@Book{1942728379,
author="Fedorov, Aleksandr",
title="CO2 Fischer-Tropsch synthesis: unleashing the power of data science and machine learning for sustainable hydrocarbon production",
year="2024",
address="Rostock",
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.<eng>",
school="Universit{\"a}t Rostock",
note="vorgelegt von Aleksandr Fedorov",
note="Enth{\"a}lt Zeitschriftenartikel",
note="GutachterInnen: David Linke (Leibniz-Institut f{\"u}r Katalyse e. V.) ; Evgeny Pidko (Delft University of Technology)",
note="Dissertation Universit{\"a}t Rostock 2024 Kumulative Dissertation",
doi="10.18453/rosdok_id00005013",
url="https://purl.uni-rostock.de/rosdok/id00005013",
url="https://nbn-resolving.org/urn:nbn:de:gbv:28-rosdok_id00005013-7",
url="https://d-nb.info/1383790965/34",
url="https://doi.org/10.18453/rosdok_id00005013",
language="English"
}