Processes

Understanding the fatigue behavior of materials is essential for designing components capable of enduring prolonged use under varying stress conditions. This study investigates the high-cycle fatigue and fatigue crack growth characteristics of X17CrNi15-2 stainless steel. Very high-cycle fatigue (VHCF) and fatigue crack growth tests were conducted on conventional fatigue and compact tension (CT) specimens fabricated from X17CrNi15-2 stainless steel. The fatigue crack growth behavior of the CT specimens was analyzed using Paris’ law. A revised version of Paris’ law was suggested based on the fatigue crack growth rate plotted against the stress intensity factor range, expanding on prior research utilizing three-point single-edge notch bend specimens. Scanning electron microscopy (SEM) was employed to examine the fracture mechanisms of both fatigue specimen types. The results indicated that the fatigue specimens failed in the VHCF regime under stress amplitudes ranging from 100 to 450 MPa. A power law correlation between stress amplitude and fatigue life was established, with material constants of 7670.3954 and −0.1663. These findings offer valuable insights into the material’s performance and are crucial for enhancing its suitability in engineering applications where high-cycle fatigue is a critical factor. Full article

Water scarcity is a problem affecting millions of people in the world, including northern Chile, with several cities declared under water scarcity by the Chilean government. This paper numerically evaluates an atmospheric water generator based on a single vapor compression refrigeration system using R410A. The monthly water harvesting rate and specific energy consumption are calculated for nine cities distributed throughout northern Chile. Every component is modeled in a modular way, using semi-empirical models, and integrated into an overall model. For the nine cities considered in this study, the monthly water harvesting varies between a maximum of 5518 L, obtained for Huasco during January, and a minimum of 0 L, in Combarbalá and Vicuña in some months during winter. In the case of the specific energy consumption, it varies between 0.355 and 1.146 kWh/L. By taking the period between December and April, the system can collect an average of 3868 L/month, with an average specific energy consumption of 0.533 kWh/L. The working domain of the system is strongly limited by the Chilean climate conditions, which is mainly influenced by the Humboldt Current. This restricts the operational efficiency of the AWGs, especially during the colder and drier months. Nonetheless, the modular modeling approach allows for flexible adaptation and optimization of the system across different geographic locations. Full article

This study reports the synthesis of high cis-1,4 polybutadiene with a narrow molecular weight distribution (Đ < 2.0) by coordinative chain transfer polymerization (CCTP) using a homogeneous ternary NdV₃/diisobutyl aluminum hydride (DIBAH)/dimethyldichlorosilane (Me₂SiCl₂) Ziegler–Natta catalyst system. The polymerization parameters, notably the monomer-to-initiator ratio ([M]/[Nd]) and the halogen-to-initiator ratio ([Cl]/[Nd]), were systematically varied to define the CCTP operational window. It was found that CCTP conditions are achieved only at low [M]/[Nd] ratios (<2500) and intermediate [Cl]/[Nd] ratios between 1.0 and 2.0, facilitating the production of polymers with molecular weights below 32 kDa and narrow dispersity. Increasing these ratios beyond these thresholds potentially induces the formation of insoluble, hyper-halogenated catalytic species and increases medium viscosity, which significantly broadens the molecular weight distribution (Đ > 4.0) and impairs CCTP control. These findings challenge previous assumptions that higher halogen concentrations are necessary for CCTP, thereby providing important mechanistic insights for tuning active species and achieving improved polymer architecture. The work demonstrates a viable pathway to control polymer microstructure and molecular weight in neodymium-based CCTP, which is critical for design of high-performance elastomeric materials. Full article

Pre-extraction gas technology is commonly used in coal mines to extract gas from single coal seams, initial protective layers, and both unprotected and protected coal seams. With the development of drilling equipment, directional long drilling, pre-extraction, coal seam gas technology has been widely applied, and negative pressure extraction is one of the key factors affecting the effectiveness of directional long drilling gas extraction. In order to determine the reasonable length of directional long boreholes, studying the negative pressure distribution and time-varying rules within such boreholes is of great significance for guiding later borehole layout and gas extraction. The COMSOL Multiphysics software v.5.3. was used to couple and solve the dynamic model of temperature, stress, and seepage in coal-containing gas, as well as the mathematical model of negative pressure attenuation in directional long boreholes. The gas pressure distribution in the coal surrounding the directional long borehole and the distribution and time-varying law of negative pressure in the borehole were studied. Then, the distribution and time-varying law of negative pressure in directional long borehole extraction were tested on site. Research has shown that the negative pressure attenuation during directional long drilling has a relatively small impact on the effectiveness of coal gas extraction, while the negative pressure at the hole opening is the key factor affecting the effectiveness of gas extraction. In the early stage of extraction, as the drilling depth increases, the pressure loss inside the hole increases and the negative pressure inside the hole decreases. As the extraction time becomes longer, the pressure loss inside the borehole decreases and the negative pressure inside the borehole gradually returns to the negative pressure value at the orifice. The gas flow velocity inside the extraction borehole gradually increases from the bottom of the hole to the hole opening, and the flow velocity at the bottom of the hole remains basically constant. The gas flow velocity inside the hole gradually decreases with the extension of extraction time, and the smaller the distance from the extraction hole opening, the greater the flow attenuation. The collapse of drilling holes during extraction affects the attenuation of negative pressure inside the hole in the short term. As the extraction time increases, the impact of the collapse on the negative pressure inside the hole is limited. The temperature of coal can significantly affect the negative pressure and gas flow distribution inside the pores. Considering the temperature effect, the gas flow velocity inside the pores is higher and the pressure loss is lower in the short term. On-site tests have determined that the depth of ultra-long directional drilling holes is shallower than 327 m, and the negative pressure changes inside the borehole are not significantly different from the negative pressure at the hole opening. The negative pressure stabilization speed near the hole opening and bottom is fast, usually reaching its peak within 3–10 min. The negative pressure stabilization process from the borehole opening to the hole bottom shows a “fast slow fast” trend. When using double-sided extraction, the time for negative pressure to reach stability is significantly shortened compared to single-sided extraction, and double-sided extraction is beneficial for improving the effectiveness of coalbed methane extraction. Full article

Hospitals often use diatrizioic acid (DTZA), an iodinated radiocontrast agent, which is poorly biodegradable and persistent in aqueous media. Therefore, the objective of this work is to remove DTZA from water using an advanced separation process, namely liquid surfactant membrane (LSM) or emulsion liquid membrane. The LSM system is composed of Aliquat 336 as extractant, Span 80 as emulsifier, kerosene as diluent, and KCl as internal stripping phase. The impacts of experimental parameters impacting the extraction of DTZA from water by LSM, namely surfactant concentration, initial pH of the contaminated solution, extractant dosage, nature of base in the contaminated solution, concentration of the internal stripping phase, nature of stripping solution, emulsion/external solution volume ratio, internal solution/organic phase volume ratio, mixing rate, nature of diluent, emulsification time, emulsification rate, and initial DTZA concentration, were investigated. A highly stable emulsion with a good degree of removal of 90.8% of DTZA in water was obtained for an emulsifier dosage of 3% (w/w), an extractant dosage of 1.0% (w/w), a pH of the contaminated solution of 10 using NH₄OH, a concentration of the inner phase of 0.3 N KCl, an internal solution/organic phase volume ratio of 1/1, an emulsion/external solution volume ratio of 20/250, a mixing speed of 250 rpm, an emulsification time of 4 min, and an emulsification speed of 20,000 rpm. Additionally, the extraction of DTZA from various natural water matrices (natural mineral water, tap water and seawater) was examined. The developed LSM method offers a fascinating enhanced separation method for the elimination of DTZA in waters with low chloride ion concentrations. Full article

Renewable energy technologies play a key role in mitigating climate change and advancing sustainable development. Among these, photovoltaic (PV) systems have experienced significant growth in recent years. However, shading, one of the most common faults in PV modules, can drastically degrade their performance. This study investigates the application of convolutional neural networks (CNNs) for the automated detection and classification of shading faults, including multiple severity levels, using current–voltage (I–V) curves. Four scenarios were simulated in Simulink: a healthy module and three levels of shading severity (light, moderate, and severe). The resulting I–V curves were transformed into grayscale images and used to train and evaluate several custom-designed CNN architectures. The goal is to assess the capability of CNN-based models to accurately identify shading faults and discriminate between severity levels. Multiple network configurations were tested, varying image resolution, network depth, and filter parameters, to explore their impact on classification accuracy. Furthermore, robustness was evaluated by introducing Gaussian noise at different levels. The best-performing models achieved classification accuracies of 99.5% under noiseless conditions and 90.1% under a 10 dB noise condition, demonstrating that CNN-based approaches can be both effective and computationally lightweight. These results underscore the potential of this methodology for integration into automated diagnostic tools for PV systems, particularly in applications requiring fast and reliable fault detection. Full article

The integrated assessment of watershed ecosystems is increasingly critical for sustainable water resource management amid global environmental change. Multi-source data integration—encompassing in situ monitoring, remote sensing, and model-based observations—has significantly expanded the spatial and temporal scales at which watershed processes can be analyzed. Concurrently, advances in model coupling strategies, ranging from loose to embedded architectures, have enabled more dynamic and holistic representations of interactions among hydrology, water quality, and ecological systems. However, a unifying operational framework that links multi-source data, cross-scale coupling, and rigorous uncertainty propagation to actionable, real-time decision support is still missing, largely due to gaps in interoperability and stakeholder engagement. Addressing these limitations demands the development of intelligent, adaptive modeling frameworks that leverage hybrid physics-informed machine learning, cross-scale process integration, and continuous real-time data assimilation. Open science practices and transparent model governance are essential for ensuring reproducibility, stakeholder trust, and policy relevance. The recent literature indicates that loose coupling predominates, physics-informed ML tends to generalize better in data-sparse settings, and uncertainty communication remains uneven. Building on these insights, this review synthesizes methods for data harmonization and cross-scale integration, compares coupling architectures and data assimilation schemes, evaluates uncertainty and interoperability practices, and introduces the Smart Integrated Watershed Eco-Assessment Framework (SIWEAF) to support adaptive, real-time, stakeholder-centered decision-making. Full article

Stirred tanks are widely used in polymerization processes, where the residence time distribution (RTD) significantly affects monomer conversion and polymer quality. In this study, the RTD in the stirred tank with both constant and variable viscosity fluids was investigated numerically. To account for the viscosity evolution during polymerization, a model relating fluid viscosity to the mean age of the fluid was developed. After verifying mesh and time step independence, the effects of impeller speed, fluid space time, and viscosity varying on RTD were examined in both single-tank and two-tank configurations. Compared to the constant-viscosity fluids, the variable-viscosity fluid shows different flow behaviors such as dead zones and short-circuiting. Analysis based on the number of tanks in series showed that increasing impeller speed and extending space time can enhance mixing efficiency, where the improved mixing in the second stage of the two-tank configuration eliminated the concentration fluctuations caused by recirculating flow in the first tank, which may result in a more uniform RTD curves. Full article

The zeolite structure, with its precisely distinct pores, settled cages, and adsorption sites, enables the formation and stabilization of isolated metal centers. These well-defined structures make metal-loaded zeolites promising catalysts. Three different β zeolites were synthesized by an aqueous ion-exchange procedure, firstly with cobalt (Co), and secondly with zinc (Zn), cerium (Ce), and copper (Cu), to make three bimetallic CoZn/β, CoCe/β, and CoCu/β zeolites, respectively. X-ray powder diffraction analysis, Fourier transform infrared spectroscopy, scanning electron microscopy with energy dispersive spectroscopy, and low-temperature nitrogen adsorption analysis revealed the structural, morphological, and surface properties of the studied materials, while optical properties were investigated by UV-Vis diffuse reflectance spectroscopy. The lowest onset potential of 1.67 V was obtained for both CoZn/β and CoCe/β, while the somewhat positive value of 1.70 V was observed for CoCu/β. CoZn/β exhibited the lowest value of Tafel slope of 89 mV dec^−1, while slightly higher values of 109 and 113 mV dec⁻¹ were calculated for CoCe/β and CoCu/β during ORR, respectively. CoZn/β showed four-electron pathways of ORR, CoCu/β showed a mixed ORR mechanism, while CoCe/β offered two-electron pathways of ORR. All presented results established that CoZn/β had the highest OER/ORR activity, followed by CoCu/β, while CoCe/β had the lowest activity detected. Full article

This work presents a systematic literature review of powder-gas jet stream (PGJS) characterisation techniques for coaxial nozzles in the laser directed energy deposition process (L-DEDp). The analysis includes thirty-four camera-based and four weight-based techniques. In weight-based techniques, the mapping of powder concentration is made by measuring the powder flow rate in certain areas within the PGJS. Despite being cost-effective, these methods are time-consuming, invasive, and less suitable for real-time monitoring. Camera-based techniques use laser light and a camera to capture particle intensities, allowing for the non-intrusive measurement of powder distribution. Despite its advantage, limitations are reported in the literature regarding the techniques. Detecting dense or fine powder flows accurately is challenging. Two-dimensional images cannot fully represent the jet’s three-dimensional structure, relying on image processing algorithms for the results. However, the non-existence of a common standard metric for evaluating and comparing results across various setups is a significant gap, as each characterisation often needs to be performed on a case-by-case basis. To address these challenges, a basic reporting structure is suggested to enable a standardised assessment of PGJS measurements, thereby supporting process control and quality assurance in L-DEDp applications. Full article

Carbon dioxide (CO₂) is a non-toxic asphyxiant gas that, once released, can pose severe risks, including suffocation, poisoning, frostbite, and even death. As a critical component of carbon capture, utilization, and storage (CCUS) technology, CO₂ pipeline transportation requires reliable leakage detection and precise localization to safeguard the environment, ensure pipeline operational safety, and support emergency response strategies. This study proposes an inversion model that integrates wireless sensor networks (WSNs) with the Gaussian plume model for CO₂ pipeline leakage monitoring. The WSN is employed to collect real-time CO₂ concentration data and environmental parameters around the pipeline, while the Gaussian plume model is used to simulate and invert the dispersion process, enabling both leak source localization and emission rate estimation. Simulation results demonstrate that the proposed model achieves a source localization error of 12.5% and an emission rate error of 3.5%. Field experiments further confirm the model’s applicability, with predicted concentrations closely matching the measurements, yielding an error range of 3.5–14.7%. These findings indicate that the model satisfies engineering accuracy requirements and provides a technical foundation for emergency response following CO₂ pipeline leakage. Full article

Against the backdrop of ever-increasing energy demand and growing awareness of environmental protection, the research and optimization of hydrogen-related multi-energy systems have become a key and hot issue due to their zero-carbon and clean characteristics. In the scheduling of such multi-energy systems, a typical problem is how to describe and deal with the uncertainties of multiple types of energy. Scenario-based methods and robust optimization methods are the two most widely used methods. The first one combines probability to describe uncertainties with typical scenarios, and the second one essentially selects the worst scenario in the uncertainty set to characterize uncertainties. The selection of these scenarios is essentially a trade-off between the economy and robustness of the solution. In this paper, to achieve a better balance between economy and robustness while avoiding the complex min-max structure in robust optimization, we leverage artificial intelligence (AI) technology to generate enough scenarios, from which economic scenarios and feasible scenarios are screened out. While applying a simple single-layer framework of scenario-based methods, it also achieves both economy and robustness. Specifically, first, a Transformer architecture is used to predict uncertainty realizations. Then, a Generative Adversarial Network (GAN) is employed to generate enough uncertainty scenarios satisfying the actual operation. Finally, based on the forecast data, the economic scenarios and feasible scenarios are sequentially screened out from the large number of generated scenarios, and a balance between economy and robustness is maintained. On this basis, a multi-energy collaborative optimization method is proposed for a hydrogen-based multi-energy microgrid with consideration of the coupling relationships between energy sources. The effectiveness of this method has been fully verified through numerical experiments. Data show that on the premise of ensuring scheduling feasibility, the economic cost of the proposed method is 0.67% higher than that of the method considering only economic scenarios. It not only has a certain degree of robustness but also possesses good economic performance. Full article

Heavy-oil reservoirs exhibit a high water–oil mobility ratio. During cyclic steam stimulation or water flooding in the later stages, severe fingering occurs, making it difficult to produce the remaining oil. Chemical flooding methods such as polymer flooding, surfactant–polymer flooding, weak gel flooding, and gel flooding have achieved significant enhanced oil recovery (EOR) effects in the development of high-water-cut oilfields in China. However, the reservoir applicability conditions for each chemical flooding method differ. How to quickly select the appropriate chemical flooding method based on reservoir conditions remains a challenge. This paper uses the basic parameters of a heavy-oil reservoir in Shengli Oilfield as a reference and establishes numerical simulation models for different chemical flooding methods. Then, using the permeability variation coefficient as an indicator to evaluate reservoir heterogeneity, the suitable permeability variation coefficient ranges for different chemical flooding methods are obtained and used as the first-level decision method. Subsequently, based on the differences in temperature and salt tolerance of each chemical flooding method, the applicable ranges for different chemical flooding methods are determined and used as the second-level decision method. Through this two-level decision-making process, the suitable chemical flooding development method for a target reservoir can be rapidly identified, providing support for the efficient development of heavy-oil reservoirs using chemical flooding. The findings are based on a typical heavy-oil reservoir model from Shengli Oilfield; the specific thresholds presented should be calibrated accordingly when applied to reservoirs with different characteristics. Full article

The convergence of carbon nanomaterials and functional polymers has led to the emergence of smart carbon–polymer nanocomposites (CPNCs), which possess exceptional potential for next-generation sensing and actuation systems. These hybrid materials exhibit unique combinations of electrical, thermal, and mechanical properties, along with tunable responsiveness to external stimuli such as strain, pressure, temperature, light, and chemical environments. This review provides a comprehensive overview of recent advances in the design and synthesis of CPNCs, focusing on their application in multifunctional sensors and actuator technologies. Key carbon nanomaterials including graphene, carbon nanotubes (CNTs), and MXenes were examined in the context of their integration into polymer matrices to enhance performance parameters such as sensitivity, flexibility, response time, and durability. The review also highlights novel fabrication techniques, such as 3D printing, self-assembly, and in situ polymerization, that are driving innovation in device architectures. Applications in wearable electronics, soft robotics, biomedical diagnostics, and environmental monitoring are discussed to illustrate the transformative impact of CPNCs. Finally, this review addresses current challenges and outlines future research directions toward scalable manufacturing, environmental stability, and multifunctional integration for the real-world deployment of smart sensing and actuation systems. Full article

Coalbed methane (CBM), mainly composed of methane (CH₄) and carbon dioxide (CO₂), has attracted increasing attention due to its dual significance as a clean energy resource and its role in greenhouse gas management. This research systematically examines the adsorption, desorption, diffusion, and bubble evolution dynamics of methane (CH₄) and carbon dioxide (CO₂) in graphene nanopores with diameters of 4 nm, 6 nm, and 8 nm by molecular dynamics simulations. Radial distribution function (RDF) analyses reveal strong solvation of both gases by water, with CO₂ exhibiting slightly stronger interactions. Adsorption and desorption dynamics indicate that CO₂ molecules display shorter residence times on the graphene surface (0.044–0.057 ns) compared with CH₄ (0.055–0.062 ns), reflecting faster surface exchange. Gas-phase molecular number analysis demonstrates that CH₄ accumulates more significantly in the vapor phase, while CO₂ is more prone to adsorption and re-dissolution. Mean square displacement (MSD) results confirm enhanced molecular mobility in larger pores, with CH₄ showing greater overall diffusivity. Structural evolution of the 8 nm system highlights asymmetric bubble dynamics, where large bubbles merge with the upper adsorption layer to form a thicker layer, while smaller bubbles contribute to a thinner layer near the lower surface. CH₄ and CO₂ follow similar pathways, though CO₂ diffuses farther post-desorption due to its weaker surface retention. These results provide fundamental insights into confinement-dependent gas behavior in graphene systems, offering guidance for gas separation and storage applications. Full article

Non-thermal technologies (NTTs) such as ultrasound (US), pulsed electric fields (PEF), high-pressure processing (HPP), cold plasma (CP), and pulsed light (PL) are emerging as versatile tools in food fermentation, offering microbial control and process enhancement without the detrimental heat effects of conventional methods. Operating at ambient low temperatures, these techniques preserve heat-sensitive compounds, modulate microbial activity, and improve mass transfer, enabling both quality retention and functional enrichment. Recent studies highlight their potential to stimulate metabolic pathways and enhance the release of bioactive compounds, opening new opportunities for fermented food production. The bibliometric analysis of the recent literature further reveals a growing interest in NTT applications in fermentation, with HPP and PEF showing the highest industrial maturity. Each technology exhibits distinct mechanisms and optimal niches across upstream, midstream, and downstream stages: HPP for uniform volumetric treatment, US for fermentation intensification, CP for surface-selective oxidative chemistry, PEF for membrane permeability control, and PL for rapid, residue-free decontamination. While the degree of industrial readiness varies, critical barriers such as scale-up limitations, high capital costs, energy distribution uniformity, process standardization, and techno-economic feasibility remain to be overcome. Beyond technical aspects, the successful commercialization of NTTs will also depend on addressing regulatory approval pathways, ensuring consumer trust and acceptance, and demonstrating their contribution to sustainability goals through lower energy use, reduced food waste, and environmentally responsible processing. Strategic, stand-alone, or hybrid applications of NTTs can therefore act not only as technological alternatives but also as enablers of a more sustainable, consumer-centered, and innovation-driven food system. Full article

The integration of photovoltaic (PV) systems into power grids has surged due to the global shift towards renewable energy, but this rapid adoption presents challenges like voltage regulation and inverter degradation. High PV penetration can lead to overvoltage conditions and transient voltage fluctuations, which stress inverters and accelerate their degradation. To address these issues, advanced modeling techniques, particularly Bayesian modeling, are employed to predict and mitigate inverter failures by incorporating prior knowledge and real-world data. This approach enables probabilistic predictions of failure, improving maintenance scheduling and inverter design. By leveraging datasets from inverter operations with induction motors, this study aims to enhance the reliability of PV systems and optimize voltage regulation strategies for sustainable renewable energy growth. Full article

Distribution grids are essential to power systems, as they ensure the safe and reliable operation of power systems. It is important to forecast the operational status of each distribution transformer. Consequently, there is an urgent need to develop load forecasting models that exhibit robust generalization and exceptional accuracy. However, distribution transformers are numerous, exhibit strong inherent features. Single-device load samples are scarce, with widespread few-shot issues. Traditional models rely on massive amounts of data and computational resources, making it challenging to balance computational costs, generalization ability, and accuracy. To address these issues, this paper analyzes the inherent features of distribution transformers and fine-tunes large language models using parameter-efficient fine-tuning methods to reduce training costs while preserving their generalization capabilities and accuracy. Experimental results demonstrate that this method achieves significantly higher accuracy than baseline models in few-shot scenarios. Full article