@article{38891,
  keywords = {proper orthogonal decomposition, model-reduction, distributed process systems, observer design, optimal sensor placement, selection, tubular reactor, location, spectral decomposition},
  author = {A. A. Alonso and C. E. Frouzakis and I. G. Kevrekidis},
  title = {Optimal sensor placement for state reconstruction of distributed process systems},
  abstract = { <p>In this contribution we propose a systematic approach to field reconstruction of distributed process systems from a limited and usually reduced number of measurements. The method exploits the time scale separation property of dissipative processes and concepts derived from principal angles between subspaces, to optimally placing a given number of sensors in the spatial domain. Basic ingredients of the approach include the identification of a low-dimensional subspace capturing most of the relevant dynamic,features of the distributed system, and the solution of a max-min optimization problem through a guided search technique. The low-dimensional subspace can be defined either through a spectral basis (eigenfunctions of a linear or linearized part of the operator) or through a semiempirical expansion known in the engineering literature as the Proper Orthogonal Decomposition (POD) or Karhunen-Loeve expansion. For both cases, the optimal sensor placement problem will be solved by taking advantage of the underlying algebraic structure of the low-dimensional subspace. The implications of this approach for dynamic observer design will be discussed together with examples illustrating the proposed methodology. (C) 2004 American Institute of Chemical Engineers.</p> },
  year = {2004},
  journal = {Aiche Journal},
  volume = {50},
  pages = {1438-1452},
  month = {07/2004},
  isbn = {0001-1541},
  language = {English},
}