Online Decoupled Data-Driven Estimation of Nonlinear Kinetic Parameters

Wilfredo Angulo; Dany De Cecchis; Santiago D. Salas

doi:10.1016/B978-0-12-823377-1.50206-8

Online Decoupled Data-Driven Estimation of Nonlinear Kinetic Parameters

Wilfredo Angulo, Dany De Cecchis, Santiago D. Salas

Research output: Contribution to journal › Article › peer-review

Abstract

A data-driven parameter estimation strategy is assessed and tested for the estimation of nonlinear parameters in a classic continuous stir tank reactor (CSTR). A decoupled version of the retrospective cost model refinement (RCMR) algorithm serves as the estimation structure. The proposed method studies the simultaneous estimation of three kinetic parameters within a CSTR considering one available measurement. The decoupled RCMR algorithm is adapted and implemented as an efficient estimation structure for the proposed problem, and contrasted with its original structure.

Original language	English
Pages (from-to)	1231-1236
Number of pages	6
Journal	Computer Aided Chemical Engineering
Volume	48
DOIs	https://doi.org/10.1016/B978-0-12-823377-1.50206-8
State	Published - Jan 2020
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2020 Elsevier B.V.

Keywords

Continuous stirred tank reactor
data-driven estimation
decoupled RCMR
nonlinear parameter estimation

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10.1016/B978-0-12-823377-1.50206-8

Cite this

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title = "Online Decoupled Data-Driven Estimation of Nonlinear Kinetic Parameters",

abstract = "A data-driven parameter estimation strategy is assessed and tested for the estimation of nonlinear parameters in a classic continuous stir tank reactor (CSTR). A decoupled version of the retrospective cost model refinement (RCMR) algorithm serves as the estimation structure. The proposed method studies the simultaneous estimation of three kinetic parameters within a CSTR considering one available measurement. The decoupled RCMR algorithm is adapted and implemented as an efficient estimation structure for the proposed problem, and contrasted with its original structure.",

keywords = "Continuous stirred tank reactor, data-driven estimation, decoupled RCMR, nonlinear parameter estimation",

author = "Wilfredo Angulo and \{De Cecchis\}, Dany and Salas, \{Santiago D.\}",

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year = "2020",

month = jan,

doi = "10.1016/B978-0-12-823377-1.50206-8",

language = "English",

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AU - Angulo, Wilfredo

AU - De Cecchis, Dany

AU - Salas, Santiago D.

PY - 2020/1

Y1 - 2020/1

N2 - A data-driven parameter estimation strategy is assessed and tested for the estimation of nonlinear parameters in a classic continuous stir tank reactor (CSTR). A decoupled version of the retrospective cost model refinement (RCMR) algorithm serves as the estimation structure. The proposed method studies the simultaneous estimation of three kinetic parameters within a CSTR considering one available measurement. The decoupled RCMR algorithm is adapted and implemented as an efficient estimation structure for the proposed problem, and contrasted with its original structure.

AB - A data-driven parameter estimation strategy is assessed and tested for the estimation of nonlinear parameters in a classic continuous stir tank reactor (CSTR). A decoupled version of the retrospective cost model refinement (RCMR) algorithm serves as the estimation structure. The proposed method studies the simultaneous estimation of three kinetic parameters within a CSTR considering one available measurement. The decoupled RCMR algorithm is adapted and implemented as an efficient estimation structure for the proposed problem, and contrasted with its original structure.

KW - Continuous stirred tank reactor

KW - data-driven estimation

KW - decoupled RCMR

KW - nonlinear parameter estimation

UR - https://www.scopus.com/pages/publications/85092780270

U2 - 10.1016/B978-0-12-823377-1.50206-8

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AN - SCOPUS:85092780270

SN - 1570-7946

VL - 48

SP - 1231

EP - 1236

JO - Computer Aided Chemical Engineering

JF - Computer Aided Chemical Engineering

ER -

Online Decoupled Data-Driven Estimation of Nonlinear Kinetic Parameters

Abstract

Bibliographical note

Keywords

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