Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/13831
Title: Data driven leak detection in a real heat exchanger in an oil refinery
Authors: Yasmal, Aslı
Kuşoğlu Kaya, Gizem
Oktay, Emirhan
Çölmekci, Ceylan
Uzunlar, Erdal
Keywords: Autoencoder
Data-based
Heat exchanger
Leak detection
Oil and gas industry
Issue Date: 2023
Publisher: Elsevier
Abstract: This study focuses on implementation of a data-based leak detection method in a heat exchanger in a petroleum refinery. We have studied on the two real leakage cases in a heat exchanger in Izmit TUPRAS Refinery. Leaks are one of the major problems that occur in operations. The autoencoder (AE) method is implemented for leak detection. Reconstruction error is used as the leak indicator. In case of leakage, the reconstruction value is expected to increase. For both cases examined, the reconstruction error is found to be around 1-5 under normal operating conditions. On the other hand, reconstruction error is observed to change between 10 and 60 under the conditions with leakage. Besides, the AE is able to indicate the start of one leakage case before the process engineers noticed it. © 2023 Elsevier B.V.
URI: https://doi.org/10.1016/B978-0-443-15274-0.50493-5
https://hdl.handle.net/11147/13831
ISSN: 1570-7946
Appears in Collections:Chemical Engineering / Kimya Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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