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Engineering    2017, Vol. 3 Issue (2) : 214 -219
Research |
Real-Time Assessment and Diagnosis of Process Operating Performance
Shabnam Sedghi,Biao Huang()
Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada

Over time, the performance of processes may deviate from the initial design due to process variations and uncertainties, making it necessary to develop systematic methods for online optimality assessment based on routine operating process data. Some processes have multiple operating modes caused by the set point change of the critical process variables to achieve different product specifications. On the other hand, the operating region in each operating mode can alter, due to uncertainties. In this paper, we will establish an optimality assessment framework for processes that typically have multi-mode, multi-region operations, as well as transitions between different modes. The kernel density approach for mode detection is adopted and improved for operating mode detection. For online mode detection, the model-based clustering discriminant analysis (MclustDA) approach is incorporated with some a priori knowledge of the system. In addition, multi-modal behavior of steady-state modes is tackled utilizing the mixture probabilistic principal component regression (MPPCR) method, and dynamic principal component regression (DPCR) is used to investigate transitions between different modes. Moreover, a probabilistic causality detection method based on the sequential forward floating search (SFFS) method is introduced for diagnosing poor or non-optimum behavior. Finally, the proposed method is tested on the Tennessee Eastman (TE) benchmark simulation process in order to evaluate its performance.

Keywords Optimality assessment      Probabilistic principal component regression      Multi-mode     
Corresponding Authors: Biao Huang   
Just Accepted Date: 16 March 2017   Online First Date: 07 April 2017    Issue Date: 27 April 2017
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Shabnam Sedghi,Biao Huang. Real-Time Assessment and Diagnosis of Process Operating Performance[J]. Engineering, 2017, 3(2): 214 -219 .
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1   Ye L, Liu Y, Fei Z, Liang J. Online probabilistic assessment of operating performance based on safety and optimality indices for multimode industrial processes. Ind Eng Chem Res 2009;48(24):10912–23
doi: 10.1021/ie801870g
2   Liu Y, Chang Y, Wang F. Online process operating performance assessment and nonoptimal cause identification for industrial processes. J Process Contr 2014;24(10):1548–55
doi: 10.1016/j.jprocont.2014.08.001
3   Liu Y, Wang F, Chang Y, Ma R. Comprehensive economic index prediction based operating optimality assessment and nonoptimal cause identification for multimode processes. Chem Eng Res Des 2015;97:77–90
doi: 10.1016/j.cherd.2015.03.008
4   Liu Y, Wang F, Chang Y, Ma R. Operating optimality assessment and nonoptimal cause identification for non-Gaussian multimode processes with transitions. Chem Eng Sci 2015;137:106–18
doi: 10.1016/j.ces.2015.06.016
5   Kariwala V, Odiowei PE, Cao Y, Chen T. A branch and bound method for isolation of faulty variables through missing variable analysis. J Process Contr 2010;20(10):1198–206
doi: 10.1016/j.jprocont.2010.07.007
6   Quiñones-Grueiro M, Prieto-Moreno A, Llanes-Santiago O. Modeling and monitoring for transitions based on local kernel density estimation and process pattern construction. Ind Eng Chem Res 2016;55(3):692–702
doi: 10.1021/acs.iecr.5b03902
7   Srinivasan R, Wang C, Ho WK, Lim KW. Dynamic principal component analysis based methodology for clustering process states in agile chemical plants. Ind Eng Chem Res 2004;43(9):2123–39
doi: 10.1021/ie034051r
8   Chen T, Sun Y. Probabilistic contribution analysis for statistical process monitoring: A missing variable approach. Control Eng Pract 2009;17(4):469–77
doi: 10.1016/j.conengprac.2008.09.005
9   Chen T, Martin E, Montague G. Robust probabilistic PCA with missing data and contribution analysis for outlier detection. Comput Stat Data Anal 2009;53(10):3706–16
doi: 10.1016/j.csda.2009.03.014
10   Pudil P, Novovičová J, Kittler J. Floating search methods in feature selection. Pattern Recognit Lett 1994;15(11):1119–25
doi: 10.1016/0167-8655(94)90127-9
11   Fraley C, Raftery AE. Model-based clustering, discriminant analysis, and density estimation. J Am Statist Assoc 2002;97(458):611–31
doi: 10.1198/016214502760047131
12   Ge Z, Song Z. Mixture Bayesian regularization method of PPCA for multimode process monitoring. AIChE J 2010;56(11):2838–49
doi: 10.1002/aic.12200
13   Downs JJ, Vogel EF. A plant-wide industrial process control problem. Comput Chem Eng 1993;17(3):245–55
doi: 10.1016/0098-1354(93)80018-I
14   Ricker NL. Decentralized control of the Tennessee Eastman Challenge Process. J Process Contr 1996;6(4):205–21
doi: 10.1016/0959-1524(96)00031-5
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