Abstract: The aim of the SINGPRO project is to merge the Big Data platforms, machine learning and data analytics methodologies with process planning and scheduling optimization technologies. It applies these technologies to provide online, reactive and anticipative tools for more sustainable and efficient operations. The currently employed classical mathematical optimization models are limited by fixed parameter sets, which are commonly updated off-line and represent only statistical averages. Such parameters could be estimated much more precisely in an on-line fashion using Big Data technologies. By creating such collaboration interfaces between scheduling optimization, big data analytics and machine learning, the process related decision-making will become much more agile, self-aware and flexible. With the sophisticated data analytics methods, one can embed to the overall key performance indicators (KPI) also all information about the process, e.g., tracking abnormal situations (anomaly detection), individual process equipment performance degradations (predictive maintenance), anticipated process timings (prediction of process behavior) and scenario simulation (e.g., artificial intelligence AI planning). This is joint work with Iiro Harjunkoski from Aalto/CHEM.

Bio: Keijo Heljanko is a Full Professor of Computer Science at the University of Helsinki. He is a vice director of the HiDATA, Helsinki Center for Data Science. His research interests include distributed and parallel computing, Big Data, data science, and data science applications.

Speaker: Keijo Heljanko

Affiliation: Professor of Computer and information Sciences, Helsinki University

Place of Seminar: Lecture Hall T5, Konemiehentie 2, Aalto University