Segmentation Methods for Model Identification from Historical Process Data

2014 ◽  
Vol 47 (3) ◽  
pp. 2836-2841 ◽  
Author(s):  
Yuri A.W. Shardt ◽  
Sirish L. Shah
10.14311/816 ◽  
2006 ◽  
Vol 46 (2) ◽  
Author(s):  
P. Pecherková ◽  
I. Nagy

Success/failure of adaptive control algorithms – especially those designed using the Linear Quadratic Gaussian criterion – depends on the quality of the process data used for model identification. One of the most harmful types of process data corruptions are outliers, i.e. ‘wrong data’ lying far away from the range of real data. The presence of outliers in the data negatively affects an estimation of the dynamics of the system. This effect is magnified when the outliers are grouped into blocks. In this paper, we propose an algorithm for outlier detection and removal. It is based on modelling the corrupted data by a two-component probabilistic mixture. The first component of the mixture models uncorrupted process data, while the second models outliers. When the outlier component is detected to be active, a prediction from the uncorrupted data component is computed and used as a reconstruction of the observed data. The resulting reconstruction filter is compared to standard methods on simulated and real data. The filter exhibits excellent properties, especially in the case of blocks of outliers. 


Processes ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 1686
Author(s):  
Nikesh Patel ◽  
Brandon Corbett ◽  
Johan Trygg ◽  
Chris McCready ◽  
Prashant Mhaskar

This manuscript addresses the problem of modeling an industrial (Sartorius) bioreactor using process data. In the context of the Sartorius Bioreactor, it is important to appropriately address the problem of dealing with a large number of variables, which are not always measured or are measured at different sampling rates, without taking recourse to simpler interpolation- or imputation-based approaches. To this end, a dynamic model for the Sartorius Bioreactor is developed via appropriately adapting a recently presented subspace model identification technique, which in turn uses nonlinear iterative partial least squares (NIPALS) algorithms to gracefully handle the missing data. The other key contribution is evaluating the ability of the identification approach to provide insight into the process by computing interpretable variables such as metabolite rates. The results demonstrate the ability of the proposed approach to model data from the Sartorius Bioreactor.


AIChE Journal ◽  
2000 ◽  
Vol 46 (10) ◽  
pp. 1989-1997 ◽  
Author(s):  
Christiane M. Jaeckle ◽  
John F. MacGregor

2005 ◽  
Vol 38 (1) ◽  
pp. 189-194
Author(s):  
V. Grosfils ◽  
C. Levrie ◽  
M. Kinnaert ◽  
A. Vande Wouwer

2006 ◽  
Vol 45 (17) ◽  
pp. 5971-5985 ◽  
Author(s):  
Nina F. Thornhill ◽  
Hallgeir Melbø ◽  
Jan Wiik

2014 ◽  
Vol 17 (1) ◽  
pp. 4-16
Author(s):  
Jade H. Coston ◽  
Corine Myers-Jennings

To better prepare the professionals and scholars of tomorrow in the field of communication sciences and disorders (CSD), a research project in which undergraduate students collected and analyzed language samples of child-parent dyads is presented. Student researchers gained broad and discipline-specific inquiry skills related to the ethical conduct of research, the literature review process, data collection using language assessment techniques, language sample analysis, and research dissemination. Undergraduate students majoring in CSD developed clinical research knowledge, skills, and dispositions necessary for future graduate level study and professional employment. In addition to the benefits of student growth and development, language samples collected through this project are helping to answer research questions regarding communicative turn-taking opportunities within the everyday routines of young children, the effects of turn-taking interactions on language development, and the construct validity of language sampling analysis techniques.


2008 ◽  
Vol 67 (2) ◽  
pp. 71-83 ◽  
Author(s):  
Yolanda A. Métrailler ◽  
Ester Reijnen ◽  
Cornelia Kneser ◽  
Klaus Opwis

This study compared individuals with pairs in a scientific problem-solving task. Participants interacted with a virtual psychological laboratory called Virtue to reason about a visual search theory. To this end, they created hypotheses, designed experiments, and analyzed and interpreted the results of their experiments in order to discover which of five possible factors affected the visual search process. Before and after their interaction with Virtue, participants took a test measuring theoretical and methodological knowledge. In addition, process data reflecting participants’ experimental activities and verbal data were collected. The results showed a significant but equal increase in knowledge for both groups. We found differences between individuals and pairs in the evaluation of hypotheses in the process data, and in descriptive and explanatory statements in the verbal data. Interacting with Virtue helped all students improve their domain-specific and domain-general psychological knowledge.


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