Search Results (3,165)

With the adoption of more ambitious emission reduction strategies in the shipping industry by the International Maritime Organization and the resulting stricter greenhouse gas emission reduction requirements, it is particularly important for all stakeholders in the global maritime shipping industry to assess the energy efficiency of shipping vessels. Forming predictive capabilities for ship fuel consumption and Carbon Intensity Indicator (CII) annual ratings, for example, are two important works. This article adopted 14 different algorithms in three categories of data-driven approaches, i.e., statistics, machine learning and deep learning, including polynomial regression, ridge regression, adaptive boosting, categorical boosting, elastic net, etc., and built the ship fuel consumption prediction model using ship noon report as the data source. The prediction accuracy and computational efficiency of model training were compared based on metrics of coefficient of determination, mean absolute percentage error and floating-point operations per amount of training data. Cross-validations were performed for all 14 algorithms to analyze their sensitivities to their respective tuned parameters. Comparisons indicated that algorithms of the statistics approach were sensitive to the quality of the data source, compared with the machine learning and the deep learning approaches. The accuracy of the elastic net algorithm was sensitive to the tuned parameters. Two algorithms, light gradient boosting machine and random forest, were selected based on their performances of prediction accuracy and computational efficiency of model training. Then, the selected algorithms were separately combined with long short-term memory as the time-series prediction algorithm to form their respective coupled framework. Both of the coupled frameworks achieved successful prediction of the CII annual discriminant and rating of the studied ships. The prediction accuracy was validated to be sufficient. Full article

The development of low-emission and reliable propulsion systems is essential for extending the operational capability of unmanned aerial vehicles (UAVs). Although aviation decarbonization is widely recognized as an important objective, it must be considered within the broader context of limited renewable-energy availability. Recent system-level analyses of transportation decarbonization have shown that the allocation of renewable electricity and sustainable fuels should prioritize sectors where direct electrification is most efficient, while hard-to-electrify sectors require alternative pathways. Aviation is one of the most difficult transport sectors to electrify because of strict energy-density requirements, especially for long-endurance airborne platforms. Therefore, sustainable liquid fuels and hybrid propulsion systems should not be considered universal replacements for electrification, but rather complementary solutions for applications where batteries alone cannot provide the required endurance, payload capacity or operational flexibility. In this context, the present study focuses on alcohol–kerosene blends for hybrid UAV power systems, where liquid-fuel energy density and partial emission reduction remain relevant engineering requirements. This work provides one of the first systematic experimental evaluations of ethanol–, butanol– and octanol–kerosene blends in a micro-turboprop engine operating as part of a hybrid UAV power-generation architecture. Unlike previous studies focused mainly on micro-turbojet thrust response, the present work evaluates the coupled influence of alcohol chain length and blending ratio on exhaust gas temperature, gaseous emissions, electrical output and operational stability under multi-load conditions representative of UAV operation. Jet-A and nine alcohol–kerosene blends containing 10%, 20% and 30% ethanol, butanol or octanol by volume were tested over four operating regimes, from idle to 2500 W electrical load. The results show that ethanol blends provided the strongest CO reduction, with E30 reducing CO by 24.9% relative to Jet-A under R3, while E10 offered the most balanced behavior across the full operating range. Higher ethanol fractions improved CO suppression but introduced NOx and low-load stability penalties. Octanol blends, particularly O20, exhibited the most kerosene-like and stable response, supporting reliable power delivery with reduced operational variability. Butanol blends showed intermediate behavior without providing a dominant advantage. A multi-criteria evaluation combining emissions, EGT behavior, relative performance, operational stability and cost identified E10 as the best overall compromise for hybrid UAV use. The study demonstrates that alcohol chain length produces nonlinear system-level effects in hybrid micro-turboprop architectures and provides an experimental basis for fuel selection in low-emission UAV power systems. Full article

The aim of this paper is to give an overview of emerging technologies for the greening of aviation, how they can be applied to different classes of aircraft, and the challenges to be overcome in achieving efficiency and environmental objectives. The following steps are part of the journey towards the greening of aviation: (i) developing and maturing new technologies, including electrification and sustainable fuels; (ii) where possible, using new technologies in the current fleet to maximize short-term benefits—i.e., EU Fit for 55; (iii) when it is not possible to retrofit new technologies to current aircraft, incorporating them into new next-generation aircraft designs from 2035; and (iv) replacing existing fleets with new, cleaner aircraft to meet the ICAO Net Zero 2050 goal. These technologies of prime importance will have to be supplemented by operational, regulatory, and economic enablers to support wide deployment. There will not be one solution that meets the requirements of all aircraft classes or mission profiles, but rather a combination of electrification, hydrogen propulsion, and sustainable aviation fuels will be required. Achievement of aviation’s environmental goals will hence not solely be a function of technological progress but also certification pathways, investment in infrastructure, and integrated policy strategies. Full article

With the rapid expansion of agricultural e-commerce in China, inefficient cloud warehouse consolidation and high environmental costs have hindered the sustainability of supply chains. To address the challenges of low vehicle loading rates and high carbon emissions, this study proposes an optimization model for collection path planning that integrates sales forecasting and three-dimensional loading constraints. First, STL decomposition is employed to identify seasonal sales patterns, and a hybrid SARIMA and ARIMA-BPNN model is constructed to achieve precise forecasting of future orders to provide data support for dynamic demand. Second, a single-objective path planning model is formulated to minimize the fixed vehicle costs, fuel consumption, and carbon emissions while maximizing the load utilization rates. To solve this complex problem, a two-stage solution framework, consisting of path planning and three-dimensional loading verification, was designed. This framework integrates an improved genetic–hill-climbing hybrid algorithm with a constructive heuristic to handle real-time spatial constraints and achieve the efficient optimization of distribution paths. Finally, a case study on the HLYX agricultural cloud warehouse in Harbin, China, demonstrated that the proposed approach significantly enhances space utilization and reduces transportation and carbon emission costs. This study provides a sustainable development path for the cost reduction, economic efficiency improvement, and carbon emission reduction of smart agricultural logistics. Full article

The sluggish kinetics of alkaline hydrogen oxidation reaction (HOR) and high cost of Pt–based catalysts have long hindered large–scale deployment of alkaline membrane fuel cells. Via first–principles calculations, we designed a series of 3d transition metal single atoms anchored on PdN₂ monolayer (TM–PdN₂, TM = Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn) and evaluated their alkaline HOR performance. Ti-, Cr-, Fe-, Co-, Ni-modified systems exhibit excellent thermodynamic and electrochemical stability under operating conditions. Single-atom doping tunes the p-band center of N and d-band center of metal sites, enabling precise modulation of H and OH adsorption strengths. Mechanistic analysis reveals HOR follows H₂ + 2OH* → H* + OH* + H₂O → 2H₂O, with the final step as rate-determining step. H adsorption contributes 3.45 times more to HOR activity than OH adsorption. Fe–PdN₂ delivers the best performance, with an ultra–low barrier of 0.11 eV and a rate constant of 2.82 × 10¹⁰ s^–1·site⁻¹, values that significantly outperform those of Pt(111) (0.22 eV, 4.5 × 10⁹ s⁻¹·site⁻¹). This work provides theoretical guidance for rational design of high–performance alkaline HOR electrocatalysts. Full article

Predicting forest fire occurrence is essential for proactive disaster preparedness and environmental protection. We introduce a machine learning-based system that forecasts next-day fire probability at high spatial resolution using satellite-derived, multi-modal geospatial data. In contrast to existing reactive systems that rely on thermal anomaly detection (e.g., MODIS or VIIRS-SNPP), our approach is fully predictive, generating pixel-wise fire risk maps a day in advance. Our study focuses on Uttarakhand, India, which is an ecologically sensitive region that experiences frequent and severe forest fires. We curated a domain-specific geospatial dataset spanning 1 April to 29 May 2016. It includes daily 30 m GeoTIFF images with 10 bands comprising weather (e.g., temperature, wind, precipitation), topography (slope, aspect), fuel map, and fire mask. We constructed this dataset from diverse sources and aligned all bands spatially and temporally. To demonstrate the usefulness of this dataset, we implement a deep convolutional neural network (CNN) using the ResUNet-A architecture, chosen for its robust performance in the semantic segmentation of high-resolution remote sensing data. Our model is trained from scratch to produce high-resolution fire probability maps and classify fire/no-fire pixels. Our solution helps with planning and decision-making for early intervention, especially in areas with high risk. It supports the UN’s SDG 13 (Climate Action) and SDG 15 (Life on Land) by enhancing resilience and conserving ecosystems. The presented dataset and methodology can serve as a benchmark for future research on wildfire risk prediction using Earth observation data. Full article

To integrate high chromaticity with visible-light-driven antibacterial functionality in yellow inorganic pigments, carbon-doped BiVO₄ (C-BiVO₄) pigments were synthesized via a one-step self-propagating combustion synthesis (SCS) using citric acid as a fuel and carbon source. The effects of citric acid dosage on phase composition, morphology, chromatic performance, and antibacterial activity were systematically investigated. The results indicate that carbon doping induces lattice expansion and oxygen vacancy formation, modulates the electronic band structure, and significantly suppresses photogenerated electron-hole recombination. At an optimal citric acid to BiVO₄ molar ratio of 1.2, the pigment exhibits excellent yellow chromaticity (b* = 79.71). Under visible-light irradiation, C-BiVO₄ achieves a methylene blue photodegradation rate of 96.63% and an E. coli inactivation efficiency of 99.99%, substantially outperforming undoped BiVO₄. Moreover, the C-BiVO₄ yellow pigment shows good dispersibility and thermal stability in PMMA and glass matrices and passes acute skin irritation and dermal toxicity tests, confirming its low toxicity and non-irritating nature. This work provides a new strategy for developing environmentally friendly inorganic pigments that combine high chromaticity with photocatalytic antibacterial functionality. Full article

To achieve scientific cost forecasting, this study investigates structural changes in mining cost driven by the widespread adoption of mechanized mining, increased mining depths, and significant operational variations. Based on the backpropagation (BP) neural network, this study systematically analyzes the cost-composition characteristics of modern mining operations and applies activity-based costing to achieve refined cost accounting for each mining operation unit. Ten key influencing factors, including working space, stope temperature, stope depth, haulage distance, worker seniority and work efficiency, scraper efficiency, equipment service life, fuel and lubricant consumption rates, are identified by analyzing cost variation patterns. Principal component analysis (PCA) is used to reduce the dimensionality of the ten factors to simplify this model and enhance prediction accuracy. The PCA-BP neural network mining cost forecasting model is built with the principal components extracted as input variables. Actual cost data from an underground metal mine in Shandong Province is used for our model training and validation, with adopting linear regression, eXtreme Gradient Boosting (XGBoost), and a traditional BP neural network as the comparison models for performance evaluation. Our prediction results indicate that the PCA-BP model achieves an average relative error of 3.80% and a root mean square error of 1.43, both significantly outperforming the comparison models. The results demonstrate superior predictive accuracy and stability of our model. Validated with data from a typical deep mechanized gold mine in eastern China, the PCA-BP cost forecasting model requires parameter retraining based on local production conditions for applications in other regions. This study confirms that the model aligns well with the cost characteristics of modern underground metal mines and produces effective predictions, offering reliable quantitative support for the development of cost control strategies and optimization of cost planning in mining enterprises. Full article

Perennial crop systems (e.g., orchards) require frequent spraying with plant protection products. Equipment plays a crucial role in assessing energy efficiency, productivity, economic performance, and the environmental impact of orchard production. In recent years some farmers have replaced conventional tractor-mounted air-blast sprayers (TMABS) and switched to unmanned ground vehicles (UGVs) or unmanned aerial vehicles (UAVs). However, there has been a lack of comparative studies on the energy and environmental assessment of these systems. This study aimed to evaluate the overall viability of different orchard spraying technologies in terms of energy efficiency, economic costs, and environmental impact. A life cycle assessment (LCA) of five sprayers was performed: a TMABS, a UGV, and three UAVs. The CML-IA methodology and SimaPro 9.5 software with the Ecoinvent v3 database were used to determine the environmental impact of the compared machines. Energy efficiency was calculated using fuel consumption data, human labor energy, and the energy embodied in the machinery. Economic viability was evaluated through capital depreciation, labor, energy consumption, consumable and maintenance cost per hectare calculation models. The results indicate that UAV systems, as compared to TMABS, can significantly reduce operational energy consumption, water use, and environmental impacts. The GWP of UAV systems was about 67% lower compared to the TMABS, while the UGV, due to lower performance efficiency, exhibited a 4% larger GWP (kg CO_2eq ha⁻¹). The findings of this study highlight that UAVs can produce the optimal results in comparison to other application methods. Full article

Open-cathode Proton Exchange Membrane Fuel Cells (PEMFCs) are a promising technology for increasing the endurance of small Unmanned Aerial Vehicles (UAVs), ground robots, e-bikes, and light electric vehicles. However, their performance under realistic operating conditions is strongly influenced by rapid variations in load, temperature, and ambient pressure, which are often neglected in design-oriented or quasi-steady-state analyses. This study experimentally investigates a 1 kW open-cathode PEMFC system, including its balance of plant and a passive supercapacitor buffer, under a representative UAV flight power profile. Steady-state and dynamic tests were conducted to assess polarization characteristics, thermal behavior, parasitic power consumption, and hydrogen utilization. Results revealed significant thermal inertia and hysteresis effects during load transients, causing voltage deviations from steady-state performance and stabilization times exceeding 90 s. The supercapacitor effectively reduced stack current ramp rates, although some high-frequency oscillations remained. Under flight-representative conditions, the system achieved stable operation with average voltaic efficiency ranging from 55.3% to 60.7% and net efficiency ranging from 50.2% to 54.2%. Auxiliary components had a measurable impact on overall performance: cooling fans accounted for 2–6% of stack power during steady operation and approximately 2.5% of total mission energy, while hydrogen purge losses can significantly reduce vehicle endurance. The findings demonstrate the importance of energy-based performance assessment, including auxiliary loads and purge losses, to obtain realistic estimates of efficiency and endurance in dynamic PEMFC-powered applications. Full article

The maritime sector accounts for approximately 3% of global greenhouse gas (GHG) emissions and faces binding decarbonization obligations under the International Maritime Organization’s (IMO) Net-Zero Framework and the FuelEU Maritime Regulation. Conventional marine fuels, including very low sulphur fuel oil (VLSFO) and liquefied natural gas (LNG), are insufficient to meet long-term regulatory intensity targets on a well-to-wake (WtW) lifecycle basis, creating an urgent need for credible fuel alternatives. This study investigates ethanol as a primary fuel for marine dual-fuel propulsion systems, assessed across four distinct production pathways, sugar beet, corn, sugarcane, and wheat straw, to determine its full decarbonization potential relative to VLSFO and LNG benchmarks. A simulation-based multi-dimensional evaluation framework is developed and applied, integrating dynamic operational simulation, energy analysis, environmental lifecycle modelling, and regulatory compliance assessment. The framework is calibrated against a high-resolution dataset from an active container ship, with scenario-specific engine data. While ethanol requires 39.1% more fuel mass than VLSFO due to its lower energy density, all four ethanol pathways deliver substantially superior WtW GHG reductions: from 50.2% (corn) to 76.9% (wheat straw), compared with 20.6% for LNG. All ethanol scenarios satisfy FuelEU compliance limits across the 2026–2045 horizon, with wheat straw ethanol achieving a GFI of 22.52 gCO₂e/MJ, compliant marginally with the 2040 IMO target. These findings demonstrate that bio-based ethanol, particularly from lignocellulosic feedstocks, is a technically viable and regulatorily superior alternative to LNG for maritime decarbonization, warranting accelerated research into production scale-up and bunkering infrastructure development. Full article

This paper introduces a refined criterion for evaluating and optimizing the aerodynamic efficiency of compound planform wings, specifically those whose half span is formed by multiple trapezoidal segments. While elliptical lift distribution is known to minimize induced drag, practical manufacturing constraints have led to widespread adoption of tapered wings. However, conventional single-trapezoid planforms deviate significantly from the ideal elliptical distribution, resulting in increased induced drag and reduced fuel efficiency. This study proposes an adjustment to the ellipticity factor, enabling quantitative assessment of how well a multi-trapezoid wing approximates elliptical chord distribution. The methodology is validated through analysis of existing transport aircraft, identifying configurations with ellipticity factors below 5% (e.g., Lockheed C-5A, Antonov An-124) that achieve near-optimal induced drag performance. A comparative case study of a virtual 40-ton aircraft with a 100 m² wing area quantifies trade-offs between three planform configurations. Computational fluid dynamics simulations confirm that increasing trapezoidal segmentation improves spanwise loading and delays flow separation. Results demonstrate that two-trapezoid configurations with total inverse taper ratios of 3.3–4.2 and break coordinates at 35–45% half span achieve ellipticity factors under 7%, offering an optimal balance between aerodynamic efficiency, structural feasibility, and tail surface requirements. The proposed criterion provides aircraft designers with a rapid, computationally efficient tool for planform optimization at the conceptual design stage. The proposed criterion is valid for subsonic cruise conditions (M ≤ 0.85) and does not account for wave drag or aeroelastic effects. Full article

The strategy of enhancing biocatalytic activity through the modification of natural cells with nanomaterials has overcome the intrinsic catalytic bottlenecks of bacteria, making significant contributions to energy production and pollution treatment. However, chemically engineered biocatalyst systems remain in their early stages of development. Herein, we report a simple and straightforward strategy for constructing an efficient biocatalyst by incorporating carbon quantum dots (CDs) into Escherichia coli (E. coli) to enhance the oxygen reduction reaction (ORR) at the cathode of microbial fuel cells (MFCs). The introduction of CDs significantly accelerates extracellular electron transfer and metabolic activity, markedly increases intracellular adenosine triphosphate (ATP) levels, and promotes substrate utilization. Furthermore, the engineered E. coli exhibits enhanced surface adhesion and increased electronegativity. Electrochemical measurements demonstrate superior ORR activity, delivering a maximum current density of 3.1 mA·cm⁻² and an onset potential of 0.67 V, outperforming many previously reported biocatalysts. When applied in an MFC system, the modified biocatalyst achieves a maximum power density of 325 μW·cm⁻², placing it among the highest-performing systems reported to date. This work provides a facile and cost-effective approach for improving MFC performance and offers a promising design strategy for next-generation biohybrid catalysts. Full article

To address the global water crisis, desalination technologies contribute about 1% of the global freshwater supply. Membrane-based desalination technologies offer high performance, operational ease, cost-effectiveness and high scalability compared to conventional thermal desalination modes. Among all membrane-based technologies, reverse osmosis is prevailing globally. However, the high energy demand of the reverse osmosis process and fouling in case of hypersaline feed streams motivate the exploration of alternative technologies, i.e., pervaporation. Pervaporation desalination involves dense hydrophilic polymer membranes to deal with high salt streams at low cost, along with less fouling than a few other membrane processes, i.e., reverse osmosis and membrane distillation. Mass transport through pervaporation desalination membranes is well-explained by solution-diffusion theory involving a tri-stage transfer, i.e., sorption, diffusion and evaporation. Since the last few decades, a green approach in all domains has offered chemical products and processes with the least hazards and minimal waste production. Application of biodegradable materials like poly(lactic acid) in combination with suitable green solvents, e.g., ethyl lactate, methyl lactate, cyrene, dimethyl isosorbide and gamma valerolactone for pervaporation desalination would be a good roadmap to meet the sustainability criterion. Some intrinsic features of poly(lactic acid) that make it a ‘material of choice’ for pervaporation desalination include hydrophilicity imparted by the presence of polar ester groups, high salt rejection, biodegradability with simple mineralization products, i.e., H₂O and CO₂, sustainable production, low toxicity, low carbon footprint, ease of processing and versatility. Poly(lactic acid) undergoes four interrelated degradation mechanisms: hydrolytic degradation, biodegradation, thermal degradation and photodegradation. The concern for poly(lactic acid) based pervaporation desalination is increased hydrolytic cleavage of poly(lactic acid) at high temperatures, which requires some modifications, e.g., nanoenhancement, additions of crosslinkers, surface modifications, addition of other polymers to prepare blends and post-treatments. These modifying strategies result in an increased stability and better performance of poly(lactic acid) films. However, optimization of various parameters relevant to such modifications leaves room for further research. This review offers a critical analysis of the need for biodegradable polymers with special focus on poly(lactic acid) rather than their fossil fuel-based alternatives, the environmental and health effects of all these polymers, cost estimation and possible performance-efficient, green and eco-friendly solutions. Full article

Against the backdrop of the global aviation industry’s commitment to achieving the “Net Zero Carbon Emissions by 2050” goal, the issue of superimposed peak loads on distribution networks—arising from the large-scale transition from fossil-fueled to electric Ground Service Equipment (GSE) at civil airports—has become increasingly prominent, emerging as a critical constraint on green airport development. Focusing on the high-value airside area, this paper presents the first systematic review of how Vehicle-to-Grid (V2G) technology can transform electric Ground Service Equipment (e-GSE) from mere “charging loads” into “dispatchable energy storage resources.” The study proposes that, through bidirectional DC charging/discharging and intelligent aggregation technologies, e-GSE fleets operating on predictable schedules can be integrated as flexible regulation units within airport microgrids. To realize this pathway, the study comprehensively examines the core technological framework, encompassing wide-power-range bidirectional charging infrastructure, grid-forming power conversion topologies, standardized communication and grid interconnection interfaces, flight-schedule-based potential assessment and dispatch algorithms, and photovoltaic storage–charging hybrid system integration schemes. The review demonstrates that this technology can not only enhance grid resilience and promote renewable energy accommodation through peak shaving, valley filling, and ancillary services but also yields significant economic benefits. Finally, the study identifies the technical, standardization, and business model barriers hindering large-scale deployment, thereby providing a theoretical reference and a technology roadmap for the energy system planning and construction of future “zero-carbon smart airports”. Full article