Nonlinear Modelling for Steam Temperature of Distillation Column: A Comparison between PRBS and MPRS Perturbation Signals

Nowadays, countless research efforts have been reported for distillation column on nonlinear modelling. Several studies have shown the importance of appropriate perturbation signal for the nonlinear system applications. This study focuses on a nonlinear modelling for steam temperature using Pseudo R...

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Published in:Proceedings - 2018 IEEE Conference on Systems, Process and Control, ICSPC 2018
Main Author: Hambali N.; Taib M.N.; Yassin A.I.M.; Rahiman M.H.F.
Format: Conference paper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2018
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065961841&doi=10.1109%2fSPC.2018.8704132&partnerID=40&md5=201ff230c38dbf566e4053c4a742e9ea
id 2-s2.0-85065961841
spelling 2-s2.0-85065961841
Hambali N.; Taib M.N.; Yassin A.I.M.; Rahiman M.H.F.
Nonlinear Modelling for Steam Temperature of Distillation Column: A Comparison between PRBS and MPRS Perturbation Signals
2018
Proceedings - 2018 IEEE Conference on Systems, Process and Control, ICSPC 2018


10.1109/SPC.2018.8704132
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065961841&doi=10.1109%2fSPC.2018.8704132&partnerID=40&md5=201ff230c38dbf566e4053c4a742e9ea
Nowadays, countless research efforts have been reported for distillation column on nonlinear modelling. Several studies have shown the importance of appropriate perturbation signal for the nonlinear system applications. This study focuses on a nonlinear modelling for steam temperature using Pseudo Random Binary Signal (PRBS) and Multi-level Pseudo Random Sequence (MPRS) perturbation signals. A Binary Particle Swarm Optimisation (BPSO) algorithm was utilised in the model structure selection for polynomial Nonlinear Auto-Regressive with eXogenous (NARX) input for Steam Distillation Pilot Plant (SDPP). Three model's selection criteria were examined; Akaike Information Criterion (AIC), Model Descriptor Length (MDL), and Final Prediction Error (FPE). The performance analysis included output model analysis and model validation of the nonlinear model. The results demonstrated fewer number of input and output lags and lesser amount of output model parameter using MPRS perturbation signal compared with PRBS. Model validation showed high R-squared and low MSE for both signals' application. © 2018 IEEE.
Institute of Electrical and Electronics Engineers Inc.

English
Conference paper

author Hambali N.; Taib M.N.; Yassin A.I.M.; Rahiman M.H.F.
spellingShingle Hambali N.; Taib M.N.; Yassin A.I.M.; Rahiman M.H.F.
Nonlinear Modelling for Steam Temperature of Distillation Column: A Comparison between PRBS and MPRS Perturbation Signals
author_facet Hambali N.; Taib M.N.; Yassin A.I.M.; Rahiman M.H.F.
author_sort Hambali N.; Taib M.N.; Yassin A.I.M.; Rahiman M.H.F.
title Nonlinear Modelling for Steam Temperature of Distillation Column: A Comparison between PRBS and MPRS Perturbation Signals
title_short Nonlinear Modelling for Steam Temperature of Distillation Column: A Comparison between PRBS and MPRS Perturbation Signals
title_full Nonlinear Modelling for Steam Temperature of Distillation Column: A Comparison between PRBS and MPRS Perturbation Signals
title_fullStr Nonlinear Modelling for Steam Temperature of Distillation Column: A Comparison between PRBS and MPRS Perturbation Signals
title_full_unstemmed Nonlinear Modelling for Steam Temperature of Distillation Column: A Comparison between PRBS and MPRS Perturbation Signals
title_sort Nonlinear Modelling for Steam Temperature of Distillation Column: A Comparison between PRBS and MPRS Perturbation Signals
publishDate 2018
container_title Proceedings - 2018 IEEE Conference on Systems, Process and Control, ICSPC 2018
container_volume
container_issue
doi_str_mv 10.1109/SPC.2018.8704132
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065961841&doi=10.1109%2fSPC.2018.8704132&partnerID=40&md5=201ff230c38dbf566e4053c4a742e9ea
description Nowadays, countless research efforts have been reported for distillation column on nonlinear modelling. Several studies have shown the importance of appropriate perturbation signal for the nonlinear system applications. This study focuses on a nonlinear modelling for steam temperature using Pseudo Random Binary Signal (PRBS) and Multi-level Pseudo Random Sequence (MPRS) perturbation signals. A Binary Particle Swarm Optimisation (BPSO) algorithm was utilised in the model structure selection for polynomial Nonlinear Auto-Regressive with eXogenous (NARX) input for Steam Distillation Pilot Plant (SDPP). Three model's selection criteria were examined; Akaike Information Criterion (AIC), Model Descriptor Length (MDL), and Final Prediction Error (FPE). The performance analysis included output model analysis and model validation of the nonlinear model. The results demonstrated fewer number of input and output lags and lesser amount of output model parameter using MPRS perturbation signal compared with PRBS. Model validation showed high R-squared and low MSE for both signals' application. © 2018 IEEE.
publisher Institute of Electrical and Electronics Engineers Inc.
issn
language English
format Conference paper
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record_format scopus
collection Scopus
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