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

Debangsu Bhattacharyya, West Virginia University

Co-Chairs

Moo Sun Hong, Massachusetts Institute of Technology
Davood Babaei Pourkargar, Kansas State University

Presentations

12:30 PM

(314a) Comparison of Machine Learning Techniques for the Modeling and Monitoring of Cryogenic Air Separation Unit Startups
Bahareh Hassani, Yajun Wang, Ankur Kumar, Jesus Flores-Cerrillo, Jin Wang, Q. Peter He

12:51 PM

(314b) Model-Predictive Fault Tolerant Control of Safety-Critical Processes, Based on Dynamic Safety Set
Ritu Ranjan, Costas Kravaris

01:12 PM

(314c) Exploring Generative Adversarial Networks for Fault Detection and Diagnosis in Chemical Processes: A Distillation Column Study
Amir Hossein Hamedi, Vladimir Mahalec, Prashant Mhaskar

01:33 PM

(314d) Interpretable Fault Detection in Chemical Processes Using Large Language Models
Abdullah Khan, Hao Chen, Gonzalo Constante-Flores, Can Li

01:54 PM

(314e) Integration of Dynamic Risk and Control for Enhanced Safety and Operational Efficiency
Sahithi Srijana Akundi, Yuanxing Liu, Austin Braniff, Beatriz Dantas, Shayan Niknezhad, Yuhe Tian, Faisal Khan, Efstratios Pistikopoulos

02:15 PM

(314f) Development of an Online Health Monitoring Framework for High Temperature Boiler Components By Using Hybrid First Principles-Data-Driven Models
Vivek Saini, Angan Mukherjee, Samuel Adeyemo, Debangsu Bhattacharyya

02:36 PM

(314g) Fault Detection for an Atomic Layer Etching Process Using Machine Learning
Henrik Wang, Matthew Tom, Feiyang Ou, Gerassimos Orkoulas, Panagiotis Christofides