An easy to use GUI for simulating big data using Tennessee Eastman process

Abstract

Data‐driven process monitoring and control techniques and their application to industrial chemical processes are gaining popularity due to the current focus on Industry 4.0, digitalization and the Internet of Things. However, for the development of such techniques, there are significant barriers that must be overcome in obtaining sufficiently large and reliable datasets. 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. Therefore, researchers need a tool that effortlessly generates large amounts of realistic and reliable process data without the requirement for additional data treatment or interpretation. In this work, we propose a data generation platform based on the Tennessee Eastman Process simulation benchmark. A graphical user interface (GUI) developed in MATLAB Simulink is presented that enables users to generate massive amounts of data for testing applicability of big data concepts in the realm of process control for continuous time‐dependent processes. An R‐Shiny app that interacts with the data generation tool is also presented for illustration purposes. The app can visualize the results generated by the Tennessee Eastman Process and can carry out a standard fault detection and diagnosis studies based on PCA. The data generator GUI is available free of charge for research purposes at https://github.com/dtuprodana/TEP.

Stream-based active learning with linear models

“…It was initially published in 1993 [49] but since then it has been further developed and improved. For this study, we used a recently released MATLAB simulator to generate the data [50,51]. We generated 50 datasets with the process running in normal operating conditions, using a sampling rate of approximately 1 minute.…”

Controller Performance Monitoring: A Survey of Problems and a Review of Approaches from a Data-Driven Perspective with a Focus on Oscillations Detection and Diagnosis

“…The Simulink codes, available in ref 63 contain four models: (i) an open-loop control strategy-based model; (ii) a multiloop model through a decentralized control strategy; (iii) a model similar to (ii) with an option to allow the coolant valve to saturate and therefore the system loses control of reactor level in case an additional override is not included that prevents the saturation by reducing the percentage open of the recycle valve, and (vi) a model similar to (ii) but with the self-optimizing control strategy. Recently, Andersen et al 64 provided a graphical user interface (GUI) for generating customized TEP data sets according to user-defined specifications. This GUI is simple and easy to use, which can be adopted to generate various data sets appropriate to test and compare CPM techniques.…”