Multiscale Framework for Real-Time Process Monitoring of Nonlinear Chemical Process Systems-Reference-Cited by-同舟云学术

Multiscale Framework for Real-Time Process Monitoring of Nonlinear Chemical Process Systems

Published:2020-09-18 Issue:41 Volume:59 Page:18595-18606
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Nawaz Muhammad¹,Maulud Abdulhalim Shah¹²^ORCID,Zabiri Haslinda¹^ORCID,Suleman Humbul³^ORCID,Tufa Lemma Dendena⁴^ORCID

Affiliation:

1. Department of Chemical Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak, Malaysia

2. Centre of Contaminant Control & Utilization (CenCoU), Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak, Malaysia

3. School of Computing, Engineering and Digital Technologies, Teesside University, TS1 3BX Middlesbrough, U. K.

4. School of Chemical and Bioengineering, Addis Ababa Institute of Technology, Addis Ababa University, King George VI St Addis Ababa 1000 Addis Ababa, Ethiopia

Funder

Universiti Teknologi Petronas

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.0c02288

Reference54 articles.

1. Distributed monitoring for large-scale processes based on multivariate statistical analysis and Bayesian method

2. Dynamic Distributed Monitoring Strategy for Large-Scale Nonstationary Processes Subject to Frequently Varying Conditions Under Closed-Loop Control

3. Deep convolutional neural network model based chemical process fault diagnosis

4. Review and Perspectives of Data-Driven Distributed Monitoring for Industrial Plant-Wide Processes

5. Survey on data-driven industrial process monitoring and diagnosis

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