Novel kinetic studies on biomass hydrothermal carbonization. (October 2018)

• Record Type: Journal Article
• Authors: Gallifuoco, Alberto; Di Giacomo, Gabriele
• Is Part Of: Bioresource technology. Volume 266(2018)
• Page Start: 189
• Page End: 193
• Publication Date: 2018-10
• Subjects: Hydrothermal carbonization -- Kinetic modeling -- Stochastic process -- Discrete Markov-chain -- Curve fitting -- Parameter determination; 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.2018.06.087
• Languages: English
• ISSNs: 0960-8524

Abstract

Highlights: Biomass hydrothermal carbonization studied as a finite Markov chain process. The analogies existing with apparently different phenomena support the method. Kinetic equation is derived by a statistical approach. Experimental data almost perfectly in accordance with the model predictions. The features of the model equation directly address process optimization. Abstract: This work aims to study kinetic data on biomass hydrothermal carbonization by a new point of view. The time course of essential properties signaling hydrochar evolution is found to be sigmoidal ubiquitously. The logistic curve here proposed, attributable to the Hill equation, furnished near-perfect regressions (R 2 as high as 0.999) in all cases studied. The broad applicability and the excellent correlations stimulated the research heuristically and routed process optimization. The evidence is reported that the success of fittings bases on profound mechanistic reasons. Arguments are discussed supporting the hypothesis that hydrochar formation is a stochastic phenomenon. The method deduces the Hill equation by applying statistical methods to the hydrothermal carbonization, modeled as a discrete Markov-chain process. The role of the model equation parameters is also discussed. The potentiality of the method proposed in this pioneering work could pave the way for a new paradigm in the modeling of hydrothermal carbonization.