(82b) Modeling Biomass-to-Fuels Processing

Fuels produced from lignocellulosic biomass are a promising renewable energy source that will aid in the transition from petroleum-based liquid fuels for transportation applications. Lignocellulosic biomass is composed of lignin, cellulose, and hemicellulose, in varying amounts for each plant species. Pyrolysis of these biopolymers results in the production of aromatic compounds, alkanes, and various oxygenated species.

The high oxygen content of the biomass pyrolysis oil must be decreased for use in conventional internal combustion engines. Hydrodeoxygenation of biomass pyrolysis oil results in a mixture of hydrocarbons with chemical properties similar to those of petroleum-derived fuels. Co-processing of hydrotreated biomass pyrolysis oil in a traditional petroleum refinery provides a transitional renewable energy source for liquid transportation fuel applications while utilizing the existing processing and distribution infrastructure. Production of biofuels involves novel molecular species and unknown chemical reaction pathways that are well-suited for kinetic modeling.

Molecular-level modeling of the thermochemical conversion of biomass into renewable fuels requires the creation of a complete reaction network that identifies each chemical pathway from the lignocellulosic biomass feedstock to the desired fuel products. The Klein research group has developed a set of software tools that are capable of rapidly generating the complete reaction network and associated kinetic model for this and other applications. A robust reaction network is first produced by INGen (the Interactive Network Generator) [1] from the molecular starting materials. KME (the Kinetic Modeling Editor) [2,3] automatically writes the corresponding rate equations and mass balances for each molecular species. The resulting molecular-level kinetic model is subsequently numerically solved to provide model predictions of the product composition at varying process conditions.

This biomass-to-fuels processing model provides insight into the competing kinetic pathways for the production of renewable liquid fuels from lignocellulosic materials. Predictions are made for product composition at varying feedstock composition and processing conditions. Comparison of the activation energy barriers for parallel deoxygenation routes will direct catalyst research to modulate between these pathways and provide the highest quality products at the mildest process conditions. The ability to rapidly generate and solve molecular-level kinetic models will enable real-time optimization of fuel production and increase the viability of biofuels in the renewable energy market.

[1]    Bennett, C.A. PhD Dissertation, Chemical & Biochemical Engineering, Rutgers University. 2009.

[2]    Hou, Z.; Bennett, C.A.; Klein M.T.; Virk, P.S. Energy & Fuels. 2010. 24 (1), 58-67.

[3]    Wei, W.; Bennett, C.A.; Tanaka, R.; Hou, G.; Klein, M.T. Jr.; Klein, M.T. Fuel Processing Technology. 2008. 89 (4), 350-363.