Instrumentation Design and Upgrade for Principal Components Analysis Monitoring

Industrial and Engineering Chemistry Research

Volumn 43, Issue 9, 2004, Pages 2150-2159

  Musulin, Estanislao ^a   Bagajewicz, Miguel ^b   Nougués, José M ^a   Puigjaner, Luis ^a  

^a UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

^b UNIVERSITY OF OKLAHOMA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GENETIC ALGORITHMS; INDUSTRIAL PLANTS; INSTRUMENTS; INVESTMENTS; MONITORING; PROBLEM SOLVING; SENSORS; SIGNALING;

FAULT DETECTION; INSTRUMENTATION DESIGN; INVESTMENT COST; UPGRADE;

PRINCIPAL COMPONENT ANALYSIS;

MONITORING;

ARTICLE; GENETIC ALGORITHM; INSTRUMENT; INSTRUMENTATION; METHODOLOGY; MONITORING; PRINCIPAL COMPONENT ANALYSIS; SENSOR; SIGNAL PROCESSING;

EID: 1942420807     PISSN: 08885885     EISSN: None     Source Type: Journal    
DOI: 10.1021/ie030607z     Document Type: Article

References (28)

1. Kourti, T.; Lee, J.; MacGregor, J. Experiences with industrial applications of proyection methods for multivariate statistical process control Comput. Chem. Eng. 1996, 20, S745-S750.
2. Pearson, K. On lines and planes of closest fit to systems of point in space. Philos. Mag. 1901, 6, 559-572.
3. Hotelling, H. Analysis of a complex of statistical variables into principal components. Educ. Psychol. 1933, 24, 417-441, 498-520.
4. Nomikos, P.; MacGregor, J. Monitoring batch processes using multiway principal component analysis. AIChE J. 1994, 40, 1361-1375.
5. Dunia, R.; Qin, S.; Edgar, T.; McAvoy, T. Identification of faulty sensors using principal component analysis. AIChE J. 1996, 42, 2797-2812.
6. Vedam, H.; Venkatasubramanian, V. PCA-SDG based process monitoring and fault diagnosis. Control Eng. Pract. 1999, 7, 903-917.
7. Kramer M. A. NonLinear PCA using Autoassociative NN. AIChE J. 1991, 37, 233-243.
8. Dong, D.; McAvoy, J. Nonlinear principal component analysis-based on principal curves and neural networks. Comput. Chem. Eng. 1996, 20, 65-77.
9. Raghuraj, R.; Bhushan, M.; Rengaswamy, R. Locating sensors in complex chemical plants based on fault diagnosis observability criteria. AIChE J. 1999, 45, 310-322.
10. Bhushan, M.; Rengaswamy, R. Design of sensor network based on the signed directed graph of the process for efficient fault diagnosis. Ind. Eng. Chem. Res. 2000, 39, 999-1019.
11. Bagajewicz, M. Design and upgrade of process plant instrumentation; CRC: 2000.
12. Bagajewicz, M. A review of techniques for instrumentation design and upgrade in process plants. Can. J. Chem. Eng. 2002, 80, 3-16.
13. Bagajewicz, M. Review of Recent Results in Instrumentation Design and Upgrade for Process Plants. In Proceedings of the 4th IFAC Workshop on On-Line Fault Detection & Supervision in the Chemical Process Industries, June 2001.
14. Wang, H.; Song, Z.; Wang, H. Statistical process monitoring using improved PCA with optimized sensor locations. J. Process Control 2002, 12, 735-744.
15. Himes, D. M.; Storer, R. H.; Georgakis, C. Determination of the number of principal components for disturbance detection and isolation. In Proceedings of the Americal Control Conference; IEEE Press: Piscataway, NJ, June 1994.
16. Jackson, J. A user's guide to principal components analysis; John Wiley: New York, 1991.
17. Jackson, J.; Mudholkar, G. Control procedures for residuals associated with principal components analysis. Technometrics 1979, 21, 341-349.
18. Dunia, R.; Qin, S. Subespace approach to multidimensional fault identification and reconstruction. AIChE J. 1998, 44.
19. Wang, H.; Song, Z.; Li, P. Fault detection behaviour and performance analysis of principal component analysis based process monitoring methods. Ind. Chem. Res. 2002, 41, 2455-2464.
20. Karr, C. Fuzzy control of pH using genetic algorithms. IEEE Trans. Fuzzy Syst. 1993, 1, 146-153.
21. Holland, J. Adaptation in natural and artificial systems; The University of Michigan Press: 1975.
22. Goldberg, D. Genetic algorithms in search, optimization, and machine learning; Addison-Wesley: 1989.
23. Belanger, P. W.; Luyben W. L. Inventory control in processes with recycle. Ind. Eng. Chem. Res. 1997, 1, 706-716.
24. Chiang, L.; Pell, R.; Seasholtz, M. Exploring process data with the use of robust outlier detection algorithms. J. Process Control 2003, 13, in press.
25. Downs, J.; Vogel, E. A plant-wide industrial process control problem. Comput. Chem. Eng. 1993, 17, 245-255.
26. McAvoy, T.; Ye, N. Base control problem for the Tennessee Eastman problem. Comput. Chem. Eng. 1994, 18, 383-413.
27. Singhal, A. Tennessee Eastman plant simulation with base control system of McAvoy and Ye. Available at http://chemen-gr.ucsb.edu/ceweb/computing/TE/tesim.pdf, accesed in Dec 2003.
28. Cheng, G.; McAvoy, T. Predictive on-line monitoring of continuous processes. J. Process Control 1998, 8, 409-420.