Skip to main content

Research Repository

Advanced Search

Gated recurrent unit autoencoder for fault detection in penicillin fermentation process.

Petrovski, Andrei; Arifeen, Murshedul; Petrovski, Sergei

Authors

Andrei Petrovski

Sergei Petrovski



Contributors

Sergey Kovalev
Editor

Igor Kotenko
Editor

Andrey Sukhanov
Editor

Abstract

The penicillin fermentation process is a fed-batch system to generate industrial-scale penicillin for antibiotic production. Any fault in the fermentation tank can lead to low-quality penicillin products, which may cause a severe impact on final antibiotic production. In this paper, we have developed a Gated Recurrent Unit-based Autoencoder deep learning model to detect faults in the batch data of the penicillin fermentation process. In particular, we have used the data shuffling strategy to minimize distribution discrepancy from different batches generated under various controlling conditions for training the deep learning model. We have also compared the model with the Feedforward Autoencoder and Long short-term memory Autoencoder model for fault detection. Experimental results show that our model trained on shuffled data from different batches outperformed the Feedforward and Long short-term memory Autoencoder model with an avergae fault detection rate of 94.74%.

Citation

PETROVSKI, A., ARIFEEN, M. and PETROVSKI, S. 2023. Gated recurrent unit autoencoder for fault detection in penicillin fermentation process. In Kovalev, S., Kotenko, I. and Sukhanov, A. (eds.) Proceedings of the 7th Intelligent information technologies for industry international scientific conference 2023 (IITI'23), 20-25 September 2023, St. Petersburg, Russia, volume 1. Lecture notes in networks and systems (LNNS), 776. Cham: Springer [online], pages 86-95. Available from: https://doi.org/10.1007/978-3-031-43789-2_8

Presentation Conference Type Conference Paper (published)
Conference Name 7th Intelligent information technologies for industry international scientific conference 2023 (IITI'23)
Start Date Sep 25, 2023
End Date Sep 30, 2023
Acceptance Date May 29, 2023
Online Publication Date Sep 21, 2023
Publication Date Dec 31, 2023
Deposit Date Oct 12, 2023
Publicly Available Date Sep 22, 2024
Publisher Springer
Peer Reviewed Peer Reviewed
Pages 86-95
Series Title Lecture notes in networks and systems (LNNS)
Series Number 776
Series ISSN 2367-3370; 2367-3389
Book Title Proceedings of the seventh international scientific conference Intelligent information technologies for industry (ITTI'23)
ISBN 9783031437885
DOI https://doi.org/10.1007/978-3-031-43789-2_8
Keywords Fault detection; Autoencoder; Gated recurrent unit; Penicillin fermentation
Public URL https://rgu-repository.worktribe.com/output/2107579

Files

PETROVSKI 2023 Gated recurrent unit (AAM) (1.2 Mb)
PDF

Copyright Statement
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG.




You might also like



Downloadable Citations