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Processes 2017, 5(2), 28; doi:10.3390/pr5020028

Outlier Detection in Dynamic Systems with Multiple Operating Points and Application to Improve Industrial Flare Monitoring

1
,
2
,
3
and
3,*
1
Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, USA
2
Analytical Technology Center, The Dow Chemical Company, Freeport, TX 77541, USA
3
Process System and Solutions, Emerson Process Management, Roundrock, TX 78681, USA
*
Author to whom correspondence should be addressed.
Academic Editor: John D. Hedengren
Received: 25 March 2017 / Revised: 8 May 2017 / Accepted: 24 May 2017 / Published: 31 May 2017
(This article belongs to the Collection Process Data Analytics)
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Abstract

In chemical industries, process operations are usually comprised of several discrete operating regions with distributions that drift over time. These complexities complicate outlier detection in the presence of intrinsic process dynamics. In this article, we consider the problem of detecting univariate outliers in dynamic systems with multiple operating points. A novel method combining the time series Kalman filter (TSKF) with the pruned exact linear time (PELT) approach to detect outliers is proposed. The proposed method outperformed benchmark methods in outlier removal performance using simulated data sets of dynamic systems with mean shifts. The method was also able to maintain the integrity of the original data set after performing outlier removal. In addition, the methodology was tested on industrial flaring data to pre-process the flare data for discriminant analysis. The industrial test case shows that performing outlier removal dramatically improves flare monitoring results through Partial Least Squares Discriminant Analysis (PLS-DA), which further confirms the importance of data cleaning in process data analytics. View Full-Text
Keywords: time series Kalman filter (TSKF); pruned exact linear time (PELT); outlier detection; dynamic system; multiple operating points; flare monitoring; PLS-DA time series Kalman filter (TSKF); pruned exact linear time (PELT); outlier detection; dynamic system; multiple operating points; flare monitoring; PLS-DA
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Xu, S.; Lu, B.; Bell, N.; Nixon, M. Outlier Detection in Dynamic Systems with Multiple Operating Points and Application to Improve Industrial Flare Monitoring. Processes 2017, 5, 28.

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