This research endeavored to boost the applicability of methanol in CI engines utilizing n-decanol as cosolvents. The work was split into binary phases. Firstly, the stabilities of pure methanol (M100) and hydrous-methanol (MH10), with diesel as a reference fuel, were examined applying various temperatures: 10 °C, 20 °C, and 30 °C. The findings showed that the M100-diesel and MH10-diesel combinations were unstable. Thus, n-decanol was utilized as a cosolvent. Following by the engine combustion and emissions characteristics were evaluated by manipulating three proportions of M100-diesel mixtures with n-decanol. Three mixtures comprised of 5, 10, and 15% M100 with 20% n-decanol, which are denoted as M5, M10, and M15, correspondingly. These combinations were assessed via thermogravimetric assessment, and their physicochemical properties were assessed corresponding to the ASTM. The maximum in-cylinder pressure, heat release rate, and pressure rise rate diminished by 10, 11, and 10%, respectively, for the M100/diesel/n-decanol combinations compared with the diesel oil. The brake thermal efficiency lowered by 10%, whereas the brake specific fuel consumption enlarged by 10% for the combinations compared with the diesel. NO and smoke opacity levels diminished by about 30 and 50%, respectively, whereas the CO and UHC enlarged by about 50 and 60% for the blends compared with the diesel oil.
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http://dx.doi.org/10.1038/s41598-022-20326-0 | DOI Listing |
Sci Rep
January 2025
Electrical Power and Machines Engineering Department, Faculty of Engineering, Helwan University, Cairo, Egypt.
To improve the inadequate reliability of the grid that has led to a worsening energy crisis and environmental issues, comprehensive research on new clean renewable energy and efficient, cost-effective, and eco-friendly energy management technologies is essential. This requires the creation of advanced energy management systems to enhance system reliability and optimize efficiency. Demand-side energy management systems are a superior solution for multiple reasons.
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January 2025
Department of Information Systems, College of Computing and Informatics, The University of Sharjah, Sharjah, UAE.
This study explores the integration of nanotechnology and Long Short-Term Memory (LSTM) machine learning algorithms to enhance the understanding and optimization of fuel spray dynamics in compression ignition (CI) engines with varying bowl geometries. The incorporation of nanotechnology, through the addition of nanoparticles to conventional fuels, improves fuel atomization, combustion efficiency, and emission control. Simultaneously, LSTM models are employed to analyze and predict the complex spray behavior under diverse operational and geometric conditions.
View Article and Find Full Text PDFEnviron Sci Technol
January 2025
Center for Environmental Research and Technology (CE-CERT), Bourns College of Engineering, University of California, 1084 Columbia Avenue, Riverside, California 92507, United States.
We assessed the real-world particulate emissions of a goods movement diesel vehicle, with an emphasis on total particle number and solid particle number emissions at different cutoff sizes. The vehicle was tested on routes in the South Coast Air Basin (SCAB) of California, representative of typical goods movement operation between the ports to warehouses and logistic centers with a mixture of urban and highway driving, as well as elevation change. We evaluated emissions during normal vehicle operation and diesel particulate filter (DPF) active regeneration events.
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January 2025
Parul Institute of Engineering and Technology, Parul University, Vadodara, Gujarat, India.
Maintaining stable voltage and frequency regulation is critical for modern power systems, particularly with the integration of renewable energy sources. This study proposes a coordinated control strategy for voltage and frequency in a deregulated power system comprising six Generation Companies (GENCOs) and six Distribution Companies (DISCOs). The system integrates thermal, diesel, wind, solar photovoltaic (PV), and hydroelectric sources.
View Article and Find Full Text PDFAm J Epidemiol
December 2024
Univ Rennes, CHU Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35000 Rennes, France.
Attributable burden of disease estimates reported population-wide do not reflect social disparities in exposures and outcomes. This makes one of the influential scientific tools in public health decision-making insensitive to the distribution of health impacts between socioeconomic groups. Our aim was to use the often-overlooked distributive property of the population attributable fraction (PAF) to quantitatively partition the population burden attributed to know risk factors into subgroups defined by their socioeconomic position (SEP).
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