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Cloud computing for big data analytics in the Process Control Industry
GSTAT, Israel.
GSTAT, Israel.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0002-9992-7791
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2017 (English)In: 2017 25th Mediterranean Conference on Control and Automation, MED 2017, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, 1373-1378 p., 7984310Conference paper, Published paper (Refereed)
Abstract [en]

The aim of this article is to present an example of a novel cloud computing infrastructure for big data analytics in the Process Control Industry. Latest innovations in the field of Process Analyzer Techniques (PAT), big data and wireless technologies have created a new environment in which almost all stages of the industrial process can be recorded and utilized, not only for safety, but also for real time optimization. Based on analysis of historical sensor data, machine learning based optimization models can be developed and deployed in real time closed control loops. However, still the local implementation of those systems requires a huge investment in hardware and software, as a direct result of the big data nature of sensors data being recorded continuously. The current technological advancements in cloud computing for big data processing, open new opportunities for the industry, while acting as an enabler for a significant reduction in costs, making the technology available to plants of all sizes. The main contribution of this article stems from the presentation for a fist time ever of a pilot cloud based architecture for the application of a data driven modeling and optimal control configuration for the field of Process Control. As it will be presented, these developments have been carried in close relationship with the process industry and pave a way for a generalized application of the cloud based approaches, towards the future of Industry 4.0

Place, publisher, year, edition, pages
Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017. 1373-1378 p., 7984310
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-65448DOI: 10.1109/MED.2017.7984310ISBN: 9781509045334 (electronic)OAI: oai:DiVA.org:ltu-65448DiVA: diva2:1137812
Conference
25th Mediterranean Conference on Control and Automation, MED 2017, University of Malta, Valletta, Malta, 3-6 July 2017
Available from: 2017-09-01 Created: 2017-09-01 Last updated: 2017-09-15Bibliographically approved

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CiteExportLink to record
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Citation style
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