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

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

Chair(s)

Yenkie, K., Rowan University

Co-chair(s)

Chen, D., Lamar 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

3:30 PM
(199a) Fast and Large-Scale Model Predictive Control Using Neural Networks
3:48 PM
(199b) Data Driven Plant Model Mismatch Estimation for Dmc with Unknown Noise Variance
4:06 PM
(199c) Model Predictive Control of Phthalic Anhydride Synthesis in a Fixed-Bed Catalytic Reactor Via Machine Learning Modeling
4:24 PM
(199d) Data-Driven Design of Hierarchical MPC Architectures
4:42 PM
(199e) Integrating Data Driven Modeling with First Principles Knowledge
5:00 PM
(199f) Optimal Control for Simulated Moving Bed Process with Deep Reinforcement Learning
5:18 PM
(199g) Input Design for Active Fault Diagnosis: Recent Developments, New Results, and Future Directions
5:36 PM
(199h) Improving Near-Infrared Spectrum-Based Soft Sensor Performance through Variable Selection

Topics 

Process Automation & Control
Sensors
Plant Operations

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Pricing

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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