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Sarbani Daud
Universiti Malaysia Pahang, Malaysia
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Mechanical Engineering

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Journal : Automotive Experiences

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Design of Experiment to Predict the Effects of Graphene Nanoplatelets Addition to Diesel Engine Performance Sarbani Daud; Mohd Adnin Hamidi; Rizalman Mamat
Automotive Experiences Vol 5 No 3 (2022)
Publisher : Automotive Laboratory of Universitas Muhammadiyah Magelang in collaboration with Association of Indonesian Vocational Educators (AIVE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.6263

Abstract

To minimise diesel exhaust emissions, a few methods are commonly used. Engine modifications, combustion optimisation, and exhaust system treatment components are among them. Fuel additives, such as zinc oxide, titanium oxide, aluminium oxide, and cerium oxide, are amongst the most effective methods to increase performance and reduce emissions. Even while positive performance and emission reduction outcomes have been demonstrated, there are worries concerning health toxicity effects. Carbon nanoparticles have been accepted as a fuel additive since they pose little risk to human health. A few studies have been undertaken to investigate the consequences of employing graphene nanoplatelets as fuel additives, thanks to advancements in graphene research. The findings of the study seemed encouraging. However, despite detecting the additive effects of graphene on performance, no more study has been undertaken to forecast the effects on engine performance. The objective of this study was to predict the effects of graphene nanoplatelets as an additive for diesel engines. The performance parameters of the trial were torque, power, BSFC, and BTE. Speed, load, and blend concentration are all considered in this model. Response surface methods and contour plotting with Minitab software were used to generate the prediction model. The results show that the prediction model is within 10% of the experimental data.
Co-Authors Mohd Adnin Hamidi Rizalman Mamat