Mathematical and Statistical Approaches for Anaerobic Digestion Feedstock Optimization
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Beschreibung
This book examines biomass mixture modeling and optimization.
The book discusses anaerobic digestion and related fermentative processes and explains their compositional dynamics. Early chapter examine macromolecules, elemental fractions, and their direct influence on methane production. Supported by an extensive data bank of substrates obtained from research, the book points out correlations that enable the estimation of global methane production for diverse biomass mixtures. Furthermore, it provides valuable insights into discerning the optimal composition capable of yielding the utmost methane output.
The book integrates cutting-edge machine learning techniques and shows how the programming language Python and Julia can be used for analysis and to optimize processes. It has many graphs, figures, and visuals.
This book examines biomass mixture modeling and optimization.
The book discusses anaerobic digestion and related fermentative processes and explains their compositional dynamics. Early chapter examine macromolecules, elemental fractions, and their direct influence on methane production. Supported by an extensive data bank of substrates obtained from research, the book points out correlations that enable the estimation of global methane production for diverse biomass mixtures. Furthermore, it provides valuable insights into discerning the optimal composition capable of yielding the utmost methane output.
The book integrates cutting-edge machine learning techniques and shows how the programming language Python and Julia can be used for analysis and to optimize processes. It has many graphs, figures, and visuals.
Details the modeling of biomass blending for methane yield optimization Uses machine learning techniques created with Python and Julia Is a contribution to improving renewable energy technology
Autor*in
Federico Moretta
Themen in »Mathematical and Statistical Approaches for Anaerobic Digestion Feedstock Optimization«
Biomass Characterization Anaerobic Digestion Machine Learning Database Management Mathematical Modeling Feedstock Characterization Data-driven Models Python Bio-Applications Process Optimization Biogas Production Maximization