A comprehensive review on the effects of diesel/biofuel blends with nanofluid additives on compression ignition engine by response surface methodology

• Published in: Energy conversion and management. X Vol. 14; p. 100177
• Main Authors: Elkelawy, Medhat, El Shenawy, E.A., Alm-Eldin Bastawissi, Hagar, Shams, Mahmoud M., Panchal, Hitesh
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-05-2022; Elsevier
• Subjects: And response surface methodology; Biodiesel; Biofuels; Compression ignition engine; Nano-particle additives; Compression ignition engine; And response surface methodology; Biofuels; Nano-particle additives; Biodiesel
• ISSN: 2590-1745; 2590-1745
• DOI: 10.1016/j.ecmx.2021.100177

Due to the emissions restrictions and the speeding requirements for energy in different sectors, diesel and gasoline can't be able to face the rapid supply of internal combustion engines. The direction for using the renewable fuel resources partially or entirely in place of fossil diesel fuel becomes inevitable due to the availability, accepted environmentally and competitive. Alternative fuels have excellent usage as fuel without any modifications in the diesel engines. Alternative fuels can dampen combustion temperature, decreasing all emission percentages compared to using fossil diesel only. Biodiesel is an oxygenated fuel and one of the alternative fuels used as a blend for operating diesel engines. Its importance is in decreasing the brake specific fuel consumption and increasing the brake thermal efficiency. Nanoparticle additives are blended with diesel fuel and its alternatives in compression ignition engines to increase the surface contact area, increase the oxidation of fuels, provide short ignition delay, improve the engine performance attributes, and decrease engine emissions. Response surface methodology is a computer application used to design, predict and optimize the response variables according to the input variables. Response surface methodology is used in many applications in industrial fields to predict the performance and quality of products due to its accuracy in the responses and time consuming. The present paper reviews the importance of using response surface methodology in predicting the optimum performance and emission characteristics for diesel engines fuelled with blends of diesel, alternative fuels, and nano-particle additives. It is accomplished that the comparison between the experimental and the modeling by response surface methodology is similar.