Korean Journal of Chemical Engineering

, Volume 18, Issue 5, pp 623–629 | Cite as

A method of model validation for chaotic chemical reaction systems based on neural network

  • Hwi Jin Kim
  • Kun Soo Chang


A chaotic system with measurable state variables fewer than the degrees of freedom of the system is identified with the Artificial Neural Network (ANN) method combined with dynamic training. Instead of using the usual method of Sum of Square Errors (SSE), the identified models are validated with the return maps (embedded trajectories), the largest Lyapunov exponent, and the correlation dimension when there is no exogenous input, and bifurcation diagram when there is an exogenous input. This method is demonstrated for nonisothermal, irreversible, first-order, series reaction A→ B → C in a CSTR.

Key words

Chaos Neural Network Dynamic Training System Identification Model Validation 



dimensionless concentrations of species A


dimensionless concentrations of species B


dimensionless temperature in the reactor


Damk ohler number


dimensionless activation energy


ratio of the two rate constants


ratio of activation energies


dimensionless adiabatic temperature rise


ratio of heat effects


dimensionless heat transfer coefficient


dimensionless coolant bath temperature


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Copyright information

© Korean Institute of Chemical Engineering 2001

Authors and Affiliations

  1. 1.Department of Chemical Engineering and Automation Research CenterPohang University of Science and TechnologyPohangKorea

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