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Aleksandr Fedorov

CO2 Fischer-Tropsch synthesis : unleashing the power of data science and machine learning for sustainable hydrocarbon production

Universität Rostock, 2024

https://doi.org/10.18453/rosdok_id00005013

Abstract: The present work focuses on applying modern data science and machine learning (ML) methods to investigate CO2 hydrogenation to higher hydrocarbons, also known CO2-Fischer-Tropsch synthesis (CO2-FTS). These methods were used for literature analysis on CO2-FT catalysts and for developing kinetic models with neural networks. New data normalization approaches and improved ML models, incorporating chemical and chemical engineering knowledge, were developed to handle limited and small data.

doctoral thesis   free access