System identification of a lumped heat exchanger using the Extended Information Filter

M. S. Al-Haik, Yousef Haik

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In this study a new Extended Information Filter (EIF) algorithm is applied to compute the heat transfer parameters for lumped heat exchanger. Many industrial applications depend on the prediction of the heat exchanger parameters. This algorithm produces an accurate prediction of the operating states and parameters. A state variable model is derived from the empirical correlation of the lumped heat exchanger. The derived system is nonlinear and stochastic. The problem of estimating the state variables and parameters is considered in the presence of random disturbances and measurement noise. The EIF is then used to produce an optimal estimate of the state and parameters of the heat exchanger. The results obtained by using EIF were compared to the results obtained using EKF and found that the estimation of dynamic nonlinear systems, is best carried out using the EIF rather than the EKF.

Original languageEnglish
Pages (from-to)9-20
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume3839
Publication statusPublished - 1999
Externally publishedYes

Fingerprint

Heat Exchanger
system identification
heat exchangers
System Identification
Heat exchangers
Identification (control systems)
Filter
filters
Nonlinear systems
nonlinear systems
Nonlinear Dynamic System
Prediction
Industrial applications
noise measurement
Industrial Application
predictions
Heat Transfer
Heat transfer
estimating
disturbances

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics

Cite this

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AB - In this study a new Extended Information Filter (EIF) algorithm is applied to compute the heat transfer parameters for lumped heat exchanger. Many industrial applications depend on the prediction of the heat exchanger parameters. This algorithm produces an accurate prediction of the operating states and parameters. A state variable model is derived from the empirical correlation of the lumped heat exchanger. The derived system is nonlinear and stochastic. The problem of estimating the state variables and parameters is considered in the presence of random disturbances and measurement noise. The EIF is then used to produce an optimal estimate of the state and parameters of the heat exchanger. The results obtained by using EIF were compared to the results obtained using EKF and found that the estimation of dynamic nonlinear systems, is best carried out using the EIF rather than the EKF.

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