Citation

BibTex format

@article{Zhou:2019:10.1021/acs.iecr.8b06138,
author = {Zhou, B and Chioua, M and Bauer, M and Schlake, JC and Thornhill, NF},
doi = {10.1021/acs.iecr.8b06138},
journal = {Industrial && Engineering Chemistry Research},
pages = {11234--11250},
title = {Improving root cause analysis by detecting and removing transient changes in oscillatory time series with application to a 1,3-butadiene process},
url = {http://dx.doi.org/10.1021/acs.iecr.8b06138},
volume = {58},
year = {2019}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Oscillations occurring in industrial process plants often reflect the presence of severe disturbances affecting process operations. Accurate detection and root-cause analysis of oscillations is of great interest for the economic viability of the process operation. Standard oscillation detection and root cause analysis methods require a large enough number of data samples. Unrelated transient changes superimposed on the oscillation pattern reduce the number of useful data samples. The present paper proposes simple heuristic methods to effectively detect and remove two types of transient changes from oscillatory signals, namely step changes and spikes. The proposed methods are used to pre-process oscillatory time series. The accuracy gained when using auto-correlation function method for oscillation detection and transfer entropy method for oscillation propagation is experimentally evaluated. The methods are carried out on a 1.3-Butadiene production process where several measurements showed an established oscillation occurring after a production level change.
AU - Zhou,B
AU - Chioua,M
AU - Bauer,M
AU - Schlake,JC
AU - Thornhill,NF
DO - 10.1021/acs.iecr.8b06138
EP - 11250
PY - 2019///
SN - 0888-5885
SP - 11234
TI - Improving root cause analysis by detecting and removing transient changes in oscillatory time series with application to a 1,3-butadiene process
T2 - Industrial && Engineering Chemistry Research
UR - http://dx.doi.org/10.1021/acs.iecr.8b06138
UR - https://pubs.acs.org/doi/10.1021/acs.iecr.8b06138
UR - http://hdl.handle.net/10044/1/70328
VL - 58
ER -

Contact us

Nina Thornhill, ABB/RAEng Professor of Process Automation
Centre for Process Systems Engineering
Department of Chemical Engineering
Imperial College London
South Kensington Campus, London SW7 2AZ

Tel: +44 (0)20 7594 6622
Email: n.thornhill@imperial.ac.uk