A Prediction of Graphene Nanoplatelets Addition Effects on Diesel Engine Emissions

• Published in: International journal of automotive and mechanical engineering 2023-09, Vol.20 (3), p.10758-10766
• Main Authors: Daud, Sarbani, Mohd Adnin Hamidi, Rizalman Mamat, Daing M. Nafiz
• Format: Article
• Language: English
• Subjects: Additives; Carbon dioxide; Carbon dioxide concentration; Carbon monoxide; Copper oxides; Diesel engines; Exhaust gases; Fuel additives; Graphene; Impact analysis; Magnesium oxide; Nitrogen oxides; Parameter identification; Platelets (materials); Prediction models; Response surface methodology; Superconductors (materials); Zinc oxide
• ISSN: 2229-8649; 2180-1606
• DOI: 10.15282/ijame.20.3.2023.17.0831

There are numerous methods for reducing diesel exhaust emissions. Engine modifications, combustion optimization, and exhaust gas treatment are all popular methods. Another proven method uses fuel additives, such as zinc oxide, copper oxide, and magnesium oxide. Those additives are proven to reduce measured emissions such as carbon monoxide and nitrogen oxide successfully; however, there are still concerns about the toxicity of the emissions, which could harm human health. As a result, carbon nanoparticles have been introduced as a fuel additive due to their low risk to human health. Because of advancements in graphene research, a few researchers began investigating the implications of using graphene nanoplatelets as a fuel additive. The study’s findings appeared to be encouraging. However, no additional research has been identified to forecast the impact on engine emissions other than analyzing the effects of graphene additives on engine emissions. The goal of this study is to forecast the effects of graphene nanoplatelets on diesel engine emissions. The emission parameters of the trial were carbon monoxide, carbon dioxide and nitrogen oxide. The factors considered in the experiment are speed, load, and blend concentration. Response surface methodology and contour plots were generated using Minitab software. The results show that the prediction model’s accuracy is within 10% of the experimental data.