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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
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| Series: | Chemical Engineering Journal Advances |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666821124001194 |
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| Summary: | 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. |
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| ISSN: | 2666-8211 |