Synergistic and Intelligent Process Optimization : First Results and Open Challenges

Iiro Harjunkoski*, Teemu Ikonen, Hossein Mostafaei, Tewodros Deneke, Keijo Heljanko

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
55 Downloads (Pure)


Data science has become an important research topic across scientific disciplines. In Process Systems Engineering, one attempt to create true value from process data is to use it proactively to improve the quality and accuracy of production planning as often a schedule based on statistical average data is outdated already when reaching the plant floor. Thus, due to the hierarchical planning structures, it is difficult to quickly adapt a schedule to changing conditions. This challenge has also been investigated in integration of scheduling and control studies (Touretzky et al. AIChE J. 2017, 63 (66), 1959-1973). The project SINGPRO investigated the merging of big data platforms, machine learning, and data analytics with process planning and scheduling optimization. The goal was to create online, reactive, and anticipative tools for more sustainable and efficient operation. In this article, we discuss selected outcomes of the project and reflect the topic of combining optimization and data science in a broader scope.

Original languageEnglish
Pages (from-to)16684-16694
Number of pages11
JournalIndustrial & Engineering Chemistry Research
Issue number38
Publication statusPublished - 23 Sept 2020
MoE publication typeA1 Journal article-refereed




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