A GRNN based frame work to test the influence of nano zinc additive biodiesel blends on CI engine performance and emissions

• Main Authors: Chiranjeeva Rao Seela, B. Ravisankar, B.M.V.A. Raju
• Format: Article
• Language: English
• Published: Elsevier 2018-12-01
• Series: Egyptian Journal of Petroleum
• Online Access: http://www.sciencedirect.com/science/article/pii/S1110062117301708

Environmental friendly fuels have increasing demand in the field of automotive industry. Hybridizing with biodiesel and addition of nanoparticles are two evident techniques to improve the performance and to control the hazardous emissions. Combination of appropriate weight fractions of nanoparticles, biodiesel, and diesel further optimize the engine outcomes. The present work aims at investigating the effect of zinc oxide (ZnO) nanoparticles suspensions in diesel and Mahua biodiesel blended fuel on single cylinder diesel engine performance characteristics. Experimental tests are performed with neat diesel fuel, biodiesel blends and ZnO added biodiesel blends. The results indicate that ZnO particulate addition yields favorable performance and emission control of the engine. A generalized regression neural network (GRNN) is implemented for predicting the performance and emissions of the engine at various operating conditions based on the experimental results. The neural network predictions are corroborated with the experimental results and are found in good agreement. A classical differential evolution algorithm (DEA) is further used on the network model to find out optimal combination of nanoparticles, biodiesel and diesel and proven through experimental validation. Keywords: Nano zinc oxide additives, Blended fuels, Emissions control, GRNN