Overview
- 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
Part of the book series: SpringerBriefs in Energy (BRIEFSENERGY)
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Table of contents (5 chapters)
Keywords
About this book
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.
Authors and Affiliations
About the authors
Federico Moretta is a chemical engineering graduate from Politecnico di Milano. Now he is a Ph.D. student at the school of Chemical Industry and Chemical Engineering at Politecnico di Milano. His main project is about the characterization and optimization of anaerobic digestion processes. He has published various research articles in literature and presented research projects in congresses about mathematical modelling and data science of fermentative reactions and microorganism metabolism.
Giulia Bozzano is an Associate Professor of Chemical Engineering at the Department of Chemistry, Materials and Chemical Engineering Giulio Natta, Politecnico di Milano, where she obtained the M.Sc. degree and holds the course Fundamentals of Chemical Engineering for undergraduate students. She is the author of more than 120 scientific papers published across various peer-reviewed international journals and has about 2500 citations. She has been active in homogeneous and heterogeneous chemical reactors modelling, non-conventional equipment modelling (glass production furnaces, soap production plants, equipment for liquid-liquid extraction), complex kinetic schemes for gas and liquid pyrolysis (visbreaking, delayed coking, steam cracking, thermal degradation of plastics), fluid dynamics of diffusional turbulent flames and bubbles movement in liquids, characterization of complex hydrocarbon mixtures. Currently, her major interest is in anaerobic digestion modelling and optimization of biological processes for the production of sustainable energy, fuels, and biochemicals (i.e., methane, methanol, acetic acid). She supervises M.Sc. and Ph.D. thesis projects, coordinates research units in national projects with public funding, and leads industrial projects on technological advances and applied science in her research area.
Bibliographic Information
Book Title: Mathematical and Statistical Approaches for Anaerobic Digestion Feedstock Optimization
Authors: Federico Moretta, Giulia Bozzano
Series Title: SpringerBriefs in Energy
DOI: https://doi.org/10.1007/978-3-031-56460-4
Publisher: Springer Cham
eBook Packages: Energy, Energy (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024
Softcover ISBN: 978-3-031-56459-8Published: 11 April 2024
eBook ISBN: 978-3-031-56460-4Published: 10 April 2024
Series ISSN: 2191-5520
Series E-ISSN: 2191-5539
Edition Number: 1
Number of Pages: VIII, 69
Number of Illustrations: 9 b/w illustrations, 16 illustrations in colour
Topics: Energy Systems, Mathematical Modeling and Industrial Mathematics, Industrial Chemistry/Chemical Engineering, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences