Duality of sensor network design models for parameter estimation

Abstract

Plant-performance evaluation relies on the estimation of specific variables, which could be model parameters or performance indicators. Thus, the availability of reliable process knowledge is essential to peformance evaluation. This information is obtained through monitoring and data reconciliation only when an adequate set of instruments has been located at the right places. The measurement arrangement should guarantee the observability and precision of the variables involved in the estimation scheme. Furthermore, the assessment of cost-optimal measurement structures for performance estimation is a challenging issue for complex plants. Several authors have addressed the problem of selecting measurement structures to determine accurate parameter values. Furthermore, several articles have appeared in the literature to design sensor networks for steady-state process. The designs satisfied different purposes, such as observability, precision, cost, reliability, and robustness. A survey of the . state of the art can be found in Bagajewicz 1997 . Among these strategies, we are concerned with maximum-precision models and minimum-cost models for parameter estimation. In this regard, there seems to be confusion in the literature as of which model is best for design. In addition, there is no study performing comparisons or giving recommendations. In this article a brief overview of the minimum-cost ap. proach Bagajewicz, 1997 and maximum-precision models are presented first. In the next section a MINLP generalized maximum-precision model for multiple-parameter estimation is proposed and its advantages over other existing techniques are highlighted. Following that, the duality between the maximum-precision model and the minimum-cost model is established. Finally, illustrative examples are solved.