Joseph J. Biernacki

Tennessee Tech University

Micro-scale modeling of biomass pyrolysis

Michael Adenson

The energy requirement of the world has been and is heavily dependent on fossil fuel resources. This over-dependence is being addressed by world governments and technology leaders who recognize that fossil fuels will diminish in decades to come, and that there is a concomitant need to reduce pollution and greenhouse gas emissions.  As a result, the search for clean renewable energy sources has become a major global interest. One promising alternative is biofuel production via biomass pyrolysis.

There exists a relatively large body of work on the interrelationship between processing conditions and the chemical product spectrum for biomass pyrolysis along with how the formation of certain components can be favored among many other products. Mathematical modelling of the pyrolysis reactions has also furthered the mechanistic understanding in areas difficult to access experimentally. These modelling efforts have focused either on molecular-scale simulation or microscopic and macroscopic continuum balances (ODE- or PDE-based equations).

Although, continuum models, in the form of ODEs or PDEs, have been used since 1946 (Prakash and Karunanithi 2009), and they have been shown to be effective in predicting mass loss, temperature and, to a lesser extent, product distribution of pyrolysis; yet, the fundamental scientific knowledge of biomass pyrolysis is still lacking, and detailed models capable of describing the chemistry, microstructure and transport in real-world reactors are still unavailable despite decades of studies (Mettler et al. 2012).

In this research, cellular automaton (CA) will be adopted to model biomass pyrolysis. Here, the physicochemical processes of pyrolysis will be modeled at an intermediate scale between the microscopic (as described by PDEs) and the molecular, typically modeled using molecular dynamics or quantum mechanical methods. The aim of the present work is to bridge the gap between these two length scales with a flexible platform that can accommodate the irregular geometry and heterogeneities of biomass pyrolysis.  For that purpose, cellular automaton has been chosen (Karapiperis and Blankleider 1994).

The first goal of this work involves modelling the transport-reaction process in biomass pyrolysis for a simple geometry using CA.  Spherical symmetry is being used with homogeneous material properties so that the CA can be benchmarked against continuum models.  Once this has been done, more details will be added to mimic real geometries, taking input from the on-going microtomography work in our group.


Karapiperis, T., and B. Blankleider, 1994, Cellular Automaton Model of Reaction-Transport Processes. Physica D: Nonlinear Phenomena 78(1-2): 30–64.

Mettler, Matthew S., Samir H. Mushrif, Alex D. Paulsen, et al., 2012, Revealing Pyrolysis Chemistry for Biofuels Production: Conversion of Cellulose to Furans and Small Oxygenates. Energy & Environmental Science 5(1): 5414–5424.

Prakash, N., and T. Karunanithi, 2009, Advances in Modeling and Simulation of Biomass Pyrolysis. Asian Journal of Scientific Research 2(1): 1–27.


Clark Templeton

Biomass pyrolysis is emerging as a key element of the sustainable energy future.  Yet in the development stage, there is much need for micro-scale modeling of pyrolysis processes.  In order to better understand how the structure of lignocellulosic matter evolves as it decomposes, I am developing a mathematical model, using Matlab 2014, to predict microstructural changes as a function of pyrolysis time and temperature.  To do this I am exploring a cellular automaton approach.  This type of mathematical model converts continuum properties, such as rates of diffusion and reaction, into probabilities and uses random numbers to effect change.  Although, there are a number of useful continuum modes and on-going work at the molecular-scale for lignocellulosic materials, my ambition is to develop a more distributed and suitable model that can be used to bridge between the molecular- and meso-scale platforms.

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