Investigating the Combined Impact of Water–Diesel Emulsion and Al2O3 Nanoparticles on the of Performance and the Emissions from a Diesel Engine via the Design of Experiment

This study aims to assess the impact of the water ratio and nanoparticle concentration of neat diesel fuel on the performance characteristics of and exhaust gas emissions from diesel engines. The experimental tests were conducted in two stages. In the first stage, the effects of adding water to neat diesel fuel in ratios of 2.5% and 5% on engine performance and emissions characteristics were examined and compared to those of neat diesel at a constant engine speed of 3000 rpm under three different engine loads. A response surface methodology (RSM) based on a central composite design (CCD) was utilized to simulate the design of the experiment. According to the test results, adding water to neat diesel fuel increased the brake-specific fuel consumption and reduced the brake thermal efficiency compared to neat diesel fuel. In the examination of exhaust emissions, hydrocarbons (HC), carbon monoxide (CO), and nitrogen oxides (NOx) in the tested fuel containing 2.5% of water were decreased in comparison to pure diesel fuel by 16.62%, 21.56%, and 60.18%, respectively, on average, through engine loading. In the second stage, due to the trade-off between emissions and performance, the emulsion fuel containing 2.5% of water is chosen as the best emulsion from the previous stage and mixed with aluminum oxide nanoparticles at two dose levels (50 and 100 ppm). With the same engine conditions, the emulsion fuel mixed with 50 ppm of aluminum oxide nanoparticles exhibited the best performance and the lowest emissions compared to the other evaluated fuels. The outcomes of the investigations showed that a low concentration of 50 ppm with a small amount of 11 nm of aluminum oxide nanoparticles combined with a water diesel emulsion is a successful method for improving diesel engine performance while lowering emissions. Additionally, it was found that the mathematical model could accurately predict engine performance parameters and pollution characteristics.