Design of the Ethylbenzene production process using machine learning
Ethylbenzene (EB) is a raw material used to produce the styrene monomer, and the EB market size has been increasing. Recently, EB has been produced by a production process using zeolite catalysts. However, the fuel consumption in this process is an issue because the energy load in the separation pro...
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Elsevier
2025-06-01
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| Series: | Case Studies in Chemical and Environmental Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666016425000647 |
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| author | Eri Ishikawa Hiromasa Kaneko |
| author_facet | Eri Ishikawa Hiromasa Kaneko |
| author_sort | Eri Ishikawa |
| collection | DOAJ |
| description | Ethylbenzene (EB) is a raw material used to produce the styrene monomer, and the EB market size has been increasing. Recently, EB has been produced by a production process using zeolite catalysts. However, the fuel consumption in this process is an issue because the energy load in the separation process is high. The objective of this study was to design an EB production process with low energy consumption per unit production. In the proposed method, the process structure is first optimized, followed by optimization of the design variables. In the conventional process design, the design variables are optimized by calculations based on knowledge of the chemical engineering and repeated simulations, but it is labor and time intensive. The proposed method uses machine learning to efficiently optimize the design variables. By using machine learning, we achieved the design of a process with a low fuel energy intensity while satisfying multiple reaction conditions. |
| format | Article |
| id | doaj-art-83afa249e8134f8dbd9d847eb9d4c739 |
| institution | OA Journals |
| issn | 2666-0164 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Chemical and Environmental Engineering |
| spelling | doaj-art-83afa249e8134f8dbd9d847eb9d4c7392025-08-20T02:04:11ZengElsevierCase Studies in Chemical and Environmental Engineering2666-01642025-06-011110115710.1016/j.cscee.2025.101157Design of the Ethylbenzene production process using machine learningEri Ishikawa0Hiromasa Kaneko1Department of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa, 214-8571, JapanCorresponding author.; Department of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa, 214-8571, JapanEthylbenzene (EB) is a raw material used to produce the styrene monomer, and the EB market size has been increasing. Recently, EB has been produced by a production process using zeolite catalysts. However, the fuel consumption in this process is an issue because the energy load in the separation process is high. The objective of this study was to design an EB production process with low energy consumption per unit production. In the proposed method, the process structure is first optimized, followed by optimization of the design variables. In the conventional process design, the design variables are optimized by calculations based on knowledge of the chemical engineering and repeated simulations, but it is labor and time intensive. The proposed method uses machine learning to efficiently optimize the design variables. By using machine learning, we achieved the design of a process with a low fuel energy intensity while satisfying multiple reaction conditions.http://www.sciencedirect.com/science/article/pii/S2666016425000647Process designEthylbenzeneSuperstructureMachine learningProcess informaticsDesign of experiments |
| spellingShingle | Eri Ishikawa Hiromasa Kaneko Design of the Ethylbenzene production process using machine learning Case Studies in Chemical and Environmental Engineering Process design Ethylbenzene Superstructure Machine learning Process informatics Design of experiments |
| title | Design of the Ethylbenzene production process using machine learning |
| title_full | Design of the Ethylbenzene production process using machine learning |
| title_fullStr | Design of the Ethylbenzene production process using machine learning |
| title_full_unstemmed | Design of the Ethylbenzene production process using machine learning |
| title_short | Design of the Ethylbenzene production process using machine learning |
| title_sort | design of the ethylbenzene production process using machine learning |
| topic | Process design Ethylbenzene Superstructure Machine learning Process informatics Design of experiments |
| url | http://www.sciencedirect.com/science/article/pii/S2666016425000647 |
| work_keys_str_mv | AT eriishikawa designoftheethylbenzeneproductionprocessusingmachinelearning AT hiromasakaneko designoftheethylbenzeneproductionprocessusingmachinelearning |