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Big Data Generation...
Big Data Generation for Time Dependent Processes : The Tennessee Eastman Process for Generating Large Quantities of Process Data
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- Andersen, Emil B. (author)
- Technical University of Denmark, Department of Chemical and Biochemical Engineering, Process and Systems Engineering Center (PROSYS), Building 229, 2800 Kongens Lyngby, Denmark
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- Udugama, Isuru A. (author)
- Technical University of Denmark, Department of Chemical and Biochemical Engineering, Process and Systems Engineering Center (PROSYS), Building 229, 2800 Kongens Lyngby, Denmark
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- Gernaey, Krist V. (author)
- Technical University of Denmark, Department of Chemical and Biochemical Engineering, Process and Systems Engineering Center (PROSYS), Building 229, 2800 Kongens Lyngby, Denmark
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- Bayer, Cristoph (author)
- TH Nurnberg, Department of Process Engineering, Wassertorstraβe 10, 90489 Nurnberg, Germany
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- Kulahci, Murat (author)
- Luleå tekniska universitet,Industriell Ekonomi,Technical University of Denmark, DTU Compute, Richard Petersens Plads 324, 2800 Kongens Lyngby, Denmark
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(creator_code:org_t)
- Elsevier, 2020
- 2020
- English.
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In: 30<sup>th</sup> European Symposium on Computer Aided Process Engineering. - : Elsevier. ; , s. 1309-1314
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- The concept of applying data-driven process monitoring and control techniques on industrial chemical processes is well established. With concepts such as Industry 4.0, Big Data and the Internet of Things receiving attention in industrial chemical production, there is a renewed focus on data-driven process monitoring and control in chemical production applications. However, there are significant barriers that must be overcome in obtaining sufficiently large and reliable plant and process data from industrial chemical processes for the development of data-driven process monitoring and control concepts, specifically in obtaining plant and process data that are required to develop and test data driven process monitoring and control tools without investing significant efforts in acquiring, treating and interpreting the data. In this manuscript a big data generation tool is presented that is based on the Tennessee Eastman Process (TEP) simulation benchmark, which has been specifically designed to generate massive amounts of process data without spending significant effort in setting up. The tool can be configured to carry out a large number of data generation runs both using a graphical user interface (GUI) and through a.CSV file. The output from the tool is a file containing process data for all runs as well as process faults (deviations) that have been activated. This tool enables users to generate massive amounts of data for testing applicability of big data concepts in the realm of process control for continuously operating time dependent processes. The tool is available for all researchers and other parties who are interested.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Tillförlitlighets- och kvalitetsteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Reliability and Maintenance (hsv//eng)
Keyword
- Data generation
- Statistical process control
- Data-driven control
- Kvalitetsteknik och logistik
- Quality technology and logistics
Publication and Content Type
- ref (subject category)
- kon (subject category)
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