Description:
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 data 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.
Sponsor:
Information Management and Intelligent Systems
Chair:
Alexander Dowling
Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN
Co-Chair:
Antonio del Rio Chanona
Centre for Process Systems Engineering, Imperial College London, London, United Kingdom
Abstract Submissions Closed
See more of this Group/Topical: Computing and Systems Technology Division