2017 Annual Meeting

Session: Big Data in Process Modeling, Estimation and Control

Contributions are invited on the topic of big data methods for off-line process modeling, on-line process state and parameter estimation and real-time feedback. Applications to industrial processes are encouraged.

Chair

Ravendra Singh, Rutgers, The State University of New Jer

Co-Chair

Matthew J. Realff, Georgia Institute of Technology

Presentations

08:00 AM

(646a) Pipeline Big Data Analysis - Leak Localization
Stevan Dubljevic, Xiaodong Xu

08:17 AM

(646b) Big Data Approach to Fault Detection, Diagnosis and Maintenance Optimization in Batch Processes
Melis Onel, Chris A. Kieslich, Yannis A. Guzman, Efstratios Pistikopoulos

08:34 AM

(646c) An Information Entropy Based Criterion for Variable Selection Performance Assessment
Kerul Suthar, Jangwon Lee, Q. Peter He

08:51 AM

(646d) Health Monitoring of Multi-Scale Systems Using an Optimal Multi-Rate Wireless Sensor Network
Edward Sablosky Mechanical & Aerospace Engineering, Katarzyna Sabolsky, Rajalekshmi Pillai, Qiao Huang, Debangsu Bhattacharyya

09:08 AM

(646e) Learning Based Automated Identification of Nuisance and Correlated Alarms
Alexander B. Smith, Yash Puranik, Bijan Sayyar-Rodsari, Neha Goyal

09:25 AM

(646f) Dynamic Latent Variable Regression for Data Modeling and Monitoring
Qinqin Zhu, S. Joe Qin

09:42 AM

(646g) Bayesian Model Averaging for Estimating the Spatial Temperature Distribution in a Steam Methane Reformer
Anh Tran, Marquis Crose, Andres Aguirre, Yangyao Ding, Madeleine Pont, Helen Durand, Panagiotis Christofides

09:59 AM

(646h) Adaptive ARX Models for Non-Linear Chemical Processes: An Industrial Application
Zhenyu Wang, Suyash Singh, Ali Esmaili

10:16 AM

(646i) Performance Evaluation of Anomaly Diagnosis System Based on Adaptive Resonance Theory
Yoshinari Hori, Yoshiharu Hayashi, Takaaki Sekiai, Hiroki Yamamoto, Shinji Hasebe