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A Prediction Model of RON Loss Based on Neural Network
Technical Paper
2022-01-0162
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
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%.
Authors
Citation
Zhang, S., Yang, G., Li, S., and Li, W., "A Prediction Model of RON Loss Based on Neural Network," SAE Technical Paper 2022-01-0162, 2022, https://doi.org/10.4271/2022-01-0162.Also In
References
- Wang , J. , Shijin , S. , Shen , Y. et al. Interpretation and Analysis of the Fourth Edition of the ‘World Fuel Code’ The Inaugural Conference of the Oil and Clean Fuel Branch of the Chinese Internal Combustion Engine Society and the Proceedings of the First Academic Annual Conference 2007 24 31
- Zhou , H. , Qi , W. , and Li , H. Analysis and Measure of S Zorb Unit Restricting Octane Number Loss Yunnan Chemical Technology 46 9 2019 90 91
- Liu , Y. and Li , J. Analysis of Factors Affecting Octane Number Loss of Gasoline in S Zorb Unit Petrochemical Design 36 4 2019 12 15
- Kong , Q. , Ye , C. , and Sun , Y. Research on Data Preprocessing Methods for Big Data Computer Technology and Development 28 5 2018 1 4
- Yang , M. , Meng , L. , Li , D. , Su , X. et al. Identification of Abnormal Data of Photovoltaic Power Based on Class 3σ Renewable Energy Resources 36 10 2018 1443 1448
- Wang , H. , Huang , P. , and Chen , X. Research and Application of a Multidimensional Association Rules Mining Method Based on OLAP International Journal of Information Technology and Web Engineering (IJITWE) 16 2021 1
- Zhang , J. , Liu , M. , Xiong , P. , Du , H. et al. A Multi-Dimensional Association Information Analysis Approach to Automated Detection and Localization of Myocardial Infarction Engineering Applications of Artificial Intelligence 97 2021 104092
- Yang , Q. , Zhang , Y. , Zhang , Q. , and Yuan , P. Research and Application of a Multidimensional Association Rules Mining Algorithm Based on Hadoop Computer Engineering & Science 41 12 2019 2127 2133
- Huang , K. Sequence Length Calculation about Grey Forecasting Model Based on Recursive Algorithm Journal of Systems Science and Mathematical Sciences 35 11 2015 1347 1357
- Zhang , L. , Huang , Z. , Liu , W. , Guo , Z. et al. Weather Radar Echo Prediction Method Based on Convolution Neural Network and Long Short-Term Memory Networks for Sustainable e-Agriculture Journal of Cleaner Production 298 2021
- Liu , Y. , Yuan , D. , Li , H. , and Gao , X. BP Neural Network System for Yield Analysis of FCC Unit Based on Open Sources Language Petroleum Processing and Petrochemicals 52 3 2021 87 92
- Zhou , D. , Guo , C. , Liu , R. et al. Hierarchical Learning Recurrent Neural Networks for 3D Motion Synthesis Int. J. Mach. Learn. Cyber. 12 2021 2255 2267 https://doi.org/10.1007/s13042-021-01304-w
- Zhou , Z. Machine Learing Beijing Tsinghua University Press 2016
- Lecun , Y. , Bottou , L. , Bengio , Y. , and Haffner , P. Gradient-Based Learning Applied to Document Recognition Proceedings of the IEEE 86 11 1998 2278 2324
- Xu , X. The Development and Status of Artificial Neural Network Microelectronics 47 2 2017 239 242
- Glorot , X. , Bordes , A. , and Bengio , Y. Deep Sparse Rectifier Neural Networks Journal of Machine Learning Research 15 2011 315 323
- Zhang , L. , Huang , Z. , Liu , W. , Guo , Z. et al. Weather Radar Echo Prediction Method Based on Convolution Neural Network and Long Short-Term Memory Networks for Sustainable e-Agriculture Journal of Cleaner Production 298 2021
- Lan , J. , Wang , D. , and Shen , X. Research Progress on Visual Image Detection Based on Convolutional Neural Network Chinese Journal of Scientific Instrument 41 4 2020 167 182
- Meng , C. , Zeng , Z. , Zhu , Y. , Zhu , W. et al. Application of Improved Generative Adversarial Networks in Image Data Generation Computer Engineering and Applications 2021 1 11
- Zhang , Y. , Chen , C. , and Wang , Z. Research on Activation Function of Deep Learning Algorithm Radio Communications Technology 47 1 2021 115 120
- Qiu , X. Neural Networks and Deep Learning Beijing China Machine Press 2020