Hasil untuk "Fuel"

J. Holechek, H. Geli, M. Sawalhah et al.

Our study evaluated the effectiveness of using eight pathways in combination for a complete to transition from fossil fuels to renewable energy by 2050. These pathways included renewable energy development; improving energy efficiency; increasing energy conservation; carbon taxes; more equitable balancing of human wellbeing and per capita energy use; cap and trade systems; carbon capture, utilization, and storage; and nuclear power development. We used the annual ‘British Petroleum statistical review of world energy 2021’ report as our primary database. Globally, fossil fuels, renewable (primarily hydro, wind and solar), nuclear energy accounted for 83%, 12.6%, and 6.3% of the total energy consumption in 2020. To achieve zero fossil fuel use by 2050, we found that renewable energy production will need to be increased by up to 6-fold or 8-fold if energy demand is held constant at, or increased 50% from, the 2020 energy demand level. Constraining 2050 world energy demand to a 25% increase over the 2020 level, improves the probability of achieving independence from fossil fuels. Improvements in energy efficiency need to accelerate beyond the current rate of ~1.5% per year. Aggressive application of energy conservation policies involving land use and taxation could potentially reduce world energy use by 10% or more by 2050. Our meta-analysis shows that the minimum level of per capita energy consumption that would allow 8 billion people to have a ‘Decent Living Standard’ is on average ~70 GJ per capita per year, which is 93% of the 2020 global average. Developed countries in temperate climates with high vehicle-dependency needed ~120 GJ per capita year−1, whereas equatorial countries with low vehicle-dependency needed 30 GJ per capita year−1. Our meta-analyses indicated replacement of fossil fuels with renewable energy by 2050 may be possible but will require aggressive application of all eight pathways, major lifestyle changes in developed countries, and close cooperation among all countries.

M. Shao, Qiaowan Chang, J. Dodelet et al.

Michael F. Wehner, R. Truby, Daniel J. Fitzgerald et al.

J. Varcoe, P. Atanassov, Dario R. Dekel et al.

This article provides an up-to-date perspective on the use of anion-exchange membranes in fuel cells, electrolysers, redox flow batteries, reverse electrodialysis cells, and bioelectrochemical systems (e.g. microbial fuel cells). The aim is to highlight key concepts, misconceptions, the current state-of-the-art, technological and scientific limitations, and the future challenges (research priorities) related to the use of anion-exchange membranes in these energy technologies. All the references that the authors deemed relevant, and were available on the web by the manuscript submission date (30th April 2014), are included.

M. Jahirul, M. Rasul, A. Chowdhury et al.

There has been an enormous amount of research in recent years in the area of thermo-chemical conversion of biomass into bio-fuels (bio-oil, bio-char and bio-gas) through pyrolysis technology due to its several socio-economic advantages as well as the fact it is an efficient conversion method compared to other thermo-chemical conversion technologies. However, this technology is not yet fully developed with respect to its commercial applications. In this study, more than two hundred publications are reviewed, discussed and summarized, with the emphasis being placed on the current status of pyrolysis technology and its potential for commercial applications for bio-fuel production. Aspects of pyrolysis technology such as pyrolysis principles, biomass sources and characteristics, types of pyrolysis, pyrolysis reactor design, pyrolysis products and their characteristics and economics of bio-fuel production are presented. It is found from this study that conversion of biomass to bio-fuel has to overcome challenges such as understanding the trade-off between the size of the pyrolysis plant and feedstock, improvement of the reliability of pyrolysis reactors and processes to become viable for commercial applications. Further study is required to achieve a better understanding of the economics of biomass pyrolysis for bio-fuel production, as well as resolving issues related to the capabilities of this technology in practical application.

S. Singh, Dipti Singh

K. Rabaey, R. Rozendal

G. Werf, D. Morton, R. DeFries et al.

M. Graboski, R. McCormick

Yan Lin, Shuzo Tanaka

J. Lindl

A. Srivastava, R. Prasad

Parham Oveissi, Gohar T. Khokhar, Kyle Hanquist et al.

This paper presents the application of a novel data-driven adaptive control technique, called dynamic mode adaptive control (DMAC), for regulating thrust in a solid fuel ramjet (SFRJ). A high-fidelity computational model incorporating compressible flow theory and equilibrium chemistry is used to simulate the combustion dynamics. An adaptive tracking controller is designed using the DMAC framework, which leverages dynamic mode decomposition to approximate the local system behavior, followed by a tracking controller synthesized around the identified model. Simulation results demonstrate that DMAC provides an effective and reliable approach for thrust regulation in SFRJs. In addition, a systematic hyperparameter sensitivity study is conducted by varying the tuning parameters over several orders of magnitude. The resulting responses show that the closed-loop performance and tracking error remain stable across wide parameter variations, indicating that DMAC exhibits strong robustness to hyper parameter tuning.

Yi-Kuan Chiang

Molecular gas, modest in mass yet pivotal within the cosmic inventory, regulates baryon cycling as the immediate fuel for star formation. Across most of cosmic history, its reservoir has remained elusive, with only the tip of the iceberg revealed by luminous carbon monoxide (CO) emitting galaxies. Here we report the first detections of the mean cosmic CO background across its rotational ladder at 7$σ$, together with ionized carbon ([CII]) at 3$σ$, over $0<z<4.2$. This uses tomographic clustering of diffuse broadband intensities with reference galaxies, directly probing aggregate emission in the cosmic web. From CO(1-0) we infer the total molecular gas density, $Ω_{\rm H_2}$, finding it about twice that resolved in galaxy surveys. The global depletion time is $\sim$1 Gyr, shorter than the Hubble time, requiring sustained inflow. CO excitation links to star-formation surface density and, with depletion time, yields a super-linear Kennicutt-Schmidt law that appears universal. Together these results establish a global picture of galaxy growth fueled by a larger, short-lived molecular reservoir. The CO and [CII] detections mark a turning point for line-intensity mapping, replacing forecasts with empirical line strengths and defining sensitivity requirements for upcoming 3D experiments poised to open new windows on galaxy formation and cosmology.

Zhanwei Wang, Shidong Fan, Zhiqiang Han

Recently, the IMO has completed the guidelines on the life cycle greenhouse gas intensity of marine fuels to accelerate the application of alternative fuels. Low-carbon fuels may persist for decades and have become a key transitional phase in replacing marine fuels. A more comprehensive methodology for evaluating the carbon emission levels of marine fuels was explored, and the carbon emissions and environmental impacts of a 150,000-ton shuttle tanker under 19 dual-fuel power scenarios were evaluated using the Energy Efficiency Design Index (EEDI) and life cycle assessment (LCA) method. The results show that liquefied natural gas (LNG) has a higher carbon control potential level compared to liquefied petroleum gas (LPG) and methanol (MeOH), while LPG is superior to MeOH based on EEDI evaluation. LCA analysis results show that MeOH (biomass) has the best carbon control potential considering the carbon emissions of the well-to-tank phase of the fuel, followed by LNG, LPG, MeOH (natural gas, NG), and MeOH (coal). However, MeOH (NG) and MeOH (coal) had greater negative environmental impacts. This study provides method support and a direction toward improvement for revising related technical specifications and regulations for dual-fuel vessel performance evaluation, considering the limitations of various maritime regulations.

Nothando Cynthia Shiba

Direct hydrogenation of CO2 to produce low-carbon footprint chemicals using multifunctional catalysts is among the most practical approaches to CO2 utilisation. Iron (Fe) and Cobalt (Co) catalysts are widely used in Fischer Tropsch Synthesis (FTS) due to their high activity and selectivity, however, their performance is significantly influenced by the choice of support material. Due to their unique structural stability, tunable redox properties, strong metal-support interactions, perovskites are used in a wide range of gas-solid reactions and have emerged as promising supports in FTS. Recent studies show that the perovskite's A- and B-site cation selection, critically affects the reducibility and dispersion of the active phase, thereby impacting the FTS activity and selectivity. Furthermore, the lattice oxygen in stoichiometric perovskites can modulate the surface chemistry, thus influencing the adsorption and activation of CO or CO2, and the hydrocarbon chain propagation. Emerging research have explored doping with high valence state elements to introduce charge imbalance to improve oxygen mobility, catalyst stability and enhance theexsolution of Co0 and Fe0 under H2, thus increasing active site density and catalyst activity. This review highlights the role of stoichiometric perovskites as functionally supportive scaffolds in FTS (both CO and CO2 hydrogenation), offering pathways to design robust, high-performance cobalt and iron catalysts for synthetic fuel production. The structural evolutions and thermochemical behaviour are discussed with respect to additional cations incorporated into the new class perovskites support lattice. The mechanisms governing this reaction are outlined; and finally, the current state of research on perovskite-supported catalysts in FTS is discussed.

Eghbal Hosseini, Barzan Saeedpour, Mohsen Banaei et al.

Accurate time-series forecasting of energy consumption and photovoltaic (PV) production is essential for effective energy management and sustainability. Deep Neural Networks (DNNs) are effective tools for learning complex patterns in such data; however, optimizing their architecture remains a significant challenge. This paper introduces a novel hybrid optimization approach that integrates Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) to enhance the DNN architecture for more accurate energy forecasting. The performance of GA-PSO is compared with leading hyperparameter optimization techniques, such as Bayesian Optimization and Evolutionary Strategy, across various optimization benchmarks and DNN hyperparameter tuning tasks. The study evaluates the GA-PSO-enhanced Optimized Deep Neural Network (ODNN) against traditional DNNs and state-of-the-art machine learning methods on multiple real-world energy forecasting tasks. The results demonstrate that ODNN outperforms the average performance of other methods, achieving a 27% improvement in forecasting accuracy and a 22% reduction in error across various metrics. These findings demonstrate the significant potential of GA-PSO as an effective tool to optimize DNN models in energy forecasting applications.

David N. Carruthers, Patrick C. Kinnunen, Yuerong Li et al.

Abstract Advances in genome engineering have improved our ability to perturb microbial metabolic networks, yet bioproduction campaigns often struggle with parsing complex metabolic datasets to efficiently enhance product titers. We address this challenge by coupling laboratory automation with machine learning to systematically optimize the production of isoprenol, a sustainable aviation fuel precursor, in Pseudomonas putida. The simultaneous downregulation through CRISPR interference of combinations of up to four gene targets, guided by machine learning, permitted us to increase isoprenol titer 5-fold in six consecutive design-build-test-learn cycles. Moreover, machine learning enabled us to swiftly explore a vast experimental design space of 800,000 possible combinations by strategically recommending approximately 400 priority constructs. High-throughput proteomics allowed us to validate CRISPRi downregulation and identify biological mechanisms driving production increases. Our work demonstrates that ML-driven automated design-build-test-learn cycles, when combined with rigorous data validation, can rapidly enhance titers without specific biological knowledge, suggesting that it can be applied to any host, product, or pathway.

Nicholas P. Nurre, Ehsan Taheri

Powered descent guidance (PDG) problems subject to six-degrees-of-freedom (6DOF) dynamics allow for enforcement of practical attitude constraints. However, numerical solutions to 6DOF PDG problems are challenging due to fast rotational dynamics coupled with translational dynamics, and the presence of highly nonlinear state/control path inequality constraints. In this work, constrained fuel- and time-optimal 6DOF PDG problems are solved leveraging a regularized indirect method, subject to inequality constraints on the thrust magnitude, thruster gimbal angle, rocket tilt angle, glideslope angle, and angular velocity magnitude. To overcome the challenges associated with solving the resulting multipoint boundary-value problems (MPBVPs), the state-only path inequality constraints (SOPICs) are enforced through an interior penalty function method, which embeds the resulting MPBVPs into a multi-parameter smooth neighboring families of two-point BVPs. Extremal solutions are obtained using an indirect multiple-shooting solution method with numerical continuation. Moreover, an empirical relation is derived for the directly-adjoined Lagrange multipliers associated with SOPICs. The fuel- and time-optimal trajectories are compared against solutions of DIDO -- a capable pseudospectral-based software for solving practical constrained optimal control problems.

Wenhao Wang, Zongmin Hu, Peng Zhang

This computational study demonstrates the formation of liquid-fueled oblique detonation waves (ODWs) induced by a liquid transverse jet, which is either reactive or non-reactive. The study employs an in-house two-phase supersonic reactive flow solver based on the rhocentralfoam framework of OpenFOAM. The findings emphasize the essential role of transverse jets in enabling successful ODW formation under conditions where detonation would otherwise fail. Specifically, the jet-inflow momentum ratio significantly influences the mechanisms of ODW formation. At lower momentum ratios, the oblique shock wave (OSW) induced by the jet is insufficient to directly initiate detonation. Instead, the atomized n-heptane jet increases the local fuel mass fraction, promoting low- and intermediate-temperature chemical reactions, which eventually lead to detonation. At higher momentum ratios, the OSW generated by the transverse jet is sufficiently strong to directly trigger detonation through intermediate-temperature chemistry, with the jet acting primarily as a combustion stabilizer rather than directly enhancing combustion. Comparative studies with non-reactive jets and wedge-strip configurations demonstrate that at higher momentum ratios, the dominant mechanism is the physical blocking effect of the jet, which generates a strong OSW capable of initiating detonation.