Data-Driven Dynamic Modeling, Estimation and Control II | AIChE

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Data-Driven Dynamic Modeling, Estimation and Control II

Chair(s)

Wang, Z., Dow Inc.

Co-chair(s)

Bispo, H., Federal University of Campina Grande

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

12:30 PM
(106a) Building a Large-Scale Kinetic Model for Saccharomyces Cerevisiae: Challenges and Insights
12:49 PM
(106b) Hybrid Modeling of Bioprocesses Based on a Kinetic Canonical Formalism
1:08 PM
(106c) Inverse Backward Analysis of Neural Approximants of Ordinary Differential Equations
1:27 PM
(106d) Experimental Demonstration of Closed-Loop Optimization of a Single Column Pressure Swing Adsorption (PSA)-Based Oxygen Concentrator Using Machine Learning
1:46 PM
(106e) Nonlinear Reactor Design Optimization with Embedded Microkinetic Model Information for Sustainable Shale Gas Processing
2:05 PM
(106f) Deep Learning Aided Koopman Predictive Control for Post-Combustion CO2 Capture Process
2:24 PM
(106g) Development of Algorithms for Augmenting and Replacing Conventional Process Control Using Reinforcement Learning
2:43 PM
(106h) Data-Driven Predictive Control of Two-Timescale Dynamics: Application to a Battery System

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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