relation: https://khub.utp.edu.my/scholars/16934/
title: Intelligent Control of an Industrial Debutanizer Column
creator: Fatima, S.A.
creator: Zabiri, H.
creator: Taqvi, S.A.A.
creator: Ramli, N.
creator: Maulud, A.S.
description: A debutanizer column located at a refinery in Malaysia produces LPG as its top product and light naphtha as its bottom product. Control of the product compositions of the column is very important to maintain the desired purities. The current control design of the debutanizer column is based on the classical proportional integral derivative approach, which has been found to be less effective in controlling the variations in the column as the process is characterized by strong nonlinear and uncertain dynamics. Here, intelligent control based on an adaptive neuro-fuzzy inference system (ANFIS) is investigated and compared against an artificial neural network, for the special case of limited training data of the industrial debutanizer column. The ANFIS-based controller outperforms the other control configurations for both set point tracking and disturbance rejection cases and has better generalization performance even when trained with limited data samples. © 2022 Wiley-VCH GmbH
publisher: John Wiley and Sons Inc
date: 2022
type: Article
type: PeerReviewed
identifier:   Fatima, S.A. and Zabiri, H. and Taqvi, S.A.A. and Ramli, N. and Maulud, A.S.  (2022) Intelligent Control of an Industrial Debutanizer Column.  Chemical Engineering and Technology, 45 (4).  pp. 667-677.  ISSN 09307516     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125037763&doi=10.1002%2fceat.202100039&partnerID=40&md5=ecdb61c61a2a103b3f76e5ad3954d026
relation: 10.1002/ceat.202100039
identifier: 10.1002/ceat.202100039