Advances in Machine Learning Methods for Process Systems Engineering
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.
Paper abstracts are public but to access Extended Abstracts, you must first purchase the conference proceedings.
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