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Predictive Models of Biohydrogen and Biomethane Production Based on the Compositional and Structural Features of Lignocellulosic Materials

In an integrated biorefinery concept, biological hydrogen and methane production from lignocellulosic substrates appears to be one of the most promising alternatives to produce energy from renewable sources. However, lignocellulosic substrates present compositional and structural features that can limit their conversion into biohydrogen and methane. In this study, biohydrogen and methane potentials of 20 lignocellulosic residues were evaluated. Compositional (lignin, cellulose, hemicelluloses, total uronic acids, proteins, and soluble sugars) as well as structural features (crystallinity) were determined for each substrate. Two predictive partial least square (PLS) models were built to determine which compositional and structural parameters affected biohydrogen or methane production from lignocellulosic substrates, among proteins, total uronic acids, soluble sugars, crystalline cellulose, amorphous holocelluloses, and lignin. Only soluble sugars had a significant positive effect on biohydrogen production. Besides, methane potentials correlated negatively to the lignin contents and, to a lower extent, crystalline cellulose showed also a negative impact, whereas soluble sugars, proteins, and amorphous hemicelluloses showed a positive impact. These findings will help to develop further pretreatment strategies for enhancing both biohydrogen and methane production.
- Polytechnic University of Milan Italy
- Département Sciences sociales, agriculture et alimentation, espace et environnement France
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement France
- National Research Institute for Agriculture, Food and Environment France
- Laboratoire de Biotechnologie de l'Environnement France
[SDE] Environmental Sciences, Energy-Generating Resources, [SPI.GPROC] Engineering Sciences [physics]/Chemical and Process Engineering, [SDV]Life Sciences [q-bio], Fructose, Magnoliopsida, Polysaccharides, [SDV.IDA]Life Sciences [q-bio]/Food engineering, [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering, Least-Squares Analysis, Cellulose, Plant Proteins, 660, [SDV.IDA] Life Sciences [q-bio]/Food engineering, Models, Theoretical, Plant Components, Aerial, [SDV] Life Sciences [q-bio], Glucose, Uronic Acids, [SDE]Environmental Sciences, Methane, Hydrogen
[SDE] Environmental Sciences, Energy-Generating Resources, [SPI.GPROC] Engineering Sciences [physics]/Chemical and Process Engineering, [SDV]Life Sciences [q-bio], Fructose, Magnoliopsida, Polysaccharides, [SDV.IDA]Life Sciences [q-bio]/Food engineering, [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering, Least-Squares Analysis, Cellulose, Plant Proteins, 660, [SDV.IDA] Life Sciences [q-bio]/Food engineering, Models, Theoretical, Plant Components, Aerial, [SDV] Life Sciences [q-bio], Glucose, Uronic Acids, [SDE]Environmental Sciences, Methane, Hydrogen
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).178 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1%
