Fault Detection and Identification in an Acid Gas Removal Unit Using Deep Autoencoders-Reference-Cited by-同舟云学术

Fault Detection and Identification in an Acid Gas Removal Unit Using Deep Autoencoders

Published:2023-05-22 Issue:22 Volume:8 Page:19273-19286
ISSN:2470-1343
Container-title:ACS Omega
language:en
Short-container-title:ACS Omega

Author:

Kathlyn Tan Kaiyun¹²,Zabiri Haslinda¹³^ORCID,Aldrich Chris⁴,Liu Xiu⁴,Mohd Amiruddin Ahmad Azharuddin Azhari¹

Affiliation:

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

2. PETRONAS, PETRONAS Twin Towers, Level 17, Tower 1, Kuala Lumpur MY 50088, Malaysia

3. CO2RES, Institute of Contaminant Management, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610 Perak, Malaysia

4. Western Australian School of Mines, Curtin University, GPO Box U1987, Perth, 6845 WA, Australia

Funder

YUTP-FRG

Publisher

American Chemical Society (ACS)

Subject

General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acsomega.2c08109

Reference28 articles.

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2. Dynamic model-based sensor network design algorithm for system efficiency maximization

3. Application of Neural Network in Predicting H2S from an Acid Gas Removal Unit (AGRU) with Different Compositions of Solvents

4. Development of an Artificial Immune System for Power Plant Abnormal Condition Detection, Identification, and Evaluation

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