In this article we extend and generalize the ideas of two previous articles devoted to linear sensor networks to nonlinear systems. In those previous articles the use of cutsets and a decomposition procedure were proposed and proved efficient to solve large scale linear problems. In this article we show that a similar procedure, now based on a variable elimination scheme, can be also used efficiently for medium size nonlinear problems, but its computational efficiency for realistic large scale problems is not satisfactory. We also propose an alternative technique for the case of tree enumeration using instruments instead of equations that is very efficient for heavily instrumented flowsheets.
In this article, we use a genetic algorithm to obtain an economically optimal preventive maintenance frequency for different equipment, the parts inventory policy (number and type of spare parts to keep in stock), and labor allocation in process plants. To assess cost, we improved a previously published Monte Carlo simulation-based maintenance model (Nguyen et al. Ind. Eng. Chem. Res.
2008, 47(6), 1910−1924). Two examples, a Tennessee Eastman example and a fluid catalytic cracking unit in a refinery, are provided.
in Wiley Online Library (wileyonlinelibrary.com).Traditionally, the sensor network design procedure was based on positioning sensors so that certain network monitoring capabilities (e.g., observability, redundancy, and error detectability) of key variables are assured at minimum sensors cost. We present a new approach that is based on maximizing economic value of information minus cost instead of the traditional approach that requires the satisfaction of performance targets. This article presents the conceptual aspect and computation issues of this new approach: the connection between the new approach and the traditional minimum-cost approaches is explored and the computational methods to solve the proposed problem are presented.
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