Search Results (720)

Phosphogypsum, the primary solid waste from the wet-process phosphoric acid industry, poses significant environmental and health risks due to large-scale stockpiling. To promote its resource utilisation, this study systematically evaluated the solidification and stabilisation performance of phosphogypsum–coal fly ash cementitious material (PAC) for Cr(VI)-contaminated soil under high-chloride conditions. Phosphogypsum reactivity was enhanced via mechanical activation and high-temperature calcination. An orthogonal experimental design was employed to analyse the effects of multiple factors—including calcination temperature and duration—on compressive strength and heavy metal leaching behaviour. Results show that PAC prepared from coal ash calcined at 600 °C for 3 h exhibits excellent mechanical properties and Cr(VI) stabilisation efficacy under high-chloride conditions, achieving a maximum compressive strength of 28.75 MPa and a Cr(VI) leaching concentration as low as 15.69 μg/L. Microstructural characterisation revealed the synergistic formation of a dense framework between C–S–H gel and calcium aluminate, conferring superior mechanical strength. Substitution and chelation mechanisms of Cl⁻ ions played a key role in enhancing corrosion resistance. This study provides theoretical support and technical guidance for the high-value utilisation of phosphogypsum-based materials in remediating saline–alkali-contaminated soils. Full article

The corrosion behavior of P110 tubing in a supercritical CO₂/oil/water environment (20 MPa, 90 °C) was investigated over a test duration of 168 h by means of weight loss testing and corrosion scale analysis. The results reveal a significant transition at 50% water cut, where the uniform corrosion rate surged by approximately two orders of magnitude, while the localized corrosion rate exhibited a distinct convex trend, peaking at this threshold due to inhomogeneous wetting dynamics. The corrosion scales were identified as Calcium-substituted Iron Carbonate solid solutions (Fe_xCa_1−xCO₃). Based on the competitive crystallization between corrosion-derived Fe²⁺ and bulk Ca²⁺, a mechanism for scale morphological evolution is proposed. This model explains the structural transition of the scale from a heterogeneous multi-layered film at a low water cut (30%) to a kinetic-controlled single layer at the critical water cut (50%), and finally, to a diffusion-controlled tri-layer gradient structure under fully water-wetted conditions (100%). Full article

The formation and evolution of secondary phases, such as sigma (σ), chi (χ), Laves, carbides (M₂₃C₆), and nitrides (Cr₂N), have a fundamental impact on the corrosion resistance of stainless steels. These stages alter the matrix’s local chemistry, compromise the passive film’s quality, and promote micro-galvanic interaction, which enhances localized corrosion issues. The thermodynamic stability, precipitation kinetics, and corrosion consequences of secondary phases in austenitic, ferritic, duplex, and lightweight (Fe–Mn–Al–C) stainless-steel systems are thoroughly reviewed and discussed in this paper. Advances in high-resolution characterization techniques, such as TEM, EBSD, atom-probe tomography, and in situ synchrotron techniques, have made it possible to map corrosion problems caused by secondary phases at the nanoscale. Computational thermodynamics (CALPHAD, DICTRA, TC-PRISMA) and emerging machine-learning models now provide quantitative prediction of phase formation and dissolution. Strategies for mitigation through alloy design, thermal treatment, and surface engineering are summarized, together with additive-manufacturing approaches for microstructural tailoring. Finally, this review highlights the integration of multi-scale modeling and sustainable alloy design to ensure phase-stable, corrosion-resistant stainless steels that enhance asset integrity and infrastructure reliability as per Sustainable Development Goals. Full article

Wind turbine blades (WTBs) are inevitably exposed to harsh environmental conditions, leading to surface damages such as cracks and corrosion that compromise power generation efficiency. While UAV-based inspection offers significant potential, it frequently encounters challenges in handling irregular defect shapes and preserving fine edge details. To address these limitations, this paper proposes DMR-YOLO, an Improved Wind Turbine Blade Surface Damage Detection Method Based on YOLOv8. The proposed framework incorporates three key innovations: First, a C2f-DCNv2-MPCA module is designed to dynamically adjust feature weights, enabling the model to more effectively focus on the geometric structural details of irregular defects. Secondly, a Multi-Scale Edge Perception Enhancement (MEPE) module is introduced to extract edge textures directly within the network. This approach prevents the decoupling of edge features from global context information, effectively resolving the issue of edge information loss and enhancing the recognition of small targets. Finally, the detection head is optimized using a Re-parameterized Shared Convolution Detection Head (RSCD) strategy. By employing weight sharing combined with Diverse Branch Blocks (DBB), this design significantly reduces computational redundancy while maintaining high localization accuracy. Experimental results demonstrate that DMR-YOLO outperforms the baseline YOLOv8n, achieving a 1.8% increase in mAP@0.5 to 82.2%, with a notable 3.2% improvement in the “damage” category. Furthermore, the computational load is reduced by 9.9% to 7.3 GFLOPs, while maintaining an inference speed of 92.6 FPS, providing an effective solution for real-time wind farm defect detection. Full article

Severe permeability reduction caused by drilling-fluid contamination has significantly impaired injectivity and deliverability in the K gas storage reservoir. This study aims to restore reservoir performance through the optimization and application of a composite acid system. A series of laboratory evaluations combined with core-flow experiments, continuous core scanning, and NMR T₂ analysis were conducted to assess acid performance and elucidate damage-removal mechanisms and pore–throat evolution. The results show that the optimized composite acid exhibits favorable compatibility, effective corrosion and precipitation control, a strong clay-stabilization capacity, and high permeability restoration. Core-scale experiments and NMR analyses indicate that the acid selectively removes near-wellbore and deep plugging while restoring pore–throat connectivity without inducing excessive dissolution or framework damage. Field application further confirms the laboratory findings, demonstrating substantial improvements in gas injection and production performance, along with enhanced reservoir energy retention and recovery. Overall, the proposed composite acid system provides an effective and practical solution for mitigating formation damage and improving the long-term injectivity and deliverability of gas storage reservoirs. Full article

Bipolar plates are critical components in high-efficiency energy conversion devices such as electrolyzers, fuel cells, and flow batteries, and their durability directly affects the overall performance and lifespan of the system. Although graphite bipolar plates exhibit excellent electrical conductivity and corrosion resistance, their inherent brittleness and porous structure render them prone to thermal-stress-induced damage under dynamic temperature conditions. In this study, a self-designed thermal shock testing system was utilized to perform 16,000 cycles of accelerated aging tests on graphite bipolar plates, alternating between high-temperature (90 °C) and low-temperature (30 °C) water bath environments. Systematic analysis was conducted on the performance degradation behaviors under such thermal cycling conditions using multi-scale characterization techniques, including scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), electrical conductivity, contact angle, surface roughness, and corrosion current density analysis. The results demonstrate that the degradation in electrical conductivity, loss of hydrophobicity, and increased surface roughness were primarily attributed to thermal-stress-induced microcrack initiation and propagation, surface oxidation, and physical structural deterioration. Notably, the corrosion current density did not increase significantly after 16,000 thermal cycles, but slightly decreased in the later stage, indicating that the aging of graphite bipolar plates is dominated by physical fatigue damage, and the graphite matrix has excellent chemical stability. The novelty of this study lies in the construction of a thermal shock testing system under long-cycle conditions, revealing the synergistic mechanism of thermal cycle-induced performance degradation of graphite bipolar plates, which provides experimental evidence and theoretical guidance for the material selection, structural design, and protection strategies of highly durable bipolar plates. Full article

Accurate prediction of the Langelier Saturation Index (LSI), an indicator of water’s scaling and corrosive potential, is vital for water treatment and infrastructure maintenance. In this study, five machine learning models (Ridge Regression, Support Vector Machine, Random Forest, Deep Neural Network, and XGBoost) were applied to predict the LSI from physicochemical characteristics of groundwater in the Morava River basin (Serbia). Rigorous data preprocessing (outlier removal, missing data handling, z-score normalization) and feature selection were performed to ensure robust model training. Models were optimized via 10-fold cross-validation on a 70/30 train–test split. All models achieved high predictive accuracy, with ensemble methods outperforming others. XGBoost yielded the best performance (R² = 0.98; RMSE = 0.06), followed closely by Random Forest (R² = 0.95). The linear Ridge model showed the lowest (yet still strong) performance (R² = 0.90) and larger errors at extreme LSI values. Feature importance analysis consistently identified pH as the most influential predictor of the LSI, followed by alkalinity and calcium. Partial dependence plots confirmed that the models captured established nonlinear LSI behavior. The LSI rises steeply with increasing pH and moderately with mineral content. Overall, this comparative study demonstrates that modern machine learning models can predict the LSI accurately, providing interpretable insights through feature importance and dependence plots. These results underscore the potential of data-driven approaches to complement traditional water stability indices for proactive water quality management. Full article

The removal of residual free chlorine ions from industrial wastewater is a critical step toward achieving sustainable and environmentally compliant water reuse. Excess chlorine in sludge collector water causes corrosion of process equipment, inhibits biological treatment, and leads to toxic discharge effects. In this study, an intelligent control strategy was developed for an ion-exchange-based dechlorination process to dynamically regulate chlorine concentration in the effluent stream. A pilot-scale ion-exchange filtration unit, designed with a nominal capacity of 500 L h⁻¹, was constructed using a strong-base anion-exchange resin to selectively adsorb chloride and free chlorine ions. A total of 200 experimental observations were obtained to characterize the nonlinear relationship between inlet flow rate and outlet chlorine concentration under varying operational conditions. Based on these experimental data, an Adaptive Neuro-Fuzzy Inference System (ANFIS) model was developed in MATLABR2025 to simulate and control the ion-exchange process. Two model-optimization techniques, Grid Partition + Hybrid and Subtractive Clustering + Hybrid, were applied. The subtractive clustering approach demonstrated faster convergence and superior accuracy, achieving RMSE = 0.147 mg L⁻¹, MAE = 0.101 mg L⁻¹, and R² = 0.993, outperforming the grid-partition model (RMSE ≈ 0.29, R² ≈ 0.97). The resulting ANFIS model was subsequently integrated into a MATLAB/Simulink-based intelligent control system for real-time regulation of chlorine concentration. A comparative dynamic simulation was performed between the proposed ANFIS controller and a conventional PID (Proportional-Differential-Integral) controller. The results revealed that the ANFIS controller achieved a faster response (rise time ≈ 28 s), lower overshoot (≈6%), and shorter settling time (≈90 s) compared to the PID controller (rise time ≈ 35 s, overshoot ≈ 18%, settling time ≈ 120 s). These improvements demonstrate the ability of the proposed model to adapt to nonlinear process behavior and to maintain stable operation under varying flow conditions. Full article

To address the challenges posed by rock bolt corrosion to engineering safety and service life, this study focuses on corrosion detection through integrated image processing, deep learning, and feature extraction methods. An automatic corrosion identification model was constructed based on computer-vision object-detection algorithms. By incorporating a Feature Pyramid Network, the model’s multi-scale object-detection capability was significantly enhanced. The corrosion features were extracted via image binarization and grayscale matrix analysis. The binary image method accurately quantified pitting density, revealing an initial increase followed by a decrease over time. The corrosion morphology was simulated using a Fractional Brownian Motion model, validating the accuracy of fractal feature calculations. The fractal dimension increased significantly with prolonged corrosion time, which not only characterize surface roughness evolution and corrosion rate, but also provide a reliable quantitative indicator for metal corrosion assessment. This research offers a technical framework integrating image processing, deep learning, and fractal theory for rock bolt corrosion monitoring and maintenance. Full article

Hard anodic oxide coatings on aluminium have long been used to enhance surface functionality. However, increasing industrial demands are driving the need for coatings with superior hardness, wear resistance, corrosion resistance and self-lubricating properties. Due to their porous structure, anodic oxide coatings can be modified by incorporating various nanoparticles. The properties of the modified coatings depend on both the type of nanoparticles used and the method employed to incorporate them. In this study, anodic oxide coatings were produced using direct and duplex methods on a semi-industrial scale to enable process control and potential industrial implementation. The coatings were modified with hard (Al₂O₃) and soft (CaCO₃, PTFE) nanoparticles in order to customise their functional properties. Their microstructure and chemical composition were characterised by SEM and TEM. Their microhardness, abrasion resistance and electrochemical behaviour were also evaluated. Among the tested production methods and methods for modifying nanoparticles, the duplex process incorporating Al₂O₃ particles proved to be the most promising. Its optimisation resulted in coatings with a microhardness of 430 HV0.05 and a mass loss of 9.4 mg after the Taber abrasion test, demonstrating the potential of this approach for industrial applications. Full article

Corrosion in harsh environments causes global economic losses exceeding 3 trillion US dollars annually. Traditional silicone epoxy (SE) coatings are prone to failure due to insufficient physical barrier properties and lack of active protection. In this study, cerium dioxide (CeO₂) was in situ grown on the surface of hexagonal boron nitride (h-BN) mediated by polydopamine (PDA) to prepare BN-PDA-CeO₂ ternary nanocomposites, which were then incorporated into SE coatings to construct a multi-scale synergistic corrosion protection system. Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), and transmission electron microscopy (TEM) confirmed the successful preparation of the composites, where PDA inhibited the agglomeration of h-BN and CeO₂ was uniformly loaded. Electrochemical tests showed that the corrosion inhibition efficiency of the extract of this composite for 2024 aluminum alloy reached 99.96%. After immersing the composite coating in 3.5 wt% NaCl solution for 120 days, the coating resistance (Rc) and charge transfer resistance (Rct) reached 8.5 × 10⁹ Ω·cm² and 1.2 × 10¹⁰ Ω·cm², respectively, which were much higher than those of pure SE coatings and coatings filled with single/binary fillers. Density functional theory (DFT) calculations revealed the synergistic mechanisms: PDA enhanced interfacial dispersion (adsorption energy of −0.58 eV), CeO₂ captured Cl⁻ (adsorption energy of −4.22 eV), and Ce³⁺ formed a passive film. This study provides key technical and theoretical support for the design of long-term corrosion protection coatings in harsh environments such as marine and petrochemical industries. Full article

High regeneration energy demand remains a critical barrier to the large-scale deployment of ethanolamine-based (MEA-based) CO₂ capture. This study adopts an Al₂O₃ ceramic-membrane heat exchanger (CMHE) to recover both sensible and latent heat from the stripped gas. Experiments confirm that heat and mass transfer within the CMHE follow a coupled mechanism in which capillary condensation governs trans-membrane water transport, while heat conduction through the ceramic membrane dominates heat transfer, which accounts for more than 80%. Guided by this mechanism, systematic structural optimization was conducted. Alumina was identified as the optimal heat exchanger material due to its combined porosity, thermal conductivity, and corrosion resistance. Among the tested pore sizes, CMHE-4 produces the strongest capillary-condensation enhancement, yielding a heat recovery flux (q value) of up to 38.8 MJ/(m² h), which is 4.3% and 304% higher than those of the stainless steel heat exchanger and plastic heat exchanger, respectively. In addition, Length-dependent analyses reveal an inherent trade-off: shorter modules achieved higher q (e.g., 14–42% greater for 200-mm vs. 300-mm CMHE-4), whereas longer modules provide greater total recovered heat (Q). Scale-up experiments demonstrated pronounced non-linear performance amplification, with a 4 times area increase boosting q by only 1.26 times under constant pressure. The techno-economic assessment indicates a simple payback period of ~2.5 months and a significant reduction in net capture cost. Overall, this work establishes key design parameters, validates the governing transport mechanism, and provides a practical, economically grounded framework for implementing high-efficiency CMHEs in MEA-based CO₂ capture. Full article

In accelerated bridge construction, precast concrete girders are connected to cast-in-place concrete slab using shear transfer reinforcement across the interface plane to ensure the composite action. The steel transverse reinforcement is prone to severe corrosion due to the extensive use of de-icing salts and severe environmental conditions. As glass fiber-reinforced polymer (GFRP) reinforcement has shown to be an effective alternative to conventional steel rebars as flexural and shear reinforcement, the present research work is exploring the performance of GFRP reinforcements as shear transfer reinforcement between precast and cast-in-place concretes. Experimental testing was carried out on forty large-scale push-off specimens. Each specimen consists of two L-shaped concrete blocks cast at different times, cold joints, where GFRP reinforcement was used as shear friction reinforcement across the interface with no special treatment applied to the concrete surface at the interface. The investigated parameters included the GFRP reinforcement shape (stirrups and headed bars), reinforcement ratio, axial stiffness, and the concrete compressive strength. The relative slip, reinforcement strain, ultimate strength, and failure modes were reported. The test results showed the effectiveness and competitive shear transfer performance of GFRP compared to steel rebars. A shear friction model for predicting the shear capacity of as-cast, cold concrete joints reinforced by GFRP reinforcement is introduced. Full article

One of the major issues in the energy intensive industries (EIIs) operation is corrosion control. Particularly, in refineries, corrosion causes 33% of malfunctions, especially due to the deterioration of the metallic materials and, therefore, the shortening of the useful life of equipment and installations. To face this problem, novel polymer-derived ceramic (PDC) coatings have been formulated, developed and characterized by physical and chemical tests. Different formulations were analyzed on a lab-scale through accelerated corrosion tests under acidic environments using electrochemical impedance spectroscopy (EIS) to evaluate their corrosion performance when exposed to near-real operating conditions. The optimized formulation will be used as a barrier in stainless-steel pipelines to improve the energy performance of EIIs by reducing energy losses due to excess cooling of components, maximizing the thermal efficiency of equipment, increasing the service life of equipment and reducing operation and maintenance (O&M) costs and downtime. Full article

This paper investigates the effectiveness of lightweight Convolutional Neural Networks (CNNs) compared with Vision Transformers (ViTs) for industrial defect detection, with a focus on wind turbine blades. While ViTs have recently attracted significant attention in computer vision research, their advantages over traditional CNNs remain unclear in highly specialized industrial applications. To address this gap, a rigorous comparative study was conducted using a labeled dataset of wind turbine blade surface defects, including corrosion, craze, hide_craze, surface_attach, surface_corrosion, surface_injure, surface_oil, thunderstrike. Experimental results demonstrate that lightweight CNNs outperform ViTs in both accuracy and efficiency. Specifically, CNN-based models achieved a maximum accuracy of 98.2%, while the best-performing ViT reached only 50.6%. Beyond accuracy, CNNs also showed superior data efficiency and robustness when trained on relatively small datasets, underscoring their suitability for industrial defect detection tasks where large-scale annotated data are often unavailable. These findings highlight the continuing relevance of lightweight CNNs in industrial settings and provide practical guidance for selecting models in safety-critical applications such as wind turbine blade inspection. This paper contributes by clarifying the limitations of ViTs under industrial conditions and reinforcing the value of lightweight CNNs as a reliable and computationally efficient solution for defect detection. Full article