2024 AIChE Annual Meeting

Session: Process monitoring & fault detection

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.

Chair

Bhattacharyya, D., West Virginia University

Co-Chairs

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

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

01:12 PM

(314c) Exploring Generative Adversarial Networks for Fault Detection and Diagnosis in Chemical Processes: A Distillation Column Study

01:33 PM

(314d) Interpretable Fault Detection in Chemical Processes Using Large Language Models

01:54 PM

(314e) Integration of Dynamic Risk and Control for Enhanced Safety and Operational Efficiency

02:15 PM

(314f) Development of an Online Health Monitoring Framework for High Temperature Boiler Components By Using Hybrid First Principles-Data-Driven Models

02:36 PM

(314g) Fault Detection for an Atomic Layer Etching Process Using Machine Learning