Open AccessModeling and identification of heat exchanger process using least squares support vector machines
Author(s) -
Mujahed AlDhaifallah,
Kottakkaran Sooppy Nisar,
Praveen Agarwal,
Alaa Elsayyad
Publication year - 2015
Publication title -
thermal science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.339
H-Index - 43
eISSN - 2334-7163
pISSN - 0354-9836
DOI - 10.2298/tsci151026204a
Subject(s) - nonlinear autoregressive exogenous model , autoregressive model , heat exchanger , a priori and a posteriori , computer science , support vector machine , process (computing) , identification (biology) , least squares function approximation , least squares support vector machine , system identification , algorithm , mathematical optimization , artificial intelligence , data modeling , mathematics , econometrics , statistics , engineering , mechanical engineering , philosophy , botany , epistemology , database , estimator , biology , operating system
In this paper, Hammerstein model and non-linear autoregressive with eXogeneous inputs (NARX) model are used to represent tubular heat exchanger. Both models have been identified using least squares support vector machines based algorithms. Both algorithms were able to model the heat exchanger system with-out requiring any a priori assumptions regarding its structure. The results indicate that the blackbox NARX model outperforms the NARX Hammerstein model in terms of accuracy and precision.
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