(348b) Progress in Automated Modeling of Biomass Derived Fuels: Updating Reaction Families and Rate Calculation Rules Conference: AIChE Annual MeetingYear: 2014Proceeding: 2014 AIChE Annual MeetingGroup: Catalysis and Reaction Engineering DivisionSession: Reaction Engineering for Biomass Conversion Time: Tuesday, November 18, 2014 - 12:50pm-1:10pm Authors: Seyedzadeh Khanshan, F., Northeastern University West, R. H., Northeastern University Thermal conversion of biofuels is very sensitive to the fuel chemistry, and the major challenge in modeling fuels derived from biomass is the presence of a wide range of cyclic oxygenated species. Since manually generating detailed chemical models for biofuels is complex, it is prefer- able to use computers instead. In this study, Reaction Mechanism Generator (RMG), an open- source software, has been used to build detailed kinetic models for gasification of bio-oil, derived from the pyrolysis of biomass. Furthermore, some progress has been made in RMG in order to improve the automated chemical modeling of all fuels derived from biomass. Biofuels contain many derivatives of lignin, hemicellulose, and cellulose, and it is important to include all reaction classes related to the primary decomposition of these components in order to propose a comprehensive mechanism. In recent work we have investigated that RMG is missing some ring opening reaction families for cylic oxygenated molecules, such as isomerization reac- tions through simultaneous H-migration and C–C or C–O bond breaking. We have now updated RMG’s kinetics database with reaction recipes for the new reaction families. We must also specify rules to predict Arrhenius rate parameters for the new reaction classes. However, the number of possible reactions in each reaction family is massive, and applying high-level electronic structure calculations for each would be prohibitively expensive. Instead, rate calculations were performed for a smaller set of reactants belonging to the particular reaction class, then the roles of the different functional groups were deliberated, and group-based rate rules were derived to estimate Arrhenius parameters for any reaction in the new reaction classes. A detailed kinetic model for bio-oil gasification at high temperature was previously built in RMG, and comparison with literature experiments showed that the RMG-built model couldn’t predict CO and CO2 formation properly. Since products from primary ring opening reactions through C–O bond breaking are major contributors to CO and CO2 formation, the bio-oil gasification mech- anism has been updated after adding these new reaction classes and associated kinetic parameter estimation rules. There are some significant differences in simulation results between the RMG- built models before and after updating the database, demonstrating the importance of these reaction families and their kinetic features when studying the thermal conversion of biofuels. 1- W. H. Green, J. W. Allen, P. Bhoorasingh, B. A. Buesser, R. W. Ashcraft, G. J. Beran, C. A. Class, C. Gao, C. F. Goldsmith, M. R. Harper, A. Jalan, F. S. Khanshan, G. R. Magoon, D. M. Matheu, S. S. Merchant, J. D. Mo, S. Pet- way, S. Raman, S. Sharma, B. Slakman, J. Song, K. M. V. Geem, J. Wen, R. H. West, A. Wong, H.-W. Wong, P. E. Yelvington, N. Yee, J. Yu, RMG — Reaction Mechanism Generator-Python, rmg.mit.edu (2013) RMG — Reaction Mechanism Generator.