Abstract
In this work, a procedure for solving the optimal design and upgrade of linear sensor networks, subject to quality constraints on a set of key variable estimates, is presented. The strategy aims to select the optimal set of flowmeters without imposing restrictions on the mathematical nature of the objective function and constraints. An evolutionary technique based on genetic algorithms (GAs) is proposed that combines the benefits of using structured populations in the form of neighborhoods and a local search strategy. Both procedures take advantage of existing process knowledge. Application examples are provided for the instrumentation design of a steam metering network of a methanol plant, which show that the algorithm has a good balance among its exploration and exploitation capabilities.
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