Journal of Process Control

This zipped file contains software programs developed using MATLAB 2015B for estimating of terminal region for discrete time Quasi-Infinite Horizon Nonlinear Model Predictive Control.

As a public benchmark of chemical industrial process, TEP is well suited for multivariable control problems. This simulation data can be downloaded from the website: http://web.mit.edu/braatzgroup/links.html The 41 measured variables, with the 22 continuous measurements are sampled with the sampling interval of 3mins, while the 19 composition variables are generated at time delays that vary from 6 to 15mins. 21 types of identified faults are inside. Each faulty state consists of 480 samples that are used as training dataset, and 960 samples are used as testing dataset with faults induced after 8 hours, which corresponds to 160 samples.

The PRONTO heterogeneous benchmark dataset is based on an industrial-scale multiphase flow facility. It includes data from heterogeneous sources, including process measurements, alarm records, high frequency ultrasonic flow and pressure measurements, an operation log and video recordings. The study collected data from various operational conditions with and without induced faults to generate a multi-rate, multi-modal dataset. The dataset is suitable for developing and validating algorithms for fault detection and diagnosis (FDD) and data fusion. When using the dataset please cite the following publication: A. Stief, R. Tan, Y. Cao, J. R. Ottewill, N. F. Thornhill, J. Baranowski, A heterogeneous benchmark dataset for data analytics: Multiphase flow facility case study, Journal of Process Control, 79 (2019) 41–55, DOI: https://doi.org/10.1016/j.jprocont.2019.04.009 The dataset has been used in the following works: A. Stief, R. Tan, Y. Cao, J. R. Ottewill. Analytics of heterogeneous process data: Multiphase flow facility case study. IFAC-PapersOnLine, 51(18):363–368, 2018. DOI: https://doi.org/10.1016/j.ifacol.2018.09.327 A. Stief, J. R. Ottewill, R. Tan, Y. Cao. Process and alarm data integration under a two-stage Bayesian framework for fault diagnostics. IFAC-PapersOnLine, 51(24):1220–1226, 2018. DOI: https://doi.org/10.1016/j.ifacol.2018.09.696 A. Stief, J. R. Ottewill, J. Baranowski. Investigation of the diagnostic properties of sensors and features in a multiphase flow facility case study. in: 12th IFAC Symposium on Dynamics and Control of Process Systems (in press), 2019 M. Lucke, X. Mei, A. Stief, M. Chioua, N. F. Thornhill. Variable selection for fault detection and identification based on mutual information of multi-valued alarm series, in: 12th IFAC Symposium on Dynamics and Control of Process Systems (in press), 2019 R. Tan, T. Cong, N. F. Thornhill, J. R. Ottewill, J. Baranowski. Statistical monitoring of processes with multiple operating modes, in: 12th IFAC Symposium on Dynamics and Control of Process Systems (in press), 2019.