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

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

Chair(s)

Chen, D., Lamar University

Co-chair(s)

Yenkie, K., Rowan University

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
(324a) Adaptive Corrective Strategies Via Integrated Data-Driven Monitoring and Explicit Fault-Tolerant Control
12:48 PM
(324b) Model Identification and Disturbance Forecasting Via Recurrent Neural Networks for Building Applications
1:06 PM
(324c) Data-Driven Input Design to Maximize Information in MIMO System Identification
1:24 PM
(324d) Gaussian Process-Based Optimal Design of Experiments Under Structural Model Uncertainty
1:42 PM
(324e) Parameter Estimation of Partial Differential Equations Using Artificial Neural Network
2:00 PM
(324f) Grey-Box Identification for a Class of Nonlinear Dynamical Systems
2:18 PM
(324g) Space-Time Dynamics of Electricity Markets Incentivize Technology Decentralization
2:36 PM
(324h) Model Predictive Control-Assisted Online Data Collection

Topics 

Plant Operations
Process Automation & Control
Sensors

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Individuals

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

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