Oscillation Detection in Process Industries by a Machine Learning-Based Approach-Reference-Cited by-同舟云学术

Oscillation Detection in Process Industries by a Machine Learning-Based Approach

Published:2019-07-05 Issue:31 Volume:58 Page:14180-14192
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Dambros Jônathan W. V.¹²^ORCID,Trierweiler Jorge O.¹,Farenzena Marcelo¹,Kloft Marius²³

Affiliation:

1. Department of Chemical Engineering, Federal University of Rio Grande do Sul, R. Eng. Luiz Englert, s/n, Campus Central, Porto Alegre, Rio Grande do Sul, Brazil

2. Department of Computer Science, University of Kaiserslautern, 67663 Kaiserslautern, Germany

3. Department of Computer Science, University of Southern California, 1002 Childs Way, Los Angeles, California, United States

Funder

Bundesministerium f?r Bildung und Forschung

Deutsche Forschungsgemeinschaft

Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior

Petrobras

Trisolutions

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.9b01456

Reference48 articles.

1. Torres, B. S.; Carvalho, F. B.; Fonseca, M. O.; Filho, C. S. Performance Assessment of Control Loops – Case Studies. In Proc. IFAC ADCHEM; Gramado, Brasil, 2006.

2. A practical approach for large-scale controller performance assessment, diagnosis, and improvement

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