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May. 16 2011

Fellows Conference Series: Ivan Castillo - "Nonlinear Model-based Fault Detection and Isolation: Improvements in the Case of Single/Multiple Faults and Uncertainties in the Model Parameters"

Iván is Ph.D. graduate in Chemical Engineering at the University of Texas at Austin (May 2011). Currently he is pursuing his Post Doc at Dow Chemical. Prior enrolling University of Texas, he received his Bachelor degree at the Pontificia Universidad Javeriana, and his Master of Science in Electronic and Computer Engineering, Major Automation and Control, from the Universidad de los Andes, both in Colombia.

In this learning activity Iván oversthe basic concepts of nonlinear fault detection and isolation (FDI) systems, which play an important role in increasing plant availability and reducing the risk of safety hazards.  He presentsa model-based approach based on residuals modeling and validatesit using two nonlinear engineering examples:  an experimental air heater and a CSTR system simulated using unit operation software.  He presents a comparison with data-driven approaches (principal component analysis (PCA) and kernel PCA).

Finally, he proposes a robust fault detection and isolation approach that deals with parameter uncertainties in which complex models can be simplified with nonlinear functions that can be formulated as differential algebraic equations (DAE). This approach is validated by using a steam generator system.

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