東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Mathematical and statistical approaches for anaerobic digestion feedstock optimization

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Mathematical and statistical approaches for anaerobic digestion feedstock optimization/ by Federico Moretta, Giulia Bozzano.
作者:	Moretta, Federico.
其他作者:	Bozzano, Giulia.
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	viii, 69 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Biomass energy. -
電子資源:	https://doi.org/10.1007/978-3-031-56460-4
ISBN:	9783031564604

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

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

683849
Biomass energy.

LC Class. No.: TP339

Dewey Class. No.: 662.88

Mathematical and statistical approaches for anaerobic digestion feedstock optimization
LDR:02132nmm a22003375a 4500 001 2370124
003 DE-He213
005 20240411084926.0
006 m d
007 cr nn 008maaau
008 241127s2024 sz s 0 eng d
020 $a 9783031564604 $q (electronic bk.)
020 $a 9783031564598 $q (paper)
024 7 $a 10.1007/978-3-031-56460-4 $2 doi
035 $a 978-3-031-56460-4
040 $a GP $c GP
041 0 $a eng
050 4 $a TP339
072 7 $a TH $2 bicssc
072 7 $a TEC031000 $2 bisacsh
072 7 $a TH $2 thema
082 0 4 $a 662.88 $2 23
090 $a TP339 $b .M845 2024
100 1 $a Moretta, Federico. $3 3715241
245 1 0 $a Mathematical and statistical approaches for anaerobic digestion feedstock optimization $h [electronic resource] / $c by Federico Moretta, Giulia Bozzano.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2024.
300 $a viii, 69 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in energy, $x 2191-5539
505 0 $a 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.
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.
650 0 $a Biomass energy. $3 683849
650 0 $a Anaerobic bacteria. $3 659823
650 1 4 $a Mechanical Power Engineering. $3 3592501
650 2 4 $a Mathematical Modeling and Industrial Mathematics. $3 891089
650 2 4 $a Chemical Engineering. $3 959567
650 2 4 $a Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 3591853
650 2 4 $a Chemical Process Engineering. $3 3595082
700 1 $a Bozzano, Giulia. $3 3715242
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a SpringerBriefs in energy. $3 1570022
856 4 0 $u https://doi.org/10.1007/978-3-031-56460-4
950 $a Energy (SpringerNature-40367)