Harnessing the Influence of Pressure and Nutrients on Biological CO<sub>2</sub> Methanation Using Response Surface Methodology and Artificial Neural Network—Genetic Algorithm Approaches

Harnessing the Influence of Pressure and Nutrients on Biological CO<sub>2</sub> Methanation Using Response Surface Methodology and Artificial Neural Network—Genetic Algorithm Approaches

The biological methanation process has emerged as a promising alternative to thermo-catalytic methods due to its ability to operate under milder conditions. However, challenges such as low hydrogen solubility and the need for precise trace element supplementation (Fe(II), Ni(II), Co(II)) constrain m...

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Bibliographic Details
Main Authors: Alexandros Chatzis, Konstantinos N. Kontogiannopoulos, Nikolaos Dimitrakakis, Anastasios Zouboulis, Panagiotis G. Kougias
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Fermentation
Subjects:
biomethanation
trace elements
CO<sub>2</sub> utilization
optimization
machine learning
Online Access:https://www.mdpi.com/2311-5637/11/1/43
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