Machine Learning Applications and Intelligent Systems
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.
Paper abstracts are public but to access Extended Abstracts, you must first purchase the conference proceedings.
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