Machine Learning Applications and Intelligent Systems | AIChE

Other Sites & Tools

Technical Groups

Machine Learning Applications and Intelligent Systems

Chair(s)

Realff, M. J., Georgia Institute of Technology

Co-chair(s)

Kieslich, C., Georgia Institute of Technology

Data-driven approaches are playing an increasingly significant role in chemical engineering. This session solicits submissions pertaining to application-driven methods and case studies demonstrating the use data and machine learning to infer correlations, develop models, as well as to improve processes/systems through data-driven optimization and control.

Presentations

12:30 PM
(344a) Coarse-Scale PDEs from Microscopic Observations Via Machine Learning
12:49 PM
(344b) Machine Learning Methods in Process Control
1:08 PM
(344c) Application of Deep Neural Networks for Artifact Removal from Sensor Data
1:27 PM
(344d) Nonlinear Dynamic Feature Extraction Based on Gaussian Process Dynamical Models for Jit-Based Adaptive Soft Sensors
1:46 PM
(344e) Attention-Based Recurrent Neural Network for Multi-Step-Ahead Prediction
2:05 PM
(344f) A Deep Reinforcement Learning Approach for Production Scheduling
2:24 PM
(344g) Decision-Making of Online Rescheduling Procedures Using Neuroevolution of Augmenting Topologies
2:43 PM
(344h) Progan: Protein Solubility Generative Adversarial Nets for Data Augmentation in Dnn Framework

Topics 

Computing and Systems Engineering

Checkout

Paper abstracts are public but to access Extended Abstracts, you must first purchase the conference proceedings.

Checkout

Do you already own this?

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

More Conference Links