Traditional sensor network design for data-reconciliation-based monitoring systems consists of optimally placing sensors to satisfy predefined precision goals as well as residual precision and gross error robustness constraints to achieve minimum sensor network cost. One existing formulation of the cost optimal sensor network design is mixed integer nonlinear [Bagajewicz, M. AIChE J. 1997, 9, 2300.] which is solved using a tree search algorithm with some bounding properties. Although the tree search method guarantees global optimality, it fails to perform computationally well for medium size problems and fails altogether in large ones. Similar problems are found when using a MILP formulation [Bagajewicz, M.; Cabrera, E. AIChE J. 2001, 48, 2271.]. Other MINLP convex formulations [Chmielewski, D.; et al. Cost Optimal Retrofit of Sensor Networks with Loss Estimation Accuracy. Presented at the AIChE Annual Meeting, Dallas, TX, November, 1999. Chmielewski, D.; et al. AIChE J. 2002, 48, 1001.] have not been tested in large problems. Thus, the field has resorted to the use of genetic algorithms [Sen, S.; et al. Comput. Chem. Eng. 1998, 22, 385. Carnero, M.; et al. Ind. Eng. Chem. Res. 2001, 40, 5578. Carnero, M.; et al. Ind. Eng. Chem. Res. 2005, 44, 358.]. In this paper, we present an alternative sensor network design algorithm based on graph theory that guarantees global optimality and is faster than the existing tree searching approaches. The efficiency of the proposed algorithm, which is good for medium size problems, is illustrated.
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