(583w) Using Reaction Mechanism Generator (RMG) to Build Detailed Kinetic Model of Biofuels | AIChE

(583w) Using Reaction Mechanism Generator (RMG) to Build Detailed Kinetic Model of Biofuels


Seyedzadeh Khanshan, F. - Presenter, Northeastern University
West, R. H., Northeastern University

Increasing interest in and expansion of the biofuels industry motivate further study of the chemical kinetics of biofuels. Future developments require detailed predictive models; building these models is easier with automated mechanism generating methods. It is therefore important that these automatic kinetic model-building tools are able to predict biofuel-related behaviors accurately. This work concentrates on building detailed kinetic models for two different biofuels systems, bio-oil gasification and biodiesel pyrolysis, using Reaction Mechanism Generator (RMG), an open source software package [1], and the new implementation RMG-Py. We investigate the influence of RMG parameters on the size and performance of the models. We describe the challenges involved in using RMG to build comprehensive models for surrogate bio-fuels containing many large compounds, and how they may be overcome.  Finally, we discuss agreements and disagreements between RMG-built models and both experimental data and other modeling efforts, in order to identify RMG’s abilities and deficiencies in building kinetic models automatically, and to guide further RMG development.

Detailed kinetic models for conversion of bio-oil to syngas through gasification have been developed using RMG. As the efficiency of bio-oil conversion is highly dependent on operation conditions of the process, the influences of pyrolysis temperature, residence time, pressure, and RMG parameters on the model predictions were investigated. The simulation results show there are challenges involved in using RMG to build a complete reaction network and in properly predicting the gasification behavior.  A major challenge for bio-oil, consisting of many large compounds, is that model size is constrained by memory (RAM) limitations, as well as CPU time. To improve the memory management in RMG we must alter the algorithm and introduce parallelization to the software. The first step in this process is identifying which parts of RMG algorithm can be parallelized easily.

Secondly, three biodiesel pyrolysis models were built in RMG, to study the influence of the surrogate size on the biodiesel pyrolysis behavior. These surrogates are methyl butanoate, methyl decanoate, and methyl palmitate, with 5, 10, and 17 saturated carbon atoms respectively. Early formation of CO and CO2, an important combustion feature of biodiesel, is examined; analysis of the kinetic models shows that under different conditions, different reaction pathways become dominant in the reaction network and these pathways are compared with other models in the literature. Comparison of the methyl butanoate model with published experiments [2] reveal some discrepancies. These differences are identified and discussed in terms of RMG’s features and kinetics estimation database. As a result of this work, RMG is able to build more reliable detailed kinetic model for larger methyl esters.

[1]      RMG - Reaction Mechanism Generator. Open-source software package. http://rmg.sourceforge.net (2013).

[2]      Farooq, A.; Ren, W.; Lam, K. Y.; Davidson, D. F.; Hanson, R. K.; Westbrook, C. K. “Shock tube studies of methyl butanoate pyrolysis with relevance to biodiesel.” Combust. Flame 2012, 159, 3235–3241 [doi:10.1016/j.combustflame.2012.05.013].


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