Abstract
The formation of foam in amine units is an issue that plant operators and field personnel are confronted with on a regular basis. The inability to take proper actions in due time may result in plant downtime and increased emissions. Steep rises in differential pressure indicate foam formation, and are monitored manually in practice. Antifoaming agent is added in order to reduce foaming, but this is usually carried out under time pressure. Hence, plant operating authorities have expressed a strong interest in a data-driven solution capable of providing an early warning against foaming. The classical univariate alarm associated with differential pressure can be ineffective for foaming detection due to high misdetection rates and its lateness of detection. Modern univariate approaches based on pattern recognition techniques may not be suitable either for an early detection, as no universally distinctive features of differential pressure are observed prior to foaming in the present study. In this contribution, the multivariate statistical process monitoring approach based on principal component analysis (PCA) is applied to the early detection of foaming in a continuously operated Shell Claus Off-gas Treating (SCOT) unit of a major refinery in Germany. The results are extended to facilitate fully automated and adaptive modeling based on exponentially weighted recursive principal component analysis (EWRPCA).
Zusammenfassung
Die Bildung von Schaum in Aminanlagen gehört zu den am häufigsten auftretenden unerwünschten Phänomenen. Ohne rechtzeitige Maßnahme führt sie zu Anlagenstillständen und erhöhten Emissionen. Ein plötzlicher Anstieg des Differenzdrucks weist auf Schaumbildung hin und wird in der Praxis manuell überwacht. Um Schaumbildung entgegenzuwirken, wird Anti-Schaummittel eingespritzt, aber dies erfolgt meistens unter Zeitdruck. Deshalb zeigten Anlagenbetreiber ein großes Interesse an einer datengetriebenen Lösung zur Frühwarnung vor Schaumbildung. Der klassische univariate Alarm ist aufgrund der hohen Fehlerkennungsrate und der späten Erkennung zur Früherkennung von Schaumbildung nicht geeignet. Moderne univariate Ansätze basierend auf Methoden der Mustererkennung können ebenfalls ungeeignet sein, da in der vorliegenden Studie keine universellen charakteristischen Merkmale des Differenzdrucks vor dem Schäumen beobachtet wurden. In diesem Beitrag wird das multivariate statistische Verfahren zur Prozessüberwachung auf Basis der Hauptkomponentenanalyse zur Früherkennung von Schaumbildung in einer kontinuierlich betriebenen SCOT-Anlage einer großen Raffinerie in Deutschland eingesetzt. In einem weiteren Schritt wird dieser Ansatz mittels exponentiell gewichteter rekursiver Hauptkomponentenanalyse um die Fähigkeit vollautomatisierter und adaptiver Modellbildung erweitert.
Funding source: Bundesministerium für Bildung und Forschung
Award Identifier / Grant number: 01IS14006D
Funding statement: The authors are grateful to the German Ministry of Education and Research (BMBF) for partially funding this work under grant number 01IS14006D.
About the authors
Hassan Enam Al Mawla, M. Sc., is a research associate at the Department of Measurement and Control at the University of Kassel. His research interests include system identification, data-driven process monitoring, and deep neural networks.
Univ.-Prof. Dr.-Ing. Andreas Kroll is head of the Department of Measurement and Control at the University of Kassel. His research areas include nonlinear identification and control methods, computational intelligence, and complex systems.
Acknowledgment
The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the article. The authors are also grateful to PCK Raffinerie GmbH Schwedt for providing the application problem and the process data.
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