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.

Papers:

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

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Individuals

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