Scalability strategies for automated reaction mechanism generation
Author(s)
Jocher, Agnes
Vandewiele, Nick
Han, Kehang
Liu, Mengjie
Gao, Connie Wu
Gillis, Ryan J.
Green Jr, William H
Date Issued
December 2019
Journal
Computers & Chemical Engineering
Publisher
Elsevier BV
Citation
Agnes, Jocher et al. "Scalability strategies for automated reaction mechanism generation." Computers & Chemical Engineering 131 (December 2019): 106578 © 2019 Elsevier Ltd
Version
Author's final manuscript
Abstract
Detailed modeling of complex chemical processes, like pollutant formation during combustion events, remains challenging and often intractable due to tedious and error-prone manual mechanism generation strategies. Automated mechanism generation methods seek to solve these problems but are held back by prohibitive computational costs associated with generating larger reaction mechanisms. Consequently, automated mechanism generation software such as the Reaction Mechanism Generator (RMG) must find novel ways to explore reaction spaces and thus understand the complex systems that have resisted other analysis techniques. In this contribution, we propose three scalability strategies — code optimization, algorithm heuristics, and parallel computing — that are shown to considerably improve RMG's performance as measured by mechanism generation time for three representative simulations (oxidation, pyrolysis, and combustion). The improvements create new opportunities for the detailed modeling of diverse real-world processes.Keywords: Chemical kinetics; Mechanism generation; Scalability; Parallel computing
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.compchemeng.2019.106578
