Abstract
In this chapter an approach based on fault detectability study for deciding the optimal measurements selection to build the principal component analysis model using combined statistics is addressed. It is believed that it will improve the monitoring systems design. The methodology applied here to the bioethanol processor with PEMFC is integrated to the previous stage of optimal sensor network and control structure selection detailed in Chap. 12. The problem dimensionality could be important so genetic algorithms for stochastic global search is preferred to solve it. The solution can be driven properly to avoid the inclusion of sensors other than those installed for control purposes. It can be done through a good definition of the functional cost. The capacity of this methodology is demonstrated with a set of typical critical faults that could occur to this complex process.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zumoffen D, Basualdo M (2008) From large chemical plant data to fault diagnosis integrated to decentralized fault-tolerant control: pulp mill process application. Ind Eng Chem Res 47(4):1201–1220
Zumoffen D, Basualdo M (2008) Improvements in fault tolerance characteristics for large chemical plants. Part I: waste water treatment plant with decentralized control. Ind Eng Chem Res 47(15):5464–5481
Zumoffen D, Basualdo M, Molina G (2008) Improvements in fault tolerance characteristics for large chemical plants. Part II: pulp mill process with model predictive control. Ind Eng Chem Res 47(15):5482–5500
Kourti T, MacGregor J (1995) Process anlysis, monitoring and diagnosis, using multivariable projection methods. Chemom Intell Lab Syst (28):3/21
Martin EB, Morris AJ, Lane S (2002) Monitoring process manufacturing performance. IEEE Control Syst Mag 22:26–39
Lane S, Martin EB, Morris AJ, Gower P (2003) Application of exponentially weighted principal component analysis for the monitoring of a polymer film manufacturing process. Transac Inst Meas Control 1(25):17–35
Yue HH, Qin SJ (2001) Reconstruction-based fault identification using a combined index. Ind Eng Chem Res 40:4403–4414
Kadu S, Bhushan M, Gudi R (2008) Optimal sensor network design for multirate system. J Process Control 18:594–609
Singh A, Hahn J (2005) Determining optimal sensor location for state and parameter estimation for stable nonlinear systems. Ind Eng Chem Res 44:5645–5659
Bhushan M, Narasimhan S, Rengaswamy R (2008) Robust sensor network design for fault diagnosis. Comput Chem Eng 32:1067–1084
Musulin E, Bagajewicz M, Nougués J, Puigjaner L (2004) Instrumentation design and upgrade for principal components analysis monitoring. Ind Eng Chem Res 43:2150–2159
Musulin E, Yélamos I, Puigjaner L (2006) Integration of principal component analysis and fuzzy logic systems for comprehensive process fault detection and diagnosis. Ind Eng Chem Res (45):1739–1750
Zumoffen D, Basualdo M (2009) A systematic approach for the design of optimal monitoring systems for large scale processes. Ind Eng Chem Res, 2010, 49(4), 1749–1761.
Li W, Yue HH, Valle-Cervantes S, Qin SJ (2000) Recursive pca for adaptive process monitoring. J Process Control 10:471–486
Wold S (1994) Exponentially weighted moving principal components analysis and projection to latent structures. Chemom Intell Lab Syst 23:149–161
Ruiz D (2001) Fault Diagnosis In Chemical Plants Integrated To The Information System. PhD thesis, Departament d’Enginyeria Química, Escola Técnica Superior d’Enginyers Industrials de Barcelona, Universitat Politécnica de Catalunya, España, March
Musulin E (2005) Process Monitoring and Abnormal Situation Management in Chemical Processes. PhD thesis, Departament d’Enginyeria Química, Escola Tècnica Superior d’Enginyers Industrials de Barcelona, Universitat Politècnica de Catalunya, España, June
Chiang L, Pell R, Seasholtz M (2003) Exploring process data with the use of robust outlier detection algorithms. J Process Control 13:437–449
ASM. Abnormal situation management consortium. http://www.asmconsortium.com
Zumoffen D, Basualdo M (2010) Monitoreo, Detección de Fallas y Control de Procesos Industriales, volume 1. Asociación Argentina de Control automático (AADECA), 1 edition
Zumoffen D, Basualdo M (2009) Optimal sensor location for chemical process accounting the best control configuration. Comput Aided Chem Eng 27:1593–1598
Molina G, Zumoffen D, Basualdo M (2009) A new systematic approach to find plantwide control structures. Comput Aided Chem Eng 27:1599–1604
Zumoffen D, Basualdo M, Ruiz J (2009) Optimal multivariable control structure design for chemical plants. AIChE Annual Meeting, Nashville, TN, USA
Zumoffen D, Molina G, Basualdo M (2010) Plant-wide control based on minimum square deviation. Proceedings of the 9th International Symposium on Dynamics and Control of Process Systems, Leuven, Belgium, p 443–448
Molina GD, Zumoffen DAR, Basualdo MS (2010) Plant-wide control strategy applied to the tennessee eastman process at two operating points. Comput Chem Eng (In Press), Corrected Proof
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag London Limited
About this chapter
Cite this chapter
Zumoffen, D., Degliuomini, L.N., Basualdo, M. (2012). Fault Detectability Index for Optimal Monitoring System Design. In: Basualdo, M., Feroldi, D., Outbib, R. (eds) PEM Fuel Cells with Bio-Ethanol Processor Systems. Green Energy and Technology. Springer, London. https://doi.org/10.1007/978-1-84996-184-4_13
Download citation
DOI: https://doi.org/10.1007/978-1-84996-184-4_13
Published:
Publisher Name: Springer, London
Print ISBN: 978-1-84996-183-7
Online ISBN: 978-1-84996-184-4
eBook Packages: EngineeringEngineering (R0)