Hasil untuk "Fuel"

Hussein A. Kazem, Miqdam T. Chaichan, Ali H.A. Al-Waeli et al.

Integrating the photovoltaic/thermal (PV/T) system in green hydrogen production is an improvement in sustainable energy technologies. In PV/T systems, solar energy is converted into electricity and thermal energy simultaneously using hot water or air together with electricity. This dual use saves a significant amount of energy and officially fights greenhouse gases. Different cooling techniques have been proposed in the literature for improving the overall performance of the PV/T systems; employing different types of agents including nanofluids and phase change materials. Hydrogen is the lightest and most abundant element in the universe and has later turned into a flexible energy carrier for transportation and other industrial applications. Issues, including the processes of Hydrogen manufacturing, preservation as well as some risks act as barriers. This paper provides an analysis of several recent publications on the efficiency of using PV/T technology in the process of green hydrogen production and indicates the potential for its increased efficiency as compared to conventional systems that rely on fossil fuels. Due to the effective integration of solar energy, the PV/T system can play an important role in the reduction of the levelized cost of hydrogen (LCOH) and hence play an important part in reducing the economic calculations of the decarbonized energy system.

Maha Rahman Rahi, M. Soltani

This study addresses the challenges of peak electricity demand and renewable energy intermittency by proposing an innovative hybrid energy system that integrates solar photovoltaic (PV), Liquid Air Energy Storage (LAES), and LNG-based power generation. Two configurations are developed and compared using Aspen HYSYS one incorporating a Solid Oxide Fuel Cell (SOFC) and another employing an oxy-fuel combustion system with carbon capture. Comprehensive thermodynamic, exergy, and economic analyses are performed to determine the optimal configuration.Results indicate that the SOFC-based system (Scenario 1) achieves a round-trip efficiency (RTE) of 61.8%و storage efficiency of 75.2%, and system exergy efficiency of 71.4%, while the oxy-fuel system (Scenario 2) attains a slightly higher RTE of 64% with comparable exergy performance. Scenario 1 demonstrates better economic feasibility, whereas Scenario 2 provides improved heat recovery potential.The originality of this work lies in its solar-assisted, storage-integrated hybrid concept and the comparative assessment of two high-temperature systems, offering new insights for developing efficient, low-emission power solutions in regions with variable solar resources such as Iraq.

Wanying Zhang, Jing Wang, Geng Qin et al.

The shipping industry, responsible for transporting 90% of global goods, is a major source of pollution and greenhouse gas (GHGs) emissions. In response to the increasingly stricter global and regional emission control regulations, the maritime industry has adopted various operational and technical measures to improve vessel energy efficiency so as to reduce emissions. However, these measures might not be able to effectively address the core issue of emissions, which arises from a heavy reliance on carbon-intensive energy sources. To reduce the emissions from the whole shipping industry more fundamentally, this review evaluates the viability of five alternative marine fuels — liquefied natural gas (LNG), methanol, ammonia, biofuel, and hydrogen — as potential solutions for maritime decarbonization. This review adopts the systematic search flow (SSF) approach, using iterative search refinement and thematic analysis for a structured synthesis of maritime alternative fuel literature. It first introduces each type of alternative fuel with an emphasis on production methods and sources, which are distinctively categorized by “color.” Following this, a comprehensive comparison of the fuels is presented, focusing on technical feasibility, economic viability, emission reduction capabilities, availability, and safety considerations. The practical application of these fuels is further explored through an analysis of their adoption in operational fleets and new orders, as well as the readiness of port infrastructure to support these changes. This review also examines the role of alternative fuels in the development of green shipping corridors, underscored by an analysis of green shipping finance initiatives. The findings provide valuable insights into the viability of these fuels, supporting the International Maritime Organization (IMO)’s 2050 decarbonization goals and paving the way towards zero emissions in global shipping.

Arif Algifari, I. G. B. N. Makertihartha, Subagjo Subagjo et al.

The increasing demand for oil fuel and the decline of crude oil reserves highlight the need for alternative energy sources. Palm oil, as a renewable resource, has potential for biofuel production through catalytic cracking. This study aims to develop and evaluate modified zeolite-based catalysts, particularly ZSM-5/HY, to produce palm oil-derived gasoline that meets European fuel standards. The research involved catalyst preparation, modification with ZSM-5 and phosphorus, and activity testing in a fixed-bed reactor. Gasoline yield and catalyst performance were analyzed using gas chromatography. The results showed nearly 100% conversion of palm oil under optimal conditions, with gasoline yield meeting European standard. The addition of ZSM-5 improved conversion and RON, while phosphorus modification reduced catalyst acidity, affecting yield and coke formation. This study concludes that modifying zeolite catalysts with ZSM-5 and phosphorus enables efficient palm oil-derived gasoline production with high RON and reduced aromatic content, contributing to sustainable energy solutions. Copyright © 2025 by Authors, Published by BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

Via Afrianti, Ma’mun Sarma

Global warming can be caused by various sectors, one of which is the transportation sector. The use of gasoline is one of the main contributors to energy consumption. Conventional cars typically use gasoline as fuel to support the mobility of their users. The use of this fuel can have negative impacts on the environment, such as greenhouse gas emissions and air pollution. Therefore, electric vehicles could be a potential solution to reduce these impacts. However, it cannot be denied that there are several barriers to the adoption of electric vehicles. Through the Theory of Planned Behavior (TPB) analysis, this study explores the influence of attitudes toward behavior, subjective norms, and perceived behavioral control on the intention to purchase electric vehicles among millennials. Additionally, it examines the motivations of millennials in their purchase intentions and the moderating role of gender in the intention to buy electric vehicles. The study sample consists of millennials living in the Jabodetabek area with an interest in electric vehicles. A quantitative method with a Structural Equation Modeling (SEM)-PLS approach is used to analyze the relationships among the variables studied. The results show that TPB and motivation influence the intention to purchase electric vehicles among millennials in Jabodetabek. Meanwhile, gender moderation only influences the relationship between perceived behavioral control and the intention to buy electric vehicles among millennials in Jabodetabek.

Alebachew Demoz

Abstract There is a huge inventory of fine tailings from mining operations in tailings storage facilities (TSFs) awaiting remediation operations. Returning TSFs to their original states as dryland is the preferred closure action and chemical additives are used in the necessary thickening (dewatering) step of the fine tailings. Synthetic fossil fuel-based polymers known as flocculants are used in the dewatering process industrially. Nanofibrillated cellulose (NFC) is a biomass-derived product which can be developed to substitute the prevailing synthetic flocculants. This review describes the pertinent subjects related to the extraction of NFC, chemical modification, their physical and chemical characterization and evaluations as flocculants. NFC has been separated from biomass by physical, biological, and chemical processes, including combinations thereof. Properties which flocculants possess that pristine NFC lacks are mainly incorporated by chemical modifications. Chemical modifications take place via the hydroxyl groups that surround the polysaccharide skeletal structure of NFC. Prospective chemical modification reactions to produce effective flocculants are discussed. Other properties that can fine tune flocculation performance of NFC such as charge type, density, and size are also covered. The preparation of new, modified-NFC flocculants needs to include characterizations to provide detailed information about flocculants’ particle dimensions, morphology, surface charge, surface chemistry, crystallinity, and flocculant rheological properties. Methods to evaluate the flocculation performances and the resulting deposits of modified NFC treated geotechnical properties are presented to wrap the review. The results of such a study will enable correlation of modified-NFCs with their efficacy in the treatment of mine waste fine tailings.

Mohammed Almomtan, Emad Al Ibrahim, Aamir Farooq

Synthetic fuels are crucial for decarbonizing the transportation sector. A significant challenge lies in the rapid and efficient characterization of these fuels. Chemometric methods using ATR-FTIR data offer a potential alternative to conventional techniques. This study expands the applicability and performance of chemometric models by providing an extensive ATR-FTIR spectral dataset and exploring various data enhancement strategies. Data enhancement was achieved by semi-supervised data generation, consistency enforcement through unsupervised data augmentation, and data imputation using synthetic spectra blending and pseudo-labeling. Models were trained on surrogate fuels and rigorously tested on real fuels, representing out-of-distribution testing conditions. We believe that this work will enhance the adoption of chemometric models for fuel characterization.

Arghya Paul, Kanak Raj, Pratim Kumar

In the present work, Jet A-Hexane binary fuel droplet impact dynamics on heated solid surfaces were studied numerically. This study is crucial for practical applications such as fuel injection in combustors and thermal management of engine components. Volume of fluid (VOF) method was used to analyse the impact dynamics, spreading behaviour, vaporisation, and heat transfer of n-hexane and Jet-A blended fuel droplets on heated stainless-steel surfaces. Droplet impact dynamics were investigated for two Weber numbers, i.e., 25 and 50, and surface temperatures ranging from 50C to 227C to capture transitions from gentle spreading to nucleate boiling and rebound phenomena. This work examines how fuel blending influences inertia, lamella formation, vapour recoil, and film boiling regimes. The results show that higher inertia in blended fuels enhances spreading but also triggers stronger vapour recoil at elevated temperatures, leading to droplet rebound. In contrast, pure hexane transitions to a stable film boiling regime at high surface temperatures, resulting in a decline in smoother heat flux. New correlations were developed linking Weber number, spreading ratio, and wall heat flux, offering predictive insights for real-world combustion scenarios. These findings advance the understanding of bi-component fuel droplet impacts on heated surfaces and provide a framework for designing efficient spray systems in combustors and thermal management in propulsion and power generation applications.

Ruimin Chu, Li Chik, Yiliao Song et al.

Early detection of fuel leakage at service stations with underground petroleum storage systems is a crucial task to prevent catastrophic hazards. Current data-driven fuel leakage detection methods employ offline statistical inventory reconciliation, leading to significant detection delays. Consequently, this can result in substantial financial loss and environmental impact on the surrounding community. In this paper, we propose a novel framework called Memory-based Online Change Point Detection (MOCPD) which operates in near real-time, enabling early detection of fuel leakage. MOCPD maintains a collection of representative historical data within a size-constrained memory, along with an adaptively computed threshold. Leaks are detected when the dissimilarity between the latest data and historical memory exceeds the current threshold. An update phase is incorporated in MOCPD to ensure diversity among historical samples in the memory. With this design, MOCPD is more robust and achieves a better recall rate while maintaining a reasonable precision score. We have conducted a variety of experiments comparing MOCPD to commonly used online change point detection (CPD) baselines on real-world fuel variance data with induced leakages, actual fuel leakage data and benchmark CPD datasets. Overall, MOCPD consistently outperforms the baseline methods in terms of detection accuracy, demonstrating its applicability to fuel leakage detection and CPD problems.

Jimmy Losfeld, Lionel Desgranges, Yves Pontillon et al.

We have developed a gas flow model in the spent nuclear fuel during the annealing. It postulates that the gas release during an isothermal plateau at 1200{\textdegree}C corresponds to the equilibrium between overpressure gas reservoirs in the fuel sample connected to the free surface at atmospheric pressure.

Mohammad Ful Hossain Seikh

Radio antennas are widely used in the field of particle astrophysics in searches for ultra-high energy cosmic rays (UHECR) and neutrinos (UHEN). It is therefore necessary to properly describe the physics of their response. In this article, we summarize the mathematics underlying parameterizations of radio antennas. As a paradigm, we focus on a half-wave dipole and also discuss measurements of characteristics, performed in an electromagnetic (EM) anechoic chamber.

María Virginia Palchetti, Fernando Zamudio, Sebastián Zeballos et al.

Plants are essential for our lives because they provide food, medicine, fuel, shelter, and immaterial resources. Understanding patterns of plant uses through large-scale plant use analysis may contribute to the development of a biocultural conservation approach. We conducted a systematic review to assess current knowledge of the ethnoflora of Argentina, as well as to identify taxonomic and geographic patterns of ethnobotanical uses of native plants at the large scale. We analyzed 124 articles reporting the use of 1706 species. We found that the most widely studied region and use category were Chaco and medicine, respectively. The number of useful native species within a family was positively related to the total native species in each family at the country level. In general, species of greatest cultural importance at the country level had a wide distribution. Almost 70% of native plants used in one phytogeographic province were exclusive to it, and species with the highest importance were characteristic elements of its vegetation. We found that southern Argentina has an exclusive ethnoflora that differs from that in a large area of central and northern Argentina. Our review highlights that plants used by people are intimately associated with the local environment, and that species with great cultural importance across phytogeographic provinces are frequent in the landscape. We provide the first analysis of ethnobotanical studies and a database of useful native plants across Argentina. This information highlights strengths and gaps in knowledge of useful native species, which is crucial for conservation, sustainability and human well-being.

Shoukun Sun, Fei Xu, Lu Cai et al.

U-10Zr-based nuclear fuel is pursued as a primary candidate for next-generation sodium-cooled fast reactors. However, more advanced characterization and analysis are needed to form a fundamental understating of the fuel performance, and make U-10Zr fuel qualify for commercial use. The movement of lanthanides across the fuel section from the hot fuel center to the cool cladding surface is one of the key factors to affect fuel performance. In the advanced annular U-10Zr fuel, the lanthanides present as fission gas bubbles. Due to a lack of annotated data, existing literature utilized a multiple-threshold method to separate the bubbles and calculate bubble statistics on an annular fuel. However, the multiple-threshold method cannot achieve robust performance on images with different qualities and contrasts, and cannot distinguish different bubbles. This paper proposes a hybrid framework for efficient bubble segmentation. We develop a bubble annotation tool and generate the first fission gas bubble dataset with more than 3000 bubbles from 24 images. A multi-task deep learning network integrating U-Net and ResNet is designed to accomplish instance-level bubble segmentation. Combining the segmentation results and image processing step achieves the best recall ratio of more than 90% with very limited annotated data. Our model shows outstanding improvement by comparing the previously proposed thresholding method. The proposed method has promising to generate a more accurate quantitative analysis of fission gas bubbles on U-10Zr annular fuels. The results will contribute to identifying the bubbles with lanthanides and finally build the relationship between the thermal gradation and lanthanides movements of U-10Zr annular fuels. Mover, the deep learning model is applicable to other similar material micro-structure segmentation tasks.

Anuradha Singh, Jyoti Yadav, Sarahana Shrestha et al.

This is a study on the potential widespread usage of alternative fuel vehicles, linking them with the socio-economic status of the respective consumers as well as the impact on the resulting air quality index. Research in this area aims to leverage machine learning techniques in order to promote appropriate policies for the proliferation of alternative fuel vehicles such as electric vehicles with due justice to different population groups. Pearson correlation coefficient is deployed in the modeling the relationships between socio-economic data, air quality index and data on alternative fuel vehicles. Linear regression is used to conduct predictive modeling on air quality index as per the adoption of alternative fuel vehicles, based on socio-economic factors. This work exemplifies artificial intelligence for social good.

Marcello Cellina, Silvia Strada, Sergio Matteo Savaresi

This paper presents a vehicle-independent, non-intrusive, and light monitoring system for accurately measuring fuel consumption in road vehicles from longitudinal speed and acceleration derived continuously in time from GNSS and IMU sensors mounted inside the vehicle. In parallel to boosting the transition to zero-carbon cars, there is an increasing interest in low-cost instruments for precise measurement of the environmental impact of the many internal combustion engine vehicles still in circulation. The main contribution of this work is the design and comparison of two innovative black-box algorithms, one based on a reduced complexity physics modeling while the other relying on a feedforward neural network for black-box fuel consumption estimation using only velocity and acceleration measurements. Based on suitable metrics, the developed algorithms outperform the state of the art best approach, both in the instantaneous and in the integral fuel consumption estimation, with errors smaller than 1\% with respect to the fuel flow ground truth. The data used for model identification, testing, and experimental validation is composed of GNSS velocity and IMU acceleration measurements collected during several trips using a diesel fuel vehicle on different roads, in different seasons, and with varying numbers of passengers. Compared to built-in vehicle monitoring systems, this methodology is not customized, uses off-the-shelf sensors, and is based on two simple algorithms that have been validated offline and could be easily implemented in a real-time environment.

Ti Yue, Ti Yue, Jianyi Chen et al.

The Upper Swirling Liquid Film (USLF) phenomenon that occurs in the upper cylinder of the Gas–Liquid Cylindrical Cyclone (GLCC) separator is the direct cause of the low separation efficiency of the liquid phase. In this study, first, the USLF formation and development were simulated by an improved Eulerian-EWF coupled simulated method. By introducing a profile-defined inlet boundary and considering entrainment droplet size distributions, the Eulerian-EWF method got reasonable results which agreed well with the experimental. Then, the flow characteristics and changing laws of the USLF including film thickness, film axial velocity, and film tangential velocity were analyzed by this method under different gas–liquid flow rates. It suggested that the liquid film thickness often reaches a maximum at the aspect ratio (z-z0)/D=(1.2–3.9) above the tangential inlet, and the film thickness appears to be more sensitive to the gas flow than to the liquid flow. For the film axial velocity, the direction of film velocity on the front and back sides seems to be generally opposite. Finally, typical distributions of the aforementioned USLF variables were presented and corresponded accordingly, and two obvious rules were found. One is that the position where the thickest liquid film is located always corresponds to the position where the axial film velocity turns from positive to negative for the first time. The other is that the tangential film velocity has a strong synchronous relationship with the film thickness. This research might provide somewhat valid information for the future LCO-prevented measurement in GLCC separators.

Yao Ma, Junmin Wang

Drivers have distinctively diverse behaviors when operating vehicles in natural traffic flow, such as preferred pedal position, car-following distance, preview time headway, etc. These highly personalized behavioral variations are known to impact vehicle fuel economy qualitatively. Nevertheless, the quantitative relationship between driving behaviors and vehicle fuel consumption remains obscure. Addressing this critical missing link will contribute to the improvement of transportation sustainability, as well as understanding drivers' behavioral diversity. This study proposed an integrated microscopic driver behavior and fuel consumption model to assess and predict vehicle fuel economy with naturalistic highway and local commuting traffic data. Through extensive Monte Carlo simulations, significant correlation results are revealed between specific individual driving preferences and fuel economy over drivers' frequent commuting routes. Correlation results indicate that the differences in fuel consumption incurred by various driving behaviors, even in the same traffic conditions, can be as much as 29% for a light-duty truck and 15% for a passenger car. A Gaussian Process Regression model is further trained, validated, and tested under different traffic and vehicle conditions to predict fuel consumption based on drivers' personalized behaviors. Such a quantitative and personalized model can be used to identify and recommend fuel-friendly driving behaviors and routes, demonstrating a strong incentive for relevant stakeholders.

Fei Xu, Lu Cai, Daniele Salvato et al.

U-10Zr-based metallic nuclear fuel is a promising fuel candidate for next-generation sodium-cooled fast reactors.The research experience of the Idaho National Laboratory for this type of fuel dates back to the 1960s. Idaho National Laboratory researchers have accumulated a considerable amount of experience and knowledge regarding fuel performance at the engineering scale. The limitation of advanced characterization and lack of proper data analysis tools prevented a mechanistic understanding of fuel microstructure evolution and properties degradation during irradiation. This paper proposed a new workflow, coupled with domain knowledge obtained by advanced post-irradiation examination methods, to provide unprecedented and quantified insights into the fission gas bubbles and pores, and lanthanide distribution in an annular fuel irradiated in the Advanced Test Reactor. In the study, researchers identify and confirm that the Zr-bearing secondary phases exist and generate the quantitative ratios of seven microstructures along the thermal gradient. Moreover, the distributions of fission gas bubbles on two samples of U-10Zr advanced fuels were quantitatively compared. Conclusive findings were obtained and allowed for evaluation of the lanthanide transportation through connected bubbles based on approximately 67,000 fission gas bubbles of the two advanced samples.

Shengnan Li, Shih-Hsin Ho, Tao Hua et al.

Microbial fuel cells (MFCs) have gained remarkable attention as a novel wastewater treatment that simultaneously generates electricity. The low activity of the oxygen reduction reaction (ORR) remains one of the most critical bottlenecks limiting the development of MFCs. To date, although research on biochar as an electrocatalyst in MFCs has made tremendous progress, further improvements are needed to make it economically practical. Recently, biochars have been considered to be ORR electrocatalysts with developmental potential. In this review, the ORR mechanism and the essential requirements of ORR catalysts in MFC applications are introduced. Moreover, the focus is to highlight the material selection, properties, and preparation of biochar electrocatalysts, as well as the evaluation and measurement of biochar electrodes. Additionally, in order to provide comprehensive information on the specific applications of biochars in the field of MFCs, their applications as electrocatalysts, are then discussed in detail, including the uses of nitrogen-doped biochar and other heteroatom-doped biochars as electrocatalysts, poisoning tests for biochar catalysts, and the cost estimation of biochar catalysts. Finally, profound insights into the current challenges and clear directions for future perspectives and research are concluded.

Gabriel Surina, Georgios Georgalis, Siddhant S. Aphale et al.

This study presents an imaging-based deep learning tool to measure the fuel regression rate in a 2D slab burner experiment for hybrid rocket fuels. The slab burner experiment is designed to verify mechanistic models of reacting boundary layer combustion in hybrid rockets by the measurement of fuel regression rates. A DSLR camera with a high intensity flash is used to capture images throughout the burn and the images are then used to find the fuel boundary to calculate the regression rate. A U-net convolutional neural network architecture is explored to segment the fuel from the experimental images. A Monte-Carlo Dropout process is used to quantify the regression rate uncertainty produced from the network. The U-net computed regression rates are compared with values from other techniques from literature and show error less than 10%. An oxidizer flux dependency study is performed and shows the U-net predictions of regression rates are accurate and independent of the oxidizer flux, when the images in the training set are not over-saturated. Training with monochrome images is explored and is not successful at predicting the fuel regression rate from images with high noise. The network is superior at filtering out noise introduced by soot, pitting, and wax deposition on the chamber glass as well as the flame when compared to traditional image processing techniques, such as threshold binary conversion and spatial filtering. U-net consistently provides low error image segmentations to allow accurate computation of the regression rate of the fuel.