2024 AIChE Annual Meeting
(173p) Automatic Chemical Reaction Mechanism Reduction Using Artificial Intelligence: Applications in Atmospheric Chemistry
Authors
Chakraborty, A. - Presenter, Columbia University In the City of New York
Wiser, F., Columbia University
Sen, S., Microsoft Research
McNeill, V. F., Columbia University
Venkatasubramanian, V., Columbia University
The objective of chemical reaction mechanism reduction is to obtain a condensed reaction mechanism that is a fairly accurate surrogate of the complete reaction mechanism and is considerably more memory and computationally efficient for subsequent tasks, such as real-time concentration prediction. When formulated as a search problem, the search space of potential candidate mechanisms could be larger than those of games such as Chess and Go combined. In this work, we present an artificially intelligent (AI) automated mechanism reduction of the gas-phase isoprene oxidation mechanism, termed Genetic Algorithmic - Automated Model Reduction (GA-AMORE). We highlight the efficacy of our approach in a well-established case study: atmospheric gas-phase isoprene oxidation. Formulated as a search problem through an astronomically large search space, we explore the same under mechanistic constraints, and subject matter expert rules, thus highlighting the benefits accrued from such a hybrid AI approach. We contrast the resultant mechanism(s) obtained to state-of-the-art manually tuned mechanisms of comparable size. Further, we obtain the optimal stoichiometric coefficients, and rate parameters, of the reduced reaction mechanisms using a derivative-free optimization strategy that significantly improves its accuracy. We posit that our approach can be extended to other mechanism reduction case studies by mere modification of the domain-specific rules, while keeping the underlying algorithm unchanged.