A validated Distributed Activation Energy Model (DAEM) to predict the chemical degradation of biomass as a function of hydrothermal treatment conditions. (December 2021)

• Record Type: Journal Article
• Authors: Lancha, Julia P.; Colin, Julien; Almeida, Giana; Guerin, Cédric; Casalinho, Joel; Perré, Patrick
• Is Part Of: Bioresource technology. Volume 341(2021)
• Publication Date: 2021-12
• Subjects: Lignocellulosic biomass -- Steam explosion -- Poplar -- Shrinkage -- Degradation kinetics; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2021.125831
• Languages: English
• ISSNs: 0960-8524

Abstract

Graphical abstract: Highlights: A DAEM approach is applied to predict lignocellulosic biomass chemical alterations. Continuous assessment of sample shrinkage during hydrothermal treatment. Shrinkage dynamic is used in DAEM model as indicator of the degree of conversion. Set of functions relating the degree of conversion with components concentrations. The model was proved to be predictive on a different validation dataset. Abstract: This study proposes a DAEM (Distributed Activation Energy Model) approach to predict the chemical alterations of lignocellulosic biomass as a function of hydrothermal treatment conditions. The model is first tuned by an original device allowing the sample shrinkage to be continuously assessed during hydrothermal treatment in saturated water vapor up to 190 °C. The shrinkage dynamic is supplied in the DAEM model as an indicator of the degree of biomass conversion. A set of chemical analyses was performed at selected residence times and treatment temperatures to correlate this degree of conversion with the resulting chemical molecules. A set of functions was then derived from this database to correlate the degree of conversion with the components concentrations. Finally, a validation database was built with different combinations of temperature levels and residence times. The model was proved to be predictive on this new dataset.