| Author | Titel | Jahr | Typ | MRT-Nr | DOI/URL |
|---|---|---|---|---|---|
| Al Mawla, H. & Kroll, A. | Multivariate statistical approaches for an early detection of foaming in a refinery SCOT unit | 2018 | DOI URL | ||
| 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). | |||||
BibTeX:
@article{
Al_Mawla-AT-2018,
author = {H. Al Mawla and A. Kroll}
, title = {Multivariate statistical approaches for an early detection of foaming in a refinery SCOT unit}
, journal = {at -- Automatisierungstechnik}
, year = {2018}
, volume = {66}
, number = {8}
, pages = {665 -- 679}
, url = {https://www.degruyter.com/view/j/auto.2018.66.issue-8/auto-2018-0048/auto-2018-0048.xml}
, doi = {https://doi.org/10.1515/auto-2018-0048}
, issn = {2196-677X}
}
| |||||
| Arengas, D. & Kroll, A. | Searching for Informative Intervals in Predominantly Stationary Data Records to Support System Identification [BibTeX] |
2017 | URL | ||
BibTeX:
@inproceedings{
Arengas2017,
author = {David Arengas and Andreas Kroll}
, title = {Searching for Informative Intervals in Predominantly Stationary Data Records to Support System Identification}
, booktitle = {Proceedings of the 26th International Conference on Information, Communication and Automation Technologies ICAT 2017}
, year = {2017}
, pages = {132 -- 137}
, address = {Sarajevo, Bosnia & Herzegovina}
, url = {http://ieeexplore.ieee.org/document/8171617/}
}
| |||||
| Arengas, D. & Kroll, A. | A Search Method for Selecting Informative Data in Predominantly Stationary Historical Records for Multivariable Systems [BibTeX] |
2017 | URL | ||
BibTeX:
@inproceedings{
Arengas2017b,
author = {David Arengas and Andreas Kroll}
, title = {A Search Method for Selecting Informative Data in Predominantly Stationary Historical Records for Multivariable Systems}
, booktitle = {Proceedings of the 21st International Conference on System Theory, Control and Computing ICSTCC 2017}
, year = {2017}
, pages = {100 -- 105}
, address = {Sinaia, Romania}
, url = {http://ieeexplore.ieee.org/document/8107018/}
}
| |||||
| Atzmueller, M., Kloepper, B., Al Mawla, H., Jäschke, B., Hollender, M., Graube, M., Arnu, D., Schmidt, A., Heinze, S., Schorer, L., Kroll, A., Stumme, G. & Urbas, L. | Big data analytics for proactive industrial decision support: Approaches & first experiences in the FEE Project [BibTeX] |
2016 | URL | ||
BibTeX:
@article{
AtzmuellerATP2016,
author = {Atzmueller, Martin and Kloepper, Benjamin and Al Mawla, Hassan and Jäschke, Benjamin and Hollender, Martin and Graube, Markus and Arnu, David and Schmidt, Andreas and Heinze, Sebastian and Schorer, Lukas and Kroll, Andreas and Stumme, Gerd and Urbas, Leon}
, title = {Big data analytics for proactive industrial decision support: Approaches & first experiences in the FEE Project}
, journal = {atp edition}
, year = {2016}
, volume = {58}
, number = {9}
, pages = {62-74}
, url = {https://www.di-verlag.de/de/Zeitschriften/atp-edition/2016/09/Big-data-analytics-for-proactive-industrial-decision-support}
, language = {english}
}
| |||||
| Heinze, S., Graube, M., Schegner, L., Arnu, D., Klinkenberg, R., Schmidt, A., Atzmüller, M., Klöpper, B., Dix, M., Hollender, M., Chioua, M., Al Mawla, H., Rehmer, A., Kroll, A., Stumme, G. & Urbas, L. | Big Data in der Prozessindustrie: Frühzeitige Erkennung und Entscheidungsunterstützung [BibTeX] |
2017 | URL | ||
BibTeX:
@inproceedings{
HeinzeAutomation2017,
author = {S. Heinze and M. Graube and L. Schegner and D. Arnu and R. Klinkenberg and A. Schmidt and M. Atzmüller and B. Klöpper and M. Dix and M. Hollender and M. Chioua and H. Al Mawla and A. Rehmer and A. Kroll and G. Stumme and L. Urbas}
, title = {Big Data in der Prozessindustrie: Frühzeitige Erkennung und Entscheidungsunterstützung}
, booktitle = {Automation 2017}
, publisher = {VDI}
, year = {2017}
, address = {Baden-Baden}
, url = {http://www.automatisierungskongress.de/}
, language = {deutsch}
}
| |||||
| Jäschke, B. & Kroll, A. | Ein Nächste-Nachbarn-Ansatz zur Anomaliedetektion bei Massendaten aus kontinuierlich betriebenen Chemieanlagen [BibTeX] |
2016 | |||
BibTeX:
@inproceedings{
JaeschkeGMA2016,
author = {Benjamin Jäschke and Andreas Kroll}
, title = {Ein Nächste-Nachbarn-Ansatz zur Anomaliedetektion bei Massendaten aus kontinuierlich betriebenen Chemieanlagen}
, booktitle = {26. Workshop Computational Intelligence}
, publisher = {KIT Scientific Publishing}
, year = {2016}
, volume = {26}
, pages = {245-260}
, address = {Dortmund}
}
| |||||
| Kroll, A., Dürrbaum, A., Arengas, D., Al Mawla, H., Kistner, L. & Rehmer, A. | µPlant: Eine automatisierungstechnisch-orientierte Modellfabrik für vernetzte heterogene Systeme | 2017 | URL | ||
| Abstract: Modellfabriken werden mittlerweile häufig in Forschung und Lehre eingesetzt, aber über ihren Aufbau und ihre Funktionen ist wenig zu lesen. Im Beitrag wird eine Übersicht über in Deuschland vorhandene Modellfabriken gegeben. Zudem werden Details der selbst konzipierten und realisierten Modellfabrik µPlant vorgestellt. Diese besteht aus mehreren mit Transportrobotern verbundenen Produktionsinseln/-zellen, die jeweils lokal über angepasste Automatisierungssysteme verfügen. Alle Module sind stofflich und informationstechnisch integriert und die Modellfabrik kann voll automatisiert betrieben werden. Der Beitrag richtet sich an Personen mit Interesse an Aufbau, Beschaffung oder Nutzung von Modellfabriken | |||||
BibTeX:
@article{
2017-Kroll_et_al-atp_edition-Modellfabrik_muPlant,
author = {Andreas Kroll AND Axel Dürrbaum AND David Arengas AND Hassan Al Mawla AND Lars Kistner AND Alexander Rehmer}
, title = {µPlant: Eine automatisierungstechnisch-orientierte Modellfabrik für vernetzte heterogene Systeme}
, journal = {atp edition}
, year = {2017}
, volume = {59}
, number = {9}
, pages = {40-53}
, url = {https://www.di-verlag.de/de/Zeitschriften/atp-edition/2017/09/Plant:-Modellfabrik-fuer-vernetzte-heterogene-Anlagen}
, language = {german}
}
| |||||
| Kroll, A., Dürrbaum, A., Arengas, D., Jäschke, B., Al Mawla, H. & Geiger, A. | µPlant: Model factory for the automatization of networked, heterogeneous and flexibly changeable multi-product plants [BibTeX] |
2016 | URL | ||
BibTeX:
@inproceedings{
muPlant_AUTOMATION_2016,
author = {A. Kroll and A. Dürrbaum and D. Arengas and B. Jäschke and H. Al Mawla and A. Geiger}
, title = {µPlant: Model factory for the automatization of networked, heterogeneous and flexibly changeable multi-product plants}
, booktitle = {Automation 2016}
, publisher = {VDI}
, year = {2016}
, volume = {VDI-Berichte 2284}
, address = {Baden-Baden}
, url = {http://www.automatisierungskongress.de/}
, isbn = {978-3-18-092284-0}
, issn = {0083-5560}
, language = {english}
}
| |||||
| Rehmer, A. & Kroll, A. | An extension to RPCA parameter selection and process monitoring | 2017 | |||
| Abstract: Multivariate Statistical Process Control (MSPC) techniques such as Principal Component Analysis (PCA) and Partial Least Squares (PLS) have found wide application especially in the statistical modeling and monitoring of chemical processes. However, real industrial processes often violate the assumptions underlying MSPC since they exhibit timevarying and non-stationary behavior. Adaptive PCA-based monitoring procedures such as Moving Window PCA (MWPCA) and Recursive PCA (RPCA) have been proposed to tackle this issue. Although the parameter selection for those procedures is critical to their proper implementation, this topic is rarely covered in the literature. This paper examines two methods for MWPCA and RPCA parameter selection using the Tennessee Eastman process as an example. Based on the findings a novel procedure for RPCA parameter selection as well as a extension to RPCA will be proposed and demonstrated. | |||||
BibTeX:
@inproceedings{
RehmerIFAC2017,
author = {Alexander Rehmer and Andreas Kroll}
, title = {An extension to RPCA parameter selection and process monitoring}
, booktitle = {Preprints of the 20th IFAC World Congress}
, year = {2017}
, pages = {15329-15334}
}
| |||||
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