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Data-Driven Techniques for Dynamic Modeling, Estimation and Control II

Chair(s):
Kwon, J., Texas A&M University
Co-chair(s):
Zavala, V. M., University of Wisconsin-Madison

The session welcomes contributions that develop and apply data-based techniques to conduct system's tasks such as dynamic model development, state/parameter estimation, identification, and control. Emerging techniques from data science and machine learning as well as approaches that combine data with first-principles models are encouraged.

Papers:
8:00 AM
(470a) Inferential Control of Distillation Column By Successive Update of Softsensor and Control Algorithm
8:15 AM
(470b) Application of Feature Engineering and Selection for Spectrometry-Based Soft Sensing
8:30 AM
(470c) Adaptive System Identification of an Industrial Ethylene Splitter: A Comparison of Subspace Identification and Artificial Neural Networks
8:45 AM
(470d) Lie-Sobolev Nonlinear System Identification and Its Effect on Nonlinear Control
9:00 AM
(470e) Data-Driven Control Via Dynamic-Mode Decomposition
9:15 AM
(470f) Learning the Dynamics of Coupled Oscillator Systems through the Discovery of Emergent PDEs Via Artificial Neural Networks and Manifold Learning
9:30 AM
(470g) Unifying Theorems for Subspace Identification and Dynamic Mode Decomposition
9:45 AM
(470h) Subspace Based Model Identification for Missing Data
Topics: 
Process Automation & Control
Process Design & Development

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Individuals

AIChE Members $150.00
AIChE Emeritus Members $105.00
AIChE Graduate Student Members Free
AIChE Undergraduate Student Members Free
Non-Members $225.00