Document Type
Article
Publication Date
2015
College/Unit
Statler College of Engineering and Mining Resources
Department/Program/Center
Civil and Environmental Engineering
Abstract
This paper introduces a novel steady-state identification (SSI) method based on the auto-regressive model with exogenous inputs (ARX). This method allows the SSI with reduced tuning by analyzing the identifiability properties of the system. In particular, the singularity of the model matrices is used as an index for steady-state determination. In this contribution, the novel SSI method is compared to other available techniques, namely the F-like test, wavelet transform and a polynomial-based approach. These methods are implemented for SSI of three different case studies. In the first case, a simulated dataset is used for calibrating the output-based SSI methods. The second case corresponds to a literature nonlinear continuous stirred-tank reactor (CSTR) example running at different steady states in which the ARX-based approach is tuned with the available input-output data. Finally, an industrial case with real data of a depropanizer column from PETROBRAS S.A. considering different pieces of equipment is analyzed. The results for a reflux drum case indicate that the wavelet and the F-like test can satisfactorily detect the steady-state periods after careful tuning and when respecting their hypothesis, i.e., smooth data for the wavelet method and the presence of variance in the data for the F-like test. Through a heat exchanger case with different measurement frequencies, we demonstrate the advantages of using the ARX-based method over the other techniques, which include the aspect of online implementation.
Digital Commons Citation
Rincon, Franklin D.; Le Roux, Galo A C; and Lima, Fernando V., "A Novel ARX-Based Approach for the Steady-State Identification Analysis of Industrial Depropanizer Column Datasets" (2015). Faculty & Staff Scholarship. 2217.
https://researchrepository.wvu.edu/faculty_publications/2217
Source Citation
Rincón, F., Roux, G., & Lima, F. (2015). A Novel ARX-Based Approach for the Steady-State Identification Analysis of Industrial Depropanizer Column Datasets. Processes, 3(2), 257–285. https://doi.org/10.3390/pr3020257
Comments
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