A Prediction Model of RON Loss Based on Neural Network
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- 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.2022-01-0162
- 掲載ページ
- 1-10(Total 10 p)
- 発行年月
- 2022年 3月
- 出版社
- SAE International
- 言語
- 英語
- イベント
- WCX SAE World Congress Experience 2022
書誌事項
| 著者（英） | 1) Sumin Zhang, 2) Gang Yang, 3) Shuai Li, 4) Wenju Li |
|---|---|
| 勤務先（英） | 1) Jilin University, 2) Jilin University, 3) Jilin University, 4) Jilin University |
| 抄録（英） | The RON(Research Octane Number) is the most important indicator of motor petrol, and the petrol refining process is one of the important links in petrol production. However, RON is often lost during petrol refining and RON Loss means the value of RON lost during petrol refining. The prediction of the RON loss of petrol during the refining process is helpful to the improvement of petrol refining process and the processing of petrol. The traditional RON prediction method relied on physical and chemical properties, and did not fully consider the high nonlinearity and strong coupling relationship of the petrol refining process. There is a lack of data-driven RON loss models. This paper studies the construction of the RON loss model in the petrol refining process. The main innovation is to use frequency statistics to summarize the operating range of data variables and preprocess the dataset, and use the variance selection method to filter out some invalid variables, then use the multi-dimensional association analysis to select 27 main operating variables in the next step. Finally, a neural network is used to establish an RON loss prediction model, and the prediction accuracy is 85.22%. 翻訳 |
