About

Synergistic and intelligent process optimization (SINGPRO) merges Big Data platforms, machine learning and data analytics methods with process planning and scheduling optimization. The project, funded by academy of Finland, is a collaborative effort between Aalto University (Professor Iiro Harjunkoski, Department of Chemical and Metallurgical Engineering) and the University of Helsinki (Professor Keijo Heljanko, Department of Computer Science). The project duration is from January 1, 2018 to December 31, 2019.

The goal of the project is to create online, reactive and anticipative tools for more sustainable and efficient operation leading to an agile, self-aware and flexible decision-making loop. SINGPRO will enable the tracking of abnormal situations (i.e., anomaly detection), identifying process equipment performance degradations (i.e., predictive maintenance) and anticipate process timing deviations (i.e., process behavior prediction). This helps to select the best production strategies in order to maintain production and energy efficiency as well as sustainability targets in rapidly changing market situations through data-driven self-adaptive scheduling models.