Process monitoring & fault detection | AIChE

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Process monitoring & fault detection

Chair(s)

Bhattacharyya, D., West Virginia University

Co-chair(s)

Hong, M. S., Massachusetts Institute of Technology
Babaei Pourkargar, D., Kansas State University

This session seeks novel contributions in the broad areas of process monitoring, fault detection, isolation, estimation and accommodation in process systems. Both data-driven and model-based approaches are welcome. Applications to industrial processes are encouraged.

Presentations

12:30 PM
(314a) Comparison of Machine Learning Techniques for the Modeling and Monitoring of Cryogenic Air Separation Unit Startups
12:51 PM
(314b) Model-Predictive Fault Tolerant Control of Safety-Critical Processes, Based on Dynamic Safety Set
1:12 PM
(314c) Exploring Generative Adversarial Networks for Fault Detection and Diagnosis in Chemical Processes: A Distillation Column Study
1:33 PM
(314d) Interpretable Fault Detection in Chemical Processes Using Large Language Models
1:54 PM
(314e) Integration of Dynamic Risk and Control for Enhanced Safety and Operational Efficiency
2:15 PM
(314f) Development of an Online Health Monitoring Framework for High Temperature Boiler Components By Using Hybrid First Principles-Data-Driven Models
2:36 PM
(314g) Fault Detection for an Atomic Layer Etching Process Using Machine Learning

Topics 

Process Automation & Control

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Pricing

Individuals

AIChE Pro Members $150.00
AIChE Emeritus Members $105.00
AIChE Graduate Student Members Free
AIChE Undergraduate Student Members Free
AIChE Explorer Members $225.00
Non-Members $225.00

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