A systematic approach for the design of optimal monitoring systems for large scale processes

Industrial and Engineering Chemistry Research

Volumn 49, Issue 4, 2010, Pages 1749-1761

  Zumoffen, David ^a   Basualdo, Marta ^b  

^a Centro Franco Argentino de Ciencias de la Información y de Sistemas ^*  (Argentina)

^b Universidad Tecnológica Nacional E Zeballos 1341   (Argentina)

Author keywords

[No Author keywords available]

Indexed keywords

DETECTABILITY INDEX; EFFICIENT MONITORING; EVENTS DETECTION; INPUT-OUTPUT; MONITORING SYSTEM; NEW CONCEPT; OBJECTIVE FUNCTIONS; OPTIMAL SENSOR LOCATIONS; OUTPUT VARIABLES; PENALTY FUNCTION; PLANTWIDE CONTROL; TENNESSEE EASTMAN;

CHEMICAL PLANTS; GENETIC ALGORITHMS; SENSORS;

MONITORING;

EID: 77649123430     PISSN: 08885885     EISSN: 15205045     Source Type: Journal    
DOI: 10.1021/ie9017836     Document Type: Article

References (25)

1. Zumoffen, D.; Basualdo, M. From large chemical plant data to fault diagnosis integrated to decentralized fault-tolerant control: Pulp mill process application. Ind. Eng. Chem. Res. 2008, 47, 1201-1220.
2. Zumoffen, D.; Basualdo, M. Improvements in fault tolerance characteristics for large chemical plants. Part I: Waste water treatment plant with decentralized control. Ind. Eng. Chem. Res. 2008, 47, 5464-5481.
3. Zumoffen, D.; Basualdo, M.; Molina, G. Improvements in fault tolerance characteristics for large chemical plants. Part II: Pulp mill process with model predictive control. Ind. Eng. Chem. Res. 2008, 47, 5482-5500.
4. Kourti, T.; MacGregor, J. Process anlysis, monitoring and diagnosis, using multivariable projection methods. Chemom. Intell. Lab. Syst. 1995, 28, 3-21.
5. Martin, E.; Morris, J.; Lane, S. Monitoring process manufacturing performance. IEEE Control Syst. Mag. 2002, 22, 26-39.
6. Lane, S.; Martin, E.; Morris, J.; Gpwer, P. Application of exponentially weighted principal component analysis for the monitoring of a polymer fil manufacturing process. Trans. Inst. Measure. Control 2003, 25, 17-35.
7. Yue, H. H.; Qin, S. J. Reconstruction-based fault identification using a combined index. Ind. Eng. Chem. Res. 2001, 40, 4403-4414.
8. Kadu, S.; Bhushan, M.; Gudi, R. Optimal sensor network design for multirate system. J. Process Control 2008, 18, 594-609.
9. Singh, A.; Hahn, J. Determining optimal sensor location for state and parameter estimation for stable nonlinear systems. Ind. Eng. Chem. Res. 2005, 44, 5645-5659.
10. Bhushan, M.; Narasimhan, S.; Rengaswamy, R. Robust sensor network design for fault diagnosis. Comput. Chem. Eng. 2008, 32, 1067-1084.
11. Musulin, E.; Bagajewicz, M.; Nougués, J. M.; Puigjaner, L. Instrumentation design and upgrade for principal components analysis monitoring. Ind. Eng. Chem. Res. 2004, 43, 2150-2159.
12. Musulin, E.; Yelamos, I.; Puigjaner, L. Integration of principal component analysis and fuzzy logic systems for comprehensive process fault detection and diagnosis. Ind. Eng. Chem. Res. 2006, 45, 1739-1750.
13. Zumoffen, D.; Basualdo, M. In Optimal sensor location for chemical process accounting the best control configuration; Alves, R. D., Nascimento, C. O., Biscaia, E., Eds.; Computer Aided Chemical Engineering, Vol. 27; Elsevier, 2009; pp 1593-1598.
14. Li, W.; Yue, H. H.; Valle-Cervantes, S.; Qin, S. J. Recursive PCA for adaptive process monitoring. J. Process Control. 2000, 10, 471-486.
15. Wold, S. Exponentially weighted moving principal components analysis and projection to latent structures. Chemom. Intell. Lab. Syst. 1994, 23, 149-161.
16. Ruiz, D. Fault diagnosis in chemical plants integrated to the information system. Ph. D. Thesis. Departament d'Enginyeria Quimica, Escola Tècnica Superior d'Enginyers Industrials de Barcelona, Universitat Politècnica de Catalunya, Spain, 2001.
17. Chipperfield, A.; Fleming, P.; Pohlheim, H.; Fonseca, C. Genetic algorithm toolbox for use with Matlab. University of Sheffield, Department of Automatic Control and Systems Engineering: Sheffield, U. K., 1994; http://www.shef.ac.uk/acse/research/ecrg.
18. Downs, J.; Vogel, E. A plant-wide industrial process control problem. Comput. Chem. Eng. 1993, 17, 245-255.
19. Ricker, N. Optimal steady-state operation of the Tennessee-Eastman challenge process. Comput. Chem. Eng. 1995, 19, 949-959.
20. Ricker, N. Decentralized control of the Tennessee-Eastman challenge process. J. Process Control 1996, 6, 205-221.
21. Ricker's Control Department of the Washington University. http://depts.washington.edu/control (Accessed 2007).
22. Abnormal situation management (ASM) consortium. http://www.asmconsortium. com (Accessed 2006).
23. Musulin, E. Process monitoring and abnormal situation management in chemical processes. Ph. D. Thesis. Departament d'Enginyeria Quimica, Escola Tècnica Superior d'Enginyers Industrials de Barcelona, Universitat Politècnica de Catalunya, Spain, 2005.
24. Chiang, L. H.; Pell, R. J.; Seasholtz, M. B. Exploring process data with the use of robust outlier detection algorithms. J. Process Control 2003, 13, 437-449.
25. Chen, G.; McAvoy, T. Predictive on-line monitoring of continuous processes. J. Process Control 1998, 8, 409-420.