Search Results (461)

The study is motivated by the application of dry finish milling for post-build processing of additive Ti6Al4V blanks, since the use of neither lubricant nor coolants has been attracting increasing attention due to its environmental benefits, non-toxicity, and the elimination of the need for additional cleaning processes. For end mills, wear patterns were investigated upon finish milling of the SLM Ti6Al4V samples under various machining conditions (by varying the values of radial depth of cut and feed values at a constant level of axial depth of cut and cutting speed). When using all the applied milling modes, the identical tool wear mechanism was revealed. Built-up edges mainly developed on the leading surfaces, increasing the surface roughness on the SLM Ti6Al4V samples but protecting the cutting edges. However, abrasive wear was mainly characteristic of the flank surfaces that accelerated peeling of the protective coatings and increased wear of the end mills. The following milling parameters have been established as being close to rational ones: Vc = 60 m/min, Vf = 400 mm/min, a_p = 4 mm, and a_e = 0.4 mm. They affected the surface roughness of the SLM Ti6Al4V samples in the following way: max cutting thickness—8 μm; built-up edge at rake surface—50 ± 3 μm; max wear of flank surface—15 ± 1 μm; maximum adherence of workpiece. Mode III provided the maximum MRR value and negligible wear of the end mill, but its main disadvantage was the high average surface roughness on the SLM Ti6Al4V sample. Mode II was characterized by both the lowest average surface roughness and the lowest wear of the end mill, as well as an insufficient MRR value. Since these two modes differed only in their feed rates, their values should be optimized in the range from 200 to 400 mm/min. Full article

With the rapid development of renewable energy, the proportion of small hydropower as a clean energy in the distribution network (DN) is increasing. However, the randomness and intermittence of small hydropower has brought new challenges to the operation of DN; especially, the problems of increasing network loss and reactive voltage exceeding the limit have become increasingly prominent. Aiming at the above problems, this paper proposes a reactive power optimization control method for DN with hydropower based on an improved discrete particle swarm optimization (PSO) algorithm. Firstly, this paper analyzes the specific characteristics of small hydropower and establishes its mathematical model. Secondly, considering the constraints of bus voltage and generator RP output, an extended minimum objective function for system power loss is established, with bus voltage violation serving as the penalty function. Then, in order to solve the following problems: that the traditional discrete PSO algorithm is easy to fall into local optimization and slow convergence, this paper proposes an improved discrete PSO algorithm, which improves the global search ability and convergence speed by introducing adaptive inertia weight. Finally, based on the IEEE-33 buses distribution system as an example, the simulation analysis shows that compared with GA optimization, the line loss can be reduced by 3.4% in the wet season and 13.6% in the dry season. Therefore, the proposed method can effectively reduce the network loss and improve the voltage quality, which verifies the effectiveness and superiority of the proposed method. Full article

Weathering steel (WS) is widely used in bridge construction due to its high corrosion resistance, durability, and low maintenance requirements. This paper reviews the performance of WS bridges in Canadian climates, focusing on the formation of protective patina, influencing factors, and long-term maintenance strategies. The protective patina, composed of stable iron oxyhydroxides, develops over time under favorable wet–dry cycles but can be disrupted by environmental aggressors such as chlorides, sulfur dioxide, and prolonged moisture exposure. Key alloying elements like Cu, Cr, Ni, and Nb enhance corrosion resistance, while design considerations—such as drainage optimization and avoidance of crevices—are critical for performance. The study highlights the vulnerability of WS bridges to microenvironments, including de-icing salt exposure, coastal humidity, and debris accumulation. Regular inspections and maintenance, such as debris removal, drainage system upkeep, and targeted cleaning, are essential to mitigate corrosion risks. Climate change exacerbates challenges, with rising temperatures, altered precipitation patterns, and ocean acidification accelerating corrosion in coastal regions. Future research directions include optimizing WS compositions with advanced alloys (e.g., rare earth elements) and integrating climate-resilient design practices. This review highlights the need for a holistic approach combining material science, proactive maintenance, and adaptive design to ensure the longevity of WS bridges in evolving environmental conditions. Full article

This study addresses the problem of agricultural waste utilization and nutrition for older adults by developing a food product based on a circular design approach. Pineapple core was used to produce a clean-label dietary powder without chemical or enzymatic treatment, relying on repeated rinsing and hot-air drying. The development process followed a structured analysis of physical, chemical, and sensory properties. The powder contained 83.46 g/100 g dietary fiber, 0° Brix sugar, pH 4.72, low water activity (aw < 0.45), and no detectable heavy metals or microbial contamination. Sensory evaluation by expert panelists confirmed that the product was acceptable in appearance, aroma, and texture, particularly for older adults. These results demonstrate the feasibility and safety of valorizing agri-waste into functional ingredients. The process was guided by the Transformative Circular Product Blueprint, which integrates clean-label processing, IoT-enabled solar drying, and decentralized production. This model supports traceability, low energy use, and adaptation at the community scale. This study contributes to sustainable food innovation and aligns with Sustainable Development Goals (SDGs) 3 (Good Health and Well-being), 9 (Industry, Innovation and Infrastructure), and 12 (Responsible Consumption and Production). Full article

Aiming at the economic problems of industrial users with wind power, photovoltaic, and small hydropower resources in clean energy consumption and trading with superior power grids, this paper proposes a distributed pumped storage capacity optimization configuration method considering clean energy systems. First, considering the maximization of the investment benefit of distributed pumped storage as the upper goal, a configuration scheme of the installed capacity is formulated. Second, under the two-part electricity price mechanism, combined with the basin hydraulic coupling relationship model, the operation strategy optimization of distributed pumped storage power stations and small hydropower stations is carried out with the minimum operation cost of the clean energy system as the lower optimization objective. Finally, the bi-level optimization model is solved by combining the alternating direction multiplier method and CPLEX solver. This study demonstrates that distributed pumped storage implementation enhances seasonal operational performance, improving clean energy utilization while reducing industrial electricity costs. A post-implementation analysis revealed monthly operating cost reductions of 2.36, 1.72, and 2.13 million RMB for wet, dry, and normal periods, respectively. Coordinated dispatch strategies significantly decreased hydropower station water wastage by 82,000, 28,000, and 52,000 cubic meters during corresponding periods, confirming simultaneous economic and resource efficiency improvements. Full article

Various pretreatment methods for the valorization of sunflower husks (SHs) for H₂ gas generation through fermentation by Escherichia coli were investigated. We analyzed thermal treatment (TT), acid hydrolysis (AH), and alkaline hydrolysis (AlkH) at different substrate concentrations (50 g L⁻¹, 75 g L⁻¹, 100 g L⁻¹, and 150 g L⁻¹) and dilution levels (undiluted, 2× diluted, and 5× diluted). A concentration of 75 g L⁻¹ SH that was acid-hydrolyzed and dissolved twice in the medium yielded optimal microbial growth, reaching 0.3 ± 0.1 g cell dry weight (CDW) L⁻¹ biomass. The highest substrate level enabling effective fermentation was 100 g L⁻¹, producing 0.37 ± 0.13 (g CDW) × L⁻¹ biomass after complete fermentation, while 150 g L⁻¹ exhibited pronounced inhibitory effects. It is worth mentioning that the sole alkaline treatment was not optimal for growth and H₂ production. Co-fermentation with glycerol significantly enhanced both biomass formation (up to 0.42 ± 0.15 (g CDW) × L⁻¹)) and H₂ production. The highest H₂ yield was observed during batch growth at 50 g L⁻¹ SH hydrolysate with 5× dilution, reaching up to 5.7 mmol H₂ (g sugar)⁻¹ with glycerol supplementation. This study introduces a dual-waste valorization strategy that combines agricultural and biodiesel industry residues to enhance clean energy generation. The novelty lies in optimizing pretreatment and co-substrate fermentation conditions to maximize both biohydrogen yield and microbial biomass using E. coli, a widely studied and scalable host. Full article

Water scarcity remains critical in Rwanda’s Eastern Savanna Agroecological Zone due to erratic rainfall, prolonged dry seasons, and rising water demands. This challenge threatens agricultural productivity, food security, and livelihoods. Stormwater harvesting presents a sustainable solution that increases water availability and mitigates the impacts of climate variability. This study utilizes Geographic Information System (GIS) tools and SCS-CN to assess stormwater harvesting potential in the region. The methodology includes analyzing land use, soil type, rainfall data (30 years, from 1994 to 2023), and topography. Key research steps involve delineating catchment areas, estimating runoff volumes, and selecting optimal storage sites using multi-criteria decision analysis. Findings include eight main water reservoirs, each with a unique code (W_R1 to W_R8), geographic coordinates (X and Y), and 10 million cubic meters storage volumes. W_R1 has the smallest volume at 0.242 × 10⁶ m³, while W_R2 has the largest volume at 8.51 × 10⁶ m³. W_R3, W_R5, and W_R7 are additional noteworthy reservoirs with sizable capacities. The findings contribute to policy formulation and Sustainable Development Goals (SDGs) related to clean water, food security, and climate action. This research provides a replicable framework for addressing water scarcity and enhancing long-term resilience in water-stressed regions. Full article

Many studies relate to long-term changes in annual precipitation in Poland, yet most of them were statistically insignificant. The primary objective of this research was to investigate the precipitation regime during the year in the context of climate change, which is more crucial than annual averages from the perspectives of agriculture and plant growth, as well as for the industrial sector and human access to clean water. For this reason, we used daily precipitation data from the Institute of Meteorology and Water Management—National Research Institute from 1966 to 2024. Each month of the study was examined for changes in monthly totals, the number of dry and wet days, precipitation intensities, and extremes. In the cold season, a considerable shift in precipitation patterns was found between November and December, which became drier, and January and February, which became wetter with more intense and extreme precipitation. Pronounced changes were also noticed in April and June, when not only the monthly totals but also the number of wet days and precipitation intensity decreased. These two months, together with winter, are essential for plant growth. On the contrary, July became slightly wetter. Interesting changes were also observed in September, including an increase in dry days and more intense rainfall. With the increase in temperature and changes in the advection of air masses, September became more similar to summer than to autumn months. The key factors driving shifts in precipitation regimes during the year were a warmer atmosphere and changes in circulation patterns. Full article

Parabolic trough collector (PTC) systems, often deployed in arid regions, are vulnerable to dust accumulation (soiling), which reduces mirror reflectivity and energy output. This study presents a physically based soiling forecast algorithm (SFA) designed to estimate soiling levels. The model was calibrated and validated using three meteorological data sources—numerical forecasts (YR), METAR observations, and on-site measurements—from a PTC facility in Limassol, Cyprus. Field campaigns covered dry, rainy, and red-rain conditions. The model demonstrated robust performance, particularly under dry summer conditions, with normalized root mean square errors (NRMSE) below 1%. Sedimentation emerged as the dominant soiling mechanism, while the contributions of impaction and Brownian motion varied according to site-specific environmental conditions. Under dry deposition conditions, the reflectivity change rate during spring and autumn was approximately twice that of summer, indicating a need for more frequent cleaning during transitional seasons. A red-rain event resulted in a pronounced drop in reflectivity, showcasing the model’s ability to capture abrupt soiling dynamics associated with extreme weather episodes. The proposed SFA offers a practical, adaptable tool for reducing soiling-related losses and supporting seasonally adjusted maintenance strategies for solar thermal systems. Full article

This paper presents a comparative analysis of machine learning-based methods for predicting shaft power in ships, a key factor in optimizing ship performance. Accurate shaft power prediction facilitates efficient operations, reducing fuel consumption, emissions, and maintenance costs, aligning with environmental regulations and promoting sustainable maritime practices. The proposed approach evaluates three machine learning methods, analyzing 431 models to determine the most accurate and reliable option for VLCC tankers. XGBoost emerged as the top-performing model, delivering a 13% improvement in accuracy over traditional methods. Using the SHAP framework, key factors influencing shaft power predictions—such as GPS speed, draft, days from dry dock, and wave height—were identified, enhancing model transparency and decision-making clarity. This explainability fosters trust in the use of AI within marine engineering. The results demonstrate that machine learning can optimize maintenance scheduling by reducing unnecessary cleaning procedures, mitigating propulsion system wear, and improving reliability. By using predictive insights, ship operators can achieve better fuel efficiency, lower emissions, and cost savings. The study underscores the potential of explainable machine learning models as transformative tools for ship performance monitoring, supporting greener and more efficient maritime operations. Full article

Solid oxide fuel cells (SOFCs) represent a pivotal technology in renewable energy due to their clean and efficient power generation capabilities. Their role in potential carbon mitigation enhances their viability. SOFCs can operate via a variety of alternative fuels, including hydrocarbons, alcohols, solid carbon, and ammonia. However, several solutions have been proposed to overcome various technical issues and to allow for stable operation in dry methane, without coking in the anode layer. To avoid coke formation thermodynamically, methane is typically reformed, contributing to an increased degradation rate through the addition of oxygen-containing gases into the fuel gas to increase the O/C ratio. The performance achieved by reforming catalytic materials, comprising active sites, supports, and electrochemical testing, significantly influences catalyst performance, showing relatively high open-circuit voltages and coking-resistance of the CH₄ reforming catalysts. In the next step, the operating principles and thermodynamics of methane reforming are explored, including their traditional catalyst materials and their accompanying challenges. This work explores the components and functions of SOFCs, particularly focusing on anode materials such as perovskites, Ruddlesden–Popper oxides, and spinels, along with their structure–property relationships, including their ionic and electronic conductivity, thermal expansion coefficients, and acidity/basicity. Mechanistic and kinetic studies of common reforming processes, including steam reforming, partial oxidation, CO₂ reforming, and the mixed steam and dry reforming of methane, are analyzed. Furthermore, this review examines catalyst deactivation mechanisms, specifically carbon and metal sulfide formation, and the performance of methane reforming and partial oxidation catalysts in SOFCs. Single-cell performance, including that of various perovskite and related oxides, activity/stability enhancement by infiltration, and the simulation and modeling of electrochemical performance, is discussed. This review also addresses research challenges in regards to methane reforming and partial oxidation within SOFCs, such as gas composition changes and large thermal gradients in stack systems. Finally, this review investigates the modeling of catalytic and non-catalytic processes using different dimension and segment simulations of steam methane reforming, presenting new engineering designs, material developments, and the latest knowledge to guide the development of and the driving force behind an oxygen concentration gradient through the external circuit to the cathode. Full article

Citrus peel, a significant by-product of fruit processing, represents a rich source of carotenoids with strong antioxidant and health-promoting properties. The present study evaluated two green extraction techniques, cloud point extraction (CPE) and supramolecular solvent (SUPRAS)-based extraction, for carotenoids recovered from citron, orange, and tangerine peels. Whereas SUPRAS methods rely on a supramolecular solvent made of water, ethanol, and octanoic acid, CPE methods use surfactants and water, and both show a high potential to extract lipophilic components. CPE demonstrated superior efficiency in extracting total carotenoids and enhancing antioxidant activity, with orange peel extracts showing the highest concentrations. CPE and SUPRAS extracts were subsequently encapsulated using freeze-drying with chickpea protein isolate, achieving high encapsulation efficiencies (82.40–88.97%). The use of encapsulation technology is an effective strategy to protect carotenoids from environmental stressors. Color, morphological, and FTIR analyses confirmed the successful encapsulation and retention of carotenoids. Environmental impact was assessed using the EcoScale tool, revealing excellent sustainability for CPE (92 points) and satisfactory performance for SUPRAS-based extraction (70 points). The use of Generally Recognized As Safe (GRAS) solvents and plant-derived encapsulation materials makes this method highly suitable for clean-label product development across the food, cosmetic, and nutraceutical industries. In summary, the results point to a practical and sustainable approach to citrus waste valorization into valuable, health-promoting ingredients—supporting both circular economy goals and eco-friendly innovation. Full article

The present work introduces a sustainable, low-carbon method to fabricate durable, non-toxic superhydrophobic surfaces using bamboo-derived cellulose. Uniform TEMPO-carboxylated cellulose particles (TOC-Ps), approximately 2 μm in diameter, were synthesized through thermal polymerization and spray drying. These particles, featuring a nano-scale convex structure formed by intertwined TOC nanofibers, were applied to substrates and modified with low-surface-energy materials to achieve superhydrophobicity. At an optimal TOC-P mass ratio of 6%, the surface displayed a water contact angle of 156.2° and a sliding angle of 7°. The coating maintained superhydrophobicity after extensive mechanical testing—120 cm of abrasion, 100 bending cycles, and continuous trampling—and exhibited robust chemical stability across harsh conditions, including subjection to high temperatures, UV irradiation, and corrosive solutions (pH 2–12). The hierarchical micro–nano structure was found to enhance both hydrophobicity and durability, offering an environmentally friendly alternative for self-cleaning surfaces, textiles, and building applications. Full article

The performance of photovoltaic systems is highly dependent on environmental conditions, with soiling due to dust accumulation often being referred to as a predominant energy degradation factor, especially in dry and semi-arid environments. This paper introduces an AI-based robotic cleaning system that can independently forecast and schedule cleaning sessions from real-time sensor and environmental data. Methods: The system integrates sources of data like embedded sensors, weather stations, and DustIQ data to create an integrated dataset for predictive modeling. Machine learning models were employed to forecast soiling loss based on significant atmospheric parameters such as relative humidity, air pressure, ambient temperature, and wind speed. Dimensionality reduction through the principal component analysis and correlation-based feature selection enhanced the model performance as well as the interpretability. A comparative study of four conventional machine learning models, including logistic regression, k-nearest neighbors, decision tree, and support vector machine, was conducted to determine the most appropriate approach to classifying cleaning needs. Results: Performance, based on accuracy, precision, recall, and F1-score, demonstrated that logistic regression and SVM provided optimal classification performance with accuracy levels over 92%, and F1-scores over 0.90, demonstrating outstanding balance between recall and precision. The KNN and decision tree models, while slightly poorer in terms of accuracy (around 85–88%), had computational efficiency benefits, making them suitable for utilization in resource-constrained applications. Conclusions: The proposed system employs a dry-cleaning mechanism that requires no water, making it highly suitable for arid regions. It reduces unnecessary cleaning operations by approximately 30%, leading to decreased mechanical wear and lower maintenance costs. Additionally, by minimizing delays in necessary cleaning, the system can improve annual energy yield by 3–5% under high-soiling conditions. Overall, the intelligent cleaning schedule minimizes manual intervention, enhances sustainability, reduces operating costs, and improves system performance in challenging environments. Full article

Tropical glaciers are highly sensitive to climate change, with their mass balance influenced by temperature and precipitation, which affects the accumulation area. In this study, we developed an open-source tool to map the accumulation area of glaciers in the Cordillera Blanca, Peru (1988–2023), using Landsat images, spectral indices, and the Otsu method. We analyzed trends and correlations between snow accumulation area, meteorological patterns from ERA5 data, and oscillation modes. The results were validated using field data and manual mapping. Greater discrepancies were observed in glaciers with debris cover or small clean glaciers (<1 km²). The Amazonian and Pacific sectors showed a significant trend in decreasing accumulation areas, with reductions of 8.99% and 10.24%, respectively, from 1988–1999 to 2010–2023. El Niño events showed higher correlations with snow accumulation, snowfall, and temperature during the wet season, indicating a stronger influence on the Pacific sector. The accumulation area was strongly anti-correlated with temperature and correlated with snowfall in both sectors at a 95% confidence level (α = 0.05). The highest correlations with meteorological parameters were observed during the dry season, suggesting that even minor changes in temperature or precipitation could significantly impact the accumulation area. Full article