Study on noise in a hydrogen dual-fuelled zinc-oxide nanoparticle blended biodiesel engine and the development of an artificial neural network model

Syed Javed, Rahmath Ulla Baig and Y.V.V. Satyanarayana Murthy

Energy, 2018, vol. 160, issue C, 774-782

Abstract: Two challenges that have motivated researchers are the mitigation of emissions and a reduction in the reliance on diesel fuel. A potential replacement for diesel is biodiesel, which is derived from animal fat or vegetable oil. The large number of studies on the performance and emission characteristics of biodiesel is notable. In such studies, the noise emissions have seldom been disregarded or treated as a trivial matter. Extending the previously published research by the authors, an experimental investigation was carried out to study the effects of new fuel types on the noise emissions. Blends of Jatropha methyl ester (JME) biodiesel suspended with zinc oxide (ZnO) nanoparticles along with hydrogen (H2) in dual-fuel mode were used as fuel for an experimental diesel engine test rig. The noise levels in decibels (dB) under variations in the biodiesel percentage, nanoparticle size, and flow rates of H2 at different loads were recorded. It was observed that 20% and 30% JME biodiesel blends suspended with ZnO nanoparticles of 40 nm in size have superior noise attenuation. To avoid a strenuous experimentation, an artificial neural network model was developed for noise prediction with a regression coefficient of 0.9992.

Keywords: Zinc oxide nanoparticle; Jatropha methyl ester blend; Artificial neural network; Noise emissions; Hydrogen (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:160:y:2018:i:c:p:774-782

DOI: 10.1016/j.energy.2018.07.041

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