Big Data Generation for Time Dependent Processes : The Tennessee Eastman Process for Generating Large Quantities of Process Data

• 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)