03153nam a22005295i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001600172072001500188072002300203072001400226100008700240245015400327250001800481264006900499300006500568336002600633337002600659338003600685347002400721490004100745505026700786520082801053650003801881650003201919650002601951650002501977650004102002650006102043650002602104650009502130650004102225700008502266710003402351773002602385776003602411776003602447830004802483856004602531912001402577912001502591999001702606978-3-031-56460-4DE-He21320251030111813.0cr nn 008mamaa240410s2024    sz |    s    |||| 0|eng d  a97830315646049978-3-031-56460-47 a10.1007/978-3-031-56460-42doi 4aTK1001-1841 7aTH2bicssc 7aTEC0310002bisacsh 7aTH2thema1 aMoretta, Federico.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut97499910aMathematical and Statistical Approaches for Anaerobic Digestion Feedstock Optimizationh[electronic resource] /cby Federico Moretta, Giulia Bozzano.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aVIII, 69 p. 25 illus., 16 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aSpringerBriefs in Energy,x2191-55390 aWhat 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.  aThis 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. . 0aElectric power production.965675 0aMathematical models.966910 0aChemical engineering. 0aStatistics .96598214aMechanical Power Engineering.96567824aMathematical Modeling and Industrial Mathematics.96691224aChemical Engineering.24aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.96691124aChemical Process Engineering.9703151 aBozzano, Giulia.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9750002 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303156459808iPrinted edition:z9783031564611 0aSpringerBriefs in Energy,x2191-553997199240uhttps://doi.org/10.1007/978-3-031-56460-4  aZDB-2-ENE  aZDB-2-SXEN  c59065d59065