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%.
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Petrovski, A., Arifeen, M., Petrovski, S. (2023). Gated Recurrent Unit Autoencoder for Fault Detection in Penicillin Fermentation Process. In: Kovalev, S., Kotenko, I., Sukhanov, A. (eds) Proceedings of the Seventh International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’23). IITI 2023. Lecture Notes in Networks and Systems, vol 776. Springer, Cham. https://doi.org/10.1007/978-3-031-43789-2_8
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DOI: https://doi.org/10.1007/978-3-031-43789-2_8
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