4.7 Article

Two-stage stacking heterogeneous ensemble learning method for gasoline octane number loss prediction

Journal

APPLIED SOFT COMPUTING
Volume 113, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.asoc.2021.107989

Keywords

RON prediction; Gasoline; Stacking method; Heterogeneous ensemble; Feature selection; Differential evolution

Funding

  1. National Natural Science Foundation of China [71533001, 71874020]
  2. National Key Research and Development Program of China [2019YFB1404702]
  3. China Scholarship Council [202006060162]

Ask authors/readers for more resources

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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available