Data-driven digital twin technology for optimized control in process systems.
Data-driven methods
Digital twin
Optimized control configuration
Process monitoring and diagnosis
Tennessee Eastman process
Journal
ISA transactions
ISSN: 1879-2022
Titre abrégé: ISA Trans
Pays: United States
ID NLM: 0374750
Informations de publication
Date de publication:
Dec 2019
Dec 2019
Historique:
received:
31
01
2019
revised:
22
04
2019
accepted:
11
05
2019
pubmed:
22
5
2019
medline:
22
5
2019
entrez:
22
5
2019
Statut:
ppublish
Résumé
Due to the installation of various apparatus in process industries, both factors of complex structures and severe operating conditions could result in higher accident frequencies and maintenance challenges. Given the importance of security in process systems, this paper presents a data-driven digital twin system for automatic process applications by integrating virtual modeling, process monitoring, diagnosis, and optimized control into a cooperative architecture. For unknown model parameters, the adaptive system identification is proposed to model closed-loop virtual systems and residual signals with fault-free case data. Performance indices are improved to make the design of robust monitoring and diagnosis system to identify the apparatus status. Soft-sensor, parameterization control, and model-matching reconfiguration are ameliorated and incorporated into the optimized control configuration to guarantee stable and safe control performance under apparatus faults. The effectiveness and performance of the proposed digital twin system are evaluated by using different simulations on the Tennessee Eastman benchmark process in the presence of realistic fault scenarios.
Identifiants
pubmed: 31109723
pii: S0019-0578(19)30233-2
doi: 10.1016/j.isatra.2019.05.011
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
221-234Informations de copyright
Copyright © 2019 ISA. Published by Elsevier Ltd. All rights reserved.