relation: https://khub.utp.edu.my/scholars/9201/
title: Artificial neural network for anomalies detection in distillation column
creator: Taqvi, S.A.
creator: Tufa, L.D.
creator: Zabiri, H.
creator: Mahadzir, S.
creator: Shah Maulud, A.
creator: Uddin, F.
description: Early detection of anomalies can assist to avoid major losses in term of product degradation, machines� damages as well as human health issues. This research aims to use Artificial Neural Network to recognize anomalies in the distillation column. The pilot scale distillation column for the ethanol-water system is selected for the study. Faults are generated by variation in feed rate, feed composition and reboiler duty using Aspen Plus® dynamic simulation. The effect of these faults on process variables i.e. changes in distillate and bottom composition, distillate and bottom temperature, bottom flow rate, and the pressure drop is observed. The network is trained using back propagation algorithm to determine root mean square error (RMSE). Based on RMSE minimization, the (6-8-6) net serves as the best choice for the case studied for efficient fault detection. The presented techniques are general in nature and easily applicable to various other industrial problems. © Springer Nature Singapore Pte Ltd. 2017.
publisher: Springer Verlag
date: 2017
type: Article
type: PeerReviewed
identifier:   Taqvi, S.A. and Tufa, L.D. and Zabiri, H. and Mahadzir, S. and Shah Maulud, A. and Uddin, F.  (2017) Artificial neural network for anomalies detection in distillation column.  Communications in Computer and Information Science, 751.  pp. 302-311.  ISSN 18650929     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028959028&doi=10.1007%2f978-981-10-6463-0_26&partnerID=40&md5=980c6b5bb27fd10ef7e69935e938799c
relation: 10.1007/978-981-10-6463-0₂₆
identifier: 10.1007/978-981-10-6463-0₂₆