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This book determines adjustable parameters in mathematical models that describe steady state or dynamic systems, presenting the most important optimization methods used for parameter estimation. It focuses on the Gauss-Newton method and its modifications for systems and processes represented by algebraic or differential equation models.
List of contents
Formulation of the parameter estimation problem; computation of parameters in linear models-linear regression; Gauss-Newton method for algebraic models; other nonlinear regression methods for algebraic models; Gauss-Newton method for ordinary differential equation (ODE) models; shortcut estimation methods for ODE models; practical guidelines for algorithm implementation; constrained parameter estimation; Gauss-Newton method for partial differential equation (PDE) models; statistical inferences; design of experiments; recursive parameter estimation; parameter estimation in nonlinear thermodynamic models - cubic equation of state; parameter estimation in nonlinear thermodynamic models - activity coefficients; parameter estimation in chemical reaction kinetic models; parameter estimation in biochemical engineering models; parameter estimation in petroleum engineering. Appendices: the Trebble-Bishnoi equation of state; derivation of the fugacity expression; listings of computer programs; contents of accompanying CD; computer program for example16.1.2; computer program for example 16.3.2.
About the author
Englezos, Peter; Kalogerakis, Nicolas
