Synergistic and Intelligent Process Optimization: First Results and Open Challenges
| Autor: | Iiro Harjunkoski, Tewodros Deneke, Hossein Mostafaei, Keijo Heljanko, Teemu J. Ikonen |
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| Přispěvatelé: | Process Control and Automation, Department of Chemical and Metallurgical Engineering, Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto-yliopisto, Aalto University, Helsinki Institute for Information Technology |
| Rok vydání: | 2020 |
| Předmět: |
Value (ethics)
021103 operations research Process (engineering) Computer science General Chemical Engineering MODELS 0211 other engineering and technologies 02 engineering and technology General Chemistry 113 Computer and information sciences Data science Industrial and Manufacturing Engineering DESIGN 020401 chemical engineering SYSTEMS SELECTIVE MAINTENANCE Process optimization NEURAL-NETWORKS 0204 chemical engineering INTEGRATION Scientific disciplines |
| Zdroj: | Industrial & Engineering Chemistry Research. 59:16684-16694 |
| ISSN: | 1520-5045 0888-5885 |
| DOI: | 10.1021/acs.iecr.0c02032 |
| Popis: | 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to improve the quality and accuracy of production planning as often a schedule based on statistical average data is outdated already when reaching the plant floor.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. |
| Databáze: | OpenAIRE |
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