%0 Book
%T CO2 Fischer-Tropsch synthesis: unleashing the power of data science and machine learning for sustainable hydrocarbon production
%A Fedorov, Aleksandr
%D 2024
%C Rostock
%C Universität Rostock
%G English
%F 1942728379
%O vorgelegt von Aleksandr Fedorov
%O Enthält Zeitschriftenartikel
%O GutachterInnen: David Linke (Leibniz-Institut für Katalyse e. V.) ; Evgeny Pidko (Delft University of Technology)
%O Dissertation Universität Rostock 2024 Kumulative Dissertation
%X 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>
%L 004
%9 theses
%9 Text
%9 Hochschulschrift
%R 10.18453/rosdok_id00005013
%U https://purl.uni-rostock.de/rosdok/id00005013
%U https://nbn-resolving.org/urn:nbn:de:gbv:28-rosdok_id00005013-7
%U https://d-nb.info/1383790965/34
%U https://doi.org/10.18453/rosdok_id00005013