Local linear model tree and Neuro-Fuzzy system for modelling and control of an experimental pH neutralization process
Autor(a) principal: | |
---|---|
Data de Publicação: | 2014 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Brazilian Journal of Chemical Engineering |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322014000200019 |
Resumo: | This paper describes the modelling and control of a pH neutralization process using a Local Linear Model Tree (LOLIMOT) and an adaptive neuro-fuzzy inference system (ANFIS). The Direct and Inverse model building using LOLIMOT and ANFIS structures is described and compared. The direct and inverse models of the pH system are identified based on experimental data for the LOLIMOT and ANFIS structures. The identified models are implemented in the experimental pH system with IMC structure using a GUI developed in the MATLAB -SIMULINK platform. The main aim is to illustrate the online modelling and control of the experimental setup. The results of real-time control of an experimental pH process using the Internal Model Control (IMC) strategy are also presented. |
id |
ABEQ-1_0be3bc09a2171138bafaad96853eb809 |
---|---|
oai_identifier_str |
oai:scielo:S0104-66322014000200019 |
network_acronym_str |
ABEQ-1 |
network_name_str |
Brazilian Journal of Chemical Engineering |
repository_id_str |
|
spelling |
Local linear model tree and Neuro-Fuzzy system for modelling and control of an experimental pH neutralization processLOLIMOTANFISInternal Model ControlpH processThis paper describes the modelling and control of a pH neutralization process using a Local Linear Model Tree (LOLIMOT) and an adaptive neuro-fuzzy inference system (ANFIS). The Direct and Inverse model building using LOLIMOT and ANFIS structures is described and compared. The direct and inverse models of the pH system are identified based on experimental data for the LOLIMOT and ANFIS structures. The identified models are implemented in the experimental pH system with IMC structure using a GUI developed in the MATLAB -SIMULINK platform. The main aim is to illustrate the online modelling and control of the experimental setup. The results of real-time control of an experimental pH process using the Internal Model Control (IMC) strategy are also presented.Brazilian Society of Chemical Engineering2014-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322014000200019Brazilian Journal of Chemical Engineering v.31 n.2 2014reponame:Brazilian Journal of Chemical Engineeringinstname:Associação Brasileira de Engenharia Química (ABEQ)instacron:ABEQ10.1590/0104-6632.20140312s00002287info:eu-repo/semantics/openAccessPetchinathan,G.Valarmathi,K.Devaraj,D.Radhakrishnan,T. K.eng2014-07-07T00:00:00Zoai:scielo:S0104-66322014000200019Revistahttps://www.scielo.br/j/bjce/https://old.scielo.br/oai/scielo-oai.phprgiudici@usp.br||rgiudici@usp.br1678-43830104-6632opendoar:2014-07-07T00:00Brazilian Journal of Chemical Engineering - Associação Brasileira de Engenharia Química (ABEQ)false |
dc.title.none.fl_str_mv |
Local linear model tree and Neuro-Fuzzy system for modelling and control of an experimental pH neutralization process |
title |
Local linear model tree and Neuro-Fuzzy system for modelling and control of an experimental pH neutralization process |
spellingShingle |
Local linear model tree and Neuro-Fuzzy system for modelling and control of an experimental pH neutralization process Petchinathan,G. LOLIMOT ANFIS Internal Model Control pH process |
title_short |
Local linear model tree and Neuro-Fuzzy system for modelling and control of an experimental pH neutralization process |
title_full |
Local linear model tree and Neuro-Fuzzy system for modelling and control of an experimental pH neutralization process |
title_fullStr |
Local linear model tree and Neuro-Fuzzy system for modelling and control of an experimental pH neutralization process |
title_full_unstemmed |
Local linear model tree and Neuro-Fuzzy system for modelling and control of an experimental pH neutralization process |
title_sort |
Local linear model tree and Neuro-Fuzzy system for modelling and control of an experimental pH neutralization process |
author |
Petchinathan,G. |
author_facet |
Petchinathan,G. Valarmathi,K. Devaraj,D. Radhakrishnan,T. K. |
author_role |
author |
author2 |
Valarmathi,K. Devaraj,D. Radhakrishnan,T. K. |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Petchinathan,G. Valarmathi,K. Devaraj,D. Radhakrishnan,T. K. |
dc.subject.por.fl_str_mv |
LOLIMOT ANFIS Internal Model Control pH process |
topic |
LOLIMOT ANFIS Internal Model Control pH process |
description |
This paper describes the modelling and control of a pH neutralization process using a Local Linear Model Tree (LOLIMOT) and an adaptive neuro-fuzzy inference system (ANFIS). The Direct and Inverse model building using LOLIMOT and ANFIS structures is described and compared. The direct and inverse models of the pH system are identified based on experimental data for the LOLIMOT and ANFIS structures. The identified models are implemented in the experimental pH system with IMC structure using a GUI developed in the MATLAB -SIMULINK platform. The main aim is to illustrate the online modelling and control of the experimental setup. The results of real-time control of an experimental pH process using the Internal Model Control (IMC) strategy are also presented. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-06-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322014000200019 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322014000200019 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0104-6632.20140312s00002287 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Brazilian Society of Chemical Engineering |
publisher.none.fl_str_mv |
Brazilian Society of Chemical Engineering |
dc.source.none.fl_str_mv |
Brazilian Journal of Chemical Engineering v.31 n.2 2014 reponame:Brazilian Journal of Chemical Engineering instname:Associação Brasileira de Engenharia Química (ABEQ) instacron:ABEQ |
instname_str |
Associação Brasileira de Engenharia Química (ABEQ) |
instacron_str |
ABEQ |
institution |
ABEQ |
reponame_str |
Brazilian Journal of Chemical Engineering |
collection |
Brazilian Journal of Chemical Engineering |
repository.name.fl_str_mv |
Brazilian Journal of Chemical Engineering - Associação Brasileira de Engenharia Química (ABEQ) |
repository.mail.fl_str_mv |
rgiudici@usp.br||rgiudici@usp.br |
_version_ |
1754213174307979264 |