期刊
APPLIED SOFT COMPUTING
卷 113, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.asoc.2021.107989
关键词
RON prediction; Gasoline; Stacking method; Heterogeneous ensemble; Feature selection; Differential evolution
资金
- National Natural Science Foundation of China [71533001, 71874020]
- National Key Research and Development Program of China [2019YFB1404702]
- China Scholarship Council [202006060162]
The study proposes a method for predicting RON loss in gasoline refining process, including feature selection and stacking heterogeneous ensemble model. Experimental results show that the method is more accurate than other machine learning methods and can promote the development of the gasoline refining industry.
Gasoline is the main fuel for small vehicles, and the exhaust emissions from its combustion have a major impact on the atmospheric environment. In the cumbersome process of gasoline refining, the aim is to reduce the sulfur and olefin contents within the raw material while maintaining its octane number as much as possible. In general, the research octane number (RON) loss is measured by using an instrument in the laboratory, which is time-consuming and expensive. Therefore, the use of algorithms to build RON loss prediction models has become a hot topic. Considering that machine learning has a good ability in fitting the non-linear complex data, we propose a stacking based heterogeneous ensemble method for RON prediction. First, we propose a fusion algorithm of sequence forward search (SFS) and feature importance score to reduce the dimension of data set. Later, the data after feature selection will be used to construct a two-stage stacking heterogeneous ensemble learning model. Finally, the differential evolution (DE) algorithm is used to optimize multiple sensitive parameters involved in the model. Experiments with data obtained in the actual gasoline refining process show that the proposed method can accurately predict the RON loss in the product. Compared to the popular machine learning methods such as support vector machines, random forests, and XGBoost, the proposed method achieves the smallest mean square error. Furthermore, we analyze the important features that affect the RON loss to promote the development of the gasoline refining industry. (C) 2021 Elsevier B.V. All rights reserved.
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