Abstract:
This paper proposes a data-driven method
for the automatic construction of chemical reaction
network graph. For a given set of chemical species
measurements and their (approximate) time derivatives,
the proposed method first estimates the nonlinear
dynamic model of the reaction using an L1 penalty
type sparse identification approach called ’constrained
least absolute shrinkage and selection operator’ (Constrained
LASSO). Using the estimated model, a network
graph construction approach that takes into account
the dynamical constraints (e.g. non-negativity
and law of mass action kinetics) of chemical reaction
networks is then presented. Simulation result is also
presented to illustrate the proposed method.
Description:
Makalah dipresentasikan pada Proceedings of 2019 6th International Conference on Instrumentation, Control, and Automation (ICA); 31 July - 2 August 2019. p. 226-230.