Estimation and Control under Uncertainty | AIChE

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Estimation and Control under Uncertainty

Chair(s)

Wang, J., Auburn University

Co-chair(s)

Babaei Pourkargar, D., Kansas State University

Operating a process/system under uncertainty is inevitable, especially under the current fast-changing environment. This session will highlight methods for characterizing, quantifying, and mitigating uncertainty in estimation, control, and real-time optimization tasks. Theoretical, algorithmic and application topics are encouraged.

Presentations

8:00 AM
(434a) End-to-End Design and Implementation of Robust MPC on Resource-Limited Hardware Using Multi-Objective Bayesian Optimization and Deep Learning
8:19 AM
(434b) On the Performance of Stochastic Predictive Control
8:38 AM
(434c) Sparse-Identification-Based Predictive Control of Nonlinear Processes Using Noisy Process Data
8:57 AM
(434d) Partial Modifier Adaptation for Economic Optimization of Process Systems Under Frequent Disturbances and Structural Model Uncertainty
9:16 AM
(434e) Machinelearning and Adaptive Model Predictive Control: Conflict or Conflux
9:35 AM
(434f) Chromate Control in an Ion Exchange Process Under Uncertainty
9:54 AM
(434g) Parameter Estimation for Real-Time Optimization Under Model Uncertainty and Measurement Noise
10:13 AM
(434h) Improving Computational Efficiency of Multi-Stage NMPC Using an Adaptive Horizon

Topics 

Process Automation & Control

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Pricing

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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