Improving catalysts and operating conditions using machine learning in Fischer-Tropsch synthesis of jet fuels (C8-C16)
Fischer-Tropsch synthesis (FTS) offers a promising route for producing sustainable jet fuels from syngas. However, optimizing catalyst design and operating conditions for the ideal C8-C16 jet fuel range is challenging. Thus, this work introduces a machine learning (ML) framework to enhance Co/Fe-sup...
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| Language: | English |
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Elsevier
2025-03-01
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| Series: | Chemical Engineering Journal Advances |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666821124001194 |
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| author | Parisa Shafiee Bogdan Dorneanu Harvey Arellano-Garcia |
| author_facet | Parisa Shafiee Bogdan Dorneanu Harvey Arellano-Garcia |
| author_sort | Parisa Shafiee |
| collection | DOAJ |
| description | Fischer-Tropsch synthesis (FTS) offers a promising route for producing sustainable jet fuels from syngas. However, optimizing catalyst design and operating conditions for the ideal C8-C16 jet fuel range is challenging. Thus, this work introduces a machine learning (ML) framework to enhance Co/Fe-supported FTS catalysts and optimize their operating conditions for a better jet fuel selectivity. For this purpose, a dataset was implemented with 21 features, including catalyst structure, preparation method, activation procedure, and FTS operating parameters. Moreover, various machine-learning models (Random Forest (RF), Gradient Boosted, CatBoost, and artificial neural networks (ANN)) were evaluated to predict CO conversion and C8-C16 selectivity. Among these, the CatBoost model achieved the highest accuracy (R2 = 0.99). Feature analysis revealed that FTS operational conditions mainly affect CO conversion (37.9 %), while catalyst properties were primarily crucial for C8-C16 selectivity (40.6 %). The proposed ML framework provides a first powerful tool for the rational design of FTS catalysts and operating conditions to maximize jet fuel productivity. |
| format | Article |
| id | doaj-art-39894fe9204345e2b264641f6bce676d |
| institution | Kabale University |
| issn | 2666-8211 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Chemical Engineering Journal Advances |
| spelling | doaj-art-39894fe9204345e2b264641f6bce676d2025-02-03T04:17:03ZengElsevierChemical Engineering Journal Advances2666-82112025-03-0121100702Improving catalysts and operating conditions using machine learning in Fischer-Tropsch synthesis of jet fuels (C8-C16)Parisa Shafiee0Bogdan Dorneanu1Harvey Arellano-Garcia2Department of Process and Plant Technology, Brandenburg University of Technology (BTU) Cottbus-Senftenberg, GermanyDepartment of Process and Plant Technology, Brandenburg University of Technology (BTU) Cottbus-Senftenberg, GermanyCorresponding author.; Department of Process and Plant Technology, Brandenburg University of Technology (BTU) Cottbus-Senftenberg, GermanyFischer-Tropsch synthesis (FTS) offers a promising route for producing sustainable jet fuels from syngas. However, optimizing catalyst design and operating conditions for the ideal C8-C16 jet fuel range is challenging. Thus, this work introduces a machine learning (ML) framework to enhance Co/Fe-supported FTS catalysts and optimize their operating conditions for a better jet fuel selectivity. For this purpose, a dataset was implemented with 21 features, including catalyst structure, preparation method, activation procedure, and FTS operating parameters. Moreover, various machine-learning models (Random Forest (RF), Gradient Boosted, CatBoost, and artificial neural networks (ANN)) were evaluated to predict CO conversion and C8-C16 selectivity. Among these, the CatBoost model achieved the highest accuracy (R2 = 0.99). Feature analysis revealed that FTS operational conditions mainly affect CO conversion (37.9 %), while catalyst properties were primarily crucial for C8-C16 selectivity (40.6 %). The proposed ML framework provides a first powerful tool for the rational design of FTS catalysts and operating conditions to maximize jet fuel productivity.http://www.sciencedirect.com/science/article/pii/S2666821124001194Jet fuels (C8-C16)Machine learning (ML)Fischer-Tropsch synthesis (FTS)Operational conditionsCatalyst preparation |
| spellingShingle | Parisa Shafiee Bogdan Dorneanu Harvey Arellano-Garcia Improving catalysts and operating conditions using machine learning in Fischer-Tropsch synthesis of jet fuels (C8-C16) Chemical Engineering Journal Advances Jet fuels (C8-C16) Machine learning (ML) Fischer-Tropsch synthesis (FTS) Operational conditions Catalyst preparation |
| title | Improving catalysts and operating conditions using machine learning in Fischer-Tropsch synthesis of jet fuels (C8-C16) |
| title_full | Improving catalysts and operating conditions using machine learning in Fischer-Tropsch synthesis of jet fuels (C8-C16) |
| title_fullStr | Improving catalysts and operating conditions using machine learning in Fischer-Tropsch synthesis of jet fuels (C8-C16) |
| title_full_unstemmed | Improving catalysts and operating conditions using machine learning in Fischer-Tropsch synthesis of jet fuels (C8-C16) |
| title_short | Improving catalysts and operating conditions using machine learning in Fischer-Tropsch synthesis of jet fuels (C8-C16) |
| title_sort | improving catalysts and operating conditions using machine learning in fischer tropsch synthesis of jet fuels c8 c16 |
| topic | Jet fuels (C8-C16) Machine learning (ML) Fischer-Tropsch synthesis (FTS) Operational conditions Catalyst preparation |
| url | http://www.sciencedirect.com/science/article/pii/S2666821124001194 |
| work_keys_str_mv | AT parisashafiee improvingcatalystsandoperatingconditionsusingmachinelearninginfischertropschsynthesisofjetfuelsc8c16 AT bogdandorneanu improvingcatalystsandoperatingconditionsusingmachinelearninginfischertropschsynthesisofjetfuelsc8c16 AT harveyarellanogarcia improvingcatalystsandoperatingconditionsusingmachinelearninginfischertropschsynthesisofjetfuelsc8c16 |