The revised Tennessee Eastman process simulator as testbed for SPC and DoE methods

References

• Bathelt, A., Ricker, N.L., and Jelali, M. (2015a). Revision of the Tennessee Eastman Process Model. IFAC-PapersOnLine, 48(8):309–314.
   Google Scholar
• Bathelt, A., Ricker, N.L., and Jelali, M. (2015b). Tennessee Eastman Challenge Archive. http://depts.washington.edu/control/LARRY/TE/down-load.html#Updated_TE_Code. (accessed 2017 May).
   Google Scholar
• Bisgaard, S., and Kulahci, M. (2005). Quality Quandaries: The Effect of Autocorrelation on Statistical Process Control Procedures. Quality Engineering, 17(3):481–489.
   Google Scholar
• Box, G.E.P. (1957). Evolutionary Operation: A Method for Increasing Industrial Productivity. Journal of the Royal Statistical Society. Series C (Applied Statistics), 6(2):81–101.
   Google Scholar
• Box, G.E.P., and Wilson, K.B. (1951). On the Experimental Attainment of Optimum Conditions. Journal of the Royal Statistical Society, Series B (Methodological) 13(1):1–45.
   Google Scholar
• Capaci, F., Bergquist, B., Kulahci, M., and Vanhatalo, E. (2017). Exploring the use of design of experiments in industrial processes operating under closed-loop control. Quality and Reliability Engineering International. 33:1601–1614. doi:10.1002/qre.2128.
   Google Scholar
• Chinosi, M., and Trombetta, A. (2012). BPMN: An Introduction to the Standard. Computer Standards & Interfaces, 34(1):124–134.
   Web of Science ®Google Scholar
• Dennis, D., and Meredith, J. (2000). An Empirical Analysis of Process Industry Transformation Systems. Management Science, 46 (8):1058–1099.
   Web of Science ®Google Scholar
• Downs, J.J., and Vogel, E.F. (1993). A Plant Wide Industrial Process Control Problem. Computers & Chemical Engineering, 17(3):245–255.
   Web of Science ®Google Scholar
• Ferrer, A. (2014). Latent Structures-Based Multivariate Statistical Process Control: A Paradigm Shift. Quality Engineering, 26(1):72–91.
   Web of Science ®Google Scholar
• Ge, Z., Song, Z., and Gao, F. (2013). Review of Recent Research on Data-Based Process Monitoring. Industrial & Engineering Chemistry Research, 52(10):3543–3562.
   Web of Science ®Google Scholar
• Hsu, C., Chen, M., and Chen, L. (2010). A Novel Process Monitoring Approach with Dynamic Independent Component Analysis. Control Engineering Practice, 18(3):242–253.
   Web of Science ®Google Scholar
• Kruger, U., Zhou, Y., and Irwin, G.W. (2004). Improved Principal Component Monitoring of Large-Scale Processes. Journal of Process Control, 14(8):879–888.
   Web of Science ®Google Scholar
• Ku, W., Storer, R.H., and Georgakis, C. (1995). Disturbance Detection and Isolation by Dynamic Principal Component Analysis. Chemometrics and Intelligent Laboratory Systems, 30(1):179–196.
   Web of Science ®Google Scholar
• Kulahci, M., and S. Bisgaard. (2006). Challenges in Multivariate Control Charts with Autocorrelated Data. Proceedings to the 12th ISSAT International Conference on Reliability and Quality in Design, Chicago, IL.
   Google Scholar
• Lee, G., Han, C., and Yoon, E.S. (2004). Multiple-Fault Diagnosis of the Tennessee Eastman Process Based on System Decomposition and Dynamic PLS. Industrial & Engineering Chemistry Research, 43(25):8037–8048.
   Web of Science ®Google Scholar
• Liu, K., Fei, Z., Yue, B., Liang, J., and Lin, H. (2015). Adaptive Sparse Principal Component Analysis for Enhanced Process Monitoring and Fault Isolation. Chemometrics and Intelligent Laboratory Systems, 146:426–436.
   Web of Science ®Google Scholar
• Lundkvist, P., and Vanhatalo, E. (2014). Identifying Process Dynamics through a Two-Level Factorial Experiment. Quality Engineering, 26(2):154–167.
   Web of Science ®Google Scholar
• Lyman, P.R., and Georgakis, C. (1995). Plant-Wide Control of the Tennessee Eastman Problem. Computers & Chemical Engineering, 19(3): 321–331.
   Web of Science ®Google Scholar
• McGregor, J., and Harris, T.J. (1990). Exponentially Moving Average Control Schemes: Properties and Enhancements – Discussion. Technometrics, 32(1): 23–26.
   Google Scholar
• Montgomery, D. (2012). Statistical process control: A modern introduction. 7th ed., Hoboken (NJ): Wiley.
   Google Scholar
• Rato, T.J., and Reis, M.S. (2013). Fault Detection in the Tennessee Eastman Benchmark Process using Dynamic Principal Components Analysis Based on Decorrelated Residuals (DPCA-DR). Chemometrics and Intelligent Laboratory Systems, 125:101–108.
   Web of Science ®Google Scholar
• Rato, T.J., and Reis, M.S. (2014). Sensitivity enhancing transformations for monitoring the process correlation structure. Journal of Process Control, 24:905–915.
   Web of Science ®Google Scholar
• Rato, T.J., and Reis, M.S. (2015). On-line process monitoring using local measures of association: Part I – Detection performance. Chemometrics and Intelligent Laboratory Systems, 142: 255–264.
   Web of Science ®Google Scholar
• Reis, M., and Kenett, R.S. (2017). A Structured Overview on the use of Computational Simulators for Teaching Statistical Methods. Quality Engineering, 29(4):730–744.
   Web of Science ®Google Scholar
• Ricker, L.N. (1996). Decentralized Control of the Tennessee Eastman Challenge Process. Journal of Process Control, 6(4):205–221.
   Web of Science ®Google Scholar
• Ricker, L.N. (2005). Tennessee Eastman Challenge Archive. http://depts.washington.edu/control/LARRY/TE/download.html#Decentralized_control (accessed 2017 May).
   Google Scholar
• Ricker, L.N., and Lee, J.H. (1995). Non-Linear Model Predictive Control of the Tennessee Eastman Challenge Process. Computers & Chemical Engineering, 19(9): 961–981.
   Web of Science ®Google Scholar
• Vanhatalo, E., Bergquist, B., and Vännman, K. (2013). Towards Improved Analysis Methods for Two-Level Factorial Experiment with Time Series Responses. Quality and Reliability Engineering International, 29(5):725–741.
   Web of Science ®Google Scholar
• Vanhatalo, E., Kvarnström, B., Bergquist, B., and Vännman, K. (2010). A Method to Determine Transition Time for Experiments in Dynamic Processes. Quality Engineering, 23(1):30–45.
   Web of Science ®Google Scholar
• Vanhatalo, E., and Bergquist, B. (2007). Special Considerations when Planning Experiments in a Continuous Process. Quality Engineering, 19(3):155–169.
   Google Scholar
• Vanhatalo, E., Kulahci, M., and Bergquist, B. (2017). On the Structure of Dynamic Principal Component Analysis used in Statistical Process Monitoring. Chemometrics and Intelligent Laboratory Systems, 167:1–11.
   Web of Science ®Google Scholar
• Vanhatalo, E. and Kulahci, M. (2015). The Effect of Autocorrelation on the Hotelling T2 Control Chart. Quality and Reliability Engineering International, 31(8):1779–1796.
   Web of Science ®Google Scholar
• Vining, G., Kulahci, M., and Pedersen, S. (2015). Recent Advances and Future Directions for Quality Engineering. Quality and Reliability Engineering International, 32(3):11–21.
   Web of Science ®Google Scholar
• Webb, D.F., Lucas, J.M., and Borkowski, J.J. (2004). Factorial Experiments when Factor Levels are Not Necessarily Reset. Journal of Quality Technology, 36(1):1–11.
   Web of Science ®Google Scholar
• Ye, L., Cao, Y., Yuan, X., and Song, Z. (2016). Retrofit self-optimizing control of Tennessee Eastman Process. Industrial Electronics IEEE Transactions 64:4662–4670. ISSN 0278-0046.
   Google Scholar