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Mathematical and statistical approaches for anaerobic digestion feedstock optimization

Record Type:	Electronic resources : Monograph/item
Title/Author:	Mathematical and statistical approaches for anaerobic digestion feedstock optimizationby Federico Moretta, Giulia Bozzano.
Author:	Moretta, Federico.
other author:	Bozzano, Giulia.
Published:	Cham :Springer Nature Switzerland :2024.
Description:	viii, 69 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Biomass energy.
Online resource:	https://doi.org/10.1007/978-3-031-56460-4
ISBN:	9783031564604$q(electronic bk.)

Mathematical and statistical approaches for anaerobic digestion feedstock optimization
Moretta, Federico.

Mathematical and statistical approaches for anaerobic digestion feedstock optimization[electronic resource] /by Federico Moretta, Giulia Bozzano. - Cham :Springer Nature Switzerland :2024. - viii, 69 p. :ill., digital ;24 cm. - SpringerBriefs in energy,2191-5539. - SpringerBriefs in energy..

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.

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.

ISBN: 9783031564604$q(electronic bk.)

Standard No.: 10.1007/978-3-031-56460-4doiSubjects--Topical Terms:

276913
Biomass energy.

LC Class. No.: TP339

Dewey Class. No.: 662.88

Mathematical and statistical approaches for anaerobic digestion feedstock optimization
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520 $a 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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