INVESTIGADORES
BRIGNOLE Nelida Beatriz
artículos
Título:
A New Structural Algorithm for Observability Classification
Autor/es:
I. PONZONI; M.C. SANCHEZ; N.B. BRIGNOLE
Revista:
INDUSTRIAL & ENGINEERING CHEMICAL RESEARCH
Editorial:
AMER CHEMICAL SOC
Referencias:
Año: 1999 vol. 38 p. 3027 - 3035
ISSN:
0888-5885
Resumen:
A new structural method to classify unmeasured variables for plant instrumentation purposes, i.e., to carry out the observability analysis, is presented in this paper. The technique, called the global strategy with first least-connected node (GS-FLCN), basically consists of making a structural rearrangement of the process occurrence matrix. GS-FLCN is applicable to strongly nonlinear models, including special features to avoid unsolvable subsystems. The new proposal is compared with the existing structural dominant column block (DCB) approach, both theoretically and through practical examples. The results for two industrial plant sections of small and medium size are put forward. GS-FLCN proved to be much more effective than DCB, thus being highly recommendable.
