Study on noise in a hydrogen dual-fuelled zinc-oxide nanoparticle blended biodiesel engine and the development of an artificial neural network model. (1st October 2018) - Javed, Syed; Baig, Rahmath Ulla; Murthy, Y.V.V. Satyanarayana
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. Highlights: Experimentation conducted on H2 dual fuelled ZnO nano blended JME biodiesel engine. Noise emissions are registered for various fuel combinations by varying load. ANN model was developed to predict noise emanating from the engine. B20JME40 & B30JME40 emanate less noise compare to other fuel blends.