Big Data Generation for Time Dependent Processes: The Tennessee Eastman Process for Generating Large Quantities of Process DataShow others and affiliations
2020 (English)In: 30th European Symposium on Computer Aided Process Engineering: Part A / [ed] Sauro Pierucci; Flavio Manenti; Giulia Luisa Bozzano; Davide Manca, Elsevier, 2020, p. 1309-1314Conference paper, Published paper (Refereed)
Abstract [en]
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.
Place, publisher, year, edition, pages
Elsevier, 2020. p. 1309-1314
Series
Computer Aided Chemical Engineering, ISSN 1570-7946 ; 48
Keywords [en]
Data generation, Statistical process control, Data-driven control
National Category
Reliability and Maintenance
Research subject
Quality technology and logistics
Identifiers
URN: urn:nbn:se:ltu:diva-81558DOI: 10.1016/B978-0-12-823377-1.50219-6ISI: 000652152900219Scopus ID: 2-s2.0-85092791397OAI: oai:DiVA.org:ltu-81558DiVA, id: diva2:1503158
Conference
30th European Symposium of Computer Aided Process Engineering (ESCAPE30), Milan, Italy (Virtual), August 30-September 2, 2020
Note
ISBN för värdpublikation: 978-0-12-823511-9
2020-11-232020-11-232021-06-28Bibliographically approved