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