(155a) Big Data Generation with the Tennesse Eastman Process

With concepts such as Industry 4.0 and Big Data attracting great attention, there is a renewed focused on data driven process monitoring and control in chemical production applications. However, the development of data driven process monitoring and control concepts requires sufficiently large and reliable plant and process data from industrial chemical production processes that are not readily available for a variety of reasons. If researchers indeed obtain industrial data sets, they often lack enough process insight to interpret results. Furthermore, researchers are generally unable to test their developments in production scale. As a result, the use of real plant and process data in developing and testing data driven process monitoring and control tools can be difficult without investing significant efforts in acquiring, treating and interpreting the data.

Hence, there is a need to develop a tool that can be used to effortlessly generate large amounts of realistic and reliable process data without extensive data treatment or interpretation. In this work, we propose to develop a massive data generation platform based on the Tennessee Eastman Process (TEP) simulation and present a tool that enables users to generate massive amounts of data for testing the applicability of big data concepts in the realm of process control for continuously operating time dependent processes.