The Optimization Model for Reducing RON Loss in Gasoline Refining Process

As gasoline is the main fuel of small vehicles, the exhaust emissions from its combustion will affect air quality. The focus of gasoline cleaning is to reduce the sulfur and olefin content in gasoline while maintaining its RON as much as possible. The reduction of RON will bring great economic losse...

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Main Authors: Xuefei Lu, Xiaoyan Wang, Yifang Yang, Jianan Xue
Format: Article
Language:English
Published: Wiley 2021-01-01
Series:Geofluids
Online Access:http://dx.doi.org/10.1155/2021/5520942
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author Xuefei Lu
Xiaoyan Wang
Yifang Yang
Jianan Xue
author_facet Xuefei Lu
Xiaoyan Wang
Yifang Yang
Jianan Xue
author_sort Xuefei Lu
collection DOAJ
description As gasoline is the main fuel of small vehicles, the exhaust emissions from its combustion will affect air quality. The focus of gasoline cleaning is to reduce the sulfur and olefin content in gasoline while maintaining its RON as much as possible. The reduction of RON will bring great economic losses to enterprises. Therefore, it is very important for petrochemical enterprises to construct a RON loss model in the gasoline refining process. The model construction, which reduces RON loss during gasoline refining, is the main question in this paper. By Python and SPSS software, we got two variable filtering methods: the random forest importance filtering and PCA filtering, and combined with SVR and random forest models, RON of the product and sulfur content were predicted. The filtering order of the original data by Excel and Python is maximum and minimum removal, 3σ criterion removal, deletion of too many sites in incomplete data, and filling of empty values in the mean within two hours. Several RON prediction models were established with the help of Python software, and the variables selected were compared by two filtering methods: one is the SVR model based on Gaussian, linear, polynomial, and Sigmoid kernel functions; the other is the random forest model. The sulfur content and RON prediction model was constructed, which use evaluation functions such as MSE, R2, and RMSE to evaluate and sulfur content as the subject condition. We convert the problem into linear and nonlinear model variable optimization problems: the linear model is the variable selected by the SVR linear kernel function model and random forest; the nonlinear model is the combination of variables selected by the random forest model and random forest. Optimizing for each sample, the optimization method is to find the optimal solution for each variable and use the optimal method for each variable as the local optimal solution for the sample. The two models are evaluated from the perspectives of optimization degree, optimization rate, model running speed, etc.
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spelling doaj-art-e038afc91b7e4b9098fe5f03e4fdd91f2025-02-03T00:58:50ZengWileyGeofluids1468-81151468-81232021-01-01202110.1155/2021/55209425520942The Optimization Model for Reducing RON Loss in Gasoline Refining ProcessXuefei Lu0Xiaoyan Wang1Yifang Yang2Jianan Xue3College of Sciences, Xi’an Shiyou University, Xi’an, Shaanxi 710065, ChinaCollege of Sciences, Xi’an Shiyou University, Xi’an, Shaanxi 710065, ChinaCollege of Sciences, Xi’an Shiyou University, Xi’an, Shaanxi 710065, ChinaCollege of Mechanical Engineering, Xi’an Shiyou University, Xi’an, Shaanxi 710065, ChinaAs gasoline is the main fuel of small vehicles, the exhaust emissions from its combustion will affect air quality. The focus of gasoline cleaning is to reduce the sulfur and olefin content in gasoline while maintaining its RON as much as possible. The reduction of RON will bring great economic losses to enterprises. Therefore, it is very important for petrochemical enterprises to construct a RON loss model in the gasoline refining process. The model construction, which reduces RON loss during gasoline refining, is the main question in this paper. By Python and SPSS software, we got two variable filtering methods: the random forest importance filtering and PCA filtering, and combined with SVR and random forest models, RON of the product and sulfur content were predicted. The filtering order of the original data by Excel and Python is maximum and minimum removal, 3σ criterion removal, deletion of too many sites in incomplete data, and filling of empty values in the mean within two hours. Several RON prediction models were established with the help of Python software, and the variables selected were compared by two filtering methods: one is the SVR model based on Gaussian, linear, polynomial, and Sigmoid kernel functions; the other is the random forest model. The sulfur content and RON prediction model was constructed, which use evaluation functions such as MSE, R2, and RMSE to evaluate and sulfur content as the subject condition. We convert the problem into linear and nonlinear model variable optimization problems: the linear model is the variable selected by the SVR linear kernel function model and random forest; the nonlinear model is the combination of variables selected by the random forest model and random forest. Optimizing for each sample, the optimization method is to find the optimal solution for each variable and use the optimal method for each variable as the local optimal solution for the sample. The two models are evaluated from the perspectives of optimization degree, optimization rate, model running speed, etc.http://dx.doi.org/10.1155/2021/5520942
spellingShingle Xuefei Lu
Xiaoyan Wang
Yifang Yang
Jianan Xue
The Optimization Model for Reducing RON Loss in Gasoline Refining Process
Geofluids
title The Optimization Model for Reducing RON Loss in Gasoline Refining Process
title_full The Optimization Model for Reducing RON Loss in Gasoline Refining Process
title_fullStr The Optimization Model for Reducing RON Loss in Gasoline Refining Process
title_full_unstemmed The Optimization Model for Reducing RON Loss in Gasoline Refining Process
title_short The Optimization Model for Reducing RON Loss in Gasoline Refining Process
title_sort optimization model for reducing ron loss in gasoline refining process
url http://dx.doi.org/10.1155/2021/5520942
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