooling, both the slag and the metallic phases were analyzed for their chemical and phase compositions. Additionally, the reduction process was modeled using a combination of approaches, including the thermochemical software FactSage 8.1, models for density, surface tension, and viscosity, as well as a diffusion model. The injection of hydrogen resulted in the reduction of up to 40% of the iron oxide content in the liquid slag. In addition, the fraction of reacted hydrogen gas was calculated. Full article

Infrastructure construction is a major contributor to carbon emissions, primarily due to the extensive use of mineral materials such as cement and aggregates, which release significant amounts of carbon dioxide during production and use. While existing research has predominantly centered on the applications of concrete, the present study extends the investigation to encompass inorganic–organic composites, alloy materials, and wastewater treatment systems, with particular attention to bridging the gap between theoretical potential and practical implementation. This study identifies China, the USA, and India as leaders in this field, attributing their progress to abundant material resources and sustained policy support. Key findings reveal that while geopolymers can fully replace cement, substitution rates of less than 50% are optimal for high-performance concrete to maintain structural integrity and decarbonization benefits. Aggregate replacements using materials such as air-cooled blast furnace slag show 50–100% feasibility. This review further highlights the multifunctional potential of red mud, rice husk ash, fly ash, and blast furnace slag as cement replacements, aggregates, reinforcers, catalysts, adsorbents, and composite fillers. However, challenges such as unstable raw material supply, lack of standardization, and insufficient international collaboration persist; these issues have often been overlooked in prior research and viable solutions have not been proposed. To address these barriers, a triple-objective framework is introduced in this study, integrating sustainable infrastructure, resource recycling, and environmental remediation, supported by optimized production processes and policy models from leading nations. Future research directions emphasize comprehensive life cycle assessments and enhanced global cooperation to bridge the divide between resource-rich and resource-scarce regions. By synthesizing cross-disciplinary applications and actionable solutions, this work advances the transition toward sustainable infrastructure systems. Full article

Steel slag aggregate concrete (SAC) is widely recognized as a high-performance and sustainable construction material. However, its broader structural application has been impeded by the limited development of reliable constitutive models. Building upon the well-established non-uniform hardening plasticity theory, this study proposes a comprehensive theoretical framework to establish a stress–strain relationship model for SAC under complex stress states. To this end, a multiaxial elastoplastic constitutive model for SAC is developed through the following steps: (1) The Guo–Wang failure criterion is employed as the bounding surface, from which a yield criterion is formulated to capture the characteristic mechanical responses of SAC under multiaxial loading; (2) Based on fundamental plasticity theory, the stress–strain relationship is derived by integrating the proposed yield function with a non-associated flow rule using a Drucker–Prager-type plastic potential function, while ensuring consistency conditions are satisfied; (3) A parameter calibration methodology is introduced and applied using experimental data from uniaxial and multiaxial tests on SAC; (4) A numerical implementation scheme is developed in MATLAB 2024a, and the model is validated through computational simulations. The validation results confirm that the proposed model reliably captures the stress–strain behavior of SAC under complex loading conditions. Overall, this study not only delivers a robust multiaxial constitutive model for SAC, but also offers a systematic modeling approach that may serve as a reference for the further development of constitutive theories for steel slag-based concretes and their broader application in structural engineering. Full article

High-performance concrete (HPC) is an essential construction material used for modern buildings and infrastructure assets, recognized for its exceptional strength, durability, and performance under harsh situations. Nonetheless, the HPC production process frequently correlates with elevated carbon emissions, principally attributable to the high quantity of cement utilized, which significantly influences its carbon footprint. In this study, data-driven modeling and optimization strategies are employed to minimize the carbon footprint of high-performance concretes while keeping their performance properties. Starting from an experimental dataset, artificial neural networks (ANNs), ensemble techniques (ETs), and Gaussian process regression (GPR) are employed to yield predictive models for compressive strength of HPC mixes. The model’s input variables are the various components of HPC: cement, water, superplasticizer, fly ash, blast furnace slag, and coarse and fine aggregates. Models are trained using a dataset of 356 records. Results proved that the GPR-based model exhibits excellent accuracy with a determination coefficient of 0.90. The prediction model is used in a double objective optimization task formulated to identify mix configurations that allow for high mechanical performance aligned with a reduced carbon emission. The multi-objective optimization task is undertaken using genetic algorithms (GAs). Promising results are obtained when the machine learning prediction model is associated with GA optimization to identify strong yet sustainable mix configurations. Full article

The entrainment behavior of solid particles from the top liquid surface into molten steel exerts a crucial influence on rapid slagging and efficient desulfurization during the refining process. A Euler–Euler mathematical model was established to describe the multiphase flow field and the entrainment behavior of solid particles in a bottom-blown ladle. This model was validated by comparison with water model experiments. The effects of bottom-blowing tuyere number, gas flow rate, and solid particle size on the flow field and particle entrainment behavior were investigated. It was found that increasing the gas flow rate enhances the participation of particles in the ladle; however, the entrainment effect changes minimally when the gas flow rate exceeds 192 Nm³/h. Increasing the number of tuyeres adversely affects particle entrainment and mixing efficiency, while simultaneously expanding the size of the “open eyes”. The particle size of the refining slag has a significant impact on the entrainment effect: when the particle size exceeds 10 mm, the particles are hardly entrained in the ladle. Reducing the particle size is more conducive to increasing the entrainment amount, but excessively small particles will significantly enlarge the size of the “open eyes”. Full article

Pervious concrete is a promising sustainable pavement material for sponge city construction. The incorporation of Steel Slag Aggregate (SSA) as a substitute for natural aggregates has the double role of clean production with significant economic and environmental benefits. While the strength and permeability, known as two critical design parameters of pervious concrete, are closely linked to its porosity, there is limited research on the influence of the porosity on the mechanical properties of pervious concrete. In this paper, both experimental and numerical investigations were performed, focusing on the influence of target porosity on the strength and permeability of pervious concrete with and without SSA. Three different target porosities (15%, 20%, and 25%), five distinct water-to-cement (w/c) ratios (0.25, 0.28, 0.30, 0.33, and 0.35), and five SSA replacement ratios (0, 25%, 50%, 75%, and 100%) were considered in this study. A two-dimensional (2D) finite-element (FE) model was developed, with which the failure mode and the strength variation of pervious concrete under different target porosities were analyzed and verified with the experimental results. The results showed that the porosity had a significant influence on both the strength and permeability of pervious concrete, while the influence of the w/c ratio is marginal. There existed an optimal w/c ratio of 0.3, for which pervious concrete with porosities of 15%, 20%, and 25% achieved 28-day compressive strengths of 27.8, 20.6, and 15.6 MPa and permeability coefficients of 0.32, 0.58, and 1.02 cm/s, respectively. Specifically, at the lowest porosity of 15%, the replacement of 100% SSA resulted in the largest improvement in the compressive strength up to 37.86%. Based on the regression analysis, a series of empirical equations correlating the porosity, strength and permeability of pervious concrete was formulated and validated against the experimental data. The findings presented herein are expected to provide references to the practical evaluation of the optimal mix proportion of previous concrete, considering specific and demanding engineering requirements. Full article