Synergistic and Intelligent Process Optimization : First Results and Open Challenges

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2020-09-23
Major/Subject
Mcode
Degree programme
Language
en
Pages
11
16684-16694
Series
Industrial & Engineering Chemistry Research, Volume 59, issue 38
Abstract
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to improve the quality and accuracy of production planning as often a schedule based on statistical average data is outdated already when reaching the plant floor.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.
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Keywords
SELECTIVE MAINTENANCE, NEURAL-NETWORKS, MODELS, INTEGRATION, SYSTEMS, DESIGN
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Citation
Harjunkoski , I , Ikonen , T , Mostafaei , H , Deneke , T & Heljanko , K 2020 , ' Synergistic and Intelligent Process Optimization : First Results and Open Challenges ' , Industrial & Engineering Chemistry Research , vol. 59 , no. 38 , pp. 16684-16694 . https://doi.org/10.1021/acs.iecr.0c02032