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Algorithms 2009, 2(1), 259-281; doi:10.3390/a2010259

Design of Sensor Networks for Chemical Plants Based on Meta-Heuristics

1
Dpto. de Ciencias Básicas, Facultad de Ingeniería, Universidad Nacional de Río Cuarto, Campus Universitario, (5800) Río Cuarto, Argentina
2
Planta Piloto de Ingeniería Química (UNS-CONICET), Camino La Carrindanga Km 7, (8000) Bahía Blanca, Argentina
*
Author to whom correspondence should be addressed.
Received: 3 November 2008 / Revised: 8 January 2009 / Accepted: 17 February 2009 / Published: 20 February 2009
(This article belongs to the Special Issue Sensor Algorithms)
Download PDF [244 KB, uploaded 20 February 2009]

Abstract

In this work the optimal design of sensor networks for chemical plants is addressed using stochastic optimization strategies. The problem consists in selecting the type, number and location of new sensors that provide the required quantity and quality of process information. Ad-hoc strategies based on Tabu Search, Scatter Search and Population Based Incremental Learning Algorithms are proposed. Regarding Tabu Search, the intensification and diversification capabilities of the technique are enhanced using Path Relinking. The strategies are applied for solving minimum cost design problems subject to quality constraints on variable estimates, and their performances are compared.
Keywords: Sensor location; Stochastic optimization; Tabu search; Scatter search; Population based incremental learning algorithms Sensor location; Stochastic optimization; Tabu search; Scatter search; Population based incremental learning algorithms
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Carnero, M.; Hernández, J.L.; Sánchez, M.C. Design of Sensor Networks for Chemical Plants Based on Meta-Heuristics. Algorithms 2009, 2, 259-281.

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