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Mathematical and Statistical Approaches for Anaerobic Digestion Feedstock Optimization: SpringerBriefs in Energy

Autor Federico Moretta, Giulia Bozzano
en Limba Engleză Paperback – 11 apr 2024
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.
 
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Specificații

ISBN-13: 9783031564598
ISBN-10: 3031564596
Ilustrații: VIII, 69 p. 25 illus., 16 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.12 kg
Ediția:2024
Editura: Springer Nature Switzerland
Colecția Springer
Seria SpringerBriefs in Energy

Locul publicării:Cham, Switzerland

Cuprins

What is anaerobic digestion?.- Reactions and operative conditions.- Energetic scenario in the XXI century.- Biomass in anaerobic digestion.- Biomass types and differences.- Principal biomass properties.- Experimental procedures.- Analytical methodologies.

Notă biografică

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.


Textul de pe ultima copertă

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.
 


Caracteristici

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