Data-driven approaches are playing an increasingly significant role in chemical engineering. This session solicits submissions pertaining to both methodological advances in machine learning as well as application-driven case studies demonstrating the use large data sets and machine learning to infer correlations, develop models, as well as to improve processes/systems through data-driven optimization and control. Particular emphasis will be given to applications which employ an adaptive data-driven approach, through which data-mining and machine learning are used to create intelligent systems, which adaptively learn from the data.

Data and Information Systems