(617bm) Simultaneous Reaction Identification and Parameter Estimation
We present an optimization-based approach that utilizes a mixed-integer non-linear programming (MINLP) formulation to identify a set of reactions that describe the chemistry observed in a steady-state continuous stirred-tank reactor, and estimate the associated kinetic rate parameters. This approach utilizes a superset of chemical reactions, which can contain different mechanistic pathways for the same reaction. A curve-fitting procedure, similar to one described previously for subset selection in multiple linear regression , is then used to minimize an objective that balances the complexity of the model with its fit in order to identify a probable subset of reactions. The utility of this methodology is demonstrated on a number of case studies.
 Warren E. Stewart and Michael Caracotsios. 2008. Computer-Aided Modeling of Reactive Systems. Wiley-Interscience, New York, NY, USA.
 Cozad, A., N. V. Sahinidis, and D. C. Miller, Automatic learning of algebraic models for optimization, AIChE Journal, 60, 2211-2227, 2014.