Progress in Modeling of Biomass Fast Pyrolysis: A Review

Abstract

Fast pyrolysis of biomass is an important technology in the conversion of lignocellulosic feedstocks to value-added fuels and chemicals. Significant efforts have been dedicated to modeling of these processes to improve the viability of large-scale operation through reactor design, feedstock selection and processing, and optimization of operating conditions, among others. This work is a review of the current progress in the field of modeling of biomass fast pyrolysis processes across multiple length and time scales. Enclosed are summaries of the current state of the art in atomistic and kinetic modeling of biomass fast pyrolysis toward production of fuels and chemicals. Decomposition of aggregate biomass and its individual components was reviewed for models at various scales, highlighting important considerations. Recent applications of machine learning methods to couple multiscale phenomena with the goal of reducing computational complexity were also included. Historical context was provided for existing models and correlations, highlighting some of those most widely applied. Some of the shortcomings and bottlenecks in existing models were identified as areas for further study. Finally, potential future directions for the field are suggested with the goal of improving the viability and sustainability of pyrolysis processes and the applications of multiscale modeling toward this goal.

Autonomous kinetic modeling of biomass pyrolysis using chemical reaction neural networks

“…On the other hand, the biomass and combustion community have moved towards mechanistic models to first unravel the intrinsic kinetics of biomass and derive reduced kinetic approaches from there. This approach has provided chemical insight, but no significant step in reduced kinetic modelling [24]. It was argued that detailed chemical kinetics is presently not suitable for problems in the fire community as well as elsewhere [25].…”

“…It was argued that detailed chemical kinetics is presently not suitable for problems in the fire community as well as elsewhere [25]. Over the last few years, the biomass community has also applied machine learning with success in some specific problems [24], but these findings are unfortunately not extendable to wildfires.…”

“…The chemical kinetics of cellulose is established and has recently been well-reviewed [24,[26][27][28]. One can divide all models into three levels of complexity.…”

Biorefinery via Catalytic Upgraded Fast Pyrolysis of Biomass

“…Intermediate pyrolysis operates with auger reactor with heating rate with few minute, flash pyrolysis operates at temperature of 300-700℃ to produce essentially gas [12].…”

Special Issue of Energy & Fuels Honoring Michael T. Klein

“…The review by Kostyteskyy and Broadbelt evaluates the modern computations and information analytics that can be directed at modeling the complex reaction intermediates and pathways involved in the fast pyrolysis of biomass. Finally, the article by Abo-Ghander and Klein revisits the Delplot method to probe its limits for examining networks containing very fast steps.…”