Estimation of Speciation Data for Hydrocarbons using Data Science
- 提供方法
- 版元よりダウンロードリンクを連絡
- 形態
- 価格
- 一般価格(税込)：￥6,600 会員価格(税込)：￥5,280
- 文献・情報種別
- SAE Paper
No.2021-24-0081
- 掲載ページ
- 1-8(Total 8 p)
- 発行年月
- 2021年 9月
- 出版社
- SAE International
- 言語
- 英語
- イベント
- International Conference on Engines and Vehicles 2021
書誌事項
| 著者（英） | 1) Kiran Yalamanchi, 2) Bingjie chen, 3) Rooppesh Sarankapani, 4) Mani Sarathy |
|---|---|
| 勤務先（英） | 1) Kaust, 2) 0, 3) 0, 4) KING ABUDLLAH UNIVERSITY OF SCIENCE & TE |
| 抄録（英） | Strict regulations on air pollution motivates clean combustion research for fossil fuels. To numerically mimic real gasoline fuel reactivity, surrogates are proposed to facilitate advanced engine design and predict emissions by chemical kinetic modelling. However, chemical kinetic models could not accurately predict non-regular emissions, e.g. aldehydes, ketones and unsaturated hydrocarbons, which are important air pollutants. In this work, we propose to use machine-learning algorithms to achieve better predictions. Combustion chemistry of fuels constituting of 10 neat fuels, 6 primary reference fuels (PRF) and 6 FGX surrogates were tested in a jet stirred reactor. Experimental data were collected in the same setup to maintain data uniformity and consistency under following conditions: residence time at 1.0 second, fuel concentration at 0.25%, equivalence ratio at 1.0, and temperature range from 750 to 1100K. Measured species profiles of methane, ethylene, propylene, hydrogen, carbon monoxide and carbon dioxide are used for machine-learning model development. The model considers both chemical effects and physical conditions. Chemical effects are described as different functional groups, viz. primary, secondary, tertiary, and quaternary carbons in molecular structures, and physical conditions as temperature. Both the Machine-learning models used in this study showed a good prediction accuracy with a test set regression score of 97.75 for support vector regression and 91.07 for random forest regression. This finding shows the great potential of machine learning application on combustion chemistry. By expanding the experimental database, machine-learning models can be further applied to many other hydrocarbons in future work. 翻訳 |
