Preliminary Program subject to change
10B05 Process Modeling and Identification
OverviewThis session focuses on theoretical and application results in the area of process modeling and identification. Topics of interest include, but are not limited to: data-driven and theoretical modeling, novel identification algorithms, design of input signals, identification and validation under conditions of inadequate and/or incomplete data, and identification of nonlinear systems. Applications in nontraditional processes and systems are of particular interest.
Primary SponsorSystems and Process Control (10b)

Chair

S. Joe Qin
Professor
University of Texas
Department of Chemical Engineering
Austin, TX 78712
Phone Number: 512-471-4417
Fax Number: 512-471-7060
Email: qin@che.utexas.edu

CoChair

Prodromos Daoutidis
Professor
University of Minnesota
Department of Chemical Engineering and Materials Science
Minneapolis, MN 55455
Phone Number: (612)625-8818
Fax Number: (612)626-7246
Email: daoutidi@cems.umn.edu
Simpca with Modified Instrumental Variable to Improve Estimation Accuracy
Jin Wang, Advanced Process Control, Advanced Micro Devices, 5204 E. Ben White Blvd, MS 613, Austin, TX 78741 and S. Joe Qin, Chemical Engineering, The University of Texas at Austin, 1 University Station C0400, Austin, TX 78712
An Integrated Methodology for Plant-Friendly Input Signal Design and Control-Relevant Estimation of Highly Interactive Processes
Hyunjin Lee, Arizona State University, Dept. of Chemical Engineering, Mail Stop 6006, Tempe, AZ 85287-6006 and Daniel E. Rivera, Arizona State University, Dept. of Chemical and Materials Engineering, Mail Stop 876006, Tempe, AZ 85287-6006
Bayesian Latent Variable Regression of High Dimensional Data with Applications to Process Identification
Hongshu Chen1, Bhavik R. Bakshi1 and Prem K. Goel2, (1)Department of Chemical & Biomolecular Engineering, The Ohio State University, 125A Koffolt Labs 140 W. 19th Ave, Columbus, OH 43210, (2)Department of Statistics, The Ohio State University, 404 Cockins Hall 1958 Neil Ave., Columbus, OH 43210
Model Maintenance for Industrial Process Control
Jinendra K. Gugaliya1, Sachin C. Patwardhan2, Ravindra D. Gudi3, Harigopal Raghavan1 and Ramprasad Yelchuru1, (1)Research and Technology Group, Honeywell Technology Solutions Laboratory, Bannerghatta Road, Bangalore, 560076, India, (2)Chemical Engineering Dept., Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India, (3)Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India
Generic Procedure for Data-Driven Predictive Modelling for Control - Exemplified on a Cement Kiln
Bao Lin1, Bodil Recke2, Torsten Vagn Jensen2, Jørgen Knudsen2 and Sten Bay Jørgensen1, (1)CAPEC, Department of Chemical Engineering, Technical University of Denmark, Lyngby, DK-2800, Denmark, (2)FLSmidth Automation, Valby, Dk-2500, Denmark
Optimal Input Design for Identification of Nonlinear Systems
Sridharakumar Narasimhan, Clarkson University, Box 5707, Clarkson University, Potsdam, NY 13699-5707 and Raghunathan Rengaswamy, Chemical Engineering, Clarkson University, Box 5705, Clarkson University, Potsdam, NY 13699-5705
Blind Identification for the Detection and Estimation of Valve Stiction
Clare B. Schoene and S. Joe Qin, Chemical Engineering, University of Texas - Austin, 1 University Station, C0400, Austin, TX 78712

Computing and Systems Technology Division