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

Abstract Engineering process control and high-dimensional, time-dependent data present great methodological challenges when applying statistical process control (SPC) and design of experiments (DoE) in continuous industrial processes. Process simulators with an ability to mimic these challenges are instrumental in research and education. This article focuses on the revised Tennessee Eastman process simulator providing guidelines for its use as a testbed for SPC and DoE methods. We provide flowcharts that can support new users to get started in the Simulink/Matlab framework, and illustrate how to run stochastic simulations for SPC and DoE applications using the Tennessee Eastman process.

[1]  Christos Georgakis,et al.  Disturbance detection and isolation by dynamic principal component analysis , 1995 .

[2]  James M. Lucas,et al.  Factorial Experiments When Factor Levels are Not Necessarily Reset , 2004 .

[3]  Zhihuan Song,et al.  Retrofit self-optimizing control of Tennessee Eastman process , 2016, 2016 IEEE International Conference on Industrial Technology (ICIT).

[4]  E. F. Vogel,et al.  A plant-wide industrial process control problem , 1993 .

[5]  Tiago J. Rato,et al.  Sensitivity enhancing transformations for monitoring the process correlation structure , 2014 .

[6]  JayHyung Lee,et al.  Nonlinear model predictive control of the Tennessee Eastman challenge process , 1995 .

[7]  Tiago J. Rato,et al.  Fault detection in the Tennessee Eastman benchmark process using dynamic principal components analysis based on decorrelated residuals (DPCA-DR) , 2013 .

[8]  Murat Kulahci,et al.  Challenges in multivariate control charts with autocorrelated data , 2006 .

[9]  George W. Irwin,et al.  Improved principal component monitoring of large-scale processes , 2004 .

[10]  Hai Lin,et al.  Adaptive sparse principal component analysis for enhanced process monitoring and fault isolation , 2015 .

[11]  M. Kulahci,et al.  On the structure of dynamic principal component analysis used in statistical process monitoring , 2017 .

[12]  Furong Gao,et al.  Review of Recent Research on Data-Based Process Monitoring , 2013 .

[13]  Ron S. Kenett,et al.  A structured overview on the use of computational simulators for teaching statistical methods , 2017 .

[14]  Kerstin Vännman,et al.  Towards Improved Analysis Methods for Two‐Level Factorial Experiments with Time Series Responses , 2013, Qual. Reliab. Eng. Int..

[15]  G. Box,et al.  On the Experimental Attainment of Optimum Conditions , 1951 .

[16]  Christos Georgakis,et al.  Plant-wide control of the Tennessee Eastman problem , 1995 .

[17]  Jack R. Meredith,et al.  An Empirical Analysis of Process Industry Transformation Systems , 2000 .

[18]  Gibaek Lee,et al.  Multiple-Fault Diagnosis of the Tennessee Eastman Process Based on System Decomposition and Dynamic PLS , 2004 .

[19]  George E. P. Box,et al.  Evolutionary Operation: a Method for Increasing Industrial Productivity , 1957 .

[20]  Alberto Ferrer,et al.  Latent Structures-Based Multivariate Statistical Process Control: A Paradigm Shift , 2014 .

[21]  Erik Vanhatalo,et al.  Special Considerations when Planning Experiments in a Continuous Process , 2007 .

[22]  Murat Kulahci,et al.  Quality Quandaries: The Effect of Autocorrelation on Statistical Process Control Procedures , 2005 .

[23]  Murat Kulahci,et al.  The Effect of Autocorrelation on the Hotelling T2 Control Chart , 2015, Qual. Reliab. Eng. Int..

[24]  Mohieddine Jelali,et al.  Revision of the Tennessee Eastman Process Model , 2015 .

[25]  Chun-Chin Hsu,et al.  A novel process monitoring approach with dynamic independent component analysis , 2010 .

[26]  Tiago J. Rato,et al.  On-line process monitoring using local measures of association: Part I — Detection performance , 2015 .

[27]  Erik Vanhatalo,et al.  Identifying Process Dynamics through a Two-Level Factorial Experiment , 2014 .

[28]  Murat Kulahci,et al.  Exploring the Use of Design of Experiments in Industrial Processes Operating Under Closed‐Loop Control , 2017, Qual. Reliab. Eng. Int..

[29]  Alberto Trombetta,et al.  BPMN: An introduction to the standard , 2012, Comput. Stand. Interfaces.

[30]  Murat Kulahci,et al.  Recent Advances and Future Directions for Quality Engineering , 2016, Qual. Reliab. Eng. Int..

[31]  Kerstin Vännman,et al.  A Method to Determine Transition Time for Experiments in Dynamic Processes , 2009 .

[32]  N. Lawrence Ricker,et al.  Decentralized control of the Tennessee Eastman Challenge Process , 1996 .