eprintid: 12606
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/26/06
datestamp: 2023-11-10 03:27:10
lastmod: 2023-11-10 03:27:10
status_changed: 2023-11-10 01:49:05
type: article
metadata_visibility: show
creators_name: Maulud, A.S.
creators_name: Nawaz, M.
creators_name: Zabiri, H.
creators_name: Suleman, H.
creators_name: Tufa, L.D.
title: Multiscale framework for real-time process monitoring of nonlinear chemical process systems
ispublished: pub
keywords: Accident prevention; Chemical industry; Error statistics; Fault detection; Monitoring; Optimization; Process control; Process monitoring; Wavelet transforms, Chemical process systems; Continuous stirred tank reactor; Kernel principal component analyses (KPCA); Monitoring techniques; Multi-scale frameworks; Process disturbances; Real-time process monitoring; Squared prediction errors, Real time systems
note: cited By 15
abstract: Process monitoring techniques are used in the chemical industry to improve both product quality and plant safety. In chemical process systems, real-time process monitoring is one of the most crucial and challenging tasks for efficient quality control of the final products and process optimization. The existing multiscale process monitoring techniques use offline decomposition tools that restrict their applications to real-time process monitoring. In this study, to improve the performance of monitoring real-time process data, we have combined moving window-based wavelet transform and kernel principal component analysis (KPCA). A case study is performed on a typical continuous stirred tank reactor system. Performance analysis (based on T2 and squared prediction error statistics and contribution plots) shows that the technique successfully detects and identifies process disturbances, sensor bias, and process faults. Moreover, a comparison with PCA and KPCA methods shows that the proposed approach provides a 100 fault detection rate for the step-change fault patterns and has considerably improved detection rates for the random and ramp-change fault patterns. © 2020 American Chemical Society
date: 2020
publisher: American Chemical Society
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096033121&doi=10.1021%2facs.iecr.0c02288&partnerID=40&md5=826402f230faa0d32700b43f35dbb54e
id_number: 10.1021/acs.iecr.0c02288
full_text_status: none
publication: Industrial and Engineering Chemistry Research
volume: 59
number: 41
pagerange: 18595-18606
refereed: TRUE
issn: 08885885
citation:   Maulud, A.S. and Nawaz, M. and Zabiri, H. and Suleman, H. and Tufa, L.D.  (2020) Multiscale framework for real-time process monitoring of nonlinear chemical process systems.  Industrial and Engineering Chemistry Research, 59 (41).  pp. 18595-18606.  ISSN 08885885