Influences of various metal oxide-based nanosized particles-added algae biodiesel on engine characteristics

Jegan, C. Dhayananth; Selvakumaran, T.; Karthe, M.; Hemachandu, P.; Gopinathan, R.; Sathish, T.; Ağbulut, Ümit

doi:10.1016/j.energy.2023.128633

Cited by 11 publications

(1 citation statement)

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“…The brake specific fuel consumption (BSFC) has an inverse correlation with speed and a positive correlation with the proportion of biodiesel fuel 2 . The combustion effect in the engine cylinder is enhanced by the presence of nanoparticles in biodiesel, leading to an increase in the fuel’s momentum and propagation 31 . Figure 5 b demonstrates that at 1200 rpm the BSFC was 215, 220, 216, and 218 g/kWh for D100, GAB20, GAB20A, and GAB20T respectively.…”

Section: Resultsmentioning

confidence: 99%

Exergy-energy, sustainability, and emissions assessment of Guizotia abyssinica (L.) fuel blends with metallic nano additives

Abishek,

Kachhap,

Rajak

et al. 2024

Sci Rep

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This study extensively examined the impact of aluminium oxide (Al2O3) and titanium dioxide (TiO2) nanoparticles addition in the biodiesel fuel derived from Guizotiaabyssinica (L.) oil. The assessment of fuel blends, which were created by combining nanoparticles and biodiesel was conducted using energy, exergy, and sustainability indices. The highest recorded power output of 2.81 kW was observed for the GAB20A engine operating at 1800 rpm. The experimental results revealed that the GAB20A exhibited the lowest fuel consumption, with a recorded value of 203 g/kWh, when operated at 1600 rpm among all the tested blend fuels. The blend GAB20A exhibited the highest level of energy efficiency at 1600 rpm of 29.5%, as determined by the study. Simultaneously, it was observed that GAB20 exhibited the lowest energy efficiency at 1200 rpm among all the blend fuels at 25%. The emission levels of nitrogen oxides (NOx) and carbon monoxide (CO) were observed to be quite low, although a little rise in carbon dioxide (CO2) was detected. For validation of results the artificial neural network (ANN) was used and an average of 1.703% difference in energy efficiency, 2.246% decrease in exergy efficiency, and 1.416% difference in sustainability index was found.

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Section: Resultsmentioning

confidence: 99%