584 Process Monitoring & Fault Detection


Description:
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.

Sponsor:
Data and Information Systems
Co-Sponsor(s):
Systems and Process Control (10B)


Chair:
Zhenyu Wang
Sr. Data Engineer
The Dow Chemical Company
Continuous Improvement Center of Excellence
Lake Jackson, TX
Email: zwang12@dow.com

Co-Chair:
Donald J. Chmielewski
Assistant Professor
Illinois Institute of Technology
Suite 127, Perlstein Hall
Chicago, IL
60616
Email: chmielewski@iit.edu


Papers:
584a
System Decomposition for Distributed Multivariate Statistical Process Monitoring
Shaaz Khatib, University of Minnesota; Prodromos Daoutidis, University of Minnesota; Ali Almansoori, The Petroleum Institute
584b
Transfer Entropy-Based Dynamic Causal Maps Generation for Fault Diagnosis in Systems with Cycles
Resmi Suresh, Columbia University; Abhishek Sivaram, Columbia University; Venkat Venkatasubramanian, Columbia University
584c
Efficient Process Monitoring and Causality Analysis of Processes Via the Integrated Use of the Graphical Lasso and Markov Random Fields Modeling
Changsoo Kim, Seoul National University; Hodong Lee, Seoul National University; Wonbo Lee, Seoul National University
584d
Transfer Learning Method for Chemical Plant Fault Diagnosis
Junyao Xie, University of Alberta; Stevan Dubljevic, University of Alberta
584e
Multi-Class Classification of Process Faults Using Nonlinear Support Vector Machine Based Feature Selection Algorithm
Melis Onel, Texas A&M Energy Institute, Texas A&M University; Efstratios N. Pistikopoulos, Texas A&M Energy Institute, Texas A&M University
584f
In Situ FT-IR Quantitative Analysis of Amine Concentration and CO2 Loading Amount in Mixture Solvent Using Deep Neural Network
Yo Sung Yoon, KAIST; Jay H. Lee, Korea Advanced Institute of Science and Technology (KAIST)
584g
Deep Learning for Pyrolysis Reactor Monitoring: From Thermal Imaging Towards Smart Monitoring System
Wenbo Zhu, Louisiana State University; Jose A. Romagnoli, Louisiana State University
584h
Model-Based Stochastic Fault Detection and Diagnosis of Lithium-Ion Batteries
Jeongeun Son, Clarkson University; Yuncheng Du, Clarkson University
584i
Implementation of Model-Predictive Safety Systems to Detect Predictively Operation Hazards in Non-Minimum-Phase Processes
Masoud Soroush, Drexel University; Jeffrey E. Arbogast, Process Control & Logistics, Air Liquide; Warren D. Seider, The University of Pennsylvania; Ahmad Arabi Shamsabadi, Drexel University
Abstract Submissions Closed
See more of this Group/Topical: Computing and Systems Technology Division
<< Previous Session | Next Session >>