TY  - BOOK
AU  - Fedorov, Aleksandr
PY  - 2024
DA  - 2024//
TI  - CO2 Fischer-Tropsch synthesis: unleashing the power of data science and machine learning for sustainable hydrocarbon production
PB  - Universität Rostock
CY  - Rostock
AB  - 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.<eng>
UR  - https://purl.uni-rostock.de/rosdok/id00005013
UR  - https://nbn-resolving.org/urn:nbn:de:gbv:28-rosdok_id00005013-7
UR  - https://d-nb.info/1383790965/34
UR  - https://doi.org/10.18453/rosdok_id00005013
DO  - 10.18453/rosdok_id00005013
LA  - English
N1  - vorgelegt von Aleksandr Fedorov
ID  - 1942728379
ER  -