Prediction of research octane number loss and sulfur content in gasoline refining using machine learning
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DOI: 10.1016/j.energy.2022.124823
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- Wang, Guoyang & Awad, Omar I. & Liu, Shiyu & Shuai, Shijin & Wang, Zhiming, 2020. "NOx emissions prediction based on mutual information and back propagation neural network using correlation quantitative analysis," Energy, Elsevier, vol. 198(C).
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- Chen, Yong & Zheng, Zunqing & Lu, Zhiyuan & Wang, Hu & Wang, Changhui & Sun, Xingyu & Xu, Linxun & Yao, Mingfa, 2024. "Machine learning-based screening of fuel properties for SI and CI engines using a hybrid group extraction method," Applied Energy, Elsevier, vol. 366(C).
- Jian Chen & Jiajun Zhu & Xu Qin & Wenxiang Xie, 2023. "Reducing Octane Number Loss in Gasoline Refining Process by Using the Improved Sparrow Search Algorithm," Sustainability, MDPI, vol. 15(8), pages 1-21, April.
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Keywords
Research octane number (RON); Sulfur content (SC); Machine learning (ML); Maximal information coefficient (MIC); Back propagation neural network (BPNN); Dragonfly algorithm (DA);All these keywords.
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