Data-driven Modeling, Estimation and Optimization for Control II | AIChE

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Data-driven Modeling, Estimation and Optimization for Control II

Chair(s)

Bispo, H., Federal University of Campina Grande

Co-chair(s)

Howsmon, D., Rensselaer Polytechnic Institute

This session highlights techniques that use data to conduct tasks such as dynamic system modeling, 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.

Presentations

8:00 AM
(433a) Grey-Box Modeling and Estimation for Health Monitoring of High Temperature Boiler Components
8:18 AM
(433b) Time-Series Modeling for Run-to-Run Control of an Area-Selective Atomic Layer Deposition Process
8:36 AM
(433c) Implicit Neural Representations for Accurately Modeling State Transition Dynamics of a Distillation Column
8:54 AM
(433d) Adversarially Robust Real-Time Optimization and Control (ARRTOC)
9:12 AM
(433e) Robust Learning and Predictive Control of Time-Delay Nonlinear Processes with Deep Recurrent Koopman Operators
9:30 AM
(433f) Next-Generation Hybrid Models: Combining Attention Mechanisms and Lstm for Improved Predictions and Process Control in the Chemical Industry
9:48 AM
(433g) Integrating Koopman Operator with Subspace Identification for Model Predictive Control (MPC) Design
10:06 AM
(433h) A Training Methodology for Hybrid Building Thermal Models with Time-Varying Heat Disturbances

Topics 

Process Automation & Control

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Individuals

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

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