Stochastic Procedures for the Optimal Sensor Location in Chemical Plants

Regarding the stochastic solution schemes, techniques based on Genetic Algorithms (GAs) were proposed initially. Benqlilou et al. (2004) applied the GA toolbox of MATLAB program to solve the design and retrofit of reliable SNs, and Gerkens and Heyen (2005) presented two ways of parallelizing the classic GA to reduce the solution time. A hybrid procedure was developed by Carnero et al. (2005a) to minimize the instrumentation cost subject to precision constraints on key variables, which makes use of a structured population in the form of neighborhoods and a local optimizer of the best current solutions. Furthermore, Carnero et al. (2005b) developed a new design strategy within the framework of Tabu Search (TS), which uses a Strategic Oscillation Technique around the feasibility boundary (SOTS).