Optimization Modeling and Empirical Research on Gasoline Octane Loss Based on Data Analysis
Gasoline is one of the most consumed light petroleum products in transportation and other industries. This paper proposes a method for optimizing gasoline octane loss using data analysis technology aimed at optimizing the production process and minimizing the loss of gasoline octane. Firstly, the da...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/5553069 |
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| _version_ | 1832546855809974272 |
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| author | Ji Guo Yujia Lou Wanyi Wang Xianhua Wu |
| author_facet | Ji Guo Yujia Lou Wanyi Wang Xianhua Wu |
| author_sort | Ji Guo |
| collection | DOAJ |
| description | Gasoline is one of the most consumed light petroleum products in transportation and other industries. This paper proposes a method for optimizing gasoline octane loss using data analysis technology aimed at optimizing the production process and minimizing the loss of gasoline octane. Firstly, the data are screened and the high-dimensional data are reduced to construct the neural network prediction model optimized by genetic algorithm. After utilizing the model for prediction, the optimal operating condition is achieved. Secondly, ensuring that the gasoline emission meets the standard, the octane loss is reduced by adjusting the operating variables. Thirdly, actual data are collected and calculated to obtain the main operating variables and their optimal operating conditions of a petrochemical company affecting the catalytic cracking gasoline S-Zorb unit, thus providing companies using S-Zorb units with reference data for optimizing gasoline catalytic cracking processes. Fourthly, the superiority of the proposed method was verified by comparing it with the other methods. This paper intends to contribute to better modeling the progress of gasoline catalytic cracking by adequately considering the impact of multiple factors, improving the quality of refined oil products of chemical enterprises, saving the economic cost of chemical enterprises, and protecting the atmospheric environment. |
| format | Article |
| id | doaj-art-1668dbca65394d529a073f4093d93b18 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-1668dbca65394d529a073f4093d93b182025-02-03T06:46:51ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/55530695553069Optimization Modeling and Empirical Research on Gasoline Octane Loss Based on Data AnalysisJi Guo0Yujia Lou1Wanyi Wang2Xianhua Wu3School of Economics and Management, Shanghai Maritime University, Shanghai 201306, ChinaSchool of Economics and Management, Shanghai Maritime University, Shanghai 201306, ChinaSchool of Economics and Management, Shanghai Maritime University, Shanghai 201306, ChinaSchool of Economics and Management, Shanghai Maritime University, Shanghai 201306, ChinaGasoline is one of the most consumed light petroleum products in transportation and other industries. This paper proposes a method for optimizing gasoline octane loss using data analysis technology aimed at optimizing the production process and minimizing the loss of gasoline octane. Firstly, the data are screened and the high-dimensional data are reduced to construct the neural network prediction model optimized by genetic algorithm. After utilizing the model for prediction, the optimal operating condition is achieved. Secondly, ensuring that the gasoline emission meets the standard, the octane loss is reduced by adjusting the operating variables. Thirdly, actual data are collected and calculated to obtain the main operating variables and their optimal operating conditions of a petrochemical company affecting the catalytic cracking gasoline S-Zorb unit, thus providing companies using S-Zorb units with reference data for optimizing gasoline catalytic cracking processes. Fourthly, the superiority of the proposed method was verified by comparing it with the other methods. This paper intends to contribute to better modeling the progress of gasoline catalytic cracking by adequately considering the impact of multiple factors, improving the quality of refined oil products of chemical enterprises, saving the economic cost of chemical enterprises, and protecting the atmospheric environment.http://dx.doi.org/10.1155/2021/5553069 |
| spellingShingle | Ji Guo Yujia Lou Wanyi Wang Xianhua Wu Optimization Modeling and Empirical Research on Gasoline Octane Loss Based on Data Analysis Journal of Advanced Transportation |
| title | Optimization Modeling and Empirical Research on Gasoline Octane Loss Based on Data Analysis |
| title_full | Optimization Modeling and Empirical Research on Gasoline Octane Loss Based on Data Analysis |
| title_fullStr | Optimization Modeling and Empirical Research on Gasoline Octane Loss Based on Data Analysis |
| title_full_unstemmed | Optimization Modeling and Empirical Research on Gasoline Octane Loss Based on Data Analysis |
| title_short | Optimization Modeling and Empirical Research on Gasoline Octane Loss Based on Data Analysis |
| title_sort | optimization modeling and empirical research on gasoline octane loss based on data analysis |
| url | http://dx.doi.org/10.1155/2021/5553069 |
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