Artificial Intelligence Methods Applied to Catalytic Cracking Processes
Fluidic Catalytic Cracking (FCC) is a complex petrochemical process affected by many highly non-linear and interrelated factors. Product yield analysis, flue gas desulfurization prediction, and abnormal condition warning are several key research directions in FCC. This paper will sort out the releva...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2023-09-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2023.9020002 |
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| _version_ | 1832570792740651008 |
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| author | Fan Yang Mao Xu Wenqiang Lei Jiancheng Lv |
| author_facet | Fan Yang Mao Xu Wenqiang Lei Jiancheng Lv |
| author_sort | Fan Yang |
| collection | DOAJ |
| description | Fluidic Catalytic Cracking (FCC) is a complex petrochemical process affected by many highly non-linear and interrelated factors. Product yield analysis, flue gas desulfurization prediction, and abnormal condition warning are several key research directions in FCC. This paper will sort out the relevant research results of the existing Artificial Intelligence (AI) algorithms applied to the analysis and optimization of catalytic cracking processes, with a view to providing help for the follow-up research. Compared with the traditional mathematical mechanism method, the AI method can effectively solve the difficulties in FCC process modeling, such as high-dimensional, nonlinear, strong correlation, and large delay. AI methods applied in product yield analysis build models based on massive data. By fitting the functional relationship between operating variables and products, the excessive simplification of mechanism model can be avoided, resulting in high model accuracy. AI methods applied in flue gas desulfurization can be usually divided into two stages: modeling and optimization. In the modeling stage, data-driven methods are often used to build the system model or rule base; In the optimization stage, heuristic search or reinforcement learning methods can be applied to find the optimal operating parameters based on the constructed model or rule base. AI methods, including data-driven and knowledge-driven algorithms, are widely used in the abnormal condition warning. Knowledge-driven methods have advantages in interpretability and generalization, but disadvantages in construction difficulty and prediction recall. While the data-driven methods are just the opposite. Thus, some studies combine these two methods to obtain better results. |
| format | Article |
| id | doaj-art-a8af2b2525fb4cb28e2cef34845c038a |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2023-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-a8af2b2525fb4cb28e2cef34845c038a2025-02-02T14:10:40ZengTsinghua University PressBig Data Mining and Analytics2096-06542023-09-016336138010.26599/BDMA.2023.9020002Artificial Intelligence Methods Applied to Catalytic Cracking ProcessesFan Yang0Mao Xu1Wenqiang Lei2Jiancheng Lv3College of Computer Science, Sichuan University, Chengdu 610041, China, and also with the Algorithm and Big Data Center, New Hope Liuhe Co Ltd, Chengdu 610000, China.Data Intelligence Lab, New Hope Liuhe Co Ltd, Chengdu 610000, China.College of Computer Science, Sichuan University, Chengdu 610041, China.College of Computer Science, Sichuan University, Chengdu 610041, China.Fluidic Catalytic Cracking (FCC) is a complex petrochemical process affected by many highly non-linear and interrelated factors. Product yield analysis, flue gas desulfurization prediction, and abnormal condition warning are several key research directions in FCC. This paper will sort out the relevant research results of the existing Artificial Intelligence (AI) algorithms applied to the analysis and optimization of catalytic cracking processes, with a view to providing help for the follow-up research. Compared with the traditional mathematical mechanism method, the AI method can effectively solve the difficulties in FCC process modeling, such as high-dimensional, nonlinear, strong correlation, and large delay. AI methods applied in product yield analysis build models based on massive data. By fitting the functional relationship between operating variables and products, the excessive simplification of mechanism model can be avoided, resulting in high model accuracy. AI methods applied in flue gas desulfurization can be usually divided into two stages: modeling and optimization. In the modeling stage, data-driven methods are often used to build the system model or rule base; In the optimization stage, heuristic search or reinforcement learning methods can be applied to find the optimal operating parameters based on the constructed model or rule base. AI methods, including data-driven and knowledge-driven algorithms, are widely used in the abnormal condition warning. Knowledge-driven methods have advantages in interpretability and generalization, but disadvantages in construction difficulty and prediction recall. While the data-driven methods are just the opposite. Thus, some studies combine these two methods to obtain better results.https://www.sciopen.com/article/10.26599/BDMA.2023.9020002intelligent optimization algorithmneural networkscatalytic crackinglumped kinetics |
| spellingShingle | Fan Yang Mao Xu Wenqiang Lei Jiancheng Lv Artificial Intelligence Methods Applied to Catalytic Cracking Processes Big Data Mining and Analytics intelligent optimization algorithm neural networks catalytic cracking lumped kinetics |
| title | Artificial Intelligence Methods Applied to Catalytic Cracking Processes |
| title_full | Artificial Intelligence Methods Applied to Catalytic Cracking Processes |
| title_fullStr | Artificial Intelligence Methods Applied to Catalytic Cracking Processes |
| title_full_unstemmed | Artificial Intelligence Methods Applied to Catalytic Cracking Processes |
| title_short | Artificial Intelligence Methods Applied to Catalytic Cracking Processes |
| title_sort | artificial intelligence methods applied to catalytic cracking processes |
| topic | intelligent optimization algorithm neural networks catalytic cracking lumped kinetics |
| url | https://www.sciopen.com/article/10.26599/BDMA.2023.9020002 |
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