Artificial neural network for anomalies detection in distillation column

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-w...

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Main Authors: Taqvi, S.A., Tufa, L.D., Zabiri, H., Mahadzir, S., Shah Maulud, A., Uddin, F.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.20267 /
Published: Springer Verlag 2017
Online Access: 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
http://eprints.utp.edu.my/20267/
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spelling utp-eprints.202672018-04-23T01:01:11Z Artificial neural network for anomalies detection in distillation column Taqvi, S.A. Tufa, L.D. Zabiri, H. Mahadzir, S. Shah Maulud, A. Uddin, F. 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. Springer Verlag 2017 Article PeerReviewed 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 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. http://eprints.utp.edu.my/20267/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
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.
format Article
author Taqvi, S.A.
Tufa, L.D.
Zabiri, H.
Mahadzir, S.
Shah Maulud, A.
Uddin, F.
spellingShingle Taqvi, S.A.
Tufa, L.D.
Zabiri, H.
Mahadzir, S.
Shah Maulud, A.
Uddin, F.
Artificial neural network for anomalies detection in distillation column
author_sort Taqvi, S.A.
title Artificial neural network for anomalies detection in distillation column
title_short Artificial neural network for anomalies detection in distillation column
title_full Artificial neural network for anomalies detection in distillation column
title_fullStr Artificial neural network for anomalies detection in distillation column
title_full_unstemmed Artificial neural network for anomalies detection in distillation column
title_sort artificial neural network for anomalies detection in distillation column
publisher Springer Verlag
publishDate 2017
url 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
http://eprints.utp.edu.my/20267/
_version_ 1741196344426496000
score 11.62408

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