Applied parameter estimation for chemical engineers /

Applied parameter estimation for chemical engineers /

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Bibliographic Details
Author / Creator:Englezos, Peter, 1958-
Imprint:New York : M. Dekker, ©2001.
Description:1 online resource (xvii, 434 pages) : illustrations
Language:English
Series:Chemical industries ; 81
Chemical industries ; v. 81.
Subject:
Chemical engineering -- Mathematical models.
Chemical engineering -- Statistical methods.
Parameter estimation.
Gaussian processes.
Chemical engineering -- Mathematical models.
Chemical engineering -- Statistical methods.
Gaussian processes.
Parameter estimation.
Electronic books.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11117111
Hidden Bibliographic Details
Varying Form of Title:Parameter estimation for chemical engineers
Other authors / contributors:Kalogerakis, Nicolas, 1954-
ISBN:9780585434209
0585434204
9780824745264
0824745264
9780203904695
0203904699
9781135553937
1135553939
082479561X
9780824795610
Digital file characteristics:data file
Notes:Includes bibliographical references (pages 391-402) and index.
Print version record.
Summary:A presentation of important optimization methods used for parameter estimation. It focuses on the Gauss-Newton method for systems and processes represented by algebraic or differential equation models.
Other form:Print version: Englezos, Peter, 1958- Applied parameter estimation for chemical engineers. New York : M. Dekker, ©2001 082479561X
Table of Contents:
  • Preface; Contents; Introduction; 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 Ordinary Differential Equation (ODE) Models; Practical Guidelines for Algorithm Implementation; Constrained Parameter Estimation; Gauss-Newton Method for Partial Differential Equation (PDE) Models; Statistical Inferences; Design of Experiments.

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