Response Surface Methodology to Predict the Effects of Graphene Nanoplatelets Addition to Diesel Engine Performance
There are a few techniques regularly applied to reduce diesel exhaust emissions. These include engine modification, combustion refinement, and use of treatment components in the exhaust system. Using fuel additive is one of the useful methods to improve performance and reduce emissions. Various nano...
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2021
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/33163/ http://umpir.ump.edu.my/id/eprint/33163/1/ICAFEV%202021%20Effects%20GNP%20to%20performance%20%5BClean%5D-Submit%20Rev1.docx |
| _version_ | 1848824189917593600 |
|---|---|
| author | Sarbani, Daud Mohd Adnin, Hamidi R., Mamat |
| author_facet | Sarbani, Daud Mohd Adnin, Hamidi R., Mamat |
| author_sort | Sarbani, Daud |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | There are a few techniques regularly applied to reduce diesel exhaust emissions. These include engine modification, combustion refinement, and use of treatment components in the exhaust system. Using fuel additive is one of the useful methods to improve performance and reduce emissions. Various nanoparticles such as zinc oxide, titanium oxide, aluminum oxide, and cerium oxide. Even though positive results are shown on performance improvement and emission reduction, there are concerns on the toxicity effects to the health. The introduction of carbon nanomaterials as a fuel additive has been very promising because it causes little health concern. With the advancement in graphene research, a few studies have been conducted to explore the effects of using graphene nanoplatelets as fuel additives. The research has shown positive results. However, the knowledge in this field can be explored deeper. This research was conducted to predict the effects of graphene nanoplatelets addition to diesel engine performance. The trial used torque, power, BSFC, and BTE as performance parameters. The factors considered in this model are speed, load, and blend concentration. The prediction model was developed using response surface methodology and contour plot with the help of Minitab software. The result shows that the prediction model by the RSM method agrees with the experimental data with the accuracy of ±10%. |
| first_indexed | 2025-11-15T03:09:05Z |
| format | Conference or Workshop Item |
| id | ump-33163 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:09:05Z |
| publishDate | 2021 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-331632022-01-19T03:19:08Z http://umpir.ump.edu.my/id/eprint/33163/ Response Surface Methodology to Predict the Effects of Graphene Nanoplatelets Addition to Diesel Engine Performance Sarbani, Daud Mohd Adnin, Hamidi R., Mamat TJ Mechanical engineering and machinery There are a few techniques regularly applied to reduce diesel exhaust emissions. These include engine modification, combustion refinement, and use of treatment components in the exhaust system. Using fuel additive is one of the useful methods to improve performance and reduce emissions. Various nanoparticles such as zinc oxide, titanium oxide, aluminum oxide, and cerium oxide. Even though positive results are shown on performance improvement and emission reduction, there are concerns on the toxicity effects to the health. The introduction of carbon nanomaterials as a fuel additive has been very promising because it causes little health concern. With the advancement in graphene research, a few studies have been conducted to explore the effects of using graphene nanoplatelets as fuel additives. The research has shown positive results. However, the knowledge in this field can be explored deeper. This research was conducted to predict the effects of graphene nanoplatelets addition to diesel engine performance. The trial used torque, power, BSFC, and BTE as performance parameters. The factors considered in this model are speed, load, and blend concentration. The prediction model was developed using response surface methodology and contour plot with the help of Minitab software. The result shows that the prediction model by the RSM method agrees with the experimental data with the accuracy of ±10%. 2021-12-10 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/33163/1/ICAFEV%202021%20Effects%20GNP%20to%20performance%20%5BClean%5D-Submit%20Rev1.docx Sarbani, Daud and Mohd Adnin, Hamidi and R., Mamat (2021) Response Surface Methodology to Predict the Effects of Graphene Nanoplatelets Addition to Diesel Engine Performance. In: International Conference on Alternative Fuels and Electric Vehicles 2021 , 9-10 December 2021 , Pune, India (Virtual). . (Published) |
| spellingShingle | TJ Mechanical engineering and machinery Sarbani, Daud Mohd Adnin, Hamidi R., Mamat Response Surface Methodology to Predict the Effects of Graphene Nanoplatelets Addition to Diesel Engine Performance |
| title | Response Surface Methodology to Predict the Effects of Graphene Nanoplatelets Addition to Diesel Engine Performance |
| title_full | Response Surface Methodology to Predict the Effects of Graphene Nanoplatelets Addition to Diesel Engine Performance |
| title_fullStr | Response Surface Methodology to Predict the Effects of Graphene Nanoplatelets Addition to Diesel Engine Performance |
| title_full_unstemmed | Response Surface Methodology to Predict the Effects of Graphene Nanoplatelets Addition to Diesel Engine Performance |
| title_short | Response Surface Methodology to Predict the Effects of Graphene Nanoplatelets Addition to Diesel Engine Performance |
| title_sort | response surface methodology to predict the effects of graphene nanoplatelets addition to diesel engine performance |
| topic | TJ Mechanical engineering and machinery |
| url | http://umpir.ump.edu.my/id/eprint/33163/ http://umpir.ump.edu.my/id/eprint/33163/1/ICAFEV%202021%20Effects%20GNP%20to%20performance%20%5BClean%5D-Submit%20Rev1.docx |